diff --git a/bf/Aug15_Generate_1000_simulated_data.ipynb b/bf/Aug15_Generate_1000_simulated_data.ipynb
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@@ -0,0 +1,13468 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bec32dab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model\n",
+    "from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a1ffd5e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hnn_simulator(parameter_set):\n",
+    "    \n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "    parameter_set0 = float(parameter_set[0])\n",
+    "    parameter_set1 = float(parameter_set[1])\n",
+    "    parameter_set2 = float(parameter_set[2])\n",
+    "    \n",
+    "    \n",
+    "    # add a distal evoked drive\n",
+    "    weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                       'L5_pyramidal': parameter_set0}\n",
+    "    weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                       'L5_pyramidal': 0.080074}\n",
+    "    synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                          'L5_pyramidal': 0.1}\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "        weights_nmda=weights_nmda_d1, location='distal',\n",
+    "        synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "    \n",
+    "    \n",
+    "    # add two proximal drives\n",
+    "    weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                       'L5_basket': 0.19934, 'L5_pyramidal': parameter_set1}\n",
+    "    synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                            'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "    # all NMDA weights are zero; pass None explicitly\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "        weights_nmda=None, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "    # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "    weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                       'L5_basket': 0.008958, 'L5_pyramidal': parameter_set2}\n",
+    "    # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "        weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "\n",
+    "    return dpl[0].data['agg']\n",
+    "#     return dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "756a44cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prior = utils.BoxUniform(low=[0, 0, 0], high=[1, 1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cde17626",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dae546ac4614e28a938a915cee1bfb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 1000 simulations in 1000 batches.:   0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
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+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 40.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 50.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "simulator, prior = prepare_for_sbi(hnn_simulator, prior)\n",
+    "inference = SNPE(prior)\n",
+    "\n",
+    "theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000, num_workers=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6573f129-539c-44c8-a3da-6af23fab8fdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # save x, theta\n",
+    "# torch.save(x, 'Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "# torch.save(theta, 'Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "23d4189d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load x, theta\n",
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "84ed246a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 341])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a4b8408a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 3])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c0dcb850",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 0.0049, -0.0011, -0.0010,  ...,  0.0200,  0.0184,  0.0155],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0144,  0.0164,  0.0140],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0087,  0.0073,  0.0069],\n",
+       "        ...,\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0113,  0.0111,  0.0105],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0190,  0.0164,  0.0142],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0015,  0.0033,  0.0069]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c2707a4f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.6249, 0.6098, 0.9125],\n",
+       "        [0.4930, 0.2235, 0.4436],\n",
+       "        [0.8427, 0.4485, 0.2682],\n",
+       "        ...,\n",
+       "        [0.8356, 0.0157, 0.1334],\n",
+       "        [0.2293, 0.7907, 0.9066],\n",
+       "        [0.4498, 0.1754, 0.4127]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "216671b9-a38f-4b02-80a4-54789e0cd075",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([341])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = torch.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b494f531-3a4f-491b-a213-50c4404020ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "47c286bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "fbc70a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x28726ffd0>]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "163da8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt b/bf/Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt
new file mode 100644
index 0000000..f16517a
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diff --git a/bf/Aug15_data1000_3ERPDrives_no_preprocessing/x.pt b/bf/Aug15_data1000_3ERPDrives_no_preprocessing/x.pt
new file mode 100644
index 0000000..8a370bc
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diff --git a/bf/Aug29_Generate_1000_simulated_data_with_exp_transform.ipynb b/bf/Aug29_Generate_1000_simulated_data_with_exp_transform.ipynb
new file mode 100644
index 0000000..930f40e
--- /dev/null
+++ b/bf/Aug29_Generate_1000_simulated_data_with_exp_transform.ipynb
@@ -0,0 +1,12777 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "bec32dab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model\n",
+    "from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "a1ffd5e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hnn_simulator(parameter_set):\n",
+    "    \n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "#     print(parameter_set)\n",
+    "    \n",
+    "    parameter_set = 10**parameter_set\n",
+    "    \n",
+    "#     print(parameter_set)\n",
+    "    \n",
+    "    parameter_set0 = float(parameter_set[0])\n",
+    "    parameter_set1 = float(parameter_set[1])\n",
+    "    parameter_set2 = float(parameter_set[2])\n",
+    "    \n",
+    "    \n",
+    "#     print(parameter_set0)\n",
+    "    \n",
+    "    \n",
+    "#     parameter_set0 = 10**parameter_set0\n",
+    "#     parameter_set1 = 10**parameter_set1\n",
+    "#     parameter_set2 = 10**parameter_set2\n",
+    "    \n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    # add a distal evoked drive\n",
+    "    weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                       'L5_pyramidal': parameter_set0}\n",
+    "    weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                       'L5_pyramidal': 0.080074}\n",
+    "    synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                          'L5_pyramidal': 0.1}\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "        weights_nmda=weights_nmda_d1, location='distal',\n",
+    "        synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "    \n",
+    "    \n",
+    "    # add two proximal drives\n",
+    "    weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                       'L5_basket': 0.19934, 'L5_pyramidal': parameter_set1}\n",
+    "    synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                            'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "    # all NMDA weights are zero; pass None explicitly\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "        weights_nmda=None, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "    # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "    weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                       'L5_basket': 0.008958, 'L5_pyramidal': parameter_set2}\n",
+    "    # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "        weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "\n",
+    "    return dpl[0].data['agg']\n",
+    "#     return dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "756a44cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BoxUniform(Uniform(low: torch.Size([3]), high: torch.Size([3])), 1)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prior = utils.BoxUniform(low=[-4, -4, -4], high=[0,0,0])\n",
+    "prior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "cde17626",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6148e8f82554e49ab246962f14b2cf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 1000 simulations in 1000 batches.:   0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "simulator, prior = prepare_for_sbi(hnn_simulator, prior)\n",
+    "inference = SNPE(prior)\n",
+    "\n",
+    "theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000, num_workers=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "96184ef7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-2.8159, -2.6743, -0.5276],\n",
+       "        [-1.6734, -1.0866, -2.5589],\n",
+       "        [-3.5870, -1.6012, -3.3245],\n",
+       "        ...,\n",
+       "        [-0.2102, -1.8647, -2.9860],\n",
+       "        [-1.2506, -1.8971, -2.9786],\n",
+       "        [-0.6901, -3.3004, -3.4009]])"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "6573f129-539c-44c8-a3da-6af23fab8fdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # save x, theta\n",
+    "# torch.save(x, 'Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/x.pt')\n",
+    "# torch.save(theta, 'Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "23d4189d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load x, theta\n",
+    "x = torch.load('Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/x.pt')\n",
+    "theta = torch.load('Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "84ed246a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 341])"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "a4b8408a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 3])"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "c0dcb850",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 0.0049, -0.0011, -0.0010,  ...,  0.0071,  0.0043,  0.0024],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0062,  0.0061,  0.0060],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0070,  0.0067,  0.0065],\n",
+       "        ...,\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0073,  0.0072,  0.0071],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0073,  0.0072,  0.0073],\n",
+       "        [ 0.0049, -0.0011, -0.0010,  ...,  0.0066,  0.0065,  0.0063]])"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "c2707a4f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-2.8159, -2.6743, -0.5276],\n",
+       "        [-1.6734, -1.0866, -2.5589],\n",
+       "        [-3.5870, -1.6012, -3.3245],\n",
+       "        ...,\n",
+       "        [-0.2102, -1.8647, -2.9860],\n",
+       "        [-1.2506, -1.8971, -2.9786],\n",
+       "        [-0.6901, -3.3004, -3.4009]])"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "216671b9-a38f-4b02-80a4-54789e0cd075",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([341])"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = torch.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "b494f531-3a4f-491b-a213-50c4404020ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "47c286bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "fbc70a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x13ae71de0>]"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "163da8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/theta.pt b/bf/Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/theta.pt
new file mode 100644
index 0000000..720b159
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diff --git a/bf/Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/x.pt b/bf/Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/x.pt
new file mode 100644
index 0000000..9170386
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diff --git a/bf/Aug3_Generate_1000_simulated_data.ipynb b/bf/Aug3_Generate_1000_simulated_data.ipynb
new file mode 100644
index 0000000..f65851a
--- /dev/null
+++ b/bf/Aug3_Generate_1000_simulated_data.ipynb
@@ -0,0 +1,14053 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bec32dab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model\n",
+    "from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a1ffd5e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hnn_simulator(parameter_set):\n",
+    "    \n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "    parameter_set0 = float(parameter_set[0])\n",
+    "    parameter_set1 = float(parameter_set[1])\n",
+    "    parameter_set2 = float(parameter_set[2])\n",
+    "    \n",
+    "    \n",
+    "    # add a distal evoked drive\n",
+    "    weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                       'L5_pyramidal': parameter_set0}\n",
+    "    weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                       'L5_pyramidal': 0.080074}\n",
+    "    synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                          'L5_pyramidal': 0.1}\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "        weights_nmda=weights_nmda_d1, location='distal',\n",
+    "        synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "    \n",
+    "    \n",
+    "    # add two proximal drives\n",
+    "    weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                       'L5_basket': 0.19934, 'L5_pyramidal': parameter_set1}\n",
+    "    synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                            'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "    # all NMDA weights are zero; pass None explicitly\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "        weights_nmda=None, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "    # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "    weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                       'L5_basket': 0.008958, 'L5_pyramidal': parameter_set2}\n",
+    "    # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "        weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "\n",
+    "    return dpl[0].copy().smooth(30).scale(3000).data['agg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "756a44cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prior = utils.BoxUniform(low=[0, 0, 0], high=[1, 1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cde17626",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52d00e1acd34431785a1404acec583ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 1000 simulations in 1000 batches.:   0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "simulator, prior = prepare_for_sbi(hnn_simulator, prior)\n",
+    "inference = SNPE(prior)\n",
+    "\n",
+    "theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000, num_workers=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a7c3071d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save x, theta\n",
+    "# torch.save(x, 'data1000_3ERPDrives_smooth_scale/x.pt')\n",
+    "# torch.save(theta, 'data1000_3ERPDrives_smooth_scale/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "23d4189d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load x, theta\n",
+    "x = torch.load('data1000_3ERPDrives_smooth_scale/x.pt')\n",
+    "theta = torch.load('data1000_3ERPDrives_smooth_scale/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "84ed246a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 341])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a4b8408a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 3])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c0dcb850",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.1558, -0.1754, -0.1949,  ..., 22.3590, 20.8787, 19.4465],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 14.7764, 13.5038, 12.3067],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ...,  4.2634,  3.2054,  2.2707],\n",
+       "        ...,\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 14.5994, 13.4982, 12.4390],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 16.6773, 15.7138, 14.7846],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 11.7446, 10.7863,  9.9005]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c2707a4f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.9906, 0.2885, 0.8750],\n",
+       "        [0.5059, 0.2366, 0.7570],\n",
+       "        [0.2346, 0.6471, 0.3556],\n",
+       "        ...,\n",
+       "        [0.9182, 0.8418, 0.1603],\n",
+       "        [0.5370, 0.2061, 0.3992],\n",
+       "        [0.0566, 0.4668, 0.2724]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "216671b9-a38f-4b02-80a4-54789e0cd075",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([341])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = torch.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b494f531-3a4f-491b-a213-50c4404020ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "47c286bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbc70a2f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/Aug3_bf_model_misspecification_1000_data.ipynb b/bf/Aug3_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..5c3c019
--- /dev/null
+++ b/bf/Aug3_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,5940 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/bayesflow/trainers.py:26: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('data1000_3ERPDrives_smooth_scale/x.pt')\n",
+    "theta = torch.load('data1000_3ERPDrives_smooth_scale/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5317fad2b70c40b78ba65030f2b42470",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.297487</td>\n",
+       "      <td>0.818212</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  5.297487  0.818212"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5bcf3d3174e4aeb9cfd9d96509e5fcf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12d1e18c77cb41d5baae6b761b13f5e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 30\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*30\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1895b419cd61433d9a4f28b66d946142",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all -30\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*(-30)\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a287ed9ba4264cb6a89dfcbb1add6082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=10, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=16, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=18, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=20, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=23, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=25, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_10', 'noise_16', 'noise_18', 'noise_20', 'noise_23', 'noise_25']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8719+nngMPdOxautDbCejaysQJPXfbzyjtjzfFzXRkp5pXovgZ7uOYuCT9+GkQqpXXR+u5wnhzXH7gzvwtk2PpN5D1NrqnFSphtGVgqRWr2urTWQ7UJ9W7XperkW2/eAQ7vjxTjz0wuHUezcCun2ro7byaoNy1Nb4b44FJ9h8RlOEBD2LfePQlKnZqRgJkjZbJA0IrvZZJPpjaAPPWmN/891f4vBQGU/uDpqaUdmgSi1wbVVdP1FBTIvamswtuFV0lSDxPB//+egesZjybn7jHeHCJ1k92lctZYJKx4UrQmdiN22RpPh4mwHtO1m/r9pmQUIl4lMJ1HAYe46NhyBp0iIJx63TkscLjeZijHf136yExLwcDV2HjndCSVJ1I6I9iZNKI/S7KSGx6Q6JnYR/27Ybf/r1X6AUbgZ511JbXVt1WST5yjeQG0O3eTTLsdDtWz3VvZzaqBz+2+JBaCA2ibSQznAgXJDUXA+FFvQpaZX7ru0WSSMcSTi5xsMNl8ciyetaEy6y8DjbtgDPR7kWeQwqNQ89BUc6Lrh3mmuLxpHnF3U2usoiOTYScAS0oebdtMfbnyy5turiSPxck4xPZt01mgGR7GlJXY1AaGpZri2vvRYJ/d5Kypyg37z3eETIt8oCaOY38nfRdrK9CdfWRHEkecu4h95OcZxskcTXHleOola7+jECxrXVcVALFObVxmkROOPUsKjRtp+u5+faWMg1R1poK9uMZnEkww3WdMpdIqVN4b+//eWf4puP7RWbRR7X1sGTUXXpVnVybOY3ltlmNpnI9nHhSJAjaivnRk4Z8nQcXbfCLZIEF6xw4Xa5a6urBAmVPCfklfQ0CcZLkPBx1ONqqrl+Lv6GggXKmoidZl1bUYc3/ffHRxprnJW3RIrsJmjoVrmwdedRPLP/pLiHKkh8aXMI/ua5E2W3NRFczfxG3vu8rRyJ1+kcSdb9068VZcgz1xbCzHYNP8kFY9SXXT9GPo7JjK4WJHnfD23WjjU+goSPQyfcXjoyjI9/6+nYph9YJNnXp/HTZOYRR81O0qgekI/jI5VYz5ZGE/JosWW5RLwGhXC9cP0gsIEWvipIdBrnSdbru1UWQD3a6fGRCs778+/gJzuCaL1hJkjazZE0cr96LZL/+pVH8bVtuzOPoxpaWWXko6it9LGrfU0cZpFUNdn5fM2l5ZHQKV0gR7pLkKgWRW7XFvk+x8kiydoM3/cvP8c//WQXnj1wUhlXvvBfGj9tdmUuSJoN/2V5JPc+fQB/9o1fSLWlGhUkef3DfH/O4/ZJyzJOvU8otCOLRH5n/Lq0KRwfjayxRu+rop5N5We7jqFS83DHj3YCkC2StgqSBqO2xBzIKYQf2nEYT+45kXmcLzZ8KpGiPy5v1BYpqHQ8/X/FjdYndytKgiSFbBfWShdIkq4SJLYiCOpNSBw/jiT6W7dx0jh1FlUeQVBTCD9ukTTv2or+1WlffDOtB1lE62O/OobvPn2gLtfWg88dwnl//h38IsdmwyGEJeKc1EMvHMbSP7sH2146xsYe/HtitIppvUHgY+sESf73RRYRjWG4HL33NAvpkReP4Ge7jjY4wjiII6nX108KUN48Lj/neqAjsjiSvKHtjhK1VZAsEp1rK14fTvc68nI0jWCs6qZWZ2g1ukqQxMj2OqO2mu0mmASdqcvhKZoOoZazRIrqpx1toUVCUUy+77OFF12TWyR1Ze1nCJI7frwTG7/znPR91vt84Lmgn8dju4+lHhcbC+OB1PH84JeDwb/PD8bGcXykirnTegC0zgKoR5BQ+fLpfUUA+V1bN296BL/1pYcbHGEc9RTg/OvvPi8sp3o5EjensKJnmJVHEoWgp99fWCSK56LiepE3gAkSfrnUEinMbdxqfOXhl/Drn/th26pAd5UgUTmOvC+IHrZq0bQKsmtL933wr3p7160vj4Q2j7Gq3sxuBHyy63zanGyvJ/omqzxFNVyk/PusTYSOterkunjSmHoL2jRGKtEm7fs+xqouyjUPc6cGgqTcIouknj3l5Fgwpum9oSCpTBxHAmS//2f2ncQXHngBj+w4Utd54j45XWhCkFB3z4RHkbdEiSpIQo+ZFLXFLUAumPIkJI6Ha2v/iTEMV1wcH21tUdEkdJUgibu28p1HpvV4WSRZrq2kDTBwF2RvoDGynYXkNqvs0L1rjPj3JEESTdSDJ8dweKiMPBCbSIILpub6sWigrPWWJJCzwAMK1AVfDC/Gw5xdL3BrAegIi2Rq6NoaYWPUhSP/4JeD+I9H9zQ5wjhyl7shC0ThJvJyJH7OvCrBkTj5ijZmZ7YH/1L2OgkobvlXXO5OlsfMP3M9H/c+fUCx8LN+Uf2gumCNRlXWi+4SJMoG8pVHXsLiW+8Wiz4JlLylupZahayERPpavX3uUutE+JFFUmtd1Ba3HHTl8Hkhu6s+8yAGPnlfzuvK11dRDa0R149apmb9Flq06nu89+kD+PDXktuhumxBq+OhbPUR5jYKIthkQTIRZPvJ0WBMNO9JgZhScrSCbf0/bMV/T3kOjUIIhgyBoJLLdVskXr68KjokK7Odjsu6P1mltE/Q/3OFLYkjUcn22x98Ae/9yqN44LlDuQXZ7925DV+4f3vqMSqIPzs6PIkskne/+92YN28eLrzwQvHZ0aNHsXbtWixfvhxr167FsWP1+a0bgUqWP7s/iILKIp3oxVPBPsL+E6MtSRbK8vNHuRryd3mLT6oWyVilMY7kxEg1tgHR0GvMzcZ/T6PJZPTMk86vhRExnueL95KZcyIEifz5wzuO4O5f7E88j/ux1eHQvbm27/l+zCKZCLL9VDkYAz1Dcm3N6Cu2N4+EXKsZPiK1T1C9/Uh0gl5/XOjayqq1leJ24rBFJrusqPA5wV2b/HpR3b/g/4lzCzwN8eN1eHb/Sfzy0FDqMSpOhW5PXUXw8UBLBMm73vUubNmyRfps48aNuOaaa7B9+3Zcc8012LhxYytu1RC4Cfqfj+7B/7f5Kel7miCcYzlwYgxX/dWDgsBtBnydpJHt6le0QPNaJDRppUmdOyLGxzV/8wP888MvJYyNmeIaMrFeiF4MSYIkdGu5vo9i6JTWrbdDp8aw+Na78ZMdh8XzU12EWZosL2MRd22FFokiSMhlQBxJq1xb9SguZJHQ+EcrLiwLmNZbbHv4L5A9F9TjaNzkOUg7P63TYOxYlSNJOKXeoo1q6SXOmyUlJNK8oXv98sCp8BgvNTRYGqfnxwISTo5VcfEnvotHXjyiPWdSurauvvpqzJ49W/ps8+bNWL9+PQBg/fr1+OY3v9mKWzUEvgn8yX8+iX9++CU8ysI5deG/e46NoOb5LSkVLmVF6wSJSExKsEgyo2FkQcInMk3WTT/cgeu++GPx+Y+3H8ZPdhzGi4NDOHRyDFXXx+GhMvYcU6w3Pxq3LsqlUUGS5daouV6YJAgUC8ktU7fuDMJY/+8jL0Vck3qv8DqJYyGLEPFqy2SRcCLbYxzJvOm9AFpZayv/sbRZ0DMcqbjoLzooFey2lkipZbxLgqqBq8enCb+8my7Awn8zLNm8wslmtbWC6wWfS5yUq1/j5P7y/KBKxanQRTpScZnrOv331Dw/9j6f2XcSJ0ar+Jvv/lJ7Ds2No20SJONW/ffgwYNYsGABAGDBggU4dKh5zT4LSROG+zKvWn4Gvv/8IP7uBzuw6XcHAOhrbR0eCl7AKeYbrxe/2HMC86f3yFFbmjEmFY/LG1VC2orQmLgrLfz70/c8J53z23f8FACwcsF0XLhwOv78LRcAAEbKcqkPztO4GoHXaOhiljYoLBLPT/V103j7igXxHGMWSUb0W1SuJX6PiCNJcG21OGqrnudJPI3LBElfqYCiY7XVIhEKRsI9Pc/HX3/veSyeMwVAtHHG5nuaRVKHG4wqKGdltuflKNRqB6RIjUgciZ6XHBGCBHhhcEj6PKlM0IPPH8LRoQpufMWi8HrxxOSeULkq1/Sleci11epW0EmYcLJ906ZNGBgYwMDAAAYHB7NPSEHS2uGd7OgF/IrxJuTb5STtkeEg+oheSCP4jS/+GFd+5kFpXHrXVvCvOt9Vn3IS1PBfySLJWCTD5RpOjdVENjzXvPnYXObayvo9eZClDVKzINf3RY8Q3bHkXugvOYkcCQmlJHBtl1uPvu9HUVsK2X5itArHtjB7Sikcb2Mbd7nm4qm9UQJlPWWnqNp1ZJHUMKXHQcGxW8bZ6PDU3hP40g92iP932Uarc1XuOzGK2x/cIdzESe8+XdiHx+T4WTQPCiku0eC47Pvy72md0Vgksj2hRMpIuK5838cxRnyPVlxtFCQA/Msjv8Lf/TB6vjUvXhSTStbrFBjf9xnZPolcWzrMnz8f+/cHBOf+/fsxb9487XEbNmzAtm3bsG3bNsydO7epeyZpHiNK6CYgb7YUKsnJ9sOnghcw1IQgAQJ3k5+hweu0rSDUMZ/GpCYk8oWR5eGgXhLEIw2XVUFC2qavHWfDri2FcFURubYisl3HH9BC7S85wqURrxBAG0C6ZuorFknVjUqRS64tPyhH0l9yhGLS6Mb9if/3DN7yhcjlmD+J1hNKjicEiYu+ooOSYzdtkRwfqeCmv3tY69r9f0/uw/+893lxbxrytX/zQ/z6538YO56+p4046d2n8XlJASlpx9pWhmsrL7cTPkqxvpjgJlQSSqSMhsd4vi9ZDyMVN1FRpBwqfj2VIyFrS9fueZgJqUnFkeiwbt063HnnnQCAO++8E9ddd9143UogsdS5ok0CauRR8JIsjUUy1IRrS71ncF/d97rj8ruP1BIpLrtJniS+quuLJEa1LDz3a+uiXJrlSJLODzgZSOG/aa6t/lKBke3yMTXNuDk4R8UXdbnmaqu3er4fNjKyUSpkd1RMw5N7jkv/n0eO3POL/Xg+JG2B6PeNVlz0l5zQtdXYeyHsGBzG1p1HReQjh+tGJVHUTfCXB+PRRfTc6RmpZLu4bg6rsa4SKU4G2Z4xB0+MVPGxzU8JYSDGL8j2pPBfjWvLk62HkUotMcim5nlSfpWOI9HV+CKcYiH57bJIWsKRvP3tb8f3v/99HD58GIsWLcInPvEJ3Hrrrbjppptwxx134JxzzsHXvva1VtwqFUkThvMcEckbvQB6SXzTPUIcyVjzPka18KDv+3jkxaN45dLZsCyLbVby5FHHnAQaf1lYJOzemdqWbJGMKK4tzt8IV4BE5qdePhGZrq0wqsX1IteWThDQQi0V7JgmKsboRRuAbsIn5ZEEHfD0Y6/UPBQdW4ytUYuECguKa2dslL7v4/3/+nP81ivOjsYfPsPhSg1TewootsAiSdtkObmeR5GIsr/lqCfVXZOHx6oraitnPxK6PlmfNdfD//n+Djx38BTufnI/io5CtpPglhIS5WsRONkuC5KIbFcFarUmP1fiCj9/33ZcsWwOLl08W5yrs0gomq/oWG3jSFoiSL761a9qP7///vtbcfncSJow3D2lqzhaUyY4AJGh3QxHQjjCsr09z8cv9p7A2//+EXzllstw1fK5Ws2kHq1fJdu5GZylwQXRJL6Y8HGyPRqDzpprtLxDVkQaZbZn5ZGMVqluU+RCjAsSWrD6sXC3CVcmxqpuQiWC4FmXCrbYZBoVJPXWh6u6vnCtEUgpGq24mDu1B8WC3XQUGV1T93p4VYJ8eR0Qx/Pz1bmTxg/prPYk0CGFjDwS/nnN81EKj//R9sP46+9F0VAz+ko4PFQWChuPkiNkWiS+L1Xl5q4t9TdVvagYJLm4a56P//39F3BitKoIkmSL5OxZ/ZM/amsikLSnDWstkujgSNOIzjkSmoStcG3x3t6u7+NQ2Flv99FRadxJiX6ZFkn4feTDjb7L2uhpklI2vPp7hfBI4Gwa6dUtXTfJImGLlkhT3a1ooQbkfPCZ6trKClrgpfL5PcoKv8XHXql5KDk2LMtCybFTW/OmIdb6IOMyUcQQn9MIPwtcW5ZlNdT6liMtWU+Qz56XyxVHz7CiuLbqsUiylAGOOEeSdM34OQAwpUfeFmk+qVGRI1IjMX0S8IjgSCKPwfTeAkarNVYZWB4gKVHBGEloe6EFKAv4MU3UFoX+Xr50Ng6cGIPv+3XXn6sX3SVIEmbMkEaQ8A2sqlk0zVokfCw8N8P1fPGiD5wck+4rWSE8Lj2TIwkmV1VnkWSGDgfx7ZQNP1JJsEg42S65BNKvn3Zf/m/8+ygCrZhikVB0Ss3zmGUhH5Nl/fCoLcm1VdX3g3G9oJET8SOlQuNRUqpFksVpkYCVA0iicNS+UgEu02gbRdr7oblZc/1Yzo72WsKVJVv+6vv8+x++iJ//6hi+9f4rY9fwNXMvCapFkhX+q/6tHk/vtqoEs4xU9RYJX7vk/uKurVlTSoFFkuTacr1YBCYJl6ryXnQ/jVxb77lqKZbOnar97a3GhIf/thJJE+aUhmzn2lBNmJHB/1ddT/gWG7VIpM2WDYvnIBw4MSrdlw+fjy/LqoiR7Tw8N9O1FWxOpNkMV2qxEFi6jq5aKSfD84KXIkmO2ooWTBpHQhVway4rKqkcx0ugcHzzsb340fZBSfuWORJXendTSk54nUC7lARJg612kyySnYeHtX1VaK5yQSKF/5aclnIkumcebW5ezKrQgZSN6J3K1yHsGBzCi4PD+vEkbLra+xFHkkG2J3GSqjUnBIliUfFHIwkS9kXEv0Y5HzP7SzJHooyvyhpm0b+0Pl0Nn6tCbS/QDnSZINF/PsQIc61FQi8njPcgITK1p9Bw+G+yph1pDAdCFxd385wYqeKZfSdl6yTLImGuAo+Zv7aV17XlYbQSCVOpMRYbmy5ypub5YkPNCz4k19P3mOB1mygpUK99Be+q6kbXSRIk6jv54L89jt+5YyuL2pLHVq7JeRHk8uCuLSAgNXXVdvMgiSP57L3P4X/855Ox47nQINAzHK1S1FbzeSRpVhzNLyqsmYZjw5Wo+ZPCRarX5mXZ4/ds3CJJ7EeikO0EVQiTAKi6yc8kKbOd36sc8hkz+opBHklCYmbV9aU1DUSkOq2LNHc3KRn9oeLTDnSVIEmaMJJrKzxEjtqS/Y404edMLaHieonZo2lQtS1ewZZcWwdPxF1bdzy0E+/48iN1cST8t1TC/IuCbcGxrdjC4/kpQLAwa64vCQ/eaU/4cT191Jbn+SKXIi/o/JIofaL5TWxhFlNcFFGZkByurSQXByPb1agt/v9Urt31fUG20+9olNxOito6NVaLRdAB0VyVXVtB+LbvA32lQktKpKS6tmgNZVgkY1UXL//L74nadmrUk6rk8Na1Kug15DBI4hxJhnACVCtCfpfqvqCbR0mtdvmYKm6gfEztcaTw31ixVvZc6VoUDJNHoNL4SdFpB7pKkCRpR3xj1IUR0qYl4t3Jl9kfZC03wpOoJbV55JFwbQmOJBr/cLmGobGadH4WD8E3jXItmIS2bcGyrNgzCVxZ0QU9z0fV86QwwhEl+Q6AiKCiv8Xv9HyRZZsXNKaehIx135czeenZ6RYoWXfVFNcWL8qoQ+TGkTcqNWpramiR+D4kiyQg21vFkQT/jlRcrVVR03IkvnhnU3qclpRI4bk3NdfD47uPs/uRqydem4yD5vkToYuOxj5Wc/Efj+6Jtditul6ysE8QPjrEORL9cUmRkUnCsep6YWSf/HnRsRIz28W9vIBz6ynY6CsWMJqWkOj5wsokJZGis2rMVZaEiuvDssavdbgOXSZI9J+f0ri2dFFb9HLo5VH5i0bcW2o0E1WRdb2ofMGJ0SrGqizpzYuiqLhrJy/ZDoTuATe0SCwrtvD8UDPi1665viRIuAXH80h0WqrnJ7u2fuMLP8YmVupBjDc8n4oxqgtP/f+Co7dcKrUo/6XmeiIRTT0uyyLhmiF/XsG7iY6bUgpdW17o2iqQa6txV5Lj6F1boxVXK5x4qG/0mS8ES18xcG2Rm7NRiNwbL2hh/NbbHxLtGGgzq2lqQHGoVRJond3+4A789689EXtm5NrSeRaSarO5no9/+PFOaf5GHEkyt0a/jV9HHeeXw1p8/HPd7+0vFRJrbfExlWsueoo2+ksORqouU9LkYznRTteqiM+yXVtVN8hxGu9ILY6uEiRJD3eoXIv5z30/Op5HbX3riX2iDtfM/qI4v9mxcIuEN4M6cGJMIp7VLOC030VQhaLr+3Cs0LWlTFLPjywuIHgOVUWQqCXTo7HJn9G9kwTJL/aeiBWL5L+HNPpY+KPye0uCI5E/58+x5mVzJFkujsAikbVU/uyTXFs9LYzaotuNVfUWiaj0rFiVkV+8IIIT1P4g9SiovEAnWRZU24vnkaS5toaVnKSsZ0Tf6y6Z9G6f3ncCf/HtZ/DQC4cBBM+NanpRGfkkPUwKGpHWUPB3n8IxVGv64p/TegsxCzF2Lz+I2uopOIEgYWR73LUVCU2V+NcFQcQ4lpon3MHtQleF/yaTaoFp2FdyYjkQju0Ijb7m+vjDrz4mvp8durZONpDdri4wXnjwxGgNPQUb5ZqHw0NlaULR+Mp19F3nk420OscJrJG4ayteboFntgNqSZloDBFJyq7n+bEFlwVawMVC6LJSxqO6ZZJcFFwrr7pe4sJMi0Di4/GYsKTzpPyC8HeSMI7I9sajpOJRW6FFUo1bJAdOjMW6ffYVndAiCYtXhq4tINgQeUqE+uvT8gtcFqorGmeV5d4aNc384lCrQmRFePFaVupzSaoYTC4fOvfuJ/fjk3c/CyCaN1/6wQ784JeDeM2KuTg8VMGtbzxf/Lbo+nGLpLeoCBJmkVhWJKAWzOjFvuNjqb/T8xEKEht9JQeVmodqQW9d0HvXWUBVNz6XR6uucLvSecU6ectm0VWCJG2elmtuIEg0k0cN6yPMasK1pV6LC5KTo1XMndaDPcdGw6Q3OieaIFnkHUeMbPcCi8SydfxDfKMOMtujz7QWieTakl1jWVFbrufjQ//+ON5z1VLsGBzCwOKgd02iRRLjl/THcSuqxhIS1ceVlbNCH/sai4RrrX2lAmwrcg+OZx6JjiN55W3xShG9RRuuF1Um6A9dW0BoefaEv9GL+/Y9H3ASFFdO9lqQkyDpOf79j17Es/vitbgIFHbfW7QxVs3ObVETFuWx6pUBEZobnsOFFwmjI8MVPLv/JH4cWi0kSOTipkzBFIJEntc8GGBmXxHHwujOM2f04YndJ4Rg1lktvh9ktpcKtoimIoUt5oplz0F1kUfWdfTZSFgaJxpnFDLfLnSVaytNOyLNhW8MNSFISMOSXxq98EYiYGJRW4pr64yp8RatHrNIdD7fJFR1FklC1BZFj8jnexiruSL6SuJIxHl6P3VAtsenET/m6HAFmx/fh3/6yS780V2P496nDgAASiFJrz73uDWnD+Pk5SF41Jb6vNREuNhY2Xm+srnw59dfcmCHAQzxPJIWRW2FlxkNfehZSkRgkXhC8ehlgoSPSdcGN+3aXPjS/KKCniTof/7SMbx4WJ/3AURBKn3FfOtIV71aHU+MJ3Mpmikkpdl64tW8de9H17cnONaXxs3HR49xZuitAIAzp/eg4noip4mKpnIFS1gkRQd9xLX59J2ivIT/W9W4DtXCkQDws53HpHlbdb22RmwBXSdIkr+jEF5JC1EEiDrRSZCoG919zxzEa//n99M7uinnkOZZdX2cGqtFvb5dVZDQeOuzSEj7KoeCpGBbwaannBu4tuIb91jFFcJtREO2A0xTUtw/JU3UFr8HPfv9YQImbTAi/Fd5jOrzFiVSVEFS466tSONWH1cSqSm+Z64t1W3Hr9VfcmCHvFNFcW21staW6/nielnX7S06YWXZ4Fn0FG1M6QneRx6/fRJ4JKMIOS7LFolKpqsg60DdkJMgLBKNwEnKPSEXMK1d7u7kNdd0azUpUiuySDSurXAcPNnvzBl9AIDBU0FeGA2f53G4Xki2Ozb6levq3Gr0eRJHwtflH/zrz/HdZw5K1ygmmZrjhK4SJGnZnrQx8z2Klx8A4pO0N0GT+ti3nsbOw8M4cGIMH/q3x7H41rtj90viSKg/QJJFErm29LV7dKi5vpicZJHYoUWiDf+txSfnaNXFnKmBlsVLyfPnJcjQHBYJ//307A+GCZgjYaHFUjjZYxaJqz47ygeQ71FOsEhi5GWWRSIWZ5ZrywmSPHWurZZxJLI1Wql5uO2eZ/EPP96pPb9HsUh6Cg6m9oRBIswlq/Pb57NIonNpXtB8VFsOqCCFQd2Qk6D2K+HgXB2HiGZiocUELqR1Alnlw6JxBMeqAjCwEILvZjJBsnBG0G6ZyiqREskFhiDbi3YsUTApw17HkdTYe5E+d+Xz2u3a6jKOJM21FZrlmn7jamgdgSaS2kJ0Whi9c3Ksiq8/tld7P3qxRMrROfvDJMS54aYtCRIvGhPfJDNLpHg++nscnCrXpITEIDJNPjbw78c3gFNjNczoK8KyIFUp5c9Ut9B14b++70vPjDTXQ2HeDFUYTrJIYmR7Qq0teqf9JSfMIyH/sSIoUzKS+e/x/XhFWH7P/mLg2qqEvBZZJD0tJNt935csibLr4sHnD+Gc2f3a84kjofnSU7CFv/xUmUW1abT8NPKbu/tUiySrdhmBXKTpR7F7kmKX4oZTlQTROiE8h68b/mx1Q/WUeUygea4KwIrLXVvcIgkEibBIwmN6mcDw/WCt90yxY+tFurfiiVDfkQj/VZ4Dt0CqhiNpDmlrOZpw0UOnl5JkkfSHvkw1cYqEQlqiIl2LtJL+UgGObYlKwGdoXFvcJ1+Xa8v1xKT3wsln2xZsW5Oc5wMVTTmPoXINvUUHfUVHKZESHaMLSnA9XyQWSvdgv4ssEfIhU7dBmuxJhCIhy7U1paeAmusxn7P82/imqENS1Jbq6uovFeBYlrAWW5FHojogVIuk6vpaXzmBoraEa6tgi/kpWyTpHIEKHjJN60NYJDkrdZ5iVQfqge7wpKKNUR2s0CJhzy4rIS/RteV5KNiWxLEE94hcW9wiWRC6tlSLpE9nkRSc2CbPfy/nsnR5OlFCot7lReNsd9RWVwkSnWuLhAZpKp7vR9FCimalurD6SuFGp0io6b3BJEprY0kTk4g1x7YwrbeAvWHrUp1riyfEcddWHrK9hyX3eV6UkKhORB1HAgSLvrdoo7foSCQ2f6a6qBpdHonq2z14ckz6nvzY6nvgv4ejmFB8j97ptJ5CrjySLItELdrIo+iAwLVlWRHJ34qorTifI5erqdS81BpUvUUn9L9HYyKLROpZorFI0lymNWZ90uZGUVt5S9Tzgpr1II1sV6eu2v6WPzvLSs+dURtbEapu0ANHV3WA7jeDCZI5U0so2FbMIokLkiCgRRUkuiKPQLC2kgJR6PdevGiG9HlwDU+4jduFrhIkug2XrArSVFy28WWZ6L3CtaW3SI6ldB8TFklo3tpWUGJjb2iRENkuk2vMtZXQKCfpXvSbXD+0SCwrIIY1UVt6QVJDn8Yi4WfrLBJdra2grEZ0zKFTZel7ct0QYakmfMaqAiRkKHOLhJdIUadBVO4DWvCIINnakmttEdlOc4lbJI3WttLl+XDCuFLzUEspC99TsCVB0lNwROKkrn3CZ35zNf7yrRcG90q1SCI+jDg1yiPJUmwIZBHV+2x0ZHuSVSlyLkQpES5IrFiTM47khMQoM1y1auj6M1jUVsG2MGdqKWaR9EqCJCyRUrRRKiS3DuBKK+9Loo6TPv7j168IjuW1A2tRD592ocsESfwzSiIrM6K4VFAskoSFQUJIJVKnhRbJsVSLJCTchCCxMK23KDZpskjKCWQ7XxB5orZ49jd1FdSXSNFHsJRrQcJmT9FODD2mDUXS2jUciRpiPHhKtkhIs6USNGqSnbrxJJVIIctgqnBtpVskia4tRtLzQ3QWiWNFgoQEXDMWiWpF+74cbRX0pvC11y86FgoOcSQuLCv4THAkGrK95NhRAdE0i4StDbWZVppiw7X4Rl1b+jyM4N+kPBJBtjNr2s4SJNK7jgsSIO4eo/XLLRLLsnDG1B4cDttzE0cphf96ya6tpKitmoYjUQtfCqWYpwAY11Zz0G0UfUKQRBEn6sNP0sz6EiySKAIr2gBjBK9wbXFBEsU20CaalEeiCpg0BK4tJ7xvMAGjEilxjTepmx9xJGN1cCSeF2z0fL2piVTEkRBooyTC8qTCNamuxKQ8EnqnU3vJtRUfMx9vYkKiF52nCkmVI7EsS2wmPUrUVlZTKu29Na4t/vzLtcAaKWsEScEOhEJNbFKBFt0TtgCWXVvB+ZRfBOSP2lLzSNLO47zCqUZdWylkeyxqiwSJF1fAbCveLVN3TUAWXjU36rGjurfInaomK/YVHTEfiaPkuRzprq3ob7kcvRezzlSliCvFNdfDE7uPG9dWs9CtY+ohQZqK60UcyVC5hrGqm7hRlwo2LEtHBgf/f3Q4skiS6kUJi8QOfPlAMMGn9RRQdCxZYHi+mFRyiZSUH41gk4hKsgdWjUN5JNrwX/0Fe4tOAxyJB8eyJFPa85CLIxElaBSLJIlsTyqPMaXkSCVS6N9KzcO+46ORaythA6T360Mh272Is5pScrBwZi9szpGI6r9RjlC90L0flSNJEiRFJ3jHrhdpu0CgIau9dEShzNBSBYDNj+/Dtl1HteNSq/8C8TwSHYpsHpwSrq06LRLN4UmNraLwX41rC1kWid61VUmxSGiuOMp1HduKohpdjUXiRyVSVEHiK0KMoAuyUAuQlkTAio/vPXMQ193+EH51ZMSE/zYD3QTvVyySIFw1+Oymv3sYM/qKiZpk0bFQtOP+b3qZnGyvuT54tCBpEn3FqPQ4WSTT+4pCYxlVSrZryfZM11bEUxA/4YioLflYz4tnthN6i7YmaitudvNQTM9nWm54GhU1JKgcCUVtUXZwzLWlDDqZbI80PJdZJPQ+v/bobnz67me1GdP8nfMcIzWm3/N9LJjRi4f/9BoAwW9VORL6l+eW5IWWbOccieuhluDaKhUCi0Qku7F7T+0taMl2x7bh2MG1Pnvv83jz6gWiZA0HtwBovg/lECTcIslzvA46V1hW1FZkkXDXVgbZnsCRBGtZb5HQGrCVzwuOFSmrfuARiAuM4J2pWeeqEOOfx6O2KGgo+H9ukRwOFdtT5ZoJ/20GOsuCSn+XWXQHX+wnRquJJGzRCVwEqquFXi532RwbqeDIUPT/qkVSdX3BrVDUV6lgx+pa6VxbeTokcovEDS0SXdRWEkcCBOZ5r8qR8NBEJY+ELu3YcoSLSrbz5wJEeSRTehyUCnasKGbctaWv/ksaXiEkuz1lXEeGKnJyJTudX4pH9JHQLoTJnK7vS1qtbWkECa9tVSfiHIkf6wXOw3s5CrYNxwldWyGRS5jaU1Q4Ek/8Lr4JjiUkFXIXCm1uNFfTOBL+rPJwKle8bE7sMx2lQlMipkyoHAl7TplkO7tWMkdih9ei70KBHLNIbCl4x3EsrVLRU3BEsdJoHFyIMdLc9RKjtkThU2aRcKXUCJImoJuvxFFQ3w/PRyzvIUljCmLJ7dhCoOP3HBsRn12x8QG84pP3xY7hZVYiiyT4t+jY0qZBGxeNN2t8QFQxONJMwokcbhi6qKAkYrgvdG3JUVtsgSmZ7TQux7akvhqeJyckqsOn3+xYFqb3FmOurTjZnpyQGNSWsrS1tmKd7hJ84rRJ+340Vm7lcPeGZUWblyiRwiySeqFzbfHNnTZjLdlesITA464tIHCdDpXjfXgKzLUFyBsvB9+woj7x2RYGD1zIY4isv2IxHv7T10mf6TPbE1xbwiKJu7ayOJK0hEQK8CDLRBTCJK5J4SCKtoWoh0vg2tJVfOgpxl1bOmWNrpOURxLnSDypbL8aGTbe6CpBonNRlZzAlAzapoafqXkPmvOow5iu2xy93LTwX5rYJMhqri/CMinio1SwJTeG63HXVj6ynSYed20FgsROySPRX484Eimrnh2qthqla9uWbJGori0VdF5P0cGMvoLocqh+T4gSEuXrkCAp2HZQ/deXx6yOQQoSYM90jFskfrTh0kLmXgyta4uKJDZgkWhdW5q2x1pBYtuwrcBiznJtCU3atsD3Me4K4hCbIrMuRRn5VEGCzGM4KN9Jd2/pugo3QIhKrsddW5ZlxVxQ8n1kN2b0d0RWkxJB/T2EINFwJDyx2baSLJKMPBKpBXicI1GDDqLkal/qbGoskiag23DtUDMoV6OkLl1JDxVFO4iAoU3qF3tO4MKP3YvBU+XMvA4gySLRubY4R5Ivs33LU/uxK6y6SgKLtFEv3PwcC2GBQblelJfi2uoNXVuSRZLCkdBYC7ZMtqsJiUmY3lvA9L5ijCOJF23URxmNVT0RocTdADTmWME7roGyW3D+LCgFDsE9eL4vbUY2i9ri4b9A6ywS/vxpfujJds6ReLIgUcj2aOOxJXcP1+A5eE0n4qxGq65WSwaYIpOw4SfBtuObfRrZ7vtKAAhFY1LnSMUiSXNtJXEkVdcTFomI3hIWSSSQb7vhItxy5ZLwe0tybRWUqC1CT8GJfZ5WIiWWrKvUjetxIkWVu8mNIGkC/JmTJlGwLfQUbfz4hUF84YHtAJBKdhHInVJwLFQ9Dy8dHcZQuYaDJ8dStS0eAghEuSg118d01SJxFI6EbfrlFNfWh/79CfzTT3ZJ9+EJidwi4cIJCBZhIkdSSg//VbVNYZHYcuKW70fCgMfbq5jaW8CMvmKMI4lltheSOJLQIgkXsauY/WmuLb54aZMm15ZtRSX4PYUj4ZntPS2wSNT91lfIdrICdMpLwbFkjoS5tmJkO0UbKaU/kgSJ4Eg8X9rcRquulgznFnFwnvayMThW3CLRlnNhP9/1fDx/4BTKNVfKI6EWyISAI0m+N59PMdeWrVgkimvLtiy8/bJz8NG3XAAgsJr5urDtONkOBOtUrcyra20R/O3F3rvvQ6pKTXwLb7fMx9sudJcgYQ+dXlZgkTj45cEhfOGBFwDELRKdXCiwCVRlGaaeHy9bwEHatcgjoQrCni8SxSijuydGtiPTIqGifhQxRlo3bWaeTxxJsAh+tusY3vcvP5fukbTh9RZswZEklRsJxhNeizgSS47Wcb3IfcaL26mY1lvE9F6NRaKS7QkdEseqHnqLtljEVbGQg+9VgSRtRlyQVCn8FyG5DlGC3/NkNwb/Oxa11ZBrSxmjFwgSylMYrtR0pwGILBLKUeDzelpPQSbb3ch6lC2SJNdWZFnw+T5SruktEqr1Fn6VNwlRJf8BvRDia3uoXMNbvvAj/Oeje1nBVT9mtQUcSYpFkhh2G0XfkaUtuk7WIoGs/g5eUFEN/yX0FGxtxWd+bz4mV6P08V7u5FZ2PU9xbRmOpGHwRTlnSpA5ThYJh+4Fq/MtmkiWlDUdmJuQOpJxnBihbF7FteVy11ZEtksciR8lwElku0Z7juoYydpxkIsSlEigBfo91qtAzTrn6CsFHInvR24az9f3zOC/0VEsEpdZPbwBEIdlBQUtZ/TFyfZ4U7DkEilBpnBwb3pmJARj3Ba3zNhXY8y1RRYIJfq5oauLYGsEiaqt1oM4RxIoIzR/03p+lJzA6uQJiYSpPQWRzAhEz7RgyxuZLhoMUCwStskOV9x011adFolqzQZj1Vkk0T1PjdVQdX0cGSpLZLtqXWVZJPx1eYpQUS2SAuMi+OcEzpGIhETNPkOlV+RxyNYQ4aUjw9gdllTiqHlR8iutPWORtBCeDyye048tH7wKK86cBiBY+D1K4yXtC1Zq05AmQqGlPFrC9TwsmtWnHQNp16RBEdnuer6I2uJkO9cieNFGubx8NLlI8NDmS1q4FP4bTmTdIkqqtQVEZDsAjFHrXT9eklrNrnVsWxI2vGLsrNAiKSrk5dSeAmzbwvS+Ak6O1RLrDQXnBIJH3b/KZJGE4+NhvEAdUVvsPD90bdm2Fban9WNRW4SSypG0wiLxfRw8NYazZwfzaySl50fBseDYNvyQV+lhiUw076KQXS86J4dFoiuRAgT1s3QWfI9wP9L5+S2SJEWFg2+29JtGqpFrq+rKQQpANkci19qKPufhv4VY1JZekHCOhIqm6vYZ3WdJ4b//64EXcIemD03NixROYZF0O0eyZcsWrFixAsuWLcPGjRvH9V5EjJ5/5nSxiRZsK1bOQEeCqSWjowkkh5ZSCZI+pTkN4XjMIglL0buB8Fk2byouWjQzGEfBVvIcksj26Pq0WIQLTYnaIoJOFw0DhHkkmjLyAETRRoBr6ZooN4UjCdxojGz3o4qxVG578ZwpuPq8uSJvgLL8Z/QV4Xq+9BxUi4RqJqmhn2O1YPOkjYhbUYCGbE/gSMR5YdScHUbsEd/ENyO+gagWSSNku67W1qGTZSyc0QfLSrdIio4t5ulwWY7aomfC+3/T51IeSRZH4gfPkWrWnRrTRyqSAiIi+nKS7ZZms9cFavD3RWtgpFyT8sPU35JVa8v1eSVwOX9DzWwnRTMpaotzJFQ0VU0zAPQbPP22n+06ir//0YuJ4yXUWM4UzdWa50tzpatcW67r4g/+4A/wne98B8888wy++tWv4plnnhm3+5E2CUT/Opp4btXVBcTdNzTBCqHJSnsE36h1iDiS4IQoaitISLzvQ6/GmrNnintUFIGhy2zni5IWCxHUtGFTtr7vR+GHutDHbIsk+N1k+QSVANIFiW1Z0sT1vMiXTK6t6X1F/PO7L8PLw99Obj76l29QKrfhhNaVqqmWqx56df0dwuNi4b9S1BZ3F0YuMYls94KSNWrUFoHu25NhkXzl4V3YdzzuogjGIf+/6/s4dGoM86b3ouTYGRxJ5BYardRkQcIS1QDOkciurZrnxyxAfh5VQqD3qAZGED547Xm45JyZdbu2qq4f0+7/astzuOWffiZ9xnUImpsjFZeF/3pa6ypJjvx4+2H8Ys8JMW/VUGCRP2LLlgk9K7W4blAiheWRJCYkxj8LIq5q+K0vPYwdg8P6AfPjQ8XWCjkgKtzJLZJ6Kyw0i3G929atW7Fs2TIsXboUpVIJN998MzZv3jxu9/P8KOafJqejiZ7QaQoF5bMoassOW15G7o+aoqVyHB+VLRKeR6JCF4YsLJKqJ/yfkmuLBMmoXBBPjdpKskg8P1lzphIpgGKRJMS9SwmJama7J5PtJKDI/UI5NZHmnOzaEhaJ6tqquegp2jFrUrhXlOs8s+8k3v+vP8ftD74gXSvKI4FYoJSD4/tBKHU0lujvmGtL81yPDlfw0c1P43fu+Gnsu+Cefuz4qutj3rSewGItJ7u2iiFHAgTcBXfhCnJYcCShJu3EtfQxjQDkyXU11xcBImqBTcKrz5uLq5bPFeG5eV1bNdePuWB3DA7j/ucOKeOJK1PctVVz/VhyZZpF8rFvPYXnD54SEYFS4cRaFP6rRm1VklxbtiUpWLoSKYB+g3/x8DCu/sz3tePUgSwShynLXc2R7N27F2effbb4/0WLFmHvXrk17aZNmzAwMICBgQEMDg42dT/uhuAWyajiZ+a+ZIJqYRBHUgoz27nZShrHne++DCvmT5POU11OPI9EhS4Mme4zVnNFaKTOIhkNFxFtFJwjIbJP1yEuN0dCkUwai0TNFVBLpPCorVmhJkubHGlkxBcVEjRCDipAqbqBxsgisfUWiWrZ/NNPduHbT+7HPz60S5/ZjqjgpbBIFKXBFhuLxf5OJtvpzEGl3lg0Vvn/D4StmOeHFslIRtQWf8fc0lYLXYqijRpOQufekqK2XA8zwmoMamBEdL9o0w5CVBOHLaHqepmkOKB3bY1WXNm1paxz206utUUCUeva8vwYR1JS3nFsv3CUEikpZLt0Xnidw0P6+aEDBfzQ86aAoK5NSNQl+qkRCxs2bMC2bduwbds2zJ07t6n7kVsCiBa8Y1uxxklajkSZGKSpFMJaW7QniYQ/28arz5uLT1y3SjrvxEgFT+w+jucPngIA9BejPBIVujBk2qTJ5LdtVRuLJvzJsaqYvFLUVtgqVOfa8n19iZRCaLmRIJFcWwkWiQj/TY3aikKdAWaRiErIcjRM8LdKtgcbTTwh0Q3Jdj1Zm2R5jVVd2bWlWCSRBaRPSATkOZRGttNYTo7VcPVnHsQjLx6RvlfXSCRIsi2SgiPnhMiuLbJIZNeW+q6AdEHieUFY9YwMi4QHd6i5S3mQ1Bb3ln/6GW79zye1ASfD5Zp45iPVWoxfSLJIfN8XyZpFIUii7wOORA4OEfkaLI+EQ7VICna8NzsQX/Pq3M0DKiZK+hO51XiwQVdxJIsWLcLu3bvF/+/ZswcLFy4ct/v5/OGSi8uyYn5m3QtWXVuUu1CwKY8k0nw4R6IKoBOjVVx3+0P49pP7Ja1Ep63qGkLxBai2y33guYPYtuuY+P7kaFWMS1drSzeXPB84qintQi4tckGNsfpTapE5NbM9XkY+8ruTS4QEVGSRBJ/TQlXDLzl0rq2a64VVj53Yu4vIdr0gGSrXpEUXJSQGHEngd6aqALJWS3/zd1dS3B4c/H3+6ugI/vTrv1DGKp+z/2TApcyb1hsGY8Q3bnpmJdUikVxbxJEo4b9KZjugj9ziUVtV18PMPn3Jfw4SuK7vI6uv+9IzpuCPrlmOq8+bK/0mFfc/dwh3/Wy35HoaVaxyAHhq70k8+Lzs0UjaSits040qS8vzT0Rtsn0gOFfv2iILlmrf2bZeYS2pLrMGOhnWXA+eF7m2io6NobIrJbfq7j2eGNe7XXrppdi+fTt27tyJSqWCu+66C+vWrRu3+/EsZOHacixRbZagFyTJUVsBRxJ8Tq1sOQfDcZwtNC5IdJnJulIJfO9zQvcJbdwf+9bT+F9hdj4QCC1Ra4vlWvCijSo8z8ehk2OiQyOhN3TBiSKXaRaJQuDaSmSc55N7wBLRWSRASKBMS+FItGS7LRegpM2/t2gLoU+I8kiStWLuaiKN3Av9+xQ2Spoff8fCItEJEi3XII9h52GZTFWnxcETwbjmTe8JinpqLJL+8Bmqobz6qC3SkiOXTD0WCXEkU3oKcGxLSnJUwV1bWQFss6aU8N/Wnheto7TqipAtN8GRMLKd8IfXLBd/W5YF3QzgpWOSOwwqFonCOeksEgAi9yjRIiFBQm4ptu+o0aVJoHtw970aBKEqV+ONcb1boVDAF7/4RbzhDW/AypUrcdNNN2HVqlXZJzYI0iYB5trSWSS6sDxFM4h8pLbYUADZdQQAam9krrHxWPI8ri2X3YfG7rANdLgsJ4OdHKtF0TiOHeZaRGXkdWZ9zfNx6FQZC2f2Sp/TJO4tyGS778f9rbE8EsvCh99wPv70jeeL76u1IIRyiiJIhEWiuLbkJkOeZOk5oQ9dV/erVLBji+Znu47h1zY+kNoK+RBr/yv6kbBwX3ruujLydF9CmmtLl7yXVJoDCDaxab0F9BaDmkw69xwJ+zSOROVtkoo2AsD//O7z+KnicqN55fuRq6e/5CRGbQGQXFtZZLsqzNKKKwLycyTX1tBYPMv+t16xSBqPLieFu+cEl6TkcohoLZHZbovvAJ3iGXFSxFHqIrRorkSJjnFeKws1N8x3Yl4RVcC327U17o2t3vSmN+FNb3rTeN8GgBy1xaO3Fs3qxwuHhsRxOk1Bndg0cYqh/1FodozMBuITimvBjh0VbqtqFlY8bFVeMI5tAV70mUq8nhytRtFP1KOdWSQ6Le/wUBmu52PhjD48ueeE+DxybRFHEm2uOhccwKK2HAsrzpwmyrYQ4V+wo/7hqmuLxq1r+1r1gkZdtXDDCEhTxbVFrhoNeczftYqg34onWSSiaKMnN+pyvSgcmEBrnb87VVvl0AmSvcdHsWhWP4Bgo543rQfvuWopbvvOs/D86F0khXCSICk5tsKRRK4tNRM7qWgjAHz/+UHMm9aDy5dGvUEkiyQMLplSKqS7toRSkE22q+8sybUlxsM2empDcHw0rij0skAaXYdQQFb21MhIeucq2R4r2phgkVRDt1PQgiL+m1TuhSu1aaHeHDXPCxWe6DdQ/logPLvMtdVueIq5R//+y+9djrdfdo44Th9NoZ8YAdnuSyGvNFH4cQS+mQQEdvD9h649L3ZPXfgvJxWJMKeGS6ov+8RoVUSHUUlx4kiSyPZ9xwNNfOFMOTOfNi8R/svcPapmFUtIVJ55ELWVZJGQa0vmSLjrr1rzpMg627JgKdFrUehxnGxPw5nTA0vs0EkuSIgjiYf/ep6vcCTxDYB61ue1SJ7bf0r87fk+zp3Tj/dcvVSKwlHvwdHHXVtMiy0V4tqtCP8VLhn9pr3/hNwOmc93Io/7e5xEsh2IcjY415eUbxXjGBJI8ejv6HOySHTcDncPWZZeoHGryg7fNSl6VcXiSCojr64tPvcpPUBnYahhxXzu5o1PUF2ugUUS/CaKkiwmKCHjhe4SJF40IfminD+9F69fNV8cRy4jjhjZLrm2PEljSeNIuCBxwlL0uza+GR9gvluCboOWyHYn6imiln8Agla/vPRF0FqXJ9XFTsGBEwGZq7q2aOMm98j2Q0NYfOvd+NXReP/neGa77E70fD9sV2pjWm8BU3sKmDst4GQoHHqaYpFwzXG4UhP1yOiYJNeWWsI+C2fOCH73IAu3pMv6iJJaefivjiNR312poHdD6QSJ2mvGUuYsNU3SJc4C0TPkeSTqmESfClb/qegEAlknSA6eVARJOG6y1koFG1NKhcTMdj5+cv/SGHVQBYxO6VGbPBGSsvGBwCIR81ETMg5AcgO5vo+Z/UUcC9vUimraShn5WGMrRfDxviDCIkmxsrgQqBeUR2IxBY4E/JypJWm87UJ3CRI/KrCnhgFzDkQXAhl3bUWaCG8wE4/akh8hn/xZk4RrkFNKTsy11V9ywg0NSrKRhfPmT8U/PrQLLx0ZEZ/ZliW04oJtwdLErew7kW6R9BQCruUnOw6z++nDf+lfNcCBclUKThBSfN+HXo23XRpYhBeeNQMf/40L8GqK1rHirq1TYzWp/LzgLNg+zYVYPf7g6b1F9BUdySIhREUbw14uvrzRB2MJ/lWtyaJSpUCMU7ORSRn2fnRNug3N1f6EMjxUdkflSPqK3LUlR225KcoPELdI6Lwym0/9JUe4hXRTO+JIIrI9yT2nS+hTMaqUDxKfK4KEeweoIjKgr88GyK6tctXDwpl92DE4jBV//h1sfjzIc4ssEtnFVWUBJvLv4RxJkEycZimriY4cqxZO157DyyBxpZnvQcIi6abw33aDtEkgLvH5g7U12kLMtcUtEldu4xpYJPIEI9QkiyRDkDgUyWRjztQesZERKFJGLRHdW3Twv995CY4MV/CNx/aG9wq0U25663z2+0+MwrEtzJumRG2F2q9lWegtONrIFkJURh7SM4iEgpzUdeaMXolkfNevLRG+bB1HMlSuibBhOkb1d0fhrFZdESpFx8YZ00qSRULgZHsh9JvzDGI+XvWZ9CRYJLogCx4a6+siDcN7TO3Rl+Cf3lfApYtn4cKzZkjzmEfiRT77KIKNNhydG+nUWE2q1UQuUyFIQjclab5qIVQ+bnqOQLJmHCPbNWPiAkOXR0Igzki4/JhFoifbmSCpeThrZh8efekYyjUPf/u97dK4kyySeAJz5PoioZ1mKeuitgDgXVcsxt1/eJX2HPqdVc8T7Q4A+VmSIDEcSRPgSTrq4uSbjc615cSitqIJVPV85s4h7S44Tp1QlQRzXAeanBedNUNEmGgtEl+2SPqKDpaeMRUARLJlwbZgWfJE12nI+4+PYe7UntgC59psX8mRzH/1edGiJq01sv6i311jSV1p0AqSsRqmhkKU7q9ql1ERQlsUhsyDomNhzpQeHFJcOQCF/1JyHYX/yguVrBN1oZYcW2oAFV1TJ0jke+p4PSBy/6noKTj42n+9Aq8+b67UO3ze9EiQRFFGNG89sWklRUgdYM+EBHWZ5VtwC0nndrOsuCApJcwBdZPVKV1ckLgpFslZoXXdoygnyRZJNLfHqq7k5iUFK5aQmKOMPBC5px3bwvzpPdhw9VJ8+A0rYmMQ3KCiaOgivYDA2qM16oZNvHQBPzNFtW0jSBqGnEcSfMbzQQi2rclkj2kYtjiv5kZteqlkhNDu2HlBb/hokmdVg90f8hUXnjUjJMp9aeJPKRUCAePJdXT6So6wqkg7IzcHD/PUtWc9OVbF9L6CNO7503skV1evol3btvw7XWad8WfANdIqe0ZpEAuQbRRD5VCQMAEVs0jY7zwroaS/DgXHxtSeAk5pqur6zLXl2BAdK7myTI9BXajFJItEs5Nxi4S7Yy3l2kmCRA2NJsxmvV+iqK2QRGbu2KScjQPMvRVxJKSYBBwJoVfTMlbKIyGyPadFohMkchvq6HOVIzkvLFPUV5JDdZM4EtUiWTAjmj/E5akJiboOiRw8Ss4Nq1JYloU/e9PKWBkl/nvVNZokSAp2VAePijaqwRkAMIMESZvJ9nEP/20nXB8SAQVw0l21SOQHrZqYkWlrBwl2otS4rKXy6/aVHGny6zRUjovDSrg3vHwRfrT9cEw76e8hjkRuv0oTquhEHRYLYSVY2sz43xxB6RU5BPQ//usVkjbbq/jmLUsOJebWGcCitsJ/qSPjxYtmpP5+GicQuVKA0CLpDYWdy0I0JddWZHnVo30Vw7IiuggZz4+UkYJti1a7fJNLcm2plZzFNbWCRP4+2bWVIEik3APZZSt+p4jaCt8Vq7KbZJFwnqSmCJKiY6G/J5oXvUU7FmAQcSRR9GGSVRp3bcWPGUtybakWSahIUA4UrWWKwlMhcSQ1V1Ki6B2KxnaOLJzo+zjHE/EX1CFR/DbN9BStD5Q5k8Yp9QhBElSljhS46JzFc6agp2AnKiHjha4SJD7zG9rKRs8ntKPhSOJRW7LZSKGGtZBM4+HBhP6SI7WNLWdYJFctn4vtn3pjGNsfrzEVWCRB2CsXUD1CkETRGsUwQoy3Ak0qa+4oFsb0vqLk8+5V/N8W5IU+XK7hhUNDsagttY5aHu6CNk9ev2uoUsM05tqyNa4t4WIIn/+cKSUcGU5OQCRwMlYFL5FCFQXUhETh2tKQ7XnzSJLIdmFFkyBJ2AzUuawDL3vueT5eGBwSJH3S73/pSJR1r7YzKDqyRbLqrBk4OlTB0eGKqCsn3qUXBafk5Uj0FglzbaVwJORN6BUcSWQ9aF1bzG1LHAmBkljJjadyrJFFov89xJFwYa1r95tskegDLApOVD2CorbU+QIAb11zFq5dOR/Te/O7e1uBrhIknBiNiKjgXz6hdZVx44JFnkA8aS0pAobzDEC+jnncDFdrNZFF4inhv33hhOKbmRMmJFbdHIJECQ1Wn4XatEvtbXLoVBnX/s0P8Pmb10jnJ0W+pUGt/jtSDWoGTestSmSi2thKkLnhprFoVl8uQaL24+DwfIi8EceKomN0me268F+dK5FvgEG5HbkPN7d48lokuuKMankNEWXk+fiPR/fg0ZeO4ZNvvVC6PseVy87AVx55CbdcuQQz+0tCEJBFU1AsknddsRiXLp4dbtZyJJPPog+TNOx4QmL8OF6wkguEUaXvBikskRURuZl4Ayi6BrdIfF8OhSdFsE/hW3h/F3JbcfC+JqMVV1LGdIEEkSBRK5MnP68+ZpFwS5bzZL1FG32lHu01xhPdxZF4zLUlFmU8uiroFaA3TQnctQVEmgNltusSEpO6JuaBbVmxIoNTSoFWfv9zh/BHdz0e3YcynxUXh23JmbdJ5eKJTCaoPnN1U7ItfQE8cj3QMNTrZEWt8XNo46E8BeHaCo/pLcr8E+dIAOTmSYpOejSNKC9jE2clJyTStFH5gYJt4UfbD+OB5w7Grkeg5EyuL5AFBET/pnEkf/u2i/E7r1ws/p/eI/V4j8ZD2quHR186hjOmlvDOy8+RziFcu3I+/uxNK3F8pIqv/zyIAlQtqYJti7I2gBy+Slo0d23VG7WlO4xHkSWF/545vTcWoXnb9RfhwrOm46yZfcKFOYvxRyfHqlJ4+ewpJdw0EJRWIYukN2aRRNaAjmOiMYxVPQxXXCV8Pf6bo+OVOoAJz+sV584WrnA35EjU310qxPvBtwvdJUjYoo+4kuD/+YTW5ZHEw4Fllxi98KDCp1ymnpAU958Hjm3FOZKSo9VmRK0lKQHNhp2DIwFI6CT7cFXLyrL0WfIkXNWoLcKx4eTktejewbm0UVDYMY/askNtTIriYeG/ALDkjCmZ9wLiuRcqXC/IGynYURl5XUKiqmkvnx9E0X3+/hekzznZTq6hePgvXTv4Ny3899dXLcA5c/rF/9NzO2NqSToucm35YYJnMcYfAsBTn3gDNv3OK3BeOH7avFU3a6lgSSHZOvcYL5FCAjTJKo1ZJJp5zkuGcKVotOriZXOn4OzZffj09RdJEZYAcMWyM/DtD1yFUsEWz+d/vPF8/NdXvwxAEOrMw98ty8JnfvNirF40Q7iwhUWieCYqrpfAeQQfHg2tYurfwp+LTpCohUWTLJIvrx/Af7lisTjH9eJ8cBJR3w50lWtLl0fiKNEXALTlC+Jku2zS0qbJE7QAiE2n5vlSnZ96YVvxKK8pPYVY4cGiYwmzmRaOZUXunzyuLUrwI6iLWG38ZVv6ul0kXOlZqkJPl6uhQq1SS9FUU3sL0jh7i46koYqOf+H3f/DaZai6Ph587hC2p9TaUnt4qKCOfU74Tn1f9nEncSSfvv4iVF0fP/ilXMqcu+PIVSWH/8bJdsGRaFxbqhA8Ggrr2VNkQSLIds/DSMWV3FL8EqVQAbHCYVZDt4nKLRRsO5YkqoI+ksn2hHBW5XfoFBXeR4i7DceqHmb0FfH19/0aAOCXIUeje68kSBbO6BNzeKTi4uzZ/bFjuTtK5Ug475JmkRwZDuY8RU8B8ffKj1eRxJEA0V5E5erVFIS0c8cb3WeRkJuFuUUAWXvXWSR8wv/RNcvx5tVB3xR6STSRo6gN+XpAcxaJbVsxLbC/5IjMdcKyedOwKHTjRIUlo1DkXByJLVsY6rNQyXbb0hOGVNhRfeaEpK6A8rVDi6/mYs+xEWGRENlO76+v6Ejka9SDnJ59AX/2ppXCfZSENLIdgEhIJE7GYws2+I3RdTgsy8K5s/sxeKosuSu4RUKbOXfT8DwSQZ6GG6LOtaU+48uWzMZ586fiT379fOlzwT25PobKNYko5x0JSWEihcgNk91UFBxLEiS6TZuXSFFLjcSul8ciSRAkgFJXzJE3ew56/MGcD/6uuZ5WSHNrIOJIZM9EzfW1Qo++PzpEFkmGayvBBUXP69sfuBKb/+DXpO8kQp+H/4bnGIukRXBZOQtLeXlyiZT4QuAT+5arloioB9owKDGL/PT8+IJtoYy4S6ge2JaFak1ewHzxE77++1eISUvJXjwBi66RlEdC3znsOalCguLxCZYlLwbeDpjfP6k/dhro3H/96a/w6bufxadvuAhAZJHQJftKjnaDVjePLFqmqBQ6JAI8uq4nFA3euCu6vt4iAYBFswMBv/f4KF42N3AV6SwS7sKU80jkDVG32am/b0ZfEd/9b6+OHScyrb0g4m/eNLm2mmNbsBVri9yrukizoiNbJDphHOURIdMiUcl13ebMyfaywiVIlY41XgcC5ZEUnMidy3NqOLhHoVfUM5PdZhXXwxSNwhhZJBpBovAs/PjbbrgIh0+V8dWtv8K+E2NCmF141gxxnfPPnCb9PrUqNX2et5/JeKCrLBLfj+c0RFEX8mYQI/sUMp5QUDgSXRw5/d2n2fjzwrbipea5O4LQV3ISo1QcK+JFCraFL7zj5Xj9BfNj1+DamU4zilsk0SLkPm9qUas+63pAi2DwVBnDFVf4mIkjEc82gyPhY02/n2yRqGGSvB8J1VXTurY0G+TZYWn4PcdGxWeSRRJuQFzj5+5Y1bJTw3+D6gX5nrFwuboeRspuzFp27LiLLwhh1guSgi1bJLooK3qsvPpvUmJcUokRjiTXFpBgkWhdWzTe6NnVXE87V7lGH4/aSj83zpHELRL+G0lJOHdOPz5wzXLWZkF+T0987PX4t/e+Sh6DJ1eljjgS49pqCTjZroakqv7JtInMNyPSIoiE0wkSOqYp15YmympKqYDff83LhEaiIgoIiFxbFUaAX3LOLGz63YHYeQ7jPHSaoBp9JrtCoimjWiRcKL1s7hT86+9dnvBrI9D9aaM4FvZVmNZTlCyn3qIjhX2qHIm4XsZGW3RkJWK6Ul6FSoBT1WXfV5WG4F+dG4F6jOw+GrkjuRuLKtOqme10eVXDVLX5rOZPKgpOwPMMV2ox6zaIXJSvT2PTZeMXHVuuf6blSMgi4Xkk+jHnySORXVuqRcLC+S3986KxAFFUY/BZ9CwvOWemOLaX1epKqrXFLQEO2uSJI+HPyrLie9DGG1fjD177MrxySdADhgRjUrh0cH4UOcZTEARHMoEWSVe5tjhxqUY0cFJcH7XF80yiz9U8Es5BEFrCkVjxqK0pPQ7+x6+fj/dctRSX/OX3Yueok91iwiiNUOZ5NFqLJEa2x0NTgagdr3jG7Lm999UvwxXLzkgcA4HuT8+XmmNN6XFgW9GCD1xbHo4MlTGlp8Bqbcnjz1LYVY5kuqL1U79tskh8S3Ynpbm25k3rQdGxZIuEvdO+ohMKqOgcLdmeM9IpC0U7SJIcLrsx7si244Kk6FhSXTnLikrs9xYd2TWTwpH43LWVEGqdRwHgFklaBjhvI6zCZxaJGvL+7F/8urbVLXdRqxyJbuz83ke0HAm912jMc6f14MNviHgt0cYhRZA4dtBxdaRSU3ovTTxH0l0WiZRHEnzGJxfX3NdecKZUA6eouL4Iah4Jdx1Fx0Rac6PgRDmBFv+sfn2WqipIHDu5FpB0LxbOq1sUcUESucL4GEX4r0Yo5Z3UjhDUkUXSWwySzHjyYF/RQcX1sO6LD+F/P/hCLI+EjzUNBUduzZtokdhR+C+3BNIEiW1bOGtmH/YciywSV2ORSGQ7m7MRr5ew+dbJQRWcYE4NV2qY0qNxbWmUKdeN2uRy910s4VEzb+gjKbO90BqLRG1ixedXlDSZrs3L69pCX0kWjuTS5SWCdNahbqz02dHhCnqLtuRmisq9J78/tRV1Es6YUsLhoYq21pZxbbUIvsa1JU0exiXccuUS/P5rXsa+YxOlXo7Ead4isax4TDmVtEjyi/cIjiQKvxWLKq2EtSOT7Sp0CYkiuorxFCIhUcOR5BYkTIsFgs2DNEKeOEmf7T0+iv0nxpLJ9ozbBgmJKa4t1xPNnygzWpfZXnL073rWlJJUJoeT7SRIuJXip7i2VOisgDQUHBtDYzX4fjSXxLUSXFunylW85rPfByALEnWT0m2mPCcoi2yPNbbKsEjKNVeaq3w8SdapOl7JskxRoGSLJJ8g4RzJzD45FFsN/NGhJ4drCwDmTO3BkeGyVO+P/s06dzzRVa4tV+Pa4qarKEdCQkZjVQTnInYO7QdRfwZ2XU0jovWvOhdvumhB7rE7djyznV/vliuXSDWBgrHJv9FmZHvahsrLwusmdyz6jJHtXNilRW3lndTq/UcqNdaONOIkuJY4WnUjd0adZLuakDhDY5E4oeCkUh9y1BZdR3+fab1FqQRHTRIkdtwiSQn/BYDP/OZq7Ds+is/dt70B15YlhJpqkQSuLfl6RcfCrsMjIsy6VLCBcjR2Dp11pEtIVIMSKEouKSG4VIiKX/Jw74rroWhHRSJLkkWS7c5Vk3D1Lt24a2vZvKk4a2YfFrIKwbpzORGuzqnomOQ1QYIxy6qYM7WEI0OVMHggvK6GB243usoi4SSa1iJRXDCO5jvOBwBxjaqckkfCXUI3X3YOLl86J/fYuTWhu/dH33IB3n3lEu33JMhsi/cjSfe12ppnQNBxJLo5OqpwJHxzSNLYdWORrln1xObDgwL44h6tuMwikc9Xrbd//C+X4t82vFL8v9oCVY3a8phrC5CLKgLR/EoSlNN65Ha0OrK9ppLt4aV0FslNA2fjhpcvkr7Pi4Jj4zgJktxkO68NlmKRaDmS4N9yzY1V0RVjSgjVFZFH7J6cFylX5f42XEBlFYik6/MlkWaRcKVl5YLpeOjW12E2qxygO1cq5a4IEk74J6FHUz9PhzOm9uDwUDlMSCRFMnLZTxS6yiLRlZvQcSSRsInOdRJehqq1CdeWEn8PyO6Deltd2pYVC//NQlEN/7UtRi6m30sI2hwWSZDPET+urDwLfqm8ESSqIBut1MSzs+2Iy+FjGqm4scrD0Vjl68+f1osLWOvSomNLm+D0PnkJ1MJwX37dvBwJECQR8qZgMbLdVsn2eK0tlSOh8darcRacZIskCP9VrQVbsgKo4rRtxedzWomUd/z9T5m2HLdIRqua98YFdGgFVVxPBMiUa1443mB8PRLZnr1RFxx5DuvWB12zTzN3nQxrJi0SkBe+TEJejoQskjlTSzHFw1gkLQJ3E8zoK8GxLSlaRe16JoX52vJiJqimuS5qiyaI1DM7R1MnDtuKeIK/vG4V/vP3X5V5Do2toBl7EmFLxwsuSbMoVCGQZJEQR2Jr7p+31afNTHQgEBIFbpGQIGFJkqNVN5ELUrX2kkL2FhxLiiTS55HI71fmSIJ/kxa8KkhIG/3Q2vPwxovOhGPJVYyzlB/+//VyJEXbFm62GEeicW05tiXl6tB87Ck4sXWh0375IXSuSrZHLZdVV5n8PRAobWQllGuuNF6+Wf/uq87FDS8/C+99dcR5qtBFbano0XAkut+m5UjYfFctkjyCLnJtpa+buVN7UHE9HB+psqgtvQLSTnSZIIkW5doL5uPeD14lOp4BkDYoQBUG8ncE1Vyu1OJRW/QCef5FvR3K+FjOmtWHV5w7O/MctflOltZECDZvK7ZhErRFGzXXG6t6iYujHuKPj3Ws6kqWI88jIYxWIo4kK2pLV9U5XXv0RB6JGJ/OIklw3U3rLYaCLupfAwC/d9USzJvWK0h8gkrmA3HNNS1UOw1ZFok6twuOLUK633XFYqwPiwTyDfw3XxG42XSKkvo7bCs+5iTXls5lWHU9sbGWa5403pksknFabxF/87Y1idwE3VfnouQQZLsmaMZOmA/R9ZNdW0k5Txw9BRslJ7t675zQxXboVDnGqRmLpEXwWBayY1tYNk9O5BM8CPmkJbdX3D0DxDdEUV1XItvJteXEPssLXWRQForKBNJZSQCwirl2guMgjtcpMdrw33BMG2+4CB9aex6AYNNPSpKrJ6adX2OkEmmePExZcm1Va4kciTocVaBTh0SCTntU84zkSsnxDY+D6mNR6Krqgksj20m+JFokdbu2bGG5xfJILP19yCJ5/ar5gnzmv3XjDRfhsY+uTQ2B5f+vzmWyUGLHCgEth/XSXPR9eU7P7Jcjo7KgjkVbIiX8nbow/iRXp+57dU6RAJyrlKnhuOJlc/CWi7ODc86YGijGlVqUYU93rtdibSW6S5AkZJ0SiqpFIk0smT9RzyHoLZK41pynOyBHlumsg5rZLkebRf/z1Q2vxDdZAbgo9Fdf1VddSJYVCd9503vwtkvPBhBoiYnF5+oQJPxZjjKLhGe2cy1xtOIJF1SsimzMooxrxJJF0qvjSFQrBLG/kzgwKn1B7i0hSJiCo1okNGSqCxVz+wgfeH1ziiszMbLdtjREuCUR12qXUSCY17Om6Ddxddo6drz9gMh9Ut8L1Y9TxsSjxSSLJMX60KGguFDzhv+K4/n61Lx62SKRn/XAubPw8d+4AJ+6/sLE8b1+1Zn4m5vWJH5P4H1neEdKdQztRpcJErlSq4qCwpHostPVTVz19asEM1236Mg+53rJdmksOS2SWK0tbpEoPAAvs0IbFdf4ORbO7MUbVs3HwhmBBhVwJGSxRS6RsaqbKPQadW35Prcc9RbJaKUWNhfTuFfYR3OmlGIciJrZrrNI1I6QWtdWokUSXO9kGLmls0iqNQ/feGwPPOptIywSfVmRtMCINKhtoDmCVgrJSlPRsVnl7Hz3jfEolhW3EBNaDiQ9V67UcG5rVpMWSZoCpedI9PNB99kMJYHYsiy869eWtKT97RnT4tFjJPwbKZraKnRV1JbO38xRdGyp2q3O1E0yxQlVbdHGIAu75Oi1pzzQuU+yoJLtuuRLgmzx0L96i6Sn4ODvfmcA/+Uft2LfiTEpaisQJNEEThIk9WTZqr+3xKJndLH9I1U3sXgejfPKZWfg/2pqfakdEqepZLtPrXaja+s2kSRBQhYOt0hsi1dcsLDl6QPY8vQBVGtyB0ZeYJAjskjqFCTsd6qurRsuOSu2GcuKSOTeSypzokJdO6qLEIjWU1L4r6q4SQEs3CWZUO0hCbE8Es2zFFFbCYnFFB6dNO/IokvjapqFVDgzHMakt0i+9rWvYdWqVbBtG9u2bZO+u+2227Bs2TKsWLEC9957b1ODzAseSqlD0UkmUXlSn3yOYpFokp+KthU2TGIugCY4knpdW7rGUuqC1Akq20q/l/DBMs1SJWmTSlfXw5Ekaca3vnElPn19UFaex/b7PjBccVNDUJOEcVAiJfpOHT9ZCJLrkl2KHmNPCtkOMEGidFjkfx88OSYpP17ChkDvt36OJHrP6vvYcPXL8FsDZ0ufSXkarG1rXqVI59qK9zbX/5ZcFgkbx7SMvjMqYpntKRZJUqkjvgaS7gHErdxWosSCRYRrS5TKnzgHU1MWyYUXXoivf/3reO973yt9/swzz+Cuu+7C008/jX379uHaa6/FL3/5Szg5k9QaBQ+l1KFg24kuCxEpZKnnyB8kNbYqOjZrElT/os+a5Dqo/mZ+DXVS6cx6HRmqO4e7tixLXtBJPVjyhv8C8U2fnjlvn6ve59RYLaG5knwNFWr1Xx5CSZodd6mp45vVX0J/yUFvSf/7iGwfKgeuLc9LFiTVsK8Er5oL6N5d/Nw8IAE0s7+Uq/x8zCIJ/zeva0trkaiCxJbnrHpvVeDx/+dzql43n9ouWvcsddYvR/D7kr0eRcdGueZhRl99brd6YFlB2+mhck38Hted5BbJypUrsWLFitjnmzdvxs0334yenh4sWbIEy5Ytw9atW5u5VS5kk+0pFkkCR2JZlnZTlIo2hvWbeKZ53r4RurHk3TBIe9NpefHSIfGx61wPujHZlpx3wrW7pB4s9Sz02GajsWa4oAaAU2NVbdy8WklXdx01UGLXxjfjb9+2hl0jcv+p17rxkkW494NXJ7rupimurZpaYoULEteTlJ8k1xa5TeoVJPS81H7uSSgortl6SX69a0sZk4ja0gcUxCySkt611QiyrP7ZU0qY2lPA4jPibXj5+RNpkQCR642GUVN4uInAuNhCe/fuxdlnR2bzokWLsHfv3vG4lQTyRydBzSGQCXM9CRicF/9M5UiCjU6vbeWBpbEYspCeYCm/WkuzmamksorItcW15uA7el66LOB6EfOjJ4yJuxyGxmpaDYx+ZpJ2FmzIdux41SJMCscuFWxtv2/CVA1HolNYgKBAJFd+0nzdth0nx7NA74hCRjOPVyx0EeqcO7lU/n9H4tai69J30rE25TUpgqTQeCRkbHzslrpNd1pvET//6Fq8dsW81PPT5hbQBkGiNN3qBI4k07V17bXX4sCBA7HPP/WpT+G6667TnuOzOHlCkoa+adMmbNq0CQAwODiYNZxE0D3TNsaCoyYlse9oc9XM1WLBBipyYx0+ES9bMlt09FNdP3khacA5T6cFzkuKEJLcPh7vIsncF/oxkSCJLDl6ziUnKK6nZkw3gpggSXh+fUUHNdfHaNXFqbGadjPI0hqLBZkjcTQWTGABcNdlzh+CIMigVLClqK0k5aXqymR7FP4bH3tDFkl4/JzcgoRbJNE7z6sYxSKxmGuL3D5E8M9UyPI3Xngm+osOdgwOSZ9L4b8NbJRXLT8DP9p+ODa+RsLWuQKmg2MHbqfxrsJLEXi0p1IgRb0BCK1E5i5w33331X3RRYsWYffu3eL/9+zZg4ULF2qP3bBhAzZs2AAAGBiId/PLC9KUU11bymLUhv9qLZL4xODn/s4rz5WOrTf0F1AjyPJNRJVs57fVa+sWwPpr8JpbOvAaWjQ8es70G5vpwSLuowqShIX4gdctw4GTY7j9wR04NVbVHpfFJxSVVrtq5V36m7/yet2U01mZlIBsl/k0QuDailt7SfOtUbI9t2tLUkQii6RRsr1gRwmvJEhWL5qB9756Kc4/U06SXb1oJlYvmokP/fvj0ud8ftXLiwDAHesvFcVF+Wts5FqZSopjj7s1AkTPhNbnLVcuQalg4x2XnTPu907CuIjOdevW4a677kK5XMbOnTuxfft2XHbZZeNxKwEiKtPmR0EhWnlPDp17iJDFkXAUbSu3IOCQ/bf5zlHzSGyh/emLLKqmeSbZLnEktNnJlVab6cFCyOva+p1XLcbrzg960DdqkahzINpQZKUij/aahP5SQWxerivnNvF787bIQPRskyySel0X5DvP7dpSqus6bD7lgc4iideDsmJChEPNtQiix4K/630PdD5p6vI7rftSYv0kCSHHttoiSES/nnAYpYKNW65cMnmjtr7xjW/gAx/4AAYHB/HmN78Za9aswb333otVq1bhpptuwgUXXIBCoYDbb7993CO2aBGmaY+vv+BMnDk9KlPAO/ulhY1mcSTSsQU7d5QLR2MlUmSyXZeJzGGFUSd8UeexSPi1VUGSFOFSD7Lqm3GQ4DpVrmGh3Rf7Xm2xrKLABL1qhUR/y2VU6rUEeASY68uJk3x+id4xwtpL9nU7Soe/PKCCjXktEjkhUbYm8kCXZBgpN8kcJMcHr12OS86dhT/86mMAgmdBfUgc28KH37ACV+Zo4Zw1vkaI6TSvBY21HYKE1kAjVtV4oSlBcv311+P666/XfveRj3wEH/nIR5q5fF3wc7i2rr1gPq69YL74f4cJDx3pStAtpKTNuujYuRO4OLKIQB1KSt0iYXEkqFtCo2LunLR7ifBCj/vxg39JuCYlb9WDWPhvysal614nXStDe+Ul4nVhwMEx8f+vB7YVxfZTD3gCFxLlqtyqWA1k4CjYVt1BHMdHSJDks0iS3L6NChKP5dAUFas5CTP7S1gyJwr7dsLfXXGD9/AHr12Wayz68bG/G9iERbO8hHP7exxRVHE8QZFsjVho44WuyWwXOQB1PFuuYaibLIduISXJimIDC56PJWkM2nuJcGN5Y0xa+Do3Q9q9CpIgif7m92iFIFEXZinl+fVnhIOK35jRLQ9QI9nkazQSjs2Pp9j+eIfFuEUS8U9pFokcbZYHVPk3N9lO9a7CKrRU8DGvIFGnEldAojI+2ddRBW8x7EPSbHgrf9+NRDhlubY+c+PFotbaeKK/2GUWSSchzb+cBN5NMc0lonNtJeURFAt27oXHkVXLRwe1jHyWxqQKkkyyPfyu5vmiSB4R3HStVri26rJISs1ZJMH14y4KSahYSNz888CxbckiSQr/jRpHyUJa97tmTSliVp1ROcfrdG2JBklUAkcIkny/X32PNTdSQPK6ttRjgtD6fNZM9nX198gLXbFXjgsWJnM/rUSUR2IESctBboF6Imx4VFJaIptuYSdtoIELornw37yCRM1bcZRFq0KQluH1z5s/DWfOSC5tTcd5no+PX7cK5y+YhquXB/5p6ubYErJd+blpglhuHhZ/TlkcSXCejiPh1klyGfk8cOyo3Eks/Jf9PVx2pXGQ21D3u/7hXZfWLbQXzuzD4KmyVDE2DfRc6PlTT5W8ipG6wUqurYRijdrrSM9LXwKoETTLkYicmAm2BNSExE5A1wgSP0fUlgrelyTNbI2TiMlaWsCRNGI2179xiTwSZWNM5khkgfO/3v7y1OuLhCffx/TeIjZcHXWgI+1ZDf+95coldWvOunasicc6QXHMoA1rcma7ulG8/bJz8M3H9krfyS105Ws4Cd/lgWNZwiIJClvqw3+HFYskqUQKAMxL6WWRhC//7gCe2nsit/uRBBg9/6qmrlwa1OdUZRaJrkJ18nVki+TAyTEAwOwpzRHZLSPbJy44CkBrvACtRtcIkjx5JCq4qarLJyCowqW3GG89SuAZ7vWgmaKN6saYzJFAOi7vmEhocJDbQ53UH33LBbmuLd1HLaOR8fym9RZwZLhSV62t2264CLfdcJH0nUSos/Bf25KfUb2bjm1HUVtqawPZIgkESazWVotUzbnTevDa8/VZ2jqQAIsskij5NA/UNeF6XhRJqEQWpoEfwp/Fe65ammsciddNKHuT+3wxtyZWktCao/DxTkAXCZLGyXZp09C5tqy4IEnCObP7Y21N86CRvIWZ/UUsnTsF582fJp2XxZHk3aho//B0gqSFri1dO9w0kCDRciSad6pCH7XFrmHJORt1u7YsFv6rWCT8WqprK3ykTZPKjUJ03FQskkYTEjnZvmROPxbN7MMl58zMvI7qCvzbt12MWf0lzJtev1Umj685iySrjlu7EPWxN4Kk5aDNrh6ORMoj0WipBHXSpZmWX8hwFyWOhW9kORWe3qKDB/74Ney8dIukXh8vbYC1FIukd1zCf9PHR33WG6m1BegrPfN5QxVWk8aXBSmPxPMTKw4MlfWurUYqI7QCatRf/YJEtUiiiLW+UgGfCy3Ceq5TsC1c//JFuc6r57oNWSQd4tqK+ti7GUe2DxP8SFqHpMqpaZDLqQef6TZxdSPpSSlUqJYgz4tmXCniGoo/Ov59eP2ci8hRNjgO2mT6W5KQKP9/liuFKuymZbanbRS6Kgb8Uo5lYdm8qVg+b6r4/3rg2FFfdtdLTkhU7x1FHk7MsiwonNvvvmoxLlsyG++4PF/pDfWZB22Lk13GiddpwVrQXjdBoNd7/kST7RStaVxb44DmXFvJJC0Q3+h66+j+lxfNakvBecG/SQmR0W/Mdz06TseR0GetyCNRN84sDZjKaOhI6SSOhEPHkajEu2VZ2Pz+X8N/PLoHA4tnpf8ABY5tYayanZBIiFVWnqCNSpDtYaLr3Gk9+Pf3vir3+eq04xFrjSh49Z6XhUZC7DnUYJWJAqUeGNfWOCBPiRQVXHul03SbuNr8qBWbpwpeMbTRxUPnZVokeftLsKgtFVVPT7Y3AlUeZLm2yCJppEMiECUrJnIk4f/0lwr43VctTh2LDrZlIfT8wfV9lOys3Bf5s4niSEQeSYMWkc4isVPWVeJ1GgiFz3VdzTuu73x6PhMtSMi11TmCpGtcW3lKpKjgme0UtaMVJEpYZFJ72WbwG6sXRONqUOMRCYkZHElei0Rktrs6jiSYxK2xSOTfm+3aKmrPA7KTMvl38mPmHEnq7TMRKBzB86l5vmLtJLu2xPgmiCPhme2NIG3t1PNMG6mEXe91G1ljPF1gIkGu9U7iSLrOIqnLF8t8npHmFD+OJl3JsTFW9cbFtTVvei++80dX4ZEXjzQ8UTOjtmgh5OVIwhN0Fgm5YVpjkcibRSbZ3pucTxBZXWmCxI4do0ZtNYNAkAR/eyrZnlLWRR1fuyESEhsoOgrohUVWNrgO4+Xakl2ZjZ/fMa6taudYJF0jSKJaW/lfsmVZohxGGkdCk65UcADUWhKppMPKBdOxckHjZRay3AhZJdZVLAvJ5vPPnJZ4TEsaWynDyRP+CwBj1bhGluc3ajmSBOK9ETiWJaII1YRE3btRP+oW1xb/rB7lSGrQ1kqOhLvMGsojqf+3jAc60bXVNYIkKpFS33lOGGWVFu1jC4skdG2Ng0XSCkQhzAnf1ylIXn3eXGz54FVYMT9ZkPS0oBucujCzwl9JkIxUdIIk+DerqrFlJVshjbp2CI4dZbZ7npyQmMbrECYq/FdNSKwXaUpYPetSyiNp4bNoPo8k+HeiOZIFYVmjq5Y3Vk5/PNA1giStTWkagnIYkbaSlkdC/vfx4EhagbTmXACrtVXHqk5qQvRXN16EO368syXambowM6O2wjwSnSDJU2uL7plUKr7Zro+2zS0STxv+W3Si6rqxXucTZZE4MhdYL3TDbsQdpIZitwrSdRt4xo1EoI0H5k3vxdY/uyZ3Ved2oGsESSMlUgDKIclwbdWR2T6RyNLG67VI0vC2S8/B2y5tTWtPdTx5XVujWosk38alNvXi77jZ9+tYURKn5+tdNVN7CjgW9gvpHI6EBElj99dFTGYpNzqoCYmtQsuKNk4wRwKg6Sz/VqMzVesG0AjZDkT8CJ2mmyNxQdKZj43GmTTPOyWhSkVUajyfRkxkOxU9lK8V/Ju1URRtW8lmj75r9v06ti1ltvPNkMY1tTfS4dQNeKJeDwmwVkaNpQWxJKGZXjDpY9ErDvnPD/6daIukE9GZO2IDaCSPBIAoGZ6akKgk5pXGuW1wo8iyODolDl4FjYe4p7wJiakWScZvdBxL4i74xtLTrEViQ8ps121gU1iQgjrUeudwq9Bs+K8ODZHt3CIZB6EGNOfa6jRFrBPQNYKkkTwSIHRx5ExIHAvjthsNjxxv8LwYHTrJNOeghUnRcFn9XKamkO2CB6qTI+GHNxtModba4oKb/p7WywUJCZeJVVDUxlbN4rz5U3OVrFEhb/jjlEcyiRMSOxFdw5GsWjgdOz79JtT7im3LkkqkpCVVkQbcSo2tlchyXXWqaS4K+xXJIkkfH23Cb12zMH6tnIQo74oJyFZAs64t24pqbcUSEsO/p/TELZItH7waz+4/2dS9m4Ha2KoZPPjfX4M5U0s4IXig/OdSWL7ntzj8l12qEWVKBHJ0mCLWCegaQWJZViwfIQ8KwrUV/L9uDdHEobjtUgtCXscDrQ7/bRcoxJMESZagLjo2nvrEG7TJkHm1xoJtS/NFskiadm1ZjGzXWyS8tzdtUGfP7sfZs/ubuncziKK2mp/fS86YAgA4NRZWOG4gmtLz/ZZaz1boefAbFFA0X4xrK47O3BHbCOJH0tw+6ryZ6MY2WUjmSNK/nyiQoM7r2gKCjTitZ3vWBsR5MfX4psN/rci1VXM9OTpMI0g6xdWodkhsBfK+j9h5LXazReNpXJmqt+jp6YTT/pGQWyvNLVRvwtxEwRdJmekcSaeZ5rSB9RXJtdL4+KJ6YxkWiSOX+29l1FaB5ZF4vuKbt3SCpKnbtQwkwFupKDlCQWvsvFYrPc1ELnYqx9gJ6BrXVqNwLEvyl+vm1x++bjmOj1RxYrSKB5471LGurawQ6E61SGwhSEKOpImNLC+5W2DuTPX4lpDtPktIZEKNhCbvadMpGxONrZXzu9HNV1SybvFcDcbjN6RMkSXS6R6JicBp/0Tk8ih6bX3WlBL+9m1rhKbcqWQ7uVOSFkmn1ApSQeOd0lNAybGbGh+dmrXYHduWBGrrM9uDvz1PyV/QJP11iBxpOrNdh0YSEoH80Xf1IrJIGjnXuLaSYCwSK8onCCK4kiculbRoBRk5HhCFKydZHgmR7e+6YjFufEVzbVUjYZl+XDz8N/q72fphjmWJnvZBiZS4RcLnUOdYJK13bc3oK+KtaxbisiWz6zovskhau9aaqeCb1o77dMdpL0je/7plmDMlqFnDS6XoIHpYd6hriziS5DyS4N9OWwg03rNm9WHBjL6mrkU/LWsDevk5M7W5HEDzFpttW/D8oGCjWiJF7W2j3nsiMXtKCTe8/Cy86mVzWnZNx7bwuZtfXv95ORWCetEasr0z3lcn4bQXJNetOSv6Hyt9knx83Sp8+u5ncXmd2lW7QH75LIuk0xZCK7XPvO6Hv7juQuW8pm8tQFbHgZNjAIKWtep3UiHHDnkdjm3hb962ZqKHAYA3KGu1RRL820z1305bP52A016QcPzWKxbhihRt7GVzp+KOd13axhHVh4hsTxAk4ZrstKit/lIBtiUT0I1C/MZ6N6AWPhLaaHYdGQYAnD0rsrJEXbEC50g66310AhzxHlvMkeRMWNVhvCLJugFNrdwPf/jDOP/887F69Wpcf/31OH78uPjutttuw7Jly7BixQrce++9zY6zLfjU9RfhNSvmTfQwGobnpUdtUWnKVvZ4aAXe+vKFuGvDq1I7H+ZFoz7wVrqX6Fq7Do8AgJRkSJtQkbu7Out1dASyun02ikZKthA6NXy+E9CUIFm7di2eeuopPPnkkzjvvPNw2223AQCeeeYZ3HXXXXj66aexZcsWvO9974Prdk5/4W4FtXdtZT+SdqC/VKibjE1C3n4kKlopSEibfim0SM6aGVkkOrLdaLhxNPoes9AK11anRT12ApoSJK9//etRKATesVe+8pXYs2cPAGDz5s24+eab0dPTgyVLlmDZsmXYunVr86M1SIWXkyPp5jD4otio6xUkrRsDPeedh4dx5vReKZxYl7FtXFtxjG8eSWPKVKc0tupEtGxL+Yd/+Ae88Y1vBADs3bsXZ599tvhu0aJF2Lt3r/a8TZs2YWBgAAMDAxgcHGzVcE5LkCBJziNB6vfdgFevmIu/vG6V6DefF63czGnze+nICM6e3af9ruTYQtiZfSmO8dq0W5HZbgRJHJlk+7XXXosDBw7EPv/Upz6F6667TvxdKBTwzne+E0DU9pYjaaFu2LABGzZsAAAMDAzkH7lBDEKQJKgHnRq11Ur0lwr4nVctrvu8Vj4STra/+aIFyn3IIrFRdGxUXbdjwn9bgT/59RU4e1bzhSctK3gnrbbWbMtqeP6fDopYo8gUJPfdd1/q93feeSe+/e1v4/777xcvfdGiRdi9e7c4Zs+ePVi4MF7y26C1uGblfNz+4A68+jx9wACV5zaulDhaSraHO0655kmhv0Dk0io4VsiTdJcged9rlrXkOo5ljUspkmYEiXFtJaOpN7Vlyxb81V/9Fb71rW+hvz/SQtatW4e77roL5XIZO3fuxPbt23HZZZc1PViDdFxyzizs2vhmXLRohvZ7OyNP5nRGK/dyrrGqdatWLZyOmy89G5ecM0sQ7l0kR1oGx258w0+DZTVuUTQT8dXtaCqP5P3vfz/K5TLWrl0LICDcv/SlL2HVqlW46aabcMEFF6BQKOD222+H06HtaU8nZGXun84YD4sEiNdl6y8VsPHG1eF3ZmNKgm1Z41LKpznXlrFIktCUIHnhhRcSv/vIRz6Cj3zkI81c3qDFsO3Oq7PVKWjlXs6fcVo5Hfqum6PoGoVtj0+YrW01zoeZzPZkmCl8GsHKKEp5OqO1eSRMkKTUaiFrxWplWn2XwOlEi8RwJIkwguQ0QjOLqNtBgmR6b/NVg7hQKqXks5CQoUrBBhHsceJImrluM5WDux2m1tZphKR+KwaBlvmJdatw1fIzWnItQppFQq6tSs0IEhXjx5E0blE008uk22EEyWkEY5GkY/0Vi1tyHW6RpLu2guOoz41BBMeyxqUmnG1ZTUdtmTUUh5GtpxEsE/7bFvBnnNa2dmZ/CYA+gfd0RxAY0vrtqRme0HAkyTAWyWmES86ZZUJN24BCTtfWZ25cjX9Z9FLLClZ2E+wwebb1123etWXcw3EYQXIa4bdfeS5++5XnTvQwuh5SHkkhedOZNaWE979ueTuGNOng2OOY2W5cWy2HcW0ZGLQYTk6OxCAZ48Xn2Vbj+Sk0HhNCH4exSAwMWgyuSBtB0hjOmd2PvmLrq2FYTVgkndrPpxNgBImBQYthLJLm8ZdvvXBcrtsMR3LN+fNxYqSK/pIp96TCCBIDgxaDN63qSYnaMmg/mnGZrThzGv70TStbPKLugJnlBgYtRt48EoP2wzZlgsYFZpYbGLQYcma72bQ6CUEZ+YkeRffBuLYMDFoMY5F0LuZO60HVNSVpWg0jSAwMWoy8me0G7cdnfnP1RA+hK2EEiYFBi8Ez29XGVgYTi/6S2fLGA2aWGxi0GJzMTWtsZWDQLTCz3MCgxZDzSAyza9D9MILEwKDFkKK2TPMKg9MAZpYbGLQY3LVlchYMTgcYQWJg0GKMR2c/A4NOhhEkBgYthun5YnC6wQgSA4MWw/SrMDjdYASJgUGLYcqMG5xuMILEwKDFMIFaBqcbzJQ3MGgxxqNFrIFBJ8PMeAODFsPIEYPTDWbKGxi0GIYjMTjd0JQg+ehHP4rVq1djzZo1eP3rX499+/aJ72677TYsW7YMK1aswL333tv0QA0MJgtM1JbB6YamBMmHP/xhPPnkk3j88cfxlre8BX/xF38BAHjmmWdw11134emnn8aWLVvwvve9D67rtmTABgadDstYJAanGZoSJNOnTxd/Dw8PiwW0efNm3Hzzzejp6cGSJUuwbNkybN26tbmRGhgYGBh0JJouzv+Rj3wE//zP/4wZM2bgwQcfBADs3bsXr3zlK8UxixYtwt69e5u9lYGBgYFBByLTIrn22mtx4YUXxv7bvHkzAOBTn/oUdu/ejXe+85344he/CADwfT92nSRzf9OmTRgYGMDAwAAGBweb+S0GBgYGBhOATIvkvvvuy3Whd7zjHXjzm9+MT3ziE1i0aBF2794tvtuzZw8WLlyoPW/Dhg3YsGEDAGBgYCDXvQwMDAwMOgdNcSTbt28Xf3/rW9/C+eefDwBYt24d7rrrLpTLZezcuRPbt2/HZZdd1txIDQwMDAw6Ek1xJLfeeiuef/552LaNc889F1/60pcAAKtWrcJNN92ECy64AIVCAbfffjscx2nJgA0MJgtMOXmD0wWWryM0JggDAwPYtm3bRA/DwKBpbNt1FAtn9mHhzL6JHoqBQcPIuyc3HbVlYGAQx8Di2RM9BAODtsGUSDEwMDAwaApGkBgYGBgYNAUjSAwMDAwMmoIRJAYGBgYGTcEIEgMDAwODpmAEiYGBgYFBUzCCxMDAwMCgKRhBYmBgYGDQFIwgMTAwMDBoCkaQGBgYGBg0hY6qtXXGGWdg8eLFTV1jcHAQc+fObc2A2ozJPHbAjH+iMZnHP5nHDnTv+Hft2oXDhw9nnt9RgqQVmMyFHyfz2AEz/onGZB7/ZB47YMZvXFsGBgYGBk3BCBIDAwMDg6bQdYKE2vZORkzmsQNm/BONyTz+yTx2wIy/6zgSAwMDA4P2oussEgMDAwOD9qJrBMmWLVuwYsUKLFu2DBs3bpzo4eTC4sWLcdFFF2HNmjUYGBgAABw9ehRr167F8uXLsXbtWhw7dmyCRxnh3e9+N+bNm4cLL7xQfJY23ttuuw3Lli3DihUrcO+9907EkAV0Y//4xz+Os846C2vWrMGaNWtwzz33iO86aewAsHv3brz2ta/FypUrsWrVKnz+858HMDmef9LYJ8vzHxsbw2WXXYaLL74Yq1atwsc+9jEAk+PZA8njb+nz97sAtVrNX7p0qb9jxw6/XC77q1ev9p9++umJHlYmzj33XH9wcFD67MMf/rB/2223+b7v+7fddpv/J3/yJxMxNC1+8IMf+I8++qi/atUq8VnSeJ9++ml/9erV/tjYmP/iiy/6S5cu9Wu12oSM2/f1Y//Yxz7mf/azn40d22lj933f37dvn//oo4/6vu/7J0+e9JcvX+4//fTTk+L5J419sjx/z/P8U6dO+b7v+5VKxb/sssv8hx9+eFI8+7Txt/L5d4VFsnXrVixbtgxLly5FqVTCzTffjM2bN0/0sBrC5s2bsX79egDA+vXr8c1vfnNiB8Rw9dVXY/ZsuRd50ng3b96Mm2++GT09PViyZAmWLVuGrVu3tnvIArqxJ6HTxg4ACxYswCWXXAIAmDZtGlauXIm9e/dOiuefNPYkdNLYAcCyLEydOhUAUK1WUa1WYVnWpHj2QPL4k9DI+LtCkOzduxdnn322+P9FixalTtROgWVZeP3rX49XvOIV2LRpEwDg4MGDWLBgAYBgAR46dGgih5iJpPFOlnfyxS9+EatXr8a73/1u4Zro9LHv2rULjz32GC6//PJJ9/z52IHJ8/xd18WaNWswb948rF27dtI9e934gdY9/64QJL4m8CxN4nYKHnroIfz85z/Hd77zHdx+++344Q9/ONFDahkmwzv5/d//fezYsQOPP/44FixYgD/+4z8G0NljHxoawo033ojPfe5zmD59euJxnfgb1LFPpufvOA4ef/xx7NmzB1u3bsVTTz2VeOxkGX8rn39XCJJFixZh9+7d4v/37NmDhQsXTuCI8oHGOG/ePFx//fXYunUr5s+fj/379wMA9u/fj3nz5k3kEDORNN7J8E7mz58Px3Fg2zbe8573CPO9U8derVZx44034p3vfCduuOEGAJPn+SeNfTI9fwCYOXMmXvOa12DLli2T5tlzqONv1fPvCkFy6aWXYvv27di5cycqlQruuusurFu3bqKHlYrh4WGcOnVK/P3d734XF154IdatW4c777wTAHDnnXfiuuuum8hhZiJpvOvWrcNdd92FcrmMnTt3Yvv27bjssssmcqgx0CYAAN/4xjdERFcnjt33fdxyyy1YuXIlPvShD4nPJ8PzTxr7ZHn+g4ODOH78OABgdHQU9913H84///xJ8eyB5PG39Pm3MjpgInH33Xf7y5cv95cuXep/8pOfnOjhZGLHjh3+6tWr/dWrV/sXXHCBGPPhw4f9173udf6yZcv8173udf6RI0cmeKQRbr75Zv/MM8/0C4WCf9ZZZ/lf/vKXU8f7yU9+0l+6dKl/3nnn+ffcc88Ejlw/9t/+7d/2L7zwQv+iiy7yf+M3fsPft2+fOL6Txu77vv+jH/3IB+BfdNFF/sUXX+xffPHF/t133z0pnn/S2CfL83/iiSf8NWvW+BdddJG/atUq/xOf+ITv++lrdTKMv5XP32S2GxgYGBg0ha5wbRkYGBgYTByMIDEwMDAwaApGkBgYGBgYNAUjSAwMDAwMmoIRJAYGBgYGTcEIEgMDAwODpmAEiYGBgYFBUzCCxMDAwMCgKfz//sRh44/Ly0gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_18\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_20\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_25\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38f7d188535d4d728701dfdd3cee6d80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe129ffd0234418aaebe3522b7b8ff36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77b26a13332b4352a9c73efe1ea96256",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32c04b3fddca4073a67ca5c4c805a012",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6241b53dd06a465888cf70e9bb5bed22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7e7aabde8d14c3ba1fb2cf7a819c010",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f393d8734efc412092e1b470fad8ab1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4+vQpLly4gMGDB+PIkSNFLo/OKs6YFxoHSsrDzYsRrGeDfayhcJPKT2PDX9kIj2Ns38ZA9ujOWxYVlsSiw5PVfl4++cDOH3nBls3yZz3qqV+noXAT86qxmx367TGTSKR5xkDjQFXdvXuXjRo1irm4uDADAwNmbGzMXF1d2XfffcfevHmT5zm54zF3797Nzpw5w9q2bcuMjIyYubk5GzBgAHvx4oXK8cHBwWzmzJnMzc2N2djYMJFIxJydnZm3tze7efNmnu9x/fp1NmjQIObg4MCEQiGzsbFhTZs2ZTNnzmT3PhkL6OzsrDY2Mj+1a9dmAJiVlRXLzs7O97js7Gy2adMm1q5dO2ZmZsZEIhGrXr068/LyYuvWrVMbU5qdnc0WL17MatSooSjfDz/8wLKysoo1DpQxxmQyGdu0aROrX78+E4lEzMHBgU2dOpUlJSXlW9bdu3ezpk2bMiMjI2Ztbc2++OIL9vjxY+bj48MAMD8/P5Xj379/z4YOHcrs7OwYn89nAJiPj4/i9Zs3bzJPT09mYWHBTExMmLu7Oztx4gTz8/NTO7ZclPM4UB5j//U/L4KWLVvi/v37hR9IiAayMnOwZv5NHNqiOiuLsZkQI75ugs+H1IWhkXoPxsK8fp6Iy6de4/yfIWq107qNrbFwkwdatFf9xp7bC5NTHHciIkTnfJyfWhb8N1Maf+PUhEu0QkxkKr4ZeB7PHn5Q7NPT42Hk9KYYPLER9ISaP66vWd8SNeu7YcS0Jjh7+CWObH+K5ER5s/DLJ/EY6XEc/Uc3wOwV7rCwMijkaoQQIkediAjnHt19hy/b/amSPOs1scah6wMxbGqTEiXPjxkaCzFogisO+Htj3HfNVZ6Bntj7DAPcDuPu1ahSeS9CSMVHCZRw6vqFCIzrfhIJH5QzmIyZ2QybjvWCpY36lG6lwcBQD0OnNMYu335w76acpu59dDrG9ziJtT/cQjGebBBCKilKoIQzV06/xrQB55CVKR8CIBTxsXxPVwyf2qRI08eVlL2jCRZt8cSiLR4ws9AHADAG/L46AHHvMyAR5z3+jRBCAEqghCP+58Mxc+gFxbhMQ2M9bD3dB27uZb8I7qfcuzlh+7k+aOGu7EgkyZbh9fPEAsebEkIqN0qgpNw9ffAes4ZeQI5EnjxNzETY+XdfONUyL+TMsmNtZ4Rlu7pi0jw38Pny2q9EIkPYy0QkxmUWcjYhpDKiXrikXMVEpODr/spmWyMTIbad7Q07BxOOIwP4fB4GjXdF7YZW4PHlk3UzBkRHpEIslsLOwbhcmpYJIbqBaqCk3KQmZ2PqF2cR9y4DAKAn5GPTsV5akTw/1rydA6xtDaFvoOyl++FtBmIiU6lzESFEgRIoKRdSqQyzhl1ASJB83k4eD/hpuxenzbYFEejxUb2mOYxNlRM3JMZlIfJVMmRSmk+XEEIJlJSTbcvu49ZF5aTU0/7XGm4dyr/DUHHw+TxUdTJV9NAFgNRkMSJeJUNKSZSQSo8SKClz969H47eflIsO9/qyDvqOqM9hREXH4/FQpZoxrGyVY1LTUyWICKUkSkhlRwmUlKmk+Ex8P+ofyGTyZ4fVa5rhm8VtOI6quHiwsTeCbRXlcloZaRJEhCRBSsujEVJpUQIlZYYxhh/GX8b76HQA8okSVuzpBoFAN//bWdoYwtbhoySanoOI0CSqiRJSSenmnYzohD93BMH/fLhie96ajrB1MOYuoFJgaW0Iu4/KkJGeI+9YJKPeubpszJgx4PF4eS7ozRUXFxfFotVEO9E4UFIm3r5JxZr5NxXbnr1d0KmnM4cRlR4LawMYhS2FJDFMvuMtkBEmgLGpsHTHiWbHKf+tb1O8c42cgKZLShyCi4sLIiIi8nzN3t4e7969U9knFouxZMkSHDx4EAkJCWjRogXWrFmDFi1aqJ1/8eJFdO/eHWfPnsXnn39e4lh1jYeHB/z9/WlolA6jBEpKHWMMS6dfRXqqfBo8M0t9zF7uznFUpcuQxQAWtZCRLi+jBIBUIoCZpT54KKUkyhd99IbF7LGcHl46MQAwNzfHt99+q7bfxER9/O68efOwbt06eHt7o1q1ati/fz88PT3x/PlzODgop0pMS0vDxIkTMWLECK1InsuWLcO8efPg6OjIdSgKly9f5joEUghKoKTUnT8SAv/zylrLwo2dVZYOqygMjfXAGENmhnxWpewsKdJSxDAxE5VeEtUCFhYWWLRoUaHHMcawbds2jB07Frt27QIA9O/fHx4eHti/fz++//57xbFz585FVlYWNmzYUFZhF4uDg4NKgtcGtWrV4joEUgh6BkpKVWJcJpbNuqbY7tzLGc3aVuEworJlZCKEgaHye2hmeg4y03M4jIg7Hz58QEZGBlq3bq3Yl/vvj5uBr127ht9++w2//vorrKysNHqv3OeDKSkpmDVrFlxcXCAUClUS/fPnzzFmzBhUr14d+vr6sLe3x7Bhw/DixQu16xX0DPTu3bsYOHAgqlSpApFIhOrVq2Py5MmIiYnJM7aEhAQsWLAAjRo1gpGREczNzdG0aVPMmzcP6enpCA8PB4/Hg7+/PwD5UKncHw8PD7Uyfio7OxvLly9HkyZNYGRkBDMzM3Ts2BF//vmn2rG57zVmzBiEh4djyJAhsLGxgYGBAVq2bImzZ88W/Iv+xMmTJzFixAjUrVsXxsbGMDExgZubGzZu3AiZTL0zXe7v9fXr19i0aROaNGkCQ0NDlXImJCRg/vz5aNCgAQwNDWFubo4uXbrgn3/+UbtecnIyVq1aBS8vL1SrVg0ikQi2trbo27cv7ty5U6yylAaqgZJStfL7G0iMywIAGBkLMevn9hxHVPaMTYWQyRjE2fLlz9JSxODzeSqJVZdlZ2fjwIEDiIyMhLGxMZo0aYJOnTpBIFBtVbC1tYWhoSEePHig2Hf//n0AgLOz/Pl3ZmYmJkyYAG9vb3h7e5coLrFYDC8vLyQkJKB79+4wMzNDjRo1AAAXLlzAgAEDIJFI0KdPH9SuXRtRUVH466+/cO7cOfj5+eX5XPZTu3fvxsSJE6Gvr4++ffuievXqCAkJwc6dO3HmzBncuXMHTk5OiuPDwsLg6emJiIgIuLm54auvvoJMJsPLly+xbt06TJkyBRYWFvDx8cGePXsQEREBHx8fxfmFdRoSi8Xo0aMH/P39Ub9+fXz99dfIyMjAsWPH8OWXX+Lhw4f45Zdf1M6LiIhA69atUbNmTYwcORIJCQk4cuQI+vXrh0uXLsHT07NIv/N58+aBz+ejTZs2cHR0RHJyMq5cuYIZM2bg3r172L9/f57nzZgxA9evX8fnn3+OXr16Kf7vREREwMPDA+Hh4ejYsSN69uyJ9PR0nD17Fj179sS2bdswceJExXWePXuGBQsWoFOnTvj8889haWmJyMhInD59Gn///TfOrFmDnu3L755TMf7CiVZ4dPcdTh9QfrufvaI9jEyEBZxRcZiYiZCSlK1YYSYlKRt8AQ8ike43Xb979w4jR45U2VejRg3s3r0bnTt3Vuzj8XiYNGkSNm7ciOTkZDg6OmL//v0wNTXF8OHDAQA//vgjEhIS8Ouvv5Y4rrdv36Jhw4bw9/eHsbGyZ3RiYiKGDh0KIyMjXLt2DQ0bNlS8FhQUhDZt2mDChAkICAgo8PovX77E5MmT4eLiAn9/f5Xno1euXEG3bt0wY8YMnDhxQrF/xIgRiIiIwC+//IL58+erXC8uLg4mJiYwMDDAokWLcPXqVURERBSpeTzXmjVr4O/vj88++wynT5+Gnp78Fu7j44PWrVtj2bJl6N27N9p/kkSuXr2KRYsWqSTrYcOGoWfPnli1alWRE+i5c+fUmpZlMhnGjh2Lffv2Ydq0aWjTRn2cd0BAAAIDAxVfcHKNHj0aEREROHz4MIYMGaLYn5SUBA8PD3zzzTfo27cv7O3tAQANGjRATEwMbGxUO9VFRUWhdevWmLluXbkmUGrCJaVCJmP4Zaay6bZhc1t07FExet0WBY8HmJqLIBAon32mJGbp/BjRsWPH4vLly3j37h3S09Px5MkTTJ48GeHh4fjss8/w6NEjleNXrFiBuXPn4t9//8Xvv/8OV1dXXL58GY6Ojrh79y7Wr1+PDRs2wMbGBosXL4aDgwP09PTg5uaGmzdv5hNF/tasWaOSPAFg3759SEpKwuLFi1WSJwC4urpi4sSJCAwMRHBwcIHX/u233yCRSLBhwwa1zkVeXl7o27cvzpw5g9TUVADAgwcPcOvWLTRr1gxz585Vu15u02lJ7Nq1CzweD2vXrlUkTwCws7PDwoULAQA7d+5UO8/Z2Rk//vijyr4ePXrAyckJ//77b5HfP6/nsnw+HzNmzAAA+Pr65nne999/r5Y8Hz16BH9/f3h7e6skT0D+3H3x4sXIysrC8ePHFfvNzc3VkicAVKtWDQMHDsTz8HBEftIzvCxRDZSUitMHnuPp/VgAAI8PzF1VsXrdFgWfz4OZhT6SE7MgkwEyGZCckAULG0PwdXQZtI9rLADQqFEjbN26FSYmJlizZg0WLVqkUgPT19fHsmXLsGzZMpXzxGIxxo0bh169emH48OFYv369okbk7u6On3/+GT179kRoaKiitlEYAwMDNGnSRG3/7du3Achv0HnV7l6+fAlA3hz4aYLN6zr+/v64d++e2uuxsbGQSqV4+fIl3NzcFM/gevToAT6/9OsmqampCA0NhaOjI+rXV58K08vLCwAQGBio9lqzZs3UmtwBoHr16opyFkV8fDxWrVqF8+fP4/Xr10hPT1d5PTo6Os/zPn4univ3fZOTk/P8nD58+ABA/jl97ObNm9iwYQNu376N2NhYiMVi1RhiY+FUpXz6XVACJSWWnirGuh+Vf4Sff1kXVZ3NOIyIO3wBDybm+khJzAYA5OQwpCZll+7wFi0wZcoUrFmzBteuXSv8YACLFy9GdHS0omPIqlWr0KVLF8WNs169enBxccGvv/6KJUuKNn7Vzs4uz3G38fHxAIAdO3YUeH5aWlqBr+deZ9WqVUW6TlJSEgCU2VCY5GT5GrX59RbO3Z8bx8csLCzyPEdPTy/Pzj95SUpKQqtWrRAWFobWrVtj1KhRsLKygp6eHpKSkrBhwwZkZ2fneW6VPBJa7u/34sWLuHjxYr7v+/HndOLECQwcOBAGBgbo1q0batWqBWNjY/D5fFy9ehX+/v7IlkiKVJ7SQAmUlNj25fcVa3waGOlh0lw3jiPillDIh4mpEGn/jYPNzpIiPVUCE1NRIWfqDjs7OwBQq4HkJTAwECtXrsTWrVvh6OiIlJQUxMTEKJ6LAoCTkxNsbGwQFBRU5Bjym7TC3Fy+RN6jR4/yrKEWVe51kpOTYWZW+BfC3CSVXy2spHLj+XTyilxv375VOa607dy5E2FhYfDx8VGrMd6+fbvAIUl5fVa5cW7YsAHffPNNkWJYuHAhRCIR7t+/jwYNGqi8NnnyZEXP5vJCz0BJicREpGDvhoeK7cnzW8LQuHJ0HCqIvqEeDIyU308z0iTIyqw4w1tym99q1qxZ4HE5OTkYN24cPD09MX78eJXXPq2tZGVllUpsbdu2BQBcv369XK+Te7yvr2+RanW5TapSqbRI1zc1NUWtWrUQHR2NkJAQtdf9/PwAoEi9izURGhoKAHn2ntYkcWnyOYWGhqJhw4ZqyVMmk+HGjRvFjqGkKIGSEvl16b+QiOU3C3tHY/QaXIfjiLSHsbEQQpHyTyw1KRsSSdFultogKCgICQkJavsjIiIwbdo0APJepwVZtmwZQkNDVZpTzczM4OjoiAsXLiAnR/6lwt/fH6mpqXB1dS1x3GPHjlV0Qsmrg4xMJsPVq1cLvc60adMgFAoxc+ZMxXPTj4nFYpWbv5ubG9q3b4+HDx9ixYoVasfHx8erfEmwtrYGAERGRhalWACAcePGgTGGOXPmqCTeuLg4LF26VHFMWcgdYvPp7y4wMFDtmXdRtGzZEh07dsRff/2lmHjjU0+ePEFsbKxKDCEhISpjcBljWLx4caGdwsoCNeESjYUGJ6gMW5n1S3vw+RXnOV+J8QBTMxGSE7MhlTIwACmJ2bC0MdSJ39PRo0exfPlyeHp6okaNGjA1NcWrV69w7tw5ZGVloVevXpg9e3a+5wcFBeGnn37C2rVrFeNAc33//feYMWMGOnbsiDZt2uDgwYMwMTHB119/XeK4ra2tcezYMfTv3x9t27ZFly5d4OrqCj6fj8jISNy+fVstmeWlfv362LVrF8aNGwdXV1f07NkTdevWhUQiQWRkJK5fvw5bW1s8f/5ccc6BAwfg4eGBH374AcePH4eHhwcYYwgJCcE///yD58+fKxJRly5dcPToUQwYMAC9evWCoaEhnJ2d1YYMfWz27Nn4+++/cerUKTRt2hS9evVCRkYGjh49itjYWHz//ffo0KFDiX+HeRk1ahRWrVqFb7/9Fn5+fqhTpw5CQkJw9uxZDBgwAEeOHCn2NQ8dOgQvLy+MHz8eGzduRJs2bWBhYYGoqCg8fvwYT58+xe3btxWPDGbOnIkpU6agefPm8Pb2hlAoxM2bNxEcHIw+ffrgzJkzpV3sAlECJRrb5HNHsQpJrfqWaNFeu6ZC0wY8Pg+m5vKeuYwBUilDarJudCry9PTEixcvEBgYiNu3byM9PR0WFhbo0KEDRo4ciZEjR+b7HFIqlWLcuHFo06YNpk6dqvb69OnTkZKSgq1btyIgIABNmzbFunXritwDtzBdunTB48ePsXr1avj6+uL69esQiUSoWrUqvLy8ijyJw4gRI9C0aVOsWbMGfn5++Oeff2BsbIyqVati4MCB+PLLL1WOr1GjBgICArBy5UqcPHkSmzdvhoGBAVxcXPDdd98pEgEATJgwAREREfjjjz+wcuVK5OTkoHPnzgUmUJFIhIsXL2Lt2rU4dOgQNm3aBD09PTRt2hTr16/H0KFDNfuFFUHVqlVx/fp1zJs3Dzdu3ICvry/q16+PLVu2oGvXrhol0GrVquHBgwfYtGkTjh8/joMHD0IqlaJKlSpo2LAhpk+fjsaNGyuOnzx5MvT19bF+/Xrs3bsXhoaG6NixI3bv3o3jx4+XewLlsWIsBdCyZUvFzCKkcnt87z2Guh9VbP96ohfqNirmiiFaLCHlDerWVR8qkIsf5ANeRtGb3iRiGTIzlL0DDY2Ehc9UpAWrsVQWQ4YMwZEjRxATE6N1c+KSYvg4P7VsWeChz549U3uWWlxUAyUa2bBQOWylSSv7CpU8i0LmurhYx/MBpL9NR1K8vNmQxwNq1LOEUUEdruI/uhlYF3wzICXz8uVL6Ovrw9bWlutQiA6hTkSk2O74ReHOlSjF9rc/teUwGt1hW8UIBobynpeMAW9eJyMnR7dnKtJ1GzZsgLe3NwIDA9GvXz+V2X0IKQwlUFJsv/2k7NnY1tMR1WuWzbiziobH48Ghuqliuj+JWIbo8BRaUJlD69evx507dzBu3Dhs27aN63CIjqGvW6RY7l2Lxv3r8i7kPB4wzUd94miSP6FIAPtqJoiJkM+fmposRnxsJmzsjTiOrHIKCwvjOgSiw6gGSopl6y/KOUHbeFaDvaMJh9HoJhNTEaxslJOKv49OQ1ZG+U0/RggpHZRASZEF3Hqr8uxz6oJWHEaj26ztVZ+HRoWlKIYEEUJ0AyVQUmQf1z5bdaoKBydTDqPRbTweD1WqmSJ3PoWsLCneRxc8uTkhRLtQAiVF8vjee9z8RznuceqP6ssTkeIR6Qtg66BcyzI+NhNpKXmvZkEI0T6UQEmRbF+mHJPYvF0VVKtROZcrK23mVvowMVWOBY0OT6WhLYToCEqgpFCvniXA76yyt+LUhfTss/TwYO9oAj29/4a2SGSIiUiloS2E6ABKoKRQe9YpV7iv18QaLnUsOYym4hHo8VV6M6ckZSM5gZpyCdF2lEBJgd5Hp+H0QeWKK5Pn05RyZcHYVAQLK33F9ts3qZBI6c+TEG1Gf6GkQPs3PUKORP5MrqqzKRq3LJ3VMog6myrGEArlf5JSKcPbeCtQQy4h2osSKMlXSlI2/tzxVLE9YXbZrHRP5Ph8Huwdlb1yUzKMkJJOMxQRoq0ogZJ8/bnjKdJT5TPkWFgbwL27E8cRVXxGJiKYW37UlJtgiRwtaMrt1q0beDwe/P398z1mypQp4PF42LFjRzlGRgh3uP/LJFpJLJbiwOZHiu2R05qAz9fuBaArClsHI0VTbo5UgLcJ3HfaCggIgEAggJubW77H3L17F4B83WBCKgNKoCRPvkdD8eFtBgDAwEgPPQfV4TiiyoPP58OuqrIpNzndGClJ3PXKffXqFRISEuDq6goTk7znPs7IyMDTp0+hr6+PRo0alXOEhHCDEihRwxjDvo0PFdt9htWFSF/AXUCVkLGpCBYmyqn9YiJSOJtg4f59+SQabdrkv/LOgwcPkJOTg6ZNm0IoLGCRcEIqEEqgRE3ArbcIDvwAQN6xZfAEqlFwoYpVEoQCKQAgJ4chlqO5couSQO/cuQOAmm9J5ULrgRI1BzYpn3228awGC2uDAo4mZUWPL4ODdQIiY20BAAlxWbCwNoCRiahc48hNoEeOHIGfn1+ex9DzT1IZUQIlKmIiUnDp5GvF9thZzbgLhsDUKBOmRplIzTAEAMREpKJmA6ty69DFGENgoHwmqosXLxZ6PCVQUplQAiUqDv32RLEuZY26FqhRl/seoNqmuelvXIdQZEHiaSU6/+XLl0hOTka7du1w69atPI9JTEyElZUVjIyM0LBhQ5XXtmzZglWrVuHt27dwdXXF+vXr0bFjxxLFRIi2oGegRCE9TYxjvwcptsd824y7YIhWyG2+bd68eb7HBAQEAACaNWsGgUDZ2ezIkSOYMWMGfvjhBwQGBqJ9+/b47LPPEBkZmd+lCNEplECJwqn9z5GaLAYAmFnoo22X6hxHRLiWm0BbtMh/FqrcBPpp8+3atWsxZswYTJw4EQ0aNMCmTZvg4OCA337TnRo8IQWhJlwCAJDJGA5ufqzYHjzRlSZOyEdg6lfl8j6iNOUqOFJRFWRm8hDxRg8M8s/FqaYZzCzLtoNXURJo7jPSjxOoWCzGgwcPMHv2bJVju3fvnm9TMCG6hmqgBABwwzcC4SFJAAA9IR99h9fjNiCixtCQwcJcORb07Zs0SKVlNzZUJpPh4cOHEIlEcHV1zfe4vGqgcXFxkEqlsLdXXXzA3t4e7969K5uACSlnlEAJAPmqK7k8e7vA0JgGw2sjW1sp9ATyTl4SiUwxW1RZePbsGdLS0uDq6gqRKO+hM2lpaQgJCYGJiQnq1VP/0sXjqbZiMMbU9hGiqyiBEoS9SMStS28U2yOnN+UwGlIQAR+ws5UqtuNjM5CdlVMm7/XgwQMABTffPnr0CDKZDC1atACfr7yd2NjYQCAQqNU2Y2Nj1WqlhOgqSqAEf2xXLllWv6k1HKqbchgNKYyZmQyGBvKmW8aAd2/SwFjprxw6atQoMMawc+fOfI9xd3cHY0xtlRaRSAQ3Nze1saMXL15E+/btSz1WQrhAnYgquYx0CU7te6bYptqn9uMBqGInRXgkDww8pKaIkZoshpmFfqHnlqdZs2Zh5MiRaN26Ndzd3bF161bExMRgypQpXIdGSKmgBFrJnT/yUmXoSsuOjhxHRIrCwEDeoSgxWT7u8t2bVJiYibSq5/SXX36J+Ph4/PTTT3j79i0aNWqE8+fPw9nZmevQCCkVlEArMcYYDv/2RLHdf3R9rboBk4LZ2siQksqHVMaDWCxD/PsM2DoYF35iOZo6dSqmTp3KdRiElAl6BlqJPbr7Ds8fxQGQr7rSd0R9jiMixSEQMNjaKDsUfXibDolYWsAZhJDSRAm0Evtjq7LzUBtPR617hkYKZ2Ehg76+vAORjAHvorhZ8oyQyogSaCWV8CETF46FKLZHTKPOQ7pI3qFIOYwlOTEb6Wli7gIipBKhBFpJ/bUnGBKxfCiEQ3UT1G1kzXFERFNGhgxmpsoZicpqWAshRBUl0EpIKpXhzx3K5tshUxpzGA0pDXY2UvB58qSZmZGD5MRsjiMipOKjBFoJXb8QgejwVACASF+Arv1qchwRKSmhkMHSUlkLfR+VpljXlRBSNiiBVkJ/bFPWPrv2qwmRvqCAoysvXWsGtbZSnSc3Prbs5sklRJeV1t82JdBKJvJVMm74Rii2v5zUiMNotBePJ0BOTtnMMVtWBHzAxlp1WEuOhIa1EPIpiUSisvi7piiBVjJ/7niK3C9fdRpZo6ozzXubF6GeAVJTU7gOo9gsLGTQF/03rEUGxJbhai2E6KqUlBSYmpb83kcJtBLJyszBX3uCFdsjplHnofwYiEwRn5CAhIR4SCQSnWnO5UF1tZbED5nIytStmjQhZYExBrFYjLi4OCQmJsLKyqrE16Sp/CqRC0dDkJwg751pYiZCG49qHEekvfQEQpga2iIlORXx8QlgrPybQgXZcYp/ywTFG9uZmCKAOFs+LWPiv29gbWdUqrERopXilH8zePZM7WWBQABTU1M4OTlBX7/kE8dQAq1EPu481HdEPQgE1ABRED2BECaGJf+WqqmqN5XPp1OrzyvWudkRevhqsh0YkyfR7ef6wr2bU6nGR4jWadhQ+e9yaDWiO2gl8fTBezy59x4AwOMD/Uc14DgiUpbq1MpBn8+Uzz9XzL4GqVRWwBmEkOKiBFpJHPmo9tmivQMsrA04jIaUh8ljUxQLb796loS/9qg3aRFCNEcJtBJISsjCuT9eKrZHfUPz3lYG1lYyjByqnFx+4/9uIyNdwmFEhFQslEArgVP7nyM7S94JxsbeCA2a2XIcESkvwwamwfa/saEJH7Kwf+MjjiMipOKgBFrByWQMR7YpF83+cpIreDxaNLuyMDBgmDBaOZ5156r7SIzL5DAiQioOSqAV3F2/KESEJgMA9IR89BhYm+OISHn7vGcGnJ3kTbcZaTnYvuI+xxERUjFQAq3g/tiqrH126O4EQyMhh9EQLugJgKnjlbXQQ1seIzpc92ZZIkTbUAKtwN5FpcHvbJhie8S0JhxGQ7jUyT0LjRvKJ9HIkTBsWnSH44gI0X2UQCuwY78HQSqVDyauXsMMzrUtuA2IcIbHA76eqKx1nj38Es8fxRVwBiGkMJRAKyiJRIpju4IU28O+ptpnZdesiRgd2sk7EDEGrFtwk+OICNFtlEArqCunw/Dhv5U49A0F8Ojlwm1ARCt8NT4FfL68VeLGP29w92oUxxERorsogVZQR7YrZx7q3r8W9IT0UROgVo0c9OqunOJvzbybOrPSDCHahu6qFdDr54m466esWQyZQsuWEaWJo1MhEsqTZlDAB/zz1yuOIyJEN1ECrYCO7FDWPus1sYadgzGH0RBtY28nxaD+yin+1i24CYmk/JdrI0TXUQKtYDLSJTi1Tzlp+MjpNO8tUTdqaCpMTeQTzb95nYq/dtNE84QUFyXQCubvP0OQmixffNnUXIRWnRw5johoI3MzhpFDUhXbmxfdRnpa8RbtJqSyowRagTDGVGYe6ju8Hvh8mveW5G3wgDTY2vw30XxcNk00T0gxUQKtQJ7cj0Vw4AcAAJ/Pw4AxtGg2yZ+BPjDxo4nmf1/1AAkfaKJ5QoqKEmgF8nHts1m7KjCzpEWzScF69ciAS+5E8+k00TwhxUEJtIJIis/EhaMhiu3R3zbjLhiiM/QE8skVch3+7TFiImiieUKKghJoBXFy30eLZlcxQoOmNhxHRHSF2kTzi+9yHBEhuoESaAUgkzGVsZ+DJ9Ci2aToeDxg6kcTzZ85+AIvn9BE84QUhhJoBXD78htEfrRods9BtGg2KZ7mTcRwb5MF4L+J5n+8zXFEhGg/SqAVwMFfHyv+3bEHLZpNNPPVhGTwePIp/q79HYEHN2I4jogQ7UYJVMe9eZ2Ma3+HK7ZHfdOMs1iIbqtdMwc9uyqHsayZTxPNE1IQSqA67vDWJ8i9x9Wsb4lqNcy4DYjotIljUiD8b6L5R3ffw+9MGMcREaK9KIHqsPQ0Mf7aHazYHj2D5r0lJVO1ihTefdMV22sX3EJOjozDiAjRXpRAddjZQy9V5r1t61Wd44hIRTBmeCqMjORJM+xFEk4feM5xRIRoJ0qgOooxptJ5qP/oBjTvLSkVFuYyjBisXO5s06I7yMrM4TAiQrQTJVAdddcvCq+eJQAABAKa95aUriED02BlKZ+YIzYmA4d/e1zIGYRUPpRAddTHtc92XavD2FTEYTSkojEyZBg3Qrnc2bZl95CSlM1hRIRoH0qgOig6PAVXz4UrtsfQvLekDPT7PB3VqsqbblOTJfh91QOOIyJEu1AC1UGHtz6BTCYfauBSxwLOtS24DYhUSEIhMHmscoq//ZseIjYmrYAzCKlcKIHqmMwMCY7T0BVSTrp4ZKJubXlP7+wsGbb8dI/jiAjRHpRAdcyZgy+Qkih/FmViJkK7rjR0hZQdPh/4+qOJ5o/vDkLYi0QOIyJEe1AC1SEyGcPe9Q8V2/1H14dAQB8hKVut3bLRsoV8onmZFNjwP5ponhCAEqhO8T8fjvCQJACAnh4f3mMbchsQqRR4PODrCcpa6MUTr/H43nsOIyJEO1AC1SF71wcq/u3R24WGrpBy06CeBF06Zyi21/5AE80TQglURzx98B73rv23vBQPGDurObcBkUpn8thUCPjypHnPPwY3/onkOCJCuEUJVEfsWfdQ8e8mrexh52DMXTCkUnKqnoO+vZS10NVzb0AqpYnmSeVFCVQHxESm4p/joYrtyfNbchgNqcwmjE6BoYE8aYYGJ+KvPc84jogQ7lAC1QEHNj+CVCpvOqtWwwx1G1lzHBGprKytZBg5RDmZwsb/3UZ6qpjDiAjhDiVQLZeanI1jvwcptsd9R88+CbeGDUqDrY18ovmED1nYuSqA44gI4QYlUC13fFcw0lMlAAAzS324d3PiOCJS2RkYMHw1PlmxvWddAN6+SS3gDEIqJkqgWkwikWL/5keK7aGTG9Gan0Qr9OyaiXp15E234mwZNiy8w3FEhJQ/SqBa7MKfoXj3Rv68SaQvQN8R9TmOiBA5Ph/4ZoqyFnrm0As8fUCTK5DKhRKolpLJGHasVC4f1XtoXYj0BRxGRIgqt2ZidGqfqdheOYcmVyCVCyVQLeV3JgyvniUAAAQCHkZMa8JxRISomzYpBQKBPGk+uBGDy6decxwRIeWHEqgWYoxh+4r7iu0u/WrC1Fyfw4gIyZtT9Rx4901XbK+edxNisZTDiAgpP5RAtdCdK1F4ej8WgHwi7/GzW3AcESH5Gz8yFaYm8skV3rxOwR9bn3AcESHlgxKoFvq49tm+a3VY2RpyGA0hBTM3l2HMcOUwli1L7yIpPrOAMwipGCiBapmHd97i36vRiu0pP7TiMBpCimbQF2lwdMgBAKQmS7DR5y7HERFS9iiBaplty5S1zxbuDqhSzYTDaAgpGpEImPGVcljLnzueIjgwlsOICCl7lEC1yON/3+Ha3xGK7akLqPZJdEfH9llo2yoLAMAY8NM3/pDJaFgLqbgogWqRX5f+q/h3k9b2cK5jwV0whBQTjwfM+joZenrypPno7nucOfiC46gIKTuUQLXEwztvccNXuUDxN4vbcBgNIZpxqp6DoQOVq7WsnncDqcnZHEZESNmhBKolPq59Nm1jD+faFtwFQ0gJjB2RCltr5Wotv/18j+OICCkblEC1QODtt7h18Y1ie8aSthxGQ0jJGBkylXly9296hNCgeA4jIqRsUALVApsXK7v8N2/vgOo1zTmMhpCS6+qZieZN5U23MinDLzOv0Ty5pMKhBMqx25ff4M6VKMX2N4tbcxgNIaWDxwO+m5YEAV+eNO9ejcbZQy85joqQ0kUJlEMyGcO6BbcU2209q6GaC9U+ScVQu2YOBvVXzpO7/LtrSIyjGYpIxUEJlEO+x0IRFPABAMDj07NPUvFMGpuCKnbyGYqSErKxau5NjiMipPRQAuWIRCLFBp87iu1uX9SCTRUjDiMipPQZGTLMmZGk2D61/znuXHmT/wmE6BBKoBw59nsw3ryS91TUE/Ix5YeWHEdESNlwb5uNLh4Ziu1FU/2QlZnDYUSElA5KoBxITxOrjI3zHtuA1vskFdqsr5NhYqxc8mwrjQ0lFQAlUA7sXPEA8e/l38gNjfQwcnpTjiMipGxZW8kwbbJybOiutQF48TiOw4gIKTlKoOUsKiwFe9Y/VGyPn90C+gZ63AVESDnp+1kGmjWWjw2V5jD4TLkCqVTGcVSEaI4SaDlbPe8mxNnyac7sqhqjz/B6HEdESPng84G5M5MgFMrHhj65H4vdawM5jooQzVECLUd3r0bh4olXiu25q9zB5/M4jIiQ8lXDOQdjR6QqtjcvuouQpzTNH9FNlEDLiVQqw/Lvriu2m7axR5PWVTiMiBBujBqaigb1xAAAiUSG+eMvQSKRchwVIcVHCbSc/LkjCC+fyL9p8/k8zF3VgeOICOGGngD43/eJEP3XlPss8AN2LH/AcVSEFB8l0HLw4W061v94W7Hdb0Q92DoYcxgRIdyq4ZKDyeNSFNtbl93D0wfvOYyIkOKjBFoOln93HWkp8iYrUwsRJnzvxnFEhHBviHcamjRS9sqdM/IfpKeJOY6KkKKjBFrGrl+IwIVjoYrteas7QqQv4DAiQrSDQAD4zE2EkZF8KEtkaDKWzbxeyFmEaA9KoGUoM0OCpd9cVWw3a1sFrTs7chcQIVrGsaoUc75JUmyf2PsMf/8Zwl1AhBQDJdAy9OuSfxEdLu+yryfkY8H6ThxHRIj2+axbJnp0Uc6V6/PVFUSHpxRwBiHagRJoGQm8/RZ7P5pxaMKcFrCwNuAuIEK02PczklDVQT7BfHqqBLOGXVBMOEKItqIEWgYy0iX4YfwlyGTybvpOtc0xYEwDjqMiRHsZGzMs+SEBAoH8b+bp/VismHOD46gIKRgl0DKwbsEtRIbKJ84WCHj4ZWcX8Hg04xAhBWnUUILpH004/8fWJzh76AWHERFSMEqgpezOlTc4tOWJYnvi926wdzThMCJCdMeXA9LRpbPq89DQIJrqj2gnSqClKCkhCwsmXlZs13a1woCx1HRLSFHxeMAP3yXBuboEAJCVKcU3g84jKSGL48gIUUcJtJQwxrBg/CW8e5MGABAK+fh5BzXdElJcxsYMyxYlwEBfPj40IjQZ3w27QPPlEq1DCbSU7N3wEFfPhSu2v1veHla2htwFRIgOq+mSg//NS1Rs37kShWWzaJIFol0ogZaCR3ffYd0PyrluPT53QZe+NTmMiBDd59UpC5PGKMeDHtn2FId+e8xhRISoogRaQknxmfhuuC9ycuTNTbZVjPD9SneOoyKkYhg7IhXdPJWdipbNvAa/s2EcRkSIEiXQEpBIpPh2yAW8jZTPNiTQ42H1wR4QimiuW0JKA48HLJiTiIb/rR8qkwGzhl1A4O23HEdGCCVQjTHG8Mu313DPP1qxb84Kd1R1MuUwKkIqHgN9YNVP8XD8b6YicZYUU/udpeEthHOUQDV06Lcn+HNHkGL7i1H16bknIWXE2kqG9SviYGkh74mbkpSNiZ+fRkwEzZlLuEMJVAM3/onAiu+UPQIbtbTD1B9bcRgRIRVfdUcp1i2Lh6GhvL9BbEw6xnY7iZj/HqEQUt4ogRbTo7vv8O3gvyGVyufstHUwwvLdXWm8JyHloH5dCVYsToBQT/73FxWegrHdTuDtG0qipPxRAi2G0KB4TOl7BpkZ8mcxBkZ62PjnZ9A30OM4MkIqj9Zu2VixJF6ZRMPkSfRdVBrHkZHKhhJoEUWHp2Di56eRkpgNQL6+56ajn8GmijHHkRFS+bRvk41lixKg918SffM6BSM9jiP8ZWIhZxJSeiiBFkFUWArG9TiJ2Jh0AACfz8Pq/d3hUteS48gIqbw6tMvCMh9lEo2JTMVIz78QFBDLcWSksqAEWojwl4kY3eUvRIXJe/vxeMCSbZ5wdbPjODJCSMf2WVi5JB76/82bm/AhE2O6nsCdK284joxUBpRACxDyNB6juiifrfB4wIL1ndDGoxrHkRFCcrVvk43Nq+JhaiIf4pKRJsGk3qfx586nHEdGKjpKoPm4fz0ao7v+hfj38mnEeHxg0RYPdO7lwm1ghBA1jV3F2LYhDrY28g5+0hyGxVOv4udv/WkVF1JmKIHm4dT+5xjf8xSSE+QdhvgCHpb93hXtuzpxHBkhJD81XXKwc1Mc6tYWK/Yd2vIEk3ufQXxsRgFnEqIZSqAfkckYNiy8jR/GX0KORP5MRSjiY+3BHnDrUJXj6AghhbG3k2Lb+jh4dcpU7LvrF4UBLf+g56Kk1FEC/U/Ch0x81e8Mtq94oNhnbqWP3y/0ow5DhOgQQ0OGnxYmYMJo5TR/ce8yMOGzU9jwvzuKlZMIKSlKoJA/7/Ru9Qdu+EYq9tWsb4m9l/rDoTpNDk+IruHzgQmjUrFuWRwszOXPQBkDti+/j2EdjuLF4ziOIyQVQaVOoOJsKTb63MHYbsoxngDQrX9NbDnxOYxNRRxGRwgpqXats7F/eyzcmmUp9gUFfMDgtn/i1yV3IRZTByOiuUqbQB/eeQvvVn9g27L7kMnkA7H1hHwsWN8J36/sAIFepf3VEFKh2NrIsHFlPKZOSIZQKG++zcmRYctP9zCgxWFcvxDBcYREV1W6LJEUn4mfZvhjROfjeP1cOe2Xo4sp9l3uD4/PXbgLjhBSJgQCYNTQNOzf/gGNG2Yr9oe9TMKUvmcwpe8ZlfsBIUVRaRKoOFuKPesC8VmDAzj82xMweaUTfAEPo75pil2+X8DWgea1JaQic3HKwdb1cZj5dRKMjZTNt9cvRKBfs0OYP+4iIkKTuAuQ6BQeY7mppHAtW7bE/fv3yzKeUifOluLUgefYufKBYjq+XDXrWWLJNk/YO5pwFB0h+at601zx79Tq8ziMpGJKSORj224znD5vBMaUyxEKBDz0GV4PY2c2R21Xaw4jJMX28bKSRU9tmr9dRU2g6ali/LXnGXavDcD76HSV10zMRZju0xqevWvQOp5Ea1ECLR8vQoTYvN0M9wIM1F5r37U6Rn3TDO7dncDn071C61ECLZkXj+NwZPtTnDn0AhlpEpXXhCI+hkxpjKGTG0EoEnAUISFFQwm0fD16KsKO3Sa4/9BQ7bWqzqb4YmR99BvZANVqmHEQHSkSSqDF9z46DX8fDcH5IyEIeqC+lJG+gQD9RzfAsK8aw9BYyEGEhBQfJVBuPHwiwh/HjHHtpiFkTL3W2cLdAd0H1EbXL2rSOHFtQwm0cIwxvHwSj2sXIuB/PhwPb7/N83dlbmWAQeMbov/oBhDpU42T6BZKoNyKeSvAnydMcM7XAKlpenke4+pmh/ZdqqNtl+po3q4K9A3yPo6UE0qg6qRSGUKDEhBw6y0CbsbgwY0YteeauXg8+X/qkdOboHk7B3rGSXQWJVDtIBYD128b4Mzfhvj3ft61UkDe0uXWoSpadXJE41b2aNjCFuaW6s9VSRmq7Ak0KzMHr58n4OWTeIQGJ+DFkzg8vvseaSniAs9zrmOB3kProEvfmjA11y/TGAkpD5RAtU9CIh/Xbhrgsr8+Ah4aQCoreCSgU21zNHKzQ/2mtqhZ3xI161nCsYYZ9GiilrJRGRJocmIWIl8lIzosBVHhKYgOT0HUf/+Oep2imBmoIEIRH/Wb2aBrv5po16U6LK3VH/wTossogWq31DQeHjzUx7/3Rbh7T4jod0WrbeoJ+XCubQGnWuZwcDKBQ3VTVHUyhYOTKWwdjGFjb0SPnDRVzgmUkwb7FbNv4NT+58U6x9BID7UaWKFVZ0e07FgVtepb0nR7hBDOmJoweHTIgkcH+Ty7794LcD9QH0+D9PA0WICwyLxrqDkSGV49S8CrZwn5XtvMQh829kbo0q8mvv2pXZmVgZQMJwm0sG7gZpb6qFbDDPWb2KBhC1vUdbVGleom9DyTEKK1qthL0btnBnr3lG9ni4HQ10I8eyHC6zABXofxEBklREJS4Y+YUpKykZKUjeYfaCFwbcZJAq1ZzxK1Xa0QGpSAVp2roloNc9g5GMPa3hA29kZqPdlSU8RIDcr/2xohFdHHS7gnvVcfnkW0n6Ml4NgWQFvlvvQMPqLeGuD9BxE+xIvw/oMe3scKEBsnQmKyCCnpIsj+q7kaGXETNymaYj0DtbGxgYuLS6m9efjLJEilJV/cNjsnFfp6FXM8FpVNd5VG+UwM0iAU5JRSRKUnNTMbpoYVs7OetpSNgQ/Lak7g80v3UdWHDx9ga2tbqtfUFmVRNhsbG1y4cCHP14qVQLWVtoxPLQtUNt1VkctHZdNdFbl85V026oVDCCGEaIASKCGEEKKBCpFAJ02axHUIZYbKprsqcvmobLqrIpevvMtWIZ6BEkIIIeWtQtRACSGEkPKmtQk0KioK06dPR7t27WBkZAQej4fw8PAin//s2TMMGjQINjY2MDQ0RL169bBhw4ayC7gYSlK2yMhIjB49Gk5OTjAyMkLdunXx448/Ij0978n1y9uxY8fg7e0NZ2dnxe99/vz5SE1NLfTcrKwszJkzBw4ODjA0NES7du1w7dq1coi6aDQt2/379zFp0iTUr18fRkZGcHJywvDhwxEWFlZOkReuJJ/bx5YtWwYej4cOHTqUUaSaKWn5tPl+UpKyafv9xNfXF15eXqhSpQr09fVRrVo1DB48GMHBwYWeWy73E6al/Pz8mJ2dHfvss89Y9+7dGQAWFhZWpHPv3bvHTE1NWZ8+fdiJEyfYlStX2LZt29iaNWvKNugi0rRsaWlprE6dOszFxYXt2bOHXblyha1YsYIZGBiwwYMHl33gRdCmTRs2aNAgduDAAXb16lW2bt06Zm5uztq0acOkUmmB5w4bNoyZm5uz7du3s0uXLrH+/fszAwMDFhgYWD7BF0LTsn333Xesffv27Ndff2VXr15lBw8eZPXr12dWVlYsMjKyHEuQv5J8brlevXrFjI2NmZ2dHXN3dy/jiIunJOXT9vuJpmXThfvJoUOH2OzZs9nRo0fZ1atX2b59+1jDhg2ZqakpCw8PL/Dc8rifaG0C/fiD37FjR5GTjFQqZQ0bNmRffPFFGUZXMpqWzdfXlwFgvr6+Kvvnzp3LBAIBS09PL+1Qiy02NlZt3969exkAdvny5XzPe/jwIQPAdu3apdgnkUhY3bp1WZ8+fcok1uLStGx5nRceHs54PB5buHBhqcaoKU3L9rHu3buzSZMmsc6dO2tdAtW0fLpwP9G0bLpwP8nL8+fPGQC2evXqfI8pr/uJ1jbhajr7xtWrVxEcHIxZs2aVckSlR9OyicXyJd3MzFTnErawsIBMJgPTgv5gec0C0qpVKwBAdHR0vuedPn0aQqEQX375pWKfnp4ehgwZAl9fX2RnZ5d+sMWkadnyOs/Z2Rm2trYFnleeNC1brkOHDiEgIADLli0r9dhKg6bl04X7iaZl04X7SV6sra0BAEKhMN9jyut+orUJVFM3btwAIG//btu2LYRCIezs7PDNN98gMzOT4+hKpmvXrqhTpw7mzp2L4OBgpKWl4cqVK9iwYQOmTJkCY2NjrkPMk7+/PwCgQYMG+R4TFBSEGjVqwOiTyT9dXV0hFosRGhpapjFqqihly8uzZ88QGxtb7PPKU1HLlpiYiJkzZ2LlypWwsrIqj9BKRVHKp6v3k6KUTZfuJ1KpFGKxGCEhIZg8eTKqVKmCIUOG5Ht8ud1PSq0uW4aK08w5efJkBoBZWlqyhQsXMj8/P7Zq1SpmaGiolc0wxSkbY4y9f/+edejQgQFQ/EyYMKHIz6nKW1RUFLO1tWVdu3Yt8Lhu3bqxNm3aqO2/ePEiA8CuXbtWViFqrKhl+5REImGdOnVitra2LCEhoYyiK5nilG38+PGsQ4cOTCaTMcaYVjbhfqqo5dO1+wljxfvsdOV+4ubmpoivdu3aLDg4uMDjy+t+wslqLGVJJpNPTj9ixAgsWbIEAODh4QGpVIp58+YhODgYDRs25DJEjWVlZeHLL79EbGws9u/fDycnJ/z7779YsmQJ9PT08Ntvv3Edooq0tDT069cPenp62L17d4HHMsbyXK6OaWkzUnHK9qlp06bh1q1bOHfuHCwtLcsoQs0Vp2zXr1/Hvn37EBAQoDPLDRanfLp2PylO2XTpfrJ//36kpKTg9evXWL16Nbp164YbN27ku7hJud1PSiUNl7Hi1NLmzZvHALDTp0+r7A8ICGAA2MGDB8soSs0Up2ybN29mAFhoaKjK/u3btzMA7OHDh2UUZfFlZmYyT09PZmlpyR4/flzo8YMHD2Z169ZV23/kyBEGgD19+rQswtRIccv2sXnz5jEej8f27dtXRtGVTHHL1qBBAzZlyhSWmJio+HF3d2dt27ZliYmJLCsrqxyiLrrilk+X7ifFLZsu3U8+lpiYyMzNzdnkyZPzPaa87icV7hmoq6srAKh9+2D/ffMo7aWBytOTJ09gaWmJWrVqqexv3bo1APlzNW0gkUjg7e2Nf//9F+fPn0fjxo0LPcfV1RVhYWHIyFBdQDg4OBgikQi1a9cuq3CLRZOy5fr555+xfPlybNiwASNHjizDKDWjSdmePXuGrVu3wtLSUvFz8+ZN3LlzB5aWllpVi9H0/yWg/fcTTcqmK/eTT1lYWKB27doFPscsr/uJdnz6peizzz6Dvr6+2vptvr6+AOTL3eiqKlWqIDExUe0/zt27dwEAjo6OXISlQiaTYfjw4bh8+TJOnTqFtm3bFn4SgL59+0IikeDo0aOKfTk5OThy5Ai6d+8OfX3u12fUtGwAsHHjRvz444/4+eefMX369DKMUjOals3Pz0/tp2nTpmjUqBH8/PwwcODAMo68aDQtny7cTzQtmy7cT/Ly/v17PH/+XC3xf6zc7ielUo8tI0ePHmVHjx5lU6ZMYQDYli1bFANqGZOPpRMIBGzx4sUq5y1atIgJBAI2f/58dvHiRbZs2TJmYGDARo8ezUEp8qZJ2cLCwpipqSmrU6eOYuDzypUrmampKXNzc9OKB/+55VmwYAG7ffu2ys+bN28YY/l/bl9++SWzsLBgO3bsYJcuXWLe3t5MX1+fPXjwgIuiqNG0bIcPH2Y8Ho/17NlT7bygoCCuiqOiJJ/bp7SxE1FJyqft9xNNy6YL95MvvviCLVmyhJ08eZJduXKFbd26ldWrV4+Zm5uzFy9eMMa4vZ9odQLFRz3DPv7p3LkzY0z+HwAA8/HxUTlPJpOxNWvWsFq1ajGhUMicnJzYwoULmVgsLv9C5EPTsgUFBbFBgwaxatWqMQMDA1anTh323XffaU1vTmdn53zLlluW/MqWkZHBZs6cyezt7Zm+vj5r3bo18/PzK/cy5EfTso0ePbrQz5trJfncPqWNCbQk5dP2+0lJyqbt95Ply5ezFi1aMHNzc2ZoaMjq1q3LJk2apNJnhMv7Ca3GQgghhGigwj0DJYQQQsoDJVBCCCFEA5RACSGEEA1QAiWEEEI0QAmUEEII0QAlUEIIIUQDlEAJIYQQDVACJaSU8Hg88Hg88Pl8vHr1Kt/jPD09Fcfu2bNH5bUxY8YoXvPx8cn3Gnv37lUc5+HhofLa1atXFa/l/hgZGcHBwQGdOnXCnDlzEBgYWJKiEkJACZSQUqWnpwfGGH7//fc8Xw8JCYG/vz/09ApeSVBPTw+7du2CVCrN8/UdO3YUeg1nZ2f4+PjAx8cH3377LXr37o309HSsXr0aLVq0wPDhw5GWlla0ghFC1FACJaQU2dvbo2XLlti9ezdycnLUXt+5cycYY+jdu3eB1+nduzeioqLUJjEH5Ktk3Lx5E3369CnwGi4uLli0aBEWLVqEX375BTt27MCDBw8QGBiIxo0b49ChQxg0aFDxCkgIUaAESkgpmzhxIt69e4ezZ8+q7JdIJNi7dy/at2+vWCYrP8OHD4ehoSF27Nih9lruvgkTJmgUX7NmzXDp0iXY2triwoULOHnypEbXIaSyowRKSCkbOnQojI2NsXPnTpX9p0+fxvv37zFx4sRCr2FhYYFBgwbh3LlzePv2rWJ/dnY29u3bh86dO6Nu3boax2hnZ4fJkycDAA4ePKjxdQipzCiBElLKTE1NMWTIEFy4cAFRUVGK/Tt27ICZmRkGDx5cpOtMnDgROTk5Kh2NTpw4gfj4+CIl4cLkdj76999/S3wtQiojSqCElIGJEydCKpVi165dAICIiAhcvHgRw4cPh5GRUZGu0aFDBzRo0EDx3BSQJ2FLS0t4e3uXOMbcBZM/fPhQ4msRUhlRAiWkDLRp0waNGzfGrl27IJPJsHPnTshksmLXHCdMmIDXr1/jypUrCA0NhZ+fH0aOHAkDA4MSx5iblHk8XomvRUhlRAmUkDIyceJERERE4MKFC9i9ezfc3NzQvHnzYl1j1KhR0NfXx86dOxU10dJovgWAmJgYAICtrW2pXI+QyoYSKCFlZOTIkTA0NMTkyZMRHR2NSZMmFfsaNjY26N+/P06cOIFdu3ahXbt2aNSoUanE5+fnB0BeWyaEFB8lUELKiIWFBQYOHIioqCgYGxtj6NChGl1n4sSJyM7OxocPH0qt9hkbG4tt27YBkA+ZIYQUX8FTmRBCSuSnn37CgAEDYGtrC1NTU42u4enpiVOnTkEmk6FHjx4ljunRo0cYNWoU4uLi0KtXL/Tt27fE1ySkMqIESkgZcnJygpOTU4muwePxNEpy4eHhWLRoEQD5JA5xcXF48OABHjx4AEBe88ythRJCio8SKCEVVEREBBYvXgwAMDAwgIWFBerUqYPZs2dj+PDhaNasGbcBEqLjeCy3LzshhBBCiow6ERFCCCEaoARKCCGEaIASKCGEEKIBSqCEEEKIBiiBEkIIIRqgBEoIIYRogBIoIYQQogFKoIQQQogGKIESQgghGqAESgghhGjg/+Zup7fGyR6BAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5871ce423f04eea93b790e3ea8f7304",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "239872e15e3540f5944ca993c7f99c2e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df78507b2e1a49549a1ebff1f2253f4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bef74ea8403e410bb7c878bf22fa1600",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28317dd0ee2049b4a38aaddb36d70f9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce7821f1088749abbb04b465c3267fae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8eccd63c06bb4fb29676174099d3d67d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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znjx5gvPnz2PgwIE4fPhwoctTJhWl3w71cyVcCQ9NZJP7nGJ1hVs0fjq67WJLp1xmvkdfsFfP4tjbsKQ8f4ICPrBT+4PZDzOusG619uR5rhZ2v7L1C6+zJ0+ecl3kgpVCP9fcbt++zUaOHMnc3d2ZsbExMzMzYx4eHuzbb79lb968yfOYnP6mPj4+7PTp06xVq1bM1NSUWVlZsX79+rHnz5+r7R8UFMRmzZrFmjZtyuzt7ZlIJGJubm7M29ubXb9+Pc/XuHr1KhswYABzcnJiQqGQ2dvbs4YNG7JZs2axux/1dXRzc9Po+5mf6tWrMwDM1taWZWVl5btfVlYW27JlC2vdujWztLRkIpGIVa5cmXl5ebENGzZo9JnNyspiy5YtY1WqVFGWb+HChSwzM7NI/VwZY0wul7MtW7aw2rVrM5FIxJycnNiUKVNYYmJivmX18fFhDRs2ZKampszOzo59+eWX7NGjR2zJkiUMAPPz81Pb/8OHD2zIkCHM0dGR8fl8BoAtWbJEuf369eusU6dOzNrampmbmzNPT092/Phx5ufnp7FvqdCjfq48xlj+99E+0qxZM9y7d+/TOxKiJYwx/PHrE6ybfx0Z6aqaJ48PtO3qhlHTG8KthnWxzh0Wkgi/06/x95EQJMRmqm3berYFGjaqDxt740J1RSl1pdCgiRCDkzs/NSv4c5HTIlxX6LYw0VspSVlYOPYiLp9+rba+VScXzFjRCvYVTPM5snDca1hjzDeNMXxqA1w69Rp//PoYb8NSAAByGfAuIgUJsRlwqmwBU3PN7hOEEJIfSq5EL4W9SMCUL88gPFQ1CLm1nTGW/dIRdRs7FnBk0QlFAnTvXx1d+1XD1fPh+HWl6ttvRroUr58nwL6iKRyczMDn62EtlhCidyi5Er0TePM9vu57Bknxqg7oHXu6Y85qT4jEggKOLBk+n4cOPdzRspMLouPDweMBjCkGx42JSkdKUhZc3C1hbEq1WEJIwagrDtEr1y9EYFy3E8rEyuMDs75vhe82ttdpYs3N2MQI5hYiVKlpDVMz1ffPzAwZXj5LQHxMOorQVIEQUg5RzZXojavnwzF9wDlln1OhiI+f9nRFvWbavQ1cWEZCAVyqWCIxLhOxUemQM0VN9l1EKtLTpKhU2Rx8bY34RAgpUyi5Er3w8HYUZg76W5lYjU2NsO1ET1SuYvWJI3WNB2s7E5iaixD1JgWZmYr4EuMykZmeDddqVhCJ6WNECFFHX7sJ514/VzReysxQdLUxMTPCb2d660FiVRGJBahczQpW1qoBJjIzZHj1LAHpNIwiIeQjlFwJp2Ki0jCp9ykkxin6mQpFfPz8V084Vbb4xJGlj8fjoYKLGSpUMkNO11eplCHsRSKS4jMLPpgQUq5QciWcSU/Lxld9zij7lvL5PKzd1xWVq+pPjVUTD1a2xqhcxRICI0WGlTPgzetkxESlUUMnQggASq6EI4wxLJvih+AHMcp1iza1R90m3DReKipjUyFcq1pBJFZ9hD68TcOHt6mUYAkhlFwJN47sfIozh14ol8fPaYJ23d04jKjohCIBKle1UuuuE/shA+8iUijBElLOUXIlpe5pQDR+nPWvcrlVJxcMmliPw4iKTyDgw9ndEhaWIuW6hNhMRL5OLnD6O0JI2UbJlZSqpIRMfDPkPLIlcgCAfQVTLNrcnuOoSobH48Gpsjksc7UkTkrIQuSrJEqwhJRTlFxJqVo25QoiXycDAARGPKzZ3wVi4zLQT5THQ0UXM1jbGStXJSdJqAZrIEaPHg0ej5fnZO1ccXd3V05ITgxPGbiqEUNx/kgIfI+FKpe/XdkGLu763DI4f/ynS8BLj9BY7wTANkOKrP8Gw8B7IOOlAKbmQu1OXZcVq/pdbK+53dQVaLi8xC/j7u6O8PDwPLdVqFABUVFRauskEgmWL1+OAwcOID4+Hk2aNMG6devQpEkTjeMvXLiArl274syZM+jZs2eJYzU0HTt2hL+/Pz2fL6MouZJSEfshHSum+yuXW3m5oMuX1TiMqGR46RGAmXue28RmgDQ1G5n/zT+bzQBptgCWNmLwoKUEy1c944VJJc3taWHaeR0AVlZWmDlzpsZ6c3NzjXXz58/Hhg0b4O3tDRcXF+zbtw+dOnXCs2fP4OTkpNwvNTUVEyZMwPDhw/Uisa5cuRLz58+Hs7Mz16EoXbp0iesQSAlQciU6xxjDiqlXlANFmJoLMX9tW46j0i0zcyHAoBx1KitThtQkCcytRNpLsKXE2toaS5cu/eR+jDH8+uuvGDNmDHbt2gUA6Nu3Lzp27Ih9+/Zh7ty5yn3nzZuHzMxMbNq0SVdhF4mTk5Na8tcH1aoZ7pdPQs9cSSk4+8cLXDz5Srm8cEM7mFmICjiibDCzEMLYRPX9NSNdirSUbA4j0q2YmBikp6ejRYsWynU5v+e+tfzvv//il19+wc8//wxbW9tivVbO88jk5GR88803cHd3h1AoVPsS8OzZM4wePRqVK1eGWCxGhQoVMHToUDx//lzjfAU9c719+zb69++PihUrQiQSoXLlypg0aRLevXuXZ2zx8fH47rvvUK9ePZiamsLKygoNGzbE/PnzkZaWhrCwMPB4PPj7K+7k8Hg85U/Hjh01yvixrKwsrFq1Cg0aNICpqSksLS3Rrl07/Pnnnxr75rzW6NGjERYWhsGDB8Pe3h7GxsZo1qwZzpw5U/Af+iMnTpzA8OHDUbNmTZiZmcHc3BxNmzbF5s2bIZfLNfbP+bu+evUKW7ZsQYMGDWBiYqJWzvj4eCxYsAB16tSBiYkJrKys0LlzZ/zzzz8a50tKSsKaNWvg5eUFFxcXiEQiODg4oE+fPrh161aRyqJrVHMlOhUfk6HW7aZtV1e07OjCYUSly8xCCCZnymew6anZ4PN5MDUznDlhs7KysH//fkRERMDMzAwNGjRA+/btIRCoTwHo4OAAExMT3L9/X7nu3j3FxPNuboo+zBkZGRg/fjy8vb3h7e1dorgkEgm8vLwQHx+Prl27wtLSElWqVAEAnD9/Hv369UN2djZ69+6N6tWrIzIyEn/99RfOnj0LPz+/PJ8Df8zHxwcTJkyAWCxGnz59ULlyZYSEhGDnzp04ffo0bt26BVdXV+X+r1+/RqdOnRAeHo6mTZviq6++glwux4sXL7BhwwZMnjwZ1tbWWLJkCXbv3o3w8HAsWbJEefynGjBJJBJ069YN/v7+qF27Nr7++mukp6fj6NGjGDRoEB48eIAff/xR47jw8HC0aNECVatWxYgRIxAfH4/Dhw/jiy++wMWLF9GpU6dC/c3nz58PPp+Pli1bwtnZGUlJSbh8+TJmzJiBu3fvYt++fXkeN2PGDFy9ehU9e/ZEjx49lP93wsPD0bFjR4SFhaFdu3bo3r070tLScObMGXTv3h2//vorJkyYoDxPcHAwvvvuO7Rv3x49e/aEjY0NIiIicOrUKfz99984vW4durdpU6iy6BolV6JTG767oZyb1dRciDmrPTmOqPSZWYogT8pSdj9KTZaAz+ep1Wr1WVRUFEaMGKG2rkqVKvDx8UGHDh2U63g8HiZOnIjNmzcjKSkJzs7O2LdvHywsLDBs2DAAwKJFixAfH4+ff/65xHG9f/8edevWhb+/P8zMzJTrExISMGTIEJiamuLff/9F3bp1lduePn2Kli1bYvz48QgICCjw/C9evMCkSZPg7u4Of39/teexly9fRpcuXTBjxgwcP35cuX748OEIDw/Hjz/+iAULFqidLzY2Fubm5jA2NsbSpUtx5coVhIeHF+qWe45169bB398fn3/+OU6dOgUjI8X/oSVLlqBFixZYuXIlevXqhTYfJZgrV65g6dKlaol86NCh6N69O9asWVPo5Hr27FmN29VyuRxjxozB3r17MXXqVLRs2VLjuICAAAQGBiq//OQYNWoUwsPDcejQIQwePFi5PjExER07dsT06dPRp08fVKhQAQBQp04dvHv3Dvb26o34IiMj0aJFC8zasEFvkivdFiY6E3jzPf7aHaxcnrPaE6bmhlNj0xYeD7CwEsNIqPq4JSdmQSKRcRhV4YwZMwaXLl1CVFQU0tLS8PjxY0yaNAlhYWH4/PPP8fDhQ7X9V69ejXnz5uHOnTv4/fff4eHhgUuXLsHZ2Rm3b9/Gxo0bsWnTJtjb22PZsmVwcnKCkZERmjZtiuvXrxc5vnXr1qklVgDYu3cvEhMTsWzZMrXECgAeHh6YMGECAgMDERQUVOC5f/nlF2RnZ2PTpk0aDZ28vLzQp08fnD59GikpirGx79+/jxs3bqBRo0aYN2+exvlybseWxK5du8Dj8bB+/XplYgUAR0dHLF68GACwc+dOjePc3NywaNEitXXdunWDq6sr7ty5U+jXz+s5MJ/Px4wZMwAAvr6+eR43d+5cjcT68OFD+Pv7w9vbWy2xAorn/MuWLUNmZiaOHTumXG9lZaWRWAHAxcUF/fv3x7OwMER81IKdK4bx1ZkYHKlUju9ztQ6u08gebbu6FnBE2cbjAZZWIiQlZEEmU3S9SIrPhI29CYyM9Pc7bu6aDgDUq1cP27dvh7m5OdatW4elS5eq1dzEYjFWrlyJlStXqh0nkUgwduxY9OjRA8OGDcPGjRuVNSlPT0/88MMP6N69O0JDQ5W1lE8xNjZGgwYNNNbfvHkTgOLinVet8MULxbCbwcHBGsk3r/P4+/vj7t27Gtujo6Mhk8nw4sULNG3aVPnMr1u3buDztf+epqSkIDQ0FM7Ozqhdu7bGdi8vLwBAYGCgxrZGjRpp3MYHgMqVKyvLWRhxcXFYs2YNzp07h1evXiEtLU1t+9u3b/M8Lvdz+Bw5r5uUlJTn+xQToxh3PDg4WG399evXsWnTJty8eRPR0dGQSNSnfHwbHQ3XihULXSZdoeRKdOLwb0/w7KGiLyafz8PCDYY9CpM28Pg8WFiLkZyQCbkcYCwnwRrr5GKsS5MnT8a6devw77//fnpnAMuWLcPbt2+VjVTWrFmDzp07Ky+qtWrVgru7O37++WcsX164/rmOjo559h2Oi4sDAOzYsaPA41NTUwvcnnOeNWvWFOo8iYmJAKCz7jxJSUkAkG+r5pz1OXHkZm1tnecxRkZGeTZEyktiYiKaN2+O169fo0WLFhg5ciRsbW1hZGSExMREbNq0CVlZWXkeWzGPZJfz971w4QIuXLiQ7+vmfp+OHz+O/v37w9jYGF26dEG1atVgZmYGPp+PK1euwN/fH1nZ+tFokJIr0brYD+nYskTVcs97TB1UdNHsE1keCQQ8WFiJkZyYBcYAmYwhKT4L1nbG2h1kQsccHRWzF31cc8lLYGAgfvrpJ2zfvh3Ozs5ITk7Gu3fvlM9hAcDV1RX29vZ4+vRpoWPI7+9lZaUYmOThw4d51mwLK+c8SUlJsLS0/OT+OQksv9pbSeXE8/HAHTnev3+vtp+27dy5E69fv8aSJUs0apo3b94ssFtVXu9VTpybNm3C9OnTCxXD4sWLIRKJcO/ePdSpU0dt26RJk5QtsPWBYX1dJgZh85JbSElS3KqxsBJh9KzGHEekX4yEfJjnGug/O1uOlCQJGAxnpJ6cW3pVq1YtcD+pVIqxY8eiU6dOGDdunNq2j2s5mZnamXC+VatWAICrV6+W6nly9vf19S1UbTDnNq1MVrhn7xYWFqhWrRrevn2LkJAQje1+fn4AUKhW0MURGqoYXS2vVt7FSWrFeZ9CQ0NRt25djcQql8tx7dq1IsegS5RciVaFPo3D8VyNmGavagORWPNZT3knEgvUGndlZkiRkSblMCJNT58+RXx8vMb68PBwTJ06FYCidWxBVq5cidDQULVbtJaWlnB2dsb58+chlSrK7O/vj5SUFHh4eJQ47jFjxigbxOTVWEcul+PKlSufPM/UqVMhFAoxa9Ys5XPa3CQSiVpiaNq0Kdq0aYMHDx5g9erVGvvHxcWpfYGws7MDAEREaA6jmZ+xY8eCMYY5c+aoJeXY2FisWLFCuY8u5HQT+vhvFxgYqPGMvTCaNWuGdu3a4a+//lIOOvKxx48fIzo6Wi2GkJAQtT7GjDEsW7bskw3UShvdFiZatf67G8qB6qvWtkGbz8pvI6ZPMTE1gkwqR1am4iKZmiyBkZAPkUg/vowcOXIEq1atQqdOnVClShVYWFjg5cuXOHv2LDIzM9GjRw/Mnj073+OfPn2K77//HuvXr1f2c80xd+5czJgxA+3atUPLli1x4MABmJub4+uvvy5x3HZ2djh69Cj69u2LVq1aoXPnzvDw8ACfz0dERARu3rypkejyUrt2bezatQtjx46Fh4cHunfvjpo1ayI7OxsRERG4evUqHBwc8OzZM+Ux+/fvR8eOHbFw4UIcO3YMHTt2BGMMISEh+Oeff/Ds2TNlkurcuTOOHDmCfv36oUePHjAxMYGbm5tGt6fcZs+ejb///hsnT55Ew4YN0aNHD6Snp+PIkSOIjo7G3Llz0batbkY/GzlyJNasWYOZM2fCz88PNWrUQEhICM6cOYN+/frh8OHDRT7nwYMH4eXlhXHjxmHz5s1o2bIlrK2tERkZiUePHuHJkye4efOm8jHErFmzMHnyZDRu3Bje3t4QCoW4fv06goKC0Lt3b5w+fVrbxS42Sq5Ea25fiYT/OdVIPHN/Kn99WovKzEIEmTQTUul/LYgTMmFrbwKBgPubSp06dcLz588RGBiImzdvIi0tDdbW1mjbti1GjBiBESNG5PvcUyaTYezYsWjZsiWmTJmisX3atGlITk7G9u3bERAQgIYNG2LDhg2Fbin8KZ07d8ajR4+wdu1a+Pr64urVqxCJRKhUqRK8vLwKPYDF8OHD0bBhQ6xbtw5+fn74559/YGZmhkqVKqF///4YNGiQ2v5VqlRBQEAAfvrpJ5w4cQJbt26FsbEx3N3d8e233yqTBACMHz8e4eHh+OOPP/DTTz9BKpWiQ4cOBSZXkUiECxcuYP369Th48CC2bNkCIyMjNGzYEBs3bsSQIUOK9wcrhEqVKuHq1auYP38+rl27Bl9fX9SuXRvbtm3DZ599Vqzk6uLigvv372PLli04duwYDhw4AJlMhooVK6Ju3bqYNm0a6tevr9x/0qRJEIvF2LhxI/bs2QMTExO0a9cOPj4+OHbsmF4lVx4rwpQMzZo1U464QkhucjnDoNZ/IihQ0Xy+RQdn/LCzM8dRFV988hvUrKnZ3SFHfrPiFAeTA2kpEsj/+ygKBDyYW4nBL6iBUynNilNeDB48GIcPH8a7d+/0boxhUgS581OzZgXuGhwcrPHsVpuo5kq04tzhF8rEyufzMHNFK44j0i25xzKtno+Xno13r5OR81XXykYMlyqW+bcgjst1EbEr+CJCPu3FixcQi8VwcHDgOhRSRnB/74kYPIlEhs25ut58PrA6HJzMCjiCfMzYVAjHSqq/WVJCFuJjMjiMqHzYtGkTvL29ERgYiC+++EJt1CNCSoL+J5ES+8snCG/DFEPAicQCTJjblOOIDJOVjTGyMmRIjFc0tImKTIWJmdCgBvk3NBs3blSOHrVu3TquwyFlCCVXUiJZmVL8tkp1i3LAuLrlYjo5XXFwMkVmRjYyM2RgDIh8lYRqdWwh0OMhEg3Z69evuQ6BlFH0iSUlcmTnU3x4qxilR2wiwODJ9T9xBCkIj8eDU2UL8PmKZ60SiRxvw1NQhHaHhBA9QMmVFFtGejZ2/KSau3PI5PoGM42aPhOKBKjoonr+mpyYhfhoev5KiCGh5EqK7fCvTxAblQ4AMDEzwoBxJR9dhyiYW4phY6eanizqbSrS0/RjQHJCyKdRciXFkpYqwc41qlrr8K8b0DCHWmZf0RTGJoq/ac7zV5m0cDOYEEK4RcmVFMsf258gIVbRqtXUXIi+o3TXGbu8yuv56/s3KRxHRQgpDEqupMgyM6TYs+mBcnnEtAYQ6sl4uGWNUCRARWfV89fE+CwkxWtn9hhCiO5QciVFdswnCHEfVM9avxie/zCBpOTMrcSwtBYrl99FpEAipS8zhOgzSq6kSCQSGXatC1AuD5pYj2qtpcCxkimEIsXHVSZjeBdrZ0CzvxJS/lByJUVy5uBzRL1JBQCIjQXwHlOX44jKBz6fj4ou5sgZaTg10xhxyRacxkQIyR8lV1JoMpkcO3P1a+07sg71ay1FJqZC2DiYKJejE6yRmU1DIxKijyi5kkLzPRqK8NAkAICRkI/Bk+txHFH5Y+doouyeI2c8RMbYKSen16UuXbqAx+PB398/330mT54MHo+HHTt26DweQvQdJVdSKIwxtdGYegysQWMIc4DH46Giizn4PEV/10yJCDHv03T+ugEBARAIBGjaNP9JGW7fvg1AMe8zIeUdJVdSKDcvvcGLx3EAAL6Ah1EzGnIcUfklEhuhgm2icjk2Kh0Z6bobvenly5eIj4+Hh4cHzM3N89wnPT0dT548gVgsRr16dEeDEEqupFB81gcqf2/f3Q2WNsYF7E10zdYiFWbGiv6uDMDbsGSd3R6+d08x61HLli3z3ef+/fuQSqVo2LAhhEJ6DkwIJVfySc8exuLGxTfK5bHfNOYwGgIAPACV7ONVt4czZIj9r++xthUmud66dQsA3RImJAc19SSftGejqtZav7kjnFypC4g+EBtJ4WiThKh4GwBAzPs0WFqLtd6COye5Hj58GH5+fnnuQ89bCVFHyZUUKCoyFecOhyiXx89twmE05GN2lilISrdCRiYfjCluD1etbQMej/fpgwuBMYbAQMWXqwsXLnxyf0quhChQciUF2r/1IaT/zcRSuaol6jZy5Dgi7jW2+IXrEArtqWRqiY5/8eIFkpKS0Lp1a9y4cSPPfRISEmBrawtTU1PUras+qMi2bduwZs0avH//Hh4eHti4cSPatWtXopgIMQT0zJXkKzVZgiM7nyqXx35LtdbyJueWcOPG+T9nDwhQDIfZqFEjCASqoTAPHz6MGTNmYOHChQgMDESbNm3w+eefIyIiQrdBE6IHKLmSfB39/SlSkyUAAGtbY7T5rDLHEZHSlpNcmzTJ/4tVTnL9+Jbw+vXrMXr0aEyYMAF16tTBli1b4OTkhF9+MZyaPyHFRbeFSZ6ys2XYt+WhcnnolPrKeUXLu8CUr7gOAaJUVSMzmagiACAzi4ewcCOw/0YgdnYrecOzwiTXnGeyuZOrRCLB/fv3MXv2bLV9u3btmu/tZULKEqq5kjz5HglFVKRqgP6eg2tyHBH5FGMxg62NXLkcFZkKaba8gCMKJpfL8eDBA4hEInh4eOS7X14119jYWMhkMlSoUEFt3woVKiAqKqrYMRFiKCi5Eg2MMfhsUNWMeg2tCZGYppUzBPZ2MgiFisEkZDKGD29Ti32u4OBgpKamwsPDAyJR3kNdpqamIiQkBObm5qhVq5bG9o9bLTPGtNaSmRB9RsmVaLh39R2ePYwFAPD5PAyZVJ/jiEhh8flARUeZcjkhLhNpKZJinev+fcVY0gXdEn748CHkcjmaNGkCPl91ObG3t4dAINCopUZHR2vUZgkpiyi5Eg37t6qetbbycoGVLQ11aEjMzeSwNFfdDn4XnlKsoRFHjhwJxhh27tyZ7z6enp5gjGnMliMSidC0aVONvrEXLlxAmzZtihwLIYaGGjQRNW/DknH51Gvl8hga6tAgOTrKkJrOg1zOQ1aWYmhERyezUo3hm2++wYgRI9CiRQt4enpi+/btePfuHSZPnlyqcRDCBUquRM2h7Y+VtZwqNa3hXsOa24BIsQiNGBzsZfgQrfiIx7xPg5WNGGLj0vvIDxo0CHFxcfj+++/x/v171KtXD+fOnYObm1upxUAIVyi5EqX0tGwc2xWkXB41sxF3wZASs7GWIylZjsz/hkZ8/yYVbtWtSrVB0ZQpUzBlypRSez1C9AU9cyVKZw4+R3JiFgDAwlqEVl4uHEdESoIHwMlRBh4UdyJSkyVISSpe4yZCSNFQciUAFF0k9m99pFzuP6YuBAL672HojI0ZrK1VjZvevyle4yZCSNHQ1ZMAAG77ReJlcDwAQGDEwxcjanMcEdEWBzsZBAJFQs2WyBEblcZxRISUfZRcCQCoDXXYvrsbzCzyHjSAGB6BAHC0V/V9jYlKhyRLymFEhJR9lFwJIl4mwf9cmHJ5xLSG3AVDdMLKSg5jY8XtYcaAqDfFH7mJEPJplFwJDm1/BPbfY7jqHraoXNWK24CI1vGgPnJTcpIEKUlZ3AVESBlHybWcS0uR4C+fYOXyaOp+o8RY2Wr4Y2LMYG2pSrDUuImUV6Xx2abkWs6d3P9MOWerla0Yzds7cxyRfuDxBJBKy95zSQcHOQR8xYVFkiVHXHQ6xxERUvqys7MhEOh2MhJKruWYXM5w8GdV95uB4z1oztb/CI2MkZKSzHUYWmckYLDP3bjpfRqyJbICjiCk7ElOToaFRcnnOy4IJddy7PqFCLx+kQgAMBLy0WuI5pRh5ZWxyAJx8fGIj49DdnZ2mbpFbGMth1ikKI9cDkS/o645pOxjjEEikSA2NhYJCQmwtbXV6evR8Ifl2IFctdaOPd1hai7kMBr9YiQQwsLEAclJKYiLiwdj+lW7E2TFKn+XC4o+6pJEwkN8guK22IcYIC7JFEIRzdlLDFys6nOB4GCNzQKBABYWFnB1dYVYLNZpKJRcy6mwFwm4ej5cuTxiKnW/+ZiRQAhzE91+uy2uStfrKX9PqTy/WOf49kdbXL9lAgBo2tYJey71o4nMiWGrW1f1O8d3m+i2cDl1YJuq1lqrgR0quen2+QPRP9MnJytHbrp/7T0uHH/JcUSElB2UXMuhlKQsnNj7TLlMs9+UT26VpRjwpep565q515CVWfZaSBPCBUqu5dCJvc+QnpoNALCxM0aztpU4johwZeyIZFhaKEZueheRqjYMJiGk+Ci5ljNyOVO7JTxgggc9ZyvHLC0YJo5WdTna/uMdxNDA/oSUGCXXcubq+XC8eZkEIKf7TU2OIyJc+7J3Gqq4Ke5kZKTJsGXpbY4jIsTwUXItZ3LXWjv1dIeJKXW/Ke+MBMCMr5KUy3/5BCH4QQyHERFi+Ci5liOvniXg+j8RyuUR06n7DVFo1TwLbVpmAlD0YFj17b9lauAMQkobJddy5OAvqlprzfp2cKpM3W+IyvTJScpxh+9dfY9LJ19xHBEhhouSazmRkpSFk/tU3W/GzGrEXTBEL7m7SuH9hXrXHAmNO0xIsVByLSdyd7+xtjNGU+p+Q/IwbqSqa05kWAoO/fKY44gIMUyUXMsBuZyp3RIeMK4udb8hebKyZBg7QtU1Z9uK20iMy+AwIkIMEyXXcuCabzgiQlXdb3oPo9lvSP68+6TBxVkxUlNqcjZ++fEexxERYngouZYD+3PNftOhhxt1vyEFEgqBaRNVXXMObXuEsBcJHEZEiOGh5FrGvX6u3v1m1PRG3AVDDEZ7z0w0bpAFAJDJGNYtuMFxRIQYFkquZVzuZ6016tnCyZW635BP4/HUB5a4fPo17vhHchgRIYaFkmsZlposUZv9ZszMxhxGQwxN7ZrZ6NElXbm8evZVyOU0sAQhhUHJtQw7sTdY2f3GylaMZu2p+w0pmsnjkiAWK7rmPHsYh1P7n33iCEIIQMm1zNKY/WYczX5Dis7RQY5hA1KVyxsX3UB6WjaHERFiGCi5llHX/4lQ637Th7rfkGIaPjgVdraKkZpiojKwe30gxxERov8ouZZR+39WTXrdvrsbTMyo+w0pHlMThsljVQNL/L72PqLfpRZwBCGEkmsZFPYiAdd8Vd1vRs9sxF0wpEzo0TUdNapJAACZGTJsXkJzvhJSEEquZdDBXOPBUvcbog0CATB9sqr2emJvMIIDac5XQvJDybWMSU2W4PieYOUydb8h2tK8SRY8WynGGWYM+GnuNZrzlZB8UHItY475BKlmv7E1pu43RKumTUpWzvl6x/8trpwN4zYgQvQUJdcyRCqV40CuhkyDJlH3G6Jd7q5S9O2Ta87XedeQnU1zvhLyMUquZcilk6/wNiwFACAU8dF7KHW/Ido3fmQKzM0UA0uEhyThz9+ecBwRIfqHkmsZsnfTA+Xv3fpVh9jYiLtgSJllbSXHmOEpyuWty+8gKSGTw4gI0T+UXMuIh7ej8OBWFADFoOvDpzXgOCJSlg34MhWVnBRzviYnZOG3VTTnKyG5UXItI3LXWhu3cYKdoyl3wZAyTyQCpk5QzZqzf+tDRLxMKuAIQsoXSq5lwLvwZFw4/lK5PGFuEw6jIeVFp/aZaFBPMeerNJth/UKa85WQHJRcy4AD2x5BJlN0j6hc1RLV69pxHBEpD3g8YPpkVW31wvGXuH/tHYcREaI/KLkauLQUCY7+HqRcHvstDRpBSk+9Otno2lk15+tPc6/RnK+EgJKrwftrdzBSkxVjvlraiNHmM1eOIyLlzVfjkiESKhLqk3vROPfHC44jIoR7lFwNmEwmx/6tuQaNmOABPp8GjSCly6mCDEP6q2bJ2bDoJjIzpBxGRAj3KLkasIvHXyHytWIwdaGIjz7Da3McESmvRg5JgY21YqSmqMhUtdbrhJRHlFwNFGMMv6+9r1zu5l0dxiY0aAThhpkZw6Qxqllzdqy+h5iotAKOIKRso+RqoG5djsTTAMWUX3w+DyNnNOQ4IlLe9fo8HVXdFZNGpKdJsXUZzflKyi9Krgbq97UByt9bd3aBjZ0Jh9EQAhgJ1Lvm/OUThBePYzmMiBDuUHI1QE/uf8DNS2+UyxPnN+UwGkJUWjXPQqvminGG5XJgzbzrNOcrKZcouRqg39eoaq31mjmikqslh9EQom765CTw/5vz9cbFN7jmG8FxRISUPkquBiY8JFFtqMMpi5tzGA0hmqq6S/FFD/WBJaRSOYcREVL6KLkamF3rA5Bzl61KTWvUoKEOiR4aPyoZpqaKhPrqWQKO/v6U44gIKV2UXA1IzPs0nNz3TLk8eWEzDqMhJH92tnKMHpprztdlt5GSlMVhRISULkquBmTv5ofIlihqAxWczdC4jRPHERGSv0HeqahYQTFSU0JsJnasvv+JIwgpOyi5GojkxCwc/u2xcnncnCbg8WioQ6K/xCJgynjVwBJ7Nz1QjihGSFlHydVAHPrlMdJSFB30rWzF6PC5O7cBEVIIXTplwKOOYmKJ7Gw5Ni66yXFEhJQOSq4GIC1Fgj2bApXLI6c3pAH6iUHg8YAZX6kGlvj7SAge3HrPYUSElA5Krgbg4LZHSIpXNAYxtxSh56CaHEdESOE18JCgc0dV15xV316lOV9JmUfJVc+lpUqwe+MD5fKIaQ0gMKK3jRiWr8cnQ/jfnK+P70bjxN5gjiMiRLfoKq3n/tj+BIlxiuHkTM2F6DOMppUjhqeSkwxDB6jmfF03/zoS4zM5jIgQ3aLkqsfS07Kxe4PqWevwqQ1gJKS3jBim0UNTUNFR0TUnMT4Lm/93i+OICNEdulLrsT9/e4L4mAwAgKmZEF+OoForMVwmJgyzvlY1bvpzxxM8vveBw4gI0R1KrnoqLVWiNq3c0K/rQygScBgRISXX3jMTbVoqbgczBqyYegUyGY07TMoeSq56av+WR8paq4mZEH1H1uE4IkJKjscDvpmaCNF/jZueBsTg2K4gjqMiRPsoueqhxPhM+KxX1VpHz2wIkZhqraRscKkkw4ghqnGH1y+8ofwiSUhZQclVD/msC0BKkmJUGwtrEb4YTs9aSdkyYnAKnJ0UjZtSkiTY8N0NjiMiRLsoueqZmKg0HPj5kXJ50rxm1K+VlDnGYsXt4Rx/7Q7GvatvuQuIEC2jq7ae2bH6PjLSFd/obR1N0KVfNY4jIkQ3PFtloYOn6nbw/yZdRlamlMOICNEeSq565G1YMg7/9kS5PG1JCxpDmJRp305LhNl/k6qHhyZh+w93OY6IEO2g5KpHNiy6CWm24kLjVNkcnl1cOY6IEN1ydJDj64mqvq+/rwvAs4exHEZEiHZQctUTj+5E4e8/Q5TLs1d50nytpFz4smc6GtVXTEwhkzL8b9Il6vtKDB4lVz3AGMPqOdeUy3UbO6BBiwocRkRI6eHzgQXfJioH9n8aEIN9Wx5yHBUhJUPJVQ/889dLPLgZBUDRyX7uGk+OIyKkdLlVlmLsiGTl8ub/3cKbV0kFHEGIfqPkyjGJRKbWx69DT3c4u1lyGBEh3Bg+MBXVqmQDALIyZVg6xQ+M0byvxDBRcuXYwW2P8OaV4hu7kZCPaUtachwRIdwQCoGFsxPA5ysS6q3LkThKQyMSA0XJlUMx79Pwy/eqrgeDJ9WDpbWYw4gI4ZZH7WwM6qea93X17KuIfJ1cwBGE6CdKrhxat+AGUpMVwxxaWosx9Kv6HEdECPcmjU2GW2XF7eGMNCm+G3cRcjndHiaGhZIrR+5dfYvTB58rl+f85ElTyhECxdCI/5ufAMF/t4fvXXuHvZsfcBsUIUVEyZUD2dkyfD/dX7lct7EDWnVy4TAiQvSLR+1sjByqmjln46KbCA2K5zAiQoqGkisHDv3yGCFPFRcKPp+HhRvbcRwRIfpn7PAU1KyueGySLZFj3qh/IMmScRwVIYVDybWURUWmYuuy28rlfmPqoEIlcw4jIkQ/CYXAkvkJyonVnz2MxXqamo4YCEqupYgxhuVTryAtRdFYw9JajLHfNOY4KkL0V7UqUkydpBpMYt/mh/j37zDuAiKkkCi5lqK//wyB/7kw5fJ3G9tRIyZCPmHAl2nwbKWamm7huIuIeZ/GYUSEfBol11KSEJuBH2f9q1xu3dkFTTwrcRgRIYaBxwMWzUmEvZ3ieWtCbCYWjL1Ig/sTvUbJtZSs+vYqEmIzAQDGJkaYt6YtxxERYjhsrOVYMj8BPJ7i+evNS2/w64/3OI6KkPxRci0FV86+xplDL5TLs1e1gZmFiMOICDE8zZtkYeQQ1ehN276/g6u+4RxGREj+KLnqWFx0Ov436bJyuWHLiujQw527gAgxYBNGJ6NZY8UdIMaAeSP/wbtwGh6R6B9KrjrEGMPiiZcQF61ojCE2FmDx5vYcR0WI4TISAMu/S4C9nRQAkJSQhZmD/6b+r0TvUHLVocO/PYH/OdVtqwXr28HK1pjDiAgxfLY2cvy4JAECwX+Tq9+PwbKvaXo6ol8ouerIq2cJWDP3unK5XXdXeHZx5TAiQsqOBh4SzPhK1f/1xN5n2L0hkMOICFFHyVUHMjOkmDPCF5kZiltX1rbGmL+WhjgkRJsGfJmGXt1V/V3XLbiBK2dfcxgRISqUXLWMMYbvp/vj2cNYAIo+eit9OkMkpsEiCNEmHg+YOyMRDetlAVA0cJoz3BchT+I4jowQSq5ad3RXEI7vCVYuj57ZCNXr2nEYESFll0gErFwaj4oVFHeJ0tOk+OqL04h+l/qJIwnRLUquWvT43gf8MEM1lVzj1hUxhCZAJ0SnbG3kWPt9HExNFCM2vX+Tiom9TiE5MYvjyEh5RslVS+JjMjBr8HlkSxQfcGs7Yyz/1Qs8Ho/jyAgp+6pXleKH/8UrWxCHPInH9P5nkZUp5TgyUl5RctWCrEwppvU/i/cRismdBUY8bPijO4xNjDiOjJDyo3WLLCyak6BcvvvvO8wbdYHGICacoORaQnI5w8KxF/HgZpRy3bw1beHibslhVISUT593ycDUiaouOheOv8SiCZcowZJSR8m1hDYtvonzR0OVy0O+qodOvapwGBEh5duwgakY7K1q0HRq/3Ms/coPcjkNMkFKDyXXEvhz5xPsXBOgXPbsUhljZtHk54RwiccDpk9Owhc9VX1g/9odjGVfU4IlpYeSazGdOfgcy7++olyuXtcGi7d0oAZMhOgBPh+YNzNRbZCJo78HYdnXfnSLmJQKSq7F8M9foVg47iJyhjK1r2iK9Qe7QyCgPych+oLPBxZ8k4jPu6Qr1x39PQhzhv9DA/0TnaNsUET+58IwZ8Q/kMkUmdXKVoxfT/WCiZmQ48gIIR8TCIBFcxLQI1eC9T0Wiq/7nkFaqoTDyEhZR8m1CC6dfIWZg/6GNFtxW8ncUoTfzvSGpQ3NdEOIvhIIgEVzEzCon6qR042LbzC++0nEfkgv4EhCio+SayEd3xOEmYNU80aamgvx65lesHUw5TgyQsin8PnAzClJmDhaNbH6ozsfMNjzCJ4/iuUwMlJWUXIthN0bArFowmVlS0MzCyG2n+oFRydzjiMjhBQWjweMHZGCOTMSwecrPsvvI1IwrMNRXD5Ns+kQ7aLkWgCpVI6V317FmnmqeVlt7I2xy/cLOFW24DAyQkhxefdJUxuLOCNNiun9z2Lb93eoJTHRGkqu+UiMz8Tk3qexf8tD5bpKrhbw+edLuhVMiIFr0zILO7fGoFJFxdjDjAE/L7+DiT1P0XNYohWUXPMQGhSPwZ5HcPPSG+W6Oo3s8dvZ3jCzEHEYGSFEW6q6S7FrWwyaNFTNnnPrciS8m/+BW36RHEZGygJKrrkwxnB011MMbvMn3rxUjU/ac3ANbDz8OcTGNBA/IWWJtZUcm9fEYuzwZPB4iuewsVHpGNftBH6Y6Y/0tGyOIySGipLrfxLjMzFz0N9YMtkPGemKW0V8Pg/f/NgaM1e0Bp9PIy8RUhYZCYCJY1KwcVUcrK1Ug0sc3PYY/Zoewt1/33IYHTFUlFwB+J15jX5ND+HiiVfKdZY2Yvx8vAc+H1CDw8gIIaWlZbMs7N8RDc9WGcp1b14lY/Rnx/Hd+Iv0LJYUSblOrlGRqZgx8Bym9juLD29VY5C27uyCA/7eqF7XjsPoCCGlzd5OjrXfx2Px3ASYmalqsSf2PkNPj/3Yu/kBsrNp6ETyaeUyuWZlSrFrXQB6NzigVlsVivhYsL4dlm/3oonOCSmneDygZ7d0HPo9Gu09VbXV1GQJVs++hj4ND+LMoec0ww4pEI8xVuj/Ic2aNcO9e/d0GY9OyWRynDrwHFuX3UbUm1S1bc3aVcKCdW1pKENiECpdt1L+nlJ5PoeRlH237oqxbosl3rxV7ylQw8MWX/+vJTp/UZXaZOiL3LOSFT616US5SK4SiQzn/niB39cG4NWzBLVtljZiLFzfDk3bVuIoOkKKjpJr6crOBg7/ZY7dB8yRmiZQ2+ZewxqjZjZCn+G16Y4X1yi5lo7kxCz8tTsI+zY/RFSkek1VJBZg2JT6GDDeA0KRIJ8zEKKfKLlyIyWVhwN/muOPo2bIzFK/btg6mKDvqDrwHlMXbjWsuQmwvKPkqjuMMQTceI+jvz/FP8deIjNDqrZdIOCh19BaGPdtY5omjhgsSq7cSkjk48Cf5vjrlCnSMzS/nDfv4Iy+I+vAq08VWFiJOYiwnKLkql2MMTx7GIt/joXi/LFQRIQmaewjNhag19BaGDalPv1nJwaPkqt+SEvj4dTfpjh01AzRMZpf1oUiPtp2dUP3/tXRtrsbrG2pTYdOUXItuYz0bNz79x2u/ROOf8+H55lQAcCuggn6j62LPsNqQySm27+kbKDkql+kMuDmbWOcOGOCm3dMIGeaDZz4fB4atKyA9t3d0crLBXUaO0BEj6S0i5Jr0cXHZODBrSg8vPUeD25F4dGdD8q5VT9mZMRH8w6VMGxKA9Ssbwcej1rykbKFkqv+io7hw/eSKf65LEbIy/xrqsYmRqjfogKatHFCE08n1G9eAVbUW6FkKLnmTyqV483LJIQGxSM0KB4vg+LxNDA635ppDoERD/WaVUCfobXQspMzjQNMyjRKrobhzVsBLl0xwb/XRQh+YQyWR402t4ou5qjVwA4169mjZn07VKtji8rVrGBK7UMKp7wn18T4TES+TsLbsBS8C0/G2/D//g1LQcTLpHxrpB+zsTdGs7aV4PVFFTRoXpFu+5Jyg5Kr4UlM4uPWXTFu3xUh8JEQUdGFb/thX9EUlataoXJVSzi7WcLByQwOFU1h72QGRycz2FUwgVBI1z99Sq6cVO/WzruO43uCi3QMn89DBWcz1GvmiJYdXeDR1BH2FWheVUKIYbC2kqP7Zxno/pli7OKYWD4ePhHjwSMhHj4WICxCjGxp3gkyNiodsVHpCLzxPs/tPB5gY28C+4qmGD+nKXoOrqmzcpDC4SS5VnKzKHC7qZkQFSubo1odG9Rt7IBqdWxRrY4t1UwJIWWGg70cn3XMwGcdFclWKgPevDFCyCshQl4a4UUIH28iBfgQI4ZMXvBItYwp2qXEx2QgK1Na4L6kdHCSXKvUtEGNenYIeRKH5u0roZKbBRydzGDraAr7CqYwNdd8vhAWklj6gRKip3KPJ5b4IZqzOIh22ZgALTwUPzlkMiAmToR3H8R4Hy1CTKwIcfF8xMbxkZAoREKyGMlpYuXzXIeKZhxFT3Ir0jNXe3t7uLu7a+3Fw14kQiaTa+VcWdIUiI0KrhEbOipj2aGtclqZJoPP085nSNtSMrJgYVL2+5TrVTkFYtg6O2v9tDExMXBwcND6efVNXuW0t7fH+fPni3yuIiVXfaZPfXB1hcpYdpSHcpaHMgLlo5zloYyAdstZLqecI4QQQnSJkishhBCiZWUmuU6cOJHrEHSOylh2lIdylocyAuWjnOWhjIB2y1lmnrkSQggh+qLM1FwJIYQQfaHXyTUyMhLTpk1D69atYWpqCh6Ph7CwsEIfHxwcjAEDBsDe3h4mJiaoVasWNm3apLuAi6EkZYyIiMCoUaPg6uoKU1NT1KxZE4sWLUJaWppugy6io0ePwtvbG25ubsr3YcGCBUhJSfnksZmZmZgzZw6cnJxgYmKC1q1b499//y2FqIumuGW8d+8eJk6ciNq1a8PU1BSurq4YNmwYXr9+XUqRF01J3svcVq5cCR6Ph7Zt2+oo0uIraRkN4boDlKychnLt8fX1hZeXFypWrAixWAwXFxcMHDgQQUFBnzy2xNcepsf8/PyYo6Mj+/zzz1nXrl0ZAPb69etCHXv37l1mYWHBevfuzY4fP84uX77Mfv31V7Zu3TrdBl1ExS1jamoqq1GjBnN3d2e7d+9mly9fZqtXr2bGxsZs4MCBug+8CFq2bMkGDBjA9u/fz65cucI2bNjArKysWMuWLZlMJivw2KFDhzIrKyv222+/sYsXL7K+ffsyY2NjFhgYWDrBF1Jxy/jtt9+yNm3asJ9//plduXKFHThwgNWuXZvZ2tqyiIiIUixB4ZTkvczx8uVLZmZmxhwdHZmnp6eOIy66kpTRUK47jBW/nIZ07Tl48CCbPXs2O3LkCLty5Qrbu3cvq1u3LrOwsGBhYWEFHlvSa49eJ9fcb/COHTsKnXhkMhmrW7cu+/LLL3UYnXYUt4y+vr4MAPP19VVbP2/ePCYQCFhaWpq2Qy226OhojXV79uxhANilS5fyPe7BgwcMANu1a5dyXXZ2NqtZsybr3bu3TmItruKWMa/jwsLCGI/HY4sXL9ZqjNpQ3HLm1rVrVzZx4kTWoUMHvUyuxS2jIV13GCt+OQ3p2pOXZ8+eMQBs7dq1+e6jjWuPXt8W5vOLF96VK1cQFBSEb775RssRaV9xyyiRSAAAlpaWauutra0hl8vB9KidWl4juzRv3hwA8Pbt23yPO3XqFIRCIQYNGqRcZ2RkhMGDB8PX1xdZWVnaD7aYilvGvI5zc3ODg4NDgcdxpbjlzHHw4EEEBARg5cqVWo9NW4pbRkO67gDFL6chXXvyYmdnBwAQCvOfxk8b1x69Tq7Fde3aNQCKe+atWrWCUCiEo6Mjpk+fjoyMDI6j047PPvsMNWrUwLx58xAUFITU1FRcvnwZmzZtwuTJk2Fmpt/ji/r7+wMA6tSpk+8+T58+RZUqVWBqqj77kYeHByQSCUJDQ3UaY0kVpox5CQ4ORnR0dJGP40phy5mQkIBZs2bhp59+gq2tbWmEpjWFKWNZuO4UppyGeO2RyWSQSCQICQnBpEmTULFiRQwePDjf/bVy7SlhDbvUFOWW6aRJkxgAZmNjwxYvXsz8/PzYmjVrmImJiV7fsilKGRlj7MOHD6xt27YMgPJn/PjxhX72xZXIyEjm4ODAPvvsswL369KlC2vZsqXG+gsXLjAA7N9//9VViCVW2DJ+LDs7m7Vv3545ODiw+Ph4HUWnPUUp57hx41jbtm2ZXC5njDG9vS38scKW0VCvOzmK8l4a2rWnadOmyjirV6/OgoKCCtxfG9ceTmbF0TW5XDGQ+fDhw7F8+XIAQMeOHSGTyTB//nwEBQWhbt26XIZYYpmZmRg0aBCio6Oxb98+uLq64s6dO1i+fDmMjIzwyy+/cB1inlJTU/HFF1/AyMgIPj4+Be7LGAMv9+THudbrs6KU8WNTp07FjRs3cPbsWdjY2OgoQu0oSjmvXr2KvXv3IiAgIM/3VF8VpYyGfN0pSjkN8dqzb98+JCcn49WrV1i7di26dOmCa9eu5TsRjVauPcX6GsCBotTq5s+fzwCwU6dOqa0PCAhgANiBAwd0FGXJFKWMW7duZQBYaGio2vrffvuNAWAPHjzQUZTFl5GRwTp16sRsbGzYo0ePPrn/wIEDWc2aNTXWHz58mAFgT5480UWYJVLUMuY2f/58xuPx2N69e3UUnfYUtZx16tRhkydPZgkJCcofT09P1qpVK5aQkMAyMzNLIeqiKWoZDfW6U9RyGuK1J7eEhARmZWXFJk2alO8+2rj2lMlnrh4eiskQP/7mwf771lHcRkT65PHjx7CxsUG1atXU1rdo0QKA4rmdPsnOzoa3tzfu3LmDc+fOoX79+p88xsPDA69fv0Z6erra+qCgIIhEIlSvXl1X4RZLccqY44cffsCqVauwadMmjBgxQodRllxxyhkcHIzt27fDxsZG+XP9+nXcunULNjY2elfbKe7/V8CwrjvFKaehXXs+Zm1tjerVqxf43FQb1x79e7e14PPPP4dYLNaYg8/X1xeAYlohQ1exYkUkJCRo/Ae5ffs2AMBZB3M6FpdcLsewYcNw6dIlnDx5Eq1atSrUcX369EF2djaOHDmiXCeVSnH48GF07doVYrGezKGJ4pcRADZv3oxFixbhhx9+wLRp03QYZckVt5x+fn4aPw0bNkS9evXg5+eH/v376zjywituGQ3tulPcchrStScvHz58wLNnzzS+HOSmlWtPCWrXpeLIkSPsyJEjbPLkyQwA27Ztm7JDMGOKPoECgYAtW7ZM7bilS5cygUDAFixYwC5cuMBWrlzJjI2N2ahRozgoRcGKU8bXr18zCwsLVqNGDWVH7p9++olZWFiwpk2b6lXDgpxyfffdd+zmzZtqP2/evGGM5f8+Dho0iFlbW7MdO3awixcvMm9vbyYWi9n9+/e5KEq+ilvGQ4cOMR6Px7p3765x3NOnT7kqTr5K8l5+TF8bNJWkjIZ03SluOQ3p2vPll1+y5cuXsxMnTrDLly+z7du3s1q1ajErKyv2/Plzxpjurj16n1yRqzVa7p8OHTowxhRvNAC2ZMkStePkcjlbt24dq1atGhMKhczV1ZUtXryYSSSS0i/EJxS3jE+fPmUDBgxgLi4uzNjYmNWoUYN9++23etfK1M3NLd8y5pQpvzKmp6ezWbNmsQoVKjCxWMxatGjB/Pz8Sr0Mn1LcMo4aNeqT778+Kcl7+TF9Ta4lKaMhXXdKUk5DufasWrWKNWnShFlZWTETExNWs2ZNNnHiRLV2Lbq69tCsOIQQQoiWlclnroQQQgiXKLkSQgghWkbJlRBCCNEySq6EEEKIllFyJYQQQrSMkishhBCiZZRcCSGEEC2j5EqIlvB4PPB4PPD5fLx8+TLf/Tp16qTcd/fu3WrbRo8erdy2ZMmSfM+xZ88e5X4dO3ZU23blyhXltpwfU1NTODk5oX379pgzZw4CAwNLUlRCyCdQciVEi4yMjMAYw++//57n9pCQEPj7+8PIqODZHo2MjLBr1y7IZLI8t+/YseOT53Bzc8OSJUuwZMkSzJw5E7169UJaWhrWrl2LJk2aYNiwYUhNTS1cwQghRULJlRAtqlChApo1awYfHx9IpVKN7Tt37gRjDL169SrwPL169UJkZKTGIPCAYtaR69evo3fv3gWew93dHUuXLsXSpUvx448/YseOHbh//z4CAwNRv359HDx4EAMGDChaAQkhhULJlRAtmzBhAqKionDmzBm19dnZ2dizZw/atGmjnJ4sP8OGDYOJiQl27NihsS1n3fjx44sVX6NGjXDx4kU4ODjg/PnzOHHiRLHOQwjJHyVXQrRsyJAhMDMzw86dO9XWnzp1Ch8+fMCECRM+eQ5ra2sMGDAAZ8+exfv375Xrs7KysHfvXnTo0AE1a9YsdoyOjo6YNGkSAODAgQPFPg8hJG+UXAnRMgsLCwwePBjnz59HZGSkcv2OHTtgaWmJgQMHFuo8EyZMgFQqVWv0dPz4ccTFxRUqQX9KTkOoO3fulPhchBB1lFwJ0YEJEyZAJpNh165dAIDw8HBcuHABw4YNg6mpaaHO0bZtW9SpU0f5nBZQJGgbGxt4e3uXOMacSa1jYmJKfC5CiDpKroToQMuWLVG/fn3s2rULcrkcO3fuhFwuL3KNc/z48Xj16hUuX76M0NBQ+Pn5YcSIETA2Ni5xjDkJm8fjlfhchBB1lFwJ0ZEJEyYgPDwc58+fh4+PD5o2bYrGjRsX6RwjR46EWCzGzp07lTVYbdwSBoB3794BABwcHLRyPkKICiVXQnRkxIgRMDExwaRJk/D27VtMnDixyOewt7dH3759cfz4cezatQutW7dGvXr1tBKfn58fAEUtmxCiXZRcCdERa2tr9O/fH5GRkTAzM8OQIUOKdZ4JEyYgKysLMTExWqu1RkdH49dffwWg6PZDCNGugod4IYSUyPfff49+/frBwcEBFhYWxTpHp06dcPLkScjlcnTr1q3EMT18+BAjR45EbGwsevTogT59+pT4nIQQdZRcCdEhV1dXuLq6lugcPB6vWAkwLCwMS5cuBaAYwCI2Nhb379/H/fv3AShqrDm1V0KIdlFyJaSMCg8Px7JlywAAxsbGsLa2Ro0aNTB79mwMGzYMjRo14jZAQsowHstpj08IIYQQraAGTYQQQoiWUXIlhBBCtIySKyGEEKJllFwJIYQQLaPkSgghhGgZJVdCCCFEyyi5EkIIIVpGyZUQQgjRMkquhBBCiJZRciWEEEK07P9SkOhDgwkrSQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b000cc4d4d048e4bebca2637fa249cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01922062b48546c595ec7eff52203bcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fb5e49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/FINAL_v2+v3+v4+v5_Sep9_bf_model_misspecification_1000_data.ipynb b/bf/FINAL_v2+v3+v4+v5_Sep9_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..3e2764a
--- /dev/null
+++ b/bf/FINAL_v2+v3+v4+v5_Sep9_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,17219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/bayesflow/trainers.py:26: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72\n",
+    "\n",
+    "from hnn_core.utils import smooth_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array = np.random.uniform(-4, 0, size=(3,))\n",
+    "#     random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "#     random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "#     combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "#     combined_array = np.squeeze(combined_array)\n",
+    "    return random_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 2.7467337\n",
+    "    maxAbs = 196.12401\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    params = 10**params\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v2: scaling\n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "#         temp = dpl[0].data['agg']\n",
+    "        temp = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "        temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms..."
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.TimeSeriesTransformer(input_dim=341, summary_dim=3)  #10\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/x.pt')\n",
+    "theta = torch.load('Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scale\n",
+    "x = x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# Smoothing\n",
+    "for i in range(len(x)):\n",
+    "    x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "25f9ecff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.7467337"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f38d976e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "196.12401"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f79ba503c7dd4d20a1227cbe9858260c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6be09fb51654f01b8a048aea41a235a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 2:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3509fb3cbd5449e9aa78a062c0679270",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 3:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee6a9601ba0a4322bd32b5753c80ac53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 4:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec68c2cb59f34fe796fd15c35fe4d837",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 5:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dca6c2c469a94c038e2fcce0a619fc3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 6:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65ece1257e6c45dca465fdafe6f5f38b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 7:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63636d3d4d1041b78b41104f120f9f75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 8:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37dbda30e47e4822a282c0b325249756",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 9:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4df098b254a4f7ba0ccef8cdc9b938a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 10:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c327d348bade4bc7b3588151b477df71",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 11:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27450e72ee234de1a3d57dae3a501ef5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 12:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c61037ef41154e678fff83e2b0c14d4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 992:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d1adb7550cd47a0bb272069edb00d94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 993:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fbbc4dcf69445afa59d19177bdf3c80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 994:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8c1af3254da4c85b1b69722c18f594f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 995:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04bcc7cbd21f4b3a9e33d86c252033bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 996:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc2f58764b8e4bf791018b2692029a93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 997:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e37c0e6fef64cd5b3a5284421e189c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 998:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd02ebc61927467281c157d761064f63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 999:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eed9664ea8fe44a78388f2f09a357b49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1000:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(max_epochs=1000 ,presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.362765</td>\n",
+       "      <td>0.822048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19.410067</td>\n",
+       "      <td>0.806924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16.760113</td>\n",
+       "      <td>0.796277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.000216</td>\n",
+       "      <td>0.786801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11.414811</td>\n",
+       "      <td>0.778039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>0.782097</td>\n",
+       "      <td>0.179129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>0.798313</td>\n",
+       "      <td>0.179221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>0.772982</td>\n",
+       "      <td>0.179291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>0.660337</td>\n",
+       "      <td>0.179271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>0.656200</td>\n",
+       "      <td>0.179197</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Loss   W.Decay\n",
+       "0    14.362765  0.822048\n",
+       "1    19.410067  0.806924\n",
+       "2    16.760113  0.796277\n",
+       "3    14.000216  0.786801\n",
+       "4    11.414811  0.778039\n",
+       "..         ...       ...\n",
+       "995   0.782097  0.179129\n",
+       "996   0.798313  0.179221\n",
+       "997   0.772982  0.179291\n",
+       "998   0.660337  0.179271\n",
+       "999   0.656200  0.179197\n",
+       "\n",
+       "[1000 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cc5cd3d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: >"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "63800080",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=np.array((math.log10(0.142300), math.log10(0.00865), math.log10(0.684013))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfbc518f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f5526ad5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_statistics = trainer.amortizer.summary_net(x)\n",
+    "summary_statistics_obs = trainer.amortizer.summary_net(data_HNN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8edb7299",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 1], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_obs[:, 0], summary_statistics_obs[:, 1], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a0c39e52",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 1], summary_statistics[:, 2], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_obs[:, 1], summary_statistics_obs[:, 2], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "57658c8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 2], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_obs[:, 0], summary_statistics_obs[:, 2], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "5fa9f167",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4d6d586c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x28a656200>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 540x540 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(pd.DataFrame(summary_statistics))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0552dda5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71a4c794c27f45d09e0f4360f74650b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## 10 Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ten_priors(seed=222):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(-4, 0, size=(10,3))\n",
+    "#     random_array2 = np.random.uniform(0, 1, size=(10,))\n",
+    "#     random_array3 = np.random.uniform(0, 1, size=(10,))\n",
+    "#     combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "#     combined_array = np.squeeze(combined_array)\n",
+    "    return random_array1.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 10)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ten_params = ten_priors(0)\n",
+    "ten_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "ten_data_HNN = simulator(params=ten_params, n_obs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 341)"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ten_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1795935b0>]"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vs7i4eEe6YV6vF7/fz+bmJl/+8peJx+NkMhmRgT1KyExFIpE8kly9epVOp8PMzAzdbhedTkepVCIQCKDT6dBoNLc14ep0Ouzs7HD16lXS6TSKouD1egmHw/j9fvR6PYVCgbW1NdbX16nVakxOTorG/M7Ozi1HjmdnZ2k2m8Tj8TseD15aWqJYLDIej3E6nWQymbvKdh4WZKYikUgeOQaDARcvXiQYDDIajTAYDLTbbXQ6HeFwmFqthtvtvql8/Wg0Ip1Ok8/n0el0hEIhgsHgTV87HA4pl8vkcjm2t7dxOp1Eo1F0Oh2JRIKtrS1isdgNysJer5fhcMjBwQGJRIKZmZnPfE+xWIwLFy6wtbXF1NQUFy9eJJ/PEw6H7/2DegDITEUikTxyXLlyhW63K5rwo9GIwWCA0+nEaDQyGo1uakfearVYW1sjn88TCAQ4c+YMk5OTt/RO0ev1BINBzpw5w/T0NM1mk2vXrqHRaJibm6PVarG5uXnTxnwwGCQUClEsFm85OfZJzGYz0WiUra0tJicnReB61JBBRSKRPFL0+30uXrwoGvE6nU5kKy6Xi0ajgdlsxuFwHPq+crnMxsYGiqKwvLxMNBq9Qfb+Vmg0GoLBIKurq9hsNvb396nVaszNzdHtdllbW6PZbN7wfZFIRAwPNBqNzzzP0tISnU6HfD5PMBgkl8vd9Yjyg0YGFYlE8khx6dIlOp0OwWAQrVbLYDDAbDaj0+lwOp20Wq0bspRCoUA8Hsdut7OysnLP8vVGo5HFxUUikYgoi8ViMbRaLZubm2QymUM9FDWjMZvN7O7ufuZy5PT0NEajkZ2dHaanpxkOh7ccY35YkUFFIpE8MrRaLS5duoTT6cRms6EoCiaTCUVRsFgsdLtdtFrtIf/5YrEodkBisdgdZye3QqPREA6HmZ+fp91us7+/z/z8PG63m3Q6zcbGxqEdE51Ox9zcHKPRiP39/dseW33t3t4ePp8Pi8XCwcHBI6UFJoOKRCJ5ZHj33XfpdDpMT0+j1+tRFAWfz0e/38fv91Mul/F6vaJxXqvV2N/fx+l0srCwcKy2wB6Ph6WlJYbDIVtbW4RCoUPlsEKhIF5rtVqJRCLCUfJ2LCwsMBgMyOVyYmflZjIxDysyqEgkkkeCfD7P5uam2BsZjUb4fD5arRYGgwFFURiPx6L01Wq12N3dxWq1Mj8/f1dWwIqiMBwOGQ6Ht80S7HY7p06dEuUvo9HI6uoqdrudRCLBxsaGUB2emJjAZrORTCZvOyociUQwm81sb28TCoUYj8dks9k7/6AeMHKkWCKRPBK8/fbbDIdDZmdngetKwT6fj83NTSYnJykWi9hsNqxWK/1+n+3tbfR6/U1Hfj+NoijU63Wq1Sr1ep1+v3/o60ajUdgMOxwO7Ha7yHrMZjPLy8tsbm6ytbXFwsICi4uLFAoFMpkMOzs7GI1GAoEAkUiEra0t0uk009PTN70WrVbL/Pw8m5ubPPPMM9jtdsrlMq1WC5vNdvQP8oSRQUUikTz0bG9vk8lkiEQiaLVaNBoN0WiUUqmEVqvFZDLR7XaZnZ1lPB6LxcTl5eVbjgvD9X0X1VZ4MBig0+lwOBz4fD7Re1Hl6DudDvV6nWw2i1arxePx4Pf7sdvtGI1GlpeX2draYnt7m/n5eQKBAH6/n2q1SqFQEOrJiqKQyWTw+/1YLJabXtfCwgLXrl2jVCrhcDio1Wrk83nm5uZO5PM9TmRQkUgkDzX9fp8LFy6IBjlcF3C02WzE43H8fj+VSgWdTofH4+Hg4IB2u83CwsIt7X7H4zG5XI5sNis22Kenp3G73bctk6l2v9VqlXK5TKlUwmq1EgqFhI3x9vY2Ozs7zM3N4fV68Xg8eDweOp0O2WyWYrFINptFr9fzzDPP3PQ8oVAIq9VKPB5nbm6OZrNJpVJhamrqtkHyYUAGFYlE8lDz3nvvUalUOH36NIqiYDQamZ6eJpfLAeDz+djY2CAQCFAulykWi+ImfzPa7TbxeJxutytMsu7Ua16r1eJyuXC5XExPT1Mqlcjlcuzu7mI2mwmFQsRiMXZ2dojH44eWMC0WC3Nzc4RCIUajEXt7e+j1epaXl4W1sYpGo2FhYYGrV6+ysrKC2WwWWVUkErn3D/M+cCyN+h/+8IcsLy8Ti8X4zne+c8PX/8t/+S+cPXuWs2fP8ku/9EtcvHjxOE4rkUgecwqFAleuXMHr9WKxWDAYDExOTqLVaikWi3i9Xur1OoqiiOa4w+G45Y23WCyyvr7OaDRicXFRTIRVq1Xy+TyZTEZkE41G47Z7JVqtlkAgwOrqqhCX3NvbEwHO6XSSSCRusBu2WCycP3+eYDBIKpUSZa5PMz8/z3g8pl6vo9frhRbZvThM3k+OnKmMRiP+4A/+gNdff52pqSnOnz/PN77xDU6fPi1eMzc3x09+8hM8Hg+vvfYaL7/8Mu+8885RTy2RSB5jxuMxP/vZzxgOh0SjURRFwel04vf7yWQyjMdjgsEg29vbWK1WkskkBoPhppNeiqKQTqfJZrM4nU5CoRC1Wo1EIkGv17vtdVitVtxuNz6fD6PReMPXNRoNXq8Xr9dLtVollUoRj8exWq1YLBbS6TSDwYDp6WlxXTqdjuXlZXZ3dwHY29ujWq0KJWS4LvNisVjIZDJCjqbf71OpVA7t4TxsHDmovPvuu8RiMebn5wH45je/yauvvnooqPzSL/2S+O/nn3+eZDJ51NNKJJLHnMuXL5PNZsVmucfjEY34fD6P2+2m1+vR7/dFRrG8vHzDcqOiKBwcHFAoFLBarYzHYzY3N9FoNDidToLBIDabDZPJhE6nE+PE3W6XZrNJvV4nnU6TTqdxuVyEw+FbTmG53W5cLhelUol0Ok2/3xcjwerkmjo15vf7yeVyaDQa4XV/9epVFhYWsNvtaDQaZmdnReO/Xq9jMpnI5/MPdVA5cvkrlUodGo2bmpq6razAn/7pn/LP/tk/u+XXX3nlFc6dO8e5c+cOLQ9JJJLPD8Vikffeew+bzYbD4cBqtRKNRjEajRQKBUajEaFQiFwuR6PRYDweMzs7e8PNXg0oakO+3W7T7XaZnJzk7NmzxGIxEVT0ej0ajQatVovRaMTpdBKJRDh16hRnzpwhHA7TbDZZX19ne3v7lhmOKrl/5swZIpEIer1ebN5/0rxLo9EQiUTodrsYjUZWVlbQ6XRsbW1Rq9UAmJ2dZTAY0Gq1GI1GOBwO2u32TXXGHhaOnKncbDHoVtMTP/rRj/jTP/1T3nrrrVse7+WXX+bll18G4Ny5c0e9PIlE8ogxHA558803GY/HRKNRAAKBAF6vl9FoJEpYgFgKDIfDeL3eQ8dRA0o8Hmc8HuNyuQiFQoRCoVtu1iuKQrvdpt1uMxgMGI/H6HQ6jEYjXq+XiYkJsX9y9epVgsGgGHP+NFqtlkgkgs/n4+DggHQ6zc7ODt1ul5WVFYxGIx6PR/RxVlZWWF5eFtNjCwsLRCIRDAYDxWIRp9OJTqdDp9ORz+fvWb/spDlyUJmamuLg4ED8OZlM3rRJdunSJf7dv/t3vPbaaw916iaRfB4YDAbk83nq9brYz7BarXg8Hlwu111tnx83b731FuVymYWFBXq9HpFIRAQX1XVxcnKSeDxOrVZjZWXlhnuOoijs7++zsbGBXq9namqKmZmZW0559Xo98vk85XL5UHNeo9EcenA2GAx4PB4WFhaEoGS1WmV2dvaWN3mTyUQsFsPj8bCxscHe3h7NZpNnnnkGi8VCOBwW70UdS97c3GRnZ4elpSWi0SjJZJJz587RaDTw+/3k83n6/f5NezwPmiMHlfPnz7O1tUU8HmdycpK/+qu/4i/+4i8OvSaRSPA7v/M7/Pmf/zlLS0tHPaVEIjkCtVpNjLuqpaXhcEi9XqdcLmM2m29aSrofXLt2jc3NTWZnZ1EUBYfDwezsLHq9nn6/Ty6XExnL9va2uMF/MggqisL29jbr6+tYrVZWV1eZmJi4aaAcDoek02mKxSJwvSfi8Xiw2Wzihj0ej+n1erRaLWq1GoVCgXw+j9PpZHJykkKhwMbGBhMTE7fMWuD66PO5c+fY2Nhgd3eXn/70p5w/fx6Px0M6nSaTyeB2u9HpdCwuLrKxscH29jbhcJidnR3RO4pGo+RyOYrF4kM5XnzkoKLX6/nud7/L17/+dUajEd/61rdYXV3lT/7kTwD49re/zf/2v/1vlEol/sN/+A/ie95///2jnloikdwl9XqdnZ0dsTPxySd3RVGoVqskk0nW19c/8yZ53KTTad5++20mJiaE4vDc3Jwodam92omJCT744AMUReHJJ588JMGiKAqXL19mZ2cHj8fDs88+e4Ovikq9Xmdvb4/BYEAgECAcDqPX60XPQ1EU0WNRJVr8fj/D4ZBisUgul6Ner+N0OrFYLORyOWq12m0DstFo5MyZM3i9Xj766CPeeustzp07RygUEh4tLpcLvV7P4uIia2trQjKmXC7jdDrp9Xq43W4KhcJtS3kPCo3yEGsqnzt3TgYfieSY6Pf7XLp0CY1Gw+LiIjab7aaaWKPRiGQySbFYxGw2Mzc3h9VqPdFry2az/OAHP8DpdDI7O0sqlWJxcZGVlRU0Gg31el0oAddqNaGxdebMGXGM4XDIe++9RyaTIRwOc+7cuVtun2cyGdLptChnqc3wT2t+mUwmrFYrTqcTt9t9aLJMnUJTPVTsdjvtdlsMEUQikduWEev1Or/4xS9ot9ucPXuWdrst5F4++ZqtrS0uXbqE2WxmZWVFjDdvbm4yMzOD3++/14/9rrmTe/LDFeIkEsmJ0O12efvttzk4OBBS7RcvXmR3d/eQ9wdc36GYmZlhcXGR0WjE+vo66XT6xJbu9vb2eO2117DZbDz99NOkUin8fj+Li4toNBrG4zGJRAKTyUS73SabzeL1esUaA0Cz2eSnP/0pmUyGWCzG888/f9OAoigKiUSCvb09er2e6C1Vq1XgehAxm82YTCYMBgOj0YhKpcL+/j6XLl1iZ2dHTF5ptVpCoRBnzpzB4/EIZ0eTyUQ2m2VjY+O2OzBOp5MvfelLuFwuLl68KEaYP+kQqU6gORwOMcFWr9dF2TKXyz10XisyqEgkjzm1Wo0PPviAWq3G0tISKysrLCwsEAgEqNVqXL16lYODgxuChtPp5PTp02JC6dq1a3dkiXun9Pt9fvGLX/AP//APuN1ufuVXfoVr165hMpl45plnRFBIp9P0ej10Oh3VahWDwSC0sdSlxp/97Gc0m02efPJJzp49e9MMQVEUdnd3WV9fp91uYzAYRHlLURT6/T69Xo9utysCjtq0H4/HjEYjCoUC6+vrbG1tieBiMBiYm5vj1KlTGI1Gca2tVotr165RLpdv+RmYzWa++MUv4vV6SSQSoqz2SUKhEPPz80KCZjAY0Ol0mJiYoNvtUq/Xj+tHcixI7S+J5DGm1Wqxvb1Nq9VidnaWWCwmdjs0Gg0ej4fRaEQ+nxee65/sB+j1eubm5vD5fCQSCTY3N3G73YTD4XsuiY1GI65evcrHH39Mt9vl9OnTLCws8NFHH6EoCs8995xQ7200GuIm2263MZvNaLVawuEw3W6X7e1toaH13HPPic3zm7G2tsbW1hZWqxW73Y6iKKJs5XQ6sdvtmM1mUeIaDof0+31arRaNRoNarYZGo6HX65HNZqlWq/h8PqanpzEYDNhsNlZWVsjlcqTTabFEGY/HqdfrTE9P37TcqNfrefHFF/n5z38ugvvU1JTod2k0GlZWVvjRj37E5uYmL7zwAvV6nWAwSDKZJJfL4XK57ulncRLIoCKRPKYMh0N2dnYYDAZiqmltbU2o8iqKQrlcRqPRiKxlY2ODycnJG27OataSy+XEGK3b7SYSidxSvv3TjMdjNjY2+PDDD2m1WkxOTrK0tES/3+fy5csAPPfcc3g8HnH9apnKYDDg9XppNpvYbDYajYbYkne5XJw9e/aWN1ZFUfj444/Z3d0VfihGo5FgMEggELjlWK6qt2W1WgkEAozHY6rVKrlcjlarRbPZpNVqUa1WmZycJBgMotFohJjl/v4+jUZDTK01m03m5+dvGoz1ej2//Mu/zE9+8hPi8TgXL17kueeeE1+3WCycPXuWH//4x2IKbWJiQuiHtdvtE+973SkyqEgkjympVIrBYIDFYhH2tL1eD6fTSavVQqfTYbPZ6HQ65PN58fSfTCZpNpvMzs4eerJWM4RAIEA+nxfBxePxEA6Hbxlcms0m8XicK1eu0Gg0CAaDPPnkk8D1RnSv18NsNjM/Py+CmaIoxONxqtUqOp0Ot9uN0WikWq1is9koFou0221CoRCnTp265bTVYDAQvSOz2UwwGMTv9zM5OXnXEvJarVZofDUaDTKZDJVKRRholUolotGoyHiWlpYoFoukUik6nQ61Wo319fWbBm24Hli++MUvUqvVuHLlChMTE8KQDK43yd966y3i8Tg2m43xeCx00PL5/KHXPkhkUJFIHkPa7TbFYhGXy0WlUqFWq4k6vNlsxmw2MxwOhRJvpVIRelqKorC+vs7BwQFnzpy5YbpIr9cTiUQIBoPkcjny+TyVSgW73S78Q9rtNtvb28TjcaHAGwgEOHPmjCghWSwW3G43lUqFUCjE5OSkOIe6gQ7XveCNRiMff/wxBoNB9EKmpqZYWFi4QTZepdVqcfXqVXZ3dzGZTMzMzDAzM3NLSfy7weFwCPOsZDJJuVzm4OCAWq1GJBJhcnISvV5PIBDA5XKxv79PpVKh2Wyyt7dHvV5ndnb2hsBmMBj4yle+wt/+7d/yi1/8Qsjpq5/DzMwM2WyWmZkZGo0GLpcLv99PoVC4p0B5EsigIpE8hiSTSfR6PTqdjlwuR7fbZX5+ntOnTx/aTRkOh2xubpJOp4WV7vLyMna7nf39fX72s58xPT3N6dOnbyiv6PV68dRdKBQol8tcunSJtbU1arUaVquVubk5zp8/j8vlotPp0O12sVgsRKNROp0OyWQSu92O2+2mVCoxGo3IZDLs7u4KEUk1ewLEWK/X62VmZuaWNsHFYlEsZVssFtG3+bTY5FFxuVw4nU4KhQLJZJJKpcLW1hblcpnp6Wn8fj9Go1HYCyeTSRqNBtlslna7fWgP55PHfPrpp/noo4947733ePHFF0VJ8IknnuC1116jWCxSqVRwuVwEg0Hy+Tz5fP5QYH5QyKAikTxmqI3lSCTC7u4ulUqFaDTKU089dWhRbjAYsLOzQ6fT4YknnkCj0XD16lVSqRTPP/88p06d4uLFi6RSKQqFAktLS8zNzd2wbKfX6wkGg+zs7Ihlvfn5ebxer7DOzeVyGI1G7HY7vV6Pq1evUi6XsVgsGI1G4vE4cH3BL5PJoNPpmJycZGpqiuFwSC6Xw2azYbFYmJ6evkHnS0VRFJLJJIlEgkQigcVi4ZlnniEajZ6Y9IxGoyEYDOL1ekkmk+L9NptNgsEg0WgUi8UiPFbi8TiVSoVSqUSv12N6elo4WqpEo1HK5TL5fF4EFqvVyuzsLB6Ph0qlwsHBAbOzs5hMpkPLkLcKtPcLGVQkkseMfD6PVqtFq9WSSCSwWq08++yzh4JBr9djc3OT4XDIwsKCKAm5XC7efvttLly4wDPPPMO5c+fIZrOsra2J3kQsFiMSiYiyU7/f5+///u/JZDI888wzzM/PUygUqNfrouw2Go0wGAx0Oh00Gg3dbldI2asTV+qUVCAQ4NSpU1itVsrlMpubm0K6RN16vxnD4ZDd3V2y2Sy5XA6LxcLzzz8vykcnjV6vZ3Z2Fr/fTyKRoFAosLe3J0pi6me2vLxMLpcjmUyKrf5er8fMzIwIfKpvDEC1WuXDDz/kC1/4ghDWrFarFItFWq0WNpuNUCgk/u52E3D3A7lRL5E8RgwGAy5fvkwgECCVSrG2tsazzz7L2bNnxWtGoxFra2uMRiNisdgNTe5yucy7776LTqdjenpajLfG43G2t7fpdDrYbDYCgQBms5m3336bbrfLr/7qr2IwGIRTobrbYbPZRKM9m81ycHCAwWDA5XKhKIrwK+n1ejgcDqanp0XAUl0dn376aVECuhnNZpPd3V0KhQLNZhOdTifkTx4EiqKQz+c5ODigUqmg1+vx+XxEo1ERwFutFjs7O5RKJXQ6ndhHUYNmqVQSGVwqlSIajXL27FkuXLjARx99xMTEBMvLy+Jnu7m5SbfbFVnnSXAn92SZqUgkjxGFQuEGa11V4Rf+aQGw3+8LqZZP4/V6OXPmDDs7O1SrVVqtFg6Hg0AgwNzcHIlEQozzXrp0iXq9zhe+8AV2d3cZjUZ4PB76/T4Gg4GpqSnC4TAmk4l0Ok232xUZx97eHgcHB9Trdex2O0888QThcBidToderxeb/uFw+JYBRS2vHRwcUK1WURQFi8XC6urqAwsocL0kNjExgcfjIZFICDkXtSQ2PT0t9lp2d3fJ5XJC7WBxcVHIxxwcHAjBz1QqJQQ2r169ymAwIJPJsLKygsFgYGJigu3tbUql0n2Vbvk0MqhIJI8JiqKIvY1kMkmv12Nubu7Q/kY2m6VerzMzM3NLoUWASCQixn0DgQCVSoXd3V20Wi0Oh4OZmRl++MMfUqlU+MpXvoLL5aLZbGK328nn8wDY7XYqlYpQ1O33+9hsNgqFAhcvXgTAZrNx5swZ5ufnD01xtdttdnd3cTgcN/QbVFqtFvv7++TzeQaDgdiwn56efiga1nBdQDIWi4nl0VKpxP7+vthtmZiYYGlpCYvFIoL1aDRieXkZo9Eo3CFjsRidTod4PM7q6iomk4nRaES73SaXyzE1NYXL5RLClj6f74HZF8igIpE8JqjjwTabje3tbVG+Um8unU6HTCaDx+P5zCdZrVbL3Nwca2trdLtdzpw5Q6PREBNi6+vrJBIJZmZmxAZ5NBqlWCxiMpmYmJhgOBxSKpXI5XJ0Oh0AURpzOp3i5u9yuQ4FlG63y9bWlpA/+fTNsdPpiL6JOmWmWgu7XC5mZmaO+ZM9Oh6PB6fTSSqVIpPJUK1W6XQ6YrclGo1iNpvZ2toimUwyHo9ZWVkhEAiQy+Xo9/vEYjGuXr1KPB4nEAiwv78vDMDUDC8UCh3yZnkQyKAikTwmVCoVtFqtECX0eDzCEE81rVIDzZ1gsViECV+hUCAYDOJ0Omk0Gly4cIHV1VVOnTrF1tYWnU6Ha9eu4XQ6mZmZEbsxzWYTo9FIJBJhYmJC9FbUZcBUKkUqlcJgMOB0OjGbzaTTabRaLYuLi6IM1u12abfb1Ot1Go2GsNcNBAL4fD7K5TIOh4NYLPbQScGr6HQ6otEoPp+P/f19yuUy2WyWVqslpsT0ej0bGxtC5v/06dM4nU6KxSKrq6tUKhUxXg3X+2PNZlNMfnk8HlKpFNlsVgYViURy7yiKQqVSEeUljUaDy+USPRN1Umhubu6uFuSCwSD1ep2DgwNMJhM2m43vfe97ZLNZfuVXfoVkMim8RtRMaW1tDbh+E52amuL06dOEQqEbprampqbo9/vU63Xq9TqFQkFoZgWDQa5du3ZIgVdRFGGYZbVa8fl8+Hw+4vE4BoNBBKGHHbWXks/nSaVS1Ot1UqkUzWaTubk5VldXuXr1KslkEp1OJ/pP9Xqd+fl5Go2G+F+n08FkMpHP5wkGg2i1WiYmJjg4OBDlyPvNw/8TkEgkn0m9Xmc0GqEoihjbVd0OR6MR6XQah8Nxy/2O2zE3N8fGxgYXL16kWCyyvr7OL//yL+PxeERTXR1tVQOa3W4nHA6j1WrJ5XK0220CgQBut/tQOUvtG+j1eur1OpFIRHyfapKl0WhEZqPRaPD7/UxNTaEoCjs7O+j1epaWlh6KbfI7Rf35uFwudnd3qVarlMtler0e4XCY1dVVLl++zN7eHhqNBr1eL3aFZmZmWF9fR6fTkclkiEQiQiYmEAgI6ZZsNkssFrvv700GFYnkMUAtffV6PYbDISaTSZS+MpkMw+GQqampez6+TqcTo8YLCwt86Utf4sqVK/R6PaF4bDAYePrppwmFQpRKJarVKqPRiE6nQ7VaFdmO3+/H6/UKi2C1TGaxWFhZWUGv19NoNEQGo3qSOJ1OQqEQDodDNLzNZjOxWOyh9Gq/E8xmM6dOnRJqw51Oh1QqhcvlYmVlRfRQ1B5Vt9slEokIF8hEIiFGvHO5HH6/H61WSzAYJJ1O0+l07ljw87iQQUUiecRRFIVarYbBYKDb7YopK9V6Np/P4/f770nFVpVx6XQ6OBwOLBYLkUiEjz76iGKxiEajEZvy0WhUDAa43W4GgwGVSoVGo0G73aZardJoNMjn82g0GqxWKw6HQyz6GQwG9vb2aLVaKIoiJs3UXo7ZbGY0GrG3t0epVMLhcLCwsPDAN8iPilarFUKU6iJkpVIRzfm1tTXy+TwWi4VCocD09DRzc3MUCgXW1tZIJBKcPXuWXq8nGvSBQIBsNks2m2Vubu6+vh8ZVCSSR5x2u81wOERRFAaDAePxmEAggFarJZPJANdHhO+W0WjE9vY23W6X2dlZfvCDH/D0008TjUZ59913GY/HBINB3G43MzMzN5zDYDAQDAYJBoPA9eA3Go2ECGM+n6dcLlOpVITIpdvtJhgMin6Q2nQfDAZi4ms4HBIOhwmHww9sbPYk8Hq9mEwmIZ3TbrfJZDLMzs6ytbVFqVTCZDIxOTmJw+Fgfn4et9vN7u4uKysrWK1W0aDX6/X4/X6hB3Y/MzkZVB5RxuOxmLAZDAbCb+JBTXxIHhz1ep3xeMx4PBb2u8FgkF6vR6lUIhgM3nW/QZWeb7VaLCwskM1m6XQ6fOlLXxKNeKvVSr/fF9vit2I4HNJsNsXklrrU6Ha7GY1GDIdDIePS7XZFGU+n0zEajej1emIkWbXXvZXU/aOO2sTf3t6m2WwyGo2oVqtMTU2xublJIpFgdnZW6KKdPn2an/zkJ+zt7fHMM88Inxe73S6EPnO53B1P/B0HMqg8gtTrdfb398XWstlsptlsUqlUcDqdzM3NPRJTMJLjQbWT1Wq1tNttTCYTLpeLTCYjTKPuFnUHRJUVeeONN4RooeoRHwwGRb3+ypUrWCwWTCbTIedEdRxYvT5VHt9ut2O1WkXpSh2NVY2ver0eo9EInU4nHpjU5b7HHYPBwNLSEtvb29TrdYbDIePxmMnJSa5du8aHH35IOBzGYDDwhS98gXfeeYe9vT1isRgmk0k06I1GI16vl2KxeFvNtONG3nkeMVQ9IbPZLGQ21BKBKq29sbHxyE3DSO6N0WhEq9UCEFNSdrsdjUZzz1lKq9UinU6L2nwqlaJYLBKNRsX4rsfjIRgMEovFGA6HFItFms0m3W6X4XCIRqNBp9NhNpvxer04HA5sNtsty1U6nQ6Xy/VQ2eI+SFQBTXUyTKPRiAZ8JpPh6tWrPPHEE0xPT7O4uMi1a9dIpVKcPXuWarVKt9vFbDYzMTFBqVQin8/fUwn0XpBB5RGiWCxycHCAzWbDZDIRj8fFP2CbzcbExASLi4tsb2+zu7vL0tLSY1VzltxIo9FgPB4zHA6xWq1CSv1es5TxeMze3h4Gg0Fspl+8eJFqtcry8jK1Wg2bzYbf7xfb7gaD4ZZSKpJ7R6vVsrCwIDTY1AGIYrHI5cuXhZ3wc889JzxxpqamhIfOzMwMFosFl8slliPvx2Low7l6KrmBVqtFIpEQ/51Op+n3+2i1WvR6Pe12m52dHeGd0Ww2RZNW8vhSr9dFGXQ0GjEej3G5XGJn4W6zFNXQS93ubjQafPDBB4RCIXq9HuPxmFAodCKGV5Ib0Wg0zM/P43Q6URQFk8mEx+Oh0+nw0Ucf0e12OX36NIFAgGKxSDabxWKxUCqVGAwGAIRCIZFN3g+OJaj88Ic/ZHl5mVgsxne+850bvr6+vs4LL7yAyWTif//f//fjOOXnClVio9PpiJl/1dVPo9GIiR9FUYQ5kNfrJZvNinq25PGk0WgA18slrVYLo9FIu92+pyyl1+uRyWRwu924XC76/T5vvPEGvV5PZClOp5PZ2dnPRW/jYUGr1RKLxXA4HCiKQiAQwGg0UigUuHLlCnq9nieffJJutytUpVXpfbgu7Gm328nlctwPp5MjB5XRaMQf/MEf8Nprr3Ht2jX+8i//kmvXrh16jdfr5f/+v/9v/pf/5X856uk+l+RyOcrlMu12m1arhd/vZ3FxkaeeeoqnnnqKM2fOEAwG0Wg09Pt98vk8JpMJrVYrNIQkjx+DwYBut8toNMJut1Ov1zGbzTQajXvKUpLJJBqNhunpacrlMmtra+zs7BCLxcRS5dTUlBgRltw/1FKYxWLBYDDg8/kYDAbs7+9zcHDAqVOncDqdQu5GVawejUbA9Wyl3+/TbDZP/lqPeoB3332XWCzG/Pw8RqORb37zm7z66quHXhMMBjl//rxsHN8D/X5fOMT1ej0mJyc5ffo0Xq9X1EdNJhPT09MsLCxgNptptVpkMhmcTqdQQ5U8fjSbTQaDATqdDqPRSLfbFQ3du81S1OlBq9XK3t4e8Xicdrstmr3FYhGr1cqpU6dkn+4BodfricViQuXZZrPRarW4dOkSHo+HiYkJMbo9HA7p9Xqi5OV0OlldXb2t3cGxXedRD5BKpQ7NQE9NTfHOO+/c8/FeeeUVXnnlFeD6NNPnnXQ6LbwopqamWF5evmVwVpfQdnd3aTQamM1msQA3Pz9/n69cctI0Gg36/T5Wq5XBYEC/30ej0RAIBOh2u0KmQ5U5UbffTSYTJpMJg8HAYDCg1+uxs7NDu90WlrdTU1PCP8VkMlGpVITvh+TBYTKZiMVibGxsYDabAajValy5coVYLEY2m2U8HotSaC6XE1UM9fUnzZGDys1qdEd5knn55Zd5+eWXgevWlZ9nOp0OBwcHtFotPB4Pp06d+sxsz+/302w22d/fF5INlUrlgWgASU6WRqOBoiii5DUYDDAajdTrdXK5HDqdTniNqEuRagmkXC6L4wwGAwaDAfPz80xPT+N0OtFoNLzxxhuYzWbq9ToWi4XFxcUH+G4lKjabjfn5eWq1mtjlSSQSzM3N4Xa7qdfr2Gw2MWhRLpdvu5x63Bw5qKh+CyrJZPK+zUM/7qRSKfL5PHq9nuXlZex2O81mUzTj1WkQdZ9AncaZnp6mVquRz+exWq1oNBpyuRyzs7MP9g1Jjo3BYECn00FRFJxOJ2tra/T7fXq9Hv1+n+npaSEueDNU/3idTsfa2hp2u53V1VXxQFgoFKhUKgSDQYbDIaFQSO6QPES43W4WFhb44IMPsNls9Ho90um0cNuE6z/jZrNJNpu9r0HlyD2V8+fPs7W1RTwep9/v81d/9Vd84xvfOI5r+1zTbrdJJBIMBgMmJycJh8McHBywsbFBs9nE5XLh8/nQarWk02muXr1KrVYD/snHwmw2U6vVMBqNlMtlMWIoefRpNpv0ej2MRiNms5l8Ps9oNMLn87GysiK8NW6FVqvFYDBQLpfpdrtMTk4eqjCoEi1qCfVhdFP8vDM9PU0wGKTdbhMOh0U5VM081YeGfD4v7g33gyNnKnq9nu9+97t8/etfZzQa8a1vfYvV1VX+5E/+BIBvf/vbZLNZzp07R71eR6vV8kd/9EfCJU5yc1KpFLlcDqfTyfLyMvF4nGq1SjAYZHJy8tANo91us7e3x/b2NjMzM0JaXPXFVkeOVbkGyaNPo9EQ9rn1ep1KpcL09DQrKyt3LB6oKArZbBar1YrH4zn0te3tbXEcp9N5Tz4skpNFo9Fw9uxZfvSjHzEej/H5fGQyGdGsV8uezWZTyOnfD45le+mll17ipZdeOvR33/72t8V/h0KhQxaYktvT6/XY3d1lNBqxsLBAuVymWq2KJ5NPY7VaWV5eZnd3l/39ffR6PW63m3A4TKFQoNFo4HQ6xVatnN559FHLn3a7nUuXLgHw7LPP3pUabbVapdfr3TDEUalUSKVSwoTL6/Xek2y+5ORxOBzMzc0Rj8cJBoOit5ZOp5mfn8dgMNDr9Ugmk8zMzNwXIU65Uf8QkkqlKBQK+Hw+IcsQCoVuux+g0+lYWFjAZrMRj8fpdru43W58Pp/QhhoMBlSr1fv0LiQnxSf1vtrtNsViEY/Hc9djxNlsVvTkPsnu7i71eh2fz4fRaMTn88kHkYeYmZkZXC4X3W6XqakpvF4vqVSKSqWCXq/HZDLRarXY39+/L9cjg8pDxmAwYHt7G0VRmJubI5vNYrfb72j4QavVMj8/j0ajEb9A4XAYk8kk3PnkmPajj6riazAYSCQSKIpy154Z9Xqddrt908xVHVe12WzYbDZpp/CQ43Q6mZiYEIM7p06dYjAYcPHiRYxGIw6HA51OJx42TxoZVB4ystks+Xxe1LAVRWFmZuaOnxSNRiNTU1NibNTr9eLxeGi1Wmi1WlGLlzy6qEGlVqvR6/XuqeeRzWaFpPwn6XQ67O3t4XK5MBqNWCyW+7IwJzka4XBYyLj4/X5mZmbY2dkhk8lgt9vFz1CV9TlJZFB5iBiNRmxsbDAej4lEIrRaLSKRyF0vLfl8Pmw2G8lkktFoxMTEBADdbhdFUe6bsJzkZFAnvyqVCna7XYgM3imtVotGo8HExMQNE2IHBwfiazqdDqfTeV+UbSVHQ3XKNBgMGI1GseP3i1/8QtgKRKNRAoHAiV+L/G15iMjlcsIOVKPRYLFYDvVRGo0Ge3t7XLt2jatXrwo/70+j0WiYmZlhOBySzWbx+/04HA6RrZRKpfsiLCc5fhRFodFoUCwW0ev1Qq7nbkpU2WwWnU6H3++/4WvXrl0TzpEmk0lOaD5CTE5OoigKDoeDQCBALBZjf3+fjz/+mImJCfr9vsxUPk8oisL6+rrILEajEdFoFI1GQ7fbZWNjg83NTarVqthNqFarrK+vk0wmbwgSFosFn89HoVBAo9EwMTFBr9cTPub3c25dcnx0u10ymQy9Xo9IJMJ4PMZkMt3xdJaqZBsMBoXrooqiKGxubuJ2u7HZbOh0Oln6eoRwOp04nU6azSZ+v5+nn34avV7PlStXhMma3W4/8euQQeUhoVgskk6nRblBtVytVCqsra0Jj4uzZ88Si8VYWFjgiSeewO/3k8vliMfjNwSWUCjEeDwmn88TCASE2KR6PsmjRyqVolQqYTKZWFxcpNlsCuXaOyGbzQp9sE+Ty+UolUpMTk6i1+vFw4vk0WFqaorRaIRGo2FxcZHp6WlyuRzvvPOOkN85aWRQeQhQFIWrV68yGAzw+Xzo9XomJyfJ5/Ps7u5isVhYWVkhEAgcqm/rdDpmZmaYnJykUqkckssBhJWrKtfidrtpt9vo9XpqtRr9fv9+v1XJEWg0Guzu7orfE4fDIRYg74TBYEC5XMbv9980CK2trdHr9YhGo4zHY5mlPIJYLBb8fj+FQoFIJMITTzyBXq8nl8vx+uuv35drkEHlIaBSqZBMJrHZbJjNZkKhEPV6nYODA9xuN8vLy7cdFw2FQkxMTFAoFMhmszd8Td2mDwaDKIpCv99HUZRDooKShxvV2XM4HGIwGPD7/XS7XQaDwR33U1STJnVw49NsbGxgs9kIBAJCU0zy6DE5OYlOpyObzfLlL3+ZYDBIqVRid3eXeDx+4ueXQeUh4PLly/T7fbG5bDab2d/fx+l0ir2Tz2JychKPx0MqlTrUvLdYLGKb3uv1YrPZaDab6HQ6SqXSSb4tyTFRq9XY2NgAEI6f4XCYSqUifMs/i9FoJJYkTSbTDV9XpX4ikYjIYmSm8miiVjrUpvwLL7yAVqtFo9HIjfrPA5VKhUQigcViweVyEQwG2d/fx2KxsLCwIAKKariTzWYpFos37JqoE19Go5F4PC4c3+C6SZrqEujz+eh0OmIA4GbTY5LjZzgcUigU2N/fZ29vj2w2K2xfb8V4PCaVSrG9vY3ZbGZyclL4ZIRCITG0cSc3CtUF8FZb9+vr63S7XWZmZhiPx3fVp5E8fPj9fqxWK8lkkhdffFEsR96PoHIs2l+Se+fSpUv0+32CwSBOp1M00BcWFtBqtbRaLZLJ5E1tQNXZc7U0ptPpmJubY2NjQ2j9wPWpEJPJRKFQwO/3C/Mmg8FAqVS6L79on1cURSGXy5HJZBiPx+j1ejQajcgS9Xo9LpcLu92O1WrFYDAwGo2EJ4qawUajUSEWODk5icFgoNFoYLFYPnOTXh3WcDgct5wSUzOhWCxGu92WlsGPOBqNhmg0yvr6OsVikS996Uv86Ec/otVqnfi/dxlUHiC1Wo1EIoHJZMLr9aLT6Wg2m8RiMYxGI5lMhnQ6jcFgYHJyErfbjdFopN/vU6lUyGazXLt2jYWFBVGqsNvtTExMkMvl8Pv92Gw2Me2TTCYJhULY7XZarRY+n49yucz09LTUdjoBWq0We3t7QoctHA6Lm/pgMKDRaFCr1ahWqzctRVqtVmZnZ8XPVpW3V8fDu93uHel9qbYHt/LTGY/HbG5uEg6HsVgstFot2U95DLDZbASDQfL5PLFYjPF4fF9GimVQeYBcunSJbrfL9PQ0DoeDWq1GIBDA6XSyv79PqVQST6mf3Ckwm82Ew2G8Xi/b29tsbW2xuLgobj7hcJhyuUwikRCe4j6fj3Q6TbVaJRAIUKlUGI/HAMIhUnJ8FItFEokEBoOB+fl5rFYr3W6XSqWCyWTCYrHg9Xrxer0oikKv16PdbjMcDoVj4yedOtWMx2AwEAqFhD/9Z23Sq99ntVpvGSj29vaoVqucP38euP6Uez9uPpKTZ3JyklqtRiaT4bnnnrthN+kkkEHlAVGtVonH42KSp9PpiLp5PB6nUqkQDodvKyRpMplYXl5mY2ODnZ0dVlZWMJlMwqQrHo9TLBaFK6TH4xGZidlspt1uY7FYKJVKMqgcI2qGabfbhVzOp8e31b6I3+8X/uG32wlpt9vUajURHFTB0M8KKpVKhW63y9zc3C1foy7dnjp1ina7jc1mk9IsjwlarZbZ2Vk2NjbY39+/webgRM554meQ3JQPP/xQlC+0Wi2KojA7O0symaRSqTA1NXVHysR6vZ5YLAbAzs6OyD7U5cl0Oi2a9n6/X9jIOp1O2u02Op2OWq3GcDg8uTcL9Pt9EokEly9f5uOPP2ZjY+OxlIsplUpiiXU4HJLL5TCbzUSjUZaXlzl9+jSzs7MYjUYSicShn9lnHbfX64kdk2q1isFguG19XFEU0uk0FovllsFHURS2trZwOBxMTEzQbrfl1Ndjht1uF7ts9Xr9xM8ng8oDoFQqsb+/L7wsxuMx4XCYer0u3BlvtUtwM0wmE/Pz83Q6HRKJhPj7yclJhsMh+XweuP7LZbFYKJfLYhdhOBwyHo9PdGel2WyytrZGsVjEarXi9XoZDofs7e2xu7t7RzfVRwE1cFqtVuEVv7CwwOLiIoFAQHz+Pp+P5eVlpqenxbjwZ1k95/N5xuOx6KHU63XMZvNtm/TqlOCnrYI/SavVIpVKEQ6HxbSXLH09fkxMTBCLxe5Lr0wGlQfAe++9R7/fJxwOC/c+o9FIOp3G6/XeUYbyaZxOJ6FQiFKpJJ5G7HY7brebbDYrblp+v592u43ZbMZqtYrx4pPaWWm1WmxtbaHT6Th9+jQLCwtEo1FOnz7N1NQU1Wr1vpkHnTSqu6lGo6Hf7xOLxW5bVgwGg8RiMbrdLpubm7fNFvP5vDDM6vf7dDqd28pujEYjIXt+u4373d1dut0u8/PzDAYD2U95TNFoNPfNTlgGlftMNpslmUyKUVCz2Yzf72d/fx+Hw3HLCZ07QTXkSiQS4ul/cnKS8XgsNu19Ph9arZZOp4PL5RJBpd1uH7uBz2AwYGdnB71ez/Ly8qGegSpyGYlEKJfLj7wWWbPZpFKpCH21ycnJOyojuVwuYrEY/X7/loGl0+lQq9Ww2+1icq/f79/2JpFOpxkMBkxNTd32/FtbW4zHY5aXl2k0GlitVtlPkRwJ+dtzH1EURWQpgUAAi8UiRn3VEtZRRnu1Wi3RaJReryeCiBq0CoUCvV4PnU6Hx+OhUqng8XjEU7WiKMearSiKQjweZzgcsrCwcMtFOnXEOZlMnnhf5yRJJpPodDq63a4Y5bxTHA4HCwsLdLtdtre3bygHFgoF+v2+sPetVqsoinJLY65WqyVERG/XcxmNRmxvb+Pz+UQGK/spkqMig8p9ZHNzk1QqhcPhEBv0qtRGLBZDrz/6MJ7T6cTj8ZDL5UTJKxKJoNFoSKfTAAQCAcbjsZBt6HQ6QrbluBrn2WyWRqNBNBq9rSy7uqSllmseRarVKq1WC4PBwHg8vqe9H6fTydzcHK1W6wbFaXU/RS2L3q5JPx6P2d/fF7tNt0PddVKzGbUUK5EcBRlU7hO9Xo/33nuP8XiM1+vF7XYzHA4ZDofEYrGb6jHdK6pZjxpEDAYDgUCAcrlMp9PBZrNhsVhoNBp4PJ5DPivHYeLT6XTIZDJ4PJ6bGkF9GrV5XSwWP7Nh/TCSy+XQarVCBudeN5Y9Hs+hPpMaWAqFghjqgOtNenXX5ZMoisLe3h6dToeZmZnP3EnY3d2l3+9z6tQpIdcjg4rkqMigcp94++23qVareDwe0dfo9XrMzc0du2yCyWQiEAhQLBZFn0QdXVazgUAgQKfTEdIgqtXwUafA1BubTqcjGo3e8fepasqFQuFI57/ftNttIaGj0Wg+Mzv4LCYmJgiHw5RKJfb29oR6gtVqxW63MxwOabVa2O32G7KhdDotxtHvpCm7vr4uRtLVfsr9WI6TPN7IoHIf2NjYYH19HaPRSDAYRK/XC9mMk1o6DIfDaLVaUqkUcH2fZWJigkqlQrvdFja0w+EQu91Op9NBq9VSqVQOiVHeLfl8nna7TTQaFeU8RVGo1+tkMhmy2exNBwLMZrPQPnuUdldUOfnRaHRLn5K7JRKJiAGGDz74gH6/j8fjwWg00m63b5C7VxSFg4MDYR19J+PovV6P/f19AoEAHo+HVqsl+ymSY+FYgsoPf/hDlpeXicVifOc737nh64qi8D/+j/8jsViMs2fP8uGHHx7HaR8JKpUKP//5zxkOh0xMTGCxWNBoNExNTeHz+W77vaPRiGazSaPRoNVq3dU+h16vF0q26pP0xMQEOp2OdDqNTqfD6/XSaDREKU5RFMbjMdVq9Z7e62AwIJPJ4HK5xLJdq9VibW2Nra0t0uk0qVSKq1evitLcJwkEAo+U1fFgMBA9MY1Gc0c6XHdKOBxmbm6OYrFIvV7H4XCgKAq1Wo3RaCQ+39FoRDweJ5/PEwwG7zg7LBaLFAoF5ubmGAwGsp8iOTaO3BkejUb8wR/8Aa+//jpTU1OcP3+eb3zjG5w+fVq85rXXXmNra4utrS3eeecd/v2///e88847Rz31Q0+z2eQHP/gBjUZDqBAbjcbbLje2223K5TK1Wu2mT/SqiZLH4/nM0U/VuCuVSrG8vIxOpyMUCpFKpWg2m6JEptFoMJlM9Ho9TCYTpVLpMwPezUin04zHY9H4rVQqQopmbm4Ot9vNaDQilUqRyWQYjUZMT0+L73e5XOj1esrl8iMhG1MqlcTEWiAQ+Ey14LvF6/ViMBgwGAy0Wi2hQgDX+2S5XE4MZExOTt5VUNve3mY0GrG8vCweOmRQkRwHRw4q7777LrFYTGjKfPOb3+TVV189FFReffVVfu/3fg+NRsPzzz9PtVolk8kQDoePevqHllarxfe+9z2KxSIul0vIpkxPT98QUNSN9kKhQLvdRqPR4HA4hGmXRqNhPB7TarWoVqvs7e2RTqeZmZm57YasVqslFApxcHBAvV7H6XQSDAbJ5XKk02mWlpawWq30+30cDgflchmz2Uyj0aDb7d6VP3m73RbukmazmVqtRjwex2azsbCwIEphqhaRTqcjn89jt9vFU7dqOFUulxmPxw/9voQ6LadmhceNmjUGAgGWlpYoFoskk0l6vR7xeBytVovdbmdhYeGu+nKKorC+vo7JZGJhYYFCoYDFYjmW6UOJ5Mi/RalU6tDT5tTU1A1ZyM1eo0pDfJpXXnmFV155BeCRa9qqZLNZXnvtNSqVCi6Xi4mJCXw+H3Nzc4cygFarRbFYFH0Mi8VCNBrF4/Hc9B+42+1mcnJSWA1vbW0RCARuO8Lq9/vJZrNCj0oNNMlkkkajQSAQEKZggBg1LhQKh35mn8XBwQF6vZ5IJCJuemazmVgsdtPm79TUFM1mk0QigdPpFK/xeDwUi0VqtdpniiU+SFqtFp1Oh9FohNvtvmES6zioVqtC7ysUChEIBNjb28Pj8TA/Py/sp++WZrNJKpViYmICp9PJ3t7eHU3pSSR3wpGDys2aqp++wd3Ja1RefvllXn75ZQDOnTt3z9d1P8xoPo2iKHz88cf84he/EM1wtem6uLiIxWJhMBhQKpUolUp0u120Wq242Xe7XTY2Nmg2m6L0pS4rBoNBZmZmMJlMOJ1OVlZWSKVS5PN5+v0+c3NzN715a7VawuEwiUSCWq2Gy+UiEAiQy+VIpVIsLi5ycHCAoihYLBaxaV8qlZicnLyjbKFSqdBsNolGo2g0GnZ3d4HrRmO3mib6pIlQNpsVU1MOhwO9Xi+WMx9WSqUS/X5fqEyfBPl8nuFwKD6bTqcj9L/upTz5yeOWSiWefvpput3uffPZkHw+OHJQmZqa4uDgQPw5mUzeoF11J685bn7v934PvV4v9K+mpqZ49tln+eIXv3gi56tUKvzjP/4jyWRS2LZOTU0RjUaJRqPi6bBarYryUr/fp9frUS6X6ff7DIdDsdRmMpkwGAwoikKxWOTatWvC2fHcuXM4nU4hYZ9IJNja2mJpaemmQeCT2YrL5ToUaOr1Oj6fj0KhgNVqFTL4o9GIcrn8mTfM8XgsZGdUV8l2u83CwsJn7t7YbDY8Hg/5fJ6JiQnhiviwl8DU0WtFUTAYDCcW/NT9F7Vc2mg0GA6HRz7fxsYGWq2WxcVFsZckg4rkuDhyUDl//jxbW1vE43EmJyf5q7/6K/7iL/7i0Gu+8Y1v8N3vfpdvfvObvPPOO7hcrhPtp7RaLSKRCPV6nWq1Sj6fZ3NzkzfffJP/8//8P8XMv9PpJBAIEI1GOX/+vJCQvxsqlQrvvPMOGxsbdLtdUcKampoiFArR6XR48803yWazlEolms0mo9FIlJlUPSefz4fT6RReFjqdDqPRKG60w+GQer0ulH2feOIJzp8/L7xSdnd3icfjN5V60Wg0hMNh9vf3qVaruN1uId2STCaZm5ujUCig1+tFxmQ0GoX98O3IZrP0+32Wlpbo9Xrkcjmx3HknhEIhKpUKhUJB/E6oJbB6vf5QNuzr9bqQtlF3jk4CtdehjvpWKhV0Ot2RlGaHwyE7OztYLBbm5+dJpVKYzWbpRy85No4cVPR6Pd/97nf5+te/zmg04lvf+harq6v8yZ/8CQDf/va3eemll/jBD35ALBbDarXy//6//++RL/x22Gw2/p//5/8Rf97e3uYnP/kJu7u7FItFms0mhUKBTCYjvLn/4i/+Ap1Oh8FgwGg0Hvp/nU6HTqdDo9EI75PxeMx4PEZRFPFnvV6P0WgklUrx05/+VOwUqN+nZk1q497tdmMymURWot7UVcbjMb1eD4PBgEajEYuT+Xyeixcvkslk+NrXvobH42F6epqDgwOSyeRNeyE+n+9QtqLRaJienmZzc5N6vY7NZqPdbmO322m321itVtrttmjw34xOp0M2m8Xn8+FwONjc3ESr1X6miOEnsVqtOBwOisUioVBIDClotdqH1pFSNb6yWq0nVvpqt9u0Wi1CoZC44ddqNYxG421lbz6Ler1OOp1mamoKi8VCs9k8UilNIvk0xzLu8dJLL/HSSy8d+rtvf/vb4r81Gg1//Md/fBynuidisdhNs5Dt7W0+/PBD4ZDYarXo9Xr0ej2hDKsGj5uh7idotVr0ej16vV6MgNpsNvx+Pw6HQ/QxrFar8MBQA4j6/SaTScin2Gw2dDod7XabRqNBtVplNBqJgKYed319nb/5m7/hn//zf04wGKTX65HP53E4HDfcjNVsRbWO9Xg8OBwOPB4P2WyWiYkJWq0Wer1eSLbodDqy2exNg4qiKOzv7wuXyXK5TKPRYHp6+q6feoPBIDs7OyKIaDQanE7nfTEUulsURRGCjp+2/D1OVLsCtUw8Ho+p1+tCAeFeSafT1Go1vvSlL4kejSx9SY6Tz/UM4a2CzacZDoe0223a7TadTueGwQM1KKhWvmqw+CQ3+/NwOBRmTr1ej1arRaVSAa5vmAcCAWZmZohGo+TzeXK5HOPxWNxUnnjiCa5evcr3v/99fuM3foPJyUkajQb7+/tYrdYb9ia8Xq/IVtSb9/T0NI1Gg0qlglarFRlXp9MRy5E3G3pIp9O0Wi1mZ2fRaDQkk0msViuBQOCOPntFUWg0GnQ6HYxGIzqd7tB+itPppFqt3vVo80nTaDTo9XpotdpbqgQfB6lU6pDsS7vdpt/v35V5281YW1sTVgSynyI5CT7XQeVO0ev1OJ3O++Ka1u/3qVarlMtlDg4OSKfTRCIRwuEwfr+fvb09Go2GmKpSPer/7u/+jt/+7d9mfn6etbU19vf3WVxcPHRsNVuJx+NUKhWxXDczM8POzg5w/YnYZDKJ0p1OpyOTyRwKvuVyWUiC+Hw+Dg4OGAwGLCws3JE6b6vVYm9v79ByZ61Wo1arCXkX9bNWHQ4fFqrVqigTnmRQKRQKmM1m0ZRvNpsMh8MjlQPb7TZ7e3s4HA6i0Sh7e3uYTKZjX9qUfL55+EZrPueo+mCnTp3i1KlT2Gw2Dg4O2NjYQFEUFhcXiUQijEYj0QNaXFyk3W7z93//9+j1erHLcjNxSI/Hg8ViIZ1Oi4zL7XYTCATo9/vCt1418rLZbNRqNfFUW6/X2d/fx263E41G79i7A/7JM319fZ3xeMzc3BxPPvkkS0tL+Hw+SqUSV65cEYHNZDI9VJItiqJQqVRQFEWoI5wE3W6XRqOBz+cTQbparaLVao/k3lev18lmsywsLKDVamk0GjJLkRw7Mqg8xNhsNhYXF5mbm6Pb7bK2tkaj0SAcDjM7Oyv6LKrBVy6X46c//ano3xwcHNxgfKXRaMSC4ieDzvT0ND6fT2iNqTs1/X4fo9FIMpkkl8uxvb0tzgfcsXeHql6cyWTw+XycPn0ar9eLXq/H4XBw9uxZgsEgmUxGGFW5XC6azeZD42GvlkD1ev2JZilqP+WTE5KVSuXITfp4PE6v1xMWxqPRSIpISo4dGVQeAbxeLysrKxiNRra2tiiXy/h8PhYWFlAUBZ1Oh8vlYmpqiu3tbTY2NpiZmWE4HAqV4k/idruxWq2HshWNRsPCwoLwuS8UCoxGI3ET3dvbY2NjA6fTydLSktCe6nQ6TE9P31YyXVEUdnd3KZfLRCIRIdPySTQaDbOzs9hsNqrVKru7uzgcDsbjsdCmetBUq1U6nQ5ms/lEp9ISiQRarVY06VVhUXXI414Yj8dsbGxgMBhkP0Vyosig8ohgMplYXl7G4XCIaTW3283MzIzYeQmFQthsNn7+85/T6XSYmJigWCze1HgrEonQ7/fJ5XLi73Q6HU8//TTBYJBms0k+n2dvb4+trS0URUGj0TA3N4der6fX692gSHwr1KXP6enp2+4neb1ebDYbbrebWq1GtVpFo9Eci3HYcaAqBLtcrhPb6xiPx+TzeaxWq/hcO52O8KS/V7vpRqNBKpXC6/WKn6/RaDxWcziJBGRQeaTQ6XTEYjGcTif7+/tUKhX8fr+4UatP+wA/+tGPCIVCGI1GEonEDRNrLpcLt9tNOp2m1+uJv9dqtZw9e5bp6WkcDgc+nw+Px8PKygpWq5VkMin851WpldtRKBTI5XIEg8HP9G1Xx2X1ej3BYFAINj4Mo8X9fp96vY5erz9R+ZhyuSwkflT9t2azSb/fP1J2VC6XyefzLC0tiUAtsxTJSSCDyiOGVqtlYWEBu91OPB4XPRaXy4WiKBiNRqanpykWi3z88cdEo1G63e6hjERFFaJMJBKH/t7pdOL1erFYLNjtdlwuF9VqVVj+bm9v02q1mJmZuW05plarkUgkRGnuTnC5XNTrdSYnJ7FYLLRaLTH59CCp1Wq0220sFsuJlr7S6TSj0ehQj6pWqwmNuHtlc3NTDHp0u12Gw6Hsp0hOBBlUHkHUwGIymdjZ2RGCkurorcfjweVy8dFHHwmXwEwmQ7/fP3Qco9EoJsXy+fyhr0WjUaxWK7VaDY1GIxYiR6MR165d+8yyV7vdZnd3F6vVytzc3B2XbVwuF6PRSAQtvV5PvV5/4CWwWq0msoWTHMHNZrMYjcZDgatarR6pSd/pdNjd3RUlVNlPkZwkMqg8oqje4gC7u7ui36EuXs7NzTEej/nxj38snno/KeqpEggEcLlcQgpfxWw2MzU1hdFopFKpYLfbhR6XXq8XT7s3o9/vs729jU6nO6RUPBqNqNfrFItFcrkchUKBZrN5qDTncDjQaDTUajVsNhuhUIhmsymWQh8Eqt+Nqhh9UqjqCQ6HQwSQ0WhEo9HAbDbf875OrVYT8j02m41Go4HBYHio9n8kjw8yqDzCmEwmZmdnabfbHBwcYLPZxMSQaoZVKBS4cuUK4XCYarV6g1Ww2ocxmUxsb28fCixqH6TRaFAqlajX67TbbZ588kn6/T5ra2s39Dt6vR6bm5uMRiNisRhGo5HBYEAikeDSpUtsbW2xv79PMpkkkUiwsbHBtWvXxHF0Oh12u13sp0xOTmIymW4o0d1P6vU6zWYTi8VyokFlb2+P0WgkSo/wT5v0qj3CvZBMJqnVapw6dQq4Hrxk6UtyUsig8ojjdrsJhUIUi0Wq1SqhUAi73Y6iKHg8HtxuNx9//DFGoxGz2czBwcENex96vZ7FxUUxspzJZMRE2fz8PGazmfX1dSGZX6lUxEDA1tYWm5ublMtlms0mGxsbDIdDMXacTCa5cuUKxWIRj8fD4uIiTzzxBE8++SRPPPEEc3NzKIrC1taWKMG5XC5hDWAymQiFQkJb7EGgWjt7vd4TK30pikIqlRLab+pUltqkv9dgNhqNWFtbw2AwcOrUKTqdjuynSE4UGVQeAyKRCFarlf39fYbDIbOzsxiNRhRFEfsqP/vZz5ienqbf75PJZG44htFoZHl5WUyEXbx4kWvXrrG2tobVasVqtdJsNpmYmGA0GnFwcEAkEmFqaoput8vVq1d54403xMLl5cuX+fGPf8zly5ep1+toNBoxplyr1cRQgdfr5fTp07hcLg4ODigUCmJrXM1e1J7M3t7e/fxYBaVS6cS1vvL5PO12W7hIfnqT/l6b9KpLqKpkrQbm+yE5JPl8IoPKY4BawhqNRuzv72M2m4lEIkKyPxKJkMlkSCaT+Hw+crncId0tFb1ez/z8PMvLy/j9foxGIx6Ph9OnT/PP/tk/w2QycenSJWH0tbe3x/7+PuVyWZhqAWQyGYrFojh3JBIRC5fdbpdEIsHly5eF/pc6eOByuUgkErRaLQwGg7gBulwunE4nmUzmvk+BtdttarXaiZe+VMVnu91+qCFfqVSEgvW9UCqVyGaznDp1SowSS70vyUkiBSUfEywWC5OTkySTSYrFIsFgUJSM1C35d999l9/6rd+iWq2SSCRYWlq66bFU47BP89xzz/Hee++xvb2N1+ul0+nQbrcZj8f4/X6h/2W324UnyqcZDoc0m03K5TKVSkWU0lTf9e3tbfb39zGZTIfKXVNTU6yvr5PP50/cNfSTqAKS0Wj00KKgoihiv8dkMt3zUiJc70Nls1m8Xi8ajUb0U1TlaovFck9NdUVRuHz5MhqNhtOnTwtl6IfZplny6CODymNEMBikWq2STCZxuVzMzMywtrbGaDRifn6eK1eu8N577/HUU0+RSCQol8t3VdLx+XycOXOGg4MDDAYDExMTmEwm3G73LTfMVR8Q1cf+k2PNWq2WXq/Hzs4OCwsLeDweFhYWWF9fp1wuYzQahfS9z+cTBmihUOi+2Qyr+mifNOMqFAqk02mRNRkMBkKhEIFA4FBwURSF0WiERqO5rYzNzs6OsKBWPVPgupqzKnd/L0Gr1Wqxvb2NzWZjeXmZTqcj9b4kJ44MKo8RGo2GmZkZrl27xsHBAfPz84RCIdLpNBaLhVAoxM7OjnDgPDg4wOl0is3tO2FycpLhcEipVKLb7eLxeG74/sFgQL1eF3L24/FY2OAGAgGMRiPD4ZBKpcJoNKJWq7Gzs8Pp06exWq3EYjEuXbpELpejVqthNptxOBw4nU7hOXM/3AoHgwHFYlGUvhRFIZFIUCwWsdvt+P1+4Vd/cHBAPp9ndnYWrVYrrn00GgHXM0nVKuCTAabT6YgFUYvFQr1eF5lKo9EQe0b3QrFYJJlM8oUvfAGDwUCpVAKQQUVyosig8phhNpsJh8PC4U+d1mq320xPT1MqlXj77bf59V//dXZ2dkgmk2KS605Q+zeqfL66HGkymdBqtfT7ffEEr6r5qi6Tn37aVqVYdnd3yefzQiLGZDKxuLhIJpMhkUiIjMjpdFIoFCgUCvclqKhb9OFwGLPZTCqVolgsitKeKtbpcDiYmpoil8tx4cIFMTnX7XYZDAai12S1WoU4qKqAsL29Tbfb5fTp0yIrU7MwVZn4XpcUL1++jKIonDlzBkDsu0g/eslJIoPKY4g6gptIJDh9+jTRaJStrS3G4zHz8/Osr69z9epVZmdnyWazYgP/bpiYmMDv9wvl3l6vx3g8xmq1igDwySmmW+Hz+TCZTIzHY1KpFHa7naWlJdxuN36/n3Q6LW62drudSqVCq9Wi0+mcmJWvSrlcZjgcMjExQa1WI5vNir6EwWDA6XSiKAq1Wk0MKnQ6HYrFInBdIDMQCAjvkkajITS4zp49KwKT3+8nEomwtrYmGvLj8ZhqtXrPTfp2u836+jp2u51Tp06hKIr0o5fcF2RQeQxRy2AbGxuk02nhlVIoFHA6nfh8Pq5cucL8/DwWi4X9/X1Onz59V2UwuL6oeBw3KbvdzurqKp1Oh62tLdxuN8FgUHjE7OzssLq6isPhwGw2MxwOKRQKnylmeRTG4zHZbFbon+3t7dFqtYRl8tTUlMgohsMhH330EfF4nMFgIDbifT4foVBIKBoUi0U2NjZIpVL89Kc/xel0YrPZhDdNv98XdsytVoter4fL5bonJeF8Ps/BwQHnz5/HZDIJXxpZ+pKcNHKk+DHFbrcTCATI5/O0Wi0huQIICZe33nqLaDTKcDgkHo/foGR8v6/37NmzaDQaLl26JJYN3W43+Xyeer2Ow+FAp9NhNBoPjTCfBI1Gg1arhcvlolQqUavVMBgMBINBotHooUEBdTzbYDDQ7/ex2+08//zzTE5OUiwWuXr1KuVyGbvdLsRA2+02mUxGNPk7nQ6AaNI3Gg263e49B+0PPvgARVF45plnAIRCgdxPkZw0Mqg8xkxOTmIwGMQORDQaFVbBMzMzFAoFNjc3mZ6epl6v31TJ+H6i9huazSaXL1/GYDDg8/kYDodkMhmxX2EwGBiNRieqB1Yul+n1ejidTvL5PKPRSCwQfhJFUdjf36fZbKLX61laWiISiRCPx9FqtaysrGAymYQldC6XY3JykmAwiEajoVgsip4XIEp6pVIJvV5/T/bBrVaLy5cv4/f7WV5eBq4HFbvdftspNInkOJBB5TFGp9MxPT1Np9Mhl8vh8XjEjoLP58NqtfLhhx8KocRUKvVAhRsBZmdnmZqaIpVKkUqlRPlH7UnY7XZGoxEmk0n0Lk4CNYh1Oh1qtRpOp/OmasuFQoF6vU6v18NsNnP69GnOnDmDz+cjk8mIKbzV1VWmp6fxer0Mh0O8Xi/z8/P0+33ef/99EZQMBgPD4ZByuSz6SHfL5uYmhUKBZ599Fr1eT7/fp9PpnKhkv0SiInsqjzmq/lc6ncbtdgupjk6nQywW48qVK7zxxhv81m/9FoPBgHg8LsZ/HwQajYYnnniCYrHItWvXeOqpp7BarYzHYzKZDF6vl3K5jNvtplgsiib+caIGEr1eLyamPr38CIhr6vf79Pt9YrGYCNqzs7M4HA52d3f5/ve/Tz6fp1Ao0Ov10Gq1+Hw+fD4fGo2GUqnEYDAQWUWj0RBZ0t0OI4zHYy5cuIDRaOTcuXPAP5W+7iXrkUjuliNlKuVyma997WssLi7yta997ZZPud/61rcIBoNitFFyf1F7APv7++j1eqanp9Hr9UKWpVQqceHCBRYWFjCbzWxvb4udhgeByWRiZWWFVqtFNptFo9Fgt9tpNBqifKOOxZ5EtlIsFul0Omg0Gjqdjtgv+TT5fJ5ut0uv18Nqtd5QGuv3+1y7do2trS263S6rq6v8+q//Ov/6X/9rXnzxRVGavHr1Knt7e6IEVq/X6ff7IujcDfl8nq2tLRYXF4XTZq1Ww2QySal7yX3hSEHlO9/5Dl/5ylfY2triK1/5Ct/5zndu+rr//r//7/nhD394lFNJjoDBYGBqaopms0mhUBANcLiuchwIBFhbW2Nzc5OlpSVsNht7e3skEokH5riolopUEUm9Xo9Wq6VarWIwGOh2u7jdbmE5fJyk02nG4zGj0Qi73S4cMlXG4zG7u7u89957ZDIZ2u02oVDo0GTVxsYGf/d3f4fZbOb3fu/3+J/+p/+J3/3d3+WXfumXWFlZ4dy5c/zLf/kveemll1AUhQ8//JCLFy8yGAyoVCrodLp7mtT6+c9/Trfb5Vd+5VfQarVC0UBmKZL7xZGCyquvvsrv//7vA/D7v//7fO9737vp6774xS+eqMKr5LPx+/04nU5SqRT9fp+ZmRksFotQMrbZbLz99tvCxzwYDAovlnQ6LaaTbsZJTI1ptVqeeOIJRqMRpVKJdruNz+cTvYZGoyGa+Gp55zjo9/sUCgXgevCYmJg4dHOv1+u88cYbXLhwgWw2SzqdFlYBKpcuXeInP/kJ4XCY3/7t376lVplWq+Xpp5/mt37rtzCZTLz11lu89tprYqv+boNKvV7nvffeY3p6Wui6qYrQMqhI7hdH6qnkcjnC4TAA4XD4Bkvae+GVV17hlVdeARD/uCXHQzQa5dq1a+zv77O4uMjs7Czb29sMh0NisRjr6+v8wz/8A7/xG78hdltSqRSZTIZMJoNGoxFjyePxWDzNA8JJ0O12H5vlrtfrJRQKkUgksNvtwnRM3do3mUwYDAYKhcKxNaFzuRyNRkPc1EOhkPharVbjrbfeYjweEw6H0ev15PN5Op0O29vbYtnzwoULzM/P86u/+qt3pFEWDof5+te/zltvvcXf/u3f8uSTT/LCCy/ctX3wm2++SaPR4Bvf+Ibo/5RKJQwGg9xPkdw3PjOofPWrXyWbzd7w9//xP/7HE7mgl19+mZdffhlANBolx4PJZGJyclL4lgQCAYLBINlsltFoxPLyMpubm/zd3/0dv/7rv47f72dxcZHBYCCMqgaDAXD9KVv9n0ajod/vCwfKg4ODGxYE75WVlRXS6TTZbFaITqr9nmazid/vJ5PJHFvD/uDgQHjR+/1+MX3V6/V49913GY/HnD17lmKxSKvVwmw28/zzz7O9vc3PfvYzKpUKy8vLfPnLX77j964OTQSDQf7yL/+SCxcuEA6HefbZZ+/4uvP5PG+//TZzc3Osrq4C15cy6/W6GF+WSO4HnxlU3njjjVt+bWJigkwmQzgcJpPJiMag5OElEAgIz3K73c7U1BTtdptqtYpGo+HUqVNsbm7y/e9/ny9/+cvMzs5iMBgOqfTejm63Kyadms0mi4uLR9KacrvdTE5Osra2RrFYZHZ2lkqlQqfTodFoMD09TTabJZ/PH3nDfjAYkEwmMRgM2O128fusKAqXLl2i1Wrx9NNPMxwOGQ6H9Ho9HA4HExMTuFwu/u7v/g64/mB0N/sg7XZbjHs/88wzXLx4kQsXLmCz2fjyl7/8mcFyNBrx6quv0u/3+dKXviRKXZVKBUVRZOlZcl850mPkN77xDf7sz/4MgD/7sz/jN3/zN4/loiQnhyoIqdVqxRb9wsICVqsVjUaDRqNhdXUVp9PJP/zDP/CLX/xCZCd3gjp+G4vFDvnVH4WVlRX0ej07OztYrVbsdjv9fp96vY7BYMDr9VIqlY48VKAahFksFlwulxgPzmQypNNpJicnCYfDwo2x0+kQCoXQ6/VcvnyZUCjEysoKly5duuNz9vt9RqOR+PytViunT58WgfS//tf/elvHS0VR+NGPfsTGxgZPP/30IY+cUqmExWK56zKaRHIUjhRU/vAP/5DXX3+dxcVFXn/9df7wD/8QuD4989JLL4nX/at/9a944YUX2NjYYGpqij/90z892lVLjoTBYGB2dpZOpyPGjGOxmLAgVj3ml5aWuHz5Mn/913/NtWvX7uqm7XK5WFhYoNfrsbu7e6RmvsvlIhgMUiwWRTlHp9MJKZNgMMh4PD7yeLEquunxeETJaDAYsLGxIcacq9UqiqKI0pfqYXPlyhXOnz/PuXPnyOVy7O7u3tE51TFis9lMuVzG6XQSCoWYmJgQmmH/8A//wH/7b/+NRCJx6HPs9/v86Ec/4he/+AWhUIjz58+LLKXb7dJqtWSWIrnvHKlR7/P5+Md//Mcb/j4SifCDH/xA/Pkv//Ivj3IayQngcrmIRCKk02msVisTExMsLi6yubnJYDCg0+ng8/n42te+xsWLF3nrrbd47733iMViLCws3JFxlNPpZHp6mkQiQT6fZ2Ji4p6vVzUHu3btGs8//zxOp5N0Ok2j0SAQCOBwOMjlcgSDwXvq47TbbbLZLFarVfi+AKRSKWq1GsvLy9jtdlKpFOPxmF6vh9vtxuVy8eabb6LX6zl//jxGo5F8Ps/Vq1fFxN3tUKfqBoMBjUYDm81GOBxmb2+PfD7PzMwMVquVa9eu8cMf/hCr1SqCqGo94HQ6OXv2LAsLC+K4+XwejUZzx2VLieS4kDItn2NCoRBut5tkMkmlUsFms7G4uCgmh7rdLpVKheeee47f+I3fYHJykvX1df72b/+WP//zP+fNN98UfiC3IhAI4Ha7SaVSwn73XohEItjtdvb391EURZiFqROHkUjk0J/vlmvXrjEYDPB4PPj9fnQ6HYPBgL29PaxWK9FolH6/T7PZBK5/NqFQiGq1ys7ODmfOnBFeKM888wyKovDRRx99ZnbXbrcxmUwUCgW63S52u525uTlOnTqF3W7n4OAAgN/8zd/kq1/9KqFQiFqtRrPZJBQKEYlEWFxc5MknnxQ/N9VEzev13rXytERyVGRQ+Ryj0WiYm5vDZrMRj8ep1+vCf8NqtaIoCv1+n1QqRblc5plnnuHf/tt/y1e+8hWmp6dJJpO8+eab/Of//J/53ve+x/b29k3LXOryYDKZPPK1tlot9vf3hXZZOp0GrqscO51OcrncXfdwhsMh29vb6PV6fD6fyKgymQz1ep1IJILNZhONb9XLxefz8eGHH2IwGHjiiSfE8TweD4uLi5TLZdbX129b+lMzlXq9jlarFU6aU1NTnDlzBoPBwPr6Oh988AGdToeVlRV+9Vd/ldXVVYbDITabjXPnzh0qc+VyObFjI5Hcb+RjzOccrVZLLBZjc3OTnZ0d5ubmcLvdrKyskEqlKBQKYjS10+lgNpsJBAK8+OKL6PV6isUiBwcH7Ozs8Oabb/LBBx/wxS9+UewvARiNRmFr3Gq17sl0ChAGYxsbG8zOzhKJRNjY2KBWq4ly3vr6Orlc7pYLhzdja2tLjCd7vV5MJhOj0YhEIoHZbGZqagq4Pk2lOjo6nU4GgwE7Ozs8/fTTN0xoLS4uUiqViMfjeDwe0R/5JKPRSIxpj0YjbDabKLtpNBoWFhbQarVcunSJSqUi7IVV3G43zz777KHFxsFgQD6fx+PxnLiJmURyM2RQkQjJ9u3tbXZ2dpiamhK+IV6vl3Q6Tb1ep9vt0m63KZVKmM1mfD4fgUCAp59+mqeffpq9vT3eeecdvv/97/PCCy8cenoPBoPkcjkymQyxWOyertPlcomtenWEeHNzk0QiwRNPPIHNZsPj8ZDNZoWj5GfR6/VYW1tDp9OJvR1AeKiEQiFcLhf9fp9WqyWCytLSElevXkWn0x16n5/8TJ944gkuXLjA2toaTqfzhgXEdrtNs9lEp9MJWZZPqxLPzc2h0+lIpVIEAgHMZjP9fl98Fp8mlUqhKMpdBVWJ5DiR5S8J8E+BRe2xbG1t0ev1hL3vysoKMzMzeDwezGYzzWaT9fV1Lly4wM9//nP29vaYnp7mX/yLf8Hc3By/+MUv+Oijj8TxdTodwWBQLFHeC0ajkUgkgqIobG1t4XA4RMO+3+8DMDU1hUajIZFI3NEx19bWhKS+1+sVN/5MJiNuzhqNhnq9Lqa+VIvfzc1NFhcXb7lH4nK5WFpaotPpcOnSJTHppVIul6lUKuj1ekwm06Ht/U8yPT2N3++nVqvhcDiYn5+/aUCp1WqUSiWCwaAUj5Q8MGRQkQi0Wi0LCwvMzMzQarW4cuUK8XicRqMhVHjPnj3LU089xdNPP82ZM2dwu900Gg0++ugj3njjDQ4ODvjqV7/K4uIi7733Htvb2+L4gUAAjUZzJPkdr9eL3W6nWCxSq9WYnp6m2+2K3orRaGRycpJ6vU65XL7tsfL5PIlEAr1eL8aI4fqmfrlcxuFwiF6Fqi/W7Xax2Wwkk0mGw+FnKm/PzMwwOztLuVzm8uXL5HI5ut0uxWKRra0ttFotBoMBl8t1S30ujUYjrJ93d3ep1+s3vKbb7bK3t4fFYpFZiuSBIstfkhvw+/24XC5yuRyFQoFyuYxWq8VqtWIymYRisNlsZn5+ntFoRK1WIx6P89FHH5FIJDh//jzNZpOf/vSnBINBnE4nBoNBKAtPTk7e0+ivw+HA5/ORy+XY3t4WYpjZbJZgMCg85MvlMvv7+1it1ps+tTcaDTY2NhgMBlgsFtxutwgghUKBTqfD7OwsJpNJKP3qdDra7TbT09N88MEHTE5OfuYeiFar5dSpUwwGA4rFIru7u0IXbTQaYTabMRgMn6kGoNPpWFxcZGtri62tLYLBIMFgEKPRSKVS4eDg4FAfRiJ5UMjfPslNUeXy1f0H9eZZr9cpFApiy/zg4EA04NXx4Ww2y1tvvcXTTz+NRqPhZz/7mTiu3+8XQehecDgcWCwW7HY7mUxGWO4OBgMODg5QFEU82Wu1WlHG+yT1ep3NzU0ajQYGg0Foomk0GjGWrFoZw/XMZTwe0+/30el0YrFQ1di6k89yZWWFUCiERqPBYDBgsVjodDoYDAbm5+fvSIDTYDCwvLyM3+8nn89z5coVPvzwQ+LxOAaDgaWlpTvqI0kkJ4nMVCS3RafTCeXhT6MqFQ+HQ3GjNZlMKIpCKpXiww8/FA3tjY0NlpeXcTgcGAwGSqWSkEG5G4xGI0ajEZ/Px8HBAalUSqgVN5tNsWRpNBrFMufa2hoTExMYDAbq9TqVSoVWq4XVaqXZbOJyucTUlSoUabfbxfWpAVDdKUmn00KO5k6xWCxioq5Wq4mN/FOnTt2VgrBOp2NmZkbsq4xGIyErI0UjJQ8DMqhI7hlVpViv1wvZ+8nJSWq1GgaDgXg8DlzPLi5cuMDc3BxGoxGPx0OhUGA0Gt2V8KKKw+FgOBzicrmEB3y73RZ+MS6XC7PZjNVqZWVlhUQiIXouOp0Ol8vFeDym3W4zGo2IRqPo9XoURaFYLDIajfB6vSJ7UK2FO50OTqeT/f19VldX77rMpMrjwPXgpdVq71lGxWQySQFXyUOJLH9Jjh2Xy8WLL75ILBajXC6j1+tptVpcvnwZuL5foSjKTRvOd4LD4WA8HhONRsXW/2g0wufzodPp2NnZEQuQJpOJxcVFzp49y+rqKqurq3S7XbRaLd1u95BnSqPRoNFoYDKZxM2+3+/T6/XEImin02E8Hh8SbrwXOp0OWq1Wlqskjx0yqEhOBK1Wyxe+8AXC4TCtVgu47oioSpHodDqq1eo9HVvd5VBLVKpxV7vdZm5ujm63y/b29qHNeoPBgMFgYHd3l36/j8lkot1uMzMzIzKSQqFAr9cT+y5wPdDA9SBgNBrJ5XL4fL6bjvTeDe12W5h6SSSPEzKoSE4Mg8HAs88+i91ux2g0UiqV+Pjjj9FoNLjdbmF1e7eYTCaMRiPtdpulpSVGoxGVSoVqtYrT6WRubo5ms8na2hqVSkUoAqyvr9NsNolEImQyGex2u9h07/f7QtLe6XQKzSxV60tt1qseMUdBURTa7baUpJc8lsigIjlR/H4/p06dwmg0MhwOuXr1qnBWHI1GIhO4WxwOB41Gg1AoRDAYFE36Xq+H1+sV5and3V0uXrzI1tYWo9GIxcVF0YxfWFgQ48bFYpFer3eo9AXXMxW9Xk+v16PVaqHVapmfnz/SZ9Lv9xmPxzKoSB5LZFCRnDhzc3NC7iSVSrG5uYnT6USr1d7zaLHdbhdTZ2fOnBHikplMBrgedFZXV1lcXGRqaor5+XnOnDlDv98nkUjg9XrF9NZ4PKZQKIhxX3XS7ZP9FFWaPhAI3CClcreom/VSm0vyOCKDiuTEsdlsQqhSlSzRarXY7fYjNevhelnK4XBw5swZRqMRly5dEsq/Go0Gp9PJxMQEHo+HTqfD2toaAMvLy6LEValUGAwGaLVaXC6XmEj7ZD9lNBqJns1RkUFF8jgjg4rkvqBa8Xq9XtbW1sjlcjidTrrdrtDtuhvUvop6449Go0SjUdG3KRQKh/o15XKZa9euUavViEajh8ZxVQ8WVa5FpdlsotVqabVaVKtVjEbjsQQVVTpfbr5LHkfknorkvmC1WpmamiKTyfD2229z9epVXnjhBeD6hvu9OBR+MtNRzbHK5TK5XA6tVksqlcJqtYrJsGq1SigUYnFxUUxdNZtN2u02RqOR0Wh0aMlT3bjv9XrU63Wi0egt9bnuhna7fVcLjxLJo4R8VJLcNwKBAOFwGJfLxbvvvis24Y/SrFf7KnB9P0ZdsByPx8A/NcXVPZbl5eVDuyHZbBatVstoNMLlconsQe2nALRaLfr9vlhcPAr9fp/BYHDPnjISycOODCqS+4bT6cTn8wnDrkwmg8PhELLyd4v6tP/JoDQ1NYXX68VqtTIajej1egwGA+x2u5CJUWk2m0JOfjwe3zD1BddLVbVaDYvFwvT09L2+dYG6syODiuRxRQYVyX1Do9Hg8/mYnZ1lMBjw/vvv43Q6GQ6Horl+N9ws01HlVVwuF6dOnSIajTI/P8/q6uoNU1vpdFooLut0OpxOp/iaap7VarWo1WrY7XahD3YUVKMv2aSXPK7IoCK5r/j9foLBIG63mw8//FBkDvc6BeZ0Og9lOnq9Hr/fT7lcxmAwEAgE8Hg8NzTF6/U6jUaDiYkJ6vU6brf70GsajYZYsGw2m8zMzBxLY10VspRNesnjivzNltxXVEHJaDRKoVAgl8sJJ8l7weVyMRqNRFkJEFpeqVTqpt8zHo9JJBJigkwVkFQZDAb0ej00Gg3FYhG9Xi986o+C3KSXfB6QQUVy33G73cRiMUajEe+88w52u51ms3lPfRWn04lGozm0RGk0GgmFQpTL5ZsuVyYSCXq9HjMzM8LO95O9FjVAqWKVNpvtWPop3W6X8Xgs+ymSx5ojBZVyuczXvvY1FhcX+drXvkalUrnhNQcHB3z5y19mZWWF1dVV/q//6/86yikljwFutxuPx4PX6+Xq1avY7XZGo9E99VV0Oh12u/2G371QKITFYiEej4sgoSgKyWSSUqlEOBzGYrFQq9Xw+XyHhB2bzSYajUb0U0Kh0LEEAtmkl3weOFJQ+c53vsNXvvIVtra2+MpXvsJ3vvOdG16j1+v5P/6P/4O1tTUuXLjAH//xH3Pt2rWjnFbyiGMwGHA4HEQiEbLZrBjdvdcSmMfjodfriU11uL63EovF0Ol0bGxssLm5yZUrV8jlcgSDQcLhMKVSCUVRblAcbjabmEwmarXasW3Rw/WgotPppNy95LHmSEHl1Vdf5fd///cB+P3f/32+973v3fCacDjMM888A1wfAVXd7ySfb9xuN7Ozs4zHYy5evIjRaDxSUAFuyFaMRiMrKysEAgHhkBiLxZienkaj0VAqlbBarYcmsVTzLo1GQyaTwWq1HkvpC64HFZvNJuXuJY81R9qoz+VyhMNh4HrwUOUubsXe3h4fffQRzz333C1f88orr/DKK68A1/0tJI8nbrebcDiM2Wzm6tWrnDp16p4nwFSf+lKpRCQSOXTT1uv1Nw0K7XabTqdzgyVwu90Whlz5fB6XyyUa/0dhPB7T6XRuassskTxOfGZQ+epXv0o2m73h7//jf/yPd3WiZrPJ7/7u7/JHf/RHh/YBPs3LL7/Myy+/DMC5c+fu6hySRweTyYTVamVycpJEIoFerxfb8aoc/d3g8/mo1WrU6/U7klLJ5/NotdpDWl/wTyW4VqtFvV7nzJkz92R5/GnU0pyc/JI87nxmUHnjjTdu+bWJiQkymQzhcJhMJnNLz+zBYMDv/u7v8m/+zb/hd37nd+79aiWPFS6Xi6mpKXZ2dojH49hsNhqNxj0FFbfbjV6vp1AofGZQGQwGlMtl/H6/UCpWabVaGAwGDg4OGA6Hx9pPAdmklzz+HKmn8o1vfIM/+7M/A+DP/uzP+M3f/M0bXqMoCv/D//A/sLKywv/8P//PRzmd5DHD6XQyOzuLVqtlY2MDg8Fwz30VjUZDIBCgVqsJLbBbkcvlUBSFiYmJG76mbtKn02lsNptwhjwqrVYLo9GIwWA4luNJJA8rRwoqf/iHf8jrr7/O4uIir7/+On/4h38IXJe/eOmllwD4+c9/zp//+Z/z5ptv8tRTT/HUU0/xgx/84OhXLnnksdvtWK1WAoEA8Xgci8Vyz+KSAMFgUDTYb4XaK/H5fDdMYXW7XYbDIcPhkEKhgN/vv6E8dq80m80jm3tJJI8CR2rU+3w+/vEf//GGv49EIiJwvPjii/e01CZ5/NFoNDgcDsLhMJcuXRI+8Kqt792i1+sJBoPkcjkmJiZu2r9IJpMAYsDkk6hZUrvdpl6vc/78+WOZ1Op2u0LUUiJ53JEb9ZIHisvlYnp6GkVRiMfjwL3vq8D1YKHX69nb2xPy9yrlcplKpUI4HL5p0FI96DOZDOPx+Nj6Ker7kR4qks8DMqhIHihOp5NgMIjJZGJ/fx+tVnukoKLT6ZidnaXT6RCPx0Vgqdfr7O/vY7fbbzki3Gw2MRqNop9yHHpfcF2cUq/X39MAgkTyqCGdHyUPFKPRiM1mIxgMkslk0Gg0R+qrwD9lPwcHB1y5cgWTyUSz2cRisTA/P3/TkpY6zmwwGMT+1XFlFrKfIvk8ITMVyQPH6XQyOTlJvV6nUqkIY62jEAwGWVpawmq1Mh6PCYVCLC8v33L6Sh35bTab1Ot1YrHYkc6v0u/36ff7svQl+dwgMxXJA0dt1ut0Ovb393niiSdoNBqH5Ojv9bh3ejNXRSTVbOm4goqadclMRfJ5QWYqkgeOw+HA6/Vis9nIZDIMh8Mj9VXuhWaziV6v5+DgQGz6HweNRgOdTiedHiWfG2RQkTxwdDodDoeDUChEsVhkMBjc16CiKIoof+Xzeaampo6tqV6v14Xni0TyeUAGFclDgcPhIBgM0u12KZfLdDodRqPRfTm3KiLZbDap1WosLS0dy3E7nQ6DweC2WncSyeOGDCqShwKn00k4HMZgMJBKpRiPx/ctW1HPk0ql0Ol0LC4uHstxVddJGVQknydkUJE8FNhsNtxuN3a7nWKxSLfbva9BRe2nHKfeV71ex2KxYDQaj+V4EsmjgAwqkocCrVYr+irqWPH9CiqtVguNRkMul2NmZuYG5eJ7YTQa0Ww2ZZYi+dwhg4rkoUHdrh8MBhSLRVqt1g1SK8eNuhPTaDSo1+ucOnXqWI7baDRQFOWOvF0kkscJGVQkDw1qs95oNJLP5+n3+4d8508CNRva399Hr9cfWz+lWq2i0+nkforkc4cMKpKHBqvVitvtxul0UiqVaLVaJ14CU0UkU6kUdrv9purFd8t4PKZareJ2u+UoseRzhwwqkocGVQp/YmKCRqNBp9M5sg7YZ9FsNtFqtWSzWWEYdlRqtRqj0ejIigASyaOIDCqShwqHw4Hf72c8HlMqlWg2myfmxzMajeh0OlSrVVqtFisrK8dy3HK5jF6vl3pfks8lMqhIHiqcTieBQACTySRKYJ1O50TOpW7RHxwcoNfrWV5ePvIxR6MRtVoNr9crS1+SzyUyqEgeKsxmMx6PB5fLRaVSodFonFhfRT3uwcEBLpfrpp71d0ulUkFRFFn6knxukUFF8tChlsA6nQ7NZvNEg4pWqyWXyx2by2OhUMBsNmOz2Y7leBLJo4YMKpKHDqfTid/vB66P5lYqlWM/h6r1VSqV6Ha7x7Kf0mw2abfbBIPBY7hCieTRRAYVyUOHw+EgEAhgNBopl8vUajV6vd6xnkMVkUwmk+h0Ok6fPn3kYxYKBXQ6nSx9ST7XyKAieegwGo2ir9JsNmk0Gsc+WqweL5lM4vV6jxwIBoMBlUoFn8+HTqc7jkuUSB5JZFCRPJQ4HA58Ph+dTodWq0W9/v9r7/5i2ir/P4C/C7TA6Cgt0FIKDDvKHwul2zpmjJoNqBdmMucSQ+ISkjlJvDMaDXdosgWiNy7Rm0UvSLwg8cJhBNGh8U+WGSIOzEg2S9e6lhboKKWl/ynP98If/Tlp+Xfq2tN9XlfjnKfnvPeU7rOe85zn8ab0+JtDlRcWFnD48GHOx1tcXARjjC59kcceFRWSkTbvq+Tl5WF1dRUPHjxI2bE376csLS1hfX0dWq2W0/Gi0ShcLhdkMhny8/NTlJIQfuJUVNxuN4xGIzQaDYxGY8IbqqFQCG1tbWhtbYVWq0V/fz+XU5LHxD/vq6yursLj8SAUCqXk2KFQCLFYDPfv30dubi7nojI/Pw/GGCorK1OSjxA+41RUBgcH0dHRAZPJhI6ODgwODm5pk5+fjx9++AEzMzOYnp7G+Pg4fv31Vy6nJY+BvLw8SKVSFBcXIxgMwuv1pmxo8ealL7vdDqVSiQMHDnA61vLyMuRyOX1LIQQci8rIyAh6enoAAD09Pbh27dqWNgKBID5TazQaRTQapSeNya4UFxdDKpUiGAwiEAikbGjx2toaQqEQlpeXOS0dvLGxAavVCpFIlJKJKAnJBpyKyuLiYvzDpFQqsbS0lLBdLBaDXq+HXC6H0WjEiRMnkh7z6tWrMBgMMBgMcLlcXOIRnvv3JbBkv197tba2BofDAcYYp0tfVqsV4XAYtbW1NOKLkP+z4xJ3nZ2dWFhY2LL98uXLuz5Jbm4upqen4fF4cPbsWdy+fRvNzc0J2/b29qK3txcAYDAYdn0Okn3EYjEUCgVEIhF8Ph/cbjdCoRAKCgr2fcxIJIJIJAKbzYaCgoJ9z/dls9mwsrIClUpFE0cS8g87FpWJiYmk+xQKBZxOJ5RKJZxO547DKUtKSnDy5EmMj48nLSqEbMrNzYVEIoFMJoPT6YyvzsilqGzeT3E4HPtaOpgxBpvNBpfLBblcjoqKin1nISQbcbr81dXVhaGhIQDA0NAQzpw5s6WNy+WCx+MBAASDQUxMTKRsyVaS/TbvqwCIDwPmwuv1xpcq3uvvIWMMVqsVLpcLCoUC1dXVnLIQko04FZW+vj5cv34dGo0G169fR19fHwDA4XDghRdeAAA4nU6cOnUKOp0Ox48fh9FoxOnTp7knJ4+F4uJiVFRUoKCgAKurq3A4HJyO5/V6YbPZIBAIcOTIkV2/bmNjA2azGW63GyqVClVVVZxyEJKt9vbd/19KS0vx/fffb9leWVmJsbExAIBOp8OtW7e4nIY8xoqKiiCTyVBYWIhAIAC32w2/37+vWYCDwSCi0Sju37+PsrKyXV+6Wl9fh9lsxtraGmpqalBeXr7ncxPyuKAn6klGEwgEkEgkkEgkiMViCAQCCQeO7Mbm2iwul2vXqzyGQiHcuXMHfr8farWaCgohO6CiQjLe5pQtIpEIfr8fNpttX8fxer2wWCwAgKNHj+7Y3ufz4c6dO4jFYqivr4/f2yGEJEdFhWQ8iUQCuVyOnJwchEIhLC4uIhKJ7OkYjDH4fD7cv38fBw8e3HEo8fLyMkwmE4RCIRobG+MP8BJCtkdFhWQ8oVAIqVQKsViM9fV1BINBOJ3OPR1jcwEtp9OJxsbGbWd1cDqdsFqtEIvFaGhooOlXCNkDKiqEFyQSCUpKSiAQCBCJRPDXX3/t6fUejwdzc3NgjG37UK3D4YDD4UBpaSk0Gs2en2Mh5HFHRYXwQklJCSorK5GTk4NAIACHw4FoNLrr16+srODevXsQi8VJH7xdWFiA0+lEaWkpDh06RHPUEbIPVFQILxQVFcVnAt68BLbbG/Z+vx9utxsLCwvQ6XQJ5+laWVnB/Pw8pFIpFRRCOKCiQnhDKpWirKwMjDFEIhHMzc3t6nUejwezs7MQCAR45plntuz3+/2wWq0oKipCbW0tFRRCOKCiQnhDKpVCpVLF76ssLi7GpwDazvLyMiwWCxQKBdRq9UP7IpEIzGYz8vLycPjwYeTk0EeCEC7oE0R4o6ioCKWlpfEJJSORCP78889tXxMIBGA2m+HxeNDW1vbQvlgshrm5OcRiMdTV1UEoFP5n2Ql5XFBRIbwik8lQXl6OjY0NCAQCWCyWbVeEdLlcmJmZwYEDB/Dss8/Gt29ODhkMBqFWq1FYWPgo4hOS9aioEF4pLS1FdXU11tfXsbGxgUAggLt37yZsyxjD7OwsHA4HDAYDiouL4/ucTic8Hg+qq6shkUgeVXxCsh4VFcIrBQUFKCsrg1QqRTgchlAohNlsTrhKqM1mw9TUFAoKCtDR0RHf7na74XQ6UVZWtuMaQISQvaGiQnhHLpdDrVYjEomAMYZgMIjbt28jEAjE24TDYdy8eRN2ux0nTpyASqUC8PeT9ZtPy9fU1KTrr0BI1qLHhQnvSCQSlJeXo7i4GH6/HzKZDA6HA7Ozs6iuro5f9vrjjz8gk8nQ2dkJ4O+hwyaTCSKRCIcPH6ahw4T8B6ioEN4RCARQqVSor6/H1NQUwuEwCgsLYbFY4PP5EA6HMTc3h7W1Nbz00ktQKBTw+Xwwm80QCoWor6+n6VcI+Y/Q5S/CSyUlJaioqEB1dTVWV1fBGINIJEIwGMTq6irsdjv0ej2eeuopzM/Px2ccrq+vh0gkSnd8QrIW/XeN8FZtbS2CwWB8LrD8/HwsLS1hYWEBlZWVOHLkCO7evYtYLAaZTIaampqEU7QQQlKHigrhLaFQCI1GA4FAgHv37sFisUAoFOLYsWPQ6XTIy8uDWCxGeXn5vpYfJoTsHRUVwmuFhYXQarWoqqrC008/DbFYTA8yEpJGVFQI7+Xk5NBSv4RkCLpRTwghJGWoqBBCCEkZKiqEEEJShlNRcbvdMBqN0Gg0MBqNWFlZSdo2FovhyJEjOH36NJdTEkIIyWCcisrg4CA6OjpgMpnQ0dGBwcHBpG2vXLmCpqYmLqcjhBCS4TgVlZGREfT09AAAenp6cO3atYTt7HY7RkdHcfHiRS6nI4QQkuE4FZXFxUUolUoAgFKpxNLSUsJ2b775Jj744INdLdV69epVGAwGGAyGhNOZE0IIyVw7PqfS2dmJhYWFLdsvX768qxN8/fXXkMvlOHbsGH788ccd2/f29qK3txcAYDAYdnUOQgghmWHHojIxMZF0n0KhgNPphFKphNPpTLjg0Y0bN/DVV19hbGwMoVAIXq8X58+fx+eff84tOSGEkIzD6fJXV1cXhoaGAABDQ0M4c+bMljYDAwOw2+2wWq0YHh5Ge3s7FRRCCMlSnKZp6evrwyuvvILPPvsMNTU1+OKLLwAADocDFy9exNjYGKdwVquV0yUwl8uF8vJyThnSic/5+ZwdoPzpxuf8fM4ObJ/farXu+HoBY4ylOFPGMBgM+O2339IdY9/4nJ/P2QHKn258zs/n7AD3/PREPSGEkJShokIIISRlsrqobA5N5is+5+dzdoDypxuf8/M5O8A9f1bfUyGEEPJoZfU3FUIIIY9WVhaV8fFxNDQ0oK6ubttJLjNJbW0tWlpaoNfr48Oo9zIL9KN24cIFyOVyNDc3x7dtl3dgYAB1dXVoaGjAt99+m47ID0mU/7333oNKpYJer4der39oSHwm5bfZbDh16hSampqg1Wpx5coVAPzp/2T5+dD/oVAIbW1taG1thVarRX9/PwD+9H2y/Cnte5Zl1tfXmVqtZmazmYXDYabT6djs7Gy6Y+3o0KFDzOVyPbTtnXfeYQMDA4wxxgYGBti7776bjmgJ/fTTT2xqaopptdr4tmR5Z2dnmU6nY6FQiN27d4+p1Wq2vr6eltybEuXv7+9nH3744Za2mZbf4XCwqakpxhhjXq+XaTQaNjs7y5v+T5afD/2/sbHBfD4fY4yxSCTC2tra2M2bN3nT98nyp7Lvs+6byuTkJOrq6qBWqyESidDd3Y2RkZF0x9qX3c4CnQ7PPfccZDLZQ9uS5R0ZGUF3dzfy8/PxxBNPoK6uDpOTk4868kMS5U8m0/IrlUocPXoUAHDw4EE0NTVhfn6eN/2fLH8ymZRfIBBALBYDAKLRKKLRKAQCAW/6Pln+ZPaTP+uKyvz8PKqrq+M/V1VVbfsLmykEAgGef/55HDt2DFevXgWw+1mgM0WyvHx6Tz7++GPodDpcuHAhfgkjk/NbrVbcunULJ06c4GX//zM/wI/+j8Vi0Ov1kMvlMBqNvOv7RPmB1PV91hUVlmAw23aVOFPcuHEDv//+O7755ht88skn+Pnnn9MdKWX48p688cYbMJvNmJ6ehlKpxNtvvw0gc/Ovra3h3Llz+Oijj1BcXJy0HV/y86X/c3NzMT09DbvdjsnJSdy+fTtp20zLDiTOn8q+z7qiUlVVBZvNFv/ZbrejsrIyjYl2ZzOjXC7H2bNnMTk5GZ8FGkDSWaAzSbK8fHlPFAoFcnNzkZOTg9dffz3+NT8T80ejUZw7dw6vvvoqXn75ZQD86v9k+fnS/wBQUlKCkydPYnx8nFd9v+nf+VPV91lXVI4fPw6TyQSLxYJIJILh4WF0dXWlO9a2/H4/fD5f/M/fffcdmpubdzULdCZJlrerqwvDw8MIh8OwWCwwmUxoa2tLZ9SENv9RAIAvv/wyPjIs0/IzxvDaa6+hqakJb731Vnw7X/o/WX4+9L/L5YLH4wEABINBTExMoLGxkTd9nyx/Svs+lSMLMsXo6CjTaDRMrVazS5cupTvOjsxmM9PpdEyn07Enn3wynvnBgwesvb2d1dXVsfb2dra8vJzmpP+vu7ubVVRUsLy8PKZSqdinn366bd5Lly4xtVrN6uvr2djYWBqT/y1R/vPnz7Pm5mbW0tLCXnzxReZwOOLtMyn/L7/8wgCwlpYW1traylpbW9no6Chv+j9Zfj70/8zMDNPr9aylpYVptVr2/vvvM8a2/6xmSnbGkudPZd/TE/WEEEJSJusufxFCCEkfKiqEEEJShooKIYSQlKGiQgghJGWoqBBCCEkZKiqEEEJShooKIYSQlKGiQgghJGX+BxhTJ0nBvWZFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ten_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 1, 341)"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = ten_data_HNN.shape\n",
+    "ten_data_HNN = ten_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "ten_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_ten_data_HNN = {}\n",
+    "observed_ten_data_HNN['summary_conditions'] = ten_data_HNN\n",
+    "observed_ten_data_HNN['direct_conditions'] = None\n",
+    "observed_ten_data_HNN['parameters'] = ten_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ecabb8ed19a946a1b66ff95d6651bad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_ten_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4667bd35f1a24fe9a76af63481034e45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d5ad1b321b14496a92477400884bc31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEpCAYAAABWTc9yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAAsTAAALEwEAmpwYAAATpElEQVR4nO3bX0zV9/3H8depJyxxKzam0IKHDshxeAQOB3tEr8hqY13MyuKfC4yGOuwIxESX7k9650waNVuW1WmT5sSGlDWRq25nvwl0WRfXxZWRU2wbPW45sdBwjsTBBrHVWv7s87toejYmx2N8o+cc83xcec73K7y+X1ueORzwOOecAAAweCjXAwAAhY+YAADMiAkAwIyYAADMiAkAwIyYAADMvLkesJhHH31UlZWVuZ4BAJA0OjqqycnJ256TlzGprKxULBbL9QwAgKRwOJz1HL7NBQAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAALOsMWlvb1dpaanq6uoWPe6c04EDB+T3+xUMBjU8PLzg+Pz8vBobG/Xtb397aRYDAPJO1pjs3btXAwMDGY/39/crkUgokUgoEomoq6trwfHjx48rEAjYlwIA8lbWmDQ3N2vlypUZj0ejUbW1tcnj8Wjjxo2anp7W+Pi4JCmZTOrMmTN6/vnnl24xACDvmN8zSaVSqqioSD/2+XxKpVKSpO9///v66U9/qoce4q0ZAHiQmb/KO+duec7j8eh3v/udSktL9eSTT97Rx4lEIgqHwwqHw5qYmLDOAgDcR+aY+Hw+jY2NpR8nk0mVl5fr3Llz+u1vf6vKykq1trbqj3/8o/bs2ZPx43R0dCgWiykWi6mkpMQ6CwBwH5lj0tLSop6eHjnnNDg4qBUrVqisrExHjx5VMpnU6Oioent7tWnTJr3xxhtLsRkAkGe82U7YtWuXzp49q8nJSfl8Ph0+fFizs7OSpM7OTm3dulV9fX3y+/1avny5uru77/loAEB+8bjF3vTIsXA4rFgslusZAADd2ddkfswKAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZsQEAGBGTAAAZllj0t7ertLSUtXV1S163DmnAwcOyO/3KxgManh4WJI0Njamp556SoFAQLW1tTp+/PjSLgcA5I2sMdm7d68GBgYyHu/v71cikVAikVAkElFXV5ckyev16uc//7kuXbqkwcFBvfLKK4rH40u3HACQN7LGpLm5WStXrsx4PBqNqq2tTR6PRxs3btT09LTGx8dVVlamdevWSZIefvhhBQIBpVKppVsOAMgb5vdMUqmUKioq0o99Pt8t0RgdHdX58+e1YcOGjB8nEokoHA4rHA5rYmLCOgsAcB+ZY+Kcu+U5j8eT/vOnn36qHTt26OWXX1ZxcXHGj9PR0aFYLKZYLKaSkhLrLADAfWSOic/n09jYWPpxMplUeXm5JGl2dlY7duzQ7t27tX37duunAgDkKXNMWlpa1NPTI+ecBgcHtWLFCpWVlck5p3379ikQCOiFF15Yiq0AgDzlzXbCrl27dPbsWU1OTsrn8+nw4cOanZ2VJHV2dmrr1q3q6+uT3+/X8uXL1d3dLUk6d+6cfvWrX6m+vl6hUEiSdOTIEW3duvXeXQ0AICc8brE3PXIsHA4rFovlegYAQHf2NZnfgAcAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmBETAIAZMQEAmGWNSXt7u0pLS1VXV7foceecDhw4IL/fr2AwqOHh4fSxgYEB1dTUyO/369ixY0u3GgCQV7LGZO/evRoYGMh4vL+/X4lEQolEQpFIRF1dXZKk+fl57d+/X/39/YrH4zp9+rTi8fjSLQcA5I2sMWlubtbKlSszHo9Go2pra5PH49HGjRs1PT2t8fFxDQ0Nye/3q7q6WkVFRWptbVU0Gl3S8QCA/OC1foBUKqWKior0Y5/Pp1Qqtejzf/3rX62fLqvD/3dR8SvX7vnnAYBCsLa8WIeerb3nn8ccE+fcLc95PJ6Mz2cSiUQUiUQkSRMTE9ZZAID7yBwTn8+nsbGx9ONkMqny8nLNzMws+nwmHR0d6ujokCSFw+G73nM/CgwAWMj8o8EtLS3q6emRc06Dg4NasWKFysrKtH79eiUSCY2MjGhmZka9vb1qaWlZis0AgDyT9ZXJrl27dPbsWU1OTsrn8+nw4cOanZ2VJHV2dmrr1q3q6+uT3+/X8uXL1d3d/cUH9np18uRJbdmyRfPz82pvb1dtLa8aAOBB5HGLvbmRY+FwWLFYLNczAAC6s6/J/AY8AMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzO4oJgMDA6qpqZHf79exY8duOT41NaVt27YpGAyqqalJFy5cSB/7xS9+odraWtXV1WnXrl26efPm0q0HAOSFrDGZn5/X/v371d/fr3g8rtOnTysejy8458iRIwqFQvrwww/V09OjgwcPSpJSqZR++ctfKhaL6cKFC5qfn1dvb++9uRIAQM5kjcnQ0JD8fr+qq6tVVFSk1tZWRaPRBefE43E9/fTTkqQ1a9ZodHRUV69elSTNzc3ps88+09zcnG7cuKHy8vJ7cBkAgFzKGpNUKqWKior0Y5/Pp1QqteCchoYGvfnmm5K+iM/HH3+sZDKpVatW6Yc//KGeeOIJlZWVacWKFXrmmWcW/TyRSEThcFjhcFgTExOWawIA3GdZY+Kcu+U5j8ez4PGLL76oqakphUIhnThxQo2NjfJ6vZqamlI0GtXIyIiuXLmi69ev64033lj083R0dCgWiykWi6mkpOQuLwcAkAvebCf4fD6NjY2lHyeTyVu+VVVcXKzu7m5JX8SnqqpKVVVVeuutt1RVVZWOw/bt2/WXv/xFe/bsWcprAADkWNZXJuvXr1cikdDIyIhmZmbU29urlpaWBedMT09rZmZGknTq1Ck1NzeruLhYTzzxhAYHB3Xjxg055/T2228rEAjcmysBAORM1lcmXq9XJ0+e1JYtWzQ/P6/29nbV1tbq1VdflSR1dnbq0qVLamtr07Jly7R27Vq99tprkqQNGzZo586dWrdunbxerxobG9XR0XFvrwgAcN953GJviuRYOBxWLBbL9QwAgO7sazK/AQ8AMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMCMmAAAzYgIAMLujmAwMDKimpkZ+v1/Hjh275fjU1JS2bdumYDCopqYmXbhwIX1senpaO3fu1Jo1axQIBPTuu+8u3XoAQF7IGpP5+Xnt379f/f39isfjOn36tOLx+IJzjhw5olAopA8//FA9PT06ePBg+tjBgwf1rW99S3/729/0wQcfKBAILP1VAAByKmtMhoaG5Pf7VV1draKiIrW2tioajS44Jx6P6+mnn5YkrVmzRqOjo7p69aquXbumd955R/v27ZMkFRUV6ZFHHln6qwAA5FTWmKRSKVVUVKQf+3w+pVKpBec0NDTozTfflPRFfD7++GMlk0l99NFHKikp0Xe/+101Njbq+eef1/Xr15f4EgAAuZY1Js65W57zeDwLHr/44ouamppSKBTSiRMn1NjYKK/Xq7m5OQ0PD6urq0vnz5/XV7/61UXfc5GkSCSicDiscDisiYmJu7wcAEAueLOd4PP5NDY2ln6cTCZVXl6+4Jzi4mJ1d3dL+iI+VVVVqqqq0o0bN+Tz+bRhwwZJ0s6dOzPGpKOjQx0dHZKkcDh8d1cDAMiJrK9M1q9fr0QioZGREc3MzKi3t1ctLS0LzpmentbMzIwk6dSpU2publZxcbEef/xxVVRU6O9//7sk6e2339batWvvwWUAAHIp6ysTr9erkydPasuWLZqfn1d7e7tqa2v16quvSpI6Ozt16dIltbW1admyZVq7dq1ee+219N8/ceKEdu/erZmZGVVXV6dfwQAAHhwet9ibIjkWDocVi8VyPQMAoDv7msxvwAMAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzIgJAMCMmAAAzDzOOZfrEf/r0UcfVWVl5V3//YmJCZWUlCzdoPuskPcX8naJ/blWyPsLebt0+/2jo6OanJy87d/Py5hYhcNhxWKxXM+4a4W8v5C3S+zPtULeX8jbJft+vs0FADAjJgAAswcyJh0dHbmeYFLI+wt5u8T+XCvk/YW8XbLvfyDfMwEA3F8P5CsTAMD99UDFZGBgQDU1NfL7/Tp27Fiu59yRyspK1dfXKxQKKRwOS5L+9a9/afPmzVq9erU2b96sqampHK/8j/b2dpWWlqquri793O32Hj16VH6/XzU1NXrrrbdyMXmBxfb/5Cc/0apVqxQKhRQKhdTX15c+lk/7x8bG9NRTTykQCKi2tlbHjx+XVDj3P9P+Qrj/N2/eVFNTkxoaGlRbW6tDhw5JKpx7n2n/kt5794CYm5tz1dXV7vLly+7zzz93wWDQXbx4Mdezsvr617/uJiYmFjz3ox/9yB09etQ559zRo0fdj3/841xMW9Sf/vQn995777na2tr0c5n2Xrx40QWDQXfz5k330Ucfuerqajc3N5eT3V9abP+hQ4fcz372s1vOzbf9V65cce+9955zzrlr16651atXu4sXLxbM/c+0vxDu/7///W/3ySefOOecm5mZcU1NTe7dd98tmHufaf9S3vsH5pXJ0NCQ/H6/qqurVVRUpNbWVkWj0VzPuivRaFTPPfecJOm5557Tb37zm9wO+i/Nzc1auXLlgucy7Y1Go2ptbdVXvvIVVVVVye/3a2ho6H5PXmCx/Znk2/6ysjKtW7dOkvTwww8rEAgolUoVzP3PtD+TfNrv8Xj0ta99TZI0Ozur2dlZeTyegrn3mfZncjf7H5iYpFIpVVRUpB/7fL7b/oeaLzwej5555hk9+eSTikQikqSrV6+qrKxM0hf/A/7jH//I5cSsMu0tpH+TkydPKhgMqr29Pf2tinzePzo6qvPnz2vDhg0Fef//e79UGPd/fn5eoVBIpaWl2rx5c8Hd+8X2S0t37x+YmLhFfijtduXNF+fOndPw8LD6+/v1yiuv6J133sn1pCVTKP8mXV1dunz5st5//32VlZXpBz/4gaT83f/pp59qx44devnll1VcXJzxvELZXyj3f9myZXr//feVTCY1NDSkCxcuZDw337ZLi+9fynv/wMTE5/NpbGws/TiZTKq8vDyHi+7MlxtLS0u1bds2DQ0N6bHHHtP4+LgkaXx8XKWlpbmcmFWmvYXyb/LYY49p2bJleuihh/S9730v/XI+H/fPzs5qx44d2r17t7Zv3y6psO5/pv2Fcv8l6ZFHHtE3v/lNDQwMFNS9/9L/7l+qe//AxGT9+vVKJBIaGRnRzMyMent71dLSkutZt3X9+nV98skn6T///ve/V11dnVpaWvT6669Lkl5//XV95zvfyeXMrDLtbWlpUW9vrz7//HONjIwokUioqakpl1MX9eUXA0n69a9/nf5Jr3zb75zTvn37FAgE9MILL6SfL5T7n2l/Idz/iYkJTU9PS5I+++wz/eEPf9CaNWsK5t5n2r+k934pf2Ig186cOeNWr17tqqur3UsvvZTrOVldvnzZBYNBFwwG3dq1a9ObJycn3aZNm5zf73ebNm1y//znP3O89D9aW1vd448/7rxer1u1apU7derUbfe+9NJLrrq62n3jG99wfX19OVz+hcX279mzx9XV1bn6+nr37LPPuitXrqTPz6f9f/7zn50kV19f7xoaGlxDQ4M7c+ZMwdz/TPsL4f5/8MEHLhQKufr6eldbW+sOHz7snLv9/6v5st25zPuX8t7zG/AAALMH5ttcAIDcISYAADNiAgAwIyYAADNiAgAwIyYAADNiAgAwIyYAALP/B+qpyLmDKoYwAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e215f9d6f6a34322b490cb22b5357bdf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YXz7VvF1if7lV8/47bZ+enta1a9dWfY2qDI5fsVhMmUym3DM+NfaXTzVvl9hfbtW8fz228yU1AIAJggMAMHFPBieRSJR7gi/sL59q3i6xv9yqef96bL8nv4cDALB3T36GAwCwd88FZ3x8XE1NTfI8TwMDA+Wes6qGhga1tLSora1NsVhMknT9+nV1dHSosbFRHR0dunHjRplX/ltvb69qa2u1bdu2hfvutLe/v1+e56mpqUm/+tWvyjF5keX2f//739fWrVvV1tamtrY2jY6OLjxWSfvz+byeeOIJNTc3KxqN6vjx45Kq5/qvtL9arv+HH36onTt3avv27YpGozp69Kik6rn+K+1f1+vv7iEff/yxi0Qi7tKlS+6jjz5yra2t7uLFi+WedUdf/OIX3ezs7KL7XnrpJdff3++cc66/v99973vfK8e0Zf32t791b731lotGowv3rbT34sWLrrW11X344Yfur3/9q4tEIu7jjz8uy+7bltt/9OhR96Mf/WjJ2Urbf/nyZffWW28555x7//33XWNjo7t48WLVXP+V9lfL9f/Xv/7lbt686Zxzbn5+3u3cudP94Q9/qJrrv9L+9bz+99RnOOl0Wp7nKRKJqKamRt3d3UqlUuWe9R9LpVLq6emRJPX09Oj06dPlHfR/fOUrX9HnP//5RfettDeVSqm7u1uf+cxn9KUvfUme5ymdTltPXmS5/SuptP2hUEg7duyQJD3wwANqbm5WsVismuu/0v6VVNr+QCCgz372s5KkW7du6datWwoEAlVz/Vfav5JPs/+eCk6xWFR9ff3C7XA4fMf/oStBIBDQU089pccee0zJZFKSdOXKFYVCIUmf/CO9evVqOSeuaqW91fT3MTg4qNbWVvX29i58SaSS909PT+vtt9/W448/XpXX///ul6rn+pdKJbW1tam2tlYdHR1Vd/2X2y+t3/W/p4LjlvmBvDsVvBKcP39ek5OTGhsb0+uvv67f/e535Z60bqrl7+PQoUO6dOmS3nnnHYVCIX33u9+VVLn7P/jgA+3bt0+vvfaaNm7cuOK5atlfTdf//vvv1zvvvKNCoaB0Oq133313xbPVsn89r/89FZxwOKx8Pr9wu1AoqK6uroyLVnd7X21trZ555hml02lt2bJFMzMzkqSZmRnV1taWc+KqVtpbLX8fW7Zs0f3336/77rtP3/zmNxe+bFCJ+2/duqV9+/Zp//79evbZZyVV1/VfaX+1XP/bPve5z+mrX/2qxsfHq+r63/b/96/X9b+ngtPe3q5cLqepqSnNz89rZGRE8Xi83LNW9Pe//103b95c+O8333xT27ZtUzwe19DQkCRpaGhIe/fuLefMVa20Nx6Pa2RkRB999JGmpqaUy+W0c+fOck5d1u0PFpL0y1/+cuEn2Cptv3NOL7zwgpqbm/Xiiy8u3F8t13+l/dVy/WdnZzU3NydJ+uc//6lz587pkUceqZrrv9L+db3+6/lTDtXg7NmzrrGx0UUiEffKK6+Ue84dXbp0ybW2trrW1lb36KOPLuy9du2a2717t/M8z+3evdv97W9/K/PSf+vu7nYPPfSQCwaDbuvWre6nP/3pHfe+8sorLhKJuIcfftiNjo6Wcfknltv//PPPu23btrmWlhb39NNPu8uXLy+cr6T9v//9750k19LS4rZv3+62b9/uzp49WzXXf6X91XL9//SnP7m2tjbX0tLiotGoe/nll51zd/73Wg371/P685sGAAAm7qkvqQEAyofgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE/8LaDBMDsHNza4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d009d57035b048bfbf3e47c2906feaa1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6efac8bacdbe40b9b1255bcdd7a83a2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cbc1b60bd9e4d21bce5b819fb64ebc0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10d19054aa364a109c903aafd243f1be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2aebe8c3aa7476fb07bbd4965d87d0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9fc70a7d170a458c9e3c1e6165509f1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fbe299354f5045a9b6d0ffdcb06134b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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sLCyYv78/++STT9ijR49UHlM2HnPDhg1s//79rEOHDszc3JzZ2NiwwYMHs9u3byuUT0xMZDNnzmTBwcHMwcGBCQQC5unpySIiItiZM2dUfsapU6fY0KFDmaurKzMxMWEODg4sMDCQzZw5k118bSygp6en0tjIijRp0oQBYPb29kwkElVYTiQSsTVr1rCOHTsya2trJhAIWKNGjVh4eDhbuXKl0phSkUjEFixYwLy9vWX1+/LLL1lRUVG1xoEyxphEImFr1qxhzZs3ZwKBgLm6urLp06ez7OzsCuu6YcMGFhgYyMzNzVmDBg3Y22+/za5du8bmzZvHALCYmBiF8i9evGAjRoxgTk5OjMvlMgBs3rx5svfPnDnDwsLCmK2tLbO0tGSdO3dme/bsYTExMUpl9ULP40A5jP2//7ka2rZti0uXLlVdkOjNL99exI8LzgOQDv9bsqkXgjqW//XJGMNviy5h9wZpd3o+n4u/zg1Ds1bK48FIubJemAZhJJ2ICKl15PNT28qvHW1c49SWV4ulPc3DH0svy16P/yhIIXkC0s4Ykz8PRvNAacIsKZHgq8nHUFpKnYoIIaQmKIHWYqvnnUdhgbTzgr2jGd6ZGqCyHI/Hxaffd4aJQPrjTkp4icM7lSfyJoQQoj5KoLXU44c52Lflluz1J4s6gcer+Mfp4WODYZPLE+yaBeepVy4hhNQAJdBaasev1yCRSB9feza1QUg3tyqOAIZM8IOVjXSFhscPcxC59bZOYySEkLqMEmgtlJ8nxu715eO23p2pPBBdFUtrAYZOKl/T8bdFF2VJmBBCSPVQAq2F9m+7LRvzaWUrQMceVa+WUebtMc1ld6FPknNx6khKFUcQQghRhRJoLfTX2vLpyyLe9QOXW/VqGWXMLEzQd0gT2esta65qNTZCCKkvKIHWMvduZuD2tXQAAJfLwdtjmlf7HANGNUPZnNpxxx7hwa0sbYZICCH1AiXQWubgn3dk236tHWFhJaj2OVwbWSk0++78/bpWYiOEkPqEEmgtwhjDwZ3lCXTQOM1n0RgwsplsO3LrbYjFNKSFEEKqgxJoLXL1/HM8Sc4FAJgIuOgQ7q7xuVp3coGji3R1i5xsEU5TZyJCCKkWSqC1SNTue7LttqENIRBqvlA2j8dFj7fLF+LdszmpRrERQkh9Qwm0lmCMIeZAsux1/5G+NT5n70E+su3YQ8nISi+s8TkJIaS+oARaSzy4lYVH918BAHh8DoI6KK/3V12NGtugRZB0kvnSEoZ//1F/PUJC6pLx48eDw+GoXNDbULy8vGSLVhPjRAtq1xInDibLtpu3cqhR86288IGNkXRFOizm4J938M4U1RPSk9dc/S9QkKqbc4vSy7eFGi47Z+4BBH5d41C8vLyQkqL6+bizszOeP3+usE8sFuPrr7/Gtm3bkJmZiTZt2mD58uVo06aN0vHR0dHo3bs3Dhw4gDfffLPGsdY23bt3R2xsLKqxoiQxMpRAa4kTBx/KtnsN9qmkZPV06eOBnxdeAGNA/JmnePk8H44uFlo7f51VkApYeOnm3Fy5oUlmDTU7R36yVkIBABsbG3z00UdK+y0tLZX2zZ49GytXrkRERATc3d2xZcsWhIWF4datW3B1LW81ycvLw+TJkzF69GijSJ6LFi3C7Nmz4eZW9ZzS+nLs2DFDh0CqQAm0FshKL8SVuPK/9Dv39NDauRs4mSOgrTOuX3wBxoCjex9gxLSWWjs/qf1sbW0xf/78KssxxvDbb7/h3Xffxfr16wEAgwYNQvfu3bFlyxZ89tlnsrKff/45ioqKsGrVKl2FXS2urq4KCd4Y+Pho7w9lohv0DLQWiDv2SDbpu6uHFWwbmGr1/N36ecq25SdqIKQ6Xr58iYKCAoSEhMj2lW3LNwOfPHkSv/zyC3766SfY29tr9FllzwdzcnLw8ccfw8vLCyYmJgqJ/tatWxg/fjwaNWoEoVAIZ2dnjBw5ErdvK69CVNkz0PPnz2PIkCFwcXGBQCBAo0aNMHXqVDx9+lRlbJmZmfjqq68QEBAAc3Nz2NjYIDAwELNnz0Z+fj6Sk5PB4XAQGxsLQLrofdlX9+7dler4OpFIhMWLF6NVq1YwNzeHtbU1unTpgr/++kupbNlnjR8/HsnJyRg+fDgcHBxgamqKtm3b4sCBA5V/o1+zd+9ejB49Gr6+vrCwsIClpSWCg4OxevVqSCQSpfJl39cHDx5gzZo1aNWqFczMzBTqmZmZiS+++AItWrSAmZkZbGxs0KNHD/z7779K53v16hWWLl2K8PBwuLu7QyAQwNHREQMHDsS5c+eqVRdtoDvQWiDu2CPZdude6k8cr64ufTzx09fSZtwrcc+QkVaABk7mWv8cUjuJRCJs3boVqampsLCwQKtWrdC1a1fweIrP4R0dHWFmZobLly/L9l26dAkA4Okp/SOtsLAQkyZNQkREBCIiImoUl1gsRnh4ODIzM9G7d29YW1vD29sbAHDkyBEMHjwYxcXFGDBgAJo0aYLHjx/jn3/+wcGDBxETE6PyuezrNmzYgMmTJ0MoFGLgwIFo1KgR7t69i3Xr1mH//v04d+4cPDzKW4QePnyIsLAwpKSkIDg4GP/5z38gkUhw584drFy5EtOmTYOtrS3mzZuHjRs3IiUlBfPmzZMdX1WnIbFYjD59+iA2NhbNmzfHe++9h4KCAvz999945513cOXKFXz33XdKx6WkpCAkJASNGzfGmDFjkJmZiZ07d+Ktt97C0aNHERYWptb3fPbs2eByuWjfvj3c3Nzw6tUrHD9+HDNmzMDFixexZcsWlcfNmDEDp06dwptvvol+/frJ/u2kpKSge/fuSE5ORpcuXdC3b1/k5+fjwIED6Nu3L3777TdMnjxZdp6kpCR89dVX6Nq1K958803Y2dkhNTUVkZGROHz4MPYvX46+nTqpVRdtoARq5BhjOCuXQEN7a6/5toy9oxkCgp1w/VIaGANOHk7GoHF+Wv8cUjs9f/4cY8aMUdjn7e2NDRs2oFu3brJ9HA4HU6ZMwerVq/Hq1Su4ublhy5YtsLKywqhRowAAc+bMQWZmJn766acax/Xs2TP4+fkhNjYWFhblz+2zsrIwYsQImJub4+TJk/DzK/+3fPPmTbRv3x6TJk1CfHx8pee/c+cOpk6dCi8vL8TGxio8Hz1+/Dh69eqFGTNmYM+ePbL9o0ePRkpKCr777jt88cUXCudLT0+HpaUlTE1NMX/+fJw4cQIpKSlqNY+XWb58OWJjY/HGG28gMjISfL70V/i8efMQEhKCRYsWoX///uj0WhI5ceIE5s+fr5CsR44cib59+2Lp0qVqJ9CDBw8qNS1LJBK8++672Lx5M95//320b99e6bj4+HgkJCTI/sApM27cOKSkpGDHjh0YPny4bH92dja6d++ODz/8EAMHDoSzszMAoEWLFnj69CkcHBQ71z1+/BghISGYuXKlXhMoNeEauZS72Xj+KA8AwDfhollLDXtlVkF+VqOjex/o5DNI7fPuu+/i2LFjeP78OfLz83H9+nVMnToVycnJeOONN3D1quJqPt9//z0+//xzXLhwAX/88Qf8/f1x7NgxuLm54fz58/jhhx+watUqODg4YMGCBXB1dQWfz0dwcDDOnDlT7fiWL1+ukDwBYPPmzcjOzsaCBQsUkicA+Pv7Y/LkyUhISEBiYiIq88svv6C4uBirVq1S6lwUHh6OgQMHYv/+/cjNlc4OdvnyZZw9exZBQUH4/PPPlc5X1nRaE+vXrweHw8GKFStkyRMAnJycMHfuXADAunXrlI7z9PTEnDlzFPb16dMHHh4euHDhgtqfr+q5LJfLxYwZMwAAUVFRKo/77LPPlJLn1atXERsbi4iICIXkCUifuy9YsABFRUXYvXu3bL+NjY1S8gQAd3d3DBkyBLeSk5H6Ws9wXaI7UCMXd/yxbLtJC3vwTXTzN0/HHo2wdon0L/KzRx9BVFQCoSn986jv5O9YACAgIAC//vorLC0tsXz5csyfP1/hDkwoFGLRokVYtGiRwnFisRgTJkxAv379MGrUKPzwww+yO6LOnTvj22+/Rd++fXHv3j3Z3UZVTE1N0apVK6X9cXFxAKS/oFXd3d25I33On5SUpJRgVZ0nNjYWFy9eVHo/LS0NpaWluHPnDoKDg2XP4Pr06QMuV/vXaW5uLu7duwc3Nzc0b668ClN4eDgAICEhQem9oKAgpSZ3AGjUqJGsnurIyMjA0qVLcejQITx48AD5+fkK7z958kTlcfLPxcuUfe6rV69U/pxevnwJQPpzknfmzBmsWrUKcXFxSEtLg1gsVowhLQ0eLi5q16km6DekkYs7Wt5826WvZyUla6ZRYxu4e1vj8cMciEWlOB/zGF3f8NLZ55Habdq0aVi+fDlOnjypVvkFCxbgyZMnso4hS5cuRY8ePWS/OJs1awYvLy/89NNP+Ppr9cavOjk5gcNRXgs3IyMDALB27dpKj8/Ly6v0/bLzLF26VK3zZGdnA4DOhsK8eiWdSKWi3sJl+8vikGdra6vyGD6fr7LzjyrZ2dlo164dHj58iJCQEIwdOxb29vbg8/nIzs7GqlWrIBKJVB7roiKhlX1/o6OjER0dXeHnyv+c9uzZgyFDhsDU1BS9evWCj48PLCwswOVyceLECcTGxkJUXKxWfbSBEqgRKy2V4EJs+R1o+zDdjlHrEO6Ov/+QNmsdi3xACZRUyMnJCQCU7kBUSUhIwJIlS/Drr7/Czc0NOTk5ePr0qey5KAB4eHjAwcEBN2/erORMilQlT0DazAdI70BV3aGqq+w8r169grW1dZXly5JURXdhNVUWz+uTV5R59uyZQjltW7duHR4+fIh58+Yp3THGxcVVOiRJ1c+qLM5Vq1bhww8/VCuGuXPnQiAQ4NKlS2jRQnE1qqlTp8p6NusLPQM1YneuZyD3lbR5wsycDw8f3VwYZTqGl/fwPbr3Ac2QQipU1vzWuHHjSsuVlJRgwoQJCAsLw8SJExXee/1upaioSCuxdejQAQBw6tQpvZ6nrHxUVJRad3VlTaqlpeotJWhlZQUfHx88efIEd+/eVXo/JiYGANTqXayJe/eki1mo6j2tSeLS5Od07949+Pn5KSVPiUSC06dPVzuGmqIEasQuny4fZ+bbskGFf3Fri38bR1jZSmfByc4oQmLCS51+HjFuN2/eRGZmptL+lJQUvP/++wCkvU4rs2jRIty7d0+hOdXa2hpubm44cuQISkpKAEh/Aefm5sLf37/Gcb/77ruyTiiqOshIJBKcOHGiyvO8//77MDExwcyZM2XPTeWJxWKFX/7BwcHo1KkTrly5gu+//16pfEZGhsIfCQ0aNAAApKaqPyXkhAkTwBjDp59+qpB409PTsXDhQlkZXSgbYvP69y4hIUHpmbc62rZtiy5duuCff/6RTbzxuuvXryMtLU0hhrt37yqMwWWMYcGCBVV2CtMFasI1YpdOlf8j6dhD++M/X8fjcxHSzR3H9kl74Z448BD+bZx0/rnEOO3atQuLFy9GWFgYvL29YWVlhfv37+PgwYMoKipCv379MGvWrAqPv3nzJr755husWLFCNg60zGeffYYZM2agS5cuaN++PbZt2wZLS0u89957NY67QYMG+PvvvzFo0CB06NABPXr0gL+/P7hcLlJTUxEXF6eUzFRp3rw51q9fjwkTJsDf3x99+/aFr68viouLkZqailOnTsHR0RG3bt2SHbN161Z0794dX375JXbv3o3u3buDMYa7d+/i33//xa1bt2SJqEePHti1axcGDx6Mfv36wczMDJ6enkpDhuTNmjULhw8fxr59+xAYGIh+/fqhoKAAu3btQlpaGj777DOEhobW+HuoytixY7F06VJ89NFHiImJQdOmTXH37l0cOHAAgwcPxs6dO6t9zu3btyM8PBwTJ07E6tWr0b59e9ja2uLx48e4du0abty4gbi4ONkjg5kzZ2LatGlo3bo1IiIiYGJigjNnziAxMREDBgzA/v37tV3tSlECNVKMMYU70Dad9TPNWMce5Qk06p/7eO+/ymO6SP0QFhaG27dvIyEhAXFxccjPz4etrS1CQ0MxZswYjBkzpsJWkdLSUkyYMAHt27fH9OnTld7/4IMPkJOTg19//RXx8fEIDAzEypUr1e6BW5UePXrg2rVrWLZsGaKionDq1CkIBAI0bNgQ4eHhak/iMHr0aAQGBmL58uWIiYnBv//+CwsLCzRs2BBDhgzBO++8o1De29sb8fHxWLJkCfbu3Ysff/wRpqam8PLywieffCJLBAAwadIkpKSk4M8//8SSJUtQUlKCbt26VZpABQIBoqOjsWLFCmzfvh1r1qwBn89HYGAgfvjhB4wYMUKzb5gaGjZsiFOnTmH27Nk4ffo0oqKi0Lx5c/z888/o2bOnRgnU3d0dly9fxpo1a7B7925s27YNpaWlcHFxgZ+fHz744AO0bFk+tejUqVMhFArxww8/YNOmTTAzM0OXLl2wYcMG7N69W+8JlMOq8aCrbdu2splFiG49uJWFAa22AQAEQh72XxsJLle3TbgAkJ8rxpD2f6GkWPoM5+j9cXBtZKXzzzU2SUlJSs9ZFNST1Vjqi+HDh2Pnzp14+vSp0c2JS6pBPj+1bVtp0SqvcTXQHaiRunS6vCefTws7vSRPALCwEqBViDPiz0h79MUeSsbwqTS5vBJdJqcMuV8CDSr/JUC0486dOxAKhXB0dDR0KKQWoU5ERuqy/PPPcN0//5TXUW5WopgDDyspSUjttmrVKkRERCAhIQFvvfWWwuw+hFSFEqiRunrhhWw7qKN+ZtUo065b+XjTCyeeQFRUotfPJ0RffvjhB5w7dw4TJkzAb7/9ZuhwSC1Df24ZocyXhXh0XzrrCJfLQRM/zZZ80pSbpzXcvKzwJDkXYlEpLp16is69tD+JPSGG9vAhtbAQzdEdqBG6dqF8phFXD0uYCJTnsNS1dl3L70JPHUmppCQhhNRPlECN0NXz5c23Ldtpp1t/dYXINeMe309/pRNCyOsogRoh+TvQYD2N/3xdqxBnCE2ld75PknPw6MErg8RBCCHGihKokSktleD6xfI7UL/WhulWLzTlI7B9eeelU1HUjEsIIfIogRqZB0lZyM+VLsdjbmkCR1eLKo7QHfneuLGHkg0WByGEGCNKoEbmqlzzbRM/e51PIF+ZELmOROdjHtNwFkIIkUMJ1Mhck+tAFBxq2CnFGnpawd1bug5isViiMLk9IYTUd5RAjczV8+V3oAHBhumBK09hOAs9ByWEEBlKoEYk95UI95Ok6y9yOEDTAP1OoKBKu24NZdsxkTSchRBCylACNSI3LqWhbG0cx4YWMDM3MWxAAAJDXGTDWR7TcBZCCJGhBGpE5Jtv/Vsbx0LWAiEPQR1oOEt916tXL3A4HMTGxlZYZtq0aeBwOFi7dq0eIyPEcCiBGhGFCRS6GM+ahPLDWU4epgRaH8XHx4PH4yE4OLjCMufPnwcgXTeYkPqAEqiRYIwpTOFnLHegAA1nqe/u37+PzMxM+Pv7w9LSUmWZgoIC3LhxA0KhEAEBAXqOkBDDoARqJB49yEF2RhEAabOpm5eVgSMq5+pRPpxFLCrFxZNPqjiC1CWXLkkX+G7fvn2FZS5fvoySkhIEBgbCxMTwz+4J0QdKoEbi5uU02XYjH2uDTqCgivzk8qeiUg0YCdE3dRLouXPnAFDzLalfaD1QI3EzvjyBBgQbT/NtmXZd3fDPxiQAQOzBh/hieRcDR0T0pSyB7ty5EzExMSrL0PNPUh9RAjUS8gk0MMSlkpKG0SrEGaZmfBQVluDRgxyk3n8FDx8bQ4dFdIwxhoSEBABAdHR0leUpgZL6hBKoEWCMISnhpey1b8sGBoxGNYGQh8D2zjh/Qvr881RUCkZNb2XgqAzDX/CjHj/tXI2Ovil+v0bH37lzB69evULHjh1x9uxZlWWysrJgb28Pc3Nz+Pn5Kbz3888/Y+nSpXj27Bn8/f3xww8/oEsXar0gdQM9AzUCqfdfIfeVGAAgNOXBqaHhVmCpTEh3ueegR2g4S31Q1nzbunXrCsvEx8cDAIKCgsDj8WT7d+7ciRkzZuDLL79EQkICOnXqhDfeeAOpqfQMndQNlECNQGJ8+d2nh4+N0XUgKtOOhrPUO2UJtE2bNhWWKUugrzffrlixAuPHj8fkyZPRokULrFmzBq6urvjll190FzAhekRNuEZAoQNRW+PrQFTGtZEVGjW2xqMHObLhLKG9PQ0dlt7VtFm0ShmXyrcbGPaZojoJtOwZqXwCFYvFuHz5MmbNmqVQtnfv3hU2BRNS29AdqBFIlHv+2bKt4VdgqYzC6izUjFunSSQSXLlyBQKBAP7+/hWWU3UHmp6ejtLSUjg7K/57dnZ2xvPnz0FIXUAJ1MBe70DUNMD4OhDJkx8PGnso2XCBEJ1LSkpCXl4e/P39IRAIVJbJy8vD3bt3YWlpiWbNmim9//rjCMaY0T6iIKS6KIEa2KMHOcjJFgGQdiBydjPODkRlWraTDmcBIBvOQuqmy5cvA6i8+fbq1auQSCRo06YNuNzyXycODg7g8XhKd5tpaWlKd6WE1FaUQA1MfgYiY+5AVIZWZ6k/xo4dC8YY1q1bV2GZzp07gzGmtEqLQCBAcHCw0tjR6OhodOrUSSfxEqJvlEANrLZ0IJInv8g2PQclFfn444+xceNGrFu3DklJSZgxYwaePn2KadOmGTo0QrSCeuEaWG3qQFTm9eEsRYUlsmZdQsq88847yMjIwDfffINnz54hICAAhw4dgqdn/eu5TeomugM1oNrWgaiMdDiLdBo/Wp2FVGb69OlITk6GSCTC5cuX0bVrV0OHRIjWUAI1oNrWgUheiFwz7ml6DkoIqYcogRpQYrz8EmbG34FInnwzLg1nIYTUR5RADeim3BR+LY1wCbPKvD6cJeVetmEDIoQQPaMEakCJCfI9cGtHB6IyAiEPQR3Lh7NQMy4hpL6hBGogjDGFSeSbBtgbMBrNhMg1456k4SyEkHqGEqiByHcgEgh5cHG3NHBE1deum/JwFkIIqS8ogRqIYgci61rVgaiMi7ulbDhLsVhS54azMMYMHQIhRAe0dW1TAjUQxQ5Etev5p7y6usg2j8dDcXGxocMghOhAcXGxwuLvmqIEaiDyHYhatqvFCbRr+XjQmAMPDRiJdllZWSEnJ8fQYRBCdCAnJwdWVlY1Pg8lUANgjClM4VcbOxCVCWjrDFNz6XCWpym5dWY4i729PbKyspCeng6xWEzNuYTUcowxiMVipKenIysrC/b2Nf+9SxOYGsDjhznIyardHYjKlK3Ocu74YwDS4SyeTWwNG5QWCIVCeHh4IDMzE8nJySgtLdXfh+enl2+nJenvcwmp7dLlrp0k5WuHx+PBysoKHh4eEAqFNf44SqAGID98xd27dnYgkhfSzU2WQI/ufYBR7wUaOCLtEAqFcHV1haurq34/eLtf+fZIuvMlRG1+cteOHlqNqAnXAOSXMPNv42jASLSjY3gj2falU0+RnVFowGgIIUQ/KIEagPzzz8D2LpWUrB0cXMzRIsgBACCRsDrVmYgQQipCCVTPGGMKd6C+tWQJs6p06VO+xmPU3/cNGAkhhOgHJVA9e5Kcq9iBqFHt7UAkL7SPh2w77tgj5OWIDRgNIYToHiVQPZO/+3TztKr1HYjKuDayQhM/abfwkhIJThykZlxCSN1GCVTP5Kfw86sDHYjkyd+F/vsPNeMSQuo2SqB6Jt+BKKhD7e9AJE/+OeipIykoyKep8AghdRclUD16fQYi3wAHA0ajfR4+NvBsIp1cXiwqpTVCCSF1GiVQPXqWmovsjCIAgImAC1ePutGBSF6o3F0oNeMSQuoySqB6JL8CS0OPutOBSF4XueegMQceQlREa4QSQuomSqB6JN+BqEVQ3epAVKZxczs09JCuclBUUIITB5MNGxAhhOgIJVA9uin3/LN1p7rVgagMh8NB+EBv2evIbbcMGA0hhOgOJVA9YYwp3IH6tqxbHYjk9XirsWz71JEUmhuXEFInUQLVk2eP8pCVLu1AxDfhypo56yJ3L2s0D5T+gVBawnB41z0DR0QIIdpHCVRP5O8+G3pYgcutex2I5MnfhUZupWZcQkjdQwlUT+R74DYPqrvNt2XC3vQCjy/9I+HahRdIuZdt2IAIIUTLKIHqSWJC+R1o6451swORPBt7U7Tr6iZ7fXDHHQNGQwgh2kcJVA+kHYjk7kBb1c0hLK/rKdeMu2dzEpgeVognhBB9oQSqBy+e5CPzpbQnKt+Ei4aedbcDkbwO4e4wtzQBADxNycXV888NHBEhhGgPJVA9kO9A5NrIss53ICojNOWja9/yqf32bEwyYDSEEKJdlED1QKEDUWD9aL4t03uwj2x7/47btNA2IaTOoASqB/IdiNrU0RmIKhLQ1gleTW0BAKLCUpqZiBBSZ1AC1THGmMIdaF2egUgVDoeD/iN9Za93/HKdOhMRQuoESqA6lvY0HxkvCgBIOxC5e1sbOCL96/lWY5ia8wEAD25lIf7MMwNHRAghNUcJVMfk7z7rUwcieRZWAvQYWD6k5c/frhswGkII0Q5KoDpWH5YwU8cAuWbcqN33kJFWYMBoCCGk5iiB6tj1Sy9k20Ed6lcHInk+Lezh11r6B0RpCcM/GxINHBEhhNQMJVAdYozh+sXyBNqidf29AwWg2Jno1+soKZEYMBpCCKkZSqA6lHr/FV5ligAAAiEPbvVkBqKKdHvDC9Z2QgDS2ZmO7rlv4IgIIURzlEB1SP7u08PHBhxO/etAJE8g5GHAyGay1+uWxdOQFkJIrUUJVIfkE2irEGcDRmI83hrTDAIhDwCQlPASF08+MXBEhBCiGUqgOiSfQNt0cjVgJMbDroEZeg0qn95v3ZJ4A0ZDCCGaowSqI2JxKZKupMteN2tVv2YgqsyQiX4oa80+E52q0FOZEEJqC0qgOnLnejrEolIAgJWNALYNTA0ckfFw97JG9ze9ZK9//fai4YIhhBANUQLVkWsXyu+qvJvZGTAS4zRyeivZXeiJg8kKE+4TQkhtQAlUR+Sff7bt0tCAkRgnr6a2CO1TvlbomvnnDRgNIYRUHyVQHbl+gXrgVmXMB+V3oScPp+Dy6aeGDYgQQqqBEqgO5GSL8PBONgCAwwGa+NkbNiAj5e1rhx5vlU8yv+KrszQulBBSa1AC1YEbcr1KHV0tIDTlGzAa4zb2w0DwTaT/DK/EPcfRvQ8MHBEhhKiHEqgOXL9Y3iGmeSANX6mMayMr9B9RPkfu0s9Py3ovE0KIMaMEqgPXLjyXbbfrSh2IqjLm/VawshEAAJ4k52Lz6iuGDYgQQtRACVTLGGO4fonWAK0OaztTjJ0RJHv9y7cX8exRruECIoQQNVAC1bLHD3OQ8UK6WLSJgItGjW0MHFHtMGCEL7ya2gIAigpKsGjmKcMGRAghVaAEqmXyQzG8mtqCy63fK7Coi8fnYsbX7WWvj0U+wPH9Dw0YESGEVI4SqJbFn30m2w6mCRSqJaCtM/oObSJ7vWB6DLIziwwYESGEVIwSqJYlnClPoO26uBkwktpp8qdtYOcgnTc4/UUBFs08aeCICCFENUqgWpT5shAPbmcBADhcGsKiCWs7U3y0sIPs9YEddxD19z0DRkQIIapRAtWiBLnmWxd3S9nC0aR6OvX0QM+3y2co+u+043iSnGPAiAghRBklUC2KP1vegSgwxMWAkdR+7/83BC7ulgCAvBwxZo2OglhMEywQQowHJVAtuny6/A60Yw93A0ZS+1lYCfDlyi7g8aW9mK9deIHFH9PQFkKI8aAEqiUF+cVISngpex3QllZgqakWQY6YOKuN7PXO32/gr3U3DBgRIYSUowSqJdcvvkBJiQQAYOdgCmtboYEjqhuGTPBDWH8v2etvPozFmehUwwVECCH/RwlUS+QnUKDp+7SHw+Hg4+86yZaEKy1h+GjYYSQmpFVxJCGE6BYlUC2R74HbqWcjA0ZS95ia8bHw93A4upoDkDaXT+y7T6HJnBBC9I0SqBaUlEhw5Vz5CixBHagHrrY5OJvj23U9YGktXbUlJ0uECX330p0oIcRgKIFqwe1r6SjIKwYAmFuawKmhhYEjqpu8fe3w/aZeCkl0Yt99uBlPSZQQon+UQLVA/vmnb0ADcDg0gbyu+AY0wJLNikn03V57EHso2bCBEULqHUqgWnDhxBPZNo3/1L2m/opJND+3GO8NOoD1y+PBGDNwdISQ+oISaA2VlEhw8WR5Am3XlSaQ14em/g2wYnsfOLlKm8sZA5Z/cRZfTjyKosISA0dHCKkPKIHWUGLCS+TliAEAZhYmcPe2NnBE9Yd3Mzv8uKcfAoKdZPsit97G0JCduH7phQEjI4TUB5RAa+j88Ueybf82jvT8U8/sGpjh+0290HdI+TqiD25nYVSXv/HjgvMoLqb5cwkhukEJtIbOyz3/7PqGpwEjqb8EQh4+/q4jZn7bEaZmfABAaSnDL99exLD2f+FC7GMDR0gIqYsogdaAqKgE8WfKe+AGhzY0YDT1G4fDQb9hTfH7gQEIaFvepHvnRgbe7bUXM4cfxtMUWhKNEKI9lEBrICHuOURF0iZCGzuhrEMLMRxXDyss29obU2YHQ2Bavh7rv//cR/+W2/D9p6eQ/qLAgBESQuoKSqA1cObf8knNA2n2IaPB43ExdKI/Nv77NsIGeMn2i4pKsXnVVfTx3Yxls88g82Wh4YIkhNR6lEBr4PS/KbLtHm95GzASooqjqwW+XNEVK3f0QVN/e9n+osISbFiRgN6+m/HDnDhkZ1AiJYRUHyVQDaU9zcOd6xkAAA4HaN3B1cARkYoEtHXGT3vexIJfw+DtayvbX5hfjLVLLqNXU2kizUqnREoIUR8lUA2dOVo+fMXDxwZmFiYGjIZUhcPhoFOPRvh1/wD898du8GhiI3uvIE+aSHv7bsbKr85SIiWEqIUSqIbORJU//+zal4av1BZcLgdd+nhi7cGBmLOqKzx8FBPpuqXx6NV0M1Z8eZaekRJCKkUJVAMlJRKcOVqeQEP7UAKtbbhcDrr188LaQwMxZ3VXeMrdkRbmF+OPZfHo7bsZP319Hvl5YgNGSggxVpRANXAl7hlyskQAADMLPryb2Ro2IKIxLpeDbm944feDAzF3dTd4NbWVvVeYX4yfv7mIfn5bseuPmygpkRguUEKI0aEEqoGYAw9l2227NKTp++oALpeDrm944rcDA/DfNd0U7kjTnxdg/n9iMDRkJy7LTZxBCKnfKIFWE2MMx/eXJ1D5OVhJ7cflctClrzSRzlrcCfaOZrL37tzIwNiwf/DVpKPISKPJGAip7yiBVtODW1lIvfcKAMDjcxBEw1fqJB6Piz4RTbDp2CCM/yhIYVajvZtvoX/ANuzdnETrjxJSj1ECrSb55tvmgQ4QCHmVlCa1nakZH6Pea4UNUW8htLeHbH9OtghfTTqG9wYdQNrTPANGSAgxFEqg1XR07wPZdp/B1HxbXzg1tMS8n7rj23U94OxWPudx7KEUvBW0g+5GCamHKIFWw5PkHFy/KF2omcMBOsvdkZD6IaSbG9YeGoi3xzSX7aO7UULqJ0qg1fDvP/dk2z4t7GFtKzRgNMRQzMxN8N5/Q7Bsa2+4NLKU7Y89lIK32+xA1O57lRxNCKkrKIFWQ9Tu+7LtN0f4GjASYgwC27vg9wMDFO5GX2WK8PGII/hiQjRyX4kMGB0hRNcogarp9ebbbm/Q7ENE8W7U0dVctj9y620MbvsnLp+mcaOE1FWUQNV06K+7sm0fP3tY2VDzLSkX2N4Faw8ORI+3Gsv2PU3Jxbge/2DFl2chFpcaMDpCiC5QAlUDYwyRW27JXg8Y2cyA0RBjZWElwOxloZizqissrAUAAMaAP5bFY0TnXbh3M8PAERJCtIkSqBquX0rDg9tZAKSTJ4T19zJsQMSodevnhXUHB6JN5/JJNm5dTcfQDn9hy5qrkEhouAshdQElUDXsk7v7bNPJFWbmtPYnqZyDizkWre+J6XPagW8ivczEolIs/uQUprwZiRdPaLgLIbUdJdAqiEWlOLTzjuz10En+BoyG1CZcLgeDxrXAr/v6o3FzO9n+uGOP8HabHTiy624lRxNCjB0l0Cr8+8892dJlFlYmCGzvYuCISG3j2dQWP+7uh+FTA1C2cE9OlgifjIrCrFFRePk837ABEkI0Qgm0Cn/+dkO2/cbQpuByaekyUn0mAh4mzmqD5dv6wKlh+VSAh3fdxcBW27Hrj5v0bJSQWoYSaCVuX0tHwtlnAKRjP6n5ltRUy3bO+P3AAPSJ8JHty8kWYf5/YjCuxz9IuvLSgNERQqqDEmgldv5efvcZ0NZJYW1IQjRlYSXArMWd8f3GngpTAcafeYah7Xdi3obhyMixrOQMhBBjQAm0AtkZhYjcWt77duyHQYYLhtRJbTo3xLpDAzF8WgC4POmjAcaAv2M7o9/n/8XaA71QkF9s4CgJIRWhBFqB7b9cR2FBCQDA3skMge2dDRwRqYuEpnxM/KQN1h4ciHZdG8r25xWa4Ye/B6Jvs83YsuYqREUlBoySEKIKJVAVCguKsf3na7LX4z8KAodDnYeI7nj42OC7P3rim7Xh8HZ5IdufkVaIxZ+cQt/mW7BhRTzycsQGjJIQIo8SqAr/bEhEVnoRAOnQld6DfKo4ghDtaN/dHXu++Q4L3t0OF/ss2f60p/lYNvssejTeiOVfnKV1RwkxApRAX1NYUIy1Sy7LXg+d5A8en75NRH9M+BIM6RaHQ4u/xszp2bCzLW++zcsRY/3yePRquhlfTjyK65degDEa/kKIIVBmeM32n6/j5bMCAICpOR9DJvgZOCJSXwkFJXgnIh97d7zAFx9noZFbefNtSbEE+7bcwvBOuzCsw1/4a90N5OdR8y4h+kQJVE7uKxHWLS2/+xz9fisITfkGjIgQQCgA3nqzADs3vsSSrzPQ0q9I4f3EhJdYMP0Ewjw34OsPTuDGZborJUQfKDvI+fmbC7Jp+yytBRg8roWBIyKkHJcLdO1chK6di3AjyQR7Ii0QHWMGcbH07+D83GLs/O0Gdv52A0387PH22OboP7IZHF0sqjgzIUQTHFaNP1Xbtm2LS5cu6TIeg7lzPR1DQnaitFT67Zi1qBP6DGli4KhIfdTwjI1sO7fR7ErL5uRycPhfc+yONEPqY+VF3nk8DkL7eOCtMS0Q1t8bAiFP6/ESYjTkR0vooRWGEigAiYTh3V57cOnUUwBAo8bW+OPIWzR0hRhEdRJoGcaAhGsCHDhsjmOxphCJlROltZ0Q/d7xxdtjmyMg2In+fZO6hxKo/m376Sq+m3lK9vq3AwPQuJldJUcQojuaJFB5+QUcxJw0w4EjprhyXfX0kz4tpE28A0Y2g6MrNfGSOoISqH49uJWFISF/QlRUCgDoO6QJPlnUycBRkfqspglU3tNnPByKNseBw6Z4niZQep/L5aBTr0YYOKo5wgd602LxpHajBKo/hQXFGN1tN25dTQcA2DuaYUvMYHpORAxKmwm0jEQCXLkuwMEj0ibeIpHyv3ELKxP0iWiCgaObIzi0IS3dR2ofSqD6wRjD7PHROLDjDgDp9/2nvW+iqV8DA0dG6jtdJFB5BYXlTbwJ11Q38Tb0tMKAkc0wcFQzePnS4wxSS1AC1Y/fFl3C6nnnZK8nzmqN4VNbGjAiQqR0nUDlPX3OQ9QxMxw4bIonz5R78QJAYHtnDBzdHH2HNoWtvalO4yGkRiiB6t6fv13Hwg9iZa87hLlj4e/hBoyIkHL6TKBlGANu3jLBoSgz/HvcDHn5ykPETQRcdOvnhbdGN0doX08IBPSogxgZSqC6tfXHq1j0cXmPW88mNvh5b3967kmMhiESqDyxGDh7wRQHj5gi7oIZSkqVJyyzbWCKHm81Ru/BPmgf5g4TE7p+iBGgBKobpaUSrJp7Dn8si5ftc3azwNqDA2FmQT0PifEwdAKVl/2Ki6MxZjhwRIhbd1U/L7W2FSJsgDd6DfJBxx6NYGpGE5wRA6EEqn3pLwrw+bh/ce74Y9k+10aW+PGfN2Ftq/q5DyGGYkwJVF5yKh+Ho81xKEqIlxnKQ2IAQCDkoW3XhujcywOhvT3h08KOJmwg+kMJVHskEobIrbfw/aenZXPcAkATf3us3NGX/lImRslYE2gZiQS4mSTA8VhTHIsVIi1ddTIFABd3S7Tt0hCt2rugVYgzmrVyoGenRHcogdYcYwyno1Kxet45JCa8VHiv/whfvD8vBDweLURDjJOxJ1B5jAFJt01w/KQZTp4WIPVJ5S06AiEPfq0d0TzIEd6+tvDytYW3rx1cPaxo3CmpOUqgmstIK8Dhv+7ir7U3cT8pU+E9c0sTfLmiC9qHuRsoOkLUU5sS6Ouev+Dh3CUh4s4JcClBiPxC9Vp5hKY8uHlZw8HFHI4uFnBwMYeDsznsHM1gYWkCc0sTWFgJYG4h3Ta3NIGpOR+mZnz6Y5iU03MCrdVtmDnZIty49ALXLrzAmehUXIl7DolE8ZvG4XLQJ8IH0+e0o2nKCNExF+dSvP1mAd5+swAlpcCtOya4mSjAtRt83Eji48VL1eNIRUWleHArCw9uZVX7M/l8LoRmPAhN+RCa8SEU8iAw5cHUjA+hKQ8CUz5MTfmyfQJTHoSmvP/v48PUjAehGR/mFtIkbWH1//9bmsDcSvD/5G1CPY2JEqNNoGJRKbIzCpGdUYRXWSJkZxQh7WkeHj3MwaP7r5B8JxvJd7MrPJ7H56BrX09M+bwtHFzM9Rc4IQQAwOcBAS2KEdCiGO9ESPdlZHKRdFuAhyl8JKdwkJzMxaOnAuTkVfwctSolJRKU5EqQn1uspchVEwh5suRqLndXbGFZnnjNLcsTroWlXDK2MpHePcslaD6f7pxrO4Mk0J2/30DktlsoFpVCLCqF6P//LxaVQlQk3RaLSjU6t5unFQaOaoY+Q5rAwkrzi5IQon0N7CUI7ViE0I6K+1/lcJD2kofMLB4yMrnIyOQhPZ2DzEygoIChoJCDwkIOCou4KCriolDEg0jMhbiYB8b08+y07PdSVnqRVs4nNOXBRMAD34QLHp8LPp8DHv//2zy5bbn9XC4HHM7/Wyo5ZduK/8dr+waMbIY3h/tqJWaiyCAJ9PnjPFyJe66Vc1nbCuDZ1BaB7V0QEOwEq/8PS3mSkquV8xOibw3ltrNfpBksDn1ztJR+oZH6xzAGlJRyIBZzIC7myv2fC3Hxa9sq9onE0mMKi4DCAg7yC7n/T9Q8FBXxUCjioUjER6GID8a0e8coKiqVrQKlS4HtXXT+GfVVtToROTg4wMvLS2sffvt6OkqKJUr7S1keeBxLpf1cLgd8E26t6a0nKsmFkG9l6DC0guqiX6YmRTATVH6nk1sogpVZ3RjHXDfqIkFOoQjWpiZqd2CRlWIcqDqCyf6j4e88Lh+OXt4aHfry5Us4Ojpq9rlGpKb1cHBwwJEjR1S+V60Eqi/G3ttXXXWlHgDVxRjVlXoAdacudaUeQN2piy7rQU+xCSGEEA1QAiWEEEI0YJQJdMqUKYYOQSvqSj0Aqosxqiv1AOpOXepKPYC6Uxdd1sMon4ESQgghxs4o70AJIYQQY6eXBPr3338jIiICnp6eMDMzQ7NmzfDFF18gN7d6YzUXLVoEDoeD0NBQHUVatZrWJSkpCUOHDoWDg4Ps+FWrVuk4amU1qUdqairGjRsHDw8PmJubw9fXF3PmzEF+fr4eIlcWFRWF8PBwuLi4QCgUwt3dHcOGDUNiYmKVxxYVFeHTTz+Fq6srzMzM0LFjR5w8eVIPUaumaV0uXbqEKVOmoHnz5jA3N4eHhwdGjRqFhw8f6ilyRTX5mcgzhmu+pnUxlmu+JvUwtmv+dX379gWHw8GcOXOqLKvNa14vEyksW7YMHh4e+O677+Du7o6EhATMnz8fMTExOHv2LLjcqvP4gwcP8O2338LJyUkPEVesJnW5dOkSwsPD0b17d6xbtw42Nja4e/cu8vLy9FgDKU3rkZ+fj549e6K4uBgLFy6Eh4cHLl68iHnz5uHu3bvYuXOnnmsCZGZmIjg4GNOnT4ejoyNSU1OxePFidOjQAdevX4enp2eFx06cOBEHDx7E0qVL0bhxY/z000/o06cP4uLiEBQUpL9K/J+mdfnzzz9x8+ZNfPjhh/D398eTJ0+wcOFCtG3bFleuXEGjRtWYncCA9ZBnLNd8TepiTNe8pvUwxmte3o4dO3D16lW1y2v1mmd6kJaWprRv06ZNDAA7duyYWufo3bs3mzJlCuvWrRvr3LmztkNUm6Z1KS0tZX5+fuztt9/WZXhq07QeUVFRDACLiopS2P/5558zHo/H8vPztR6rJm7dusUAsGXLllVY5sqVKwwAW79+vWxfcXEx8/X1ZQMGDNBHmGpRpy6qfp7JycmMw+GwuXPn6jI8talTD3nGcs2rok5djO2aV0WdehjzNZ+VlcWcnZ3Z9u3bGQD21VdfVVpe29e8XppwVc0C0a5dOwDAkydPqjx++/btiI+Px6JFi7QeW3VpWpcTJ04gMTERH3/8sc5iqw5N6yEWiwEA1tbWCvttbW0hkUjAjKRPWoMGDQAAJiYVr8ATGRkJExMTvPPOO7J9fD4fw4cPR1RUFEQiUYXH6pM6dVH18/T09ISjo6Na15g+qFOPMsZ0zauiTl2M7ZpXRZ16GPM1/9lnn8Hf3x8jRoxQq7y2r3mDdSKKjY0FALRo0aLScllZWZg5cyaWLFkCe3t7fYRWberU5fTp0wCk7e8dOnSAiYkJnJyc8OGHH6KwsFAvcVZFnXr07NkTTZs2xeeff47ExETk5eXh+PHjWLVqFaZNmwYLCwt9hauktLQUYrEYd+/exdSpU+Hi4oLhw4dXWP7mzZvw9vaGubniaj3+/v4Qi8W4d++erkOuUHXrokpSUhLS0tKqvMZ0SZN6GOs1X926GOs1X916GOs1f/r0aWzevBk///yz2sdo/Zqv9j2rFjx+/Jg5Ojqynj17Vll24sSJLDQ0lEkkEsYYM7rmHHXrMnXqVAaA2dnZsblz57KYmBi2dOlSZmZmZhRNPNX5mbx48YKFhoYySGfpZADYpEmTWGlpqR4irVhwcLAsniZNmrDExMRKy/fq1Yu1b99eaX90dDQDwE6ePKmrUKtU3bq8rri4mHXt2pU5OjqyzMxMHUVZNU3qYazXfHXrYqzXvCY/E2O75sViMfPz81NosoUaTbjavub1nkBzc3NZcHAwc3V1ZY8ePaq07MmTJ5mJiQm7fv26bJ+xXEyMVa8ukydPZgDYBx98oLB/8eLFDAC7efOmLkOtVHXqUVhYyLp37858fX3Zli1bWGxsLFu6dCmzsrJi06ZN01PEqiUmJrJz586x7du3szZt2jA3Nzf28OHDCsv37NmTdejQQWn/v//+a/AEWt26vG7q1KmMz+crPbfSt+rWw5iv+erWxViv+erWwxiv+YULFzJvb29WUFAg26dOAtX2Na/XBFpYWMjCwsKYnZ0du3btWpXlW7RowaZNm8aysrJkX507d2YdOnRgWVlZrKioSA9Rq1bdusyePZsBYJGRkQr74+PjGQC2bds2XYVaqerW48cff2QA2L179xT2//777wwAu3Lliq5CrZasrCxmY2PDpk6dWmGZYcOGMV9fX6X9O3fuZADYjRs3dBmi2tSpi7zZs2czDofDNm/erOPIqkedehjzNS9PnboY6zUvT516GNs1n5KSwkxNTdnWrVsV/p0AYLNmzWJZWVmspKRE5bHavub19gy0uLgYERERuHDhAg4dOoSWLVtWeUxSUhJ+/fVX2NnZyb7OnDmDc+fOwc7ODr/88oseIlemSV38/f0BSBe4lcf+/wBenaE82qZJPa5fvw47Ozv4+Pgo7A8JCQEg/ZkZA1tbWzRp0qTSZxr+/v54+PAhCgoKFPYnJiZCIBCgSZMmug5TLerUpcy3336LxYsXY9WqVRgzZoweolOfOvUw1mv+der++wKM65p/nTr1MLZr/sGDBygqKsLo0aMV/p0A0uF5dnZ2uH79uspjtX3N6+UnKJFIMGrUKBw7dgz79u1Dhw4d1DouJiZG6SswMBABAQGIiYnBkCFDdBy5Mk3r8sYbb0AoFCqtKxcVFQVAuuSOPmlaDxcXF2RlZSldcOfPnwcAuLm5aT1WTbx48QK3bt1SuujlDRw4EMXFxdi1a5dsX0lJCXbu3InevXtDKDSO9SnVqQsArF69GnPmzMG3336LDz74QE/RqU+dehjjNa+KOnUxtmteFXXqYWzXfFBQkMp/JwAwevRoxMTEVJgItX7Na3AHXW3Tpk2TtU/HxcUpfJU9c0tOTmY8Ho8tWLCg0nMZ+nlITeoyf/58xuPx2BdffMGio6PZokWLmKmpKRs3blytqcfDhw+ZlZUVa9q0Kdu4cSM7fvw4W7JkCbOysmLBwcEG6VTw9ttvs6+//prt3buXHT9+nP3666+sWbNmzMbGht2+fbvCujDG2DvvvMNsbW3Z2rVr2dGjR1lERAQTCoXs8uXLeq8HY5rXZceOHYzD4bC+ffsq/TwN8aytJj+T1xn6mq9JXYzpmte0HsZ4zauC156B6uOa10sC9fT0VOi9Jf81b948xpj0hyT/uiKGvphqUheJRMKWL1/OfHx8mImJCfPw8GBz585lYrG4VtXj5s2bbOjQoczd3Z2Zmpqypk2bsk8++cRgvT0XL17M2rRpw2xsbJiZmRnz9fVlU6ZMUegYUVFdCgoK2MyZM5mzszMTCoUsJCSExcTE6DV+eZrWZdy4cRX+PLt161Zr6qGKoa/5mtTFmK75mtTD2K55VV5PoPq45mk1FkIIIUQDhn+KTQghhNRClEAJIYQQDVACJYQQQjRACZQQQgjRACVQQgghRAOUQAkhhBANUAIlhBBCNEAJlBAt4XA44HA44HK5uH//foXlwsLCZGU3btyo8N748eNl782bN6/Cc2zatElWrnv37grvnThxQvZe2Ze5uTlcXV3RtWtXfPrpp0hISKhJVQkhoARKiFbx+XwwxvDHH3+ofP/u3buIjY0Fn8+v8jzr169HaWmpyvfXrl1b5Tk8PT0xb948zJs3Dx999BH69++P/Px8LFu2DG3atMGoUaOQl5enXsUIIUoogRKiRc7Ozmjbti02bNiAkpISpffXrVsHxhj69+9f6Xn69++Px48fK01EDkhXvzhz5gwGDBhQ6Tm8vLwwf/58zJ8/H9999x3Wrl2Ly5cvIyEhAS1btsT27dsxdOjQ6lWQECJDCZQQLZs8eTKeP3+OAwcOKOwvLi7Gpk2b0KlTJ9lSVxUZNWoUzMzMsHbtWqX3yvZNmjRJo/iCgoJw9OhRODo64siRI9i7d69G5yGkvqMESoiWjRgxAhYWFli3bp3C/sjISLx48QKTJ0+u8hy2trYYOnQoDh48iGfPnsn2i0QibN68Gd26dYOvr6/GMTo5OWHq1KkAgG3btml8HkLqM0qghGiZlZUVhg8fjiNHjuDx48ey/WvXroW1tTWGDRum1nkmT56MkpIShY5Ge/bsQUZGhlpJuCplnY8uXLhQ43MRUh9RAiVEByZPnozS0lKsX78eAJCSkoLo6GiMGjUK5ubmap0jNDQULVq0kD03BaRJ2M7ODhERETWOsWwh5JcvX9b4XITUR5RACdGB9u3bo2XLlli/fj0kEgnWrVsHiURS7TvHSZMm4cGDBzh+/Dju3buHmJgYjBkzBqampjWOsSwpczicGp+LkPqIEighOjJ58mSkpKTgyJEj2LBhA4KDg9G6detqnWPs2LEQCoVYt26d7E5UG823APD06VMAgKOjo1bOR0h9QwmUEB0ZM2YMzMzMMHXqVDx58gRTpkyp9jkcHBwwaNAg7NmzB+vXr0fHjh0REBCglfhiYmIASO+WCSHVRwmUEB2xtbXFkCFD8PjxY1hYWGDEiBEanWfy5MkQiUR4+fKl1u4+09LS8NtvvwGQDpkhhFRf5VOZEEJq5JtvvsHgwYPh6OgIKysrjc4RFhaGffv2QSKRoE+fPjWO6erVqxg7dizS09PRr18/DBw4sMbnJKQ+ogRKiA55eHjAw8OjRufgcDgaJbnk5GTMnz8fgHQSh/T0dFy+fBmXL18GIL3zLLsLJYRUHyVQQuqolJQULFiwAABgamoKW1tbNG3aFLNmzcKoUaMQFBRk2AAJqeU4rKwvOyGEEELURp2ICCGEEA1QAiWEEEI0QAmUEEII0QAlUEIIIUQDlEAJIYQQDVACJYQQQjRACZQQQgjRACVQQgghRAOUQAkhhBANUAIlhBBCNPA/XRYva8mVsHQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16b055b1bfd44db98833d5c70581185f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c743968c793945bcbfaa6fda6d91482c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eedf290a06eb459eb4396a208e375a24",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "115367706a034e3e99215531decaffc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd6b479aa9f64d5a87f7e4a912a4e59a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a865e7b25b784b56bbbf6664020b0fb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26d67b577d764d1dbdf68620060070d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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dd5mTkxPT09NjhoaGzMPDg3322Wfs5cuXlb6m7H7M7Zv+j53Ys5Z5eXkxAwMDZmpqysaOHcsePXokVz4yMpItWLCAeXp6MktLSyYSiZijoyPz8/NjoaGhlb7HpUuX2Pjx45mtrS0TCoXM0tKSdejQgS1YsIDduHFDrqyjo6PCvZFVadWqFQPALCwsWFFRUZXlioqK2KZNm1iPHj2YiYkJE4lErHnz5szX15etW7dO4Z7SoqIitnz5ctaiRQtZ/b7++mtWWFhYq/tAGWNMIpGwTZs2MXd3dyYSiZitrS374IMPWGZmZpV13b59O+vQoQMzMDBgTZo0YW+99Ra7e/cuW7p0KQPAgoKC5MonJSWxSZMmMWtra8bn8xkAtnTpUtnzoaGhrF+/fszMzIwZGRmxXr16sSNHjrCgoCCFsupGGX/jPMYYe1OCrahLly64efOmajI5USvXQ+LgP/Q4SkqkU7NZNzPEhgNDYGmjeP+cRMJwYMsD/PlTeV9X1z522HpmdO0GGFUc4FHzX0uVKBuFSeoprcL5okkX7uIg5DXK+BunPlCiICEmGwsmnpUlT8umBvjz7OhKkycA8Pk8TJzTFvOWdZPtuxESj7VfX2mQeAkhhAuUQIkcxhiWfRiMzDTpIAeRrgAbDgyBnn713eWjprhj+icdZds719/GmQOKE4YTQog2oARK5Jw9+BSh/5bfOP+/Lb6wtjWq8esnvd8OPfrby7aXfxSElMQ8pcZICCHqgBIokcnOLMLKz8pvN/AZ4ohOPRRH870Jn8/DwtXeaGovTbo5mcX4/pOLSo2TEELUASVQIvP7yhtIS8oHAOjp6+Cz73vU6TiGxiIs+J+XbPvff57h3NGaD9EnhBBNQAmUAABSXuVh36/laxJ+tLRbve7l7NyrGYaMayXb/n7BRRQW0CQLhBDtQQmUAAC2/HgLRYXSKbssmxpg0FjFZYpqa/YiT5hZSJdtSorPw+7Nd+p9TEIIUReUQAlevczBgS33ZdsfLulWoyWsqmNsqot3Py5fweL3lTcVpgIkhBBNRQmUYMe6CIiLpfd82tgZotfA6hdQrqmhE1zQvKV05Yj8XDF+/e660o5NCCFcogTayOVkFeGfHeWL/n6gpNZnGR0hH/5fli+ovP+P+3j1MkdpxyeEEK5QAm3k/tkRhfxcMQDArIkeevjaV/OK2vPytYeHpxUAoLSEYetqxeWtCCFE01ACbcRKSyXY+8td2fbbs9sqtfVZhsfj4Z0Py/tCD/35AEnxuUp/H0IIaUiUQBuxkFPRiHuRDQAQivgYMclVZe/l6W2L1h0tAQAlYoncxPOEEKKJKIE2YvsrjLwd8FbLGs13W1c8Hg/vfFTeCj2w5T6NyCVqY/r06eDxeJUu6M0VJycn2aLVRD3RgtqN1KuXOXJz3k5+v73K37Nr72Zw8bDAkwfpEBdL8Pfv9/DB4m7Vv1Ad3fk/ID+2+nJcMXAAOnxb78M4OTkhJiam0udsbGyQmJgot6+4uBjffvst9uzZg/T0dHRu54I1336Czh3cFV4fGBiIQYMG4eTJkxg+fHi9Y9U0ffv2RUhICGqxoiRRM5RAG6njux/Jltx0cjGTzV2rSjweD+Pe88DKT6Xz7e7efAczP+us0pavyuTHAoZOXEdRtbxopR3K1NQUn3zyicJ+IyPF35lFixZh3bp18PPzg729Pf7atQP93pqLh2GHYNukvFxubi78/f3xzjvvqEXyXLlyJRYtWgQ7OzuuQ5E5f/481yGQamjgmYvUl0TCcGRnpGx7/Kw2DfbevYc44s+fwpGckIes9CIc3/MQE2a1bbD3J7VnZmaGZcuWVVuOMYbff/8dM2bMwLZt2wAAY3xbo+/oufjrwGl8uXyIrOzChQtRWFiIDRs2qCrsWrG1tYWtbe0WTlA1Z+f6zwZGVIv6QBuhW5cT8PK5dPCQjpCPPsOcGuy9dYR8jJ1evgr89jURdAlLS6SkpCA/Px/dupVflu/W2QMAEBP3Srbv4sWL+PXXX/Hzzz/DwsKiTu9V1j+YnZ2NTz/9FE5OThAKhXKJ/uHDh5g+fTqaN28OXV1d2NjYYPLkyXj06JHC8d7UB3rt2jWMGzcOTZs2hUgkQvPmzTFnzhwkJCRUGlt6ejq++eYbtG3bFgYGBjA1NUWHDh2waNEi5OXlITo6GjweDyEhIQCkV2bKvvr27atQx9cVFRVh1apVaN++PQwMDGBiYgIfHx8cOHBAoWzZe02fPh3R0dGYOHEiLC0toaenhy5duuDkyZNv/kG/5ujRo3jnnXfg6uoKQ0NDGBkZwdPTExs3boREIlEoX/Zzff78OTZt2oT27dtDX19frp7p6en46quv0Lp1a+jr68PU1BT9+/fHv//+q3C8rKwsrF69Gr6+vrC3t4dIJIKVlRVGjRqFq1ev1qouykAt0Ebo6K7yiRN6+NpDV69hfw2GjnfBro13kJ8rRuyzLFwLioOXr/JmPyLKVVRUhN27dyM2NhaGhoZo3749evfuDYFAIFfOysoK+vr6uHXrlmzfzdvS3zVHe2nrrqCgALNmzYKfnx/8/PzqFVdxcTF8fX2Rnp6OQYMGwcTEBC1atAAAnD17FmPHjoVYLMbIkSPRqlUrxMXF4Z9//sGpU6cQFBSEzp07V/MOwPbt2+Hv7w9dXV2MGjUKzZs3x5MnT7B161acOHECV69ehYODg6z8ixcv0K9fP8TExMDT0xPvv/8+JBIJHj9+jHXr1mHu3LkwMzPD0qVLsWPHDsTExGDp0qWy11c3aKi4uBiDBw9GSEgI3N3d8eGHHyI/Px+HDh3C22+/jdu3b+P7779XeF1MTAy6deuGli1bYurUqUhPT8f+/fsxevRonDt3Dv369avRz3zRokXg8/no3r077OzskJWVhQsXLmD+/Pm4ceMG/vrrr0pfN3/+fFy6dAnDhw/HsGHDZL87MTEx6Nu3L6Kjo+Hj44MhQ4YgLy8PJ0+exJAhQ/D777/D399fdpyoqCh888036N27N4YPHw5zc3PExsbi+PHjOHPmDE6cOIEhQ4ZUGoMqUAJtZIqLSnHu6HPZ9vhZHg0eg4GREIPGOOPoXw8BAHt/vUcJVI0lJiZi6tSpcvtatGiB7du3o0+fPrJ9PB4Ps2fPxsaNG5GVlQU7Ozv8tWs7jI0MMWW89KS2ePFipKen4+eff653XK9evUKbNm0QEhICQ0ND2f6MjAxMmjQJBgYGuHjxItq0Ke+iePDgAbp3745Zs2YhPPzNt1I9fvwYc+bMgZOTE0JCQuT6Ry9cuICBAwdi/vz5OHLkiGz/O++8g5iYGHz//ff46quv5I6XmpoKIyMj6OnpYdmyZQgODkZMTEyNLo+XWbNmDUJCQjB06FAcP34cOjrSU/jSpUvRrVs3rFy5EiNGjEDPnj3lXhccHIxly5bJJevJkydjyJAhWL16dY0T6KlTpxQuLUskEsyYMQO7du3CRx99hO7duyu8Ljw8HBEREbJ/cMpMmzYNMTEx2LdvHyZOnCjbn5mZib59++Ljjz/GqFGjYGNjAwBo3bo1EhISYGlpKXecuLg4dOvWDQsWLGjQBEqXcBuZywExyM0uBgAYm4rg3sGymleoRsV7ToNOvKCJFdTUjBkzcP78eSQmJiIvLw/37t3DnDlzEB0djaFDh+LOHfkVdn744QcsXLgQ169fx59//gkPt5Y4/8/PsLO1xrVr17B+/Xps2LABlpaWWL58OWxtbaGjowNPT0+EhobWOr41a9bIJU8A2LVrFzIzM7F8+XK55AkAHh4e8Pf3R0REBCIjI/Emv/76K8RiMTZs2KAwuMjX1xejRo3CiRMnkJMjnZry1q1buHLlCjp27IiFCxcqHK/s0ml9bNu2DTweD2vXrpUlTwCwtrbGkiVLAABbt25VeJ2joyMWL14st2/w4MFwcHDA9es1n5+6sn5ZPp+P+fPnAwACAgIqfd2XX36pkDzv3LmDkJAQ+Pn5ySVPQNrvvnz5chQWFuLw4cOy/aampgrJEwDs7e0xbtw4PHz4ELGxDTc6nlqgjczpA09kj/sOd1LJzEM14ehihg7dbXDnWhIkEoZDfz7Ah5xEQt6kYosFANq2bYvffvsNRkZGWLNmDZYtWybXAtPV1cXKlSuxcuVK6Y60mwCA4mIxZs6chWHDhmHKlClYv369rEXUq1cvfPfddxgyZAiePn0qa21UR09PD+3bK95+FRYWBkB6gq6sdff48WMA0suBryfYyo4TEhKCGzduKDyfnJyM0tJSPH78GJ6enrI+uMGDB4PPV37bJCcnB0+fPoWdnR3c3RVvC/L19QUAREREKDzXsWNHhUvuANC8eXNZPWsiLS0Nq1evxunTp/H8+XPk5eXJPR8fH1/p6yr2i5cpe9+srKxKP6eUlBQA0s+potDQUGzYsAFhYWFITk5GcXGxQgwVL6urEiXQRiQ/T4zgky9k2yMnu3EYjfT971xLAgD8/ft9zAYfQigORCDqZ+7cuVizZg0uXrxYo/LLV29BfHy8bGDI6tWr0b9/f9mJ083NDU5OTvj555/x7bc1u3/V2tq60n8A09LSAABbtmx54+tzc9981aPsOKtXr67RcTIzMwFAZbfCZGVlAUCVo4XL9pfFUZGZmVmlr9HR0al08E9lMjMz0bVrV7x48QLdunXDu+++CwsLC+jo6CAzMxMbNmxAUVFRpa9t2rSpwr6yn29gYCACAwOrfN+Kn9ORI0cwbtw46OnpYeDAgXB2doahoSH4fD6Cg4MREhJSZQyqQAm0EQk5FY2C/BIAgLmlHpxczTiNp+eA5rCw0kd6SgHSUwoQxGuHQYwW3dYE1tbWAKDQAqlMxN1H+HHTLvz22++ws7NDdnY2EhISMGXKFFkZBwcHWFpa4sGDBzWOoaqrJ6am0uXz7ty5U2kLtabKjpOVlQUTE5Nqy5clqapaYfVVFs/rk1eUefXqlVw5Zdu6dStevHiBpUuXKrQYw8LC3nhLUmWfVVmcGzZswMcff1yjGJYsWQKRSISbN2+idevWcs/NmTNHNrK5oVAfaCNy9mD55dv+o1tydvm2jFAkwNDxrWTb+/k+HEZDaqPs8lvLli3fWK6kpAQz53+Lft5d8N5778k993pLobCwUCmxeXl5AQAuXbrUoMcpKx8QEFCjVl3ZJdXS0tIaHd/Y2BjOzs6Ij4/HkydPFJ4PCgoCgBqNLq6Lp0+fAkClo6frkrjq8jk9ffoUbdq0UUieEokEly9frnUM9UUJtJEoyBfjcoWp+4ZNcOEwmnLDJrqCz5cm8qt8N7yANccRkTIPHjxAenq6wv6YmBh89NFHAKSjTt9k5fodePo8DlvWfSPbZ2JiAjs7O5w9exYlJdIrIiEhIcjJyYGHR/1Hhc+YMUM2CKWyATISiQTBwcHVHuejjz6CUCjEggULZP2mFRUXF8ud/D09PdGzZ0/cvn0bP/zwg0L5tLQ0uX8SmjSRTs1Um0EvM2fOBGMMX3zxhVziTU1NxYoVK2RlVKHsFpvXf3YRERHlfd610KVLF/j4+OCff/6RTbzxunv37iE5OVkuhidPnsjdg8sYw/Lly6sdFKYKdAm3kbgS+BKFBdKTlVkTPTRvqZrLPLVlbWsIL197XDn3EgCwn++NRZJ/OI6KAMDBgwexatUq9OvXDy1atICxsTGePXuGU6dOobCwEMOGDcPnn39e5esfPHiA/63dhrUrPoFjc/l+uy+//BLz58+Hj48Punfvjj179sDIyAgfflj/oWRNmjTBoUOHMGbMGHh5eaF///7w8PAAn89HbGwswsLCFJJZZdzd3bFt2zbMnDkTHh4eGDJkCFxdXSEWixEbG4tLly7BysoKDx8+lL1m9+7d6Nu3L77++mscPnwYffv2BWMMT548wb///ouHDx/KElH//v1x8OBBjB07FsOGDYO+vj4cHR0Vbhmq6PPPP8eZM2dw7NgxdOjQAcOGDUN+fj4OHjyI5ORkfPnll/D29q73z7Ay7777LlavXo1PPvkEQUFBcHFxwZMnT3Dy5EmMHTsW+/fvr/Ux9+7dC19fX7z33nvYuHEjunfvDjMzM8TFxeHu3bu4f/8+wsLCZF0GCxYswNy5c9GpUyf4+flBKBQiNDQUkZGRGDlyJE6cOKHsar8RJdBG4tyxZ7LHPkMaZoRaTY2c7CZLoMf43fGJ5ATqN9ifKEO/fv3w6NEjREREICwsDHl5eTAzM4O3tzemTp2KqVOnVtkNUFpaipkzZ6J757b4YOZ4hefnzZuH7Oxs/PbbbwgPD0eHDh2wbt26Go/ArU7//v1x9+5d/PTTTwgICMClS5cgEonQrFkz+Pr61ngSh3feeQcdOnTAmjVrEBQUhH///ReGhoZo1qwZxo0bh7fffluufIsWLRAeHo4ff/wRR48exebNm6GnpwcnJyd89tlnskQAALNmzUJMTAz+/vtv/PjjjygpKUGfPn3emEBFIhECAwOxdu1a7N27F5s2bYKOjg46dOiA9evXY9KkSXX7gdVAs2bNcOnSJSxatAiXL19GQEAA3N3d8csvv2DAgAF1SqD29va4desWNm3ahMOHD2PPnj0oLS1F06ZN0aZNG8ybNw/t2rWTlZ8zZw50dXWxfv167Ny5E/r6+vDx8cH27dtx+PDhBk+gPFaLedS6dOmCmzdvqjIeogJicSl6229Ddoa0z2nz4WFwa8/N/Z+VkUgYZgw8ioRY6f10P5TswAgJt79nUVFRCv0schrJaiz1llbhc2zShbs4qjFx4kTs378fCQkJajcnLlGNav/Ga4BaoI3ArUsJsuRpYCSEa7sm1byiYfH5PAzyc8aOdbcBAEf5XhjBbUjVU4fkRJTm8ePH0NXVhZWVFdehEA1Cg4gagXPHyqfu69bHjvPRt5UZOMYZPCYduXiV54qEmGyOIyKNwYYNG+Dn54eIiAiMHj1abnYfQqpDCVTLMcYQfCpatl3xthF1Ym1riJ5MOhiD8fiyeXIJUaX169fj6tWrmDlzJn7//XeuwyEahv7d0nJPH6Tj1X99izpCPtp3U5wRRF2MkVxDKF86tdqhPx9g7tddZbe4EKIKL168qL4QIVWgFqiWCz4dLXvcuqMldITq+5H7srswYdKZbZLi83DjompmdCGEEGVQ37MpUYqLZ6Jljwe8pd4r3OuiRG707eHtDX9jNCGE1BQlUC2WmVaA22Hl82b26G/PYTQ1M0ZSvqr8v4efIier4SaGJoSQ2qAEqsUu/xsLiUR6m69tcyOYN9HnOKLqtUYc3FgcAEBcLMGZg4pzfhJCiDqgBKrFQir0f/oMceQukFrgQb4VeujPmq/OQQghDYkSqJaSSJhsejxAuni2phghuSkb7PTgVgqeRSlOaE4IIVyjBKqlom6nIDNNOlm2nr4OnFtbcBxRzZkjDz18y/trj+yMekNpQgjhBiVQLXUlsHyeVg9PK427n3LwuPIJH47siIJYXLM1EwkhpKFQAtVSoRUu3/Yb0YLDSOqmi3czWFhLBz1lphficoAaT9xOCGmUKIFqobzcYkRceSXb7uLTjMNo6kagw8egMeX3rdI9oYQQdUMJVAvdCIlHiVg6MbtlUwM0sTbgOKK6GexXfhk35HQ00pLzOYymcRs4cCB4PB5CQkKqLDN37lzweDxs2bKlASMjhDuUQLVQxdG33fvacRhJ/di3MIGHp3R5KUkpw8l9jzmOqPEKDw+HQCCAp6dnlWWuXbsGQLpuMCGNASVQLVRxAJHPYM24/7Mqg8aWt0IPbXuAWqz/TpTk2bNnSE9Ph4eHB4yMjCotk5+fj/v370NXVxdt27Zt4AgJ4QYlUC2TEJONF48zAQB8AQ9tu1hzG1A99RnqCF09AQDgeVQGIiNSOI6o8bl5Uzo/cffu3assc+vWLZSUlKBDhw4QCoUNFRohnKIEqmWunC+/fOvsbg5dPc1esc7QWCQ3i9LRXXRPaEOrSQK9elU6exRdviWNiWafXYmCK4HlCVRTpu+rzmC/Vjh39DkA4PieR/jiB2+IdAUcR9V4lCXQ/fv3IygoqNIy1P9JGiNKoFqktFSCsAvlCdTLV/1XX6mJ9t1s0NTeCIlxucjNKkbQiRdyEy0Q1WGMISIiAgAQGBhYbXlKoKQxoQSqRR6EpyA7Q7r8l76hDpxczLgNSEn4fB4GjmmJvzbdBQD8szOS0wTqIdrM2XvX1oPij+r1+sePHyMrKws9evTAlStXKi2TkZEBCwsLGBgYoE2bNnLP/bLtIFZv3o1XSanw8GiL9evXw8fHp14xEaIuqA9Ui4T+Wz76tl0XG/B4mjV935sMrDCpQmhgLJITcjmMpvEou3zbqVOnKsuEh4cDADp27AiBoPzS+v79+zH/6zX4+pPpiAjajZ49e2Lo0KGIjaVZpYh2oASqRa6cKz8x9RvpxF0gKmDb3BgdutsAAJhE2hdKVK8sgXbu3LnKMmUJ9PXLt2vXrsX0iSPg/+4YtHZtgU2bNsHW1ha//vqr6gImpAHRJVwtkZ8nxt1rSbJtT2/Nm76vOoP9WuHOf3U8vC0S733emZNWdn0vi2qSmiTQsj7Sigm0uLgYt27dwudz/idXdtCgQVVeCiZE01ALVEuEhyagpEQ6fV8Ta32YN9HnOCLl8x7sAAND6T2Gsc+ycPd6UjWvIPUhkUhw+/ZtiEQieHh4VFmushZoamoqSktLYWMlv4yejY0NEhMTVRMwIQ2MEqiWuBYUL3vcqacth5Gojr6BEL2Hld+ac2QHTTCvSlFRUcjNzYWHhwdEIlGlZXJzc/HkyRMYGRnBzc1N4fnXrxAwxrSqb540bpRAtcS14DjZY5/BDhxGolqDx5YPJjr59xMU5Is5jEa73bp1C8CbL9/euXMHEokEnTt3Bp9ffjqxtLSEQCBAYnKaXPnk5GTY2NioJmBCGhglUC2QlVGIqApT3LXv1pTDaFTLw9Madk7GAICCPDHOH3vOcUTa69133wVjDFu3bq2yTK9evcAYU1ilRSQSwdPTE4HB1+T2BwYGomfPniqJl5CGRglUC9y6lACJRDrJelN7IxiZVH65TRvweDy5Ceb/ocu4auvTTz/Fjr9PYutfRxH1+AXmz5+PhIQEzJ07l+vQCFEKGoWrBSpevu3aW/tG375uwFstsWNdBBgDrgfHIyEmG80cTbgOi7zm7bffRlpsOP63dhteJaWibdt2OH36NBwdtWOKSUKoBaoFrgWVJ9AeA5pzGEnDsLY1ROde0oFSjAFHdtIE8+rqg5njER1xHEUJV3Dr1i307t2b65AIURpKoBouLTkfTx6kAwB4PKCtp2YvX1ZTQye4yB4f2PoApaUSDqMhhDRGlEA13I2Q8ttX7FuYQN+gcazF2LN/c5hZ6AEAUhPzcTmApocjhDQsSqAarmL/Z/d+2rH6Sk0IRQIMqnBLy8GtDziMhhDSGFEC1XAV+z+9GlECBeQv44acjqYJ5gkhDYoSqAZ79TIHMU+zAAB8AQ+tO1pxHFHDsm9hgvbdpDflSyQMR3Y+5DgiQkhjQglUg10PLu//dHIxg0hX8IbS2mn42+Wt0INb78vuhyWEEFWjBKrBKvZ/9ujfuC7flvEe7AhjM+nEEa9e5uLqhZdKOzZjlIwJ0UbK+tumBKqhGGNyCbRbn8aZQEW6AgwYXT6Y6MAW5QwmEggEEItpnl1CtJFYLJZb/L2uKIFqqJfPs5H4UjpoRkfIh1u7JhxHxJ1hFS7jnj/+HKlJ+fU+prGxMbKzs+t9HEKI+snOzoaxsXG9j0MJVENVbH06tzaHQKfxfpROLmZo01k6gEpSypSyzJmFhQUyMjKQmpqK4uJiupxLiIZjjKG4uBipqanIyMiAhYVF9S+qBs2Fq6Eq3r7Sa6D2Ll9WUyMmuSIyXLoizd5f7mHGZ52hU49/KnR1deHg4ID09HRER0ejtLRUWaE2Lnmp5Y+TacpFwi2BQABjY2M4ODhAV1e33sejBKqBGGO4LjeBvB2H0aiHPkOd8MfKW8hML0TyqzwEn3yBAW85V//CN9DV1YWtrS1sbbVzgfIGsbdN+ePJ1Ion2qXxXvfTYM8i05GWXABAOoimpbs5xxFxT6QrwNAKfaF7fr7LYTSEkMaAEqgGqtj/6dquCfh8HofRqI8Rk1zBF0h/FtdD4vE0Mp3jiAgh2owSqAa6FlQ+gYL3IOr/LGNta4heFZZz2/srtUIJIapDCVTDlJZKcONieQLt4qP9C2jXxuip7rLHx3Y9RE5WEYfREEK0GSVQDfPwTiqyM6VJQd9ABw7OphxHpF7ad7OBk6sZAKCwoATH/qL5cQkhqkEJVMNUvH3FvaMVeDzq/6yIx+Nh9DvlrdDdm+/S/LiEEJWgBKphrlZIoL2HOHIYifrqP6oFDI2lC4u/fJ6F0EBabJsQonyUQDVIcXEpwi8nyLap/7Ny+oZCDPZrJdvevjaCw2gIIdqKEqgGuXcjCQX5JQAAI1MRmtobcRyR+hozrbXslpZrQXGIikjhOCJCiLahBKpBKvZ/tvW05jAS9dfU3kjuEve2teEcRkMI0UaUQDVIxQTaZyj1f1Zngr+H7PHZg0+REEOrqxBClIcSqIYoyBfj9tVE2XbnXtT/WR0Xjybo1LMpAEAiYdi58Q7HERFCtAklUA0RceUVSsQSAIBZEz1YWOlzHJFmGD+rvBV6cMt9ZKYXchgNIUSbUALVEBUv33bwasphJJqli3cz2WT7RYWl2P/7fY4jIoRoC0qgGuIq9X/WCY/Hw7j3ypfU2rXxNooKSziMiBCiLSiBaoDszCLZYtEA0JFaoLXSb3gLWNkaAAAy0wpxdBdN70cIqT9KoBrg5qUE2XR0VrYGMDat/0rqjYmOkA+/GeWt0N9X3kBxcSmHERFCtAElUA1wLeil7LEnjb6tk2Fvu8CsiR4AICk+D0d2RHEcESFE01EC1QAVBxD1pv7POtE3EMrdF/rb9zdQXEStUEJI3VECVXOpSfl48iAdAMDjAW270AxEdTVyshvMLaWt0OSEPBzeHslxRIQQTUYJVM3dCClfPLuZozH0DYQcRqPZ9PR18PbstrLt31feoBG5hJA6owSq5q5W6P/s1seOw0i0w/CJrrJWaMqrfBzaRq1QQkjdUAJVc9eCy1ug3oOo/7O+9PR1MLFCK/SPVTepFUoIqRNKoGosISYbL59lAQD4Ah7cO1hyHJF2GD7JFRbW0qkQUxPzsf8Pmp2IEFJ7lEDV2LXg8tG3Ti5mEOkKOIxGe+jqybdCf/3uBrIziziMiBCiiSiBqrFrQeWXb3v0t+cwEu0zfKKrbEHy7IwibPnhFscREUI0DSVQNcUYk2uB9vBtzmE02kekK8B7n3eSbe/aeBvx0bReKCGk5iiBqqnnDzOQnJAHABCK+GjlYcFxRNqnzzAnWb9yiViC9UvCOI6IEKJJKIGqqdDAWNljl7ZNIBDQR6VsPB4Pc77qIts+vf8J7t5I4jAiQogmobOymqqYQPsOd+IuEC3X1tMaPoMdZNurv7wMxhiHERFCNAUlUDVUVFiCmxcTZNte/WgAkSq990VnCHR4AIDw0Fc4f+w5xxERQjQBJVA1FB76CoUF0pv7Tcx1YdvcmOOItJudowlGTXGXba/67BLy88QcRkQI0QSUQNVQxcu3nXrQ4tkN4Z0P28HEXLrO6quXufj9+xscR0QIUXeUQNVQxQTqO6olh5E0HibmevD/orNse/u6CDyNTOcwIkKIuqMEqmZSXuXh8b00ANLlyzp5UQu0oQzyawUPTysAQGkJw4qPg2lAESGkSpRA1UzF1qe9kwn0DWn5sobC5/Mwf7mXbEDRzYsJOL77EcdREULUFSVQNXPlXPnyZb0GObyhJFGFFm7m8JveRrb945eXkZleyGFEhBB1RQlUjUgkDFfOlbdAew+l5cu48M5H7WFlawAAyEwrxJpFoRxHRAhRR5RA1UjU7RRkpEpbO7p6Aji3pun7uKBvKMSHS7rJtv/ZEYWLZ6K5C4gQopYogaqRKxX6P9t0tgKfz+Mwmsat10AHuSsAS+ZcoEu5hBA5lEDViNztKyNacBgJAYCPl3WHWRM9ANKFt1cuuMhxRIQQdUIJVE3k5RQjIixRtt2tL03fxzVTCz0s+K6HbPvkvsf495+nHEZECFEnlEDVxJVzL1EilgAALG0MYGGlz3FEBAB69m+OgWOcZdvLPwxGWnI+hxERQtQFJVA1EXzqhexxzwG0eLY6+WBxV1jalI/KXTo3iCZYIIRQAlUHpaUSXDwTI9se8BZN36dOjExE+GxlT9l20MkX+GvTHQ4jIoSoA0qgauDu9SSkpxQAAPT0deDW3pLjiMjruvg0w5hprWXba766QotvE9LIUQJVA8Enyy/ftu9uQ7evqCn/LzvDrX0TAECJWILPJp9FdmYRx1ERQrhCCVQNBJ+Klj0eOq4Vd4GQNxKKBPhmfW8YGkvnJ06IycES//PUH0pII0UJlGMvn2fJls3i8wFPn2YcR0TexLa5sVx/6Lljz7F7810OIyKEcIUSKMcqtj6d3Myhb0Crr6g7n8GOeGuqu2x79cLLuBYcx2FEhBAuUALlWMXbVwaOptG3msJ/oaesP7S0hGHBxLN4+TyL46gIIQ2JEiiHcrKKcPNigmy773Cavk9TiHQFWLq5r2zCi6z0QszzO4W8nGKOIyOENBRKoBy6HBCLkpL/Zh9qagDLpgYcR0Rqw8rWEEt/7gsdofTP6MmDdCyaHgiJhAYVEdIYUALlUMXLtz5DaO1PTdSmkxUW/K98vtwLJ15g09KrHEZECGkolEA5UlIiwcWz5bMPDaTZhzTWoLHO8JvZRrb9xw+3cGDLfQ4jIoQ0BEqgHAkPTUB2hvQmfANDIVq1ocWzNZn/F53RtXf5LUgr5oXgwvHnHEZECFE1SqAcCTzyTPa4i48teDyafUiTCXT4WLKxD1zaSkfmSiQMn78TgIiwVxxHRghRFUqgHJBImFwCHTHZjcNoiLLoGwrx3RZfNHMwBgAUFZbiw7dO4vnDDI4jI4SoAiVQDkSEvULKK+makrp6ArTvasNxRERZzC318f22/jC10AUAZGUUwX/YMcRHZ3McGSFE2SiBcuDffypevm0GgQ59DNrEztEE323pD119AQAgMS4XMwYewauXORxHRghRJjpzNzCJhOGc3OVbVw6jIari1t4Sy3/pB6FI+icWH5ODGQOPICk+l+PICCHKQgm0gd29nojEOOlJVKQrQMfuthxHRFTF07uZdKKF/64wvHyejfcGH0VKYh7HkRFClIESaAM7vf+J7HHnXrayWWyIdure1x5LNvWGQCAdZf3icSbeG3wMyQnUEiVE09HZuwGVlEhw9mB5Ah0zzf0NpYm26DnAAV+v6w3+f0n0WVQ6pvb7B3EvaGARIZqMEmgDuhYUh7TkAgCAnoEOOnRvynFEpKH0HuqIr9Z4y5Jo3ItsTO13WLYWLCFE81ACbUCn/n4se+w9yAECAf34G5O+w1tg2c99ZQOLkhPyMK3/P7h/K4njyAghdUFn8AZSWFCCc0fLR9++9S5dvm2MevRvjpXbBkDPQAcAkJlWiBkDj+LimWhuAyOE1Bol0AYSfPIF8nLEAAATc124/jflG2l8OnRvitW7BsHYVAQAyM8V48Mxp7D317scR0YIqQ1KoA3kyK4o2eMBo1vS3LeNnHsHS6zdNwTWzQwBSO8P/m7+Raz87BJKSyUcR0cIqQlKoA0gKT4XVwJfyrbHTGvNYTREXTi5mGHToWFwa1d+NWL3pjv4eNxp5GQVcRgZIaQmKIE2gOO7H0IiYQAAx1amaGpvxHFERF1YWOnjpz2D4T3IQbYv+FQ03u5xAI/vpXIYGSGkOpRAVYwxhiM7yy/fVlx4mRAA0NPXwZJNfTBhlodsX8zTLEzyPoQTex5xGBkh5E0ogapYRFgiYp5mAQB0dPjoN6IFxxERdcTn8+C/0BNfr/ORTUJfWFCCRTMC8e28YBQWlHAcISHkdZRAVezAH/dlj7v2bgY9fR0OoyHqrt+IFvj58HA0b2ki27f/9/sY330/IiOSOYyMEPI6SqAqlJFagLOHyqfum/Jhew6jIZrC0cUMmw8Ph88QR9m+5w8zMKnXIfz2/Q2UlNAoXULUASVQFTqyMwriYunJzrqZIdzaW3IcEdEUBkZCLNnYG5+s8JJd0i0pkWDTsmuY2vcwDTAiRA1QAlURiYThwNYHsu23/T3eUJoQRTweD8MnuuL3EyPRumP5P193rydhXLf9WL3wMvJyijmMkJDGjRKoioQGxuLlM+ngIaGQj0F+rTiOiGgqO0cTrNs3BDM+7QSBjnQCjtJShh3rbmNk+z0IOPQUjDGOoySk8aEEqiK7Nt6WPfYZ6kiDh0i9CHT4mPx+O/x+fCTad7OR7U+Kz8Onk8/inT6HcT0kjsMICWl8KIGqwON7qXIzD02f35G7YIhWcXQxw0+7B2HRT94wNdeV7b99NREzBh6F//BjeBBOo3UJaQiUQFVg18Y7ssdtOlnB1sGYw2iItuHxeOg/uiV2nBuDsdNbQ0en/M/4SuBLTPA6AP/hxxAaGEuXdglRIUqgSpaSmIeT+8pnj/Ff6MlhNESbGZmI8P43XbHj3FsY7OeMiusTXAl8idnDj2Os5984siuKJmIgRAUogSrZXxvvyG5daWpvhLae1hxHRLSdjZ0RPl/VC1tOj0KfoY5yifTx/TQsnnUefR224dt5wbh/K4lapYQoCY/V4q+pS5cuuHnzpirj0WgZqQUY6LILBXnSdT+/WusD35E0dV9NNXMylT1OiM7iMBLN9io2B//siMLpA09QXFSq8Lxr2yYYOsEFg8Y6w8nVXLXB7K2QzSdT4ibahRKoEm1YEoY/frgFADC31MPfoePB59O6nzVFCVS5sjOLcHr/Y5zY+xjJCXmVlnFt1wQDxzij3/AWcO9oqfx1aimBEi1GCVRJMtMLMchlJ/JypK3PL3/ohYFjnTmOSrNQAlUNiYTh3o0knD34FCFnomVdDK+zbGqAXgMd4DPYET0HNoepuV7935wSKNFilECVZP3iMGz5Udr6NLPQw99XxkEgoC7m2qAEqnp5OcW4eiEOwaejceNiPEpLKv/z5/N5aN/dBr0GOsB7kAM8PK3r9vtMCZRoMUqgSpAYl4thbf5CUaG0v+nT73tg6HgXjqPSPJRAG1Z+rhjXguNw5dxLXA+JR36uuMqypha66OHbHL0GOaDnAIeaLwpPCZRoMZoeRwl+/vaaLHlaNjXAYJq2j2gAAyMh+o1ogX4jWqC0VILH99Jw42I8QgNf4vmjDKBCvstKL8LZQ09x9tBTAIBzawv0GtQcvQY6oIuPHc20RRolaoHW05P7aRjb5W9IJNIf43db+6NbHzuOo9JM1AJVH5lphQgPTcCNSwm4HhyP7MyiKsuKdAXo4tMMvQY6oM8wJ7RwqzCyl1qgRItRAq0HxhhmDj6K68HxAIAWbmb44+QojqPSXJRA1RNjDC8eZeLmpXhcD4nH/VvJVfadAtLW6YC3WmLgW85wj7Quvy+VEijRMpRA6+HkvkdYOC1Qtv3b8RFwbm3BYUSajRKoZigsKMHd60m4eSkB14LjkBCTU2VZe6tUDPC8g8FdI9Bu8Qvl3yZDCIcogdZRTlYRRrTbg9TEfACA78gW+GqtD8dRaTZKoJopOSEXNy+/wtULcbh5Kb7K22QcW5lixGQ3jJjkBgdn00rLEKJJKIHW0bcfBWP/H/cBAHr6Ovg7dBwMjUUcR6XZKIFqvsKCEty4GI/LAbG4cu5llXPwduzRFKOmuGPwuFYws1DC/aaEcIASaB1c/jcGc0ackG3TbSvKQQlUu4iLSxH7pzcCbnRE4M2OyC3QVyijI+Sjz1AnjJzihj7DnCDSFXAQKSF1Qwm0lrIyCjGm8z4kxUunRmvd0RIbDgylvh0loASqfZqFSj/TwmIhzrxcjDP/6uPqTV2UlipOymBqoYuhE1wxeqo72nWxpr8povYogdYCYwwLJp5F4JFnAAChLh97Q8bBrAldglIGSqDapyyBAkBO80UAgIxMPs4F6+N0gB6iHlf+t9PSzRyjprpj5GS3mk/aQEgDo7nmamHnhtuy5AkAn33Xk5InIbVkbibB+LfysP3XNBzYkYQZ72TDxqpYrszzRxlYvzgMA5x3YNbQYzix5xHy86qeKYkQLlALtIZuXU7AjIFHUFoq/XH1HuKIJZv6cByVdqEWqPaprAVaGYkEiLgrwukAA5wP0UNhkWJfqIGREIPGOmPIeBd4+dpDKKT+UsItSqA1EP04A5N7H0JWunQ2Fhs7Q2z/9y0IRfQHrEyUQLVPTRNoRQUFPARf1sPJM3oIv6sPxhT7Qk3MdeE7sgUG+7WCV//mENHfIuEAJdBqpCXnY0rvQ3j5PBsAINQVYHvAaNjYUb+MslEC1T51SaAVJSULcPacPk6e1cPLeN1KyxibitBvRAv0GeaEHgOUtAwbITVACfQNMlILMHPQUTy+nwYA4PGBtXuGoG0Xa44j006UQLVPfRNoGcaABw+FOB+kj/MhukhOrfyea4GAh/bdm8JnsCO697NDm87W1DolKkMJtArpKQV4b3B58gSARWu80X9USw6j0m6UQLWPshJoRYwBkY+EOB+sh/PBukhKqbxlCgD6Bjro4NUUXXzs0L6bDTw8rWniBqI0tAZRJWKeZGLu6BOIfVp+Ev/w/7pS8iREDfB4gIe7GB7uYsybk4OHj4W4HKaHy2FCPH6mJ9dnWpBfgqsX4nD1QpxsX3NnU7TtbA2Xtk3Q0t0czq0t0NzZhAYlkVqjBPqaGxfj8cnbZ5CZVijb9+H/dcVbU1tzGBUhpDI8HtDaTYzWbmL4T5feY3r1hi5u3BIh/LYQiZW0Tl8+y8LLZ1k4c/CJbJ+ODh8OrUzR0t0czVuaws7JBM0cjGHnaAxbR2MYGtE0nUQRJdD/SCQMW364hc3Lr8nW9uTxgM9X9cKgsc4cR0cIqQlzMwmGDizA0IEFAKSDkMLviHD3vggPIvl4HqOHkkpmQSopkeD5www8f5hR6XHNmujBupkhrJsZwqqpIayaGsDSVvrdyva/fbYG0NWjU2pjQp82gOcPM7D0/QsID30l2ycU8bFqx0C072rDYWSEkPqwsS6VS6jFxcCzF0I8eiLEi2gdPH0OxMQKkZrx5n7RzLRCZKYV4vG9tDeWMzHTlSZUWwNY2xrCytYQNnZl341g3cwQlk0N6HKxlmjUCTQnqwh/rg7HjvURckswNXM0xuq/BsLalm5VIUSbiETll3wrysvnISZWBzEvdfAqUQdx8UDCKx4Sk3SQkl753L2Vyc4sQnZmEZ5FpVdZhscDLKylCda6WXmCbWpvhGYOxrB1MIZtc2OaWF8DNMoEmp1ZhANb7mPHughkpBbKPTd8ogvmLe0OgQ7NckhIY2FowNDGXYw27orTBUokQGoaH2npAqSkCZCWxkdqGh/JKTykpACp6XxkZAiRkS2ERFL9eYMxIC0pH2lJ+Yi6nVJpGR4PsLI1RDMHYzRzNJb2xzqZwNbBWLZP30BY73qT+mlUCTQyIhmHt0fhxJ6HyMuR/0OxbmaIZb/2hUubJhxFRwhRR3w+YG0lgbWVBK1R9Xy8EgmQmcVHWjofqWkCpKYJkJIqQHIKD0nJQEoqH2lpQmTmCCudXakixoDkhDwkJ+Th9tXESstYWOnLkmvZd1sHE9m2iVnVt/cQ5dDqBFpUWIJ7N5IQ+m8szh19juePFAcIGBoLMXuhJ4aMdwGfT8snEULqhs8HLMwlsDCXwMW58oXEAaCkBEjP4CM5tWKS5SMxkYeEVzwkpQiRliGqNsmmpxQgPaUA928mV/q8kYlIdkm4maP0snDFZNvExoDOefWkNQlULC7Fy2fZeBaVjqjbKbh1OQF3ryehuKi00vJmFnp4Z157DJvgQnPaEkIajI5OeYsWVbRoS0qA5BQBXiUJ8CpRB4nJAiQk8JCQwENisg5S0kQoreZycW52MR7fT5ObDKYika5ANqipbBSx7Pt/I4strPVhaqFHszlVQa0TqFhcitysYuRkFSM3u0j6PasYGWkFSI7PQ1JCLpIT8hAfnYOYp5koEUveeDyBgIf23WwwcW47dPRqSv99EULUko4O0My2FM1sSwEUKzxfWgqkpfORkKiDxCQBEpMESHhVlmAFSE7VRbH4zUmvuKgUcS+yEfciu9p4DI2FMGuiB1NzPel3C+l3EzNdGBqLYGAkhIGhEIbGQhgYCaX7DIXQNxRCpMuHSFcAoUgAoa4AQiFfaxZL5ySB7vvtHo7vfojiolKIiyUQi0vLHxeXQvzf45KSNyfEmjAx10WbTlYYOKYluvWxh56+Wv/PQAgh1RIIylqxxUA7xecZk/bHJiZJW7GJSTr/JVggMUmA5FQRcvNrPggpL0eMvBwx4qNzlBK/NKFKE2tZcuULeODzeNLv/P++/nvM4/MgEFS+j8fjgccD8F9O5vF4+GBxV3T0slVKrG/CSTZJjs/F3etJSj+uSFcAC2t9OLQ0RVtPa7TysIBphXkvY5/R/KrqrFmFx1VddiKapeJnmplUeV8dUQ0eAFsz6RfcFJ/PL+AjLUOI9Mz/vjJ0kJYuRGoaH+mZAqRnipCTJ0RuvhCMKfeuhOIiaaPp9cGcyjJxdluVHPd1tZpM3tLSEk5OTkp780f3UuUuu5ayXAh41d97yePxoKPDh0BHOy4DFJXkQFfHmOswGhzVu3Hg8xhM9LORW1gIY/3GNzI0p6BIy+vNACb577t8OskuFMNET7Glyyo8YKj6PF5+uJqf6wW6RrCwU17r09LSEmfPnq30uVolUFVrTKu9VET1blyo3o1LY603oP11p9kCCCGEkDqgBEoIIYTUgVol0NmzZ3MdAieo3o0L1btxaaz1BrS/7mrVB0oIIYRoCrVqgRJCCCGaQqUJ9NChQ/Dz84OjoyP09fXh5uaGr776Cjk5tbsZd+XKleDxePD29lZRpMpV33pHRUVh/PjxsLS0lL1+w4YNKo66/upT79jYWEybNg0ODg4wMDCAq6srFi9ejLy8vAaIvP4CAgLg6+uLpk2bQldXF/b29pgwYQIiIyOrfW1hYSG++OIL2NraQl9fHz169MDFixcbIOr6q2u9b968idmzZ8Pd3R0GBgZwcHDAlClT8OLFiwaKvH7q83lXpGnntvrWW1PPbVVR6SVcLy8vODg4YPTo0bC3t0dERASWLVsGd3d3XLlyBXx+9fn7+fPnaN++PQwNDeHi4oLLly+rKlylqU+9b968CV9fX/Tt2xczZ86Eqakpnjx5gtzcXHz66acNWIvaq2u98/Ly0KlTJ4jFYixbtgwODg64ceMGli5dilGjRmH//v0NXJPa27dvH8LDw9G9e3dYWVkhNjYWq1atwsuXL3Hv3j04OjpW+dopU6bg1KlTWL16NVq2bImff/4ZZ86cQVhYGDp27NhwlaiDutb7888/R1hYGKZMmQIPDw/Ex8djxYoVSE5Oxu3bt9G8efMGrknt1OfzLqOJ57b61FuTz21VYiqUnJyssG/nzp0MADt//nyNjjFo0CA2e/Zs1qdPH9arVy9lh6gSda13aWkpa9OmDXvrrbdUGZ7K1LXeAQEBDAALCAiQ279w4UImEAhYXl6e0mNtCA8fPmQA2E8//VRlmdu3bzMAbNu2bbJ9YrGYubq6spEjRzZEmEpXk3pX9rsSHR3NeDweW7JkiSrDU5ma1LsiTTy3VaYm9db0c1tVVHoJ18rKSmFf165dAQDx8fHVvn7v3r0IDw/HypUrlR6bKtW13sHBwYiMjNTY/8bqWu/iYulk2SYmJnL7zczMIJFIwDR0nFuTJtK1ZYXCquccPX78OIRCId5++23ZPh0dHUycOBEBAQEoKipSeZzKVpN6V/a74ujoCCsrqxqdG9RRTepdRlPPbZWpSb01/dxWlQYfRBQSEgIAaN269RvLZWRkYMGCBfjxxx9hYWHREKGpVE3qXXYJp7CwEF5eXhAKhbC2tsbHH3+MgoKCBolT2WpS7wEDBsDFxQULFy5EZGQkcnNzceHCBWzYsAFz586FoaFhQ4Vbb6WlpSguLsaTJ08wZ84cNG3aFBMnTqyy/IMHD9CiRQsYGBjI7ffw8EBxcTGePn2q6pCVorb1rkxUVBSSk5OrPTeok7rUWxvObbWttzae2wCo9hLu6+Li4piVlRUbMGBAtWXfe+895u3tzSQSCWOMafRljprWe86cOQwAMzc3Z0uWLGFBQUFs9erVTF9fXyMvfdTm805KSmLe3t4M0mkyGQA2a9YsVlpa2gCRKo+np6cs/latWrHIyMg3lh84cCDr3r27wv7AwEAGgF28eFFVoSpVbev9OrFYzHr37s2srKxYenq6iqJUvrrUWxvObbWtt7ad28o0WALNyclhnp6ezNbWlr18+fKNZS9evMiEQiG7d++ebJ8m/pIxVrt6+/v7MwBs3rx5cvtXrVrFALAHDx6oMlSlqk29CwoKWN++fZmrqyv766+/WEhICFu9ejUzNjZmc+fObaCIlSMyMpJdvXqV7d27l3Xu3JnZ2dmxFy9eVFl+wIABzMvLS2H/v//+q1EJtLb1ft2cOXOYjo6OQj+4uqttvbXl3FbbemvTua2iBkmgBQUFrF+/fszc3JzdvXu32vKtW7dmc+fOZRkZGbKvXr16MS8vL5aRkcEKCwsbIOr6q229Fy1axACw48ePy+0PDw9nANiePXtUFapS1bbemzdvZgDY06dP5fb/8ccfDAC7ffu2qkJVqYyMDGZqasrmzJlTZZkJEyYwV1dXhf379+9nANj9+/dVGaJK1KTeFS1atIjxeDy2a9cuFUemWjWpt7ac2yqqSb215dz2OpX3gYrFYvj5+eH69es4ffo02rWrZPXX10RFReG3336Dubm57Cs0NBRXr16Fubk5fv31V1WHXW91qbeHhwcAKKzWzv4bRFOT2364Vpd637t3D+bm5nB2dpbb361bNwDS3wdNZGZmhlatWr2xH9PDwwMvXrxAfn6+3P7IyEiIRCK0atVK1WEqXU3qXea7777DqlWrsGHDBkydOrUBolOdmtRbG85tr6vp7zmg2ee2yqg0aolEgilTpuD8+fM4duwYvLy8avS6oKAgha8OHTqgbdu2CAoKwrhx41QZdr3Vtd5Dhw6Frq6uwtpzAQEBAKRLA6mzuta7adOmyMjIUPgDvHbtGgDAzs5O6bE2hKSkJDx8+FDhH4OKRo0aBbFYjIMHD8r2lZSUYP/+/Rg0aBB0dTVvHcma1BsANm7ciMWLF+O7777DvHnzGig61alJvTX93FaZmtRb089tVVJl83bu3LkMAPvmm29YWFiY3FdZv1h0dDQTCARs+fLlbzyWJvUT1Kfey5YtYwKBgH311VcsMDCQrVy5kunp6bFp06ZxUJPaqWu9X7x4wYyNjZmLiwvbsWMHu3DhAvvxxx+ZsbEx8/T01IiBRG+99Rb79ttv2dGjR9mFCxfYb7/9xtzc3JipqSl79OgRY6zqz/ztt99mZmZmbMuWLezcuXPMz8+P6erqslu3bnFRlVqpa7337dvHeDweGzJkiMLviib0h9Xn836dJp3b6lNvTT63VUWlCdTR0VFuVGXFr6VLlzLGpCfPittV0aRfsvrUWyKRsDVr1jBnZ2cmFAqZg4MDW7JkCSsuLm74itRSfer94MEDNn78eGZvb8/09PSYi4sL++yzzzRmROaqVatY586dmampKdPX12eurq5s9uzZcgMrqqp7fn4+W7BgAbOxsWG6urqsW7duLCgoqEHjr6u61nvatGlV/q706dOnwetRW/X5vF+nSee2+tRbk89tVaHVWAghhJA60MyeW0IIIYRjlEAJIYSQOqAESgghhNQBJVBCCCGkDiiBEkIIIXVACZQQQgipA0qghBBCSB1QAiVESXg8Hng8Hvh8Pp49e1ZluX79+snK7tixQ+656dOny55bunRplcfYuXOnrFzfvn3lngsODpY9V/ZlYGAAW1tb9O7dG1988QUiIiLqU1VCCCiBEqJUOjo6YIzhzz//rPT5J0+eICQkBDo6OtUeZ9u2bSgtLa30+S1btlR7DEdHRyxduhRLly7FJ598ghEjRiAvLw8//fQTOnfujClTpiA3N7dmFSOEKKAESogS2djYoEuXLti+fTtKSkoUnt+6dSsYYxgxYsQbjzNixAjExcUpTL4NSFf0CA0NxciRI994DCcnJyxbtgzLli3D999/jy1btuDWrVuIiIhAu3btsHfvXowfP752FSSEyFACJUTJ/P39kZiYiJMnT8rtF4vF2LlzJ3r27Clb3qkqU6ZMgb6+PrZs2aLwXNm+WbNm1Sm+jh074ty5c7CyssLZs2dx9OjROh2HkMaOEighSjZp0iQYGhpi69atcvuPHz+OpKQk+Pv7V3sMMzMzjB8/HqdOncKrV69k+4uKirBr1y706dMHrq6udY7R2toac+bMAQDs2bOnzschpDGjBEqIkhkbG2PixIk4e/Ys4uLiZPu3bNkCExMTTJgwoUbH8ff3R0lJidxAoyNHjiAtLa1GSbg6ZYOPrl+/Xu9jEdIYUQIlRAX8/f1RWlqKbdu2AQBiYmIQGBiIKVOmwMDAoEbH8Pb2RuvWrWX9poA0CZubm8PPz6/eMZYtVJ6SklLvYxHSGFECJUQFunfvjnbt2mHbtm2QSCTYunUrJBJJrVuOs2bNwvPnz3HhwgU8ffoUQUFBmDp1KvT09OodY1lS5vF49T4WIY0RJVBCVMTf3x8xMTE4e/Ystm/fDk9PT3Tq1KlWx3j33Xehq6uLrVu3ylqiyrh8CwAJCQkAACsrK6Ucj5DGhhIoISoydepU6OvrY86cOYiPj8fs2bNrfQxLS0uMGTMGR44cwbZt29CjRw+0bdtWKfEFBQUBkLaWCSG1RwmUEBUxMzPDuHHjEBcXB0NDQ0yaNKlOx/H390dRURFSUlKU1vpMTk7G77//DkB6ywwhpPbePJUJIaRe/ve//2Hs2LGwsrKCsbFxnY7Rr18/HDt2DBKJBIMHD653THfu3MG7776L1NRUDBs2DKNGjar3MQlpjCiBEqJCDg4OcHBwqNcxeDxenZJcdHQ0li1bBkA6iUNqaipu3bqFW7duAZC2PMtaoYSQ2qMESoiWiomJwfLlywEAenp6MDMzg4uLCz7//HNMmTIFHTt25DZAQjQcj5WNZSeEEEJIjdEgIkIIIaQOKIESQgghdUAJlBBCCKkDSqCEEEJIHVACJYQQQuqAEighhBBSB5RACSGEkDqgBEoIIYTUASVQQgghpA4ogRJCCCF18P8x08WJUq1b6gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# scale\n",
+    "ori_x = ori_x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# Smoothing\n",
+    "for i in range(len(ori_x)):\n",
+    "    ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "# yes_data = yes_data / 3000\n",
+    "# no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "58b1f931",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_statistics = trainer.amortizer.summary_net(x)\n",
+    "summary_statistics_yes = trainer.amortizer.summary_net(data_ERP_yes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "de69afbb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 1], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_yes[:, 0], summary_statistics_yes[:, 1], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "0c07bc2d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 1], summary_statistics[:, 2], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_yes[:, 1], summary_statistics_yes[:, 2], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "8a20c0bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 2], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_yes[:, 0], summary_statistics_yes[:, 2], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b48320faff64848a679f3bf25a29396",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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m+ruLnJrI9Hh0I67rFs6ePXuGfC8oKAgajQZSqRQajQaBgYGDlpHJZKiqqhr4urq6GiEhIQPrX/bQQw9h+fLlIwpPQ+vo7Uf2SQ3+ebQa+eXNkEiAeZP88PN54Vg8NQhSb9crljcYBGz57gLSd57B0tf349W74/lcdxpzcr8/unmFRzeiMOqUWlJSEjIzM5GWlobMzEwkJycPWmbmzJlQq9UoLy9HaGgotm7dik8//RQABsoKALZt24bY2Fhj4hCA5s4+/Dn7NHac0KBbp0eEvzt+tzQKd04LRYiP65Dr2dlJsGb2BCRGB+HhT47hd/88DnmgB6KlXmZMTzR6BOHSfTcT/NxwB49uRGFU4aSlpeGee+7B+++/j/Hjx+Mf//gHAKC2tha/+MUvkJ2dDQcHB2zcuBFLly6FXq/HunXrEBMTAwB46qmnUFRUBIlEgvDwcLzzzjvG/x/ZsLqLPfjZ+9+isrkLq6bLcNcMGaaP9xnRNZlALxe8df90LH/jINZ/fBSf/2o+vFwcRzE1kXnsOd2A4to2vHyXgkc3IpEIV7vIYuFUKhUKCgrEjmFRShs68MCH+Wjp7MP7D8zEnAg/oz4vv7wZ972bh8XRgdh0/wwOJCCrpjcIuP2NA+jW6bH3iZtZOCY23N/J3OtjwOHSRqx86xC6+/T49KE5RpcNAMya6Iunb5uCXcX1ePfAeROkJBLPtsIanKlrx2+XRLFsRMQ9b+U+K6jC2g/yEejlgu2P3IT4MB+TffaD8yfitthg/CXnLL6raDbZ5xKZU49Oj7/tPguFzBu3x0nFjmPTjLqGQ6PvSFkTztS1IdzfHZP8PWBnB1Q1d6O6pQtHK1uw9bsqzJf748010+HtatprLRKJBH+9S4HiNw7iN1uLkP3rBSbfBtFoyzxcgdqLPXjlnnjY2fHUsJhYOBbqVM1F/CXnDA6oG4dcxk4C3D9nPJ5dEQPHUTpN4OniiA2rlbhr0xH8fvspvLFayes5ZDUudunw5teluHlyAOZN8hc7js1j4Vig9w6cxws7TsPHzRG/vz0aK+JDcKG5C2UNHRAAhI1zg2ycK0J8XOHkMPpnRaeNH4ffJETi1dxzWBQVgJXTZaO+TSJTeOubUrT39iPttiliRyGwcCzOkbIm/Dn7NJZMDcIr98QPDEkO8nLBzHBf0XI9vEiOA+pG/DGrGHMn+Q26cZTI0tS0duPDwxW4c1oo7yezEBw0YEEa2nvwqy2FCPd3x9/uVVrU/S/2dhK8cnc8enR6vP1NmdhxiK5JEAT8179PQgLgySVRYseh77FwLES/3oDHthSio1eHt9fMgIez5R18jvdzw90qGbbmV6HuYo/YcYiG9NGRSuw7p8Uzt0cj9BozbJB5sXAsxN9yzyHvfDNevCMOUcGeYscZ0sO3yGEQBLz9TanYUYiu6lx9O/6cfRqLogLwszkTxI5DP8DCsQDfVTTjrW/KsHpmGFbNsOwL8mG+blg1XYYt3/EohyxPb78ej20phIezA/56VzxHVFoYFo4FyNh/Hr7uTnh2RYzYUYblkUVyGAwCNu3jtRyyLK/sOoszde34610KBHg6ix2HfoSFI7Kq5i7sPV2P1TPD4OpkL3acYRnv54aV00Pxaf4F1LfxKIcsQ0FFM949UI7754xHQnTQ9Vcgs2PhiOyTby8AANZY2bnmRxdFQm8QOGKNLILBIOD5L0oQ7OWC/1oWLXYcGgILR0Q9Oj3+97sLWDI12OpG0oz3c8PKaaHYkn8BDTzKIZFtK6zByZqL+M/bouDmZHkjPOkSFo6Ivjhei5YuHdbOs66jm8sevVWOfoOAt3kth0TU1dePv+46g3iZN5LjQ8WOQ9fAwhGJIAjIPFKByEAPzDXB4wTEMMHPHXdOC8Wn3/Ioh8Szad951Lf14o8rpnJyTgvHwhHJsQutOFXThrXzwq166Oajiy4d5Wzax2fmkPmd13YgY38ZliukmDFBvKmfaHhYOCL56EgFPJ0dsHKadZ8CCPd3xx3KUHz8bSWqmrvEjkM2pEenx8OfHIOroz2euZ0DBawBC0cEDe09yD6pwV0qGdwtcAqbkXpyyWTYSYCXdp4WOwrZkOe/KMaZunb87V4lJ5O1EiwcEWzNr4JOL4yZaTdCfFyx/mY5sk/WIe98k9hxyAZsL6zBlvwqPHzLJCyKChQ7Dg0TC8fMdHoDPvm2EgsnByAiwEPsOCaTujACId4ueP6LEugNgthxaIzq7O1H+s4z+N0/j2NWuC+eSJwsdiQaAaMKp7m5GYmJiYiMjERiYiJaWlquuty6desQGBiI2NjYG1p/LNldXI/6tl48YKVDoYfi6mSPp5dF47SmDZ8VVIkdh8YYQRDwxfFaJLy6D5v2lSFZGYp3fjYDDqP0pFsaHUb9aaWnpyMhIQFqtRoJCQlIT0+/6nIPPPAAcnJybnj9sSTzcAXG+7rh5slj7zTAcoUUM8PH4ZVdZ9HWoxM7Dlm5Hp0eB9WNSN95BsveOIhfbSmEn4cT/rV+Ll65Ox7j3J3EjkgjZFThZGVlISUlBQCQkpKC7du3X3W5hQsXwtd38JDF4a4/VvyjoAr5Fc1YO3cC7Mfg/QISiQR/XB6D5q4+vPkVH19AI1fd0oU3vy7FT9/Ng+L53bj//W/x/sHz8HR2wJ/vjMPnj87n8GcrZtQQqfr6ekilUgCAVCpFQ0PDqK2fkZGBjIwMAIBWq73BxOI5Ud2KZ7afwrxJfnhgXrjYcUZNnMwby2Kl+KygCr9dGgVHnvKgYWho78GbX5Xi0/wL0OkFREu9kDJ3AubJ/TEr3HdMjOakYRTO4sWLUVdXN+j1F198cVQCDSU1NRWpqakAAJVKZdZtG6uxoxf/sfkoAjycsfGn08f8eeckZQh2nNTgSFkTFk4OEDsOWbCuvn68sbcUfz9cjn69gLtVYXj0VrnVzS1Iw3PdwtmzZ8+Q7wUFBUGj0UAqlUKj0SAwcGTXJYxd3xr06w149NNjaOrsw7/Wz4OvDZx3vnlyADycHbDjhIaFQ0Pq7dfjl5uP4mBpI5LjQ/CbxZMR7u8udiwaRUb9VTspKQmZmZkAgMzMTCQnJ5t1fUun0xvwx8+LkXe+GS+tjENsqLfYkczCxdEeiVODkFNch75+g9hxyAL16w34zdYiHFA34q+rFHh99TSWjQ0wqnDS0tKQm5uLyMhI5ObmIi0tDQBQW1uLZcuWDSx33333Ye7cuTh79ixkMhnef//9a65vDQRBQG1rN2pbu9HQ1oOmjl706PQD75c2tGPV24fx6bcX8MuFEVg53bIfHW1qyxVSXOzW4VBZo9hRyMIIgoD/2nYSO0/V4Q/Lp+JuVZjYkchMJIIgWN1deiqVCgUFBaJtv7ShA89sO4lvy5uveF0iAUJ9XBHu5478ima4O9njxTvjsCxOKlJS8fT266F6YQ+WTA3Gq/fEix2HLIQgCPhz9mm8e6Acj90qxxNLosSORCYw3N/JHPoxAjq9Af+zV42395XB1dEe//mTKfB1d0S/QYDeIKC5sw/ntZ0439iBpTHB+MPyaAR6uogdWxTODvZYGhOMXcV16O2PhbODdTw+m0bXW9+U4d0D5UiZOwGPc5YAm8PCGYHXcs/hrW/KcOe0UDxzezT8PZzFjmTRbldI8c+j1ThwrhGLp/IZ87Zu85EKvLzrLO5QhuDZFTFW/VgOujEsnGH6rqIZm/aV4V5VGP5yl0LsOFZhvtwf3q6O2HmqjoVjw/LON+GNvWocLmtCwpRAvHx3PB+UZqNYOMPQ3qPD4/9bBNk4N/xhxVSx41gNR3s7LIj0xwG1FoIg8G+0NqS9R4cjZU1472A58subEeDpjN/fHo2fzZ3Am4FtGAtnGP70ZQlqW7vx2S/nwoN3PI/IwskB+PKEBmfq2hEt9RI7Do0Snd6AwgutOFjaiEOljSiqaoXeICDIyxnPrZiK1bPGw8WR1/FsHX97Xseu4jp8VlCNRxZNgiqccziN1MLISzd+HlBrWThj0Kmai3j/YDl2F9ehs08POwkQJ/PB+psnYe4kP6jCx3HACA1g4VyDtr0XT//7JGJCvPDrBI6ouRHB3i6YHOSB/ecakbpwkthxyAQMBgH7zmnx7oHzOFzWBDcneyQrQ3Hz5ADMjfCDt5uj2BHJQrFwhiAIAtL+dQKdvf14/V4lnBx43vlGLYwMwEd5leju08PViX/btVbdfXpkFdXgvYPlKG3oQLCXC9Jum4L7Zo2HtytLhq6PhTOErd9VYe+ZBjy7YioigzzFjmPVFk4OwHsHy5FX3sTHAVsZQRBwtLIF/zxajS9PaNDR24+pUi+8dm88bo8L4V/EaERYOFdR0diJP31Zgvlyf6TMDRc7jtWbNdEXzg52OHCukYVjRQovtOC3/ziOMm0n3JzscXucFHfNkGHWRF+OOKQbwsL5EYNBwJP/OA4HOwlevlvB+wVMwMXRHrMm+mK/2vqeY2SrPv32Ap77vBhB3s545e543BYbzGfSkNF4PPwjOcV1OFrZgt8vnwqpN5/JYSo3Tw5AaUMHalu7xY5C19Cj0yPtXyfwX9tOYs4kP3zx6HzcNUPGsiGTYOH8QL/egFd3n0VkoAdW2djszqPt8nNx9p3jUY6l0hsE3P/et9j6XRUeXSTHhw/MhI/b2H9+E5kPC+cHthXWoEzbiSeXRMGep9JMKjLQA6E+rthTUi92FBrCruI6FFS24M93xuG3S/kzQKbHwvleb78er+9RQyHzxtIYzvtlahKJBEtjgnGgtBEdvf1ix6EfEQQB7+w/jwl+brh3Jp9PQ6ODhfO9rflVqGntxm+XRHEEzihZGhOEvn4DvjnbIHYU+pGCyhYcr2rFL+ZP5JENjRoWDi4N/3x511nMnuiLBZH+YscZs1ThvvBzd8KuYp5WszQZ+89jnJsj7prBoxsaPTZfOKdqLmLtB/nwdXfC66uVPLoZRfZ2EiRODcLXZxrQ26+//gpkFmXaDuw5XY+fzQ3nTBA0qmxurOP/fncBh0qbIBvnigBPZ2zYq4aXiyM+fWg2h0GbwdKYYGz9rgqHS5uwaApvArUE7x0oh5O9HdbOnSB2FBrjbK5wmjr7UFTViuyTGvQbBEi9XbDloTmQjXMTO5pNmCf3g4ezA3YV17FwLMDl+dHuUIbyCbY06myucB6+RY6Hb5FDbxBQ19YDP3cnPqfDjJwd7LFoSiByS+rx4p0CL1CL7KszDejq0yN5WojYUcgGGHUNp7m5GYmJiYiMjERiYiJaWlquuty6desQGBiI2NjYK15/7rnnEBoaCqVSCaVSiezsbGPijIi9nQShPq4sGxEsjQlCU2cfCiqaxY5i8748UYsAT2fMnugndhSyAUYVTnp6OhISEqBWq5GQkID09PSrLvfAAw8gJyfnqu89/vjjKCoqQlFREZYtW2ZMHLISt0QFwsnBjqPVRNbR24+vzjRgWWwwjzTJLIwqnKysLKSkpAAAUlJSsH379qsut3DhQvj68mmZdImHswMWyP2xq7gOgiCIHcdm7T1dj95+A5bH83QamYdRhVNfXw+pVAoAkEqlaGgY+Q19GzduhEKhwLp164Y8JQcAGRkZUKlUUKlU0Go5H5e1WxoTjJrWbhTXtokdxWZ9cVyDYC8XzBg/TuwoZCOuWziLFy9GbGzsoH+ysrKM3vj69etRVlaGoqIiSKVSPPnkk0Mum5qaioKCAhQUFCAgIMDobZO45n9/g+3RyqH/kkGj52K3DvvPaXG7QspHcJDZXHeU2p49e4Z8LygoCBqNBlKpFBqNBoGBIxvmGhT0/3OWPfTQQ1i+fPmI1ifrJfV2gb+HM05UXxQ7ik3KLalHn96A5Qqp2FHIhhh1Si0pKQmZmZkAgMzMTCQnJ49ofY1GM/Df27ZtGzSKjcYuiUQChcwbJ6pbxY5ik7KKahDq4wplmI/YUciGGFU4aWlpyM3NRWRkJHJzc5GWlgYAqK2tvWLE2X333Ye5c+fi7NmzkMlkeP/99wEATz31FOLi4qBQKPD111/jtddeMyYOWRmFzBul2g50cvZos6ps6sQBdSPuVsk4lROZlVE3fvr5+WHv3r2DXg8JCbninpotW7Zcdf3Nmzcbs3mycgqZNwQBKK5tw6yJHMVoLp9+ewH2dhKsnjle7ChkY2x+8k4ST1yoDwDwtJoZ9ej0+KygConRQQj2dhE7DtkYFg6JJsDTGSHeLhw4YEY7T2nQ0qXD/XM4USeZHwuHRBUn88bJGhaOuXycdwET/d0xbxKnsiHzY+GQqBQyH5Q3duJit07sKGPeaU0bjla2YM3s8bz3hkTBwiFRKWTeAC49CI9G18d5lXB2sMNdM2RiRyEbxcIhUcWFXiqc4xw4MKrae3TYVliDFfEh8HFzEjsO2SgWDonKx80J433dcJIDB0ZVVlEtuvr0HCxAomLhkOguzTjAwhlNOafqIA/0QPz3pzCJxMDCIdEpZN6oae1GU0ev2FHGpM7efuSXN2NRVABnFiBRsXBIdAM3gHLgwKg4XNaEPr0Bi6JGNrkukamxcEh0saFekEjA6zij5JuzDXBzsocqnNMHkbhYOCQ6TxdHRPi7c4qbUSAIAvartZg3yQ9ODvxxJ3HxO5AsQrzMhwMHRkFFUxeqmruxcDIfWkjiY+GQRYiTeaOhvRd1F3vEjjKm7D936XHsCyNZOCQ+Fg5ZhNjvbwAtruVRjikdUGsx3tcN4f7uYkchYuGQZYiWXho4cKqmTewoY0ZfvwFHypqwcLK/2FGIALBwyEJ4ODtgor87TvEIx2SOVrags0/P02lkMVg4ZDFiQ7xRzHtxTGa/WgsHOwnm8lEEZCFYOGQxYkO9UHuxB82dfWJHGRP2n9Ni+oRx8HRxFDsKEQAWDlmQ2BAOHDAVbXsvimvbcDOHQ5MFYeGQxYgJufxsHA4cMNbBUg6HJstjVOE0NzcjMTERkZGRSExMREtLy6BlqqqqsGjRIkRHRyMmJgYbNmwY0fpkO7zdHBHm68qBAyaw/1wj/NydEBPiJXYUogFGFU56ejoSEhKgVquRkJCA9PT0Qcs4ODjg1VdfxenTp5GXl4c333wTJSUlw16fbAsHDhjPYBBwQK3F/Eh/PkqaLIpRhZOVlYWUlBQAQEpKCrZv3z5oGalUiunTpwMAPD09ER0djZqammGvT7YlTuaNiqYuNPJRBTesRNOGxo4+nk4ji2NU4dTX10MqlQK4VCwNDQ3XXL6iogKFhYWYPXv2iNfPyMiASqWCSqWCVqs1JjZZsHmTLt2keKi0UeQk1mu/+tLPxwLe8EkWxuF6CyxevBh1dXWDXn/xxRdHtKGOjg6sWrUKr7/+Ory8Rn5eOTU1FampqQAAlUo14vXJOsSFesPb1REH1Y1IVoaKHccq7T+nRbTUC4GeLmJHIbrCdQtnz549Q74XFBQEjUYDqVQKjUaDwMCrP+BJp9Nh1apVWLNmDVauXDni9cl22NtJcJPcDwfUjRAEgU+oHKHO3n4crWzBuvkTxY5CNIhRp9SSkpKQmZkJAMjMzERycvKgZQRBwIMPPojo6Gg88cQTI16fbM+CyADUtfWgTNshdhSrc0CthU4v4GZevyELZFThpKWlITc3F5GRkcjNzUVaWhoAoLa2FsuWLQMAHDp0CJs3b8ZXX30FpVIJpVKJ7Ozsa65Ptm2+/NK1hwNqXscZqayiWvh7OGHWRD7dkyzPdU+pXYufnx/27t076PWQkJCBUpk/fz4EQRjR+mTbwnzdMNHfHQfUjfj5TTw1NFwXu3XYe7oBa+aMh4M97+kmy8PvSrJI8+X+yDvfhL5+g9hRrEbOKQ369AbcwcEWZKFYOGSRFkT6o6tPj2MXOPvEcG0vrEWEvzsUMm+xoxBdFQuHLNKcSX6wt5PgIK/jDIvmYjfyypuQrAzlyD6yWCwcskheLo5QhvnggJo3+Q7H50W1EAQgWRkidhSiIbFwyGItiPTHiZqLaO3i83GuZ1thDaaN90G4v7vYUYiGxMIhi7Ug0h+CABwqbRI7ikU7U9eGM3XtHCxAFo+FQxYrXuYDT2eHgWe70NVtL6yFvZ0EyxVSsaMQXRMLhyyWg70d5k7yw/5zjUPey2XrDAYBnxfVYGGkP/w8nMWOQ3RNLByyaAsmB6CmtRsVTV1iR7FIRdWtqL3YgyQOFiArwMIhi7bg+2luDnK02lXtLq6Hg50Et04JEjsK0XWxcMiiTfBzg2ycK/bzfpyr2l1ShzkRfvB2dRQ7CtF1sXDIokkkEiyIDEBeWRN0ek5z80OlDR04r+3Ekhge3ZB1YOGQxVsQ6Y/23n4cr2oVO4pFyS2pBwAsjmbhkHVg4ZDFmzfJD3YSPq7gx3JL6hAX6o0QH1exoxANCwuHLJ6PmxPiZD44WMrCuayhvQeFVa1InMqjG7IeLByyCjdN8sPxqlZ09PaLHcUi7D3dAEEAr9+QVWHhkFW4Se6PfoOA/HJOcwMAu4vrEObriqggT7GjEA0bC4eswowJ4+DkYMd51QC0dPbhUGkTlkwN5qMIyKqwcMgquDjaY06EH3aX1Nn8NDf/LqxBn96Au2bIxI5CNCIsHLIayfEhqGrutumngAqCgK35FxAf5oNoqZfYcYhGhIVDVmNpbDBcHO2wvbBW7CiiOXahBeqGDvx0VpjYUYhGzKjCaW5uRmJiIiIjI5GYmIiWlsF/86yqqsKiRYsQHR2NmJgYbNiwYeC95557DqGhoVAqlVAqlcjOzjYmDo1xHs4OSJwajC9P1NrsrANb8qvg7mSP5QpO1knWx6jCSU9PR0JCAtRqNRISEpCenj5oGQcHB7z66qs4ffo08vLy8Oabb6KkpGTg/ccffxxFRUUoKirCsmXLjIlDNuAOZQhaunTYf872JvO82K3DlydqkaQMhbuzg9hxiEbMqMLJyspCSkoKACAlJQXbt28ftIxUKsX06dMBAJ6enoiOjkZNTY0xmyUbtnByAMa5OWJboe19D31eVIMenQH38XQaWSmjCqe+vh5S6aWnDEqlUjQ0NFxz+YqKChQWFmL27NkDr23cuBEKhQLr1q276im5yzIyMqBSqaBSqaDV2t7fbukSR3s7LFeEILekHu09OrHjmE1fvwF/P1yBqVIvxIV6ix2H6IZct3AWL16M2NjYQf9kZWWNaEMdHR1YtWoVXn/9dXh5XRpds379epSVlaGoqAhSqRRPPvnkkOunpqaioKAABQUFCAgIGNG2aWy5Y1oIevsN2HmqTuwoZvPWN6Uo03biySWTee8NWa3rngjes2fPkO8FBQVBo9FAKpVCo9EgMDDwqsvpdDqsWrUKa9aswcqVK69Y/7KHHnoIy5cvH0l2slHTx4/DlGBPvLzrLBKmBI75RyufrWvHm1+XIlkZggTODE1WzKhTaklJScjMzAQAZGZmIjk5edAygiDgwQcfRHR0NJ544okr3tNoNAP/vW3bNsTGxhoTh2yERCLBa/cqcbFbh9/+4zgMhrF7I6jeIOCpf52Ap4sjnl0RI3YcIqMYVThpaWnIzc1FZGQkcnNzkZaWBgCora0dGHF26NAhbN68GV999dWg4c9PPfUU4uLioFAo8PXXX+O1114z8n+HbEW01Au/vz0aX5/V4sPDFWLHGTUfHCzH8apWPJcUA193J7HjEBlFIljhPCEqlQoFBQVixyCRCYKA1M1H8c3ZBmx7+CbEjrGL6RWNnfjJhv2YLw/Au2tn8NoNWazh/k7mTANktSQSCf66SgE/d2f8akshOsfYowue/6IYjvZ2ePHOWJYNjQksHLJq49yd8PpqJSqbOvHYlkL0j5EZCPLLm/H1WS0eXSRHkJeL2HGITIKFQ1ZvToQf/js5FnvPNOCZbaesfjZpQRDw8q4zCPR0xtq54WLHITIZzo9BY8L9cyagoa0Hb3xViiAvZzyxJErsSDfsm3NafFfRgj/dEQtXJ3ux4xCZDAuHxozHEyejvq0Xb3xVil69AU8tnQJ7O+u69iEIAl7LPYcwX1fcq+IUNjS2sHBozJBIJHjxzlg4Okjwzr7zUNd3YMNqJTxdHMWONmxfn23AieqL+MuqODg58Iw3jS38jqYxxcHeDi/cEYc/Jcdg3zktVr51GBeausSONSyCIGDDHjVk41yxcjqf5kljDwuHxqSfzQ3H5nWz0NDei6Q3D+JwWaPYka7rm3NaHK++iEcWyeFozx9NGnv4XU1j1jy5P7IeuQn+Hs5Y+34+Ps6rFDvSkC4f3YT6uGIVj25ojGLh0JgW7u+Ofz88Dwsi/fH77afw4o4Sixw2vV/diKKqVjy8aBKv3dCYxe9sGvO8XBzxXspM3D9nPN49UI5/HK0WO9IVLh3dnEOItwvunsGRaTR2sXDIJtjbSfB8UizmRvjhj1mncK6+XexIA/arG3HsQivWL5Lz6IbGNH53k82wt5Ngw31KeDg74uFPjqGrT/y51/QGAX/ecRphvq64R8VrNzS2sXDIpgR6umDDaiXKtB14+t8nRX+Wzv9+V4Wz9e14+rZoODtwVgEa21g4ZHNukvvjt0uikFVUi5d2nhZtEEF7jw5/yz2LWeG+uC02WJQMRObEmQbIJj18yyQ0tPXg3QPl8HV3xvpbJpk9w5tfl6Gxow8fPBDNxw+QTWDhkE2SSCR4dkUMWrp0+EvOGfi6O+LemePNtv2q5i58cLAcK6eHQiHzMdt2icTEwiGbZWcnwSt3x6O1W4en/30S3q6O+Ems1CzbTs85A3s7CZ5aOsUs2yOyBLyGQzbNycEOm+6fjmnjx+HRTwux86Rm1Ld5quYidpzQIHVhBIK9+XA1sh0sHLJ5bk4O+PvPZyI+zAePbinEjhOjWzof51XC1dEeDy6YOKrbIbI0LBwiAJ4ujshcNwvTx/vgsa2F2H9OOyrbaevRIauoFsnKEHhZ0WMTiEzBqMJpbm5GYmIiIiMjkZiYiJaWlkHL9PT0YNasWYiPj0dMTAyeffbZEa1PZC4ezg74+89nIdTHFW/sVY/KNrYdq0G3To81syeMyucTWTKjCic9PR0JCQlQq9VISEhAenr6oGWcnZ3x1Vdf4fjx4ygqKkJOTg7y8vKGvT6RObk7O2Dt3AkoqGzBqZqLJv1sQRDwybeVUMi8ESfzNulnE1kDowonKysLKSkpAICUlBRs37590DISiQQeHh4AAJ1OB51ON3DPwXDWJzK3u2eEwdXRHpuPmPZxBgWVLThX34E1s803/JrIkhhVOPX19ZBKLw0jlUqlaGhouOpyer0eSqUSgYGBSExMxOzZs0e0PgBkZGRApVJBpVJBqx2d8+tEAODt5og7poVie1ENWrv6TPa5H+dVwtPFASviQ0z2mUTW5LqFs3jxYsTGxg76Jysra9gbsbe3R1FREaqrq5Gfn49Tp06NOGhqaioKCgpQUFCAgICAEa9PNBJr505Ab78BnxVUmeTzGtp7sPNkHVZOC4WbE29/I9t03e/8PXv2DPleUFAQNBoNpFIpNBoNAgMDr/lZPj4+uOWWW5CTk4PY2NgRr09kLtFSL8ya6IvNeZV4cH4E7O2Mm3rm/YPl6DcY8MBNHApNtsuoU2pJSUnIzMwEAGRmZiI5OXnQMlqtFq2trQCA7u5u7NmzB1OmTBn2+kRieWBeOKqau40eIn2xW4dP8i7gtjgpJvq7mygdkfUxqnDS0tKQm5uLyMhI5ObmIi0tDQBQW1uLZcuWAQA0Gg0WLVoEhUKBmTNnIjExEcuXL7/m+kSWIHFqEHzcHLG9qMaoz/k4rxIdvf1Yf7P5JwglsiRGnUz28/PD3r17B70eEhKC7OxsAIBCoUBhYeGI1ieyBI72drgtVorthTXo6uu/oWsv3X16fHCwHDdPDkBsKIdCk23jTANE15CsDEG3To89p4ceQXktnxVUoamzDw+L8PgDIkvDwiG6hlnhvgj2csHnRbUjXre5sw9vf1OGGRPGYdZE31FIR2RdWDhE12BnJ8FyhRT7zjXgYpdu2Ov16w341ZZjaO7swx+XT+UD1ojAwiG6riRlCHR6ATtPDX8W6Zd3ncWh0ia8cEcs4sN8Ri8ckRVh4RBdR1yoN8L93PD58eGdVvvieC3e2X8e988Zj3tmho1yOiLrwcIhug6JRIIkZSiOnG9CZVPnNZc9rWnDU/88gRkTxuGPy2PMlJDIOrBwiIbh3plh8HB2wPqPj6G7T3/VZVq7+vDLzUfh6eKAt9dMh5MDf7yIfog/EUTDEOrjijdWT8PpujY89a8TEAThivf1BgG/3loEzcVuvH3/DAR68dHRRD/GwiEapkVTAvG7pVED12h+6G+5Z7HvnBbPJ8VixoRxIiUksmyctpZoBNbfPAnFtW34S84ZhPi4wsXBDt9VNOPdA+W4b9Z4/JTPuiEaEguHaAQkEglevkuB89pOPLbl0pRN9nYSLI4OxHNJU0VOR2TZWDhEI+Tm5IC//3wm9p5uQFSwJ6ZKveDqZC92LCKLx8IhugFBXi48fUY0Qhw0QEREZsHCISIis2DhEBGRWbBwiIjILFg4RERkFiwcIiIyCxYOERGZBQuHiIjMgoVDRERmwcIhIiKzkAg/frCHFfD390d4ePgNr6/VahEQEGC6QGbG/OKx5uwA84vNmvNfK3tFRQUaGxuv+xlWWTjGUqlUKCgoEDvGDWN+8VhzdoD5xWbN+U2RnafUiIjILFg4RERkFjZZOKmpqWJHMArzi8easwPMLzZrzm+K7DZ5DYeIiMzPJo9wiIjI/GyucHJychAVFQW5XI709HSx41xXeHg44uLioFQqoVKpAADNzc1ITExEZGQkEhMT0dLSInLK/7du3ToEBgYiNjZ24LVr5X3ppZcgl8sRFRWFXbt2iRH5ClfL/9xzzyE0NBRKpRJKpRLZ2dkD71lS/qqqKixatAjR0dGIiYnBhg0bAFjP/h8qv7Xs/56eHsyaNQvx8fGIiYnBs88+C8B69v9Q+U26/wUb0t/fL0RERAhlZWVCb2+voFAohOLiYrFjXdOECRMErVZ7xWu/+93vhJdeekkQBEF46aWXhKeeekqMaFe1b98+4ejRo0JMTMzAa0PlLS4uFhQKhdDT0yOcP39eiIiIEPr7+0XJfdnV8j/77LPCyy+/PGhZS8tfW1srHD16VBAEQWhraxMiIyOF4uJiq9n/Q+W3lv1vMBiE9vZ2QRAEoa+vT5g1a5Zw5MgRq9n/Q+U35f63qSOc/Px8yOVyREREwMnJCatXr0ZWVpbYsUYsKysLKSkpAICUlBRs375d3EA/sHDhQvj6+l7x2lB5s7KysHr1ajg7O2PixImQy+XIz883d+QrXC3/UCwtv1QqxfTp0wEAnp6eiI6ORk1NjdXs/6HyD8XS8kskEnh4eAAAdDoddDodJBKJ1ez/ofIP5Uby21Th1NTUICwsbOBrmUx2zW9oSyCRSLBkyRLMmDEDGRkZAID6+npIpVIAl35IGxoaxIx4XUPltaY/j40bN0KhUGDdunUDp0QsOX9FRQUKCwsxe/Zsq9z/P8wPWM/+1+v1UCqVCAwMRGJiotXt/6vlB0y3/22qcISrDMi7VoNbgkOHDuHYsWPYuXMn3nzzTezfv1/sSCZjLX8e69evR1lZGYqKiiCVSvHkk08CsNz8HR0dWLVqFV5//XV4eXkNuZy15Lem/W9vb4+ioiJUV1cjPz8fp06dGnJZa8lvyv1vU4Ujk8lQVVU18HV1dTVCQkJETHR9l/MFBgbizjvvRH5+PoKCgqDRaAAAGo0GgYGBYka8rqHyWsufR1BQEOzt7WFnZ4eHHnpo4LSBJebX6XRYtWoV1qxZg5UrVwKwrv0/VH5r2f+X+fj44JZbbkFOTo5V7f/LfpzfVPvfpgpn5syZUKvVKC8vR19fH7Zu3YqkpCSxYw2ps7MT7e3tA/+9e/duxMbGIikpCZmZmQCAzMxMJCcnixnzuobKm5SUhK1bt6K3txfl5eVQq9WYNWuWmFGv6vIvCwDYtm3bwAg2S8svCAIefPBBREdH44knnhh43Vr2/1D5rWX/a7VatLa2AgC6u7uxZ88eTJkyxWr2/1D5Tbr/TTnKwRrs2LFDiIyMFCIiIoQXXnhB7DjXVFZWJigUCkGhUAhTp04dyNvY2CjceuutglwuF2699VahqalJ5KT/b/Xq1UJwcLDg4OAghIaGCu+99941877wwgtCRESEMHnyZCE7O1vE5JdcLf/9998vxMbGCnFxccKKFSuE2trageUtKf+BAwcEAEJcXJwQHx8vxMfHCzt27LCa/T9UfmvZ/8ePHxeUSqUQFxcnxMTECM8//7wgCNf+ebWG/Kbc/5xpgIiIzMKmTqkREZF4WDhERGQWLBwiIjILFg4REZkFC4eIiMyChUNERGbBwiEiIrNg4RARkVn8Hwd9vImxj5MoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "251760fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_statistics = trainer.amortizer.summary_net(x)\n",
+    "summary_statistics_no = trainer.amortizer.summary_net(data_ERP_no)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "a6bf6c1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 1], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_no[:, 0], summary_statistics_no[:, 1], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "488cb981",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 1], summary_statistics[:, 2], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_no[:, 1], summary_statistics_no[:, 2], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "38cdbd72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "colors = cm.viridis(np.linspace(0.1, 0.9, 2))\n",
+    "ax.scatter(summary_statistics[:, 0], summary_statistics[:, 2], color=colors[1], label=r\"Well-specified: $h_{\\psi}(x)$\")\n",
+    "ax.scatter(\n",
+    "    summary_statistics_no[:, 0], summary_statistics_no[:, 2], color=colors[0], label=r\"Observed: $h_{\\psi}(x_{obs})$\"\n",
+    ")\n",
+    "ax.legend()\n",
+    "ax.grid(alpha=0.2)\n",
+    "plt.gca().set_aspect(\"equal\")\n",
+    "sns.despine(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77da1105128e4baba42b7866f6d410bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ac3a1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e2a8705",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "716ee1bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(ten_data_HNN.reshape(10,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c950ec5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/HNN-GUI_Real_ERP_data/no_trial_S1_ERP_all_avg.txt b/bf/HNN-GUI_Real_ERP_data/no_trial_S1_ERP_all_avg.txt
new file mode 100644
index 0000000..89e066e
--- /dev/null
+++ b/bf/HNN-GUI_Real_ERP_data/no_trial_S1_ERP_all_avg.txt
@@ -0,0 +1,103 @@
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diff --git a/bf/HNN-GUI_Real_ERP_data/yes_trial_S1_ERP_all_avg.txt b/bf/HNN-GUI_Real_ERP_data/yes_trial_S1_ERP_all_avg.txt
new file mode 100644
index 0000000..b60c520
--- /dev/null
+++ b/bf/HNN-GUI_Real_ERP_data/yes_trial_S1_ERP_all_avg.txt
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diff --git a/bf/Jul25_Explore_real_ERP_data.ipynb b/bf/Jul25_Explore_real_ERP_data.ipynb
new file mode 100644
index 0000000..660c1c5
--- /dev/null
+++ b/bf/Jul25_Explore_real_ERP_data.ipynb
@@ -0,0 +1,725 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "730178e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53261d5e",
+   "metadata": {},
+   "source": [
+    "### Load in real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "d3f559f5",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
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+       "              0          1\n",
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+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
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+   ],
+   "source": [
+    "yes_data = np.loadtxt(\"HNN-GUI_Real_ERP_data/yes_trial_S1_ERP_all_avg.txt\", dtype=float)\n",
+    "yes_data = pd.DataFrame(yes_data)\n",
+    "yes_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "02083910",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x29f3bb730>]"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(yes_data.iloc[:,1].T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "bd2e8ad0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-17.391097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.664960</td>\n",
+       "      <td>-17.522803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.329920</td>\n",
+       "      <td>-16.434230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.994880</td>\n",
+       "      <td>-14.408149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.659840</td>\n",
+       "      <td>-12.348419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>163.166091</td>\n",
+       "      <td>24.583799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>164.831051</td>\n",
+       "      <td>23.377644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>166.496011</td>\n",
+       "      <td>22.072480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>168.160971</td>\n",
+       "      <td>20.803446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>169.825931</td>\n",
+       "      <td>20.339855</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>103 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              0          1\n",
+       "0      0.000000 -17.391097\n",
+       "1      1.664960 -17.522803\n",
+       "2      3.329920 -16.434230\n",
+       "3      4.994880 -14.408149\n",
+       "4      6.659840 -12.348419\n",
+       "..          ...        ...\n",
+       "98   163.166091  24.583799\n",
+       "99   164.831051  23.377644\n",
+       "100  166.496011  22.072480\n",
+       "101  168.160971  20.803446\n",
+       "102  169.825931  20.339855\n",
+       "\n",
+       "[103 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_data = np.loadtxt(\"HNN-GUI_Real_ERP_data/no_trial_S1_ERP_all_avg.txt\", dtype=float)\n",
+    "no_data = pd.DataFrame(no_data)\n",
+    "no_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "e625d27c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2c110e200>]"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(no_data[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55d3cc69",
+   "metadata": {},
+   "source": [
+    "### resample the data so that the number of time points matches the hnn-core simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "3d85fc54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:00.000</th>\n",
+       "      <td>-3.638991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:00.500</th>\n",
+       "      <td>-2.922918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:01.000</th>\n",
+       "      <td>-2.206844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:01.500</th>\n",
+       "      <td>-1.490771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:02.000</th>\n",
+       "      <td>-1.000855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:48.000</th>\n",
+       "      <td>46.364935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:48.500</th>\n",
+       "      <td>45.878566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:49.000</th>\n",
+       "      <td>45.392197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:49.500</th>\n",
+       "      <td>44.905828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:50.000</th>\n",
+       "      <td>44.662643</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             value\n",
+       "time                              \n",
+       "1970-01-01 00:00:00.000  -3.638991\n",
+       "1970-01-01 00:00:00.500  -2.922918\n",
+       "1970-01-01 00:00:01.000  -2.206844\n",
+       "1970-01-01 00:00:01.500  -1.490771\n",
+       "1970-01-01 00:00:02.000  -1.000855\n",
+       "...                            ...\n",
+       "1970-01-01 00:02:48.000  46.364935\n",
+       "1970-01-01 00:02:48.500  45.878566\n",
+       "1970-01-01 00:02:49.000  45.392197\n",
+       "1970-01-01 00:02:49.500  44.905828\n",
+       "1970-01-01 00:02:50.000  44.662643\n",
+       "\n",
+       "[341 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data['time'] = pd.to_datetime(yes_data[0], unit='s')\n",
+    "yes_data = yes_data.drop(0, axis=1)\n",
+    "yes_data.set_index('time', inplace=True)\n",
+    "yes_data.columns = ['value']\n",
+    "yes_data=yes_data.resample('500ms').first().interpolate()\n",
+    "time_at_341 = pd.to_datetime('1970-01-01 00:02:50.000')\n",
+    "value_at_341 = yes_data.iloc[-1]['value'] + (yes_data.iloc[-1]['value'] - yes_data.iloc[-2]['value']) / 2\n",
+    "yes_data.loc[time_at_341] = value_at_341\n",
+    "yes_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "3d185bc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2c14a01c0>]"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(yes_data.iloc[:,0].T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "f2bafca8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:00.000</th>\n",
+       "      <td>-17.391097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:00.500</th>\n",
+       "      <td>-17.434999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:01.000</th>\n",
+       "      <td>-17.478901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:01.500</th>\n",
+       "      <td>-17.522803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:00:02.000</th>\n",
+       "      <td>-17.159945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:48.000</th>\n",
+       "      <td>20.803446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:48.500</th>\n",
+       "      <td>20.648916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:49.000</th>\n",
+       "      <td>20.494385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:49.500</th>\n",
+       "      <td>20.339855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01 00:02:50.000</th>\n",
+       "      <td>20.262590</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>341 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             value\n",
+       "time                              \n",
+       "1970-01-01 00:00:00.000 -17.391097\n",
+       "1970-01-01 00:00:00.500 -17.434999\n",
+       "1970-01-01 00:00:01.000 -17.478901\n",
+       "1970-01-01 00:00:01.500 -17.522803\n",
+       "1970-01-01 00:00:02.000 -17.159945\n",
+       "...                            ...\n",
+       "1970-01-01 00:02:48.000  20.803446\n",
+       "1970-01-01 00:02:48.500  20.648916\n",
+       "1970-01-01 00:02:49.000  20.494385\n",
+       "1970-01-01 00:02:49.500  20.339855\n",
+       "1970-01-01 00:02:50.000  20.262590\n",
+       "\n",
+       "[341 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_data['time'] = pd.to_datetime(no_data[0], unit='s')\n",
+    "no_data = no_data.drop(0, axis=1)\n",
+    "no_data.set_index('time', inplace=True)\n",
+    "no_data.columns = ['value']\n",
+    "no_data=no_data.resample('500ms').first().interpolate()\n",
+    "time_at_341 = pd.to_datetime('1970-01-01 00:02:50.000')\n",
+    "value_at_341 = no_data.iloc[-1]['value'] + (no_data.iloc[-1]['value'] - no_data.iloc[-2]['value']) / 2\n",
+    "no_data.loc[time_at_341] = value_at_341\n",
+    "no_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "9d7ac3b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2c124f1c0>]"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(no_data.iloc[:,0].T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18edda66",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "7e7f8f65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save yes_data, no_data\n",
+    "\n",
+    "np_yes_data = np.array(yes_data['value'])\n",
+    "np_no_data = np.array(no_data['value'])\n",
+    "\n",
+    "torch.save(np_yes_data, 'Real_ERP_data/np_yes_data.pt')\n",
+    "torch.save(np_no_data, 'Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30391c89",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0831b25b",
+   "metadata": {},
+   "source": [
+    "### scale the simulated out: Read the tutorial for the correct “scaling factor”"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aece6345",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9542bae8",
+   "metadata": {},
+   "source": [
+    "### smooth the hnn-core output waveform (smoothing window of 30 ms) (write inside of simulator function so it always returns a smoothed signal)\n",
+    "\n",
+    "signal = dpl[0].copy().smooth(30).data['agg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d18f059",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/Jul27_Generate_1000_simulated_data.ipynb b/bf/Jul27_Generate_1000_simulated_data.ipynb
new file mode 100644
index 0000000..3bf640b
--- /dev/null
+++ b/bf/Jul27_Generate_1000_simulated_data.ipynb
@@ -0,0 +1,14107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bec32dab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model\n",
+    "from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a1ffd5e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hnn_simulator(parameter_set):\n",
+    "    \n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "    parameter_set0 = float(parameter_set[0])\n",
+    "    parameter_set1 = float(parameter_set[1])\n",
+    "    parameter_set2 = float(parameter_set[2])\n",
+    "    \n",
+    "    \n",
+    "    # add a distal evoked drive\n",
+    "    weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                       'L5_pyramidal': parameter_set0}\n",
+    "    weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                       'L5_pyramidal': 0.080074}\n",
+    "    synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                          'L5_pyramidal': 0.1}\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "        weights_nmda=weights_nmda_d1, location='distal',\n",
+    "        synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "    \n",
+    "    \n",
+    "    # add two proximal drives\n",
+    "    weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                       'L5_basket': 0.19934, 'L5_pyramidal': parameter_set1}\n",
+    "    synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                            'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "    # all NMDA weights are zero; pass None explicitly\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "        weights_nmda=None, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "    # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "    weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                       'L5_basket': 0.008958, 'L5_pyramidal': parameter_set2}\n",
+    "    # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "    net_sim.add_evoked_drive(\n",
+    "        'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "        weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "        synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "\n",
+    "    return dpl[0].copy().smooth(30).scale(3000).data['agg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "756a44cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prior = utils.BoxUniform(low=[0, 0, 0], high=[1, 1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cde17626",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52d00e1acd34431785a1404acec583ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 1000 simulations in 1000 batches.:   0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "simulator, prior = prepare_for_sbi(hnn_simulator, prior)\n",
+    "inference = SNPE(prior)\n",
+    "\n",
+    "theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000, num_workers=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a7c3071d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save x, theta\n",
+    "torch.save(x, 'data1000_3ERPDrives_smooth_scale/x.pt')\n",
+    "torch.save(theta, 'data1000_3ERPDrives_smooth_scale/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "23d4189d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load x, theta\n",
+    "x = torch.load('data1000_3ERPDrives_smooth_scale/x.pt')\n",
+    "theta = torch.load('data1000_3ERPDrives_smooth_scale/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "84ed246a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 341])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a4b8408a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 3])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c0dcb850",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-0.1558, -0.1754, -0.1949,  ..., 22.3590, 20.8787, 19.4465],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 14.7764, 13.5038, 12.3067],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ...,  4.2634,  3.2054,  2.2707],\n",
+       "        ...,\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 14.5994, 13.4982, 12.4390],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 16.6773, 15.7138, 14.7846],\n",
+       "        [-0.1558, -0.1754, -0.1949,  ..., 11.7446, 10.7863,  9.9005]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c2707a4f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.9906, 0.2885, 0.8750],\n",
+       "        [0.5059, 0.2366, 0.7570],\n",
+       "        [0.2346, 0.6471, 0.3556],\n",
+       "        ...,\n",
+       "        [0.9182, 0.8418, 0.1603],\n",
+       "        [0.5370, 0.2061, 0.3992],\n",
+       "        [0.0566, 0.4668, 0.2724]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "47c286bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x150ffd090>]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x[0].T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbc70a2f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/Jul27_bf_model_misspecification_1000_data.ipynb b/bf/Jul27_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..2df07b0
--- /dev/null
+++ b/bf/Jul27_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,5924 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet()\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('data1000_3ERPDrives_smooth_scale/x.pt')\n",
+    "theta = torch.load('data1000_3ERPDrives_smooth_scale/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f5945f9c2414d5584a13195589c8e68",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.162854</td>\n",
+       "      <td>1.786506</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  9.162854  1.786506"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bae86409c534b7ca48ca3d412fd142a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Y62j8s8wx4z3/Zulp8uP1KkTjQOvMlStX2NSpU5mLiwszNDRkJiYmzN3dnX3++efs6dOnCo8pG48ZFBTETpw4wXr16sWMjY2Zubk5GzVqFHvw4IFM+fj4eLZw4ULm4eHBrK2tGZ/PZ87Ozszf359FRUUpfI+LFy+yMWPGMDs7O6avr8+sra1Zp06d2MKFC9m1t8YCOjs7y42NrEiLFi0YAGZlZcWKi4srLFdcXMy2bNnCevfuzczMzBifz2eOjo7Mz8+PbdiwQW5MaXFxMVu2bBlzdXWV1O+bb75hRUVFNRoHyhhjIpGIbdmyhbVp04bx+XxmZ2fHPvroI5aTk1NhXYOCglinTp2YsbExa9y4MXv//ffZrVu3WEBAAAPAIiIiZMq/fPmSTZgwgdna2jIdHR0GgAUEBEhej4qKYr6+vszCwoI1atSIeXp6sqNHj7KIiAi5svWinseB8hir/kOnbt264fr163WTyYnGi4lKw8Jxp5GZLu3KbufYCEu39ENLd/legW97kZqPrcuu4MoFadd3B1cz/Bn6PhxczSo58v/tL9dBZGLtnqWW9cIkhGiQ8vmpW7dKi6ri/zg9AyUqceHkE3ww6JhM8pzySUcEhb9freQJAE0dGmHFH3748Nvu0NERJ8PUJ3mYMfAoMp4X1EnchBCiLEqgpNYunHyCBWNPS5536hvoYPXuAZj6aWfo6tbsnxiPx8Oo6W2x7Fdf6PPFx6Ylv8ZH74egIF9QxdGEEFJ/KIGSWrkYlozPxp1GaYk4eZqY6uPP0yPQpbd8J4aa6OXngICffaCjK74TjY/LwOcTw1Baqni1D0IIqW+UQInSHtx6hUUTQiV3niZmfPweMgx2jqZVHFk9PX0csGB5L8n2xdBk/PZDxbO+EEJIfaIESpSS8aIAH40MwZt88ZyfRiZ6+D14KJo0a6TS9xkytiUmfihdSuv3ldcR81/9rPVHCCGVoQRKakxQLMSno0/hxVPxPJ+6ujxsODAYTexVmzzLTF3QCR17iOf8FIkYvpp6Bnk52j3HJiFE/VECJTW2/ttLuHVVOr/pd5u94dbWqpIjakdXVweL13rB1Fy8QPCL1HysXPhvnb0fIYRUByVQUiPnTzzBns03JdsTPmwPr4HOdf6+tnYmWPh9b8l28L4HuPavdi+VRAhRb5RASbU9f/oa380+K9lu09EaMxbKTwReV/oOdob3u9JkvfyTCygpUTzRNSGE1DVKoKRaGGP437zzyM0SP3s0MtbDD9v96n0Zsnlfd4OhsXh+3Mf3srF3y80qjiCEkLpBCZRUy9Fd93ApXLoqQ+CvvjCzNKz3OGzsTDD1E+nCxluXX0XGC5qliBBS/yiBkiq9SM3HT19I1wvsP9wVXfvUbqKE2hg5rS1cWloAAIrelGLbTzGcxUIIabgogZJKMcawfP4F5OeJp9EzNefjs3Kdebigp6+DWV92lWz/9fttPEvK4zAiQkhDRAmUVCoiJAmRp5Ik20u39IOhEfdrdPbwsUe7rjYAAGEpw88rrnIcEdFk06dPB4/HU7igN1dcXFwki1YT9cT9lZCoraLCUqz6XDresnd/h1rPcasqPB4PH3zeFZ9PCgMABO+7j5ltmqKF/Yv6CeDm/4A3KfXzXsowdgI6La/1aVxcXJCcnKzwtSZNmuDFC9nPWyAQYPny5di3bx+ysrLQtWtXrFu3Dl27dpU7Pjw8HAMHDkRISAjee++9WseqaXx8fBAZGYkarChJ1AwlUFKh7atj8CzpNQBAn6+DL1b24TgiWR17NEF372a49m8amAj448RArJ63u37e/E0KYOJSP++ljIIklZ3K3Nwcn332mdz+Ro3kZ55asmQJNmzYAH9/fzg4OGDPnj3w9fXF/fv3YWcn/eMrPz8fs2fPxuTJk9Uiea5cuRJLliyBvb0916FInDt3jusQSBUogRKFUp/k4c+1sZLtmYu6cNLrtirTPuuMa/+K58Y9fcUDn4w6CUfbTI6j0i4WFhYIDAysshxjDL///jtmzJiBHTt2AABGjhwJHx8f7NmzB1999ZWk7OLFi1FUVIRNmzbVVdg1YmdnJ5Pg1YGbmxvXIZAq0DNQotDG76IhKBZPUmDbzASjZrTjOCLFWnewRpc+TQEAIqaDnaF+HEfUcGVkZODNmzfo0aOHZF/Z7+Wbgf/991/8+uuv+Pnnn2FlpdwUkGXPB/Py8rBo0SK4uLhAX19fJtHfv38f06dPh6OjIwwMDNCkSRNMnDgRDx48kDtfZc9Ar1y5gtGjR6Np06bg8/lwdHTE3LlzkZameFGDrKwsfPvtt2jfvj2MjY1hbm6OTp06YcmSJSgoKEBSUhJ4PB4iIyMBiB9HlP34+PjI1fFtxcXFWLVqFTp27AhjY2OYmZmhb9+++Pvvv+XKlr3X9OnTkZSUhPHjx8Pa2hqGhobo1q0bQkJCKv+g33Ls2DFMnjwZrVq1gomJCRo1agQPDw9s3rwZIpH8UoNln+vjx4+xZcsWdOzYEUZGRjL1zMrKwtdff422bdvCyMgI5ubm6N+/P86cOSN3vtzcXKxZswZ+fn5wcHAAn8+HjY0Nhg8fjsuXL9eoLqpAd6BEzq1rL3H6UIJke8laL+jo1O+ECTUxfk57xF0SP4s7erEXPnr/NBpzHJM2KS4uxt69e5GSkgITExN07NgR3t7e0NXVlSlnY2MDIyMjxMRIhxVdv34dAODsLJ5BqrCwELNmzYK/vz/8/f1rFZdAIICfnx+ysrIwcOBAmJmZwdXVFQAQGhqKUaNGoaSkBMOGDUOLFi2QmpqKf/75BydPnkRERITC57JvCwoKwuzZs2FgYIDhw4fD0dERCQkJ2L59O06cOIHLly/DyclJUv7Jkyfw9fVFcnIyPDw88OGHH0IkEuHhw4fYsGED5s2bBwsLCwQEBGDnzp1ITk5GQECA5PiqOg0JBAIMGjQIkZGRaNOmDT7++GO8efMGhw8fxrhx43Djxg38+OOPcsclJyejR48eaN68OaZMmYKsrCwcPHgQI0aMwNmzZ+Hr61utz3zJkiXQ0dFBz549YW9vj9zcXJw/fx4LFizAtWvXsGfPHoXHLViwABcvXsR7772HIUOGSP7tJCcnw8fHB0lJSejbty8GDx6MgoIChISEYPDgwfj9998xe/ZsyXnu3buHb7/9Ft7e3njvvfdgaWmJlJQUBAcH4/Tp0zixbh0G96m/R02UQIkMxhjWLYmSbLt72KBD9yYcRlS1Ln3s0LJ9YyTcyURxCR97w/thwTyuo9IeL168wJQpU2T2ubq6IigoCP369ZPs4/F4mDNnDjZv3ozc3FzY29tjz549MDU1xaRJkwAA3333HbKysvDzzz/XOq7nz5+jXbt2iIyMhImJiWR/dnY2JkyYAGNjY/z7779o107aenL37l307NkTs2bNQmxsrKLTSjx8+BBz586Fi4sLIiMjZZ6Pnj9/HgMGDMCCBQtw9OhRyf7JkycjOTkZP/74I77++muZ87169QqNGjWCoaEhAgMDceHCBSQnJ1erebzMunXrEBkZiXfffRfBwcHQ0xNfwgMCAtCjRw+sXLkSQ4cORZ+3ksiFCxcQGBgok6wnTpyIwYMHY82aNdVOoCdPnpRrWhaJRJgxYwZ2796N+fPno2fPnnLHxcbGIi4uTvIHTplp06YhOTkZBw4cwPjx4yX7c3Jy4OPjg08//RTDhw9Hkybia1Dbtm2RlpYGa2trmfOkpqaiR48eWLhhQ70mUGrCJTIiQpJw/eL/N03xoHYdhxTh8XgYP6e9ZPuvc94ofFPCYUTaY8aMGTh37hxevHiBgoIC3L59G3PnzkVSUhLeffdd3LwpO5XiTz/9hMWLF+Pq1av4888/4e7ujnPnzsHe3h5XrlzBxo0bsWnTJlhbW2PZsmWws7ODnp4ePDw8EBUVVUEUFVu3bp1M8gSA3bt3IycnB8uWLZNJngDg7u6O2bNnIy4uDvHx8ZWe+9dff0VJSQk2bdok17nIz88Pw4cPx4kTJ/D6tbijXUxMDC5duoTOnTtj8eLFcucrazqtjR07doDH42H9+vWS5AkAtra2WLp0KQBg+/btcsc5Ozvju+++k9k3aNAgODk54erV6g8BU/RcVkdHBwsWLAAAhIWFKTzuq6++kkueN2/eRGRkJPz9/WWSJyB+7r5s2TIUFRXhyJEjkv3m5uZyyRMAHBwcMHr0aNxPSkLKi3rqiQ+6AyXliEQMm/8XLdnu964zHFzNOYyo+jwHOsLRJgNPM2yQ98YYJ/96iNEz3bkOS+OVv2MBgPbt2+O3335Do0aNsG7dOgQGBsrcgRkYGGDlypVYuXKlzHECgQAzZ87EkCFDMGnSJGzcuFFyR+Tp6YkffvgBgwcPRmJiouRuoyqGhobo2LGj3P7oaPG/4Zs3byq8u3v48CEAcXPg2wlW0XkiIyNx7do1udfT09MhFArx8OFDeHh4SJ7BDRo0CDo6qr83ef36NRITE2Fvb482bdrIve7nJ37+HxcXJ/da586d5ZrcAcDR0VFSz+rIzMzEmjVrcOrUKTx+/BgFBbLTaD57pniFpPLPxcuUvW9ubq7C7ykjIwOA+HsqLyoqCps2bUJ0dDTS09MhEAhkY0hPh1PTptWuU21QAiUSoYcSkHA3CwCgo8vD/P/JN8WoK11dHYzvfxFr/hoFANizOQ7+M9rV+2T3DcW8efOwbt06/Ptv9dZlXbZsGZ49eybpGLJmzRr0799fcuFs3bo1XFxc8PPPP2P58uqNX7W1tVX4/WZminthb9u2rdLj8/PzK3297Dxr1qyp1nlycnIAoM6GwuTm5gJAhb2Fy/aXxVGehYWFwmP09PQUdv5RJCcnB927d8eTJ0/Qo0cPTJ06FVZWVtDT00NOTg42bdqE4mLFC903VZDQyj7f8PBwhIeHV/i+5b+no0ePYvTo0TA0NMSAAQPg5uYGExMT6Ojo4MKFC4iMjERxSf21PlECJQAAoVCEX8rN5vPumBawaKx+w1YqM7LvZWz5ZyiKBHwkxucgNuo5PLyacR2WVrK1tQUAuTsQReLi4rB69Wr89ttvsLe3R15eHtLS0iTPRQHAyckJ1tbWuHv3brVjqOiPI3NzcavJzZs3Fd6hVlfZeXJzc2FmZlZl+bIkVdFdWG2VxfP25BVlnj9/LlNO1bZv344nT54gICBA7o4xOjq60iFJir6rsjg3bdqETz/9tFoxLF26FHw+H9evX0fbtm1lXps7d66kZ3N9oWegBABw8sBDPHmYAwDQ09PBzM+r7qGobsxNCjG0t7Spbd/PtNRZXSlrfmvevHml5UpLSzFz5kz4+vrigw8+kHnt7buVoqIilcTWq1cvAMDFixfr9Txl5cPCwqp1V1fWpCoUVm9NW1NTU7i5ueHZs2dISEiQez0iIgIAqtW7WBmJiYkAoLD3tDKJS5nvKTExEe3atZNLniKRCP/9918FR9UdSqAEpaUi/PKDNPEMndAKZhYGHEakvInvSJsUw48+wstnlTfTkYrdvXsXWVlZcvuTk5Mxf/58AOJep5VZuXIlEhMTZZpTzczMYG9vj9DQUJSWlgIQX4Bfv34Nd/faP7eeMWOGpBOKog4yIpEIFy5cqPI88+fPh76+PhYuXCh5blqeQCCQufh7eHigT58+uHHjBn766Se58pmZmTJ/JDRuLB5slZJS/SkhZ86cCcYYvvzyS5nE++rVK6xYsUJSpi6UDbF5+7OLi4uTe+ZdHd26dUPfvn3xzz//SCbeeNvt27eRnp4uE0NCQoLMGFzGGJYtW1Zlp7C6QE24BGGHE/H0kfj5ip6+DqYv7MxtQLXQ2jEN3Von4PqDlhCJxOuYzvumO9dhaaRDhw5h1apV8PX1haurK0xNTfHo0SOcPHkSRUVFGDJkCL744osKj7979y6+//57rF+/XjIOtMxXX32FBQsWoG/fvujZsyf27duHRo0a4eOPP6513I0bN8bhw4cxcuRI9OrVC/3794e7uzt0dHSQkpKC6OhouWSmSJs2bbBjxw7MnDkT7u7uGDx4MFq1aoWSkhKkpKTg4sWLsLGxwf379yXH7N27Fz4+Pvjmm29w5MgR+Pj4gDGGhIQEnDlzBvfv35ckov79++PQoUMYNWoUhgwZAiMjIzg7O8sNGSrviy++wOnTp3H8+HF06tQJQ4YMwZs3b3Do0CGkp6fjq6++gpeXV60/Q0WmTp2KNWvW4LPPPkNERARatmyJhIQEhISEYNSoUTh48GCNz7l//374+fnhgw8+wObNm9GzZ09YWFggNTUVt27dwp07dxAdHS15ZLBw4ULMmzcPXbp0gb+/P/T19REVFYX4+HgMGzYMJ06cUHW1K0UJtIFjjGH7GunA96HjW8HElM9hRLU3xicK1x+0BAAc2n4bc5Z0U+uJINSVr68vHjx4gLi4OERHR6OgoAAWFhbw8vLClClTMGXKlAqfQwqFQsycORM9e/bERx99JPf6J598gry8PPz222+IjY1Fp06dsGHDhmr3wK1K//79cevWLaxduxZhYWG4ePEi+Hw+mjVrBj8/v2pP4jB58mR06tQJ69atQ0REBM6cOQMTExM0a9YMo0ePxrhx42TKu7q6IjY2FqtXr8axY8ewdetWGBoawsXFBZ9//rkkEQDArFmzkJycjL/++gurV69GaWkp+vXrV2kC5fP5CA8Px/r167F//35s2bIFenp66NSpEzZu3IgJEyYo94FVQ7NmzXDx4kUsWbIE//33H8LCwtCmTRv88ssveOedd5RKoA4ODoiJicGWLVtw5MgR7Nu3D0KhEE2bNkW7du3wySefoEOHDpLyc+fOhYGBATZu3Ihdu3bByMgIffv2RVBQEI4cOVLvCZTHarAUQLdu3SQzixDt8O/pJHw4Qjydl44uD4eix6jlnLfV0SxK3CmhWKAHn8/XIe+1+AnFn6Ej0MvPsUbnunfvntxzFhkNZDWWhmL8+PE4ePAg0tLS1G5OXFID5fNTt26VFq3y/3g10B1oA7dttfTu02+Yq8Ymz/IM+KUY9M4bHDoqXi3k8J93a5xAq0TJSas8fPgQBgYGsLGx4ToUokGoE1EDFhOVhtgocdd3Hg+YsagLxxGpzvB3pcMrwo89Qk6Wanp4Eu2yadMm+Pv7Iy4uDiNGjJCZ3YeQqlACbcD+XCOdC7SHjz1s7UwqKa1ZWrqVom1r8QwlpSUMIfvlV+AgZOPGjbh8+TJmzpyJ33//netwiIahP7caqIe3XyHyVJJke87iyp8XaKLh7xbg3gNxh6gjQXcweX4njiMi6ubJkydch0A0GN2BNlBB66XzZbbragMnN82Y87YmBvgWgq8v7iP38HY2Ht2TH9NICCHKogTaAGW8KMCpv6Uzmcz7WvvuPgGgUSMGz17SZ58hB+QHwxNCiLIogTZAf/9xB6Ul4qnG7JwaoW1n7e15OOidN5Lfj+2+gxqM2iKEkEpRAm1gBMVCHPzjjmR76ifa/VywT48imDYS/7GQnlaEuOj6WyuQEKLdKIE2MKGHEpCZXggAMDbRh897rlUcodn4fMCvX6FkO2T//UpKE0JI9VECbUAYY9i7VbpCyfApraGnr/3/BAb1lzbjnjxwHwJB9Va/IISQymj/1ZNIxEW/wN1Y8SrvOjo8jJ7RjuOI6kfnDgI0sRWv+pH/WoioM2o8BR8hRGNQAm1A9m6R3n329LWHuZXmT9tXHTo6wEA/aTNu8N57HEZDCNEWlEAbiBep+Th77JFke8ZC7Zm2rzrKN+NGhDxBfp6Aw2gIIdqAEmgDcfjPuxAKxUM4HN3M4NrakuOI6leL5qVo0bwEAFAiYDgf/JjjiAghmo4SaANQWirCkSDpau2TPurIYTTcGeArvQsNPUSTKhBCaocSaAMQeTIJ6Wni1UkMjfTgPdiZ44i4UX44S9TZp9SMWwMDBgwAj8dDZGRkhWXmzZsHHo+Hbdu21WNkhHCHEmgD8Pd26cQJA95vDn2+LofRcMfRXohWLaQrtFwIoYnEqys2Nha6urrw8PCosMyVK1cAAN2qWMiYEG1BCVTLpT7Jkxm2MWa2O4fRcM/XW3oXevoQLXFWHY8ePUJWVhbc3d3RqFEjhWXevHmDO3fuwMDAAO3bt6/nCAnhBiVQLXf4z7som/61RTsr2DmachsQx/p7SyeX/+/MUxS8pmbcqly/fh0A0LNnzwrLxMTEoLS0FJ06dYK+vn59hUYIpyiBajGBQIh/dpbvPNSBw2jUg5OjtDduaQnDhZNJ3AakAaqTQC9fvgyAmm9Jw0ILamuxiOAnknlvjUz00bu/I8cRqQc/70IkPhbfJYUeeoj3xrfiOCL1VpZADx48iIiICIVl6PknaYgogWqx8p2HBo92g64eNTgA4t64f+w0AwBcDE1GQb4AJo341T7enb+1rkJTubuC+bU6njGGuDjx4uvh4eFVlqcEShoSuqJqqWdJebh8PlWyPfqDht15qDwXp1K4uf7/pAolDP+eSuY4IvX18OFD5Obmonfv3mCMKfzJysoCABgbG6NdO9n5lX/55Re4urrC0NAQHh4euHjxIhfVIKROUALVUsf3SJftcmtrCVs7Ew6jUT9+Mr1xaVKFipQ133bpUvHUj7GxsQCAzp07Q1dXOkTq4MGDWLBgAb755hvExcWhT58+ePfdd5GSQpP5E+1ATbhaSCRiOLpbOmH62AY+dEURP+9CbNtV1oybhKLCUhgaVe+/Q22bRTVJWQLt2rVrhWXKEujbzbfr16/H9OnTMXv2bADAli1bEBoail9//RUrV66so4gJqT90B6qFrkY+Q1ryawAA30AXXgMb5sxDlXF1KYWzo7gZV1DMEH3uKccRqafqJNCyZ6TlE6hAIEBMTAwGDhwoU3bgwIG4dOlSHURKSP2jBKqFjpYbuuI1yAl8g4Y581BVvD2lY0LPlVuphoiJRCLcuHEDfD4f7u4Vt2IougN99eoVhEIhmjRpIlO2SZMmePHiRd0ETEg9owSqZV7nFiP8qDQZjJnZMBbNVkY/T+lz0LPHEyEUijiMRv3cu3cP+fn5cHd3B5+vuJdyfn4+EhIS0KhRI7Ru3VrudR6PJ7PNGJPbR4imogSqZU4fSkBxkRAAYN3UGC3cG3Mckfpq16YEja3En9Xr3FLciKY7o/JiYmIAVN58e/PmTYhEInTt2hU6OtLLibW1NXR1deXuNtPT0+XuSgnRVJRAtczRndLOQ+9PacNhJOpPRwfw7lOuGfc4NeOWN3XqVDDGsH379grLeHp6gjEmt0oLn8+Hh4eH3NjR8PBw9OnTp07iJaS+UQLVIonxWbh19SUAgMcDBo9uwXFE6q+fl7QZN/TQA7CyiYNJrS1atAg7d+7E9u3bce/ePSxYsABpaWmYN28e16ERohI0jEWLHCs3dMXdwxbmVoYcRqMZPDoXw8REhIICHbxMK0LCnUy06mDNdVhaYdy4ccjMzMT333+P58+fo3379jh16hScnalXONEOdAeqJUpKhAjeJ12ea9wcWlKqOvT1gT49yjXjBtMaoar00UcfISkpCcXFxYiJiYG3tzfXIRGiMpRAtcR/YSnIfPkGAGBkrIfufZtxHJHmKD+cJfTQ/UpKEkKIFCVQLVG++dZ3mCtNHF8DfXoUQU9P/OwzMT4XaSmvOY6IEKIJ6CqrBXKzixB5Kkmy7T+Dxn7WhIkJQ7cuxZLtiBOPOYyGEKIpKIFqgTP/PEKJQDwJgG0zEzi5mXMckeYpP6lC2GGaXJ4QUjVKoFogZL+089DQCbQ4tDL6lhsPGhf9EiIhDWchhFSOEqiGe5aUh+sX08QbNPZTadaNRWjfVgAAEImAosJSGhNKiJZS1f9tSqAa7uRf0ubGFu2sYGltxGE0ms27XDNuXrYAJSUlHEZDCKkrJSUlMmvXKosSqAZjjOFEubGfo6a15TAazVd+OEvUmefIzc3lMBpCSF3Jy8uDqalprc9DCVSD3buRgccPsgEAuno8eA1y4jgizebiVAonB/FdZ/jfL/A0+SVevXoFgUBAzbmEaDjGGAQCAV69eoXs7GxYWVnV+pw0lZ8GO1Gu81DXPs1gZKzPYTTaoW+fIuz7Wx+vXhTjzF8ZmPZ5YyQlJUEoFHIdGiGkKq9eSX+/d0/uZV1dXZiamsLJyQkGBga1fjtKoBqqtFSEk38lSLZHTqeVV1RBnEDFTTsn9j7EV6t9YGdnx3FUhJBqaVduDHw9tBpRE66Gunz+qczUfV1700VeFTq0E8DSQny3mZNVgtvXXnIcESFEXVEC1VAh+6W9b/sNcaGp+1REVxfw7CXtTHQ+mGYlIoQoRlddDVSQL8DZY9LFn0dMbs1hNNqn/CLbZ/5JqKQkIaQhowSqgc4ff4LCN6UAACsbI7i1q31vMiLVw6MYBgbiqRFTHr1GckIOtwERQtQSJVANdOKAtPftu2NagMfjcRiN9jE0ZOjhUW5y+RBaI5QQIo8SqIbJeFGA6LNPJdvvjm3JYTTaq3wzbvhRasYlhMijBKphTv+dAJFI3D3bqYU5mtg34jgi7dSnZxF4PPHnfPNKOrJfFVZxBCGkoaEEqmHKT91HnYfqTmMrEdq3E08uzxhwodx6q4QQAlAC1SiJ8VmIj8sAAPB0AL9hzTmOSLuVb8Y9d/xRJSUJIQ0RJVANcrJc5yH3rrZoZMbnMBrtV35y+UtnUlBUWMphNIQQdUMJVEOIRAwh5ZYuGzWdVl6pa86OpXB2FE8uX1wswuXzT6s4ghDSkFAC1RBxl54jLfk1AIBvoItevg4cR9Qw9C3XjHv+BA1nIYRIUQLVEMH77kt+7+FjD31+7ReDJVUr34x7/niipAc0IYRQAtUAgmIhzhyRdmLxp+bbeuPeRjq5fHamALeuvuA4IkKIuqAEqgEiTyUhL0c8M04jMz7cPWw5jqjh0NUFvGQml6dmXEKIGCVQDRBSbuFsv2GuNHVfPevrWX44SyKHkRBC1AklUDWXm12EyNNJku0RU2jh7PrWo6t0cvmkhDyaXJ4QAoASqNo7888jlAjEF2/bZiZwcjPnOKKGx9CQoWe5yeXPn6A1QgkhlEDVXvnm2yHjaOJ4rpTvjXuWZiUihIASqFpLS87D9Ytpku13x1AC5Ypnr/KTy79EVgZNLk9IQ0cJVI2dLDfzkGtrC1jZGHEYTcNmaSFCB/f/n1xeBESeot64hDR0lEDVFGMMJ/ZLE+j7U6nzENdkJ5enBEpIQ0cJVE3dv/kKj+5lAQB0dHnwfc+V44hI+QQaFZ5Mk8sT0sBRAlVTIeVWXunQrQmMTPQ5jIYAgJNjKZydxJPLC4pFiD5Hk8sT0pBRAlVDQqFI5vnn6JntOIyGlFf+LjSCJpcnpEGjBKqGrl54hoznbwAABoa66O7djOOISBnZ56CJEApFHEZDCOESJVA1dKLc2M9efo7Q1aOvSV24t5VOLp+TJcCtqy85jogQwhW6MquZwjclOHtMOlB/9ExaeUWd6OgAXr1pcnlCCCVQtXMhJAkFr8UdVcwsDNC6ozXHEZG3lW/GPXssgcNICCFcogSqZso3377zfnNaeUUNdfcogqGh+NlnyqPXSHqYzXFEhBAuUAJVI1kZhYg6kyLZfp9WXlFLhgZ4a3J5asYlpCGiBKpGQg8noLRUfGfT1LER7JxMOY6IVKRvuWbc8KPUjEtIQ0QJVI2cPCAd+zlsQisOIyFV8epdBB0d8eTyt69lIDP9DccREULqGyVQNZGcmIMbl18AAHg8YJB/C44jIpWxMBehY9nk8gyIPJXEbUCEkHpHCVRNHN9zX/J7y/aNYW5lyGE0pDrKN+OeOULNuIQ0NJRA1YBIxHB8rzSBjp3lzmE0pLp8vKRrgkafS8Xr3OJKShNCtA0lUDVw9UIqXjzNBwDwDXTR5x1HjiMi1WHfTIjWLcXNuKWlDBdCkrgNiBBSryiBqoFju6V3n737O0Kfr8thNKQm+veT3oWePvSwkpKEEG1DCZRj+XkChB+VTt03bg4132oSX29pAo0KT0F+noDDaAgh9YkSKMfCjiRKFma2sjFCi3ZWHEdEasLRXohWLf6/GbeE4cJJmlSBkIaCEijHju2+J/n9vXEtaeo+DVT+LjT0EPXGJaShoATKoeSEHMRGPQcgHvs5bHJrjiMiyujvLR3O8l9YMgpeUzMuIQ0BJVAOHSs39rOFuxUsGxtxGA1RlpNjKdxcxSvolJQwmlSBkAaCEihHhEIRgsuN/Rw3uz2H0ZDaKt8bN+wwNeMS0hBQAuXI1QvP8CKVxn5qC79yCfTf00koyKdmXEK0HSVQjpTvPOQ5gMZ+ajoXp1I0dxE34woENKkCIQ0BJVAO5GYXyYz9pKn7tMMAP+mKLCf23aukJCFEG1AC5UDIgYcoLhICAKybGKOFe2OOIyKqMNBX2ox76exTZL8qrKQ0IUTTUQKtZ4wxHN5+V7I9akZbDqMhqmTfTIgO7cQTyguF4kkyCCHaixJoPbt19SUe3skEAOjo8jBkbEuOIyKqNLC/9K7zxL77lZQkhGg6SqD17FC5u08PTzuYmPI5jIaoWv9+hdDVYQCAG5dfIi05j+OICCF1hRJoPXqdW4zTf0vHCE6e35HDaEhdsLIUobuHdF3QkwdpTCgh2ooSaD0KOfBQZuL4tp1tOI6I1IVB/aW9cYP3xHMYCSGkLlECrSeMMZnm2/entqGJ47WUt2cRDAxEAIDHD3Lx8PYrjiMihNQFSqD15E5MOh7cEl9IdXR4GDaRJo7XVibGDN59pBPMhxyghbYJ0UaUQOvJX7/dlvze1dMOjcyo85A2G+gn7Y17bPddCIUiDqMhhNQFSqD1ICujEKfKdSaZ8gl1HtJ2vXsUwdJCPFlGZnoxLoU/5TgiQoiqUQKtB0d23IWgWHwxtbEzps5DDYCeHvDuAGlnoiNB1JmIEG1DCbSOlZaK8NcfdyTbE+Z1oM5DDcSwwdIEev7EY2Rl0NR+hGgTSqB1LOLEE7x4Kl22bJB/C44jIvXF1aUU7duKlzUTljKEHHjAcUSEEFWiBFrH9v1yS/L7O+83B9+Ali1rSIa9WyD5/dD2O2CMcRgNIUSVKIHWoYe3X+Fa5DPJ9uSPqfNQQ/OOTyEMDf9/TOj9HNy+ns5xRIQQVaEEWof2/yodutKuqw1s7Ew4jIZwwcSEoX8/6bPPozupMxEh2oISaB3JySxEyH7pM6+Zi7pwGA3hUvnORCH776PwTQmH0RBCVIUSaB058NsdFL4Rz3vb2NYIHXs04TgiwpVOHQRwtBf/W3hTIEToYVonlBBtQAm0DhS+KcG+n29Ktqd+2omGrjRgPB4wfIi0M9H+n29wFwwhRGUogdaBY7vvI/uVeC5U40b6GDiKhq40dMPefQO+vrgHbnxcJm5de8lxRISQ2qIEqmKlpSLs3BAn2faf0RZ6+vQxN3QW5iK84yPtTHSg3PAmQohmoiu7ip05kojUJ3kAAH19HYyZ5c5xRERd+L+fL/n99N8Pkf2KZiYiRJNRAlUhxhh2rJPefQ4a3QJGxvocRkTUiXubErRtLZ6ZqKSE0fy4hGg4SqAqFH3uKe7dyAAgXvNz2oLO3AZE1M7oEdLORPu23kBJiZDDaAghtUEJVIX++ClG8rvnAEdYNDbkMBqijt7xfSNZ5iz9eSHCaEgLIRqLEqiKXI1MlZm2b/ZiDw6jIerKgA+MeV96F7pjXQzNj0uIhqIEqiK/rLgq+b1HP3vYOZpyGA1RZ6OGF8DAQDw/7oNbWbhy4VkVRxBC1BElUBW4GpmKa/+miTd4wMf/685tQEStWZiLMHSQdHq/oHUxlZQmhKgrSqC1xBjDz8tl7z6bOZlxGBHRBONH54PHEzfd/nfmKe7ffMVxRISQmqIEWktR4Sm4flF69/lJQE9uAyIawdFeiH5eRZLt31de4zAaQogyKIHWgkjEsP6baMl27/6OaOrQiMOIiCaZMem15Pcz/zxCwp1MDqMhhNQUJdBaOHngIR7cEje96ejwsGAZ3X2S6mvdsgRevaWzEdFdKCGahRKokoqLSrE58LJke/CYFmhsa8xhREQTzZwsvQsNPZyIx/ezOYyGEFITlECVtO/nW0hLFl/89Pk6mEPjPokS2rUpQe8e4mehjAFbl1+u4ghCiLqgBKqEjOcF+PUHaXPbhHkdYGLK5zAioslmTcuT/B52+BFuX6elzgjRBJRAlbDhu2i8yS8BAJhbGmDCvA4cR0Q0mXubEvj2lT4LXf/1JZqdiBANQAm0hm5eeYHje+5Ltj9f1YfW+yS1Nu+DPOjqiJPm1chniApP4TgiQkhV6MpfA6WlIvywIFKy3bpjY/T2c+QwIqItnB1LMXyIdHaitYujUFoq4jAiQkhVKIHWwN4tN3E3VrxcGY8HfLO+L8cREW3ywdQ8GBqKk2bC3Swc/OMOxxERQipDCbSanj7OxZbAK5LtUdPbopkzTdlHVMe6sQjTJ0qHtWxaGo3M9DeVHEEI4RIl0GpgjGHZxxdQVFgKALBobIhZX9KwFaJ6E8fkw8Fe/O+s4HUJNn4XXcURhBCuUAKthkPb7yL63FPJdsDWftRxiNQJPh9Y9HGOZPufnfcQE5XGXUCEkApRFqjCkwfZ+OmL/yTbvsNc0L5bEw4jItquT89i9O0jHdby3axzktYPQoj6oARaiZISIRZPD5dcvMytDPH5j304joo0BF98kgtjY3GHopRHudgSSDMUEaJuKIFWYkvAFdyNSQcg7nX74/b+MDDU4zgq0hA0sRViwbxcyfaujTcQF/2cw4gIIW+jBFqBs8ce4c+1sZLtsXPc0apDYw4jIg3N8CFv0MNDOk/u4mlnkJdTzHFUhJAylEAVSHqYjW8+OCvZdmtriZmLunIYEWmIeDzgm89zYPL/TbnPkl4jYN55muaPEDVBCfQteTnF+HTMaRS8Fs91a2Sih9W7B0JHh8dxZKQhatpEiG+/kC5xduafR/jrd5pggRB1QAm0HIFAiM/Gncaje1kAxHcAq3cNgJmFAceRkYbMr18R/IfnS7Z/+vwiDW0hRA1QAv1/jDEEfhiBKxGpkn0f/68H2nSy4TAqQsQ+/TAXrVoIAAAlJSJ8OvoUnj7OreIoQkhdogQKcfJcszhKZpWV4ZNbY8TkNhxGRYiUAR9YtSwLlhZCAEBOZhE+HhlCnYoI4VCDT6CMMWz8Lhq7Nt6Q7Ovu3Qzz/9eDu6AIUaBZUyF+Wp4FfT1xJ6JH97Lx4YgTKMgXcBwZIQ1Tg06gjDFsWnoZ29dIh6u06WSN5b/5gcejTkNE/XR0F+DbL6Wdim5Ev8D8USdppiJCONBgE2hpqQgB8yKwbXWMZF8Ldyus3z+I5rklam3wO4VY+FGOZPvqhWeYP+ok3YkSUs8aZKbIzxPg09GncCQoXrKvRTsrbDr4LvT5uhxGRkj1jPMvwIcfSDsRRZ97ipkDjyEro7CSowghqtTgEujj+9mY4HkIkaeSJPu69rHDlsNDwDeg5Ek0x7SJ+ZgzI0+yfed6Oib3OywZhkUIqVsNJoEyxhBy4AHGe/6Nxw+kz5AGjnLDqp3vULMt0UgzJ7/GV59lg8cTdyxKTszFeM9DOHvsEceREaL9GkTWyMksxBeTwrB4WrhkhiEeTzzO88ufPKnDENFoo4a9wQ//y4IBXzzl35v8EiwYexo/fBaJNwUlHEdHiPbS6gQqEjEcCYrH0A77EHo4UbLfxFQfWw4PwftTaJwn0Q5+3kXYvjUDzeykCXP/L7fh3/0vXL/4jMPICNFeWplAGWOIPvcUE7wO4X9zzyP7VZHkte7ezbD/39Fo3dGawwgJUb2WbqXY+WuGzGLcKYm5mNb/KL6YHIa0lNccRkeI9uGxGizt0K1bN1y/fr0u46kVxhgunX2KbatjcC1S9q9uIxM9LPqhN3zec+UoOlLXmkWZS35/7biEw0i4xRhw6owx1m81Q8Ebacc4A0NdjP7AHTM/74qmDo04jJCQOlL+cVw9rFqkFQk0K6MQoYcT8Ndvd+R6IPJ4wOAxLfHRd91haESLYWszSqCy0jN0sPk3c5y9YCyzX5+vg3fHtMT4eR3QsUcT6gNAtAcl0OrJzS7CxdBknDr4EFFnnqK0VCRbgAf09HHAp4E9YNuM/tpuCCiBKnbjFh8bfzHD/QT5VYVatLPCIP8WGDjKDS3cacF4ouEogSqWnyfA3dh0XL3wDJfOpuDO9XSIRPKh6+ry0HewM2Yu6gI7J1MOIiVcoQRaMcaA6KsG2LGnEe7cM1RYpnlrS/R+xxHd+jZDt772sLIxqucoCamlhp5AiwpLkZKYg+TEXCQl5ODx/SzcuZ6OJw+yK/08mjo0wrCJrfDe+FYwMeXXaYxEPVECrZ74+/o4EmyMsxFGKBZUPHmIS0sLtO5o/f8/jeHayhLNXEyhr08TjhA11RAS6O3rL3EvLgMZzwuQ8eINXr14g4wXBch4XoCXzwqqfZ4mDibwHuSMYRNb090moQRaQ28Kebh0xRDnLhjg0hXDSpNpGV1dHpo5m2LaZ10wYV6HeoiSkBqo5wTKSa+a4L33sf+X2zU6hscDrGyN0KajNfq95wIPz2Yws5B/pkMIqR5jI4Z3fArxjk8hiop4uHmHj5g4PmLi9HA/wRBCkfwoN6GQ4enjPIiEIgVnJKRh4SSB2jQ1qfR1I2M9WFobws7ZDK4tLeDUwhyOzc1l5qp9kZqPF6n5dR0q0SDNyv2e8zKdszg0VWtH8c/E4UBRMQ/JqUZ4nGKEx0mGeJLMR9pLA2Tlip+LOjY3r+JshGi/GjXhWltbw8XFRWVvfv9uCvR1tLuHbHHpaxjoaXfzsjrV0UCvGMYGql+R5HVhMUyNtLvFo9p11OHDysGh7gNSsYyMDNjY2HAdRp2iOqqetbU1QkNDFb5WowSqauo0LKauUB21A9VR82l7/QCqY33Tyqn8CCGEkLpGCZQQQghRAqcJdM6cOVy+fb2gOmoHqqPm0/b6AVTH+sbpM1BCCCFEU1ETLiGEEKIElSbQw4cPw9/fH87OzjAyMkLr1q3x9ddf4/XrqtchLCoqwpdffgk7OzsYGRmhd+/e+Pfff1UZnkooW8fr169jzpw5aNOmDYyNjeHk5IRJkybhyZMn9RR59dXmeyxv5cqV4PF48PLyqqNIlVfbOt67dw9jxoyBtbW15PhNmzbVcdQ1U5s6pqSkYNq0aXBycoKxsTFatWqF7777DgUF1Z8prD6EhYXBz88PTZs2hYGBARwcHDB27FjEx8dXeaymXHOUraMmXXNq8z2WV9/XHJU24fbq1QtOTk4YMWIEHBwcEBcXh8DAQLRp0waXLl2Cjk7F+XrSpEk4efIk1qxZg+bNm+Pnn3/G6dOnER0djc6dO6sqxFpTto5ffPEFoqOjMWnSJLi7u+PZs2dYsWIF0tPTcePGDTg6OtZzTSpWm++xzOPHj9GxY0eYmJigZcuW+O+//+oh8uqrTR2vX78OPz8/+Pj4YObMmTA3N0dCQgLy8/OxaNGieqxF5ZStY0FBAbp06YKSkhIEBgbCyckJ165dQ0BAAIYPH46DBw/Wc00qduDAAcTGxqJnz56wsbFBSkoKVq1ahadPn+L27dtwdnau8FhNueYoW0dNuubU5nssw8k1h6lQenq63L5du3YxAOzcuXMVHnfjxg0GgO3YsUOyr6SkhLVq1YoNGzZMlSHWmrJ1VHRcUlIS4/F4bOnSpSqNsbaUrWN5AwcOZHPmzGH9+vVjnp6eqg6x1pSto1AoZO3atWPvv/9+XYanEsrWMSwsjAFgYWFhMvsXL17MdHV1WUFBgcpjVaX79+8zAGzt2rUVltGka44i1amjJl1zFKlOHcvj4pqj0iZcRbNDdO/eHQDw7NmzCo8LDg6Gvr4+xo0bJ9mnp6eH8ePHIywsDMXFxaoMs1aUraOi45ydnWFjY1PpcVxQto5l9u/fj9jYWKxcuVLlsamKsnW8cOEC4uPj1epOsyLK1lEgEAAAzMzMZPZbWFhAJBKBqXm/w8aNxeua6uvrV1hGk645ilSnjpp0zVGkOnUsw9U1p847EUVGRgIA2rZtW2GZu3fvwtXVFcbGxjL73d3dIRAIkJiYWKcx1lZ16qjIvXv3kJ6eXuPjuFDdOmZnZ2PhwoVYvXo1rKys6iM0lalOHcuahYqKitCrVy/o6+vD1tYWn376KQoLVT+FoKpVp47vvPMOWrZsicWLFyM+Ph75+fk4f/48Nm3ahHnz5sHEpPK5rLkgFAohEAiQkJCAuXPnomnTphg/fnyF5TXxmlPTOiqi7tccZerI6TWnLm9vU1NTmY2NDXvnnXcqLTdgwADWs2dPuf3h4eEMAPv333/rKsRaq24d31ZSUsK8vb2ZjY0Ny8rKqqPoVKMmdfzggw+Yl5cXE4lEjDGmtk24b6tuHefOncsAMEtLS7Z06VIWERHB1qxZw4yMjNS+Wbcm3+PLly+Zl5cXAyD5mTVrFhMKhfUQac15eHhI4mzRogWLj4+vtLwmXnNqWse3acI1R5k6cnnNqbPVWPLz8zFixAjo6ekhKCioqiQOXvl13MrtV2c1qePb5s+fj0uXLuHkyZOwtLSsowhrryZ1vHjxInbv3o3Y2FiF36e6qkkdRSLxMl6TJ0/G8uXLAQA+Pj4QCoVYsmQJ4uPj0a5duzqPuaZqUseioiKMGzcO6enp2LNnD5ycnHD16lUsX74cenp6+PXXX+sp6urbs2cP8vLy8PjxY6xduxYDBgzAf//9V+HiF5p4zalpHd+mCdecmtaR82tOXWTlwsJC5uvryywtLdmtW7eqLD927FjWqlUruf0HDx5kANidO3fqIsxaqWkdy1uyZAnj8Xhs9+7ddRSdatS0jm3btmXz5s1j2dnZkh9PT0/Wq1cvlp2dzYqKiuoh6pqpaR2XLFnCALDg4GCZ/bGxsQwA27dvX12FqrSa1nHr1q0MAEtMTJTZ/8cffzAA7MaNG3UVqkpkZ2czc3NzNnfu3ArLaOI1p7zq1LE8TbnmlFedOnJ9zVF5AhUIBGzIkCHMxMSERUdHV+uYZcuWMX19fbnefQEBAYzP56vdhVeZOpb5/vvvGQC2efPmOopONZSpI8o19yn62bBhQ90GXUPK1HHPnj0MADtx4oTM/piYGAaAHThwoC5CVZoydZw7dy6ztLSU21/Wc1Xd6qiIh4cH69+/f4Wva9o1R5Gq6lhGU645ilRVR66vOSpNoEKhkI0ZM4YZGBiws2fPVvu4uLg4BoDt3LlTsq+kpIS1adOGDR06VJUh1pqydWSMsU2bNjEA7Icffqij6FRD2TpGRETI/XTq1Im1b9+eRUREsKdPn9Zh1DWjbB1fvXrFDAwM2Mcffyyz/8cff2QAWEJCgqpDVZqydQwICFBYl99//11tnw+W9+LFC2ZiYsLmzJlTYRlNuuYoUp06MqY51xxFqlNHrq85Kk2g8+bNYwDYt99+y6Kjo2V+yiqSlJTEdHV12bJly2SOHTduHLOwsGDbtm1jZ8+eZf7+/szAwIDFxMSoMsRaU7aOBw4cYDwejw0ePFjuuLt373JVHYVq8z2+TV07EdWmjoGBgUxXV5d9/fXXLDw8nK1cuZIZGhqyadOmcVCTiilbxydPnjBTU1PWsmVLtnPnTnb+/Hm2evVqZmpqyjw8PNSqI9H777/Pli9fzo4dO8bOnz/PfvvtN9a6dWtmbm7OHjx4wBjT/GuOsnXUpGtObb7Ht9XnNUelCdTZ2bnCW+mAgADGmPg/Z/ntMm/evGELFy5kTZo0YQYGBqxHjx4sIiJCleGphLJ1nDZtWoXH9evXj5O6VKQ23+Pb1DWB1qaOIpGIrVu3jrm5uTF9fX3m5OTEli5dygQCQf1XpBK1qePdu3fZmDFjmIODAzM0NGQtW7Zkn3/+udr13ly1ahXr2rUrMzc3Z0ZGRqxVq1Zszpw57MmTJ5Iymn7NUbaOmnTNqc33+Lb6vObQaiyEEEKIEmg1FkIIIUQJlEAJIYQQJVACJYQQQpRACZQQQghRAiVQQgghRAmUQAkhhBAlUAIlhBBClEAJlBAV4fF44PF40NHRwaNHjyos5+vrKym7c+dOmdemT58ueS0gIKDCc+zatUtSzsfHR+a1CxcuSF4r+zE2NoadnR28vb3x5ZdfIi4urjZVJYSAEighKqWnpwfGGP7880+FryckJCAyMhJ6epWvJKinp4cdO3ZAKBQqfH3btm1VnsPZ2RkBAQEICAjAZ599hqFDh6KgoABr165F165dMWnSJOTn51evYoQQOZRACVGhJk2aoFu3bggKCkJpaanc69u3bwdjDEOHDq30PEOHDkVqaipCQ0PlXrt37x6ioqIwbNiwSs/h4uKCwMBABAYG4scff8S2bdsQExODuLg4dOjQAfv378eYMWNqVkFCiAQlUEJUbPbs2Xjx4gVCQkJk9peUlGDXrl3o06cP3N3dKz3HpEmTYGRkhG3btsm9VrZv1qxZSsXXuXNnnD17FjY2NggNDcWxY8eUOg8hDR0lUEJUbMKECTAxMcH27dtl9gcHB+Ply5eYPXt2leewsLDAmDFjcPLkSTx//lyyv7i4GLt370a/fv3QqlUrpWO0tbXF3LlzAQD79u1T+jyENGSUQAlRMVNTU4wfPx6hoaFITU2V7N+2bRvMzMwwduzYap1n9uzZKC0tlelodPToUWRmZlYrCVelrPPR1atXa30uQhoiSqCE1IHZs2dDKBRix44dAIDk5GSEh4dj0qRJMDY2rtY5vLy80LZtW8lzU0CchC0tLeHv71/rGO3t7QEAGRkZtT4XIQ0RJVBC6kDPnj3RoUMH7NixAyKRCNu3b4dIJKrxneOsWbPw+PFjnD9/HomJiYiIiMCUKVNgaGhY6xjLkjKPx6v1uQhpiCiBElJHZs+ejeTkZISGhiIoKAgeHh7o0qVLjc4xdepUGBgYYPv27ZI7UVU03wJAWloaAMDGxkYl5yOkoaEESkgdmTJlCoyMjDB37lw8e/YMc+bMqfE5rK2tMXLkSBw9ehQ7duxA79690b59e5XEFxERAUB8t0wIqTlKoITUEQsLC4wePRqpqakwMTHBhAkTlDrP7NmzUVxcjIyMDJXdfaanp+P3338HIB4yQwipucqnMiGE1Mr333+PUaNGwcbGBqampkqdw9fXF8ePH4dIJMKgQYNqHdPNmzcxdepUvHr1CkOGDMHw4cNrfU5CGiJKoITUIScnJzg5OdXqHDweT6kkl5SUhMDAQADiSRxevXqFmJgYxMTEABDfeZbdhRJCao4SKCFaKjk5GcuWLQMAGBoawsLCAi1btsQXX3yBSZMmoXPnztwGSIiG47GyvuyEEEIIqTbqREQIIYQogRIoIYQQogRKoIQQQogSKIESQgghSqAESgghhCiBEighhBCiBEqghBBCiBIogRJCCCFKoARKCCGEKIESKCGEEKKE/wOLV/r/Akm8JwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ae3c922484e4a65989f52d342cf5469",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 30\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*30\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d417584d907f40089dbb075676a1f861",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all -30\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*(-30)\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "945ea4e54a93406fbce02875ffdde1e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=10, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=16, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=18, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=20, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=23, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=25, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_10', 'noise_16', 'noise_18', 'noise_20', 'noise_23', 'noise_25']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "94c5e02d-01ff-4007-904a-5941c63e1c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8719+nngMPdOxautDbCejaysQJPXfbzyjtjzfFzXRkp5pXovgZ7uOYuCT9+GkQqpXXR+u5wnhzXH7gzvwtk2PpN5D1NrqnFSphtGVgqRWr2urTWQ7UJ9W7XperkW2/eAQ7vjxTjz0wuHUezcCun2ro7byaoNy1Nb4b44FJ9h8RlOEBD2LfePQlKnZqRgJkjZbJA0IrvZZJPpjaAPPWmN/891f4vBQGU/uDpqaUdmgSi1wbVVdP1FBTIvamswtuFV0lSDxPB//+egesZjybn7jHeHCJ1k92lctZYJKx4UrQmdiN22RpPh4mwHtO1m/r9pmQUIl4lMJ1HAYe46NhyBp0iIJx63TkscLjeZijHf136yExLwcDV2HjndCSVJ1I6I9iZNKI/S7KSGx6Q6JnYR/27Ybf/r1X6AUbgZ511JbXVt1WST5yjeQG0O3eTTLsdDtWz3VvZzaqBz+2+JBaCA2ibSQznAgXJDUXA+FFvQpaZX7ru0WSSMcSTi5xsMNl8ciyetaEy6y8DjbtgDPR7kWeQwqNQ89BUc6Lrh3mmuLxpHnF3U2usoiOTYScAS0oebdtMfbnyy5turiSPxck4xPZt01mgGR7GlJXY1AaGpZri2vvRYJ/d5Kypyg37z3eETIt8oCaOY38nfRdrK9CdfWRHEkecu4h95OcZxskcTXHleOola7+jECxrXVcVALFObVxmkROOPUsKjRtp+u5+faWMg1R1poK9uMZnEkww3WdMpdIqVN4b+//eWf4puP7RWbRR7X1sGTUXXpVnVybOY3ltlmNpnI9nHhSJAjaivnRk4Z8nQcXbfCLZIEF6xw4Xa5a6urBAmVPCfklfQ0CcZLkPBx1ONqqrl+Lv6GggXKmoidZl1bUYc3/ffHRxprnJW3RIrsJmjoVrmwdedRPLP/pLiHKkh8aXMI/ua5E2W3NRFczfxG3vu8rRyJ1+kcSdb9068VZcgz1xbCzHYNP8kFY9SXXT9GPo7JjK4WJHnfD23WjjU+goSPQyfcXjoyjI9/6+nYph9YJNnXp/HTZOYRR81O0qgekI/jI5VYz5ZGE/JosWW5RLwGhXC9cP0gsIEWvipIdBrnSdbru1UWQD3a6fGRCs778+/gJzuCaL1hJkjazZE0cr96LZL/+pVH8bVtuzOPoxpaWWXko6it9LGrfU0cZpFUNdn5fM2l5ZHQKV0gR7pLkKgWRW7XFvk+x8kiydoM3/cvP8c//WQXnj1wUhlXvvBfGj9tdmUuSJoN/2V5JPc+fQB/9o1fSLWlGhUkef3DfH/O4/ZJyzJOvU8otCOLRH5n/Lq0KRwfjayxRu+rop5N5We7jqFS83DHj3YCkC2StgqSBqO2xBzIKYQf2nEYT+45kXmcLzZ8KpGiPy5v1BYpqHQ8/X/FjdYndytKgiSFbBfWShdIkq4SJLYiCOpNSBw/jiT6W7dx0jh1FlUeQVBTCD9ukTTv2or+1WlffDOtB1lE62O/OobvPn2gLtfWg88dwnl//h38IsdmwyGEJeKc1EMvHMbSP7sH2146xsYe/HtitIppvUHgY+sESf73RRYRjWG4HL33NAvpkReP4Ge7jjY4wjiII6nX108KUN48Lj/neqAjsjiSvKHtjhK1VZAsEp1rK14fTvc68nI0jWCs6qZWZ2g1ukqQxMj2OqO2mu0mmASdqcvhKZoOoZazRIrqpx1toUVCUUy+77OFF12TWyR1Ze1nCJI7frwTG7/znPR91vt84Lmgn8dju4+lHhcbC+OB1PH84JeDwb/PD8bGcXykirnTegC0zgKoR5BQ+fLpfUUA+V1bN296BL/1pYcbHGEc9RTg/OvvPi8sp3o5EjensKJnmJVHEoWgp99fWCSK56LiepE3gAkSfrnUEinMbdxqfOXhl/Drn/th26pAd5UgUTmOvC+IHrZq0bQKsmtL933wr3p7160vj4Q2j7Gq3sxuBHyy63zanGyvJ/omqzxFNVyk/PusTYSOterkunjSmHoL2jRGKtEm7fs+xqouyjUPc6cGgqTcIouknj3l5Fgwpum9oSCpTBxHAmS//2f2ncQXHngBj+w4Utd54j45XWhCkFB3z4RHkbdEiSpIQo+ZFLXFLUAumPIkJI6Ha2v/iTEMV1wcH21tUdEkdJUgibu28p1HpvV4WSRZrq2kDTBwF2RvoDGynYXkNqvs0L1rjPj3JEESTdSDJ8dweKiMPBCbSIILpub6sWigrPWWJJCzwAMK1AVfDC/Gw5xdL3BrAegIi2Rq6NoaYWPUhSP/4JeD+I9H9zQ5wjhyl7shC0ThJvJyJH7OvCrBkTj5ijZmZ7YH/1L2OgkobvlXXO5OlsfMP3M9H/c+fUCx8LN+Uf2gumCNRlXWi+4SJMoG8pVHXsLiW+8Wiz4JlLylupZahayERPpavX3uUutE+JFFUmtd1Ba3HHTl8Hkhu6s+8yAGPnlfzuvK11dRDa0R149apmb9Flq06nu89+kD+PDXktuhumxBq+OhbPUR5jYKIthkQTIRZPvJ0WBMNO9JgZhScrSCbf0/bMV/T3kOjUIIhgyBoJLLdVskXr68KjokK7Odjsu6P1mltE/Q/3OFLYkjUcn22x98Ae/9yqN44LlDuQXZ7925DV+4f3vqMSqIPzs6PIkskne/+92YN28eLrzwQvHZ0aNHsXbtWixfvhxr167FsWP1+a0bgUqWP7s/iILKIp3oxVPBPsL+E6MtSRbK8vNHuRryd3mLT6oWyVilMY7kxEg1tgHR0GvMzcZ/T6PJZPTMk86vhRExnueL95KZcyIEifz5wzuO4O5f7E88j/ux1eHQvbm27/l+zCKZCLL9VDkYAz1Dcm3N6Cu2N4+EXKsZPiK1T1C9/Uh0gl5/XOjayqq1leJ24rBFJrusqPA5wV2b/HpR3b/g/4lzCzwN8eN1eHb/Sfzy0FDqMSpOhW5PXUXw8UBLBMm73vUubNmyRfps48aNuOaaa7B9+3Zcc8012LhxYytu1RC4Cfqfj+7B/7f5Kel7miCcYzlwYgxX/dWDgsBtBnydpJHt6le0QPNaJDRppUmdOyLGxzV/8wP888MvJYyNmeIaMrFeiF4MSYIkdGu5vo9i6JTWrbdDp8aw+Na78ZMdh8XzU12EWZosL2MRd22FFokiSMhlQBxJq1xb9SguZJHQ+EcrLiwLmNZbbHv4L5A9F9TjaNzkOUg7P63TYOxYlSNJOKXeoo1q6SXOmyUlJNK8oXv98sCp8BgvNTRYGqfnxwISTo5VcfEnvotHXjyiPWdSurauvvpqzJ49W/ps8+bNWL9+PQBg/fr1+OY3v9mKWzUEvgn8yX8+iX9++CU8ysI5deG/e46NoOb5LSkVLmVF6wSJSExKsEgyo2FkQcInMk3WTT/cgeu++GPx+Y+3H8ZPdhzGi4NDOHRyDFXXx+GhMvYcU6w3Pxq3LsqlUUGS5daouV6YJAgUC8ktU7fuDMJY/+8jL0Vck3qv8DqJYyGLEPFqy2SRcCLbYxzJvOm9AFpZayv/sbRZ0DMcqbjoLzooFey2lkipZbxLgqqBq8enCb+8my7Awn8zLNm8wslmtbWC6wWfS5yUq1/j5P7y/KBKxanQRTpScZnrOv331Dw/9j6f2XcSJ0ar+Jvv/lJ7Ds2No20SJONW/ffgwYNYsGABAGDBggU4dKh5zT4LSROG+zKvWn4Gvv/8IP7uBzuw6XcHAOhrbR0eCl7AKeYbrxe/2HMC86f3yFFbmjEmFY/LG1VC2orQmLgrLfz70/c8J53z23f8FACwcsF0XLhwOv78LRcAAEbKcqkPztO4GoHXaOhiljYoLBLPT/V103j7igXxHGMWSUb0W1SuJX6PiCNJcG21OGqrnudJPI3LBElfqYCiY7XVIhEKRsI9Pc/HX3/veSyeMwVAtHHG5nuaRVKHG4wqKGdltuflKNRqB6RIjUgciZ6XHBGCBHhhcEj6PKlM0IPPH8LRoQpufMWi8HrxxOSeULkq1/Sleci11epW0EmYcLJ906ZNGBgYwMDAAAYHB7NPSEHS2uGd7OgF/IrxJuTb5STtkeEg+oheSCP4jS/+GFd+5kFpXHrXVvCvOt9Vn3IS1PBfySLJWCTD5RpOjdVENjzXvPnYXObayvo9eZClDVKzINf3RY8Q3bHkXugvOYkcCQmlJHBtl1uPvu9HUVsK2X5itArHtjB7Sikcb2Mbd7nm4qm9UQJlPWWnqNp1ZJHUMKXHQcGxW8bZ6PDU3hP40g92iP932Uarc1XuOzGK2x/cIdzESe8+XdiHx+T4WTQPCiku0eC47Pvy72md0Vgksj2hRMpIuK5838cxRnyPVlxtFCQA/Msjv8Lf/TB6vjUvXhSTStbrFBjf9xnZPolcWzrMnz8f+/cHBOf+/fsxb9487XEbNmzAtm3bsG3bNsydO7epeyZpHiNK6CYgb7YUKsnJ9sOnghcw1IQgAQJ3k5+hweu0rSDUMZ/GpCYk8oWR5eGgXhLEIw2XVUFC2qavHWfDri2FcFURubYisl3HH9BC7S85wqURrxBAG0C6ZuorFknVjUqRS64tPyhH0l9yhGLS6Mb9if/3DN7yhcjlmD+J1hNKjicEiYu+ooOSYzdtkRwfqeCmv3tY69r9f0/uw/+893lxbxrytX/zQ/z6538YO56+p4046d2n8XlJASlpx9pWhmsrL7cTPkqxvpjgJlQSSqSMhsd4vi9ZDyMVN1FRpBwqfj2VIyFrS9fueZgJqUnFkeiwbt063HnnnQCAO++8E9ddd9143UogsdS5ok0CauRR8JIsjUUy1IRrS71ncF/d97rj8ruP1BIpLrtJniS+quuLJEa1LDz3a+uiXJrlSJLODzgZSOG/aa6t/lKBke3yMTXNuDk4R8UXdbnmaqu3er4fNjKyUSpkd1RMw5N7jkv/n0eO3POL/Xg+JG2B6PeNVlz0l5zQtdXYeyHsGBzG1p1HReQjh+tGJVHUTfCXB+PRRfTc6RmpZLu4bg6rsa4SKU4G2Z4xB0+MVPGxzU8JYSDGL8j2pPBfjWvLk62HkUotMcim5nlSfpWOI9HV+CKcYiH57bJIWsKRvP3tb8f3v/99HD58GIsWLcInPvEJ3Hrrrbjppptwxx134JxzzsHXvva1VtwqFUkThvMcEckbvQB6SXzTPUIcyVjzPka18KDv+3jkxaN45dLZsCyLbVby5FHHnAQaf1lYJOzemdqWbJGMKK4tzt8IV4BE5qdePhGZrq0wqsX1IteWThDQQi0V7JgmKsboRRuAbsIn5ZEEHfD0Y6/UPBQdW4ytUYuECguKa2dslL7v4/3/+nP81ivOjsYfPsPhSg1TewootsAiSdtkObmeR5GIsr/lqCfVXZOHx6oraitnPxK6PlmfNdfD//n+Djx38BTufnI/io5CtpPglhIS5WsRONkuC5KIbFcFarUmP1fiCj9/33ZcsWwOLl08W5yrs0gomq/oWG3jSFoiSL761a9qP7///vtbcfncSJow3D2lqzhaUyY4AJGh3QxHQjjCsr09z8cv9p7A2//+EXzllstw1fK5Ws2kHq1fJdu5GZylwQXRJL6Y8HGyPRqDzpprtLxDVkQaZbZn5ZGMVqluU+RCjAsSWrD6sXC3CVcmxqpuQiWC4FmXCrbYZBoVJPXWh6u6vnCtEUgpGq24mDu1B8WC3XQUGV1T93p4VYJ8eR0Qx/Pz1bmTxg/prPYk0CGFjDwS/nnN81EKj//R9sP46+9F0VAz+ko4PFQWChuPkiNkWiS+L1Xl5q4t9TdVvagYJLm4a56P//39F3BitKoIkmSL5OxZ/ZM/amsikLSnDWstkujgSNOIzjkSmoStcG3x3t6u7+NQ2Flv99FRadxJiX6ZFkn4feTDjb7L2uhpklI2vPp7hfBI4Gwa6dUtXTfJImGLlkhT3a1ooQbkfPCZ6trKClrgpfL5PcoKv8XHXql5KDk2LMtCybFTW/OmIdb6IOMyUcQQn9MIPwtcW5ZlNdT6liMtWU+Qz56XyxVHz7CiuLbqsUiylAGOOEeSdM34OQAwpUfeFmk+qVGRI1IjMX0S8IjgSCKPwfTeAkarNVYZWB4gKVHBGEloe6EFKAv4MU3UFoX+Xr50Ng6cGIPv+3XXn6sX3SVIEmbMkEaQ8A2sqlk0zVokfCw8N8P1fPGiD5wck+4rWSE8Lj2TIwkmV1VnkWSGDgfx7ZQNP1JJsEg42S65BNKvn3Zf/m/8+ygCrZhikVB0Ss3zmGUhH5Nl/fCoLcm1VdX3g3G9oJET8SOlQuNRUqpFksVpkYCVA0iicNS+UgEu02gbRdr7oblZc/1Yzo72WsKVJVv+6vv8+x++iJ//6hi+9f4rY9fwNXMvCapFkhX+q/6tHk/vtqoEs4xU9RYJX7vk/uKurVlTSoFFkuTacr1YBCYJl6ryXnQ/jVxb77lqKZbOnar97a3GhIf/thJJE+aUhmzn2lBNmJHB/1ddT/gWG7VIpM2WDYvnIBw4MSrdlw+fjy/LqoiR7Tw8N9O1FWxOpNkMV2qxEFi6jq5aKSfD84KXIkmO2ooWTBpHQhVway4rKqkcx0ugcHzzsb340fZBSfuWORJXendTSk54nUC7lARJg612kyySnYeHtX1VaK5yQSKF/5aclnIkumcebW5ezKrQgZSN6J3K1yHsGBzCi4PD+vEkbLra+xFHkkG2J3GSqjUnBIliUfFHIwkS9kXEv0Y5HzP7SzJHooyvyhpm0b+0Pl0Nn6tCbS/QDnSZINF/PsQIc61FQi8njPcgITK1p9Bw+G+yph1pDAdCFxd385wYqeKZfSdl6yTLImGuAo+Zv7aV17XlYbQSCVOpMRYbmy5ypub5YkPNCz4k19P3mOB1mygpUK99Be+q6kbXSRIk6jv54L89jt+5YyuL2pLHVq7JeRHk8uCuLSAgNXXVdvMgiSP57L3P4X/855Ox47nQINAzHK1S1FbzeSRpVhzNLyqsmYZjw5Wo+ZPCRarX5mXZ4/ds3CJJ7EeikO0EVQiTAKi6yc8kKbOd36sc8hkz+opBHklCYmbV9aU1DUSkOq2LNHc3KRn9oeLTDnSVIEmaMJJrKzxEjtqS/Y404edMLaHieonZo2lQtS1ewZZcWwdPxF1bdzy0E+/48iN1cST8t1TC/IuCbcGxrdjC4/kpQLAwa64vCQ/eaU/4cT191Jbn+SKXIi/o/JIofaL5TWxhFlNcFFGZkByurSQXByPb1agt/v9Urt31fUG20+9olNxOito6NVaLRdAB0VyVXVtB+LbvA32lQktKpKS6tmgNZVgkY1UXL//L74nadmrUk6rk8Na1Kug15DBI4hxJhnACVCtCfpfqvqCbR0mtdvmYKm6gfEztcaTw31ixVvZc6VoUDJNHoNL4SdFpB7pKkCRpR3xj1IUR0qYl4t3Jl9kfZC03wpOoJbV55JFwbQmOJBr/cLmGobGadH4WD8E3jXItmIS2bcGyrNgzCVxZ0QU9z0fV86QwwhEl+Q6AiKCiv8Xv9HyRZZsXNKaehIx135czeenZ6RYoWXfVFNcWL8qoQ+TGkTcqNWpramiR+D4kiyQg21vFkQT/jlRcrVVR03IkvnhnU3qclpRI4bk3NdfD47uPs/uRqydem4yD5vkToYuOxj5Wc/Efj+6Jtditul6ysE8QPjrEORL9cUmRkUnCsep6YWSf/HnRsRIz28W9vIBz6ynY6CsWMJqWkOj5wsokJZGis2rMVZaEiuvDssavdbgOXSZI9J+f0ri2dFFb9HLo5VH5i0bcW2o0E1WRdb2ofMGJ0SrGqizpzYuiqLhrJy/ZDoTuATe0SCwrtvD8UDPi1665viRIuAXH80h0WqrnJ7u2fuMLP8YmVupBjDc8n4oxqgtP/f+Co7dcKrUo/6XmeiIRTT0uyyLhmiF/XsG7iY6bUgpdW17o2iqQa6txV5Lj6F1boxVXK5x4qG/0mS8ES18xcG2Rm7NRiNwbL2hh/NbbHxLtGGgzq2lqQHGoVRJond3+4A789689EXtm5NrSeRaSarO5no9/+PFOaf5GHEkyt0a/jV9HHeeXw1p8/HPd7+0vFRJrbfExlWsueoo2+ksORqouU9LkYznRTteqiM+yXVtVN8hxGu9ILY6uEiRJD3eoXIv5z30/Op5HbX3riX2iDtfM/qI4v9mxcIuEN4M6cGJMIp7VLOC030VQhaLr+3Cs0LWlTFLPjywuIHgOVUWQqCXTo7HJn9G9kwTJL/aeiBWL5L+HNPpY+KPye0uCI5E/58+x5mVzJFkujsAikbVU/uyTXFs9LYzaotuNVfUWiaj0rFiVkV+8IIIT1P4g9SiovEAnWRZU24vnkaS5toaVnKSsZ0Tf6y6Z9G6f3ncCf/HtZ/DQC4cBBM+NanpRGfkkPUwKGpHWUPB3n8IxVGv64p/TegsxCzF2Lz+I2uopOIEgYWR73LUVCU2V+NcFQcQ4lpon3MHtQleF/yaTaoFp2FdyYjkQju0Ijb7m+vjDrz4mvp8durZONpDdri4wXnjwxGgNPQUb5ZqHw0NlaULR+Mp19F3nk420OscJrJG4ayteboFntgNqSZloDBFJyq7n+bEFlwVawMVC6LJSxqO6ZZJcFFwrr7pe4sJMi0Di4/GYsKTzpPyC8HeSMI7I9sajpOJRW6FFUo1bJAdOjMW6ffYVndAiCYtXhq4tINgQeUqE+uvT8gtcFqorGmeV5d4aNc384lCrQmRFePFaVupzSaoYTC4fOvfuJ/fjk3c/CyCaN1/6wQ784JeDeM2KuTg8VMGtbzxf/Lbo+nGLpLeoCBJmkVhWJKAWzOjFvuNjqb/T8xEKEht9JQeVmodqQW9d0HvXWUBVNz6XR6uucLvSecU6ectm0VWCJG2elmtuIEg0k0cN6yPMasK1pV6LC5KTo1XMndaDPcdGw6Q3OieaIFnkHUeMbPcCi8SydfxDfKMOMtujz7QWieTakl1jWVFbrufjQ//+ON5z1VLsGBzCwOKgd02iRRLjl/THcSuqxhIS1ceVlbNCH/sai4RrrX2lAmwrcg+OZx6JjiN55W3xShG9RRuuF1Um6A9dW0BoefaEv9GL+/Y9H3ASFFdO9lqQkyDpOf79j17Es/vitbgIFHbfW7QxVs3ObVETFuWx6pUBEZobnsOFFwmjI8MVPLv/JH4cWi0kSOTipkzBFIJEntc8GGBmXxHHwujOM2f04YndJ4Rg1lktvh9ktpcKtoimIoUt5oplz0F1kUfWdfTZSFgaJxpnFDLfLnSVaytNOyLNhW8MNSFISMOSXxq98EYiYGJRW4pr64yp8RatHrNIdD7fJFR1FklC1BZFj8jnexiruSL6SuJIxHl6P3VAtsenET/m6HAFmx/fh3/6yS780V2P496nDgAASiFJrz73uDWnD+Pk5SF41Jb6vNREuNhY2Xm+srnw59dfcmCHAQzxPJIWRW2FlxkNfehZSkRgkXhC8ehlgoSPSdcGN+3aXPjS/KKCniTof/7SMbx4WJ/3AURBKn3FfOtIV71aHU+MJ3Mpmikkpdl64tW8de9H17cnONaXxs3HR49xZuitAIAzp/eg4noip4mKpnIFS1gkRQd9xLX59J2ivIT/W9W4DtXCkQDws53HpHlbdb22RmwBXSdIkr+jEF5JC1EEiDrRSZCoG919zxzEa//n99M7uinnkOZZdX2cGqtFvb5dVZDQeOuzSEj7KoeCpGBbwaannBu4tuIb91jFFcJtREO2A0xTUtw/JU3UFr8HPfv9YQImbTAi/Fd5jOrzFiVSVEFS466tSONWH1cSqSm+Z64t1W3Hr9VfcmCHvFNFcW21staW6/nielnX7S06YWXZ4Fn0FG1M6QneRx6/fRJ4JKMIOS7LFolKpqsg60DdkJMgLBKNwEnKPSEXMK1d7u7kNdd0azUpUiuySDSurXAcPNnvzBl9AIDBU0FeGA2f53G4Xki2Ozb6levq3Gr0eRJHwtflH/zrz/HdZw5K1ygmmZrjhK4SJGnZnrQx8z2Klx8A4pO0N0GT+ti3nsbOw8M4cGIMH/q3x7H41rtj90viSKg/QJJFErm29LV7dKi5vpicZJHYoUWiDf+txSfnaNXFnKmBlsVLyfPnJcjQHBYJ//307A+GCZgjYaHFUjjZYxaJqz47ygeQ71FOsEhi5GWWRSIWZ5ZrywmSPHWurZZxJLI1Wql5uO2eZ/EPP96pPb9HsUh6Cg6m9oRBIswlq/Pb57NIonNpXtB8VFsOqCCFQd2Qk6D2K+HgXB2HiGZiocUELqR1Alnlw6JxBMeqAjCwEILvZjJBsnBG0G6ZyiqREskFhiDbi3YsUTApw17HkdTYe5E+d+Xz2u3a6jKOJM21FZrlmn7jamgdgSaS2kJ0Whi9c3Ksiq8/tld7P3qxRMrROfvDJMS54aYtCRIvGhPfJDNLpHg++nscnCrXpITEIDJNPjbw78c3gFNjNczoK8KyIFUp5c9Ut9B14b++70vPjDTXQ2HeDFUYTrJIYmR7Qq0teqf9JSfMIyH/sSIoUzKS+e/x/XhFWH7P/mLg2qqEvBZZJD0tJNt935csibLr4sHnD+Gc2f3a84kjofnSU7CFv/xUmUW1abT8NPKbu/tUiySrdhmBXKTpR7F7kmKX4oZTlQTROiE8h68b/mx1Q/WUeUygea4KwIrLXVvcIgkEibBIwmN6mcDw/WCt90yxY+tFurfiiVDfkQj/VZ4Dt0CqhiNpDmlrOZpw0UOnl5JkkfSHvkw1cYqEQlqiIl2LtJL+UgGObYlKwGdoXFvcJ1+Xa8v1xKT3wsln2xZsW5Oc5wMVTTmPoXINvUUHfUVHKZESHaMLSnA9XyQWSvdgv4ssEfIhU7dBmuxJhCIhy7U1paeAmusxn7P82/imqENS1Jbq6uovFeBYlrAWW5FHojogVIuk6vpaXzmBoraEa6tgi/kpWyTpHIEKHjJN60NYJDkrdZ5iVQfqge7wpKKNUR2s0CJhzy4rIS/RteV5KNiWxLEE94hcW9wiWRC6tlSLpE9nkRSc2CbPfy/nsnR5OlFCot7lReNsd9RWVwkSnWuLhAZpKp7vR9FCimalurD6SuFGp0io6b3BJEprY0kTk4g1x7YwrbeAvWHrUp1riyfEcddWHrK9hyX3eV6UkKhORB1HAgSLvrdoo7foSCQ2f6a6qBpdHonq2z14ckz6nvzY6nvgv4ejmFB8j97ptJ5CrjySLItELdrIo+iAwLVlWRHJ34qorTifI5erqdS81BpUvUUn9L9HYyKLROpZorFI0lymNWZ90uZGUVt5S9Tzgpr1II1sV6eu2v6WPzvLSs+dURtbEapu0ANHV3WA7jeDCZI5U0so2FbMIokLkiCgRRUkuiKPQLC2kgJR6PdevGiG9HlwDU+4jduFrhIkug2XrArSVFy28WWZ6L3CtaW3SI6ldB8TFklo3tpWUGJjb2iRENkuk2vMtZXQKCfpXvSbXD+0SCwrIIY1UVt6QVJDn8Yi4WfrLBJdra2grEZ0zKFTZel7ct0QYakmfMaqAiRkKHOLhJdIUadBVO4DWvCIINnakmttEdlOc4lbJI3WttLl+XDCuFLzUEspC99TsCVB0lNwROKkrn3CZ35zNf7yrRcG90q1SCI+jDg1yiPJUmwIZBHV+2x0ZHuSVSlyLkQpES5IrFiTM47khMQoM1y1auj6M1jUVsG2MGdqKWaR9EqCJCyRUrRRKiS3DuBKK+9Loo6TPv7j168IjuW1A2tRD592ocsESfwzSiIrM6K4VFAskoSFQUJIJVKnhRbJsVSLJCTchCCxMK23KDZpskjKCWQ7XxB5orZ49jd1FdSXSNFHsJRrQcJmT9FODD2mDUXS2jUciRpiPHhKtkhIs6USNGqSnbrxJJVIIctgqnBtpVskia4tRtLzQ3QWiWNFgoQEXDMWiWpF+74cbRX0pvC11y86FgoOcSQuLCv4THAkGrK95NhRAdE0i4StDbWZVppiw7X4Rl1b+jyM4N+kPBJBtjNr2s4SJNK7jgsSIO4eo/XLLRLLsnDG1B4cDttzE0cphf96ya6tpKitmoYjUQtfCqWYpwAY11Zz0G0UfUKQRBEn6sNP0sz6EiySKAIr2gBjBK9wbXFBEsU20CaalEeiCpg0BK4tJ7xvMAGjEilxjTepmx9xJGN1cCSeF2z0fL2piVTEkRBooyTC8qTCNamuxKQ8EnqnU3vJtRUfMx9vYkKiF52nCkmVI7EsS2wmPUrUVlZTKu29Na4t/vzLtcAaKWsEScEOhEJNbFKBFt0TtgCWXVvB+ZRfBOSP2lLzSNLO47zCqUZdWylkeyxqiwSJF1fAbCveLVN3TUAWXjU36rGjurfInaomK/YVHTEfiaPkuRzprq3ob7kcvRezzlSliCvFNdfDE7uPG9dWs9CtY+ohQZqK60UcyVC5hrGqm7hRlwo2LEtHBgf/f3Q4skiS6kUJi8QOfPlAMMGn9RRQdCxZYHi+mFRyiZSUH41gk4hKsgdWjUN5JNrwX/0Fe4tOAxyJB8eyJFPa85CLIxElaBSLJIlsTyqPMaXkSCVS6N9KzcO+46ORaythA6T360Mh272Is5pScrBwZi9szpGI6r9RjlC90L0flSNJEiRFJ3jHrhdpu0CgIau9dEShzNBSBYDNj+/Dtl1HteNSq/8C8TwSHYpsHpwSrq06LRLN4UmNraLwX41rC1kWid61VUmxSGiuOMp1HduKohpdjUXiRyVSVEHiK0KMoAuyUAuQlkTAio/vPXMQ193+EH51ZMSE/zYD3QTvVyySIFw1+Oymv3sYM/qKiZpk0bFQtOP+b3qZnGyvuT54tCBpEn3FqPQ4WSTT+4pCYxlVSrZryfZM11bEUxA/4YioLflYz4tnthN6i7YmaitudvNQTM9nWm54GhU1JKgcCUVtUXZwzLWlDDqZbI80PJdZJPQ+v/bobnz67me1GdP8nfMcIzWm3/N9LJjRi4f/9BoAwW9VORL6l+eW5IWWbOccieuhluDaKhUCi0Qku7F7T+0taMl2x7bh2MG1Pnvv83jz6gWiZA0HtwBovg/lECTcIslzvA46V1hW1FZkkXDXVgbZnsCRBGtZb5HQGrCVzwuOFSmrfuARiAuM4J2pWeeqEOOfx6O2KGgo+H9ukRwOFdtT5ZoJ/20GOsuCSn+XWXQHX+wnRquJJGzRCVwEqquFXi532RwbqeDIUPT/qkVSdX3BrVDUV6lgx+pa6VxbeTokcovEDS0SXdRWEkcCBOZ5r8qR8NBEJY+ELu3YcoSLSrbz5wJEeSRTehyUCnasKGbctaWv/ksaXiEkuz1lXEeGKnJyJTudX4pH9JHQLoTJnK7vS1qtbWkECa9tVSfiHIkf6wXOw3s5CrYNxwldWyGRS5jaU1Q4Ek/8Lr4JjiUkFXIXCm1uNFfTOBL+rPJwKle8bE7sMx2lQlMipkyoHAl7TplkO7tWMkdih9ei70KBHLNIbCl4x3EsrVLRU3BEsdJoHFyIMdLc9RKjtkThU2aRcKXUCJImoJuvxFFQ3w/PRyzvIUljCmLJ7dhCoOP3HBsRn12x8QG84pP3xY7hZVYiiyT4t+jY0qZBGxeNN2t8QFQxONJMwokcbhi6qKAkYrgvdG3JUVtsgSmZ7TQux7akvhqeJyckqsOn3+xYFqb3FmOurTjZnpyQGNSWsrS1tmKd7hJ84rRJ+340Vm7lcPeGZUWblyiRwiySeqFzbfHNnTZjLdlesITA464tIHCdDpXjfXgKzLUFyBsvB9+woj7x2RYGD1zIY4isv2IxHv7T10mf6TPbE1xbwiKJu7ayOJK0hEQK8CDLRBTCJK5J4SCKtoWoh0vg2tJVfOgpxl1bOmWNrpOURxLnSDypbL8aGTbe6CpBonNRlZzAlAzapoafqXkPmvOow5iu2xy93LTwX5rYJMhqri/CMinio1SwJTeG63HXVj6ynSYed20FgsROySPRX484Eimrnh2qthqla9uWbJGori0VdF5P0cGMvoLocqh+T4gSEuXrkCAp2HZQ/deXx6yOQQoSYM90jFskfrTh0kLmXgyta4uKJDZgkWhdW5q2x1pBYtuwrcBiznJtCU3atsD3Me4K4hCbIrMuRRn5VEGCzGM4KN9Jd2/pugo3QIhKrsddW5ZlxVxQ8n1kN2b0d0RWkxJB/T2EINFwJDyx2baSLJKMPBKpBXicI1GDDqLkal/qbGoskiag23DtUDMoV6OkLl1JDxVFO4iAoU3qF3tO4MKP3YvBU+XMvA4gySLRubY4R5Ivs33LU/uxK6y6SgKLtFEv3PwcC2GBQblelJfi2uoNXVuSRZLCkdBYC7ZMtqsJiUmY3lvA9L5ijCOJF23URxmNVT0RocTdADTmWME7roGyW3D+LCgFDsE9eL4vbUY2i9ri4b9A6ywS/vxpfujJds6ReLIgUcj2aOOxJXcP1+A5eE0n4qxGq65WSwaYIpOw4SfBtuObfRrZ7vtKAAhFY1LnSMUiSXNtJXEkVdcTFomI3hIWSSSQb7vhItxy5ZLwe0tybRWUqC1CT8GJfZ5WIiWWrKvUjetxIkWVu8mNIGkC/JmTJlGwLfQUbfz4hUF84YHtAJBKdhHInVJwLFQ9Dy8dHcZQuYaDJ8dStS0eAghEuSg118d01SJxFI6EbfrlFNfWh/79CfzTT3ZJ9+EJidwi4cIJCBZhIkdSSg//VbVNYZHYcuKW70fCgMfbq5jaW8CMvmKMI4lltheSOJLQIgkXsauY/WmuLb54aZMm15ZtRSX4PYUj4ZntPS2wSNT91lfIdrICdMpLwbFkjoS5tmJkO0UbKaU/kgSJ4Eg8X9rcRquulgznFnFwnvayMThW3CLRlnNhP9/1fDx/4BTKNVfKI6EWyISAI0m+N59PMdeWrVgkimvLtiy8/bJz8NG3XAAgsJr5urDtONkOBOtUrcyra20R/O3F3rvvQ6pKTXwLb7fMx9sudJcgYQ+dXlZgkTj45cEhfOGBFwDELRKdXCiwCVRlGaaeHy9bwEHatcgjoQrCni8SxSijuydGtiPTIqGifhQxRlo3bWaeTxxJsAh+tusY3vcvP5fukbTh9RZswZEklRsJxhNeizgSS47Wcb3IfcaL26mY1lvE9F6NRaKS7QkdEseqHnqLtljEVbGQg+9VgSRtRlyQVCn8FyG5DlGC3/NkNwb/Oxa11ZBrSxmjFwgSylMYrtR0pwGILBLKUeDzelpPQSbb3ch6lC2SJNdWZFnw+T5SruktEqr1Fn6VNwlRJf8BvRDia3uoXMNbvvAj/Oeje1nBVT9mtQUcSYpFkhh2G0XfkaUtuk7WIoGs/g5eUFEN/yX0FGxtxWd+bz4mV6P08V7u5FZ2PU9xbRmOpGHwRTlnSpA5ThYJh+4Fq/MtmkiWlDUdmJuQOpJxnBihbF7FteVy11ZEtksciR8lwElku0Z7juoYydpxkIsSlEigBfo91qtAzTrn6CsFHInvR24az9f3zOC/0VEsEpdZPbwBEIdlBQUtZ/TFyfZ4U7DkEilBpnBwb3pmJARj3Ba3zNhXY8y1RRYIJfq5oauLYGsEiaqt1oM4RxIoIzR/03p+lJzA6uQJiYSpPQWRzAhEz7RgyxuZLhoMUCwStskOV9x011adFolqzQZj1Vkk0T1PjdVQdX0cGSpLZLtqXWVZJPx1eYpQUS2SAuMi+OcEzpGIhETNPkOlV+RxyNYQ4aUjw9gdllTiqHlR8iutPWORtBCeDyye048tH7wKK86cBiBY+D1K4yXtC1Zq05AmQqGlPFrC9TwsmtWnHQNp16RBEdnuer6I2uJkO9cieNFGubx8NLlI8NDmS1q4FP4bTmTdIkqqtQVEZDsAjFHrXT9eklrNrnVsWxI2vGLsrNAiKSrk5dSeAmzbwvS+Ak6O1RLrDQXnBIJH3b/KZJGE4+NhvEAdUVvsPD90bdm2Fban9WNRW4SSypG0wiLxfRw8NYazZwfzaySl50fBseDYNvyQV+lhiUw076KQXS86J4dFoiuRAgT1s3QWfI9wP9L5+S2SJEWFg2+29JtGqpFrq+rKQQpANkci19qKPufhv4VY1JZekHCOhIqm6vYZ3WdJ4b//64EXcIemD03NixROYZF0O0eyZcsWrFixAsuWLcPGjRvH9V5EjJ5/5nSxiRZsK1bOQEeCqSWjowkkh5ZSCZI+pTkN4XjMIglL0buB8Fk2byouWjQzGEfBVvIcksj26Pq0WIQLTYnaIoJOFw0DhHkkmjLyAETRRoBr6ZooN4UjCdxojGz3o4qxVG578ZwpuPq8uSJvgLL8Z/QV4Xq+9BxUi4RqJqmhn2O1YPOkjYhbUYCGbE/gSMR5YdScHUbsEd/ENyO+gagWSSNku67W1qGTZSyc0QfLSrdIio4t5ulwWY7aomfC+3/T51IeSRZH4gfPkWrWnRrTRyqSAiIi+nKS7ZZms9cFavD3RWtgpFyT8sPU35JVa8v1eSVwOX9DzWwnRTMpaotzJFQ0VU0zAPQbPP22n+06ir//0YuJ4yXUWM4UzdWa50tzpatcW67r4g/+4A/wne98B8888wy++tWv4plnnhm3+5E2CUT/Opp4btXVBcTdNzTBCqHJSnsE36h1iDiS4IQoaitISLzvQ6/GmrNnintUFIGhy2zni5IWCxHUtGFTtr7vR+GHutDHbIsk+N1k+QSVANIFiW1Z0sT1vMiXTK6t6X1F/PO7L8PLw99Obj76l29QKrfhhNaVqqmWqx56df0dwuNi4b9S1BZ3F0YuMYls94KSNWrUFoHu25NhkXzl4V3YdzzuogjGIf+/6/s4dGoM86b3ouTYGRxJ5BYardRkQcIS1QDOkciurZrnxyxAfh5VQqD3qAZGED547Xm45JyZdbu2qq4f0+7/astzuOWffiZ9xnUImpsjFZeF/3pa6ypJjvx4+2H8Ys8JMW/VUGCRP2LLlgk9K7W4blAiheWRJCYkxj8LIq5q+K0vPYwdg8P6AfPjQ8XWCjkgKtzJLZJ6Kyw0i3G929atW7Fs2TIsXboUpVIJN998MzZv3jxu9/P8KOafJqejiZ7QaQoF5bMoassOW15G7o+aoqVyHB+VLRKeR6JCF4YsLJKqJ/yfkmuLBMmoXBBPjdpKskg8P1lzphIpgGKRJMS9SwmJama7J5PtJKDI/UI5NZHmnOzaEhaJ6tqquegp2jFrUrhXlOs8s+8k3v+vP8ftD74gXSvKI4FYoJSD4/tBKHU0lujvmGtL81yPDlfw0c1P43fu+Gnsu+Cefuz4qutj3rSewGItJ7u2iiFHAgTcBXfhCnJYcCShJu3EtfQxjQDkyXU11xcBImqBTcKrz5uLq5bPFeG5eV1bNdePuWB3DA7j/ucOKeOJK1PctVVz/VhyZZpF8rFvPYXnD54SEYFS4cRaFP6rRm1VklxbtiUpWLoSKYB+g3/x8DCu/sz3tePUgSwShynLXc2R7N27F2effbb4/0WLFmHvXrk17aZNmzAwMICBgQEMDg42dT/uhuAWyajiZ+a+ZIJqYRBHUgoz27nZShrHne++DCvmT5POU11OPI9EhS4Mme4zVnNFaKTOIhkNFxFtFJwjIbJP1yEuN0dCkUwai0TNFVBLpPCorVmhJkubHGlkxBcVEjRCDipAqbqBxsgisfUWiWrZ/NNPduHbT+7HPz60S5/ZjqjgpbBIFKXBFhuLxf5OJtvpzEGl3lg0Vvn/D4StmOeHFslIRtQWf8fc0lYLXYqijRpOQufekqK2XA8zwmoMamBEdL9o0w5CVBOHLaHqepmkOKB3bY1WXNm1paxz206utUUCUeva8vwYR1JS3nFsv3CUEikpZLt0Xnidw0P6+aEDBfzQ86aAoK5NSNQl+qkRCxs2bMC2bduwbds2zJ07t6n7kVsCiBa8Y1uxxklajkSZGKSpFMJaW7QniYQ/28arz5uLT1y3SjrvxEgFT+w+jucPngIA9BejPBIVujBk2qTJ5LdtVRuLJvzJsaqYvFLUVtgqVOfa8n19iZRCaLmRIJFcWwkWiQj/TY3aikKdAWaRiErIcjRM8LdKtgcbTTwh0Q3Jdj1Zm2R5jVVd2bWlWCSRBaRPSATkOZRGttNYTo7VcPVnHsQjLx6RvlfXSCRIsi2SgiPnhMiuLbJIZNeW+q6AdEHieUFY9YwMi4QHd6i5S3mQ1Bb3ln/6GW79zye1ASfD5Zp45iPVWoxfSLJIfN8XyZpFIUii7wOORA4OEfkaLI+EQ7VICna8NzsQX/Pq3M0DKiZK+hO51XiwQVdxJIsWLcLu3bvF/+/ZswcLFy4ct/v5/OGSi8uyYn5m3QtWXVuUu1CwKY8k0nw4R6IKoBOjVVx3+0P49pP7Ja1Ep63qGkLxBai2y33guYPYtuuY+P7kaFWMS1drSzeXPB84qintQi4tckGNsfpTapE5NbM9XkY+8ruTS4QEVGSRBJ/TQlXDLzl0rq2a64VVj53Yu4vIdr0gGSrXpEUXJSQGHEngd6aqALJWS3/zd1dS3B4c/H3+6ugI/vTrv1DGKp+z/2TApcyb1hsGY8Q3bnpmJdUikVxbxJEo4b9KZjugj9ziUVtV18PMPn3Jfw4SuK7vI6uv+9IzpuCPrlmOq8+bK/0mFfc/dwh3/Wy35HoaVaxyAHhq70k8+Lzs0UjaSits040qS8vzT0Rtsn0gOFfv2iILlmrf2bZeYS2pLrMGOhnWXA+eF7m2io6NobIrJbfq7j2eGNe7XXrppdi+fTt27tyJSqWCu+66C+vWrRu3+/EsZOHacixRbZagFyTJUVsBRxJ8Tq1sOQfDcZwtNC5IdJnJulIJfO9zQvcJbdwf+9bT+F9hdj4QCC1Ra4vlWvCijSo8z8ehk2OiQyOhN3TBiSKXaRaJQuDaSmSc55N7wBLRWSRASKBMS+FItGS7LRegpM2/t2gLoU+I8kiStWLuaiKN3Av9+xQ2Spoff8fCItEJEi3XII9h52GZTFWnxcETwbjmTe8JinpqLJL+8Bmqobz6qC3SkiOXTD0WCXEkU3oKcGxLSnJUwV1bWQFss6aU8N/Wnheto7TqipAtN8GRMLKd8IfXLBd/W5YF3QzgpWOSOwwqFonCOeksEgAi9yjRIiFBQm4ptu+o0aVJoHtw970aBKEqV+ONcb1boVDAF7/4RbzhDW/AypUrcdNNN2HVqlXZJzYI0iYB5trSWSS6sDxFM4h8pLbYUADZdQQAam9krrHxWPI8ri2X3YfG7rANdLgsJ4OdHKtF0TiOHeZaRGXkdWZ9zfNx6FQZC2f2Sp/TJO4tyGS778f9rbE8EsvCh99wPv70jeeL76u1IIRyiiJIhEWiuLbkJkOeZOk5oQ9dV/erVLBji+Znu47h1zY+kNoK+RBr/yv6kbBwX3ruujLydF9CmmtLl7yXVJoDCDaxab0F9BaDmkw69xwJ+zSOROVtkoo2AsD//O7z+KnicqN55fuRq6e/5CRGbQGQXFtZZLsqzNKKKwLycyTX1tBYPMv+t16xSBqPLieFu+cEl6TkcohoLZHZbovvAJ3iGXFSxFHqIrRorkSJjnFeKws1N8x3Yl4RVcC327U17o2t3vSmN+FNb3rTeN8GgBy1xaO3Fs3qxwuHhsRxOk1Bndg0cYqh/1FodozMBuITimvBjh0VbqtqFlY8bFVeMI5tAV70mUq8nhytRtFP1KOdWSQ6Le/wUBmu52PhjD48ueeE+DxybRFHEm2uOhccwKK2HAsrzpwmyrYQ4V+wo/7hqmuLxq1r+1r1gkZdtXDDCEhTxbVFrhoNeczftYqg34onWSSiaKMnN+pyvSgcmEBrnb87VVvl0AmSvcdHsWhWP4Bgo543rQfvuWopbvvOs/D86F0khXCSICk5tsKRRK4tNRM7qWgjAHz/+UHMm9aDy5dGvUEkiyQMLplSKqS7toRSkE22q+8sybUlxsM2empDcHw0rij0skAaXYdQQFb21MhIeucq2R4r2phgkVRDt1PQgiL+m1TuhSu1aaHeHDXPCxWe6DdQ/logPLvMtdVueIq5R//+y+9djrdfdo44Th9NoZ8YAdnuSyGvNFH4cQS+mQQEdvD9h649L3ZPXfgvJxWJMKeGS6ov+8RoVUSHUUlx4kiSyPZ9xwNNfOFMOTOfNi8R/svcPapmFUtIVJ55ELWVZJGQa0vmSLjrr1rzpMg627JgKdFrUehxnGxPw5nTA0vs0EkuSIgjiYf/ep6vcCTxDYB61ue1SJ7bf0r87fk+zp3Tj/dcvVSKwlHvwdHHXVtMiy0V4tqtCP8VLhn9pr3/hNwOmc93Io/7e5xEsh2IcjY415eUbxXjGBJI8ejv6HOySHTcDncPWZZeoHGryg7fNSl6VcXiSCojr64tPvcpPUBnYahhxXzu5o1PUF2ugUUS/CaKkiwmKCHjhe4SJF40IfminD+9F69fNV8cRy4jjhjZLrm2PEljSeNIuCBxwlL0uza+GR9gvluCboOWyHYn6imiln8Agla/vPRF0FqXJ9XFTsGBEwGZq7q2aOMm98j2Q0NYfOvd+NXReP/neGa77E70fD9sV2pjWm8BU3sKmDst4GQoHHqaYpFwzXG4UhP1yOiYJNeWWsI+C2fOCH73IAu3pMv6iJJaefivjiNR312poHdD6QSJ2mvGUuYsNU3SJc4C0TPkeSTqmESfClb/qegEAlknSA6eVARJOG6y1koFG1NKhcTMdj5+cv/SGHVQBYxO6VGbPBGSsvGBwCIR81ETMg5AcgO5vo+Z/UUcC9vUimraShn5WGMrRfDxviDCIkmxsrgQqBeUR2IxBY4E/JypJWm87UJ3CRI/KrCnhgFzDkQXAhl3bUWaCG8wE4/akh8hn/xZk4RrkFNKTsy11V9ywg0NSrKRhfPmT8U/PrQLLx0ZEZ/ZliW04oJtwdLErew7kW6R9BQCruUnOw6z++nDf+lfNcCBclUKThBSfN+HXo23XRpYhBeeNQMf/40L8GqK1rHirq1TYzWp/LzgLNg+zYVYPf7g6b1F9BUdySIhREUbw14uvrzRB2MJ/lWtyaJSpUCMU7ORSRn2fnRNug3N1f6EMjxUdkflSPqK3LUlR225KcoPELdI6Lwym0/9JUe4hXRTO+JIIrI9yT2nS+hTMaqUDxKfK4KEeweoIjKgr88GyK6tctXDwpl92DE4jBV//h1sfjzIc4ssEtnFVWUBJvLv4RxJkEycZimriY4cqxZO157DyyBxpZnvQcIi6abw33aDtEkgLvH5g7U12kLMtcUtEldu4xpYJPIEI9QkiyRDkDgUyWRjztQesZERKFJGLRHdW3Twv995CY4MV/CNx/aG9wq0U25663z2+0+MwrEtzJumRG2F2q9lWegtONrIFkJURh7SM4iEgpzUdeaMXolkfNevLRG+bB1HMlSuibBhOkb1d0fhrFZdESpFx8YZ00qSRULgZHsh9JvzDGI+XvWZ9CRYJLogCx4a6+siDcN7TO3Rl+Cf3lfApYtn4cKzZkjzmEfiRT77KIKNNhydG+nUWE2q1UQuUyFIQjclab5qIVQ+bnqOQLJmHCPbNWPiAkOXR0Igzki4/JhFoifbmSCpeThrZh8efekYyjUPf/u97dK4kyySeAJz5PoioZ1mKeuitgDgXVcsxt1/eJX2HPqdVc8T7Q4A+VmSIDEcSRPgSTrq4uSbjc615cSitqIJVPV85s4h7S44Tp1QlQRzXAeanBedNUNEmGgtEl+2SPqKDpaeMRUARLJlwbZgWfJE12nI+4+PYe7UntgC59psX8mRzH/1edGiJq01sv6i311jSV1p0AqSsRqmhkKU7q9ql1ERQlsUhsyDomNhzpQeHFJcOQCF/1JyHYX/yguVrBN1oZYcW2oAFV1TJ0jke+p4PSBy/6noKTj42n+9Aq8+b67UO3ze9EiQRFFGNG89sWklRUgdYM+EBHWZ5VtwC0nndrOsuCApJcwBdZPVKV1ckLgpFslZoXXdoygnyRZJNLfHqq7k5iUFK5aQmKOMPBC5px3bwvzpPdhw9VJ8+A0rYmMQ3KCiaOgivYDA2qM16oZNvHQBPzNFtW0jSBqGnEcSfMbzQQi2rclkj2kYtjiv5kZteqlkhNDu2HlBb/hokmdVg90f8hUXnjUjJMp9aeJPKRUCAePJdXT6So6wqkg7IzcHD/PUtWc9OVbF9L6CNO7503skV1evol3btvw7XWad8WfANdIqe0ZpEAuQbRRD5VCQMAEVs0jY7zwroaS/DgXHxtSeAk5pqur6zLXl2BAdK7myTI9BXajFJItEs5Nxi4S7Yy3l2kmCRA2NJsxmvV+iqK2QRGbu2KScjQPMvRVxJKSYBBwJoVfTMlbKIyGyPadFohMkchvq6HOVIzkvLFPUV5JDdZM4EtUiWTAjmj/E5akJiboOiRw8Ss4Nq1JYloU/e9PKWBkl/nvVNZokSAp2VAePijaqwRkAMIMESZvJ9nEP/20nXB8SAQVw0l21SOQHrZqYkWlrBwl2otS4rKXy6/aVHGny6zRUjovDSrg3vHwRfrT9cEw76e8hjkRuv0oTquhEHRYLYSVY2sz43xxB6RU5BPQ//usVkjbbq/jmLUsOJebWGcCitsJ/qSPjxYtmpP5+GicQuVKA0CLpDYWdy0I0JddWZHnVo30Vw7IiuggZz4+UkYJti1a7fJNLcm2plZzFNbWCRP4+2bWVIEik3APZZSt+p4jaCt8Vq7KbZJFwnqSmCJKiY6G/J5oXvUU7FmAQcSRR9GGSVRp3bcWPGUtybakWSahIUA4UrWWKwlMhcSQ1V1Ki6B2KxnaOLJzo+zjHE/EX1CFR/DbN9BStD5Q5k8Yp9QhBElSljhS46JzFc6agp2AnKiHjha4SJD7zG9rKRs8ntKPhSOJRW7LZSKGGtZBM4+HBhP6SI7WNLWdYJFctn4vtn3pjGNsfrzEVWCRB2CsXUD1CkETRGsUwQoy3Ak0qa+4oFsb0vqLk8+5V/N8W5IU+XK7hhUNDsagttY5aHu6CNk9ev2uoUsM05tqyNa4t4WIIn/+cKSUcGU5OQCRwMlYFL5FCFQXUhETh2tKQ7XnzSJLIdmFFkyBJ2AzUuawDL3vueT5eGBwSJH3S73/pSJR1r7YzKDqyRbLqrBk4OlTB0eGKqCsn3qUXBafk5Uj0FglzbaVwJORN6BUcSWQ9aF1bzG1LHAmBkljJjadyrJFFov89xJFwYa1r95tskegDLApOVD2CorbU+QIAb11zFq5dOR/Te/O7e1uBrhIknBiNiKjgXz6hdZVx44JFnkA8aS0pAobzDEC+jnncDFdrNZFF4inhv33hhOKbmRMmJFbdHIJECQ1Wn4XatEvtbXLoVBnX/s0P8Pmb10jnJ0W+pUGt/jtSDWoGTestSmSi2thKkLnhprFoVl8uQaL24+DwfIi8EceKomN0me268F+dK5FvgEG5HbkPN7d48lokuuKMankNEWXk+fiPR/fg0ZeO4ZNvvVC6PseVy87AVx55CbdcuQQz+0tCEJBFU1AsknddsRiXLp4dbtZyJJPPog+TNOx4QmL8OF6wkguEUaXvBikskRURuZl4Ayi6BrdIfF8OhSdFsE/hW3h/F3JbcfC+JqMVV1LGdIEEkSBRK5MnP68+ZpFwS5bzZL1FG32lHu01xhPdxZF4zLUlFmU8uiroFaA3TQnctQVEmgNltusSEpO6JuaBbVmxIoNTSoFWfv9zh/BHdz0e3YcynxUXh23JmbdJ5eKJTCaoPnN1U7ItfQE8cj3QMNTrZEWt8XNo46E8BeHaCo/pLcr8E+dIAOTmSYpOejSNKC9jE2clJyTStFH5gYJt4UfbD+OB5w7Grkeg5EyuL5AFBET/pnEkf/u2i/E7r1ws/p/eI/V4j8ZD2quHR186hjOmlvDOy8+RziFcu3I+/uxNK3F8pIqv/zyIAlQtqYJti7I2gBy+Slo0d23VG7WlO4xHkSWF/545vTcWoXnb9RfhwrOm46yZfcKFOYvxRyfHqlJ4+ewpJdw0EJRWIYukN2aRRNaAjmOiMYxVPQxXXCV8Pf6bo+OVOoAJz+sV584WrnA35EjU310qxPvBtwvdJUjYoo+4kuD/+YTW5ZHEw4Fllxi98KDCp1ymnpAU958Hjm3FOZKSo9VmRK0lKQHNhp2DIwFI6CT7cFXLyrL0WfIkXNWoLcKx4eTktejewbm0UVDYMY/askNtTIriYeG/ALDkjCmZ9wLiuRcqXC/IGynYURl5XUKiqmkvnx9E0X3+/hekzznZTq6hePgvXTv4Ny3899dXLcA5c/rF/9NzO2NqSToucm35YYJnMcYfAsBTn3gDNv3OK3BeOH7avFU3a6lgSSHZOvcYL5FCAjTJKo1ZJJp5zkuGcKVotOriZXOn4OzZffj09RdJEZYAcMWyM/DtD1yFUsEWz+d/vPF8/NdXvwxAEOrMw98ty8JnfvNirF40Q7iwhUWieCYqrpfAeQQfHg2tYurfwp+LTpCohUWTLJIvrx/Af7lisTjH9eJ8cBJR3w50lWtLl0fiKNEXALTlC+Jku2zS0qbJE7QAiE2n5vlSnZ96YVvxKK8pPYVY4cGiYwmzmRaOZUXunzyuLUrwI6iLWG38ZVv6ul0kXOlZqkJPl6uhQq1SS9FUU3sL0jh7i46koYqOf+H3f/DaZai6Ph587hC2p9TaUnt4qKCOfU74Tn1f9nEncSSfvv4iVF0fP/ilXMqcu+PIVSWH/8bJdsGRaFxbqhA8Ggrr2VNkQSLIds/DSMWV3FL8EqVQAbHCYVZDt4nKLRRsO5YkqoI+ksn2hHBW5XfoFBXeR4i7DceqHmb0FfH19/0aAOCXIUeje68kSBbO6BNzeKTi4uzZ/bFjuTtK5Ug475JmkRwZDuY8RU8B8ffKj1eRxJEA0V5E5erVFIS0c8cb3WeRkJuFuUUAWXvXWSR8wv/RNcvx5tVB3xR6STSRo6gN+XpAcxaJbVsxLbC/5IjMdcKyedOwKHTjRIUlo1DkXByJLVsY6rNQyXbb0hOGVNhRfeaEpK6A8rVDi6/mYs+xEWGRENlO76+v6Ejka9SDnJ59AX/2ppXCfZSENLIdgEhIJE7GYws2+I3RdTgsy8K5s/sxeKosuSu4RUKbOXfT8DwSQZ6GG6LOtaU+48uWzMZ586fiT379fOlzwT25PobKNYko5x0JSWEihcgNk91UFBxLEiS6TZuXSFFLjcSul8ciSRAkgFJXzJE3ew56/MGcD/6uuZ5WSHNrIOJIZM9EzfW1Qo++PzpEFkmGayvBBUXP69sfuBKb/+DXpO8kQp+H/4bnGIukRXBZOQtLeXlyiZT4QuAT+5arloioB9owKDGL/PT8+IJtoYy4S6ge2JaFak1ewHzxE77++1eISUvJXjwBi66RlEdC3znsOalCguLxCZYlLwbeDpjfP6k/dhro3H/96a/w6bufxadvuAhAZJHQJftKjnaDVjePLFqmqBQ6JAI8uq4nFA3euCu6vt4iAYBFswMBv/f4KF42N3AV6SwS7sKU80jkDVG32am/b0ZfEd/9b6+OHScyrb0g4m/eNLm2mmNbsBVri9yrukizoiNbJDphHOURIdMiUcl13ebMyfaywiVIlY41XgcC5ZEUnMidy3NqOLhHoVfUM5PdZhXXwxSNwhhZJBpBovAs/PjbbrgIh0+V8dWtv8K+E2NCmF141gxxnfPPnCb9PrUqNX2et5/JeKCrLBLfj+c0RFEX8mYQI/sUMp5QUDgSXRw5/d2n2fjzwrbipea5O4LQV3ISo1QcK+JFCraFL7zj5Xj9BfNj1+DamU4zilsk0SLkPm9qUas+63pAi2DwVBnDFVf4mIkjEc82gyPhY02/n2yRqGGSvB8J1VXTurY0G+TZYWn4PcdGxWeSRRJuQFzj5+5Y1bJTw3+D6gX5nrFwuboeRspuzFp27LiLLwhh1guSgi1bJLooK3qsvPpvUmJcUokRjiTXFpBgkWhdWzTe6NnVXE87V7lGH4/aSj83zpHELRL+G0lJOHdOPz5wzXLWZkF+T0987PX4t/e+Sh6DJ1eljjgS49pqCTjZroakqv7JtInMNyPSIoiE0wkSOqYp15YmympKqYDff83LhEaiIgoIiFxbFUaAX3LOLGz63YHYeQ7jPHSaoBp9JrtCoimjWiRcKL1s7hT86+9dnvBrI9D9aaM4FvZVmNZTlCyn3qIjhX2qHIm4XsZGW3RkJWK6Ul6FSoBT1WXfV5WG4F+dG4F6jOw+GrkjuRuLKtOqme10eVXDVLX5rOZPKgpOwPMMV2ox6zaIXJSvT2PTZeMXHVuuf6blSMgi4Xkk+jHnySORXVuqRcLC+S3986KxAFFUY/BZ9CwvOWemOLaX1epKqrXFLQEO2uSJI+HPyrLie9DGG1fjD177MrxySdADhgRjUrh0cH4UOcZTEARHMoEWSVe5tjhxqUY0cFJcH7XF80yiz9U8Es5BEFrCkVjxqK0pPQ7+x6+fj/dctRSX/OX3Yueok91iwiiNUOZ5NFqLJEa2x0NTgagdr3jG7Lm999UvwxXLzkgcA4HuT8+XmmNN6XFgW9GCD1xbHo4MlTGlp8Bqbcnjz1LYVY5kuqL1U79tskh8S3Ynpbm25k3rQdGxZIuEvdO+ohMKqOgcLdmeM9IpC0U7SJIcLrsx7si244Kk6FhSXTnLikrs9xYd2TWTwpH43LWVEGqdRwHgFklaBjhvI6zCZxaJGvL+7F/8urbVLXdRqxyJbuz83ke0HAm912jMc6f14MNviHgt0cYhRZA4dtBxdaRSU3ovTTxH0l0WiZRHEnzGJxfX3NdecKZUA6eouL4Iah4Jdx1Fx0Rac6PgRDmBFv+sfn2WqipIHDu5FpB0LxbOq1sUcUESucL4GEX4r0Yo5Z3UjhDUkUXSWwySzHjyYF/RQcX1sO6LD+F/P/hCLI+EjzUNBUduzZtokdhR+C+3BNIEiW1bOGtmH/YciywSV2ORSGQ7m7MRr5ew+dbJQRWcYE4NV2qY0qNxbWmUKdeN2uRy910s4VEzb+gjKbO90BqLRG1ixedXlDSZrs3L69pCX0kWjuTS5SWCdNahbqz02dHhCnqLtuRmisq9J78/tRV1Es6YUsLhoYq21pZxbbUIvsa1JU0exiXccuUS/P5rXsa+YxOlXo7Ead4isax4TDmVtEjyi/cIjiQKvxWLKq2EtSOT7Sp0CYkiuorxFCIhUcOR5BYkTIsFgs2DNEKeOEmf7T0+iv0nxpLJ9ozbBgmJKa4t1xPNnygzWpfZXnL073rWlJJUJoeT7SRIuJXip7i2VOisgDQUHBtDYzX4fjSXxLUSXFunylW85rPfByALEnWT0m2mPCcoi2yPNbbKsEjKNVeaq3w8SdapOl7JskxRoGSLJJ8g4RzJzD45FFsN/NGhJ4drCwDmTO3BkeGyVO+P/s06dzzRVa4tV+Pa4qarKEdCQkZjVQTnInYO7QdRfwZ2XU0jovWvOhdvumhB7rE7djyznV/vliuXSDWBgrHJv9FmZHvahsrLwusmdyz6jJHtXNilRW3lndTq/UcqNdaONOIkuJY4WnUjd0adZLuakDhDY5E4oeCkUh9y1BZdR3+fab1FqQRHTRIkdtwiSQn/BYDP/OZq7Ds+is/dt70B15YlhJpqkQSuLfl6RcfCrsMjIsy6VLCBcjR2Dp11pEtIVIMSKEouKSG4VIiKX/Jw74rroWhHRSJLkkWS7c5Vk3D1Lt24a2vZvKk4a2YfFrIKwbpzORGuzqnomOQ1QYIxy6qYM7WEI0OVMHggvK6GB243usoi4SSa1iJRXDCO5jvOBwBxjaqckkfCXUI3X3YOLl86J/fYuTWhu/dH33IB3n3lEu33JMhsi/cjSfe12ppnQNBxJLo5OqpwJHxzSNLYdWORrln1xObDgwL44h6tuMwikc9Xrbd//C+X4t82vFL8v9oCVY3a8phrC5CLKgLR/EoSlNN65Ha0OrK9ppLt4aV0FslNA2fjhpcvkr7Pi4Jj4zgJktxkO68NlmKRaDmS4N9yzY1V0RVjSgjVFZFH7J6cFylX5f42XEBlFYik6/MlkWaRcKVl5YLpeOjW12E2qxygO1cq5a4IEk74J6FHUz9PhzOm9uDwUDlMSCRFMnLZTxS6yiLRlZvQcSSRsInOdRJehqq1CdeWEn8PyO6Deltd2pYVC//NQlEN/7UtRi6m30sI2hwWSZDPET+urDwLfqm8ESSqIBut1MSzs+2Iy+FjGqm4scrD0Vjl68+f1osLWOvSomNLm+D0PnkJ1MJwX37dvBwJECQR8qZgMbLdVsn2eK0tlSOh8darcRacZIskCP9VrQVbsgKo4rRtxedzWomUd/z9T5m2HLdIRqua98YFdGgFVVxPBMiUa1443mB8PRLZnr1RFxx5DuvWB12zTzN3nQxrJi0SkBe+TEJejoQskjlTSzHFw1gkLQJ3E8zoK8GxLSlaRe16JoX52vJiJqimuS5qiyaI1DM7R1MnDtuKeIK/vG4V/vP3X5V5Do2toBl7EmFLxwsuSbMoVCGQZJEQR2Jr7p+31afNTHQgEBIFbpGQIGFJkqNVN5ELUrX2kkL2FhxLiiTS55HI71fmSIJ/kxa8KkhIG/3Q2vPwxovOhGPJVYyzlB/+//VyJEXbFm62GEeicW05tiXl6tB87Ck4sXWh0375IXSuSrZHLZdVV5n8PRAobWQllGuuNF6+Wf/uq87FDS8/C+99dcR5qtBFbano0XAkut+m5UjYfFctkjyCLnJtpa+buVN7UHE9HB+psqgtvQLSTnSZIIkW5doL5uPeD14lOp4BkDYoQBUG8ncE1Vyu1OJRW/QCef5FvR3K+FjOmtWHV5w7O/MctflOltZECDZvK7ZhErRFGzXXG6t6iYujHuKPj3Ws6kqWI88jIYxWIo4kK2pLV9U5XXv0RB6JGJ/OIklw3U3rLYaCLupfAwC/d9USzJvWK0h8gkrmA3HNNS1UOw1ZFok6twuOLUK633XFYqwPiwTyDfw3XxG42XSKkvo7bCs+5iTXls5lWHU9sbGWa5403pksknFabxF/87Y1idwE3VfnouQQZLsmaMZOmA/R9ZNdW0k5Txw9BRslJ7t675zQxXboVDnGqRmLpEXwWBayY1tYNk9O5BM8CPmkJbdX3D0DxDdEUV1XItvJteXEPssLXWRQForKBNJZSQCwirl2guMgjtcpMdrw33BMG2+4CB9aex6AYNNPSpKrJ6adX2OkEmmePExZcm1Va4kciTocVaBTh0SCTntU84zkSsnxDY+D6mNR6Krqgksj20m+JFokdbu2bGG5xfJILP19yCJ5/ar5gnzmv3XjDRfhsY+uTQ2B5f+vzmWyUGLHCgEth/XSXPR9eU7P7Jcjo7KgjkVbIiX8nbow/iRXp+57dU6RAJyrlKnhuOJlc/CWi7ODc86YGijGlVqUYU93rtdibSW6S5AkZJ0SiqpFIk0smT9RzyHoLZK41pynOyBHlumsg5rZLkebRf/z1Q2vxDdZAbgo9Fdf1VddSJYVCd9503vwtkvPBhBoiYnF5+oQJPxZjjKLhGe2cy1xtOIJF1SsimzMooxrxJJF0qvjSFQrBLG/kzgwKn1B7i0hSJiCo1okNGSqCxVz+wgfeH1ziiszMbLdtjREuCUR12qXUSCY17Om6Ddxddo6drz9gMh9Ut8L1Y9TxsSjxSSLJMX60KGguFDzhv+K4/n61Lx62SKRn/XAubPw8d+4AJ+6/sLE8b1+1Zn4m5vWJH5P4H1neEdKdQztRpcJErlSq4qCwpHostPVTVz19asEM1236Mg+53rJdmksOS2SWK0tbpEoPAAvs0IbFdf4ORbO7MUbVs3HwhmBBhVwJGSxRS6RsaqbKPQadW35Prcc9RbJaKUWNhfTuFfYR3OmlGIciJrZrrNI1I6QWtdWokUSXO9kGLmls0iqNQ/feGwPPOptIywSfVmRtMCINKhtoDmCVgrJSlPRsVnl7Hz3jfEolhW3EBNaDiQ9V67UcG5rVpMWSZoCpedI9PNB99kMJYHYsiy869eWtKT97RnT4tFjJPwbKZraKnRV1JbO38xRdGyp2q3O1E0yxQlVbdHGIAu75Oi1pzzQuU+yoJLtuuRLgmzx0L96i6Sn4ODvfmcA/+Uft2LfiTEpaisQJNEEThIk9WTZqr+3xKJndLH9I1U3sXgejfPKZWfg/2pqfakdEqepZLtPrXaja+s2kSRBQhYOt0hsi1dcsLDl6QPY8vQBVGtyB0ZeYJAjskjqFCTsd6qurRsuOSu2GcuKSOTeSypzokJdO6qLEIjWU1L4r6q4SQEs3CWZUO0hCbE8Es2zFFFbCYnFFB6dNO/IokvjapqFVDgzHMakt0i+9rWvYdWqVbBtG9u2bZO+u+2227Bs2TKsWLEC9957b1ODzAseSqlD0UkmUXlSn3yOYpFokp+KthU2TGIugCY4knpdW7rGUuqC1Akq20q/l/DBMs1SJWmTSlfXw5Ekaca3vnElPn19UFaex/b7PjBccVNDUJOEcVAiJfpOHT9ZCJLrkl2KHmNPCtkOMEGidFjkfx88OSYpP17ChkDvt36OJHrP6vvYcPXL8FsDZ0ufSXkarG1rXqVI59qK9zbX/5ZcFgkbx7SMvjMqYpntKRZJUqkjvgaS7gHErdxWosSCRYRrS5TKnzgHU1MWyYUXXoivf/3reO973yt9/swzz+Cuu+7C008/jX379uHaa6/FL3/5Szg5k9QaBQ+l1KFg24kuCxEpZKnnyB8kNbYqOjZrElT/os+a5Dqo/mZ+DXVS6cx6HRmqO4e7tixLXtBJPVjyhv8C8U2fnjlvn6ve59RYLaG5knwNFWr1Xx5CSZodd6mp45vVX0J/yUFvSf/7iGwfKgeuLc9LFiTVsK8Er5oL6N5d/Nw8IAE0s7+Uq/x8zCIJ/zeva0trkaiCxJbnrHpvVeDx/+dzql43n9ouWvcsddYvR/D7kr0eRcdGueZhRl99brd6YFlB2+mhck38Hted5BbJypUrsWLFitjnmzdvxs0334yenh4sWbIEy5Ytw9atW5u5VS5kk+0pFkkCR2JZlnZTlIo2hvWbeKZ53r4RurHk3TBIe9NpefHSIfGx61wPujHZlpx3wrW7pB4s9Sz02GajsWa4oAaAU2NVbdy8WklXdx01UGLXxjfjb9+2hl0jcv+p17rxkkW494NXJ7rupimurZpaYoULEteTlJ8k1xa5TeoVJPS81H7uSSgortl6SX69a0sZk4ja0gcUxCySkt611QiyrP7ZU0qY2lPA4jPibXj5+RNpkQCR642GUVN4uInAuNhCe/fuxdlnR2bzokWLsHfv3vG4lQTyRydBzSGQCXM9CRicF/9M5UiCjU6vbeWBpbEYspCeYCm/WkuzmamksorItcW15uA7el66LOB6EfOjJ4yJuxyGxmpaDYx+ZpJ2FmzIdux41SJMCscuFWxtv2/CVA1HolNYgKBAJFd+0nzdth0nx7NA74hCRjOPVyx0EeqcO7lU/n9H4tai69J30rE25TUpgqTQeCRkbHzslrpNd1pvET//6Fq8dsW81PPT5hbQBkGiNN3qBI4k07V17bXX4sCBA7HPP/WpT+G6667TnuOzOHlCkoa+adMmbNq0CQAwODiYNZxE0D3TNsaCoyYlse9oc9XM1WLBBipyYx0+ES9bMlt09FNdP3khacA5T6cFzkuKEJLcPh7vIsncF/oxkSCJLDl6ziUnKK6nZkw3gpggSXh+fUUHNdfHaNXFqbGadjPI0hqLBZkjcTQWTGABcNdlzh+CIMigVLClqK0k5aXqymR7FP4bH3tDFkl4/JzcgoRbJNE7z6sYxSKxmGuL3D5E8M9UyPI3Xngm+osOdgwOSZ9L4b8NbJRXLT8DP9p+ODa+RsLWuQKmg2MHbqfxrsJLEXi0p1IgRb0BCK1E5i5w33331X3RRYsWYffu3eL/9+zZg4ULF2qP3bBhAzZs2AAAGBiId/PLC9KUU11bymLUhv9qLZL4xODn/s4rz5WOrTf0F1AjyPJNRJVs57fVa+sWwPpr8JpbOvAaWjQ8es70G5vpwSLuowqShIX4gdctw4GTY7j9wR04NVbVHpfFJxSVVrtq5V36m7/yet2U01mZlIBsl/k0QuDailt7SfOtUbI9t2tLUkQii6RRsr1gRwmvJEhWL5qB9756Kc4/U06SXb1oJlYvmokP/fvj0ud8ftXLiwDAHesvFcVF+Wts5FqZSopjj7s1AkTPhNbnLVcuQalg4x2XnTPu907CuIjOdevW4a677kK5XMbOnTuxfft2XHbZZeNxKwEiKtPmR0EhWnlPDp17iJDFkXAUbSu3IOCQ/bf5zlHzSGyh/emLLKqmeSbZLnEktNnJlVab6cFCyOva+p1XLcbrzg960DdqkahzINpQZKUij/aahP5SQWxerivnNvF787bIQPRskyySel0X5DvP7dpSqus6bD7lgc4iideDsmJChEPNtQiix4K/630PdD5p6vI7rftSYv0kCSHHttoiSES/nnAYpYKNW65cMnmjtr7xjW/gAx/4AAYHB/HmN78Za9aswb333otVq1bhpptuwgUXXIBCoYDbb7993CO2aBGmaY+vv+BMnDk9KlPAO/ulhY1mcSTSsQU7d5QLR2MlUmSyXZeJzGGFUSd8UeexSPi1VUGSFOFSD7Lqm3GQ4DpVrmGh3Rf7Xm2xrKLABL1qhUR/y2VU6rUEeASY68uJk3x+id4xwtpL9nU7Soe/PKCCjXktEjkhUbYm8kCXZBgpN8kcJMcHr12OS86dhT/86mMAgmdBfUgc28KH37ACV+Zo4Zw1vkaI6TSvBY21HYKE1kAjVtV4oSlBcv311+P666/XfveRj3wEH/nIR5q5fF3wc7i2rr1gPq69YL74f4cJDx3pStAtpKTNuujYuRO4OLKIQB1KSt0iYXEkqFtCo2LunLR7ifBCj/vxg39JuCYlb9WDWPhvysal614nXStDe+Ul4nVhwMEx8f+vB7YVxfZTD3gCFxLlqtyqWA1k4CjYVt1BHMdHSJDks0iS3L6NChKP5dAUFas5CTP7S1gyJwr7dsLfXXGD9/AHr12Wayz68bG/G9iERbO8hHP7exxRVHE8QZFsjVho44WuyWwXOQB1PFuuYaibLIduISXJimIDC56PJWkM2nuJcGN5Y0xa+Do3Q9q9CpIgif7m92iFIFEXZinl+fVnhIOK35jRLQ9QI9nkazQSjs2Pp9j+eIfFuEUS8U9pFokcbZYHVPk3N9lO9a7CKrRU8DGvIFGnEldAojI+2ddRBW8x7EPSbHgrf9+NRDhlubY+c+PFotbaeKK/2GUWSSchzb+cBN5NMc0lonNtJeURFAt27oXHkVXLRwe1jHyWxqQKkkyyPfyu5vmiSB4R3HStVri26rJISs1ZJMH14y4KSahYSNz888CxbckiSQr/jRpHyUJa97tmTSliVp1ROcfrdG2JBklUAkcIkny/X32PNTdSQPK6ttRjgtD6fNZM9nX198gLXbFXjgsWJnM/rUSUR2IESctBboF6Imx4VFJaIptuYSdtoIELornw37yCRM1bcZRFq0KQluH1z5s/DWfOSC5tTcd5no+PX7cK5y+YhquXB/5p6ubYErJd+blpglhuHhZ/TlkcSXCejiPh1klyGfk8cOyo3Eks/Jf9PVx2pXGQ21D3u/7hXZfWLbQXzuzD4KmyVDE2DfRc6PlTT5W8ipG6wUqurYRijdrrSM9LXwKoETTLkYicmAm2BNSExE5A1wgSP0fUlgrelyTNbI2TiMlaWsCRNGI2179xiTwSZWNM5khkgfO/3v7y1OuLhCffx/TeIjZcHXWgI+1ZDf+95coldWvOunasicc6QXHMoA1rcma7ulG8/bJz8M3H9krfyS105Ws4Cd/lgWNZwiIJClvqw3+HFYskqUQKAMxL6WWRhC//7gCe2nsit/uRBBg9/6qmrlwa1OdUZRaJrkJ18nVki+TAyTEAwOwpzRHZLSPbJy44CkBrvACtRtcIkjx5JCq4qarLJyCowqW3GG89SuAZ7vWgmaKN6saYzJFAOi7vmEhocJDbQ53UH33LBbmuLd1HLaOR8fym9RZwZLhSV62t2264CLfdcJH0nUSos/Bf25KfUb2bjm1HUVtqawPZIgkESazWVotUzbnTevDa8/VZ2jqQAIsskij5NA/UNeF6XhRJqEQWpoEfwp/Fe65ammsciddNKHuT+3wxtyZWktCao/DxTkAXCZLGyXZp09C5tqy4IEnCObP7Y21N86CRvIWZ/UUsnTsF582fJp2XxZHk3aho//B0gqSFri1dO9w0kCDRciSad6pCH7XFrmHJORt1u7YsFv6rWCT8WqprK3ykTZPKjUJ03FQskkYTEjnZvmROPxbN7MMl58zMvI7qCvzbt12MWf0lzJtev1Umj685iySrjlu7EPWxN4Kk5aDNrh6ORMoj0WipBHXSpZmWX8hwFyWOhW9kORWe3qKDB/74Ney8dIukXh8vbYC1FIukd1zCf9PHR33WG6m1BegrPfN5QxVWk8aXBSmPxPMTKw4MlfWurUYqI7QCatRf/YJEtUiiiLW+UgGfCy3Ceq5TsC1c//JFuc6r57oNWSQd4tqK+ti7GUe2DxP8SFqHpMqpaZDLqQef6TZxdSPpSSlUqJYgz4tmXCniGoo/Ov59eP2ci8hRNjgO2mT6W5KQKP9/liuFKuymZbanbRS6Kgb8Uo5lYdm8qVg+b6r4/3rg2FFfdtdLTkhU7x1FHk7MsiwonNvvvmoxLlsyG++4PF/pDfWZB22Lk13GiddpwVrQXjdBoNd7/kST7RStaVxb44DmXFvJJC0Q3+h66+j+lxfNakvBecG/SQmR0W/Mdz06TseR0GetyCNRN84sDZjKaOhI6SSOhEPHkajEu2VZ2Pz+X8N/PLoHA4tnpf8ABY5tYayanZBIiFVWnqCNSpDtYaLr3Gk9+Pf3vir3+eq04xFrjSh49Z6XhUZC7DnUYJWJAqUeGNfWOCBPiRQVXHul03SbuNr8qBWbpwpeMbTRxUPnZVokeftLsKgtFVVPT7Y3AlUeZLm2yCJppEMiECUrJnIk4f/0lwr43VctTh2LDrZlIfT8wfV9lOys3Bf5s4niSEQeSYMWkc4isVPWVeJ1GgiFz3VdzTuu73x6PhMtSMi11TmCpGtcW3lKpKjgme0UtaMVJEpYZFJ72WbwG6sXRONqUOMRCYkZHElei0Rktrs6jiSYxK2xSOTfm+3aKmrPA7KTMvl38mPmHEnq7TMRKBzB86l5vmLtJLu2xPgmiCPhme2NIG3t1PNMG6mEXe91G1ljPF1gIkGu9U7iSLrOIqnLF8t8npHmFD+OJl3JsTFW9cbFtTVvei++80dX4ZEXjzQ8UTOjtmgh5OVIwhN0Fgm5YVpjkcibRSbZ3pucTxBZXWmCxI4do0ZtNYNAkAR/eyrZnlLWRR1fuyESEhsoOgrohUVWNrgO4+Xakl2ZjZ/fMa6taudYJF0jSKJaW/lfsmVZohxGGkdCk65UcADUWhKppMPKBdOxckHjZRay3AhZJdZVLAvJ5vPPnJZ4TEsaWynDyRP+CwBj1bhGluc3ajmSBOK9ETiWJaII1YRE3btRP+oW1xb/rB7lSGrQ1kqOhLvMGsojqf+3jAc60bXVNYIkKpFS33lOGGWVFu1jC4skdG2Ng0XSCkQhzAnf1ylIXn3eXGz54FVYMT9ZkPS0oBucujCzwl9JkIxUdIIk+DerqrFlJVshjbp2CI4dZbZ7npyQmMbrECYq/FdNSKwXaUpYPetSyiNp4bNoPo8k+HeiOZIFYVmjq5Y3Vk5/PNA1giStTWkagnIYkbaSlkdC/vfx4EhagbTmXACrtVXHqk5qQvRXN16EO368syXambowM6O2wjwSnSDJU2uL7plUKr7Zro+2zS0STxv+W3Si6rqxXucTZZE4MhdYL3TDbsQdpIZitwrSdRt4xo1EoI0H5k3vxdY/uyZ3Ved2oGsESSMlUgDKIclwbdWR2T6RyNLG67VI0vC2S8/B2y5tTWtPdTx5XVujWosk38alNvXi77jZ9+tYURKn5+tdNVN7CjgW9gvpHI6EBElj99dFTGYpNzqoCYmtQsuKNk4wRwKg6Sz/VqMzVesG0AjZDkT8CJ2mmyNxQdKZj43GmTTPOyWhSkVUajyfRkxkOxU9lK8V/Ju1URRtW8lmj75r9v06ti1ltvPNkMY1tTfS4dQNeKJeDwmwVkaNpQWxJKGZXjDpY9ErDvnPD/6daIukE9GZO2IDaCSPBIAoGZ6akKgk5pXGuW1wo8iyODolDl4FjYe4p7wJiakWScZvdBxL4i74xtLTrEViQ8ps121gU1iQgjrUeudwq9Bs+K8ODZHt3CIZB6EGNOfa6jRFrBPQNYKkkTwSIHRx5ExIHAvjthsNjxxv8LwYHTrJNOeghUnRcFn9XKamkO2CB6qTI+GHNxtModba4oKb/p7WywUJCZeJVVDUxlbN4rz5U3OVrFEhb/jjlEcyiRMSOxFdw5GsWjgdOz79JtT7im3LkkqkpCVVkQbcSo2tlchyXXWqaS4K+xXJIkkfH23Cb12zMH6tnIQo74oJyFZAs64t24pqbcUSEsO/p/TELZItH7waz+4/2dS9m4Ha2KoZPPjfX4M5U0s4IXig/OdSWL7ntzj8l12qEWVKBHJ0mCLWCegaQWJZViwfIQ8KwrUV/L9uDdHEobjtUgtCXscDrQ7/bRcoxJMESZagLjo2nvrEG7TJkHm1xoJtS/NFskiadm1ZjGzXWyS8tzdtUGfP7sfZs/ubuncziKK2mp/fS86YAgA4NRZWOG4gmtLz/ZZaz1boefAbFFA0X4xrK47O3BHbCOJH0tw+6ryZ6MY2WUjmSNK/nyiQoM7r2gKCjTitZ3vWBsR5MfX4psN/rci1VXM9OTpMI0g6xdWodkhsBfK+j9h5LXazReNpXJmqt+jp6YTT/pGQWyvNLVRvwtxEwRdJmekcSaeZ5rSB9RXJtdL4+KJ6YxkWiSOX+29l1FaB5ZF4vuKbt3SCpKnbtQwkwFupKDlCQWvsvFYrPc1ELnYqx9gJ6BrXVqNwLEvyl+vm1x++bjmOj1RxYrSKB5471LGurawQ6E61SGwhSEKOpImNLC+5W2DuTPX4lpDtPktIZEKNhCbvadMpGxONrZXzu9HNV1SybvFcDcbjN6RMkSXS6R6JicBp/0Tk8ih6bX3WlBL+9m1rhKbcqWQ7uVOSFkmn1ApSQeOd0lNAybGbGh+dmrXYHduWBGrrM9uDvz1PyV/QJP11iBxpOrNdh0YSEoH80Xf1IrJIGjnXuLaSYCwSK8onCCK4kiculbRoBRk5HhCFKydZHgmR7e+6YjFufEVzbVUjYZl+XDz8N/q72fphjmWJnvZBiZS4RcLnUOdYJK13bc3oK+KtaxbisiWz6zovskhau9aaqeCb1o77dMdpL0je/7plmDMlqFnDS6XoIHpYd6hriziS5DyS4N9OWwg03rNm9WHBjL6mrkU/LWsDevk5M7W5HEDzFpttW/D8oGCjWiJF7W2j3nsiMXtKCTe8/Cy86mVzWnZNx7bwuZtfXv95ORWCetEasr0z3lcn4bQXJNetOSv6Hyt9knx83Sp8+u5ncXmd2lW7QH75LIuk0xZCK7XPvO6Hv7juQuW8pm8tQFbHgZNjAIKWtep3UiHHDnkdjm3hb962ZqKHAYA3KGu1RRL820z1305bP52A016QcPzWKxbhihRt7GVzp+KOd13axhHVh4hsTxAk4ZrstKit/lIBtiUT0I1C/MZ6N6AWPhLaaHYdGQYAnD0rsrJEXbEC50g66310AhzxHlvMkeRMWNVhvCLJugFNrdwPf/jDOP/887F69Wpcf/31OH78uPjutttuw7Jly7BixQrce++9zY6zLfjU9RfhNSvmTfQwGobnpUdtUWnKVvZ4aAXe+vKFuGvDq1I7H+ZFoz7wVrqX6Fq7Do8AgJRkSJtQkbu7Out1dASyun02ikZKthA6NXy+E9CUIFm7di2eeuopPPnkkzjvvPNw2223AQCeeeYZ3HXXXXj66aexZcsWvO9974Prdk5/4W4FtXdtZT+SdqC/VKibjE1C3n4kKlopSEibfim0SM6aGVkkOrLdaLhxNPoes9AK11anRT12ApoSJK9//etRKATesVe+8pXYs2cPAGDz5s24+eab0dPTgyVLlmDZsmXYunVr86M1SIWXkyPp5jD4otio6xUkrRsDPeedh4dx5vReKZxYl7FtXFtxjG8eSWPKVKc0tupEtGxL+Yd/+Ae88Y1vBADs3bsXZ599tvhu0aJF2Lt3r/a8TZs2YWBgAAMDAxgcHGzVcE5LkCBJziNB6vfdgFevmIu/vG6V6DefF63czGnze+nICM6e3af9ruTYQtiZfSmO8dq0W5HZbgRJHJlk+7XXXosDBw7EPv/Upz6F6667TvxdKBTwzne+E0DU9pYjaaFu2LABGzZsAAAMDAzkH7lBDEKQJKgHnRq11Ur0lwr4nVctrvu8Vj4STra/+aIFyn3IIrFRdGxUXbdjwn9bgT/59RU4e1bzhSctK3gnrbbWbMtqeP6fDopYo8gUJPfdd1/q93feeSe+/e1v4/777xcvfdGiRdi9e7c4Zs+ePVi4MF7y26C1uGblfNz+4A68+jx9wACV5zaulDhaSraHO0655kmhv0Dk0io4VsiTdJcged9rlrXkOo5ljUspkmYEiXFtJaOpN7Vlyxb81V/9Fb71rW+hvz/SQtatW4e77roL5XIZO3fuxPbt23HZZZc1PViDdFxyzizs2vhmXLRohvZ7OyNP5nRGK/dyrrGqdatWLZyOmy89G5ecM0sQ7l0kR1oGx258w0+DZTVuUTQT8dXtaCqP5P3vfz/K5TLWrl0LICDcv/SlL2HVqlW46aabcMEFF6BQKOD222+H06HtaU8nZGXun84YD4sEiNdl6y8VsPHG1eF3ZmNKgm1Z41LKpznXlrFIktCUIHnhhRcSv/vIRz6Cj3zkI81c3qDFsO3Oq7PVKWjlXs6fcVo5Hfqum6PoGoVtj0+YrW01zoeZzPZkmCl8GsHKKEp5OqO1eSRMkKTUaiFrxWplWn2XwOlEi8RwJIkwguQ0QjOLqNtBgmR6b/NVg7hQKqXks5CQoUrBBhHsceJImrluM5WDux2m1tZphKR+KwaBlvmJdatw1fIzWnItQppFQq6tSs0IEhXjx5E0blE008uk22EEyWkEY5GkY/0Vi1tyHW6RpLu2guOoz41BBMeyxqUmnG1ZTUdtmTUUh5GtpxEsE/7bFvBnnNa2dmZ/CYA+gfd0RxAY0vrtqRme0HAkyTAWyWmES86ZZUJN24BCTtfWZ25cjX9Z9FLLClZ2E+wwebb1123etWXcw3EYQXIa4bdfeS5++5XnTvQwuh5SHkkhedOZNaWE979ueTuGNOng2OOY2W5cWy2HcW0ZGLQYTk6OxCAZ48Xn2Vbj+Sk0HhNCH4exSAwMWgyuSBtB0hjOmd2PvmLrq2FYTVgkndrPpxNgBImBQYthLJLm8ZdvvXBcrtsMR3LN+fNxYqSK/pIp96TCCBIDgxaDN63qSYnaMmg/mnGZrThzGv70TStbPKLugJnlBgYtRt48EoP2wzZlgsYFZpYbGLQYcma72bQ6CUEZ+YkeRffBuLYMDFoMY5F0LuZO60HVNSVpWg0jSAwMWoy8me0G7cdnfnP1RA+hK2EEiYFBi8Ez29XGVgYTi/6S2fLGA2aWGxi0GJzMTWtsZWDQLTCz3MCgxZDzSAyza9D9MILEwKDFkKK2TPMKg9MAZpYbGLQY3LVlchYMTgcYQWJg0GKMR2c/A4NOhhEkBgYthun5YnC6wQgSA4MWw/SrMDjdYASJgUGLYcqMG5xuMILEwKDFMIFaBqcbzJQ3MGgxxqNFrIFBJ8PMeAODFsPIEYPTDWbKGxi0GIYjMTjd0JQg+ehHP4rVq1djzZo1eP3rX499+/aJ72677TYsW7YMK1aswL333tv0QA0MJgtM1JbB6YamBMmHP/xhPPnkk3j88cfxlre8BX/xF38BAHjmmWdw11134emnn8aWLVvwvve9D67rtmTABgadDstYJAanGZoSJNOnTxd/Dw8PiwW0efNm3Hzzzejp6cGSJUuwbNkybN26tbmRGhgYGBh0JJouzv+Rj3wE//zP/4wZM2bgwQcfBADs3bsXr3zlK8UxixYtwt69e5u9lYGBgYFBByLTIrn22mtx4YUXxv7bvHkzAOBTn/oUdu/ejXe+85344he/CADwfT92nSRzf9OmTRgYGMDAwAAGBweb+S0GBgYGBhOATIvkvvvuy3Whd7zjHXjzm9+MT3ziE1i0aBF2794tvtuzZw8WLlyoPW/Dhg3YsGEDAGBgYCDXvQwMDAwMOgdNcSTbt28Xf3/rW9/C+eefDwBYt24d7rrrLpTLZezcuRPbt2/HZZdd1txIDQwMDAw6Ek1xJLfeeiuef/552LaNc889F1/60pcAAKtWrcJNN92ECy64AIVCAbfffjscx2nJgA0MJgtMOXmD0wWWryM0JggDAwPYtm3bRA/DwKBpbNt1FAtn9mHhzL6JHoqBQcPIuyc3HbVlYGAQx8Di2RM9BAODtsGUSDEwMDAwaApGkBgYGBgYNAUjSAwMDAwMmoIRJAYGBgYGTcEIEgMDAwODpmAEiYGBgYFBUzCCxMDAwMCgKRhBYmBgYGDQFIwgMTAwMDBoCkaQGBgYGBg0hY6qtXXGGWdg8eLFTV1jcHAQc+fObc2A2ozJPHbAjH+iMZnHP5nHDnTv+Hft2oXDhw9nnt9RgqQVmMyFHyfz2AEz/onGZB7/ZB47YMZvXFsGBgYGBk3BCBIDAwMDg6bQdYKE2vZORkzmsQNm/BONyTz+yTx2wIy/6zgSAwMDA4P2oussEgMDAwOD9qJrBMmWLVuwYsUKLFu2DBs3bpzo4eTC4sWLcdFFF2HNmjUYGBgAABw9ehRr167F8uXLsXbtWhw7dmyCRxnh3e9+N+bNm4cLL7xQfJY23ttuuw3Lli3DihUrcO+9907EkAV0Y//4xz+Os846C2vWrMGaNWtwzz33iO86aewAsHv3brz2ta/FypUrsWrVKnz+858HMDmef9LYJ8vzHxsbw2WXXYaLL74Yq1atwsc+9jEAk+PZA8njb+nz97sAtVrNX7p0qb9jxw6/XC77q1ev9p9++umJHlYmzj33XH9wcFD67MMf/rB/2223+b7v+7fddpv/J3/yJxMxNC1+8IMf+I8++qi/atUq8VnSeJ9++ml/9erV/tjYmP/iiy/6S5cu9Wu12oSM2/f1Y//Yxz7mf/azn40d22lj933f37dvn//oo4/6vu/7J0+e9JcvX+4//fTTk+L5J419sjx/z/P8U6dO+b7v+5VKxb/sssv8hx9+eFI8+7Txt/L5d4VFsnXrVixbtgxLly5FqVTCzTffjM2bN0/0sBrC5s2bsX79egDA+vXr8c1vfnNiB8Rw9dVXY/ZsuRd50ng3b96Mm2++GT09PViyZAmWLVuGrVu3tnvIArqxJ6HTxg4ACxYswCWXXAIAmDZtGlauXIm9e/dOiuefNPYkdNLYAcCyLEydOhUAUK1WUa1WYVnWpHj2QPL4k9DI+LtCkOzduxdnn322+P9FixalTtROgWVZeP3rX49XvOIV2LRpEwDg4MGDWLBgAYBgAR46dGgih5iJpPFOlnfyxS9+EatXr8a73/1u4Zro9LHv2rULjz32GC6//PJJ9/z52IHJ8/xd18WaNWswb948rF27dtI9e934gdY9/64QJL4m8CxN4nYKHnroIfz85z/Hd77zHdx+++344Q9/ONFDahkmwzv5/d//fezYsQOPP/44FixYgD/+4z8G0NljHxoawo033ojPfe5zmD59euJxnfgb1LFPpufvOA4ef/xx7NmzB1u3bsVTTz2VeOxkGX8rn39XCJJFixZh9+7d4v/37NmDhQsXTuCI8oHGOG/ePFx//fXYunUr5s+fj/379wMA9u/fj3nz5k3kEDORNN7J8E7mz58Px3Fg2zbe8573CPO9U8derVZx44034p3vfCduuOEGAJPn+SeNfTI9fwCYOXMmXvOa12DLli2T5tlzqONv1fPvCkFy6aWXYvv27di5cycqlQruuusurFu3bqKHlYrh4WGcOnVK/P3d734XF154IdatW4c777wTAHDnnXfiuuuum8hhZiJpvOvWrcNdd92FcrmMnTt3Yvv27bjssssmcqgx0CYAAN/4xjdERFcnjt33fdxyyy1YuXIlPvShD4nPJ8PzTxr7ZHn+g4ODOH78OABgdHQU9913H84///xJ8eyB5PG39Pm3MjpgInH33Xf7y5cv95cuXep/8pOfnOjhZGLHjh3+6tWr/dWrV/sXXHCBGPPhw4f9173udf6yZcv8173udf6RI0cmeKQRbr75Zv/MM8/0C4WCf9ZZZ/lf/vKXU8f7yU9+0l+6dKl/3nnn+ffcc88Ejlw/9t/+7d/2L7zwQv+iiy7yf+M3fsPft2+fOL6Txu77vv+jH/3IB+BfdNFF/sUXX+xffPHF/t133z0pnn/S2CfL83/iiSf8NWvW+BdddJG/atUq/xOf+ITv++lrdTKMv5XP32S2GxgYGBg0ha5wbRkYGBgYTByMIDEwMDAwaApGkBgYGBgYNAUjSAwMDAwMmoIRJAYGBgYGTcEIEgMDAwODpmAEiYGBgYFBUzCCxMDAwMCgKfz//sRh44/Ly0gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_18\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_20\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kj710HA8+eyj3OOf/ybfwoQ4Xx5Phv/n3lTpKmiAsklI7XFuthb9mgQuPotOb9uEupnp9PsgNVvGauxf8+nceHsCnNj2N996Rr3CY9i2S2d6o60wXwBPd4raCJAeNCJJqGGVqdScGq1j3hYfxwX9/XHzX1qithNBrpMEOCZLZfWXx3Z9ufAp3PbZHLCrtMMdpSD3JwtduE19aT3laYZgItPz781+P7Wnr2HTUI6GpKPCpkTRPQiCLrqdJ1xYHX5za6cNXFtmCc1JwJLXiri0ac7NClR//ZCK8R2rFLOagoDXXTB7JDX/3Y6z8003KdxPdNWYFSQ7oRSvyDGtBmKk1HB+KTfCte2VRRbGoGPbZcXgA33vmYAPjjD8bEUrk2gqYNRNG8cRvJ0dCxydyuN2aFVlNeVFbtSAO1RxvQl6cP+M5kXDgxS11S4EsEq8N3bH4MEbamCiquLYK75N2AdWbz7RtuQ0WCVVf8Asei0d85Vsk8YaNWHybdxxNJe5OdLLeCpIcBA24n6pBmKkV06ToZbH/obB20tv/6l8/hHd9+WeF/ctBE7W29iWEK0W8VMmqCcO21geie0fuh3aFFBOqBYRePT/1WNV3EhxJxu+kWXPXlh6cMCCqBLRPyAPAaBMJeVloxiKhzUYUQZK/jxAkTfJF/Pj9iYAuFRQkjVokrQqCiR4+bAVJDhpZoKtBlKltDyWLdW9ZCpI81xaVnCYyMQvD1QAhc6k1MldpAR6qBoiiSLG+Rgq4i4qCDAV62WttzvcYLRC1Va/e0Vj5n+u5SolAPzWcXQmABEs7rCsl/LWNFgm/vKLyTuaRSIFW77m0Kki4AkH3tewVs/SKCksRtVVr/HnxsOZ28JWdhBUkOZAuo/rb1sIw8+UeNGQjy6it9D7EW7x0dCDzfNUgxCtv/y7uenxPUyVSaF5GUawt8aTGdnIkpLlJQdLeF6KIG46uI0srHCuXV72oLQrpPcVLymhRbiRYWhV+URSpgqSNrhM+tsYFCedIzDvf8aOd2Hl4QGzbbCg0Pzzd56KuLa5c5vJzLeSRnBiSXJnlSLoYjZDYtSDK1LapDwd3beURr4tnxsl7eSG6w9UARwdGse/EcFOChL8Iw9VQJDXWmGurnVpvqcOurVYskrFqpkTzQz/bP/1wB6765APwE22Yu7b0ZmDUZbLV+zhSC5W5116OhAmSgiyJTEjMd20NVwN89O6tePuGH4vFuVm2iI+T7nOpsEUi/y5Sa6uZqLhjg9IjYTmSCYRaEDaUBUvProgPvRrIxVgHaZGqIIk/TYvYwum9AICXjmT3hpCuqKg5i4S9CSPVQHGP5U36/SeG8c8P7yh8HhJYQpC0nWwnoZc95modrVAXQodOjXSENzFZJGEY4c+/uQ37T0qFoJ9FbZE1SxFd0rXV2sIyXA06FrWlXF+B28jvNR+HSTmg+XNquCYj9pqcUwpHMtw8R9KpWlvHBk4ji2TXrl14/etfj5UrV2LVqlX43Oc+BwA4evQo1q5di+XLl2Pt2rU4duxYy4NtFTd/eTNe9v/KsLqf7TyKJ3Ydz9y+kYZR1SA7/JfMZs6R5JWkoLmcZ5Hw0iDNcCRc6AxXZcRZGEaKINGFyu/966P42De24cUj2W439Tzxp+c68F2n5ZpYf3Xfs3hou8wJkVFbeVphfuQM3/fEUBUv/8T9uPWup1oap/E84pnL8z38/OHUOE4ZyHZSQkgpaVUgD1UDjSMZP7KdX8poLb9ECilrruMIIdusm4+PjUKui0aAKa6tNkdt0RiOc4tkspPtvu/jL//yL/GLX/wCP/nJT/DFL34R27Ztw+23345rrrkG27dvxzXXXIPbb7+9HeNtCQ8/d0T5/3/72x/jui8+nLm9KKxYhCPJCf+VHIm83bSpaR/6Li/Lli/8rVokw7VACM0gihTtR+88SIEDJ4eKJRbS4uk5DjzXadkl848/3IH/eGS3+H+xPJJ89wK/FycTv/S//vSlpms4ZY4jGSMf6Y7DUiCT2+qU4tqKv5uSKCHkJtXnTTUI8ZWfvFh4UR2uhuAyvVN5JEWmZFbNK9OlUHSh68iglGaFKh8b3XO/oGtLtbrqz71GLIppPT4A4NjgaWSRLFq0CJdddhkAYNq0aVi5ciX27NmDjRs34uabbwYA3HzzzbjrrrtaPdWYQ7z4hRMSzQ+7XzQj4lFb2Ys/Tb5nD57KLvDHFn4uVIoiCCOh5Q5XA3GtQRgpxKseNTS1Ek/yvKQ5DhqT6zooeW7LmvRoEGLPsSHl/zTuLNQjPLNcPN99unguTxFI15aZqKV5wnkRPVBDWCSa4PzpC0fxJ3c9hUdfKmb5D6cskg7lkRR4d7LyWYzvRiDn05Dgi5obOx8bEdtNhf/mKDHNcCRSkHCLZGx4vGbRVo5k586deOyxx3DllVfiwIEDWLRoEYBY2Bw82N6XcizAeYh6qAUxeWlazEnTdJiyIzkS07HiL48PVvHMgVMZ55NjE66thqK2IvRVSJCEzD0WKaGKgxmCJK+MBwctBq6DxCJpfsGKkogy3le8WsBPXjf8Nxmj46ia39GB/PDrRiHzkuR3pjIlJrK9nkVC2xUtijlcDZT50l6OpHmLpB5HQs+Hu7bawZGQICns2irovqOx1cKosKJHyabctTXpLRJCf38/rr/+enz2s5/F9OnTC++3YcMGrFmzBmvWrMGhQ/n1kMYaphc/CzXGWeigl5xPpNCgnYpjhSGWzIoJ9x89fyT1Oz9PTLY3npAYhBGmlGOhwInXIIqU5LRBzbU1NdGWThUWJPEncSStWCTEhxw4OZxKmswT9tWCHInrOMo27a5UbCLb8+YLHwNpyqMZwQXkhita02m4qpaMaatFwrO+G7VIFI4kvS3dLwdybraDI6E/i1okSi+XAkoMUFxY0z6nlWsLAKrVKq6//nrcdNNNeOtb3woAWLBgAfbt2wcA2LdvH+bPn2/cd/369diyZQu2bNmCefPmtWM4hVBk8jXCPeS5WKj8gqm1qenYQRjhrNlTcPacKfhxpiCR52sk34UQhhH6EutiqMo4kiDSFlNVkNA+PMY9D3Q/HMeB7zmp+/PikQF85D+eEC/KM/tP4T8f3Z06DiDvcRjJLo5VFrZcbwxZi6wUJOioIJEcSf4ixHuS0P2naZJlJdOiWpTXGa4FSnBGXmb7yeEq/uGhFwpHsoWGeV50+3olUsiidRxHhkK3QZAQinIk/JS5me1B44KEjnds4DSySKIownve8x6sXLkSH/zgB8X369atwx133AEAuOOOO3Dddde1eqq2gUqb10Mj3IM0YdMPvF+UtZDfyQ6J6WNVgwie6+DCxTPwwuF+8/mYa6uZoo1BFGFqRXIk3CLhWbS6a6svcbEUFSR0nZ7jwHfdVIbuH33tCfz7lt147KXjAIA3ffZBfDCjHD9fZHYnPMlIgcz2ouG/juMo2/S3ueS9KVLP5F8/ZbBIZOCH2fKlIIiilsVIAxzJn929DR+/5xd4cPvhzG04slx32duzcdQRJPQsPVcqaLq79MDJYXz54R11BZ/p58IcicG7YN5Ojq1omRNpkUhBMtF7vrcsSB5++GH8n//zf/Dd734Xq1evxurVq3HvvffilltuwX333Yfly5fjvvvuwy233NKO8bYFYVRM2+SLax7UEiPpbQdMrq1I7ms6b8lzMbXiZ46Tj62ZMvJBCOHaGmEciR7+qyfE0XAbFSSug8QiUV8I8gcXyYvg4yKepEjyZNHwX90i6dcCCp7cfQLv+eefNawdRlGEv/n+c6K+GX/mprl1kt1bUniiKH42umVCIEtkuGD12jhqy6wt6/OILKQhplQMjQY4eMrcHE2Z04U4Evl3PYuEuyEHq2bX1jee2Is/+8Y2HKrTp6SVVKGivVxqGfc4D6bck4leIsVv9QCvfvWrMyX/Aw880OrhO4JaGDYkSOqtz/wh18IIh/tHUPJczOgtAYA53l3wLyatK4TnOuir+JmNoHiOiywAWXyyhZxsrwUKxzNSCzG7r4yjA6Op89Mk50RgHsSL78bhv1XtZpIgKeJq5As4RW4V4Ujqhv9GcnHiz1K/9kdePIoHnj6IY4OjonVwEew/OYxPb3pG/J+P1JRXc7h/FCcGq9i0dZ9odhVGap6SvnjRHCvs2qoGmZnt1TBExZURhuTu4ef80vefw8Yn9uIHH3596tgqEV1/LFGmRZLeVob/OiLwQL8XpPwcH6pi/vTs50Tj5Nxd8bL3kfFvHXxeFq23Rftwa3XSu7a6BTo5VoQsLqrpV7VIk9/9yqP4829sE98Jsl0x+ZH6jh+j5DmYWvEwMFozE/ItJiTqZLsQTEkeCdX70nuI0yQvapHQ0F0nSUjUNCvPLV5efkRxbcU5NsIiKVLvKENbp989Rrb3lryUNSbnQ92hKtAj3PLI9gvPiANVtu47gb9/aIf4PozUoqC6O2doNP6/Xn48C3lRW7r2a3pGRwZGFR8+R+OuLfm34jLKmfdhFGUGHpCSeHwwf47S8SmApOh488acGq9ikRQT8jWDcmQFyQQBN/lrYVSo5WtRTb+mWSRHBkZwZECa1UQKmrSYrIREz3XRV/ERRenIKToPjbHZWlsUUsoz24PEtTWztwTHSbu2hEXSINnuuYDvpvNIiNssIkhMrq3RQpnt8W8f+8Y2XP2Z76V+F/fNkS/7rCmllNWq8xRFMaA9P/6cdMF68ZKZAIBte09i9pQyFs3owcvPmZX0U5HXn8WRFCfbQ42bYJVmNcvN9IzCKMp0DbVCtivfG9ZOWmT5depKBHFb9ZQdupy+sp/6rh6UZ1gwaqsoz2Fyk0/6zPaJip++cARXfvJ+Fl/PylMHUWqBNKFofkaVv+BBiGpg5hmUqqj0aXJthSH8xLUVjz09Vh6f3qhZDsTXVPZc+K6TWCRMkAQhKqWYo9EtN3LFNMqRUNSWrj02YpGQBtpX9qQgqZm1Ug6uze08kq4WwP3u5H6Y1VfOFKJZNdWyoD8/xbWlPbNF03swf1oF2/adxGgQYtn8qVg8s1exSDw3Hf02JFxb2feBzzVqQUDg87Va4BnVgijzvVCyvgs81yICSY4t/k5pyaudQ1ok9dyviUVSkYKkcGSa5uHIQrUJnoO/243uO16YtILk9k1P48DJETyzP07o46U+amExQZJXD4tDt0iqQai8mHkWidG1FUTwXUcm/5kECZm/keRIGomnj60eBz0lT6n+GyTjL3suplX89GKabHeijtuAoEZtpRdA303737NA9/TceX148cgg3vzZB/GLfSfFuLNQ775wsp2y+mf3lVORfeI+N2qRZAQs0Ll5s8OS7+KCxdOxbe9J1MIQJc+F6zgIIznOHj9t2YmorRyynd+HIZ0jqeW5ttL7B1GUbUlo3w+NBnjbl36EJ3efMG6ftXjnhf8qQi1DkBS2SCq84VzuLql9TefnaMaqsBbJBMKgVqOILwpFyfZawQVa50iqgfTf8oguUwJUVokU3+MWSb5rqxmLJIwiuK6DnpKb1NqSi+RoLV7ApvWUUhwJbXdiqFpIe6NbFyckuilfr5chSEzHFoJk7lQAwNP7ZdY/339oNMADvzhg/I3wv/7rSdz+racBmBMSZ04pZwYa1JsPP9x+GNtZRQJdICklUsJIqQpd8lzMm1rByaEqaolC4TgU5h3vVyl5aYukms+RPLXnBO59ar/4/0hVrQ2nCBLdtUUWCVeEwihz0dUVpj3Hh7DlxWN4fNexjO2zjpP+joQc/0m3RvuLCpLkBFN7Ssax56EelyPHlhYGYRjhkRfN9yJiQRWqNVNckHzz53tx8ce+PabCZ9IKkgGtdSbnGWrMteW72QlIRbkH/pBrgWqRKFocd23lRG3VEmuBcjZMFomI7OAcSYNku+c4qPiekkdC4b9l38XUnrRFws9bJJxR5miYXTIyaks9lumW030+d25f5nkA4E83PoX33LEF2/bG1oqpLMu//vQl/O0PntfG6IhzzJ5Syrz2eoLknf/4U6z9qwfF/3WBpFgkQYRe5qMvew5Kvotqcn9LvgvPcZQFpuK7CMJIEUjCtZVhkfzTwzvw0Y2yovFw0hmToFokuiCJP/WKt9kWiXqtpIzoXJHcvgGLhOYJtwg0C4re/SxBsuHB5/FvP3tJjHNqJd1wrh50ZSALJsL8B9sP4fov/QgvHErniPFDNUu2f/ybv8DJ4RoO9+eHP7cTk1aQSMERJf+Xi0IQRsIicZkg+f/uexZbdh5VtgPq+3lrmrCoMUGiTgz5N32fRSj6CdkOmDkSmlicbC/qcom748XXXim5SR6JzMcYDRJBUvFTEUdc+yviSlNcW166RAoJcj000rSI0GJ33ry0IOHHfS55QYeqxWoxyfBfqTXO6itjtKa6KIsIEpPLL82RqC6iiu8Kgep7LspebLnVgggl10m5tqglr+6qArLLwVeDSGwDGKK2eOsAbdHy3XQvmTDHtRVpFkm9OmDZZLvJtUUWiVlBi88TXyeP2vrY3Vvx63/zMIarAT5579P4n19/UoyTk+1ZVvYz+0/hQ//+hOJSzhunGK/B6qNoN1MtN/5+ceFBz+Sux/bkVgUHWLj2GPIqk1aQDAiCO/7/YAZHwoXFXz+wHW/72x+L7YrnkfBomphsHxGCRO58fHAU5/7xPXjw2UOMI0kfPAgjlWw3ZOG3kpAo3E2OoyxadNzRWsKR9PhKljU/b3zdBQRJcmtcqrWV8r/Hk15v9WoSivQynTMn3yKRpHSxZlr0u5tYJK4DTE/cHXzxK2Kh7kj6tHBLNx21BTx74BSu/sz3cOjUCHzPEcUCS0kARLUWxgqF58J11TwSqiKtu/OAbNdWEIbKb8NVrUMij9rKeEb6PQ4jdeHddXQQB08OqxYJZF22rEjJbLI9/V1VuIzld3l5JIR//tFOPPbScfx0h1QUaa8+RrZnKWN/9LUn8PVHd2Nbwslxl20+PxeKYq00dlobTNGYWR6MatKm4g/+7XHc8Hc/Tu0XhpHgoMgLk8eXtRuTVpDoC7lukXBBEkVRasGk34D6mn6KbA9DoyB5ev8pRBHwuQe251b/rYYRPE+S7UaOxFAipVH/rufGvdRpktIxyCKZ1pOO2tKtr7rnYsl+vqGMPGlPuj93/4lhPK+Z/sJamFLGP7/r5XjleXPYuNJkse86CkeVBVHqPrFIqKoAoLoVTdE0OnYm/UXOnD1FfGci25872I+dRwbx0tHBWKD7JEgS11YQYTSIKxw4wiKJr7Fsskjq1NrShcNwLTtqq3+4hlu+/nM8m/A8Jh5Ldg+Vx/zQ157An31zm8YFRsKq1cvtEBpzbZFFwr8z55GYXFvfTbizWVNK4vjTeuqH/07VaszJzp/5gqQWROhJBD8JEnpGpvuh3GN2/dVaJPYzZez/8LnDeMsXfojnDvYLJWaoYCh4OzBpBQlBChJukahkey2MRFkKrkmKSVtPkLCJPFoLEUUy+Y1PMuqQ2D8skwwzS6S4rogmyQv/DcJINN4qasmKsiVJjxDe3TEII1TZYqq7tvKS4nLP5SQZxNo+rmMWJK/7zPdxzV/+QPmOtin5Dq5eMR/nL5gmxxXwRS7ezitYbZhzJCOMHwJUa7CIa+uFRJAsmiEzqvUFI4K838PVAJ4rBUnZc1HyXIwGYRK15cB1kOJIAM0iqVMiRR/zaC2bbH/2wCnc+bNdeONfPYijA6MZFknaxdM/XEP/cC2VeHuyLkdi/DrDtSWDWIB4XnFFLgwj8a6fMIT/UlsGHrXGLZIsoTZzSmyhkruMtit5bt2e7ZWSKvhzLRKulGoFH0mQeAZel9xkh06NiN8ffu4I/u1nL2WOrZ1ouUTKRMdTe07ga1t244yZveI7PbM9CCOhafAIGpGQWGe95E1nSDPklWoJNN/6R+TLpk9C0qBjsj07/Fe8yGEEx5F8SREIi8RxUPLiQoX8eGSR9Ja8uDJw4mIB0tZXPdCYvKREShbZXi/C5L9/ebMoqU9uoFlTyvKaovQLqGeDZ0FEbbkQoc8mfqqQaysRJCQgAUPxx0jel5FaiBm9qmurnFhpQ6NBksQZKtdCCxMXyjIh0XwfdbI2dk2ZLRIujL6zdT88xyBIhEXNFnEDbxJFcv4ONsqRaF//zfefw893xe4b+qniqxFsXPCbLBKyzIaroXhX+wrkkVC5I1qw6ZTlOs3aamGU4rSkRZIWJEpOGjvucDXAcPKMTAFC5MbqH6kJ19bXH92NvceH8PaXn5U5vnZh0lsk3336IP75RztxhBFbtTDCkX61RDNNuh7WV71oQiK3SOiFpheTT0x6mU8NV1mHRP1Y0mR2XQdTyl4G2S61Y75wFoFofyssklBx21SDOFnR5FrjL+3Pdx/Hv/9sV+65aHPXye+QWK98xNP7Tol7Str7jF7m2+aWElMATOfTBS53v4mItQpFzAWp7fKEE/Wy53PClCHPLRLfc4Rw8D2H+bhDlHwT2e4p4wjDSAiQLL+4PuYoUucLFzTDbIF75MVjIiBFCXkV1rp6XbqACiOptGVV3M7mSNQfPr3pGWzaul/Zp5xEsBFors6dWsaJoWrqWfPrPDEUrwFTC+SRkLChQpV0Tt9z6lT/jeTziqQQA8yuray5NVQNhOAjpY6Djtk/UhXu4kOnRpRky05i0gsSWmR5PkQQRkpoXMBcW4pFkkOIm84BSG0jjJDq404L4SlmkejH1onivopfl2wvGpYq9qV2pQaynSYkd++cZPeOL5D/96cv4eP3yJpixnOF8lymDon1CiqSIObaJQmS6b0y/t/E3dTC0Oh+0xUDPsZqYo1Ra1ueyMqTNrMgemQoxR/Tme0hEyQU9ABI1xah5LpwHJ1sV11b3ILIskj0IIcgidwj8PtPylDZc/EIa91rithT8ykSS4cNIYpkrbFsst1spRZRjMq+q2jxZP2cN28qwghKN01A5YqocdTUSjqPZPuBUzjnlntEQjMJIOInaMxl3821pmthmOK0SNgbSx8F8rgcQ6OBWFtMFgn91j9cE7+fGKoqdcQ6iUkvSKikAn9oI9UQRwdHMSvxe9aYa6unJG9JM0UbOdk5GqiRMTziJIsjoW1oMvSVPWNfDHp5wlCWqmjaIqmpGjIAkdkOqK41vnAMjgZ1hZfwZbuxlaVbCNzFY8JQNcBoLVSIQ1p0p7NEMu5bFqHRUWS0SPQx81yX0UBmkydXILYrkpDI82wI6YRENrei+DmQcCj5LkqsuRKNJWIWCQk5+r/QVJNyNybohHSqCKSBb7ni3Nl44dAAjiRKV03hodJzjlxbKYtkhDiSLNeW+n96vkXmc8V3lWdPQvuKc2cDALYmuUSEahDKnuiJl6LPkEdy1+N7AACbkiROEhYkSOgdWjZvKnYcHsi1BNOurfpRW3rL3+FqIM5har5Fxzw1UhPh2gCsRdIKuIZAGin/7lD/MKIIWDijN9mmDkdSZz6rvkyVeM/qW0BfZy1qNFn6MnqS0MujJCQWkyMKb1ESUVtqREnJczAtWahNkUtAvIDVc/sJoUUWScb1Zml1AyOBYhGVkygmAJg/vWIcl9SWzbH0WWMQri0mSPimRYo2cmuIMDgSiAoLdByegOm7LovaUi0SPyHbw0i6MHWLhBakmVNKmQ2qTAJcaNWerv3Gx7h4yQwAENFbSj91Ul406yOOglTPTa6twQyLRBcYdC/UY5vveUUrF0PvymVnz4LnOti6Vy3LMloLMW9qPG+I75ha8cWCS+ch64mEDM3Pg4kgoVNeetYsjAYhnt4nKxlw1MIobZFoUVtRFKX66pQ0YTFUDcTawgUFgazSU8M1RdBYQdIClIisQDUnAYgGQwuThagWSo6E+x9rhsgUE6oG0hNICxKOrFpbNJGERWKod8W34xxJYdeWYpE4SYSQqkmXfBk11q8FJtDYeEZ8FuhnJ+mQqLua6guSmtGtBQAXnTEDn37bxXjHlWelYu7p2KZijnrRRSlI4mrCJd8Vsf8mRSCvaKMQJGyb/pEa3nzhQrzjyrPQW/IQQX1WPGrLdx3VteURRyIVBrkwqcJ/5pRyEjhhEJ7amDnnoi9adDwSfvRs9FJA8XFUAd4MR5IpSLRjm1DWyHZ6V+b2VbBs3tSURTJUDTA3ESTk2vJdF9/6/ddgxYJp4pw0ZtH8TXNtkTJ26VkzAcR8oQlBGAlBnYraSoTVx+7eiuW3fksJVdc7NQ5XZdSWySIhBYK7tgArSJpGNQiVhZdcQNx1coAEicEiMb0s9cJ/ud+VC5KRmvmlBuQCqx9b9MYgjqTsKX56sR2LsirK5RB41BZxJPo4S57UkrkmWgsj4VopJEiY9eObLJJkzHpCImFgtKZ0DOQvkeM4uGHNmZjTV4apxHpW1JbuhlDJ9gAVzxWFFBWLhPFSWeAVAl46MoiBkRoGRwPM7C3jk79+Ec6Y1QtEqoXge44gZMu+i5LPBYnMI6Fj6xYJzTly1ZrcLLpApYRCOieH4EjE84835NUHslxbejFHJWprNDC+S/pX0rXFx59tkfB3loRVX8XDqjOm48k9JxTlZXg0wNxpcbQfVQd23Tjvp6fsiXOStUBh/CRMD/ePKO/cmbOmYE5fGU+wgpQvHhkQ60ktiFApqQmketTWHT9+UVwvPSf+TPrKnmaRGFxbLGqL3yorSJrAjsMDWPn/bsKjjCDUtQAg7lgHyFh/zpE0WhID0MIwR1WOJGvNoe3SFkkyWZIFM67Om120UclsLyhIeLa5nkdCKLFsa903TgsZxeLnCVoutDwDR1KPbB8cDRSLxGQNeG680NJCr+TEGLbXCWmeR1INYlcEuc/Umkr1w6ylRRLitZ/5Hm76h59iYLQmrDsH6TBZ1zGH/wKxhUx5JLSPyGxPro1cNAuSboAmwp3f97LvKmPQtV8SRPL5J+5hg5IVaEIjCNPuQAp0CcLIyIXlWSSbntqPh7Yfyqw1RXXHCHTtPSUPS2b24nD/iAibBYDBaoBZU8pwHNkTndyY5EIEmPDTojDDSFWgPNfBJWfOxBO7jotzvO4z38dbPv9Dcc1CMGprkW6hkUUHqO7GKRUfwwrZborakq4tfq8s2d4E9hwbQi2MhE8XkNaCKkhi83Rh8uIFGYKEE6J5qCoTuZhriyZxVtQWaR2Uy6FDhPyGPGorf5ziHMK1hYRsT0c3cV+9XgJGWiTSKsqC7EcSRyDp24Z1BEm/5toyLUR0r/TwXB7R9htXnIVz5sTZ5nrSnuLaqoXCnQSkS77z85iguwgf33UcUSRdJI6jku00fiWzXYnaisfCXZg6ebs/sbCpmKVJ8eD3veQ6+YJERO6pGdlKX3cRLALluzDR1h1m0fUP18TCaOL79OnDyfa/fmA7/uGhHZl1o8oaR0JjLfsufM9NGsOprtmy72J6T0m4tkjBJxciIKMEyf2k8q6SB3KcmEt67lC/4gl5KamHVQvDdJRd8nx0TwMPDuHPZApZJLXshER6ZqeGq6ogsRZJ46BchKMDLFyV6tuwl+vAiWFUfBczEldANQiF+0TtGS0X6zzwstu6IMlaZEmL1H+vCtdWPFkqSb8QHTw6qIimbNrXdRyUfJUjIfiuK8uXcE00UC0SGkMWRNFGlpDItfx6HMngSICTjKMxVRzWGy+Jz0CSmFevmIf3v35ZPO7RLEEi80hcA0dSxEIli0nXoImHcOAgQqQ8K1Nmu9zPhetqeSQldWHad2IYjiPLspiELV+IS76bHC/+v+7aov3p+6pwbRXgSBIBJYR7GGFgNBCBEaZIJV2ZKvky0GE4idrTm20RdIuExlryZCFMvTpD2XMxc0pJvINkfXqOI6z1wwkXMqglGAOxu5zP60uWzEQUxcnPOoIwUgImAKnI6PfCxIMBsRJSCyXXVPLi0j9f+cmLIhBFhP+O1IQrErAWSVOgDHPuUxeuk0DGc+8/OYy5Uyvi5Q7CSCxWCh9QMNHPlJBIx8pac6jUgn5onWzrKblikuw+NihCFnn1Xzp9YdcWJ9tdM0dS9qW7RbVIpM9XP575XPGnm2TR0zH0680qSa9zJCZkNcfiFonvOkIw6ho7t0iqWvivyU+fJ0gEb6EtErSoGS0Sj5HtnqvwQLQIAdLyla6t+J7tPzGMOX0VTE8WDXMNJ3l/S54rLAfAHGoKIMWRmfhDPY+E5iPdv/4k9JfcbibCXZ8+NPejKK4vlRVAAMT3osaUE2GRJMUv4zGo5/Q9R9S/AmJ3IwCRrxNFEQ4lIc9UQVp3eXNejaLbTIQ7tYTgbaZHMhISudXJAyAo6IG8GL7n4ondJ/Andz2FP/76k/Exk/H1j9QUgW8tkiZAE54/IGGW16SJCQCz+kqsOmwo/LijBoukXg0r7ofPCv/VrdEs1xa98B5zbdGL/epPfQ+v+tR34zEpHEmx6DIC5y1KXqyd6haB7zLXlvYS8VybeMw5Gjot0i6rxss5lyjfItGjtkwQtaD0yKRQugp8zxXnH9bORWPgtbZMUVuFLJJke90dKQWJyucA8WJUYa6tsm6ROFRqXyXbhWvr5DAWzejB4qQMkKnMOL/nZc8VbihAWgAEYZFoHEnV8Nz4lIuitEVC7wOVszElJeocG49yGqoGSsKsDv1e0D3yPUdEYOoWCVl5BMmRxPk6xwer4lrJalCrI4eKgjRnagVLZvUqhDuBohxdlyWhZlkkYVqRBKQgOZ54WvjT2nUsftY8IZErDVaQNIFRQWKxAo2MI6kwLaTH96QmG0hS1sSR1C3amBG1xQWJXtaAJ82ZjlViZHtctoSEpOpO4gRdw64t1xGLiL7wlTwZPcQFBa8dRMg7b8Q0N2k5pDXbkZoa0UPnGBwN6lskXvq4QCJkWYInD1tWthOZ/vFzqWSQ7YUECS1o2sJHi1Vy1ByOxODa0sqQ666t/SeGsXBGD86bF3eOfO5gumFSmmxX60XJccr7U9FcW0ayXbFIIpFHQoKTxjy9N7u3ToojEWR7LIhGa2Em2V7W5mjV8Lz1yt6x20v+XwgSN76GQ6zqxYCBIwlCKYTp2Sya0YOj/ab+IhE811UskqyERK4UqlFbSQJlonzWmDJH95PnkfC5ZwVJE6CHPai11QVijoQvgJWSq/hxA20iAizcs84CzRcwvkiN1ELhdippJgkdMgzjEiT3PrkvORa5naRrSz8uILVERZAUkyPStcWihXRXTMmTL6K+gPRori0a88FTw6IWEd+ezuUzV6L++2hN5Wno5SaLpK+snpPDVJ2W/s9dW16WIOHhv8K1Ff/WCNmeV7Kezu26SXQTO4bnxgUyXceckEhCTQgSrdbWvhNDWDSjB30VH2fM7MV2kyBhz5BKnwcGJafkuQaOJO3aksm66rMkl5lejJOKHpoFiXrP6D0NwwjDtcDI4RH0ZL/RpE6cw+abbpHEc0FeM1mfRLZzVxh3bXHOKGRzJh6zl0Rppueg78buSaE0sYREXVExR20lFgmFFIcyGpSEEQmnUS39wQqSJqBr7fF3zCJhLpmK74mJVg2lJlBj2kaRsNpf7DuJ/3x0j7Ag9PDfLIuEEEYR/ud//By/+38fxXMH+41RW0A6pFNJSMwQeP/0wx0455Z7MjkBKpECZFgkwrXFLZIw0yK54hMP4IpPPKBdX/zJLRLFRcIEiUnADIzUcHK4GudfZCCLIwmjSPAKPhOMevY3P29eZrvM5jbPhzyFwxMWSZJcyO6B7zq48eVn4YvvuCwh3lmJFJe5tgyZ7YOjNZwcrmFhEsq+fMFUbD+Qb5GUPBdRwgXEwkuGv8aCROVITILEFNFIAjIWJKo1QwuaiQtLke0euSCDpCVDtkUi7gVVeghCcT1ZHEnJc8Fz+sjNRW5HKlpZ9l2FbCdlphbKuUr7Us0tfQrUgjDmSBJ3YnxdYcLHpIN7TFFbZJFQ3kstiJT3A1CVo2OsQK0l25uAsEhGuGsrsUhqoUKwVXxXxGMHycQgzWSUafuAWqpBx/Vf+hH2HB+CAweOo4aWcteWnj1MCKNIFJbrH6kprhgAgthOWyQG15b2Qn72/mcBmMMMAZlHYtrG9xwRaaX3pM+ySLKuLz6XFKZGi0TTOilKZ2A0QP9wTZC1JuhRW3yssjeJ9IvrQpPfPyra2AxHolclUMfIyHZoFonnYOGMHvzSRYsAQHNtOWnXlrBIQhH6S6Hsy+dPxfOH+tP3QhMkFIgQF9OMz0eFNXWOhHblCoUpCTZ2bcUCRSoNMq8j/n/63ul6GgkwcivlcyRycadtySVL15UWJNI6BSTnQPk6ND/m9JXFezFSC0UINw//pcOUEwFsSvz0kxBuUlJHa6Gw0LjSy99lnpQqyXaySOR2osVyLRTeC/6srUXSBExkO7+pqkXiMj9urAlMSSY7vUj6Av3SkUF88N8eV/ylwoQPQviuI+oUAaqWrcfqE8KIu2bkYkrWUq9BkChWSJSdkEgvkP4KklygfiRAenGVCXJSkFBzJd0iKZJHQosUoEX/RNJi5O4XabrX4npFGfcPkIvWswdO4bK/uE85N++WKCySjMz2IIrnAie4lYREVt/MBLoPuqAFVLI9itR7RtYKQXVtcYskzZGQ33x2X0xmnzdvKkZqIfayqre6y63sSY7EZfeFIuvokvWw4HolUuJjml1bNI9N1ZhTCYlCuZFuJVOpGz5G7toiBZHmtt7lUy3KqZLtYSTfhdl9ZRFlNloLxYJeC6VrS4bpJxaJNsxaGHc79d243DytUbNF8IGa48KjzggkwMjSqAWhmLOyv0qAOX2y9hzBCpImYOo8VtNeIEJPibm2kgz0Xq22kP6y/OSFI/jPx/Zg9zEZFXPh4hnib991lXyV0Zrswmaqj0PHlo2DeNQWcSRp19bJoarQtMOQczn6saFcB4FHUtGLqAsSXwgSV0xWOoy+UOaTz0iux1E4KX1fnSMh9I/EWcT0wpoECv321c27RG4AnVu4Cj3OkWhaoxZoEeeRpF1b9UrR0MutR7UBzH2CdOkWPcGMz5WS5zBLWXVtBaFMpCUNl1wZXPFIVS3wHcFluI7K3/CxZOWX8GMqzzKiWluqghXfk8QiMTxj/SvSxiloJnZtZVkkabKdKgOIPJIRNVhDD7GWCYnxs6H5MTuxSKIoFgBTkkU5CEOFVwPieTlaC1P8GVkkVLCUngvl1fCIRDWPJB3+y5Nd9XdupBpiVl8JOsbKtTWpOiTqFTQBdaLz/Afu2hJaEwkScm1p4b/kbuF+Xt5S1XcdQx4JacTZHInsiS1NeFp0aVHixz0+VGWTSvpl6xWI1P9P4b+A2swIkNocNb4C5H1KCZLcPJL4N8eRwikrj8TkvhgcqQlBcs//eLVR66J7NU17abi15rvyeWeR7VRXibuTjEUbM1ydgVjoDRYJcSTJcfm81F1hmeG/mmuL14gjQULP0xRhxY9J4b+e44ixuUlhTdM4+PkBqbzo4b8koPQcpN6yGkrMkWWRkEJYDaK64b/0O3dt0bWkw38dxSJxxLNRLZI5fWUMjgZJngoER1JVXFuSIxmphQr3RRZ87FaN7xkJ40VJnb8jiuIj56uJbCdwjoT2Gw1CpWPo6jNnYtGMHqWaeScxqSySeu1ay0y7q5Rk+C9pIFNKvnIcmpzk3hChxEyj1X3dfJGMTV1ybWVYJCGUaCauQQPSJcDddccHR1myJDtWlqacYZHUI9tp3NUgxMbH92Dln24CAPQ04tqic9UJ/40ic2mPoWpcqt51HaxaPEOQyhxehiAJQzX8N9Mi0bRrz3XhwGCRiO3M84yefcVgkdC64CCdkOhpcyOTI9GiqYIwwolBVZDIBVweP51l74qoLTepgQaokXUAUi5Mk0tSd20FUbzIkgUmBElJCr9aEOK7Tx/I7MkjQ79lknBWZntZ40hqiWsSqEO2K3kk8jMMI6FUze6rJMUS1WrIfCGnfSu+l7JIlGTYJPyXjkXzeB9zQWZlthPZTqiFoRLwQa5acm8CwOtXzMeX3nm5EJKdxmklSDxXal8VX5q39CB6NNeWSEhkZjNg1va++ltXpTRLHsWRb5HEv41UQxllJCySeEzczxtbJOlrDcII2/aexKo/3YQDJ4eV75XtFLLdzJGUmGtrNAhFgx8Aqcz2LEHyJ3c9ift/cQCA6toyNUgCshv9kOacBXqO+j3mGh4/f1atLdrWAeAkh4oasUhIkBgsEiXpTS+RkseRsCABaZFIy+7EUDwvpmsWiYnPIJR9N2mupnIkjqNaR2mOhAmNMH1sWgh5QiK9SzRnRoMQ//nYHrz7n7fg35I2zbr+Q9fAkxd1i1kfI7dshSDxzILEZ+tAfN3SFRZysn1qGVEEUfVCkO1aiRQax0igcjl87rlO/N6REkOejL0n+Hsq9zElJPLjckXkfV95FAAUi0RPMu00TitBwhca7tqSFolapE4PcawaLJIwirBs/lS84rw5ipZT8uLol3pRWzzCJS47Txq0ypHwl+HEYBUmPiEMI/zLj3diYDTAd58+qIyRg8xvnkeia+n0EpYTjoQvKkXI9iCM8JWfvCSyfXlcv+LaYmN7yZCRTaXJTYXqCEIQawIijHjJCamF6ouSntDpOmBke3q7LFeeXuZdHaNcrPVe8roConRIZMmRo4G6yFAfnSllT7EgAbUagc4vlJWoLTk2KuVB49RD1tV2AqRsxf+PEkuEhAkPZAFUi4RA/df16SN5OznnTUoGYOZISppikbJIfLNF4jhSkMSN3dSwW6rgXAukIuAw11ZWCDtZJEEQiTk6f1oiSJhFEoQRguQeq4JEs0iCSJmDP3j2EADVIskLTukEJpUg4VqYaeHhHfoqvie2oYdLHAn5MUNtgZHltNUIKtJufBFGGQsALkiy8kjIvUDjkP1IVI6E+3m5a4tAcemSbzFr/XROOkfJsOgBciL6XtxnnU/MeoIkDGU0Eb8WGaarastXLZ0NxwHufmJPahxxyCQUn7YOLoj1fWX4L6u1VccicV0zR6ILHB3SIskRJEnRRj0hkcNh0XQlVw3/9ZkVWQsinByuCrcWIInqfI5EVv/llhqPrNO1djo/5Z/ovBz9n9yzOldTSUKquca+ecdR5RhyfGmLxFQ/jI5L94LGSPtnFW0suaogcbi1mLhYe0qeEH7kPpQWiRpQwMcxbHB7e64D13UUi2Rqxcf0Hh/7TqiuLcGRsDlU9l1F2dBdWwQuSLKiRDuFjp9t06ZNWLFiBZYtW4bbb7+9o+fizZF0Pz6gan49JdkbO0W213SLJJmkyf/v23YAb/2bh5OXRvqDxUvouaj4rlJsLi+PhHM1umkrXVsyuoOT7QTSMulYQ+zFS5HtBo5EBx2HyHY+sX1Pn9jq8UeDUHSSA6TGR9n9umtr8YxeXHXuHNz75H7ooDL5ee9FZsZ6yBO8HOicmNhO8/c7jmOM2tJLxOug7/PDf5PEPZ6QaJgbpJT4nisW9GqS3MYz+U8MqYKkKEcShvH1OjyPhAmpePFTx0RhyybeiAuUKlM86F0qeW5SIDRimd0B9p0YStfa0jgS2tYEWcaH8kgiFijiiH25XlnyHK3WlvyMo7YC9JY8GXabCBIlITGJeCPovA6/N5SMG4TSIqmUXMzqK2PfcdUFbcpsdx2tTJGhfxCgurayokQ7hY4KkiAI8P73vx/f+ta3sG3bNnz1q1/Ftm3bOnY+7trS/fgAlMnDLRLdtUValN4RjyySr27ehUdfOo6TQ9VEq4uPyd1BFA5I70jWgh1FkmyNE5pUi4S0Il4v6PhgFTrhW06ylWlR4NEg+pzTW+3qcJi7I+ZIVNcWX8zi46snGKnqgkQVtLq15LkOXnP+3NQ4aKz1XFtZFklMtstr9bKitnSLxEFGQmKY+k4/H5BhkTiaIFEskvS18cWQR23xulwjtbjp13RukRTgSGTUlhoEwd1cvusaeamqFjHELRG+DQ+tp2sk65bzcY+9dDzTtcWFx4BBkHBr6nvPHEIYRkaLZGg0UAhrLpzj65YWSRjF2/eWPeHaokZ4vSwhkYSwPuYhLcGQxuEmgoS/3zOnlEUyMqBGGfL30nEcXHTGDEwpe7ji3NnG8N94fGq031iio2fbvHkzli1bhqVLl6JcLuPGG2/Exo0bO3Y+/vKYXmauRVdKsgQIaQl6j2pRZG00wJe+/3xmXxDp2iKLxMHimb14Zv8pZTKZwC2SOHwwVI4lOBJmng+NBinXVinpy0AvMC8gl5lH4jhGX2rJlX75shc3vlIsEs2S0ccyUgtw0CBIssJ/PdfB9J50DDwQa+5hWI8jMQsS9cV0mUViFiT8vlDUViNFG/MsEplHkri2csh2QC5Mcavd+LvRWnwfKr6HsueifyQuaKm4trQFnI+LH5sEtMKROLJirq4sEPSS7qYGV5zwrgqfv2y1TO+R5zp4fNdxQ/ivFACEIYNri4/xrx/Yjm9v3a9YQzSGwWogvA3x8VWLmu4vlZEfqgbo8T1cdMYMOA7w8HOHATCOJEwKU7LnpvfoofsAyIAGChwB4n1nTympJVJCc2a76wB3/96r8NTH3oTXLIsVLhMffJi985OKI9mzZw/OPPNM8f8lS5Zgz560H7xdUCySHD81/a5bJKRxiITEZILvPTGMT216Go+8eFQ5HjW4ka4tOYGvvWABntxzQmgcOhlK4IJouCotEp+5GEqeo/ahNxSxo2xlWrQ5R5HiMBSLRBUQ+hhLfhz+W/EasEhqqkXC/e6AmktAfvqsDFwSBrlRWxmWBg//Ja0w3s4c/quS7TS+9HZZgqQIRyKKNnJBYrRIZAirq7m2gDjRrH+kmnJtCW5CSR7UXVsxFxCGkRa1Jf/23bhYpH7bq4Ea4mpK0lQ4kqSsCrUlqCYWScmLtexYkKjnECVSmPAwWiRa3suJoSqqtUh5d2g8fRVukeiuLZUjGaqG6Cl7mNVXxgWLpuP7z8SBK7JESigCFfQxcytKCMwkV4e7BT3XUVxRNE6h+ChFJeNn4boyVJs/34++5QIsmz8VV6+YJ76bVBaJqfy6Hte8YcMGrFmzBmvWrMGhQ4daOp8qSNJaoWKRsDLygmwXrq34//qCoUd/UDy5bpGUXAdvvGABAIiwWZ7gx8HdHCMsu5u/ID0lTxUkbIEkULgfXeJRxbWlWyTxp+eoZDtvrkTwXRfVMNIsElcRNrpQG6kFiiChR25ybVHjn74sQRLKBS8LwiIxJBry+lf1LBIR/utktNqN8gVJrkWiF21kxzD5s8kS4VxFLEji/0yt+OgfrqU5Eq30O5C2GGlu1UItj8RVLZJ4W3VsgyOBZpHQp271qJZR3B/EQS2IBJm9+syZeHL3CSXCjK4dUAX+oKFqcKzQyP8fGRhVXFt87Dwxr+Tp4b/xp8qRxMd41bK54hrJY1ENI0WBBICyF//GrShaV8jq4xyI4wCz+jRBEsXBIb5r5nAAKWAoYOQHH74a73rVubj/g68TkWDAJONIlixZgl27don/7969G4sXL1a2Wb9+PbZs2YItW7Zg3rx5+iEaAo9UMZWp4NExlZKbEI2ySB35GGNuI+2HTPUPSCaGTraXfBdL503F7L4yfrH/JAD58uqChEdqjFSl24BrqT0lT2k3W00EDp9gfawnOACtVIiZbHdd1fqo+OkxUk93vk6kLBLt+MPVUOnpwPkWfTxBspj1VdKLb/x7rAHnWSR0DbTwfOP3Xh27b0LJK9CzBgxciqZVu0wTNxZt1BbNL37vOdz6X08KDqVe+G+EIhaJI1yMJIRqgXSDTq34ODZYxeBoYLRIslxbrqMWVOS5Vfw3HsnFcXxoVBn71r0n8KPnD6dqTNF8l/1B3EQpCYUgWbFwGoaqgeAg5L4O9FtiItvj8cp7fejUCKphyCIOeQgtFyTZja042Q4Ar142N3WMIIijpvi9ybNI3OR9CSLZyTG2SFR3LoWFe9r18/OI3Dc6dsZ7MalcWy9/+cuxfft27NixA6Ojo7jzzjuxbt26jp2vrkWiLJrx76ogoVLXUcrcBtKTuZqUQElxJK7KL/Bz6y9JnOsQbzNcC5TGPITekod+FrVFrXH5NZJGT759ijShc3Bwsp1POOmX5y+Ik3KlUVw8wWyRyMVBJ9t1ItjPc22Fal6CCXp2/oVnTE9cCSGGq6GwEEwWScQEuUq2OwkxrlpPQFpwPvLiMTz83GGh+Rsz29kLn3JtGRaDEqsHJfNIVNcWuU3rcSQ8MIO7+GphpCQh0oJHf9P2gNTojw1WlbF/etMzeMff/zQzhHeUzf+SsEhC9LLKErqi47tuaoGkZ/vND7wa77v6vOSYLi45cya+/r5XYum8Phw4OYxqLZ3ZDqhWoh51KKxFjWwHgKuWzhHbFQn/5RwJzTPPkbW2eI0uakRGCKK4vQCvwhCPi98bOReAtBKi80NjhY6ezfd9fOELX8Cb3vQmrFy5EjfccANWrVrVsfNxi8RcpsKgfbuOcIlQ1NZ92w7gyMBIan89lr2mLXK65u25jnAxkEmqa/O8sCBZJK6jRpj1lFzFtTUaxP0Z+DUKQZLsxsOF6bbsOjqIR148Jq0eR+VISDClLBKNYPU8NdpLXwhGqqGRbKd9dIskz7UVRki5EXTQgjtUDRTrIwjj72gRMYUJh2xRp7VQj+IBYuFBv+uCsxqEorgkAKVdAUEs0I6TLiOfwZHQc6Cfq0EornVaxceeY7EgmTnFwJGwd4G7uTyXWzhhssjReeRzpQWLhNycqbEb5vjgqDFqLe3a0sh214XvuaiFIYZGA/SUJEepu2k9jcMApBK3ctF0EZhB13H52bOwYFoPDp4aSWptJdegKETSHZtXRj4MJdnOrwOAUqZdD/+VUVvyPRWCJHnnOZnuOg7efOFCfPptF2PN2bPiexhGwiLhFIBqkah8oO569D31XRsrdFxs/fIv/zKeffZZPP/887j11ls7eq5GOBKaFJ7riB7eZLo++Owh/ONDO1L76xnCMfGoNrcBVEFCLzQnAB1FO5WlzkdqQRw6qQXw95Q8pUSKtEjkdlMrar8HvtadGq7ihUP9ePNnH8T1X/qRdOG4qiCRHAmbuEnsv26RqMIwTbafYBYRnUIuHCpZm0e2UxOhfNeWfLnkgh0fe6QaCJelIOW1tqn6+HWfOaAu/JRHJHuZh+gfqYpt8pQYsnKU+2nkSOSinkW283Ln+r3gvUMUPsaVbWZp0RJzkwkV3TKZk5zj2MBo6n7p94fGD8hsfCqlXg3iroe9JRl+r1cENrm2BkZqgjOS80n+vmB6BQdPDSftHNRroPGIgBfN4uEcHrm2epgr7Es3XYbekoczZ02J71vi2jKF/2a5tqgfCd0mWgduWHMm/vQtF8T3MJmLcesAeW18rLp7Vhe4gqfNSDTuFCZV9V81jyR9I/kDIUHje7L0Ow8RzOuKSKBSCbQOXLJkJh7aflj0GPCVicwsEjYOTrwOV+MSKfrC0uN7YoKWPTfuMa8R4MSR6GVCAODvH3oBz+w/JSJfDp6MrQXej4SOzT8B6drS3SNcAOmLyEgtUPJepEWSDv+tR7bHiXPpF4aDu6zK7D4HmnbJo3jE8Q1cmEqMI7XPiaEqHn3pOH7w7CFcs3KBcNeQ39qckIjkmEjqXHErwRCC7ckSPtIikcl2XPByQULab1WxSOTfrqMKJs4dOY5MGtUXYyKGddcWgTeTo/EDsqIyhYxXg9giqZQ8sRDnubb4cywxy4mPEQDmT+/BgZMjcB2m0LHfefh3XCIF4pp5ZnsYAdXE9Ub4pYsW4ZcuWiRcmqRY8fbZFYMgoXeRcnVUi0ReL10P9WzX1wiuQ9HzH8mwSEzXPhYY27N1GEoeicFH6BsevO86sg+F5+Krv3UVgPp1uwCmLSfHfeOqOFLrhcMDANTFj7sM1NBZOW5KSNRdHVwo9pTcXI7E1JL0+GAVx4eqmJ4kWD2dBAC4mnV07ty+eIxKPwzXwJGoJSb05MiTwzXl/ukcieylklSKdRzhVtQhLLo8joRFKvGooyDJCSAFwXQMna/g442J8exIrf94ZHdy3jC57tgKM5HtetHGvFpbQOKK8WkciXZfC8VxeJ+JuVPV0vq8GZk+dt4oqxbEybTCjcXmJnfFxcd0Mb3Hx/FBs0VyfEjt+SFJ/0j8X0Rt1eKFmmfsc/gsCZOSAgdGglRYL18r50+rYLQWc2IiO19Tkjh34mlCCaA6aMmcycgFcp0kTDcIFTJfcCSjaY7EdeR81Hu98+uhBFrf1V1b7N6IunJmi0RYXbZoY/Pgi1fcnEj9nU+sCiNg6aF4roNXnDcHZd8VboM8krcaUHOgeJuLzpih/M4XCJp0vI4ToEVt1UKlDLYYKxMYU8p+UtwxUiLTRE9sgwAcDeKoq3MSQfGLfaeM13bVeTGxmOZI0o2Y+Eukyy4eMRZfc3IPyJVBbYIjGZqbZ3GYxsrBtS8ebRSGMtQ0Hkc6L4KKQirjFa4tJ5MXAYAfbj+sXM/xQRIk+SVSwlAVvqbIGyopwn/XORKCno9AFZsJND7qRS8sHAr/ZfeMnquvcSWuE1slxwarqfsFqA2a4nOpQsJ1pFIyLDiSeFs9PJlHLREfMjhaS0WSca19PmvFbCLby6zad1zAE2JcBNdxMJIoab1ls2Lje3HkWS2MVA6Gwn+rOVFbYaS4lfn1ApKv06O2uFDRqzhkVY7OqjbeKUwu1xZ7eaghEHdRGS0SzxUkOk2MiucqrqSh0FznR+SRiEXCwcb3vyqlOQHcZZAOnZXhv2aLhAuMKWVPJGpR/xRAWiRGQVKTheIAiGgffRJeuHh6Mla2MHtOqp86z8kA0hbJkX41UEFEtWnhv8LMzxASMUdBf+e4trz0S0lZ1EPVUIlqIj89gRo8cUhXR7pwJwfNN0r4o8U0zyIBnFT4r8kimd7jC21curbUPBIg1tj1cu9lzzVaJJWSq8y/WpJzwaO2uHWif86cUsaxwVHjvTg+qCoPvrBIYguBClEOV0PBkdB9TnVwZKXzp/fG1zk4GgiexmXjJcw18ER+yrXlCutAlKyBPIbrsLL3GRxDyXUQBJESig1Ir8GgwSKhqC2qzUffyfNy15bs8a7/Dsj7KtxmOkci3oX6rvl2YtIIkrjOjrx5IlZc0aTTpqjvOqxkQ8IR+K6YBCXPgaZsCYjibexhXnLmTHY+g0XiqJMkjGTUCpWR1xcWruH2lDwcPxkPiBOCRLZzYUqLKS0qes8RXWkR/lVmFsecjNr5zUsJEvU4VOdrWsXHqZEa82knWqpWsyrLbVXyXGYtGjdJ7V9ilh81KVo4Xbp+eCRdPIb0QsYtkjDHIhHFPJPjkSDJK9roOuke6npjKwD4419eKdwk0iJheSSJpq67tegecLKdBB0R3GIBDyJUfDnvaZEFeEiwvBcze0s4OjBqvBe6RcLDf0UBStdFLaglUVvStZWK2mILKS+dw4MC+BgBaPXG0r/HUXCO4hnQwd/LLIuEwnjjBZ9xiSIEnUdtSU9H2iJRjwmAtT9WlU3FtaXlkeiCZEpZDboZK0waQaJ3UCv56cgPtb6O1LS45gDEC6qwSHKiHyTZbl4IVUHiiE8+iTnZS64tnWyvaBYJ1d2aomTrqnH7QOxHPz5YFd8NaXkwNO7Pvn01zp3bxxYRVZMLI5XEpyxlQtoiiQXJzL5SLEi0xYmEkl6gUkeZCZI8i4S03WqghmILjoTnELguADleU+Ip50j05mYcvAcGAJwki8QQ6MGbR+lFG02CdAFz1dD9U/JIEotktpYdDciyNmKc1BOk7MU1ophA91xfCfXVI8V4G95ZU0p44XC/mSMZ1AVJWvjRMyJ3oyn4gbbTXVvxfSL+C8oYAT2XRnUjh1F8T3zPlcmKwp0kz81dSKYQbjp2NWlgpYcXA5pFQpntLi+RYuBIWNABhbrz6c6tpnqurb95x+X45x/txIoF04zj7xQmDUeiu3TIH8yRFa+vL2icI8nzNZJFkrUQKhqReAncVHIavfRUaysV/ssmdW/ZE1YHz9Z1tQkGpHkT3SKhcf/apWfgkjNninHpHIm+r56QqGfjH05cW+S7p9ugh3vyMtsm8BDGPI4kHpO6QNCLyzkSPhYCzyMR24iIHkdEV5l4AYq+oucnXVsFijZyCy9HSPIxU5Y+IElooyDROBJukfDQ2loQiRpO8XkkCU2LJE9MnDmljOMDVWNEoy5IaGEdZbxO3G5WJonSZesWjmKR9Ko1smictB3BlEtD5wQk2a5fFz8znxtZCqR0UWmurRyy3XMc0YabF20U501ORc3GPM1rwaeHJNsDOE7asjprzhT86VsuqMs5thuTTpDwMh/6C2pajNQ+G4kg8VwRwmvSLgnVIL/pkqm0geeo4+D1oIhEz4va4to1d22ZImCmJdocLSp8kvOwRzFepjkSZCteLbKGjVHv2yIsEiFISBsnEz9U9suqC1QycB9Z4BYfXYuo5KplNXN89O6t2H6wX/lO4Ugo/DfDVRCEslT9ceHaMuSR8EgwzSKpd2287pdukcwxCBKdI+E1wFw3nZDIrSXZj8RNjXvWlDJOjdRSJWbia9c4EtHETEbS+V4cITkahEoeiV5Ukof/9lV8scCnyHZ233g4NA9f5wQ7zyXxxD2Vz0F5X3PcrdUgSoQ6P0/Mv5jJ9vh8cWBH+lzctUX9jRTXluKikwpjPQVkLDFpXFu0WE6t+BipjcL30hE6RkHipbXesu+KxVBvc8khw3/Nv+thtEDykrDteTvYkWqgaJ0ETvxxK4S7tuiY3DKbJkKC4+PzSW6agryZlT5u3p52tKa638KkhhC9k1QVYGavmoFM59BLjWQJYv6i5rm2ALD8EblQkEXC/d36HPjGE3tTx9JDdQGzRQLEi/RoAxaJyGyvk5DIwS+dJyQCMuOcgxY7Mcbk79+86mxR2wxIorZclVDXORLxm+MI6+CYFpUHQElAjceQXpRLnqzQ0FPiDbu0PBLm2uopeYIr0/uMKNnp7CZxjs9j5/ZdRwS90PdZFklWR1PfixUhXuGYwF3igMz1IM6DEhkBjSNhZDv1N+LP3DU8/5FqWFcBGUtMGouEyMUpCelc9tyUeee7Dj779tV4/+vPE9/pfTboO8oJ6Msg3YCkyU+Oa8tTuAY5gfmiGEVSIxtOLJJUQmKJu7akYFNcW47UVAg81wBIlwbRIfuGpAUgb09b9tO1tvjxaGGgonQKV+Q6YmHj4b8mmJ5NFvTSEK4ba7/VIFJcg/WOA8gXl2ovAWkeiMAVgbyoLXEPnHQSpCkhUR1PelEmTmCekWx3cPDUMDY9tQ+AtEiuvWABbnj5mUrUluuorXb1xZoLVZpvA4beIHoeicnS911HFB/tLXuy5peeR+LKgIDekpequCDcbQUUEP5O+56bqm7M9QMlzDZDuHuug2oS2JPyHPieVv1XJdvDSG3hQKDrEd1AneyoLd5DaSIJkklkkcQPkDK8s1xb160+Q/mOa048+Yo0i6zoDUCS7VnaMp+LvPREuhe2JHRHamFqYeELU6Zri/iHIM2R6OfJgrDIvLQAHBoNcN68Pnz4TSuwbP401SIJzWVGpguLRH7ve64s2x6o4b9l31UsKuXZFORIuEZNPb9557giL59cPKXrI6+9ruBIBrPJdhluCkCLFKsn3ExukLlTK/jbd16OVy6bk9q+5Ll49KXj+J2vPIqff+yNYqGm+ym4iUDLIzFYJHzxJiXm1LBBkGjhv4pVy9oG0/Pt8T3pYjNwJDSOnhInyFW3lD4naP4oHImo1uAoZWdM80BXeEwoua7oR2KySHjo+7BukYShUrRRnNdAtqvWljyHP0FdW5PGIiHpT/kUcT8H3SJJX65Jc8pyJekI6pDtmRaJtj1fPPtHakrpBUC1SDJdW8m16lFb6TFlTz598QBYMbpqgOm9Jbz5wkWp49RYNIo4d8U3ViCN8zjU8F96Ib71+6/BR968QmzL96/30pRZODcQLzKkOatRW/VfPjpVnNQY/53ZzIrVISMXl7lDojxmhFj46hFSWVAEMZtTb75wobGzJJ/TAyO1VDCJGrUlI/BcJ+3S4kKV5ptJkGSF//JjKS0LchISea2pHmaRlNg443uh3jd6N8rMtcXdatRci18XR9Z95iCyndc9I1BzOcIwy/Xw3Hgu6YVBASkQA0pIdLSExIyoLVPY+Hhh0giSWVPK+O+vPAfL58elmXkIISErakv/nWvkeRwJZXxn+/jTJqlvsEji0hfx37zwIEGxSLggYWMzCZJphvpVM3vNLW0BTrano16Gk8q64nrYixYaBMm0ii8WKJ1Y1htJ0XbnzZuKd1xxljy34dlkQZbwJ60VImCCL+x0HNO9IfDFMy/8F0j3NgHi+aNPCRoXhRTXQulyq8+RpC2SPPDn9+8/243PfPuZ+HstUz62SFReSW+FwIUPLdR6gzcgHbWlFP40JAj2lrhFknZt0W9ckIjaYwaOBFC9Efo4Sp6LNWfPwhXnzjbuC2g8S04ACL33+ja6Jap3SCROFTDzHmTZ66WLTAJupBpYi6QTWDyzFx9btwqrkjIlZd9NTRZjTSNTqGBBi6QWhrn9xE2msqlMB0WxAEjakOquLY/9LX8j15bjyHON5ri2ADXePjVeY/hv/N3QqCrguDakcyQAMKXii4WLrxM83NpEtisVVRtxbWmlPTzXQX/i2uKChIY5d1qaWxDnYlFmekKi7hoxFcn0XCdVNI9RJHGb2ygSC0/9qC35dxGLii9wf3X/s/I4mpVBHRJl1JYDT0vY43kkpMQMGASJqfWzPh4+rh4mSExkO02DnpInFCY9IVFX4Gh8JkWo5Lv4wDXL8efXXRgfw3AfTRnkOqgUflyBQnNtebogoTwSCI7E1LiO/hRkew5Hwl1bYx3im4dJI0gI5BaiEikcJlOwbMhVKCpI6pPtGRaJYXvyP+sLNiDDSUueo46txAILkl3yyHYAmDElR5AYXnjK5RiuqeGOJbH4yPh3ACIR6sDJYTHpQ80iIZ+9iWw33TOgvmtLX+BdxxGlb7hri5I5edjsb79uKd7z6nPF/8Wi78iijST0SKjTvdf7v9NYuUbuOmqFWSraSG4pU4FADhNHkod6TY34IThH4rlqKR9AdcnRQm1ybeWNgUdOEfoq+eG/giPxXSyd16fsb4raAuS7appPZe3dN82nIgI7nr9xlV7dBa0HWegcCUAVl6FZHInlHsqSS6YSKvG44nNQKZWJgsknSDxXfOpuzqxOdAQR4ZHj2uKHqNXJIzFNaE4kchAhrLuQAFbynvl447HJ5jsi+9mQkMhRxCIxlX7Q81tIY6v4LoJAthC9cmnsOjg1XBPbKPXOPBn+q5PtgPoyq4tR5rCVbTnpbgqYIF8+Ly2S8knzRV+zSEiQU3Sg3v/dcdJ5AMrzdiB85W++cCH+5d1XYPHM3txra1SQ1Guzqh+PR23Jeaov2rK2WzFBwhQCLckRiHOcuDtWFW5yjL1lD+fPj5UT4tZo+FmChCtTppB2077xedOav+m6aknukClqi0MvkULXkJUoHYaR6G/EN3HY0FUFxQqSjqHEiDn9RmeV6yYUsUj6mGCRme3msbgG4aGH/xJ6mYabVSLF99Q+ILRAVnwek88TEhvjSMilwH29WSG4fjLZyVVFno2lSYXhsu8KjU0vUEgCRCfbAXOoo/69cewZvn1ATRCkRYbnX2SV7eYcie6GIwVDFyQmZURx3UEGG0wpe3jt+fNyrwsw55HkoZ5FokcE8SKIeuVcHv7bKziSjOJzGWPgPAVhek9JKHp6KG3c5jixSEoezl8Q854vHhlUxq8rhvRuctebSDLWrAWTW0iJjsok211Za8sQtSX3l03tVIskXVKJ55FEUdzfSJ0zfFz1hd14YPIJEvYiFy2Rov+uttdUBQkXLES6Zcez00uoalFGi6Qkiy5mlUih7Fx97Pxauat6aiUtNGZOSSewESq+hy/8xmV422VLxHemhE0AWH3mTLx+xXz4rqPkRXiug7t/71W47w9fyywSsGNIjkREE3l8EZHbKtE3dV4a4fYwhHeaoqi4RaJrgHzxzCraKAR/TQ2tpfP++XUX4vrkPuoLd72qxzr4PM6r/Uao14tCF0xmi0QVzK6bT7brMClofBGc1uMryk/6XY0/e0selifu0peOJoLEST9jIC50ecW5s/HKZXPFd4Ij0RZ9k1JZKLM9cc3WDInD/JorLJSd3+PRxLWlnJdbJKGpaGPaEwDUd/eOJSafICHXlqEfiWkxUia8owqSioGwf+OqBVj/2qWiwRSQvSCI+kqOw9xG6WgywBxZRBAWiataJDSPYtdW+pgmi2R6jkUCAL9y8SKlt4MpPBqIu8b9039/eRIfL11bruvg4iUzcfacPrG4couk5MkSKca6QwZ/sP69CULrNWirJg5irm6RID0GirA6MVRlpHWS+FpWXVskoGnMv3LxIlyQlOVXr09ajUV93Hw+1HNbAelF8+IlM3Dn+quMx+N8hOdkW3auI+9jf4McibByNLcsd8fq90JGbbk4e84U9Tcm3DiWzZ+Kf//tVygu3SzXVt3w3wzFhZpz6f1IAJUj4TlRnDyv1swZ6Z7rCK5R71mkKBLc0p1AHMmkSUgkLJ3Xh0UzenDW7CkGLSf9EhIJ5zITn7or8sgSwnnzpuJdrzoXX938ktJK0wReQLCua4tZOqZWuwBZJPIazpwdv2C/94blyhhWLpqO5fOnCpKSI6vPQhYqimaZ3jeOj4+MiVYlYZFoZHuohtQqXFKGa6ueS0cSsWrUEWC2SOZoFonKkdBnXLTxb773HB576biyf68mSGZNKeHQqRGVGyErlFtckNdf1MedxRtlQa/msmz+VFy1VCYuqs9IVilwHVlm3RPvhZzDruugp+QWskh0Nyidi+DwxTWII5D+4rpV2Pj4XvE7EFvJJc/Ff3/lOVhzzqx4LAYLJ3McrH4eh5Ejcc1zT72uJGorSJPdXOkqea7I4nddOY5qYE4kjEv6QERtcVerozz/+lbTeGASCpKp+PEfXwMg/aKabrypCQ5NCN7FTRyDkfmkcWRpBlyb42Xr+fekrOclzakciXyJpveUsPP2XwEA/GznUbH9OXOm4K9/49JU6Ymse5CHemVK4vh4yZHwl0SU6uYWievKEikGF0+Wa6to1BYX3gSTRaKGfWe5tmJinI+PFmlB7CaEqrRI2JgNY3EcGWRQ9Fk4GcI1Czpvo+fM6KQyz2zPtEiSzyllvxDZbi6RIl2xAJhrK9buf/MV5+A3X3FOMsZ4XxLYH1u3io0/fR1ZkOG/ulKZ3tbUiTB1vIRsD0wcCeOXfNdR3L081Nk0bNeVpXN097ceHl/x49pjlmwfI+gLvEkLoQnPlW0pSDzo2fE8PJLX0jGBl3Lgi4rUqBhxrri21MfCm3AJXkQnDw2RPaZxZcXHZ4FHohhNci+2SEhYqNqTiSNxRLgnWTFpolXdHyiQR6JFBtXjSPgtdh3NteXK7yNEikVAlyLI9sQqFQUq+QJgcME4jiN65xQhzuNxyL+L9OIe1PrO9KUEifzbd1mHRIffP9WyE1FUJc+YhKnDlMtBxybhIMn2tJZO/zc+O6GU1R2GMfQ4/t5gXWe4VTn8RBGqmhrQleQ941Yoz9UZNWTE07mDRCFLu7bUbfXaYxMBk84i4dAfQB7Zbgp57fG91ATnFskIy1w1gbsHJEHoKtYJknee183SM2bpZeLhv6b2qmKMjJtJX2+nLJK0UDBltvueg+GaRrYb/ONBpPZlKera8rXF23XUqK3b33oR9p0YTmmfJp80JSTyHvTEBekcySyTRcLcQgTHkVZNvWKN+niAYhyJ3ndGFyRqro7qdtWtKJ3Yzqs9x6FmttNiLoURIK/LtLgKjiSnAGaxCLZ4m5Rrq1mOxI2jsaIoLWyob7ver8d1ZFkkU2ABEM9XkUfipK0Qjorv4RRq1iIZKxSJ2qKFUiE0Ey28p5QmsYULxXMER5KlLXNNj47je47wnfNFpzeHbBcmM3Nt6VyHWpgwewHOa9RlQsUQfcPBSULATJbzng++6+BI/yh+5/88gqNJqf4sbZRr3/VeGr2PBt3bvoqvvIg3XnEW/nDt+WkLTiPE6TOKItFSAJBZ2FKQJK6tvnSlY1MpD34VRY3DrJDoLOiuLT2fSK1yK5s9xZ0mzZYd7WJK0DVNf57Zr5ev6dEy+nmfFTnGhL8xXK/rpO9rFkyRmABS7zU/LpBPttP91bcRa4mTziNSItQy3qMw4iVS+LjUbSuGNWu8MbktktQiahAkmiYL8KitNNnONV/Z7tJ8fq7N8ZBS3e8MqIIkFWHiOih7cQnsLIukp2S2aDzHQQDVIjhnzhRlccyDydJRrtF1EASSIzE14dHDf186OoiXjg5iQdJL3bSI6Oeu99KkysgnB+nLqJXGD+e5eoSfPEYYASdY06ZpPT76R2qiEgEpE3kWie5G4+ctApO7MA96S+VciyTD7Zr6ZK4tHRXfM3bfdJ342UsrPrEyNItE/5v+n9XulgcA1AP3ICjjM+zLv8p2bTnCE5HVN8jznNQ8EGR7zZwuwFvx6oIobZFMPEEyyS0S9f/FLZJEkJTSuSg8nLAuRyIK9anluemYikWS03yJxlLiHIn2YmRZNCaL6rsfuhpPfPSNxjHrICHGr4fDc11RIwjQ3QPx9nr4L4EWnyy3hqnqQBb0MvK0a18lfzGiY6vhv/IzjCIcGRjFr1y0CC988pdFZQDdIjH1Xski2+XvuZckx2MQznl4XZLkeEaSMT9Vuwf6M5JRW4bwX6YAAWaLJKtHvajaq/EUdIw8V5LnOqhklI4xKWJZMEWMmc4Xjyf97FLH82Qb45TnIMMicV3VjWc6NHX0JLKdB3zo4OeZKJjUgiTFbxgWQr1+D8A4EsNE5tvXdW2xInPcHBdCRStiJ8dpECS+p7m21LFl7W+6B66bLmWfhzxyz3NlHwUgvUAD6fBfwlDV/ELSfxvJbNfLyNMxTWViAK0DnZsRt884ktl9ZeHHBurnkdBx9evjV1GcI5F/F0lIfP/rl2HLn1wrwr/TZDt7Rp7DLCcnJZD1gAFeMojute5mjV25Ds6bNzU5lnpMUpq8nHnqOKq7loOmRZGotyyy3cxT1D2csekbgVsKevQe/T/TtcUsEu7aMo3TWiRjjGKZ7emXvcKitvTy6Dy5iicc5Z3fdeRiwLWNrBBVk8DrKblKHkmea8szLGb6+BtB3sT13LhRFdXdM+aRKBaJPD+VstCtK2mR1NcQCbKXCmnX8f/1RVQ/B+2r+qTlc6sFIY4PVjE7KfJYE4KESqSQRZL0p3fV4+pjV0rCF9QoG+VIXNfB3KkV4dbLEyQl11WCQng3w3jstE/82VtOu2B1pYbmLxXwpOdI74sg2xWtPW2V1nNtFVGGiKtptNZWFrIKjALqe6KH79Yj22OORPYjEY3Q8iySCSRIJjVHoj+EvFpbpt4hPb6ruGUA5ot3HRyvk0fCI4iUhET6my2UXPsyaf4V31XcBbog8ZLf4g5x2ZO90agtfi5z1JZukcjffCNHIjegDOmsOkiNFG3UW6jSZ1Y/GZ0UNyWAuY4jIrakIFEXw+GaTEgE1HvEeTH92PoY8tAoR0Igq0m3yvTIOiX8V9w/1X0irAmNixuqmuciAKxYGAsSajBGrkzil7wcoTqvQJn/IoLYS6LSstynHKYoRx1caOrvkkglcNJRWyL8NyOz3XVZq12mbJrGRGOwgmSMoN9oYxl5k2uLWSS6IFGituqF/7pyIeG8iIzakpMtL2oLAM6Z04czZvVmuhPoGPpEzQpfbgQm9584HlkkhvBf0gYDLWqLQBnS6ZwY9bzxd/kvTar8ebK9zg/o56B9dFcXbXO4XxMkAZWTj8c2kiyOUyo+yp5aUscUpsq5mOJ5JFzJKb54UIXi3DwSNmbVtQXxHR/DFKNFoj4/UiDOTwTJ8wcHAEg3YG9JLrgE/V58/NcuTHkD9G0Lhf+6jvGemTmSuofLzffiLmB9HkiLJEJPyaSQOUnRxqSQZhGOxAqSsQFfvGsZxRX17mv8u56SKyYzafu8CNxIzUwWEzy2IHF/ucl3Xo8j+fv/Zw0cBziVLL4mQdJTcnFiSNOKdYukicmnE7Acriv7KADmEMpIyyMhkKaalVzZiGurJF5i1XrKcm3p/SBMZLvjODic9OCeo7m2iGAm11bJddBX8TTXR3rsamRQ44KkSB4JgayxqZpVppD3rqMQ6otn9uA3rzobr0oKH+pkO7+fdBz9+dF1LUs4Eirdv/aChfjywzvxvquXJfvLffTna+In9fMWWUiXLZiGVYtnpL4355HUPx7PSUp1SCRLwUn3o+EJicYQajfJI6HGVsKlaLJILNk+pqD7nKdR55HtFd8DVRnpo5LtrIDiSNESKa6juAjo70UzZHHEen3Fyf1SzuBI+HdKHkkbLBKpAZqicyhqS45Tniv+Wy0jL48hXFvamGiR59dYt2ijJuy8OoJE50j4LTdpg8QNXHfJYgDAzN5YsMicAhdTe/yUb1w/l3KewoJE/l0yPPcszOkro6fkpiLX9Gqyruugr+yhr+zB91z8xa9diCWz4jpusppyvP2vXLxI7EtzS1dqaJ4smdWLD609H3/9G6sBxFbdpj94Lc5NWg0oxSwbUHBMiZ5Z+M2rzsbX3/fK1Pem8xUZQl5xVe7dUCt/q3kkRq7RyYraylZ+rUUyRpARG7EvN48jMbu2XFGp9pqVC7Dm7FmCQPR5+G/GhOZuFpdppzQ5Llg0HQ9tPwxAC//NWeyzCE7+m64NKWNqgiPxtEWaw3XVzHZl0aNWu8xDYXRtpch22pZzJHUsEsO1A0XzSLRGQmSRMCuFxvLHv7wSv3/tchw6FVsqxJGUPAd9ZV85v9G1ZYhqq4cixQRNeOdVZ+O1589LKQ9qYcz473/77VeIIqCmbWnc582bii/ddBke3H4YP3jmIAAT2S73+cA1y7Ovq4l7AUhLppXqt81bJNlBMXwtSTcGSwRJLTTyHp6rRm3xCtQ6Jl3U1oc//GG87GUvw8UXX4xf//Vfx/Hjx8Vvt912G5YtW4YVK1bg29/+dqvjbAqu0GyzySkTR1IxcCR9ZQ83XnGWmARqUbaM89MkchzM7avgva8+F687f574vkdLQhSCL2eC0KJncnGYkivTrq3GH3leJrHvqrW2lHpFpnBh9t1IEhiQVROtkUqneokUshSK5JFw4c5/UxIJmSIwrackxihcW56LVy+bi8vPmiWPa9Cc+VUUr/6bXviLoK/iY+Wi6anvVfdafGEXnjHD2D3TlAvzSxctwm1vvYhV6DVzJPXAn3sjbppGMtuz0GzUVm5xVUP4r6ONNW7ilT6u68TVfylqSyozaUxEi6QlQbJ27Vo89dRT+PnPf47zzz8ft912GwBg27ZtuPPOO7F161Zs2rQJv/u7v4sgCOocrf2g+/yK8+bgLZcszqg9lV58p1Z8uE7cllaU/NYeGtfysiagnEzx/n/yqxfg7Dl9LKTSERMzLshY7AUpea7RtWVy0+nHasYi8VmAgQ7XSaqhRlS0MV97TpXezimBwd04dYs2aiVShoQgqZ9HkhXRk0cGe5rAKnku/uRXL8AH37giNebM8N8mXFuNcCSZxzO4H7PPnRaqBBq/njjYiHUh3b+Fd1F6+zSLrMipeshr96BYJCyHjJ9vNKuMvBu7tqhoo57Hw0EW4KSptfXGN74Rvh+/qFdddRV2794NANi4cSNuvPFGVCoVnHvuuVi2bBk2b97c+mgbBN3oq8+fh8//xqXGbUzSfVZfGV9/3yuxbvViYztYQLUa6pHtqUWI+eDXXrCAHdMc865jycxenDk73edbzwEwjrsJQVLEIpGFCPOtiCx3AAcNWSmRUi9qSyuRQpaCidjUx5m2SGgc2ddC+4/U4o535hwbkyCRvxfW3OsI50Zhcm1lbsvcs+lxxZ9pjqQR66Lxfab2+LjwjOl42cK0tVUUpvMVCf9VyXbzXObhvvXcmwTXlS2YeQJzHkcyKfuR/NM//RPe/va3AwD27NmDq66SHdmWLFmCPXv2GPfbsGEDNmzYAAA4dOhQu4YDoJgJXMrQti9NXBTnzImJwZdpLgKFh6gjSPTJQBPJcYBPv+1i/NKFC7F8wbRYAx+p/1J96w9eY87SJ79sTvn1Ros2xvtka4CeG5eFl1Fb6m9ZxyKYBImpRErRMvJ0TrJITFwSPwfto3IkUtCbtufnGa4GmYu7SavkRynej0T+3QjZngV+2nrzIS/UNitqqxFhF9/XyBjIkYWS5+KbH3hN4e2zz1v/Ox25nUx96V3gEZv6tmayXXbO9Fy5n2mKkOCeQAZJfUFy7bXXYv/+/anvP/GJT+C6664Tf/u+j5tuugmAGu5JyJL269evx/r16wEAa9asKT7yAqAHlnfDuRZhwhtXLcQ3P/BqrFqsC5L62rLvphckdVwOekoefumiRcr29RaYrMWRquUq1lIqaqsJi0QsJumXPTbJZRkUvffGefP68N7XLGXnr2+R0CEaCv/N4Eh6DHWg+Dno2CaLxFTJWOyT/DZSDTO1esmRyO/yXGpZaJYjyTxeM66tnIW3SPfBLEgOpvAubYGZI6m/X1ZxVEAu8LxBmAxU4a5w83iosjRFaDqOed2kd0bPcRtP1BUk999/f+7vd9xxB775zW/igQceEBe9ZMkS7Nq1S2yze/duLF68uMWhNg56BrkWiVvfTLzwjHQcehHXVlYphyxLxdTbuhGYOBL93M342PN80mSRkO6gX9MDH7pa+X8hjsRNWyTFOyTG+1CyqKlaLZB2W/HDm8J/dRnKfd5TKmmSmm9jCgnmY62HZvNIihyvnvWgJ3iqx4k/9aKNDUVgtYE4bwZ5ZHveVCuUkOimo7bUSErzufXW3XF+UxqkSE4kQdLSrNy0aRM+9alP4e6778aUKTJ0cN26dbjzzjsxMjKCHTt2YPv27bjiiitaHmyjyNOmxDaukzT3aexWFCLbPfP5syas3i62Ueid6IB0RFlreSRmQRKGMLq28o5FKBusK7NrK/+4eq9xWY6jvmtLf2HlbzkWiZv9m34cpUSKMuaiFon8u90cSb25xrP80+OKv+QWsut0niNpB8wcSfJbznrRaNSWmMt1Qtk915G1+9g9z+NIqhNIkLTEkfze7/0eRkZGsHbtWgAx4f63f/u3WLVqFW644QZccMEF8H0fX/ziF+F5xTqrtRN5RCFHyXMbXrxN+QLp8ycTK+VfRzKu9DjyjlcP1ONc8f+3wbWVl0cSd0gMjdV/TciqT8RhdG0Vtkjiz5lJ7atpPWZrQeEJvKxaW2z7DIsEAMpZri2TAG5gEZe7FLcgCh2PHaLe8ejS8jR4Trb7Sd+coshTUjqJvDySPD5OySPJi9rS1p56Fkns2grVcTiOUYBXhGurfsvjsUJLguS5557L/O3WW2/Frbfe2srhW4ZpQTCh5LnGOlx5MJXC0JFVwTPLUhLlPZp0GMuOhOxc2sU3k0eSF4roeXH8e1FBkupHnxf+6+VrcRzL5k/FRWfMwPL5ccLoX719NR74xQGRRZ11DgBK3D7/LU8gKxZJJtme3lbnZhpFOzgSpVhinTHkKWMi/JcJkpLrNOXaGutQ1rwOiXnDV4RmqtWuVBxTHEmd4BzXkRwJv+fmoo3xeaju20TApM5sL+p/Lftuw3Vryh435/M10izXVvqYpFW3xpGQZmM6VysWiamGXtxHIRTZ6/XudZYWx8Gfm+PE7qB6oZnzp/XgGx94tfj/3KkVvP3lZ2Vur5PeRrKdDS2LbM+6Bv06xHlz3GVFUCREtR4aIe/1fiTqceJPnkdS8t2GBGQjdbPaCdO9F262Oq5weYy0Iui5cYKtKM2fnKZUgGyvCYtEbmeaIpVuJNu7GbxhTx56SuYEvzxMqdPREOBJcmZtNssiafalokWhyjSVdGOrJgRJcgxTNVbPdbSijfnHKhT+yxI2OxUrr4dj8rNIDotrkdn7mwpo8m2yOySO7eJJyBOQOrwcLV1ktnuqlt5QQuI4ke1GzqegK5xgUsrKiZtc7zDp13HTuo6D0UCNfOSlUpRzkEViBcnYIK+CJscnfu0iLGQFFIugp1zfIski1fUYc4KeVNcojBZJcg/KXlzJuBmNlrQrkwZEgsQU/ms8li5IclxbvEVpu5FXIkW6RLOtB8eRPcnrWSRKAyd2OeOVUKYWbSxmkWRlYwNq1FbJc5rjSMbYtWVMCiwQtcVhEsJl300y02UoMFA/J8pzoSQk0nhMQ6HzWotkjCDq3NSZGa9Nelw3gil1+ocASPlJCSaNF2idbPeFRSIFCfdjV5sk5+j+mSau7zpaz/b8sWdlA3M47EXqlKaasgwc+b1IGGXbZ5UICYOoQYuECbCxTp6g8zZA3vPmbDqEa0sh2xuzIkWk1DgJVQ63wbHkNaBLWSQZygRBIduZoml6n+hYtQlEtrceAjKBkWeWt4oirq1sjkT9JNBL3WxkDoUmKmQ7uR9KXlNEOyAnrkmQUB8FmtP1hLZ+L/KitrxEkHRCW82ySExcCWDWPqWQNkck5i3C/PexhqNcVzEL0rSgyaKN8vpnTymLiLkiGK+oLRMazWkx3bt50yqYNaWcum/1Mttjst1gkWQIHcBaJGMGSZq2f5L2FnBtZb2EPLOdI8uCKYrfff0yDIzU8M6rzk6d68zZvaJHeqNwcyYuVUGWRRvzj6XzLCZBwl8kz3WMJH+rSEVtie/N2xi1yOT3TNeWeJ7yO3URHx89TknGrKO0yIUw/Zt+/a4D/N1vrslMAjVhvDgSE0wuzTyY7t2/vPsK9JQ8fOUnL8bHYu86NdjLKiOfitrK4Eh84cK2gmRM0Ch51gh4L/B6ri3958yExBaLsU2t+Piz6y5UvqMX9QNvWIZXLJ3b1HFfs3wu/uOR3aIHN4frJCVSQvUlyAJ3uwH1ORLPcRB1YI3RrQ16Fmo+iXx+WS8/kEO2C7eGOXl1IhTdK1q00VxoMP4UpUEcp2GusdHFu5NoNBTZ9PzmTI17zXsGJcL3sju1mpp81XNtWYtkjJCnTbUKxbXVpEWSKpGSfN9siRQTXLHYeZlZ3vVw3eoz8Jrl80Tfcg6a1HoyVRb0yW9ahPni4rkOOvG66BFZxrIoyd9ZlkM915ZJkXEMv48n6llF4h3KIafr1avLw2RzbRFKLKdEfOe6GIa5Q6LCnRV0bdmorTECPa92xN7r4BmuWe9i1kuSxZHo/cbbgXa5DkxCBJCLoQhdrHMaffLXyyPplGtLL3Fi0oy5ZWQ+RvxDlmvLpJVOAMVbQT2LZMH0ClzH/Pz1EinNXJvI/J4QgiT5LKjH5b1TpiReIufNwiH9d1ZC4uKZcQuJdZeMff3CLExuQdLB0MJGyHb99MJvqgX3tRq1lTeGTml8IoIkyO9fT9AtEnPRxvjTIdO+w66tOPExbT3QOLItkviznmtLDf8d/wWTo56SdelZs/DIn6zFLKMgiT8rLVgkRcqSjBVMc6DI9iboUVsAyxPLcZPy88eWcvrYs/vKePov3pw578YDE2ckHUAnK4sWcW3pPTL07VMcCeWRtJGE7XR4JR23qGuLfOgkQHItksS11QlOWo/aov+pw0+7uzjoOWa90KI97wQTHo3CJEQA+exbaf06XnkkeWjHWEwWCbmus6r/pvZ1zGQ7EHtEOuFpaRaTXJDEn52430rUVsYLlJUQmRUEICySNuYX5BVcbOfxybVV7yV8/Yr5+OpvXYXfed1SAPmChDLbO7HI8ENyUtMUqZXFWUn+Kd8iUUqkZCgR3QhKyqR525Rraxw5kv92+RJ8/NdkcEpUMKn2rZeeUffYsvI3/05NUuRQLBImXCeAoVYIk9q11cnQwiJtYMki0X/OskhMrXJbRafDK3WLxCmgmrzivDl49KVjAPLzSFw3rlvUCbZdreflMI4kvU2WVkjcjd6znOC5DhZMrwifNj/+RNLAm0VcnNBl0YnNuLaSY43DivmZ/3aJ8n/yutYbyl/ecElqXx16PxKAl0xKb2/i5hynM/xuJzCpBUmjPs9mjg1kazBZZnuWpVLuANneaY1PCJKamkxVD+TGywr/5QtuJ0auuz1lAETaIskKoqPM4qxmU47j4KGPvEF5nvTXRONKmoHrJgERBu27KCZSHomsF5c/Fsdx6nZ0NAlXclnnVVIG6kdtTURMakEyViWqG89sN1skyxdMw8sWTmsv2d7hqBg6flGOhJDLkbiyNIrnOnA6YpHEn1Lw0TNJu6GyyHZaePQOgRz69QnlZhI4lR1HrTzQzLztZK5Xoyha5qcITEEuWY3uAHMTtzyOZKJhUguSrAZSbT9PpmuLFg3196wkr3WXLG57SF9ev/V2QOdIip6Gki9N/ILLTPpOCRJyZ/niWchzy3HkL/oUytxI+1snJcCKYf1rl2LZ/KkN7dNpiBI2GfO5CAQPNQEsElrMF0yvtHysvKgtk3Dg7wGP9psAt6UQJrUgcVrQlBpB1kJjSnIDuOnayVEl5+pw5FCjUVuEelFb/GXqhCCh8+gasakKcKZFEtS3SHQ47Loawf/65ZUNbT8WcBNBnFXBoQhM0U3jhRULp+FT11+EN61a2PKx8qK2THoHn0Pc3Wo5kgmAvIJzbT1PHYsklZA4hua8cDt0qNKsLkiKCiwSIGVDC2bOkcS1vNowUANoIQRgDP+VvIl5f6qmnJXZbsLk4khUi6SVPJJxKoScQl4ztEbgCz6EfeelhQtB6XuvhP+2ZTgdx6QWJDz6p5PIsnhc15FJdcq41MWrk6Br73T4r4jaKniaPIvEcdQQyE4FqTvcIjEEQMis63yOpCnXVresEDmYUvbQV/EFV9JSiZRO1DEaR5g4klITrq3OhJq0H5NckKT9lJ1Anvmp9wMHpGk7FmZrp6NiSECN1qKGwhWnVOKp11dJa/N8UZrdV+5YTSHVIlG5kvjvfLdLrQDZnj7n2FmjncYH3rAcN10ZV5puNnF0rN7RsYaJWDfxJgRFkDDXVrcEZZwWgmQ8lT9TP42xHFcny8QA8lqqQdjQOV513hz83W9ejgsWTTcek16mT11/cXsGagA/jyTbuUUSf2ZZcyKPpAnX1mRQwBdM78GC6XGlAr9Ji6SThVXHE+aorbS7i8BzkeievPGChROqDEoeJrkgST7HUZK89bIleOWyOcp3rUS5NArxonbICc27MjaykPiem0lqxu7A+O8ZDTRJahSqIEkL96J1oLKKNprQbNTWREfzrq34czJYaBzGqK2cwAKTRfK+q8/r5BDbikktSF5x3lxcf9kSzJpirhXUKmb0lnBiqJq7zW1vvSj13VhaJPSidooj4RZJu8zwsYqfd5yCCYl1htKI1ths1NZEh99kqKrgoSYK294mmKK28tx4qiDp8OA6gEktSFYsnIa/vCG/lEEr2PQHr8ELhwYa3m8s/eReh89FRPRoELXtHHP6ypll69sJ7nY0cST0n3o9OxqySJLPyaaBe67bnGtrAiUkthOiYCtXTHItkvodVycyJrUg6TQWzejFohm99TfUIPymYzBfOtHjhIPW2GqtMY4kD39w7fn47dd13qwvypHUu6xmLJLxjtqa3uPjnLl9bTue5za3AE6kEinthLRI5Hcmq5dgyiPpJlhBMg6YmkQs0Wcn8dbLzsDimb1t7brIQZpXNQjbVheot9x8N8dG4DqoI0iKacvN5ZEUH2cn8POPvamtx+PNwRrBRMpsbydMtbbyEpG5MmItEotCuGDxdHz9fa/EZWfN7Pi5lsyagrddPqVjxyf5VA3MLUQnMhzHyQ3/LWqRNOLaEj1Kuuxe1QNPTGwEk9W1ZYrayrNGTQmJ3QQrSMYJl589a7yH0BZ4wiJpH0cyVojj9LPJ9qLVoxtybWFyLpzNhv9OVteWKfkw17VlSEjsJnRhfIDFRAJN+tEg7DpNyuUWiYimUn+Pf8s/TmMcSfw52RZOr8WorW6bO/Vgskj0CEEOzpF0SxIiRxcO2WIigZdI6ba1gIcZmy2SYsdpJB9oopDt7Qbve98IOl3CZ7xgqrNX1LVlLRKL0w68sVW3uWtcV+Yv0NCdgq6IZkFH6paqrkXhe81xJJ2uTj1e8LR5BeQHWJgSErsJVpBYtAQhSMJu5Egc5r5Kux06kTgqM9vbd8yJgDiPpPH9Jqtry5TZTtdqKh2nRG114b2wZLtFSxCNrWrty2wfKygcCfuOUI9sf+gjr8ee40MNnxPoTq0zD36zri2R9Dm57oeJI6E/A4Mk4feuG62ztrz6//t//284joPDhw+L72677TYsW7YMK1aswLe//e12nMZiAoIvAN32AsQlUtzkb5NFIrcz4czZU3DV0jnmH7POKc7dXfeqHub0lTG7iVJEE6mxVTvhG/qz0zVSS98sdKOS0bJFsmvXLtx333046yzZEGbbtm248847sXXrVuzduxfXXnstnn32WXiGJkYW3Q1V4+quFyDObKe/40/H4Ipo56I/WYs2fua/XdJSQuJkux+e6+Cjb7kArz1/nviO5lMdOdJ17xHQBovkD//wD/HpT39aedk2btyIG2+8EZVKBeeeey6WLVuGzZs3t3oqiwkIRZB0mSb15lULcfWK+QB4fof8vTOVbBJXziQjSWb0ljC9p/FKza7BBTRZ8K5XnYvz5k0V//esRWLG3XffjTPOOAOXXKIWRtyzZw+uuuoq8f8lS5Zgz549xmNs2LABGzZsAAAcOnSoleFYjANMPuBuwR+9aYX421QiBXU4kmbQiUiwbsZkTUg0gR55vT5t3Xgr6gqSa6+9Fvv37099/4lPfAKf/OQn8Z3vfCf1W2SQuFnugfXr12P9+vUAgDVr1tQdsMXEgikqpRuRV7SxnUEEJi7mdIaJlJ6skFFb+ZKkG/mzuoLk/vvvN37/5JNPYseOHcIa2b17Ny677DJs3rwZS5Yswa5du8S2u3fvxuLFi9s0ZIuJBN4wq5sFiSmLXXzXRucWKVmnw8JZBEXL0EwGkNIVdqh19HiiaV3roosuwsGDB7Fz507s3LkTS5YswaOPPoqFCxdi3bp1uPPOOzEyMoIdO3Zg+/btuOKKK9o5bosJAm6RdPPi2EoZ+UZAa8jpsHAWAc2fyRb+a4II/63HtnchOpJHsmrVKtxwww244IIL4Ps+vvjFL9qIrUkKUy2hboQp+dARVkr7rovcGlaQxPAmaTVkE2T47zgPpANomyDZuXOn8v9bb70Vt956a7sObzFBwReAnlKXZSQymBISO5HZTslop8PCWQSTtfWwCTL8d/JJku598y0mBLhrq6fUvVanqdaWo322A8IiOQ0WziIQZPtpYKHlZbZ3O6wgsWgJruuIRbingU6BEw2maCp63dvphhIWyeRfNwvhdAr/7S3HDqBSh7qVjidsrS2LluG7DqpBNOlcW2Q9tJMjIUFiLZIYk7U/iwnvvOosHBsYxe+87jzj78vmT8VzB/vHeFTtgRUkFi0jXnyjhnqXTzTIKrTsy8QkaW/UFlkkk3/hLILVZ87Eq5fNxbSeyb8UVXxPSYLVsfH9r0L/SG0MR9Q+TP6nZ9Fx+K6DEXQ52W7gSCJQhFX7zkPu8dNBAy+CNefMxlfee+V4D2NCoK/io6/SnUty9775FhMG5KbpZrJdRmhx11b82c6ERHJtdWP2soVFFqwgsWgZtPhWuliQEBSynZIH2/iWCNeWffMsJhHsdLZoGaRld7Nri6yqTpPtYWg5EovJh+598y0mDKpBCKDLw3/pU7FIIuW3diAQBL4VJBaTB1aQWLSMmrBIuliQGGptdaIuVhDGQvd0qC1lcfrAChKLljEpXFumhETh2mrfed60aiEA4G1rlrTvoBYW44zujDWzmJDoaotEfPLw3xjttEjOntOHnbf/StuOZ2ExEdC9KqTFhEM3WySOISFxEpZEsrDoCLr3zbeYcOhqst2UkGhLvltYFIIVJBZtQzfnkZg5EqS+s7CwSMMKEou2oatdW8mnKY/EFli0sMhH9775FhMOXU2254T/WjFiYZEPK0gs2oaK373TSbbVld9FsJLEwqIIuvfNt5hwmGwWyWuXzwMArF25YDyGZGHRNbB5JBZtQzcLEhPZfuEZM2zOh4VFAViLxKJt6Olm11byaUN9LSwaR/e++RYTDn4X10Z3BUdiBYmFRaPo3jffwqKNkBzJ+I7DwqIbYQWJhQXMZLuFhUUxWEFiYQFZrNFaJBYWjcMKEouWMW9aZbyH0DJMtbYsLCyKwYb/WrSM7/3R1ajWwvEeRktwHQe+66DSxWVeLCzGC1aQWLSMqRUf6HKjxHMd/PO7rsAFi6eP91AsLLoOVpBYWCR49fK54z0EC4uuhLXjLSwsLCxaghUkFhYWFhYtwQoSCwsLC4uWYAWJhYWFhUVLaFmQfP7zn8eKFSuwatUqfOQjHxHf33bbbVi2bBlWrFiBb3/7262exsLCwsJigqKlqK3vfe972LhxI37+85+jUqng4MGDAIBt27bhzjvvxNatW7F3715ce+21ePbZZ+F53Vtm3MLCwsLCjJYski996Uu45ZZbUKnESQTz588HAGzcuBE33ngjKpUKzj33XCxbtgybN29ufbQWFhYWFhMOLQmSZ599Fg899BCuvPJKvO51r8PPfvYzAMCePXtw5plniu2WLFmCPXv2GI+xYcMGrFmzBmvWrMGhQ4daGY6FhYWFxTigrmvr2muvxf79+1Pff+ITn0CtVsOxY8fwk5/8BD/72c9www034IUXXkAURants2oYrV+/HuvXrwcArFmzptHxW1hYWFiMM+oKkvvvvz/zty996Ut461vfCsdxcMUVV8B1XRw+fBhLlizBrl27xHa7d+/G4sWL2zNiCwsLC4sJhZbI9l/7tV/Dd7/7XVx99dV49tlnMTo6irlz52LdunV4xzvegQ9+8IPYu3cvtm/fjiuuuKLu8Xbu3NmyVXLo0CHMmzevpWOMF7p57IAd/3ijm8ffzWMHJu/4d+7cWewAUQsYGRmJbrrppmjVqlXRpZdeGj3wwAPit49//OPR0qVLo/PPPz+69957WzlNQ7j88svH7FztRjePPYrs+Mcb3Tz+bh57FNnxt2SRlMtlfOUrXzH+duutt+LWW29t5fAWFhYWFl0Am9luYWFhYdESJp0goQiwbkQ3jx2w4x9vdPP4u3nsgB2/E0WGWF0LCwsLC4uCmHQWiYWFhYXF2GLSCJJNmzZhxYoVWLZsGW6//fbxHk4hnHPOObjooouwevVqEfZ89OhRrF27FsuXL8fatWtx7NixcR6lxLvf/W7Mnz8fF154ofgub7wTqXCnaewf+9jHcMYZZ2D16tVYvXo17r33XvHbRBo7AOzatQuvf/3rsXLlSqxatQqf+9znAHTH/c8ae7fc/+HhYVxxxRW45JJLsGrVKnz0ox8F0B33Hsgef1vvf1tix8YZtVotWrp0afT8889HIyMj0cUXXxxt3bp1vIdVF2effXZ06NAh5bsPf/jD0W233RZFURTddttt0Uc+8pHxGJoRP/jBD6JHHnkkWrVqlfgua7xbt26NLr744mh4eDh64YUXoqVLl0a1Wm1cxh1F5rF/9KMfjT7zmc+ktp1oY4+iKNq7d2/0yCOPRFEURSdPnoyWL18ebd26tSvuf9bYu+X+h2EYnTp1KoqiKBodHY2uuOKK6Mc//nFX3Pu88bfz/k8Ki2Tz5s1YtmwZli5dinK5jBtvvBEbN24c72E1hY0bN+Lmm28GANx888246667xndADK997Wsxe/Zs5bus8U60wp2msWdhoo0dABYtWoTLLrsMADBt2jSsXLkSe/bs6Yr7nzX2LEyksQNxeaepU6cCAKrVKqrVKhzH6Yp7D2SPPwvNjH9SCJJGikROJDiOgze+8Y24/PLLsWHDBgDAgQMHsGjRIgDxC0il+ScqssbbLc/kC1/4Ai6++GK8+93vFq6JiT72nTt34rHHHsOVV17Zdfefjx3onvsfBAFWr16N+fPnY+3atV13703jB9p3/yeFIIkaKBI5kfDwww/j0Ucfxbe+9S188YtfxIMPPjjeQ2obuuGZvO9978Pzzz+Pxx9/HIsWLcKHPvQhABN77P39/bj++uvx2c9+FtOnT8/cbiJegz72brr/nufh8ccfx+7du7F582Y89dRTmdt2y/jbef8nhSDp1iKRNMb58+fj13/917F582YsWLAA+/btAwDs27dP9HiZqMgabzc8kwULFsDzPLiui9/6rd8S5vtEHXu1WsX111+Pm266CW9961sBdM/9zxp7N91/AJg5cyauvvpqbNq0qWvuPYc+/nbd/0khSF7+8pdj+/bt2LFjB0ZHR3HnnXdi3bp14z2sXAwMDODUqVPi7+985zu48MILsW7dOtxxxx0AgDvuuAPXXXfdeA6zLrLGu27dOtx5550YGRnBjh07ChfuHEvQIgAA//Vf/yUiuibi2KMownve8x6sXLkSH/zgB8X33XD/s8beLff/0KFDOH78OABgaGgI999/P172spd1xb0Hssff1vvfzuiA8cQ999wTLV++PFq6dGn08Y9/fLyHUxfPP/98dPHFF0cXX3xxdMEFF4gxHz58OHrDG94QLVu2LHrDG94QHTlyZJxHKnHjjTdGCxcujHzfj84444zoH/7hH3LHO16FO00wjf2d73xndOGFF0YXXXRR9Ja3vCXau3ev2H4ijT2Kouihhx6KAEQXXXRRdMkll0SXXHJJdM8993TF/c8ae7fc/yeeeCJavXp1dNFFF0WrVq2K/uzP/iyKovx3tRvG3877bzPbLSwsLCxawqRwbVlYWFhYjB+sILGwsLCwaAlWkFhYWFhYtAQrSCwsLCwsWoIVJBYWFhYWLcEKEgsLCwuLlmAFiYWFhYVFS7CCxMLCwsKiJfz/W2gw2z8YVH8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_25\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "846a00ed58b24935882e54189dedf991",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e268c47b82c42d99a2a1393358baa31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c946974a27045c9ba796e891ea6e64b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf81cdc7f1e4461eb47a90950a9768d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e60a490ebee94820a7d0de53e8a58927",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dba9a169db0c47c9adf661894acb1914",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cac30c3be4f4e439b01f14b8a274504",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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bGxv0798fN27cUCu7a9cu1ZCbgIAA8Hg88Fq2BK9lSyzculWt3KBBg1CrVi0YGBjA1NQU7dq1w759+0pcn6qErniJ3svKzMGir/xVy+7NbdDtI1fuAqok9uzZhZycHAwePAQNGzYqsNxnn47HihXL8OLFc1y+HKBx1Xv8xDH4+p7FwD5e8GrXAvf/fYE/T56F/5UbuHLhOOrWcYW5uQJR0akYM74f3oYFo52nF/r16wcACAkJwYkTJzB48GDUqlVLddzFixdjwYIFsLS0RN++fWFra4uHDx9izZo1OHPmDK5fvw5TU/XZx2QyGby9vREfH4/u3bvD1NQUNWvWRI8ePbB161bs3r0b/fv3z+d3sQcAMHbsWNW6xMREeHt7IzAwEM2bN8dnn30GhUIBX19fjBgxAo8fP8bSpUtV5Rlj+OSTT3DixAm4urriq6++gkwmw44dO/Do0SON1yzK1KlTsWnTJtjb22PSpEkQiUQ4ceIEbt68CZlMBrFYrFb+6dOnmDdvHjp27Ig+ffrAwsICoaGhOHnyJP7++2+cOnUKPXv2BAA0bdoUCxYswKJFi+Ds7Ixx48YBEREAAK8WLVTH/Pzzz+Hu7o6OHTvC3t4ecXFxOHPmDEaPHo3nz59jyZIlJa5XlcBKoEWLFiUpTohW/Lj4JnMXbWbuos2skcEW9uh2JAsPTirTz6OH/7KszJwq/ePl1ZkBYD/9+EuRZYcNHc4AsIULFqnWbdv6G4Py1i3788/jjMXeVv2sW/kdA8A6d2rHcpLfspzkt+zw/p0MABs1fBJ7dCeKRUWkqt7jrKwslpycrFq+dOkSA8Datm3LEhIS1P4edu5UHmfq1Klq652dnRkA1qVLF5aamso+5ObmxsRiMYuNjVVbn5mZySwsLJitrS3Lzs5WrR87diwDwH744Qe18hkZGaxHjx6Mx+OxwMBA1fr9+/czAKxNmzYsIyNDtT4uLo7VqlWLAWCdOnUq8u+dMcauXr3KADBXV1cWFxen9tpt2rRhAJizs7PaPomJiSwmJkbjWG/fvmX29vasXr16GtvUYrp9O+/nP0FBQRr7ZGVlMW9vbyYUCllYWFix6sOFJ0+ecPba1NRM9FpIUCK2rbqrWh43tRksbQwq/HUFSxZBLBXq7I9gyaIy1zEyMhIA4FjDsciyuWUi3r3T2Obl1Rl9evdVW/flpDFwrekMv4CrCAkNAwAIhcpOVVKpcqKTmHdpyMrMAQCIxWKYmJio9t+0aRMAYNu2bTA3N1c79rhx49C0aVPs378/31jXrl0LIyMjjfVjx46FTCbD77//rrb+1KlTSEhIwMiRI1XTiMbFxWHfvn3w8PDAzJkz1cpLpVL88MMPYIzhwIEDqvU7d+4EACxfvlxVRwCwtLTE/Pnz8421ILnHmjdvHiwtLdVee8WKFfnuY2ZmBmtra431jo6OGDx4MJ49e4bQ0NASxeHqqtmyJBaL8eWXXyInJwcXL17MZy9CTc1EbzHGsHRKgGqifTsHIwyZ4M5xVJUH+28O5eJMr1dY2Y4dNMdRCwQCtGvbEq/ehOD+w3/h7OSITu3awKF6Nfy2azOePnuEDu26wLOtJ3r176Axb/b169chEolw+PBhHD58WOP4MpkMMTExiIuLg5WVlWq9VCpF48aN863DmDFjMH/+fOzevRtffvmlav3u3cpxyO83M9++fRtyuRw8Hg8LFy7UOFZ2tnJY1NOnT1Xr7t27Bz6fj/bt22uUL+mQnXv37gEAOnXqpLGtQwfN31euq1evYuPGjbh+/Tqio6Mhk8nUtoeHh8PJqfgjAUJDQ/HDDz/g4sWLCA0NRUZGhsbxiCZKvERv+R4JwrXzb1XL8zd3gkBAjTjlxb5aNTx//gxvQ98WWTY8LFy1z4dsbe3y3cfOzhYAkJSkfEqMqakJrl48ie+XrMWpM+dx9bpyaJiVlTW+/PILfPfddxCJlGOy4+LikJOTg0WLCr+yT01NVUu8tra2BX6RcHR0RJcuXXD+/Hk8ffoU9evXR3R0NM6ePYumTZuiSZMmqrJxcXEAlAn49u3bhb5+rqSkJFhaWqrq8L5q+fzeCpPbgcrOTvN3KxAI1Oqc69ixYxg8eDCkUim6desGV1dXGBkZgc/nw9/fHwEBASXq5PX69Wu0atUKCQkJ6NChA7p37w4zMzMIBAIEBwdj9+7dpeo0VhVQ4iV6KTVZhpXTr6iWO/etibqNNJvRKop8/gLI5y/Q2utxwdOzHfwD/HHJ7yLGj59QYDm5XI7LVwIAAG3bempsj46Oyne/qKhoAICZWV4TsqODPXb8sgaR0XzcvhuEm7f/waEjO7F48WIoFApVZx0zMzMoFIoSP1O1qKv3sWPH4vz589i9ezdWrlyJ/fv3IycnR+1qN/f1AWDatGlYt25dsV7bzMwM8fHxyM7O1ki+uc36xZX7+lFRUWodzgDl+xEXFwcHBwe19fPnz4dYLMadO3dQ/4MH2fv4+CAgIKBEMaxbtw5xcXHYuXOnsvPVew4ePKhqKSCa6PKA6KXNi26qxuxKDYSYsrg1xxFVPqNHj4VAIMCJE8fx5MnjAsvt2rUTERERcHOri44dNZs+L1+5rLFOLpfj6nXllWLTxg01tttaK1C/bl2MHDYBW7f8AQA4fvy4anubNm2QkJCAx48Ljqs0Pv74Y5iammLfvn1QKBTYvXs3hEIhRowYoVauVatW4PP5uHLlSgFH0tS8eXMoFAr8888/Gtv8/f1LFGfz5s0BIN9keeXKFeTk5GisDwoKgru7u0bSLSgmAODz+QU+Pi8oSPkQkkGDBmlsK2kSr2oo8RK98zQwBgd+fKha/vL7VjAyEReyBymNWrVqYdbM2cjOzsbHgwbi6dMnGmVOnDyBb6dPg0AgwKaNm8Hna36k+Pv74a8zp9XW/bh1D169CYFXR084Oyk7Zv375BmCQ5TN2nw+YPffdJJx8TEAAKk0r9PctGnTAAATJ05ExH/DXN6XlpamMTa1OAwMDPDJJ58gPDwc69evx4MHD9C7d2/Y2tqqlbO1tcXIkSNx584dLFmyJN9E9+rVK7x580a1/OmnnwJQdojKzMxUrY+Pj1cbdlQcuVeYy5YtU7vqz8zMxJw5c/Ldx8XFBS9fvlT7fTHGsGjRIjx5ovneAoCVlRXevs3/VkPuXNAffmnw9fXF9u3bi1mTqomamoleUSgYFn/tr5pWsGZdc/QYRGN2K8r8+QuQlp6GjRs3wKNlc3Tr1h3u7u7Izs7GjRvXcevWLRgYGGDvnv3o3Nk732P06dMXn3wyGAP7eKF2TUc8+Pclzly4CksLc2xZt0xV7qLfP5g+dzHatvZAPTdX2NhY4WVQFM5d8AWfz8eYEZ9DoWDg83no0qULVq5ciTlz5qBOnTro3bs3atasidTUVISEhCAgIADt27fH2bNnS1znsWPHYvv27aoE9mEzc64tW7bg5cuX+P7777F37160b98ednZ2iIiIwNOnT3H79m0cPHgQNWvWBAAMHz4chw4dwsmTJ9GwYUMMGDAA2dnZOHLkCFq2bIlXr14VO8Z27drh66+/xubNm9GwYUMMHjxYNY7XwsIC9vb2GvtMmzYNkydPRrNmzTBo0CCIRCJcvXoVT548Qb9+/XDq1CmNfbp06YLff/8d/fr1Qwt7ewgFAnRs1gwdPTzwxRdfYOfOnRgyZAgGDRoEBwcH/Pvvvzh79iw++eQTHDp0qNj1qXJKMvaIxvESrh3a9kg1ZreBeDO7cSm0zGN2aRxv0T//XLnGRo0czZydXZhUKmVGRkbM3b0Bmzp1GnsVFJzvPrnjeLdt/Y39+edx1sajETM0lDIzU2M2sF8P9uRugGoMb07yW/bo9iU25csJrEWzRszaypKJxWLmVMORdevSh+397TR7dCeKRb9TH3975coVNmTIEGZvb89EIhGztrZmTZo0YdOmTWO33xtvyphyHO+HY1sLUrt2bQaAWVpasqysrALLZWVlsc2bN7O2bdsyU1NTJhaLWY0aNZi3tzdbv369xpjgrKwstmjRIlazZk0mFouZs7Mzmzt3LsvMzCzROF7GGFMoFGzz5s2sXr16TCwWM3t7e/bFF1+wxMTEAuu6c+dO1qRJE2ZoaMisrKzYRx99xB4+fMgWLFjAADA/Pz+18lFRUWz48OHM1taW8fl8BoAtmDhRtf3q1ausc+fOzNzcnBkbG7N27dqxY8eOMT8/P2XZBQuKXR9t43IcL48xVuwZyT08PHDnzp2K+QZASBES4zPR230vkuKVPSX7jayLbxZWzL3d+OS3cHOrVyHHrqrEqYGq/8tFlgCveLcH4hIEiI4RAAD4PKB2AyuIJYIKiZEU4v3Pfg8P7uIoJ7k917lA93iJ3vhpyS1V0jUyEcFndosi9iCVgaWFHFKJcg5oBQPevU3hOCJCyoYSL9ELL/+Nw++/5M1n+82i1pBIqYtCVcADUM0ur2dtSpIMKUk0PpToL0q8ROcxxrBy+hXI5cq7Ii51zNC5b02OoyLaZCBlMDfNS77v3qbQc3uJ3qLES3TexROvceNSmGp5zvoOxZrGkFQuNjYKCPjKZCvLUiAuOp3jiAgpHUq8RKdlZeZg9ayrqmWv3s6oVdeykD1IZSUUMFhb5131xrxLQ7Ys/8kdCNFllHiJTtu94T7C3iQDAERiPr5e1IbjiAiXLMwVkEiUV70KBRAZllrEHoToHkq8RGdFhadi6w95j/wb+01TmJpLOIyIcI0HoJpN3ixRSQlZSEuRFbwDITqIEi/RWevmXkNGmvLxapY2Bhg8nh75RwBDQwZTE4Vq+d3bFJRgOgJCOEeJl+ikwOvvcPrgC9XyrNXtIBDSnytRsrWRg89TJtvMDDniYzKK2IMQ3UGfZETnKBQMK/6X99SXBs1t0LxddQ4jIrpGJGSwssq76o2OSENOtqKQPQjRHZR4ic756+ALPL6rfFYrjwfMWtOe44iILrK0kEMsUl71yuUM0RHU0YroB0q8RKdkZuRgw/fXVcs9h9SBfQ2TQvYgVRWfB9jZ5A0nSojNRGZ6NocREVI8lHiJTtmz8T4i3yqvXMRSAc3HTAplbKyAsZGyiZlBObyIOloRXUeJl+iM2Kh0bFv1/vChJvSAe1IkWxs5eFAm29SUbKQklXx40bhx48Dj8RAcHFzO0ZWei4uL6mHzpHKhWeaJzvhx8U2kpyqbCs2tpBj0qe4OH+I/XgBeeijXYRSIGTpB0WBRmY/j5uaKkNCQfLfZ2dkhNCRcbZ1MJsOyZUtw8PeDSEiIR7OmzfDDD6vRrFlzjf3PX7qMXh+NxIk/dqFPzy6ljlEiZjA3VyAhUfmowMiwFBibisHn6/a0ol5eXggICKAr9CqIEi/RCUGP43Dktyeq5alL2uj08CFeeihg5MJ1GAXipQWX27HMzMzw9VffaKw3MjbWWDfvuznYtGkjBg78GA4OjjhwYB+69+iKhw8ew/m9W/WpqWmY/M0sjBz6cZmSbi4bKwWSk/mQK3iQZSkQH5MBazvDYu+/YsUKzJ49Gw4ODmWOpbxcvHiR6xBIBaHES3TCmjnXVE+bqVnXHJ5da3AcEcllbmaO+fMXFFmOMYbt27dh7Jhx2Lp1OwBgQP8B6Na9C/Yf2Ie5Pl1VZecsWIXMzCys/2FhucQoEDBYWykQFaO86o15lwpzSymEouJ9ebO3t4e9vX25xFJeXF1duQ6BVBDdvaQgVca1C6G4cjavOXPmqnb09CE9FBMTg/T0dHi0bKla17JlKwBAaGhes/zla/fwy2/7sXntUlhaWpTqtVwbtoVrw7ZITk7Bt3MWwbVhW1R3dcKv21cBAORy4NqVQIwbNw41atSARCKBnZ0dRowYgefPn2scr7B7vDdv3sTgwYNRrVo1iMVi1KhRAz4+PoiIiMg3tvj4eMybNw8NGzaEoaEhzMzM0KRJE8yePRtpaWkIDg4Gj8dDQEAAAIDH46l+vLy8VMcp6B5vVlYWVq5cicaNG8PQ0BCmpqbo0KED/vjjD42yua81btw4BAcHY9iwYbC2toZUKoWHhwdOnz5djN92nuP+/hg1fz7c3NxgZGQEY2NjtGjRAps2bYJCoTmOOvf3+vr1a2zevBmNGzeGgYGBWj3j4+MxZ84c1K9fHwYGBjAzM0OXLl1w7tw5jeMlJSVh9erV8Pb2hqOjI8RiMWxsbNC/f3/cuHGjRHXhEl3xEk7J5Qqsnpn39KE2nR1R292Kw4jIh7JkWThwYD/evg2FoZERGjVshA4dOkIgEKiVs7GxgYGBAQLv3VOtu3v3DgDAyckJAJCRkYkJU5fh4/498fGA3mWKSyaToVu/oYhPSEI3744wMTFGHVdHAMA/1y5h2ozPkCPPRr9+/VC7dm2EhYXhzz//xF9//QU/Pz80b6553/lDO3fuxMSJEyGRSNC/f3/UqFEDL1++xPbt23Hq1CncuHFDVTcAePPmDTp37oyQkBC0aNECn3/+ORQKBV68eIH169dj8uTJMDc3x4IFC7Br1y6EhIRgwYK81oSiOlPJZDL06NEDAQEBqFevHr788kukp6fjyJEjGDp0KO7fv4/ly5dr7BcSEoJWrVqhVq1aGD16NOLj43Ho0CEMGDAAFy5cQOfOnYv1O5+9ZQv4fD5at2sHBwcHJCUl4dKlS5gyZQpu376NvXv35rvflClTcOXKFfTp0we9e/dW/e2EhITAy8sLwcHB6NChA3r27Im0tDScPn0aPXv2xK+//oqJEyeqjvP06VPMmzcPHTt2RJ8+fWBhYYHQ0FCcPHkSf//9N06dOoWePXsWqy5cosRLOHV8zzO8+DcOAMAX8DBlCT19SNdERkbi08/Gqq1zcamJbVu3o2PHTqp1PB4P48dPxI8/bkZSchKqV6+OAwf2w8TEBMOHjQAQje+W/4z4xCRsXruwzHG9i4xG/XpuuHTmCIyM8u7nPnocj5nzJkMqNcAfBy+hW6+8v6nHjx+jdevWmDBhAu699wUhPy9evICPjw9cXFwQEBCgdv/30qVL6NatG6ZMmYJjx46p1o8aNQohISFYvnw55syZo3a82NhYGBsbQyqVYuHChfD390dISAgWLlxY7DqvXbsWAQEB6NWrF06ePAmhUPkRvmDBArRq1QorVqxA37594enpqbafv78/Fi5cqJbkR4wYgZ49e2L16tXFTrx/bdgAV0dHwMNDtU6hUODTTz/Fnj178NVXX6F169Ya+927dw+BgYGoWbOm2vqxY8ciJCQEBw8exLBhw1TrExMT4eXlhW+++Qb9+/eHnZ0dAKB+/fqIiIiAtbW12nHCwsLQqlUrTJs2TS8SLzU1E86kpcqwaUFe89CA0fVK1CGGVLwxY8bi7N/nEBoSjoT4ZNy7ex8TJkxCSEgw+g/oi4cPH6iVX75sBaZ/OwN3bt/Grl074V7fHWf/PgcHBwfcvPsvNvz6OzYu+xbWVpZYvGI9HOu0gMTCBa069sbVG7dLHN/qZfPVki4AnLt4CCkpSfjCZwbsbWshJSlLta1BgwaYOHEiAgMD8eTJkw8Pp+bnn39GdnY2Nm7cqNHpytvbG/3798epU6eQkpICALh79y6uXbuGpk2bYtasWRrHy23iLYsdO3aAx+Nh3bp1qqQLALa2tpg/fz4AYPv27Rr7OTs747vvvlNb16NHDzg5OeHWrVvFfn1XR0eNdXw+H1OmTAEA+Pr65rvfzJkzNZLugwcPEBAQgEGDBqklXQAwNzfHokWLkJmZiaNHj6rWm5mZaSRdAHB0dMTgwYPx7NkztdsauoqueAlndq4NRGxkOgDAwFCIT6c15TYgouG7775XW27QoCF+3PITjI2NsGHDeixZuhiH/8j7YJRIJFi6dDmWLlVv7pTJZPjsm8Xo3dUTI4f0wrpfdmPxinWYP3saPNt4YMWazejz8Wg8v38FdrY2xYpNKpWgccP6Guvv3lOOBX/+8jF++nU1BEIeLG0MVP0GXrxQPnzj6dOncHcveMja9evKGdQCAgJw+7bml4Lo6GjI5XK8ePECLVq0UN1j7NGjB/j88r+mSUlJQVBQEBwcHFCvXj2N7d7e3gCAwMBAjW1NmzbVuDUAADVq1FDVszjiEhOxeu9enAkMxOvXr5GWlqa2PTw8PN/9WrVqpbEu93WTkpLyveqPiYkBoHyf3nf16lVs3LgR169fR3R0NGQy9XHb4eHhas3/uogSL+FEVHgqdq7L+4AYP6M5DAxFHEZESmLiRB9s2LAe//xzpejCAJYuXYzwdzE4d2QLAGDtpm3w9mqPBXP/BwCoW8cVrg3b4qetu7Hou+nFOqatjXW+nfDiEhIBAEeP7St0/9TUwud2jotT3gJZvXp1sY6TmKh83YoakpSUlAQABfa+zl2fG8f7zM3N891HKBTm2ykqP4kpKWg5dizeRESgVatWGDNmDCwtLSEUCpGYmIiNGzciKysr332rVaumsS7393v+/HmcP3++wNd9/306duwYBg8eDKlUim7dusHV1RVGRkbg8/nw9/dHQEBAgTHoEkq8hBObFtxAZobygeZWdgboO9yN44hISdja2AKAxhVPfu7fD8TadWvwy5rZcLC3RXJKKiLeRWH4JwNVZZxqOMDayhJPnr0o5EjqCur5bmaqHDB86e8LsLFpBAAQCHio09AKwhKMDTczMwOgTHimpqZFls9NbgVd9ZVVbjyRkZH5bn/37p1aufK2/fhxvImIwIKJE7Fw61a1bdevX8fGjRsL3De/9yo3zo0bN+KbbzTHiedn/vz5EIvFuHPnDurXV2/t8PHxUfUU13V0j5do3dP7MTix95lq+dvlnhAI6E9Rn9y4qWwmrFmzVqHlcnJyMMlnIjp18sL4UQPUtsmy1JsIM8vpSqV1y2YAgEePr6s9vSjmXdFfEt7Xpo2yU9aVK8W7qs8t7+vrW6yryNymX7lcXkRJJRMTE7i6uiI8PBwvX77U2O7n5wcAxeqtXRpBYWEAgEH/NWm/rzQJr6S/XwAICgqCu7u7RtJVKBT4559/ShwDV+jTjmgVYwyrZ11F7ix5dRpaomVH3ZktiOR58uQx4uPjNdaHhIRg6lRlZ5oRw0cUeoxVq1bi1asg/PzTr6p1pibGcKheDb4X/JGTo2z1CPjnOlJSUuFer+wtH+NGDoW5uRmWrtyAt2F3VOvjozOQlZkDhUIBf3//Io/z1VdfQSQSYdq0aar7wu+TyWRqSaNFixbw9PTE/fv38cMPP2iUj4uLQ2ZmpmrZyko5bK4knYE+++wzMMYwY8YMtYQdGxuLJUuWqMpUBJf/mrL9795VWx8YGIgVK1aU+HgeHh7o0KED/vzzT+zYsSPfMo8ePUJ0dHReDC4uePnypdoYasYYFi1aVGRnOV1CTc1EqwLOBOOmX5hqedYqetaurjp69AhWr1mFTp284OJSEyYmxnj9+jX+/vsMMjMz0bNnL0yb9m2B+z958hgrVi7HqlVr4OzsDKTmJfHpUyZi2qwl6NRjEFp7NMOBP47B2NgIX0waW+DxisvKygKH9vyCwSMnolvffvBs0wE1XeqCx+MjNu4dHjy6o5EE81OvXj3s2LEDn332GRo0aICePXvCzc0N2dnZCA0NxZUrV2BjY4Nnz/Jab/bt2wcvLy/MnTsXR48ehZeXFxhjePnyJc6dO4dnz56pxup26dIFhw8fxscff4zevXvDwMAAzs7OGD16dIExTZ8+HX///TdOnDiBJk2aoHfv3khPT8fhw4cRHR2NmTNnon37ijmnxvTpg9X79mHqunXwe/UKderUwcuXL3H69Gl8/PHHOHToUImPeeDAAXh7e2P8+PHYtGkTWrduDXNzc4SFheHhw4f4999/cf36ddjaKm9tTJs2DZMnT0azZs0waNAgiEQiXL16FU+ePEG/fv1w6tSp8q52haDES7QmJ0eBtbOvqZbbd3eCcx1z7gIiherUyQsvXrzA/Qf3cfPmDaSlpcHc3Byenu0wcsQojBw5qsD7rHK5HJN8JqBVy1aY7PO5xvavfMYiOSUDW3fsQ+CDf9G4YX2sXbmg2D2ai9LFqz0Cr53Duk2/wvdCAG7fvQmRSAQb62ro2MELQ4cNKdZxRo0ahSZNmmDt2rXw8/PDuXPnYGRkhOrVq2Pw4MEYOnSoWvmaNWvi3r17WLVqFY4fP44tW7ZAKpXCxcUF3377rSqBAMCECRMQEhKC33//HatWrUJOTg46depUaOIVi8U4f/481q1bhwMHDmDz5s0QCoVo0qQJNmzYgOHDh5fuF1YM1W1scGXrVszesgX//PMPfH19Ua9ePfz000/o2rVrqRKvo6Mj7t69i82bN+Po0aPYv38/5HI5qlWrBnd3d3z99ddo1KiRqryPjw8kEgk2bNiA3bt3w8DAAB06dMDOnTtx9OhRvUm8PFaCR2N4eHjgzp07RRckJB+///oIS75W3gsSCHk4+M9gWFgZcBxV/uKT38LNTXPIRq6q8nSi8iROzevFLhdZAjztPfIxIlKApGTlPVWpgQCu9S3VvjQMGzYMhw4dQkREhM7N2awz3v/sf28CDX319OlTjXvF2kJXvEQrUpNl+HFx3kD9j8fW19mkWxy6ltRI4WytFUhJ4UPBeMjMkCMhLhOW1nl/fy9evIBEIoGNTflccRNSGOpcRbRi+6q7iI/JAAAYGokwdmpTbgMiVYpQyGBlmdfTODo8FXK5Ahs3bsSgQYMQGBiIAQMGqM0GRUhFob8yUuEiQlOwZ9N91fLEWc0hkdKfHtEuS0s5EpP4yM7hISeHITYyHRs2bFDOqvXZZ1i7di3XIZIqgj79SIXb9P0NZGUqhz7YVDNE76E0WQbRPj4PsLGWIyJS+bEXG5WO58+CIJZoTqVISEWipmZSof69G4VTB/Kef/rtCk/w+fSsXcINU1MFDKTKJmfGlFOXEqJtlHhJhWGMYc2svGftujWyQov21TmMiFR1PAC2NnkTTyQlZCE9VVbwDoRUAEq8pML4nXqD25fzZpiZvYYmyyDcMzRgMDXJ62j17m0qSjCqkpAyo8RLKkR2thxr5+RNltGhhxNq1KqYydsJKSlbazl4PGWyzUjPQVK87j/RhlQelHhJhfhj22MEv0wEoJwsY8riNtwGRMh7RCIGS4u8q96o8FQo5MV7PB4hZUWJl5S7lKQs/LQkb7KMQZ+6w8xSymFEhGiytpRDKFBe9WZnKxAbncFxRKSqoMRLyt3WlXeRGKecgN7QSISxU5pyGxAh+eDzlcOLcsVGpiFbVrxH9BFSFpR4SbkKD07Gvi0PVMs+c1rQOEmis8zMFJBIlFe9CgUQHVGyZ/YSUhqUeEm52jD/OmRZ/02WYW+EXp/U4TgiQgrGA2D33vCixLhMZKRncxcQqRIo8ZJy8/B2FM4ceqlanvGDZ4GPjSNEVxgZKmBs9N+kGgAiaXgRqWCUeEm5YIxh9cx/VMv1mlijWVt6vBrRD7Y2cvCgTLZpqdlISaJJNUjFocRLysXFE69x7+o71fKsNe04jIaUt169e0AiFeLy5YACy3z51ReQSIX47bftWoysfEjEDBbmecOJIsNSoFDQVS+pGJR4SZnJZHKsnZs3WYZXHxc4utBkGZXJ/fuBEAgEaN68RYFlbt9WDiFr0aLgMrrM2koBwX/Di2RZCtVjLAkpb5R4SZkd+OkhQoOSAABCER9fL2zNcUSkPL169Qrx8fFwd28AY2PjfMukp6fj8eN/IZFI0KBBQy1HWD4EAgZry7yOVjHv0pCTTZNqkPJHiZeUSXxMBn5Zdlu1PHRiA5iaSziMiJS3e/fuAgBatmxVaJmcnBw0btwYIpFIW6GVOwtzBcQi5VWvXM4Q/Y6GF5HyR4mXlMnmhTdUHVFMzMUY+WVjjiMi5e3uvTsAgFaFJN6bt24CAJo399BKTBWFx1N/elFCTAYyM3I4jIhURkKuAyD66/nDWBz57YlqeeriNhCJabKMyubeXeUV7+EjfyAgwD/fMrdy7+8Wcg9YX5gYK2BkqEBaOh8MQFRYKpzrmHMdFqlEKPGSUmGMYeX0K6qeny5u5ujQ05njqLSjmcnPXIdQbIEpn5dpf8YY7j+4DwC4ePFCkeX1tWPVh2xt5AgO4YGBh5RkGVKSsmBiRrdQSPmgpmZSKheOv8Yt/3DV8tz1HWiyjEroxcsXSEpKQps2bZCVmZPvT+S7GACAoaEh6td3V9v/l19/hlvd2jA1M0IL79G4cj2Qi2qUmFTCYGb2/jN7aXgRKT+UeEmJZWXmYM3sq6rlDj2cUNPNgsOISEXJbWZu0qRZgWUCA+8pyzRuAoEg71bD4cN/4Ntvp2HWzNm4efMOPFs1Rq9hUxAaFlmxQZcTG2sFBPy84UVx0ekcR0QqC2pqJiW2Z9MDhL1JBqAcPjR1aVuOI9Kusjbf6pPcjlXNmhaSeO8rr2Kbt1DvWLVx03qMGT0W48dPAABsXjkDZy9ex887j2Dp4u8rKOLyIxQwWFvLERWt/JiMeZcGc0sp9WMgZUZXvKREYt6lYevKO6rlEZ83ouFDlVjuFW+zZgUn3gf37wNQ71glk8lw7949dO3aTa1s986tce3Ww/IPtIJYmKs/vSgqPJXjiEhlQImXlMiG+deRnqp8eou5pRQjPm/EcUSkoigUCjx4+ABisRju7g0KLJd3xZuXeGNjYyGXy2Fra6tW1s7GEpHRcRUTcAXgAahmkzecKDE+C2kpNI8zKRtKvKTYHt2JwvE9z1TL/1vRFgIh/QlVVs+ePUVqairc3RtALBbnWyY1NRVBQS9hbGyMum51NbZ/2OGOMc11us7QkMHURL2jFT29iJQFfWqSYpHLFVjydd4E+XUaWqKtdw0OIyIV7d49Zaepwu7vPnz4AAqFAs2aNgOfn/dxYm1tDYFAgKioKLXy0bHxsLOxrJiAK5CttRx8njLZZmbIaR5nUiaUeEmxHN35BI/vRgNQzu4zd30HjiMiFW3UqNHIyszBL79sLbCMp2c7ZGXm4MIFP7X1YrEYzZs3x4UPxv6e978Fz1b6N7uZSMRgZZV31RsdQfM4k9KjXs2kSAmxGdjw3XXVcp9hbvT0IVKkKd9Mw6efjUVLj5Zo6+mJHT+tQURUDCaPG8R1aKViaSFHUhIfsmyech7niFRUdzblOiyihyjxkiJtmH8dSfFZAABDYxEmz9Xv+XiJdgwZ8gni4uOwcuVyvIt8h4b1auHMwQ1wrmEPedG76xz+f/M4h0UoPzYTYjNhYW0AAyP9fSgE4QYlXlKoh7cicXTHe/MxL2kDiZT+bEjxTPb5HJN9lOOexan6MWtVYUyMFTA2UiA1TTmPc0RoCmrVs9C7DmOEW3SPlxQot0NVbgdO1/oW6Ny3JrdBEcIxO9u8jlYZ6TnU0YqUGCVeUqDD2x/jSaByHl4eD/h+SyeOIyKEe2IRg5VlXseqqPBUZMv0sfGccIUSL8lXfEwGNs6/oVruN6IuqjtRRxJCAMDKUg6JOG9Gq8i3NKMVKT5KvCRf6+ddQ3KiskOVkYkIPnOoQxUhuXg8oJpt3oxWSYlZSEnK4jAiok8o8RINgdff4c9dT1XL3y73hFhCE8MT8j5DQwYz07wm5nehKVDIaWwvKRolXqJGJpNj4ed5kyHUbWxVZR5wT0hJ2dooIBD89+hAmQIxkfToQFI0SrxEzc61gQh6Eg8A4At4+G5jR44j4g7Nx0uKIhQw2FrnXfXGRqUjMyOnkD2ILuD63KbES1SCXyTgl+W3VcvDJzdCNUcTDiPiDo8nQE4OfYCSopmZKWBooGxiZkw5tpfrD3ZSuOzsbAgE3N0+o8RLACi/AS780h+yLOW3d5tqhhj9tf7NqVteREIpUlKSuQ6D6AEegGp2cvCgTLbpqdlIiM3kNihSqOTkZJiYcHdRQYmXAACO7X6K2wHhquUFP3pBIKi6fx5SsQni4uMRHx+H7OxsuoIhhZKI1cf2Roal0NheHcMYg0wmQ2xsLBISEmBpyd1TsmjuP4LYqHSsnnVVtdz1o1qo29iaw4i4JxSIYGJgg+SkFMTFxYMx+hAtK0FWrOr/CkEGgMrVU54BiE0QQJ6jnD4yPvktrGwNuQ2qPMXmvX94+rTgcjpMIBDAxMQETk5OkEgknMVBiZdg+dTLSE7IG7M7ZXEbjiPSDUKBCMYG+vfsWF1V/WpD1f/TbEdCIal8z3NOeCHGV9OswZgy+a7c2Q39RtblOKpy4u6e939qASqTqtuWSAAAvkeC4Hs0SLU8a017SA3o+xghpdG0kQyDP0pTLS+fFoDYKBpiRNRR4q3C4qLTseQbf9VyKy8HtPWufFchhGjT5+OTYV9N2SM+OVGGZVMCOI6I6BpKvFXYsimXVb0vpYZCzF3XgeOICNF/hgYMc/6XqFo+9+cr+B4JKngHUuVQ4q2iNJqYV7eHkYmYw4gIqTxatchC/955Tc4Lv7yEmHdphexBqhJKvFXQh03MLTtWR/vuTtwFREgl9I1PEuz+e5BCcoIM830u0bA0AoASb5XDGMPiL/3Vmpjnbai600ISUlGMjRnmz0xQLV85G4LD2x9zGBHRFZR4q5g/dz3FhROvVcuz11ATMyEVxaOZDMMG5T2r94fpVxASlMhdQEQnUOKtQkJeJmLF/66oltt1d0K7btTETEhFmjw+CS5O2QCAzAw55nx2Hjk59PjAqowSbxWRnS3HrHHnkZGm/AAwNZdg9pr2HEdFSOUnlQAL5ySoHh/44EYUtv1wl+OoCJco8VYRvy6/g0e3o5QLPGDpNm+aKIMQLannlo3xY1JUyz8tvYW7VyM4jIhwiRJvFRB4/R1+XXFHtTzoU3fUb2rDYUSEVD1jhqegSUPl1KwKOcPM0b5IjKenGFVFlHgrucT4TMwYfQ4KhbKZy8HFBBNnNuc4KkKqHqEAWDwvASYmygduRIal4ftJNMSoKqLEW4kxxjD3swt4F6ps4hIIeVi5s2uVftwfIVyys5Xju+mJquWLJ1/j4C+PuAuIcII+gSuxXesDEXAmWLU8faUnqjly9/BnQgjQqX0mBn+UN8Ro1Yx/8OxBbCF7kMqGEm8lFXj9HdbPu65a9urjgq4DXDmMiBCS62ufJNRxlQEAsmUKTB16BsmJWRxHRbSFEm8llBCbgekjfSGXK+8d2VY3wsxV7TiOihCSSyIGls5PgIFUOZ737etkzB53XtUXg1RulHgrGblcgVnjziMyTNmUJRTysXZ/d4jEAo4jI4S8z7lGDr57b0rJgDPBaqMPSOVFibeSWT/vOq6eC1Utz1zdju7rEqKjunTKxIgheeN7f1x8E1fOhnAYEdEGSryVyOkDz7FzXaBqudcnddC5b00OIyKEFOWLiclo3kR5f5cxYOYYX7x9ncRxVKQiUeKtJB7fi8b3ky+plus0sMTUJW04jIgQUhxCAbB0fjxsrP97hGCiDF8P+gupyTKOIyMVhRJvJRAblY5vBp9BVqZyYL6JmRir93YHn8/jODJCSHFYWiiwYkE8hEJl56qXj+MxfZQvPUyhkqLEq+cyM3IwZcgZVWcqgYCH9b/3pEf9EaJnGrpnY+636s/vXT3zHw4jIhWFEq8ek8sVmDX2HO7fiFStm7uhA5xrm3MXFCGk1Hp3z8DYEXmdrfZteUgzW1VClHj12OpZV3HheN5D7YdPboSOPV24C4gQUmY+nybDu2OGann5tMu44ks9nSsTSrx6as+m+9i76YFquWMvZ3z2bTMOIyKElAc+H/h+VgLc6yo7VynkDNOG/o2HtyKL2JPoC0q8eujcn0FYNSPv3k/dxlaYu74DhxERQsqTVMqwakkc7GyUPZ0z0nMwud8pvHoaz3FkpDxQ4tUzV8+HYuaYc8h9klg1R2Os3d+DnjhESCVjbaXAhh/iYGaqHK2QlJCFib1PICI0pYg9ia6jT2s9cudKOL4ZfAbZMuUQA2MzMbYc7Q2JVMhxZISQilDTOQfrVsSp5nSOCk/DpN4nEB+TUcSeRJdR4tUTD29F4vMBp5GZoWx6khoK8cuJvjCzlHIcGSGkIjWol40fFueN8X3zIhGT+pxEYnwmx5GR0qLEqwee3o+BT99TSE/NBgCIJQL8dKwP7ByMOY6MEKINrVpkYfHcePB4yuT79H4Mxvc4jsQ4uvLVR5R4ddzje9GY0OuE6lmdQhEfm/7ohRq1zDiOjBCiTd6dMjH320RV8n32IBaf9TiBhFhKvvqGEq8Ou3s1Ap91P47EOGWTkkDIw7oDPeDqbslxZIQQLvTrlY550/OS7/OHsfisx3FKvnqGEq+OunYhFD59TqomShcI+Vi9tzvqN7XhODJCCJf69kzHdzPyku+LR3EY2+UY3r2l3s76ghKvDrp08jW++Og0MtKVHalEYj42/dETjTzsOI6MEKIL+vRIx/yZCark++ppPEZ2PIKgJzTOVx9Q4tUxB35+iCmf/K0aMiQ1EOLn433h1sia48gIIbqkd/cMLJqboOrtHBWehtGdj+LetXccR0aKQolXR8jlCqycfgXLplyGQqE8kYxMRNj6Vz841zHnNjhCiE7q7p2BtcviYGCg/KKenJCFCT2P49LJ10XsSbhEiVcHpKdlY8qQv9XmXraxN8RvZwfAvoYJh5ERQnRda48s/LwuFhbmyhmusjLl+GbIGWxdeQcsd4o7olMo8XIs7E0yxnT+E36n36jW1W1shR2+H8HK1pDDyAgh+qKeWza2bY6BQ3XlWH/GgI3f38D0kb5IT8vmODryIUq8HAo4E4whbQ7h6f0Y1bou/Wti0+HekBrQNJCEkOJzrC7H9s2xaNYkS7Xu7JEgjOp0FOHByRxGRj5EiZcDcrkCG7+/gS8+Oo3khP9OEh4wfnozzF7bAXw+j9sACSF6ycJcgc2rYjF4QKpq3fOHsRjS5hAunqD7vrqCEq+WRYalYmLvk9i68o5qndRAiDV7u2OYTyMOIyOEVAZCITD9myTM+V9ej+ek+Cx8M+QMFn/tr5rvnXCHEq+WMMZwct8zfNTsAG76hanWO9cxw16/gWjSuhqH0RFCKpsBfdLx87pY1TN9AeDQr/9iaNs/8PLfOA4jI5R4tSA+JgNTh/6NOZ9dQEqSTLW+73A3/HqqH8ytDDiMjhBSWTVqIMPebdHo3CFvSsmgJ/EY0uYQfll+GzKZnMPoqi5KvBVIoWA4tucpBjQ9gAvH8+6vGBmL8MPurpiyuA09wJ4QUqFMTRiWL4jH7P8lQCJWjvfNlimweeFNfNL6EB7eiuQ4wqqHPvUryPOHsRjj/Se+m3BR7aHVbbwdcfCfwWjuWZ3D6AghVQmPB3zUJx27fomBe928Xs8vH8djRIcjWPG/y0hKoOf7agsl3nKWGJeBFd9ewZDWhxD43tRtBoZCLNjSCUt+9YaBkYjDCAkhVVVN5xxs2xyLqV8kQipRXv0yBuzb8hC93ffhwM8PkZOj4DjKyo8SbzlJT8vG1pV30KPuXuzb/AByed6MMd0+qoVD14agfQ9nDiMkhBBAIACGDUrDgd+i0ap53lVuYlwmlk25jIHNDyLgTDDNelWBeKwEv10PDw/cuXOn6IJVSFZmDv7c9RS/LL+N2Mh0tW01apli/uZOqOlmwVF0hOiO6lfNVP9Psx0JhaQGh9EQQHm1e+myFJt/NUVklHpLXONWdvh8Xkt06OkMHo+nbK9+f0dSajQ9UiklJ2bh0K+PsHfLQ8RFqSdcU3MJJs/1QNePain/YAkhRAfxeECXTplo3zYTh44aY9d+Y6RnCAAAD29F4fMBp9HQwxaT57ZEJ/DAByXc8kBXvCUUEpSIP7b9iz+2PUZ6qvocqFJDIcZ83QQfj6sPgZBa8Ql5H13x6r64eD527jfBib8MkZ2t/hnmxKIxUnEZHyluwphlFHAEUhyUeItBJpPD7+QbHP7tMa5ffKuxXWooxOBP3TFsckNIpNSIQEh+KPHqj+gYPvYdMsHx04aQfZCADVkmBnzREgPH1od7Mxtq1SsFSrwFUCgYHtyMxJlDL+B7JAhx0Zrf8MytpBj1ZWP0HloHIrGAgygJ0R+UePVPbBwfB48Y4/hpQ6Sla37G1Xa3xIDR9dB3uBtsqxtzEKF+osT7HrlcgQc3o3Dp5GucPRKEd6Ep+Zar7W6JUV81RtsuNeiBBoQUEyVe/ZWewYN/r8vYx++E1zx7je08HtDM0x5dBtRC1wGucKxpykGU+qPKJ96YyDTc9AvD5b9D8M+5ECTFZ+VbzsBIiG4DXTF0YgP6ZkdIKVDi1W8mHVeCAbjJc8MfPT6D3xUpZLL8W/rqNrZGu25O8OxaA83b2dMtuA9Uqd8GYwzvQlPw4GYUbl8Ox+2AcLx+nlBgeZGYjxbtqmPg2Hpo0qYaTe9ICKnSeADasBdoMDcRM9N58LtsgL98pbj/SArG8lr/nj+MxfOHsdix9h4kUgFatK+OZp72aO5pj0Yt7WBkIuauEjqg0iZeuVyBsNfJCHoSj+ePYvHvnWj8eycq33u17zMwFKJpW3v0+qQ2PNpXp3u3hBCSDyNDhr4909G3ZzriE/i4ck0Kv8sS3LkvRU5O3kVKVqYc1y68xbULyo6pfD4PdRpaoX5Ta9Rt/N9PI6sq9bAYvU68jDEkxmUiLDgZEcEpCH2ViKAn8Qh6Eo83zxOQlVn0kzd4fMDRxRRtu9RA5z4ucHW3pF56hBBSApYWCgzok44BfdKRlsbD3fsS3Lwjxo3bIoS/k6qVVSiY6or4fXYORnBrZA3nOuZwcjWDUy0z1KhliuouphBXsgsgnUy8jDGkp2UjMTYTsVHpiI1MR1y08t/YqHREhqUiIiQZ4SEpGmNpiyIU8eHoYorm7ezRtksN1G9qTfcfCCGknBgZMXRsl4mO7ZTTUUZGCRD4UIyH/4px/6EAwW/Vm6VzRYWnISo8DVfOhqit5/N5sHcyRjVHE9jaG8GmuiFs7Y1gW90YttWNYGljAAtrA5hZSvTmdiAnGeefcyG4czkCyYlZSE7IQnLSf/8mZCI5MQspibJymahbaiiEvaMxXN0t0bRtNdRvYgPHmqbUE5kQQrSkmp0cvbploFc35W2+tDQenr0QIei1CM9fCvAiiI+Qt1Jk5+R/VatQMIQHpyA8OP9RJrl4PMDMUgoLKyn+t9wT3v1rlXtdygsniffGpTDsXBdYLscSivgws5TA1t4INWqaoW4TK9SqawGn2uYwNZeUy2sQQggpH0ZGDC2aydCimUy1LkcOvH0rxKtgEcIjBAh9y8PbMB7C34kRl1C8z3HGlA96SIzLVHtIjS7iJPGamBX9i+TxAbFYAENjEYxNxTC3ksLK1hA21Qxh9t//LW0MYGQiyveebGRYKiLDUisifEJIKbz/BOrkhEzk8KI5i4WUnMl7/0+MKv/3zsIA8Kiv/HmfTMZDVIwYcYkixMaLEJ8gQmy8EDGxfMQniJCUIkRKmhhpGXk9pS2spdBlJRrHa21tDRcXl3J78eAXiZDLy96knJWTAonQpOiCeqwq1BGgelY2+dWTB8DUMAl8nm5flZRESkYWTAwqfwubPtSTJzSARXXNST5KIiYmBjY2NgCUee/s2bPlEZpKiRKvrqqME3t8qCrUEaB6VjZUz8qF6lk+9KMLGCGEEFJJUOIlhBBCtKhSJN5JkyZxHUKFqwp1BKielQ3Vs3KhepaPSnGPlxBCCNEXleKKlxBCCNEXOpF4w8LC8PXXX6Nt27YwNDQEj8dDcHBwkfstXLgQPB4v3x+pVH0cl4uLS77ljh8/XjGV+sCRI0cwaNAgODs7w8DAAHXr1sWcOXOQklL4bCwAkJmZiRkzZsDe3h4GBgZo27YtLl++rFFOoVBgxYoVcHFxgVQqRZMmTXD06NGKqE6BSlvPO3fuYNKkSahXrx4MDQ3h5OSEkSNH4s2bNxpluX4vgbK9nwX9zd6/f1+tnD6/n/p0bgKAr68vvL29Ua1aNUgkEjg6OuKTTz7BkydPitxXX87P0tZR387NsryXWjs3mQ7w8/Njtra2rFevXqx79+4MAHvz5k2R+719+5Zdv35d7efChQtMKBSyIUOGqJV1dnZmPXr00CgfHx9fQbVS17p1azZkyBC2b98+5u/vz9avX8/MzMxY69atmVwuL3TfESNGMDMzM7Z161Z24cIFNnDgQCaVSllgYKBaublz5zKxWMxWr17NLl26xCZNmsR4PB7766+/KrBm6kpbz2+//ZZ5enqyH3/8kfn7+7P9+/ezevXqMUtLSxYaGqpWluv3krGyvZ8A2Lhx4zTiT0tLUyunz++nPp2bjDF24MABNn36dHb48GHm7+/P9uzZw9zd3ZmJiQkLDg4udF99OT9LW0d9OzfL8l5q69zUicT7/gm8bdu2Yife/OzZs4cBYKdPn1Zb7+zszEaOHFmWMMskOjpaY93u3bsZAHbx4sUC97t//z4DwHbs2KFal52dzdzc3Fi/fv1U66KiophYLGbff/+92v7e3t6sUaNG5VCD4iltPfPbLzg4mPF4PDZ//ny19Vy/l4yVvp6MKU/uefPmFVpG39/P/OjquVmQZ8+eMQBszZo1BZbRt/PzQ8Wpo76dm/kpTj0Z0965qRNNzXx++YWxe/du2NnZoUePHuV2zPKQOwvK+1q2bAkACA8PL3C/kydPQiQSYejQoap1QqEQw4YNg6+vL7KysgAom1dkMhlGjRqltv+oUaPw6NGjfJuFKkJp65nffs7OzrCxsSl0P66Utp7Fpe/vZ3509dwsiJWVFQBAJBIVWEbfzs8PFaeO+nZu5qc49Syu8ngvdSLxlpewsDD4+flh5MiREAo1p6E+deoUDA0NIZFI0KZNG63ed8hPQEAAAKB+/foFlnn8+DFq1qwJQ0NDtfUNGjSATCZDUFCQqpxEIkHt2rU1ygEo1v2NilKceubn6dOniI6Oznc/XXsvgZLV8+eff4ZEIoGhoSG8vb1x5coVte2V7f3Ul3NTLpdDJpPh5cuX8PHxQbVq1TBs2LACy+vj+VnSOuZHH87N0tZTG+dmpUq8e/fuhUKhwNixYzW29evXD5s3b4avry/2798PqVSKgQMHYt++fRxEqrxa+P7779G1a1d4eHgUWC4+Ph4WFhYa6y0tLVXbc/81NzfXeGDEh+W0rbj1/FBOTg4mT54MGxsbjB8/Xm2brr2XQMnqOWrUKPz000+4cOECtm7diri4OHh7e8Pf319VprK9n/pybrZu3RoSiQRubm54+PAhLl26BFtb2wLL6+P5WdI6fkhfzs3S1FNr52axGqS1qCz3eOvVq8eaNWtWrLI5OTnMw8ODOTo6lvh1yiolJYW1aNGC2dvbs7dv3xZatmvXrqxNmzYa68+dO8cAsMuXLzPGGJswYQKrVq2aRrkXL14wAGzPnj3lE3wJlKSeH/Lx8WFCoZD5+voWWZbL95KxstWTMcaSk5OZk5MTa9eunWpdZXs/9eXcfPLkCbtx4wY7cOAAa968OXNwcCj0s0gfz8+S1vFD+nJulrWejFXcuVlprnhv3bqFZ8+e5fuNOj8CgQBDhgxBWFgY3r17V8HR5cnMzET//v3x+vVr+Pr6wtHRsdDylpaW+X6DSkhIUG3P/TchIQHsg/lQPiynLSWt5/vmzJmDrVu3YseOHejevXuR5bl6L4Gy1TOXiYkJ+vTpg9u3b6vWVab3U1/OTUDZfN66dWsMHz4cFy9eRGpqKlauXFlgeX08P0tax/fp07lZlnrmqqhzs9Ik3t27d0MoFGLEiBHF3if3F5ff83wrQnZ2NgYNGoRbt27hzJkzaNSoUZH7NGjQAG/evEF6erra+idPnkAsFqvuMzRo0ABZWVl49eqVRjkAcHd3L6daFK009cy1bNkyrFy5Ehs3bsTo0aOLvZ+230ugbPX8EGNMLfbK8n4C+nFu5sfc3By1a9dW3afNjz6en+8rTh1z6dO5+aGS1PNDFXJului6WwtK09SclZXFLC0tWf/+/Yu9T3Z2NmvRogVzcnIqRZQlJ5fL2ZAhQ5hEImEXLlwo9n6BgYEMANu1a5dqXXZ2NqtXrx7r27eval1uF/eFCxeq7d+lSxfWsGHDslegmEpbT8YY27hxIwPAli1bVqL9tP1eMla2en4oKSmJ1ahRg3Xs2FG1rjK8n4zpx7lZkMjISGZkZMQmTZpUYBl9Oz8/VJw6MqZf52Z+ilvPD1XUuakziffw4cPs8OHDbPLkyQwA++mnn1QDoBlTjhsTCARs0aJFGvsePXqUAWBHjx7N99gHDhxgQ4cOZbt372aXLl1iBw8eZO3bt2cA2MGDByu0Xrly6zVv3jyNwdm598sKquPQoUOZubk527ZtG7tw4QIbNGgQk0gk7O7du2rlZs2axSQSCVu7di3z8/NjkydPZjwej508eVIrdSxLPQ8ePMh4PB7r2bOnxn6PHz9WldOF97Is9Vy9ejWbMGEC279/P/Pz82O7du1iDRs2ZCKRSHU/MJc+v5+59OHcZIyxjz76iC1evJgdP36cXbp0if3yyy+sbt26zMzMjD1//pwxpv/nZ2nrqG/nZmnrqc1zU2cSL4B8fzp16sQYY+zNmzcMAFuwYIHGvv3792eWlpYsKysr32Nfv36dde7cmdna2jKhUMhMTU1Zly5d2NmzZyuwRuqcnZ0LrGNunQqqY3p6Ops2bRqzs7NjEomEtWrVivn5+Wm8Rk5ODluyZAlzcnJiYrGYNWrUiB0+fLjiK/ee0tZz7NixRf4NMKYb72VZ6nny5Enm6enJrKysmFAoZJaWlqxfv37s5s2bGq+hz+9nLn04NxljbOXKlax58+bMzMyMGRgYMDc3NzZp0iS1ljd9Pz9LW0d9OzdLW09tnpv0dCJCCCFEiypN5ypCCCFEH1DiJYQQQrSIEi8hhBCiRZR4CSGEEC2ixEsIIYRoESVeQgghRIso8RJCCCFaRImXkHLC4/HA4/HA5/M15nF9X+fOnVVld+3apbZt3Lhxqm0LFiwo8Bi7d+9WlfPy8lLb5u/vr9qW+2NoaAh7e3t07NgRM2bMQGBgYFmqSggpA0q8hJQjoVAIxhh+++23fLe/fPkSAQEB+T4M/sPj7NixA3K5PN/t27ZtK/IYzs7OWLBgARYsWICpU6eib9++SEtLw5o1a9C8eXOMHDkSqampxasYIaTcUOIlpBzZ2dnBw8MDO3fuRE5Ojsb27du3gzGGvn37Fnqcvn37IiwsDGfPntXY9vTpU1y9ehX9+vUr9BguLi5YuHAhFi5ciOXLl2Pbtm24e/cuAgMD0ahRIxw4cABDhgwpWQUJIWVGiZeQcjZx4kRERkbi9OnTauuzs7Oxe/dueHp6okGDBoUeY+TIkTAwMMC2bds0tuWumzBhQqnia9q0KS5cuAAbGxucPXsWx48fL9VxCCGlQ4mXkHI2fPhwGBkZYfv27WrrT548iaioKEycOLHIY5ibm2PIkCH466+/1B4gnpWVhT179qBTp05wc3MrdYy2trbw8fEBAOzfv7/UxyGElBwlXkLKmYmJCYYNG4azZ88iLCxMtX7btm0wNTXFJ598UqzjTJw4ETk5OWodsI4dO4a4uLhiJe+i5HbKunXrVpmPRQgpPkq8hFSAiRMnQi6XY8eOHQCAkJAQnD9/HiNHjoShoWGxjtG+fXvUr19fdV8YUCZvCwsLDBo0qMwxOjg4AABiYmLKfCxCSPFR4iWkArRu3RqNGjXCjh07oFAosH37digUihJfqU6YMAGvX7/GpUuXEBQUBD8/P4wePRpSqbTMMeYmcx6PV+ZjEUKKjxIvIRVk4sSJCAkJwdmzZ7Fz5060aNECzZo1K9ExxowZA4lEgu3bt6uufMujmRkAIiIiAAA2NjblcjxCSPFQ4iWkgowePRoGBgbw8fFBeHg4Jk2aVOJjWFtbY+DAgTh27Bh27NiBtm3bomHDhuUSn5+fHwDl1TkhRHso8RJSQczNzTF48GCEhYXByMgIw4cPL9VxJk6ciKysLMTExJTb1W50dDR+/fVXAMqhS4QQ7Sl86htCSJksXboUH3/8MWxsbGBiYlKqY3Tu3BknTpyAQqFAjx49yhzTgwcPMGbMGMTGxqJ3797o379/mY9JCCk+SryEVCAnJyc4OTmV6Rg8Hq9UyTE4OBgLFy4EoJy8IzY2Fnfv3sXdu3cBKK90c696CSHaQ4mXkEoqJCQEixYtAgBIpVKYm5ujTp06mD59OkaOHImmTZtyGyAhVRSP5Y4pIIQQQkiFo85VhBBCiBZR4iWEEEK0iBIvIYQQokWUeAkhhBAtosRLCCGEaBElXkIIIUSLKPESQgghWkSJlxBCCNEiSryEEEKIFlHiJYQQQrTo/3PYUuAZWMpEAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9qYH+/fffOHXqFD744AN4eHjgzp07OHToEPz9/XH16lU0b94cgCoZ9ejRA8+ePUP//v0xZMgQMMYQGRmJo0ePYsSIEWjcuDH3ukuXLoWPjw8sLCwwePBg2NjY4N69e1i3bh3++ecfBAUFwcREfTYpqVQKT09PJCcn45133oGJiQkaNWqEAQMG4JdffsGePXswdOjQIq7FXgDApEmTuLLU1FR4enoiJCQEHTp0wJQpU6BUKnHmzBmMGzcODx8+xPfff8/tzxjDqFGjcPToUTg7O2PmzJmQSqXYuXMn7t+/X+bfz1dffYXNmzfD1tYWU6dOhUgkwtGjR3H9+nVIpVKIxeotM2FhYVi4cCF69+6N9957D+bm5oiKisKxY8dw6tQpHD9+HAMHDgQAuLq6wsfHB0uWLIGjoyMmT57MnafwPdFPP/0ULi4u6N27N2xtbZGUlIR//vkHEyZMwOPHj7Fs2bIyv68ahZWBm5tbWXYnpMK8eJzM2hn+wFxEW5iLaAtbPTeQxUSkVdrj/r0HLC9XXqcfHh59GAD24w/bWF6unLHEm9xDnhbB5OkvucfYke8zAGzJojks5lksu3/rNVvm48egqoyy48ePq/0+N23axAAwT09PruzYsWMMAPvqq680fv95eXksPT2d27548SIDwLp168ZSUlLU9t21a1eR53F0dGQAWN++fVlmZqbGazRr1oyJxWKWmJioVp6bm8vMzc2ZjY0Nk8lkXPmkSZMYALZ69Wq1/XNyctiAAQMYj8djISEhXPm+ffsYANa1a1eWk5PDlSclJbHGjRszAMzd3V0jrqJcuXKFAWDOzs4sKSlJ7bW7du3KADBHR0e1Y1JTU1lCQoLGuV6+fMlsbW1ZixYtNJ4rKaanT59qlOXl5TFPT08mFApZdHR0qd5Phbl5s+BRgtDQ0HK/HDXhkmqPMYbls/6FTKrq2WnTwBBjp7fWaUyCZUsglgir7UOwbEm53+OrV68AAPYN7Uvc195eNYFFXNxrWFkWzFAEAD17umPw4MFq+8+cORPOzs64ePEiIgsvQQVAX19f4/xisRjGxsbc9ubNmwEA27dvh5mZmdq+kydPhqurK/bt21dkrOvXr4ehoaFG+aRJkyCVSvHHH3+olR8/fhwpKSkYP348N71jUlISfv/9d3Ts2BHffPON2v4SiQSrV68GYwz79+/nynft2gUAWLFiBSSSggXeLSwssHjx4iJjLU7+uRYuXAgLCwu11165cmWRx5iamsLKykqj3N7eHiNGjMCjR48QFRVVpjicnTWb9cViMWbMmAG5XI4LFy6U6Xw1DTXhkmrv4rEXuHruJbf93VZ3CAT03a+ysTcTIpRm2rPC+wr4gJVVQQeidq26QCZVqC15JhAI0LNnTzx79gwhISFwdHSEu7s77OzssGrVKgQHB2PQoEHo0aMHXF1dIRCoL5cWFBQEkUiEP//8E3/++adGPFKpFAkJCUhKSoKlpSVXLpFI0LZt2yLfw8SJE7F48WLs2bMHM2bM4Mr37NkDQL359ubNm1AoFBr3BPPJZKoZmcLCwriy4OBg8Pl89OzZU2P/sg4VCQ5WLRrv7u6u8VyvXr2Kncf5ypUr8PPzQ1BQEOLj4yGVStWej4mJgYODQ6njiIqKwurVq3HhwgVERUUhJydH43y1GSVQUq3l5sixak7BmM9eAxzQvI3mt2hS8Wzr18fjx4/wMuplifvGxMQBAOrXtwEAmJkqIRKqkqqFhTXi47Jg56h+P7J+/foAwHV6MTExwbVr1+Dj44Njx47hzJkzAAArKyt89tlnWLRoEUQi1WQZSUlJkMvlWLLk7TXtzMxMtQRqY2NT7BcCe3t79O3bF+fOnUNYWBhatmyJ+Ph4nD59Gq6urmjXrh23b1JSEgBVIr158+ZbXz9fWloaLCwsuPdQ1LUorfxrVq9ePY3nBAKB2nvOd/jwYYwYMQISiQT9+/eHs7MzDA0NwefzcenSJQQEBJSpM9Pz58/RuXNnpKSkoFevXnjnnXdgamoKgUCAiIgI7NmzR6vOUTUJJVBSrf269jZiI1XLDonEfHz1fTcdR6SiWOwDxWIfXYdRqbp374FLAZdw0f8CPv74k2L3UygUCLgcpDqmaycAAA+AibGqyT0pOQGpibmwtDGARL/gIye/idjU1JQrs7e3x6+//grGGEJDQ3Hx4kX88MMPWLp0KZRKJdcpxdTUFEqlssxrOpZUm540aRLOnTuHPXv2YNWqVdi3bx/kcrla7bNwzLNmzcKGDRtK9dqmpqZITk6GTCbTSKL516K08l//9evXah2rANXvIykpCXZ26pOLLF68GGKxGLdu3ULL/yw2PW3aNAQEBJQphg0bNiApKQm7du1S62QEAAcOHOBq7rUZtYORauvl8zTsWBvMbX88pz1MzMq3gjwpvQkTJkEgEODo0SMIDX1Y7H67fjuI2LjXaN7UGe49u3LlenqqGujt4KtgAF5HF9TGFAoFLl++DABo3769xjl5PB5atWqFzz//HOfOnQMAHDlyhHu+a9euSElJwcOHxceljeHDh8PExAS///47lEol9uzZA6FQiHHjxqnt17lzZ/D5fAQGBhZzJk0dOnSAUqnk3ndhly5dKlOcHTp0AIAik15gYCDkcrlG+dOnT+Hi4qKRPIuLCQD4fH6xy349ffoUAODl5aXxXFmTcU1FCZRUW2vmXuYG49ezM8T7E1uWcASpSI0bN8a338yDTCbDcK8PEPpYcxzm0ROnMetbHwgEAmzZsBx8vuZHyvWblxEQeBYZ6VJkpqvuuW3duhXPnj1Dnz594OjoCAB48OABIiIiNI5//fo1AMDAwIArmzVrFgDA29sbsbGxGsdkZWVpjG0sDX19fYwaNQoxMTHYuHEj7t69i0GDBsHGxkZtPxsbG4wfPx63bt3CsmXLikxYz549w4sXBfM1f/TRRwBUHX9ycwvGyCYnJ6sNdymN/Brf8uXL1Wrhubm5mD9/fpHHODk5ITw8XO16McawZMkShIaGFnmMpaUlXr4sugk/f67d/yb/M2fOYMeOHaV8JzUbNeGSainwdKTaZPHzN/SijkM6sHixD7Kys+Dntwnt3MdhQJ9uaNWiMfKUIgRdD8aNWyHQ15dg386t8HQvekrFfp798dWcj+DZZxAaN2qMqJgn+Oeff2BhYYEff/yR2+/8+fP4+uuv0b17d7Ro0QI2NjaIjo7G0aNHwefzMXfuXG7fvn37YtWqVZg/fz6aNm2KQYMGoVGjRsjMzERkZCQCAgLQs2dPnD59uszvedKkSdixYweXiP7bfJtv69atCA8Px3fffYfffvsNPXv2RL169RAbG4uwsDDcvHkTBw4cQKNGqsk+xo4di4MHD+LYsWNo3bo1hg0bBplMhr/++gudOnXCs2fPSh1jjx498Pnnn2PLli1o3bo1RowYwY0DNTc3h62trcYxs2bNwvTp09G+fXt4eXlBJBLhypUrCA0NxZAhQ3D8+HGNY/r27Ys//vgDQ4YMgZubG4RCIXr37o3evXvjs88+w65duzBy5Eh4eXnBzs4ODx48wOnTpzFq1CgcPHiw1O+nxirLmBcaB0qqQl6unL3bci835vOjdw5X6phPGgda8uNy4FU2cfR7zMmhAZNI9JihoQFr1bIZm/X5VBYRdkNtTGj+49ef1jMA7JetG9gPG/eytm3cmL5En5mYmLLhw4ezx48fq/3eQ0ND2axZs5ibmxuzsrJiYrGYOTo6Mi8vL3blypUi/1YCAwPZyJEjma2tLROJRMzKyoq1a9eOzZo1i938z1hAR0dHjbGRxWnSpAkDwCwsLFheXl7xf6t5eWzLli2sW7duzMTEhInFYtawYUPm6enJNm7cqDGmNC8vjy1ZsoQ1atSIe38LFixgubm5ZRoHyhhjSqWSbdmyhbVo0YKJxWJma2vLPvvsM5aamlrse921axdr164dMzAwYJaWluz9999n9+7dYz4+PgwA8/f3V9v/9evXbOzYsczGxobx+XwGgPn4+HDPX7lyhfXp04eZmZkxIyMj1qNHD3b48GHm7++vsW+VqOJxoDzGSr94X8eOHXHr1q3KyeSEvLF9zW1sWqTqlCIU8bE/0AvmlppjAytTcvpLNGvWokpfs7oTF5qJKH8u3NJKSBQgMVm1v0jMR9NWluDzSx4eQ0iZFM5PHTu+ddf8ntblQW1ipFp5HZOJn1cW/Cf4cEbbKk+epOJZWCggeDO5gkyq5FbTIaQmowRKqpXNPteQk6UahG5uJcGYabqdcYhUDAEfsLIs6M2ZEJcFuZzWDCU1GyVQUm2EhsTj6G+PuO05q3pAIKQ/0drC3FSptmZo4qtsHUdESPnQpxOpFhhjWDP3MvLvyDdrY4nO7nZvP4jUKDweYF1oir+k+GzIpLRmKKm5KIGSauHisRe4+e+b8Wk84Nu1RQ+JIDWbibES+hJV0y1jqqZcQmoqSqBE56RSBdbNv8Jt93nPCQ7OZroLiFQqa6uCe58pibnIy9WchICQmoASKNG5P7bdR9RT1eTYQhEfM7/rrOOISGUyNFDC0OBNLRRAQizVQknNRAmU6FRqUg5++r5gNYux01vDxFzyliNIbWBdqEduWkoecrNlOoyGEO1QAiU69dPym0hPVS15ZGwmxtjpbXQcEakK+voMxoYFtdDXVAslNRAlUKIzLx6n4I9tD7jtz327qC26TGq3wotuZ6RJkZ1JtVBSs1ACJTqzfv5VbjB9w8Ym8BjkpNuASJWS6DGYGhd0KHodm/mWvQmpfiiBEp24fTkW/icKVlv5Zm2PEhc7JrWPlaUCPKgG/2ZlyLjlzgipCSiBkirHGMOGBVe57Q7d66NFW2sdRkR0RSxmMDUtVAuNyUQZ1rcgRKcogZIqd+Hoc9y59gqAanaaWcu76zgioktWlkrweaqkmZMtR0Za1ddCJ0+eDB6PV+SC3rri5OTELVpNqidaUJtUKblcyS1VBgB9hzZGfXsjHUakHf5DH/Cyo3QdRrGYgQOUrZaU+zzNmjkjMiqyyOfq2Vgj5mmwWplUKsWyVZtw4M8jSE5JRft2rbF2+WJ0cNXsXX3u4r949/3xOPq/3ejo9g6SU1QdyF7HZMLYVFzrm/Q9PDwQEBBANe4ajBIoqVKH94ThxZNUAIBQyMf0BW9fs6+64mVHAYZOug6jWLysiAo7l6mpKT6f+QUE0ldcmVJgACMjzS8+831Wwu+HHRg+bBDsG9ji9z8Ood/g0Xh4yx+29etx+2VmZmH6F99i/OjheG9gX8gVSqSm8qFkPOTlKpCekgdTi6obD7xy5UrMmzcPdnbVZ/7lCxcu6DoEUgJKoKTK5GTL8MPS69y215SWVfohSbRjZmqGxYt9SlxQmzGG7bv2YfKHo7Djx/UAgGFDBqDvoFH4/Y+/MferT7l953+3Arm5edi42hcAIBQwWJgruUW3X8dmwsRcr8pqoba2trC1ta2S1yotZ2dnXYdASkD3QEmV+W3zXSTEqZaw0jcQ4sOZ7XQcEalICYlJyM7OQSc3V66s85ufo6KiubJ/r1zDtl9/w5b138PCwpwrt7BQQMBXNWdK85RITcot9rXy7w+mp6fj66+/hpOTE0QiEXx9fbl9Hj16hMmTJ6Nhw4bQ09NDvXr1MG7cODx+/FjjfG+7B3r9+nWMGDEC9evXh1gsRsOGDTFt2jTExsYWGVtycjIWLlyI1q1bw8DAAKampmjXrh3mzZuHrKwsREREgMfjISAgAADA4/G4h4eHh8Z7/K+8vDysWrUKbdu2hYGBAUxMTNCrVy/873//09g3/7UmT56MiIgIjBkzBlZWVpBIJOjYsSNOnDhR7DUuypEjR/Dhhx+iWbNmMDQ0hJGREdzc3LB582YolZrru+Zf1+fPn2PLli1o27Yt9PX11d5ncnIy5s+fj5YtW0JfXx+mpqbo27cvzp49q3G+tLQ0rF27Fp6enrC3t4dYLIa1tTWGDh2Ka9eulem9VASqgZIqkZKYg1/XFdwv++jr9pDo059fTZAnzcP+/fsQ++w6DA0kaOvSFD3c34FAqF4DtbayhL6+BMF37nNlt0LuAQAcHOwBADk5OZg28xsMHzYIw4cNUjtewAcszJVISFKdNz4uC6YWEvD5RddCpVIpPD09kZycjHfeeQcmJiZo1KgRAOD06dMYPnw4ZDIZhgwZgiZNmiA6Ohp///03Tp48CX9/f3To0KHE975r1y54e3tDT08PQ4cORcOGDREeHo4dO3bg+PHjuHbtGhwcHLj9X7x4gT59+iAyMhJubm749NNPoVQq8eTJE2zcuBHTp0+HmZkZfHx8sHv3bkRGRsLHx4c7vqROQ1KpFAMGDEBAQABatGiBGTNmIDs7G3/99RdGjx6NO3fuYMWKFRrHRUZGonPnzmjcuDEmTJiA5ORkHDx4EMOGDcP58+fRp0+fEq8FAMybNw98Ph9dunSBnZ0d0tLScPHiRXz55Ze4efMmfvvttyKP+/LLLxEYGIj33nsPgwYNgkAg4OLy8PBAREQEevXqhYEDByIrKwsnTpzAwIED8fPPP8Pb25s7T1hYGBYuXIjevXvjvffeg7m5OaKionDs2DGcOnUKx9evx8DuVdcpkT7BSJX4ZfUtboyfmaUEQ8c313FEpLRevXqFj6ZMUitr5NgQO35aD/ee3bgyHo8H78njsWXbTqSlZ6CBbT3s++NvGBsbYdyo9wEAi5etRXJKKras/77I17IwVyA5lQ+FggeZVFULtbDWL3LfuLg4uLi4ICAgAIaGhlx5SkoKxo4dCwMDA/z7779wcXHhnnv48CG6dOmCTz75BMHBwUWdlvPkyRNMmzYNTk5OCAgIULs/evHiRfTv3x9ffvklDh8+zJV/+OGHiIyMxIoVKzB//ny18yUmJsLIyAgSiQS+vr64dOkSIiMj1WrNJVm/fj0CAgLw7rvv4tixYxAKVR/hPj4+6Ny5M1auXInBgwej+3+SyKVLl+Dr66uWrMeNG4eBAwdi7dq1pU6gJ0+e1GhaViqV+Oijj7B3717MnDkTXbp00TguODgYISEh3BecfJMmTUJkZCQOHDiAMWPGcOWpqanw8PDAF198gaFDh6JePdX985YtWyI2NhZWVlZq54mOjkbnzp0xa+PGKk2g1IRLKl1MRDoO/FRQK5n5XWcIhPSnVxNMnDgJp0+dRVRkDLKiAnE/8ACmTRqOiKhoDPaaiLv3Q9X2X7l0PubO+gw3b9/Brt8OwqVlM5w9dgB2DWxx/WYINv/4KzauXgIrSwssXbkR9k3doGfuhM69B+HKtZvg8wFLi4KmwPi4TCiVxfdSXb9+vVryBIC9e/ciNTUVS5YsUUueANCqVSt4e3sjJCQEoaHqsf/XTz/9BJlMBj8/P43ORZ6enhg6dCiOHz+OjIwMAMDt27dx9epVuLq64ttvv9U4X37TaXns3LkTPB4PGzZs4JInANjY2GDx4sUAgB07dmgc5+joiEWLFqmVDRgwAA4ODrhx40apX7+o+7J8Ph9ffvklAODMmTNFHvfNN99oJM+7d+8iICAAXl5easkTAMzMzLBkyRLk5ubi0KFDXLmpqalG8gQAe3t7jBgxAo8iIhD16pXG85WFaqCk0m3xvQ6ZVPWhaOdojN7vOuo4IlJaixZ9x/0szoxF65ZNsG39fBgYW2Lj1u1YunIDDu0v+MDW09PDCt95WOE7T+08UqkU3jPm4N13PDFu9Afw+2EHlq7cgMXzZqF7145YuW4L3hs+AY/vBMLa2hrJyXzIFTzIZQzJCTmwqmegEZtEIkHbtm01yoOCVMOk7t69W2Tt7smTJwBUzYH/TbBFnScgIAA3b97UeD4+Ph4KhQJPnjyBm5sbdw9uwIAB4PMr/gtiRkYGnj59Cjs7O7Ro0ULjeU9PTwBASEiIxnOurq5cs2lhDRs25N5naSQlJWHt2rX4559/8Pz5c2RlqS8CEBMTU+RxnTtrLlGY/7ppaWlF/p4SEhIAqH5PhV25cgV+fn4ICgpCfHw8pFL1ccMx8fFwqF+/1O+pPCiBkkoVdicBJw4UdNqYtaJbrR/fVxdMmzIeG7duR+CV6yXvDGDpqo2IiXuFU0d+BwCs37wNnh494bPgawBA86bOcG7dDT/+sgdLFs2BpaUCr+NVH0+Jr7JgbiWBQKCelGxsbIr8W0pKSgIAbN++/a0xZWa+fe7d/POsXbu2VOdJTU0FgEobCpOWplozt7jewvnl+XEUZmZmVuQxQqGwyM4/RUlNTUWnTp3w4sULdO7cGRMnToSFhQWEQiFSU1Ph5+eHvLy8Io+tX0RCy7++586dw7lz54p93cK/p8OHD2PEiBGQSCTo378/nJ2dYWhoCD6fj0uXLiEgIAB5sqpblIASKKlUmxYHIX+ceEtXK7TrXDXfDEnlsrG2BABkZeeUuG/I3QdYt2kbfty0EnYNbJGenoHYuNcYO+oDbh+HhnawsrRA6CNV7dDcVInkZAaZnAe5XFULta6v3lRb3BcxU1NTAKoaaFE11NLKP09aWhpMTExK3D8/SRVXCyuv/HheFdNEGRcXp7ZfRduxYwdevHgBHx8fjRpjUFAQ/Pz8ij22qN9Vfpx+fn744osvShXD4sWLIRaLcevWLbRs2VLtuWnTpnE9m6sK3YgileaafzQunymYrWfW8m5v2ZvUJEE3VB1wGjs5vHU/uVwO7xlz4NGrG6ZMVL/PJc1Tb3rLLVR74fFUE83nS3yVDYW8dDWlrl27AgACAwNLtX9FnSd//zNnzpSqVpffpKpQKErYU8XY2BjOzs6IiYlBeHi4xvP+/v4AUKrexdp4+vQpAMDLy0vjOW0Slza/p6dPn8LFxUUjeSqVSly+fLnMMZQXJVBSKZRK9Qnju/W1R6Nm5m85glQ3oaEPkZycrFEe+TIOX85V9eYcN/oDjecLW7V+K54+j8DPW9ZwZSYmxrBrUB9nzl+CXC4HAARcDkJGRiZcWjTj9jM1UUIsUjVfKBQMSfHZpYr7o48+4jqhFNVBRqlU4tKlSyWeZ+bMmRCJRJg1axZ337QwqVSq9uHv5uaG7t27486dO1i9erXG/klJScjNLRjbammpqsVHRZV+SsgpU6aAMYa5c+eqJd7ExEQsW7aM26cy5A+x+e+1CwkJwcqVK8t8vo4dO6JXr174+++/sXPnziL3uX//PuLj49ViCA8PVxuDyxjDkiVLSuwUVhmoCZdUirOHnuLhbdUfPp/Pw0wfza7tpHo7dOgvrF23Bu7uHnByagQzvWw8i4jGyXNXkJubh3ff8cTsL6YVe/zDsMdYsXYL1q1YDMc340DzzfnyU8z61gfuA7zQpWN77P/fYRgZGeKzqQXDZfJrobGv3twLfZ0NCxsDCEvowW1paYm//voLH3zwAbp27Yq+ffuiVatW4PP5iIqKQlBQkEYyK0qLFi2wc+dOTJkyBa1atcLAgQPRrFkzyGQyREVFITBQ1eHp0aNH3DG///47PDw8sGDBAhw6dAgeHh5gjCE8PBxnz57Fo0ePuETUt29f/Pnnnxg+fDgGDRoEfX19ODo6YsKECcXGNGfOHJw6dQpHjx5Fu3btMGjQIGRnZ+PPP/9EfHw8vvnmG/Ts2fOt70tbEydOxNq1a/HVV1/B398fTZs2RXh4OE6cOIHhw4fj4MGDZT7n/v374enpiY8//hibN29Gly5dYGZmhujoaNy7dw8PHjxAUFAQbGxsAACzZs3C9OnT0b59e3h5eUEkEuHKlSsIDQ3FkCFDcPz48Yp+229FCZRUOJlMgU3fFcwKMmBEE9jYGr7lCFIdubt74MmTJ7hz9w6uX7+GrKxMmJkao2eXdhg/djQ+HDuy2PuQCoUCn3w2B507uuJT70kaz8+c/hHSMzLwy87fEXL3Adq2bon1q3xQz0Z9WTsTEyWSkhnypDwolaqm3NIsPtC3b1/cu3cP69atw5kzZxAYGAixWIwGDRrA09OzyGbIonz44Ydo164d1q9fD39/f5w9exaGhoZo0KABRowYgdGjR6vt36hRIwQHB2PNmjU4cuQItm7dColEAicnJ8yePZtLBADwySefIDIyEn/88QfWrFkDuVwOd3f3tyZQsViMc+fOYcOGDdi/fz+2bNkCoVCIdu3aYdOmTRg7dmyp3pc2GjRogMDAQMybNw+XL1/GmTNn0KJFC/z444/o16+fVgnU3t4et2/fxpYtW3Do0CHs27cPCoUC9evXh4uLCz7//HO0aVOwEMG0adOgp6eHTZs2Yc+ePdDX10evXr2wa9cuHDp0qMoTKI+VYSmAjh074tatW5UZD6kFDmy7j++/UN0TEYn5OHh1JIxN9XQcVdkkp79Es2aaQwXy1ZXVWAoraS7cypKewUdMnOq7Pp8PNG1tCZGoYl97zJgxOHjwIGJjY6vdnLikDArnp45vX6giLCxM415qWVENlFSorEwpfvq+4L7T6Kmta1zyLI2KTk6keMbGSkiSlcjN43O1UNuGxhX6Gk+ePIGenh6srWlhd1J61ImIVKg9G+8gKV41tMHASISx0zXXgSSkLHgArC0LerUmJ+RAJi1dz9WS+Pn5wcvLCyEhIRg2bJja7D6ElIT+WkiFSXydjV0bC5r5Pp7THmK9qmnmI7WbkZES+hIlcnL5YAxIeJWNBg7lr4Vu2rQJUqkUU6ZMwfr16ysgUlKXUAIlFebnlTeRnamaBcTCWh/vjWlWwhGElJ61lRJR0apGs5RE1fR+5f2C9uLFi4oIjdRR1IRLKkTUszT875eH3Pbnvl00pl4jpDwMDZQw0Fc15TIGJMRllXAEIZWLPuFIhdj83TXI38wUY9/IBD36N9RxRKQ2srYquPeZmpSLvFy5DqMhdR0lUFJuD26/xqk/C6YWm72SJownlcNAn8HI4E0tFEB8LNVCie5QAiXlwhjDhgUFyyG5tLdGa7d6OoyI1HZWhWqhaSl5yM2uutU3CCmMEigpl6vnX+K6fzS3PXslTRhPKpe+hMHYqPCi26WbI5eQikYJlGjtvxPGd+/XEA7OZroLiNQZhVdqSU/NQ04W1UJJ1aMESrR28sATPLqbCEA1YfwXS2jCeFI1JHoMJsaFaqF0L5ToACVQopW8XDn8fAomjH93ZBNY2hjoMCJS11hZKsCDairvjHQpsjKlJRxBSMWiBEq0sv/He4iLygAAiPUE8P7WTccRkbpGT8xgYlKwFkZ8TBbKsDYGIeVGCZSUWWpyLn5ZdZvbHvdpGxgai3UYEamrCtdCszJlyMqge6Gk6lACJWW2Y81tpKfmAQCMTMQY5d1KxxGRukosYjAzLXwvNJNqoaTKUAIlZRITkY7ft97ltj9d2AkiMU0YX9u9O2gAeFadEHDldrH7fPbVfAhNGmLH7v1VGBlgaakEj6dKmtlZcmSm071QUjUogZIy2eJ7HTKp6hu/TQND9P+gsY4jIlXhzp0QCAQCuLUrfgHiG7dUK/G4tW9bVWEBAERCBnNT9R65VAslVYESKCm1sJAEnDjwmNuevYKm7KsLnj17huTkZLRq0RhGRkX3tM7OzsGD0MfQ09NDa5fmVRyhqhbKf1MLzcmWc7cYCKlMlEBJqW1YeBX5X+ybtLJAhx4NdBsQqRLBwapm2y4dir/XffvOPcjlcrRt3RIikaiqQuMIBQzmZlQLJVWLEigplSvnonD1/Etu+9u1PXUYDalKt4NvAQC6uLUudp/rN1XNtx2ruPm2MEsLJfh8VdLMy1UgLZlqoaRy0YLapERKJcP6+QVT9nV2t4NTUzPdBUSqVPBtVQ304JFz8L+sSqaMLwFQ0HzP3f/soLsEKhAwWJgrkZik6tQWH5cJE3M98Pl0m4FUDkqgpEQn9j/G43sFU/bNWl63J4xvb/yTrkMotZCMT8t1PGMMd+7eAQCcu3S9xP2rugPRf1mYK5CSyodCwYM0T4m05FyYW+nrNCZSe1ETLnkrjSn7RjWBVT2asq+ueBL+BGlpaejatStY4k3uIU+LgDz9JeTpL5EQeR8AYGCgD5cWzdSO/2n7HjRp0x2G1k3QufcgBF4tOQmXh4APWJoXXqklC0ol3QsllYMSKHmrfT/cw6uXmQBUU/ZN/bajjiMiVSm/+bZdu/bF73P3gWqfNq0gEBSMCf7foWOY9a0v5s2eiVuXT6FbZzcM9pqIqJcxlRqzuZkCQoEqacqkSqQk5lTq65G6i5pwSbGSE3Lw88pb3Pb4z9rAwKjqe1hWN+VtFq1J8jsQtXctPoGGvEmg/+1AtHHrdkwaPxKfTB4HAPBbtwxnzl/Ctl9/wwrfeZUUMcDnA5YWCrxOUH28JbzKgrmlBHwB1RdIxaK/KFKsrUuvc7O6mJjpYZR38b0wSe2UXwNt3774BHrnTQItfP9TKpUi+M599PfsrbZvf8/eCLp+C5XNzEwJkVBVC5XLGJITcyv9NUndQwmUFCn8QRL+3P6Q2/5yaRcIRfTnUpcolUrcvXcXYrEYLi7FjwHNr4EW7oGbmJQMhUIBGxsrtX1tbKzx+nVC5QRcCJ+nqoVy8bzKgkKhfMsRhJQdfSISDYwxrPn2Mtf5wqGJKXoNdNRxVKSqPXoUhszMTLi4tIJYXPRqO5mZWQh/9gJGRoZo3tRZ4/n/zlTFGKuy2avMTJUQid7UQuUMyfF0L5RULEqgREPg6UhcPVcwacL89T1pyr46KDg4GMDb73/efRAKpVKJ9u1ag88v+DixsrSAQCDQqG0mJCRq1EorC48HWBWuhb7OglxOtVBScSiBEjUymQJr5l7mtrv0sUMTF0sdRkR05cMPJyAvV45t234pdp8eXTtBnv4S/qf+UisXi8Xo4NoG5/0D1crP+weiW5eq68ltaqqE+E0tVKEAEl9lV9lrk9qPeuESNf/75QFePEkFAAiEPMxe2V23AZEaa9ZMb0ya+hU6ubmie9eO+OXX3xH76jWmTfmwymLgAbC2UiAmTvVRlxSfDQtrfYj1aAk+Un6UQAknNTkXPyy7wW17feQCc0uaxYVoZ5TXUCQlp2DF2s2IexWP1i7NcfyvPXB0sK/SOIyNldBPUSInlw/GVItu2zcyrdIYSO1ECZRwflp+k5uA29BYhElfuuo2IFLjfeo9CZ96T9JpDDwANtYKRL5U3bFKTc6DhY0MBoY0ppmUD90DJQCAF49T8MdP97ntmd91pmYuUmsY6DMYGxV0IHoVnUnLnZFyowRKwBjDytmBXA9Feydj9B3WWMdREVKxbKwU4EGVNLMzZcigRbdJOVECJbhw9DmunI3itudt6EXDVkitIxarL7r9KiaTJpon5UIJtI7LyZZh1ZyCYSs9+jdE8zZVM06PkKpmZamA4M2i29I8JVISaHIFoj1KoHXc9tW3EReVAQAQifk0bIXUagIBYGWpvtwZTa5AtEUJtA6LDE/FzvXB3Pbkr1xhbKqnw4gIqXzmZopCkyswJL7K0nFEpKaiBFpH5XcckklV376tbQ0x4uPiJwyvi6iXZu3E46k6FOVLis9BXq5chxGRqlZR/7cpgdZR/sdfIPB0JLe9cFMv8PnUcSgfjyeAXE4fqrWVsbESBvqqL4+MgVs0ntQNMplMbfF3bVECrYNyc+RYNadgjtKufezQqoONDiOqfkRCCTIy0nUdBqlE9awLhrVkpEtpWEsdkp6eDmNj43KfhxJoHbRjzW3ERLzpOCTiY+6anjqOqPqRiI2RlJyM5OQkyGQyas6thSQSBlPTgg5EcdEZNKylFmOMQSqVIjExESkpKbCwsCj3OWkqvzomMjwVv64r6Dg0aZYrTMyo49B/CQUiGOtbIz0tA0lJyWBMUfJBtZwgL5H7WSnIRW34/q1U8pCQxAdTqm5fpGZGU0e6miyx4G8UYWEaTwsEAhgbG8PBwQF6euX/PVMCrUMYY/Cd4Q9pnioZ2NgaYCR1HCqWUCCCkX75v6XWFg2utOZ+zrSdDiY0010wFejmGUOs32IGANDTF+Dkgw9h27D8zXtEB1xcCn6uglajmv8VkpTakd8e4calGG77u60e1HGI1HkfDMmCcyMZACAvR4H1867qOCJSU1ACrSOS4rOx9puCGYf6DmuM5m1pxiFChAJg9uep3PapP8Nx5VxU8QcQ8gYl0DpizdzL3FJlBkYifLm0i44jIqT66NBOinf6ZnPbS2f6IzeHhjGRt6MEWgdcPhuJEweecNtzV3WHvgGthUhIYV9OT+OWPIt+kYGfV97ScUSkuqMEWstlZUqxdOYlbrtt53roOcBRdwERUk1ZWigxwzuN29657jaehSXrMCJS3VECreXWz7/KjfkUivhYtLm3jiMipPoaOigbbVqpbnXI5QxLPvOnsaGkWJRAa7Fr/tE4+PMDbvvTBZ1gbqmvw4gIqd74fGDerFQIBKqkeftKHI7s1RxPSAhACbTWysqU4rtpF7jtZq0tMWR8Mx1GREjN4NxIjvGjCubGXT0nEPGxNFcu0UQJtJZSa7oV8rFkmwd4PBrzSUhpTPkwA3a2ql64mekyLJ0ZQNM5Eg2UQGshjabbRR1hVc9QhxERUrNIJAwL5qRw2/4nXuDkH0/ecgSpiyiB1jLpqXlY+Ml5brtZG0sMGddchxERUjO5uUrhNbSg6Xb5VwFIoMW3SSGUQGsRxhiWzrjErW0oFPGx7GdParolREszpqajfj1VU256ihTff0FNuaQAJdBa5Pi+xzj1Zzi3/fWKbrCwpl63hGjLQJ9h4ZxUbvv8kec49b/w4g8gdQol0Foi+kU6vv8ygNvu5G6H/u876zAiQmqHTh3y8P57BU23S2f6I+5lhg4jItUFJdBaQC5X4tvJZ5GVoVpRwtBEjMV+NGECIRXl82lpsK2vasrNSJNh/kfnoFAoSziK1HaUQGuBbctv4k7QK277+589oW9Ic90SUlEMDRl856eAz1fd/7z5byx2b7yj26CIzlECreGunIvCthU3ue3hk1uidUcbHUZESO3UrrUUk8cVNN1u/i4IoSHxOoyI6Bol0BrsVXQmvp10llt43cHZFFPnuek2KEJqsSkTMuDSQgpANVfu3AlnkZ0l03FURFcogdZQMpkCs8efRkpiLgBALBFg3e/vQCCgXykhlUUoBJYsSIa+RHX/M+JJKg1tqcPo07aG2rQoSO2+p+8PHjC3oiErhFS2hnYKfD2zYNmzo789wt+7acL5uogSaA109u+nah0YvD5qiU697XQXECF1zOCB2RjUP5vb/v7zSwi7k6DDiIguUAKtYR7dTcSCKQVT9TVpZYGp8zrqMCJC6h4eD5j7ZSqcG6nuf0qlSswacwoZaXk6joxUJUqgNUhSfDZmep1ETrZqPJqRiRird/cHn09T9RFS1fT1GVb4JMNAX3U/9OXzdCz65ALdD61DKIHWEFKpArPGnEZclKobPV/Aw4b9A2BipqfjyAipuxwbyrGg8FR/R5/jp+U3iz+A1CqUQGsAxhhWfPUvbl+O5crmre2BRs3NdRgVIQQA+nnkYNQHBau2/LD0Bs789VSHEZGqQgm0Bti5PgR/7njIbXtNaYk+QxrrMCJCSGFfTE9Dxw653Pb8KefwMJgmWajtKIFWc8f3PcaGBVe5bdeu9TGNOg0RUq0IhcDyxcmwt1P1T8jLVWDm8JNIiKP1Q2szSqDV2NXzUVjkfYHbtm9kguU7+tL6noRUQ6YmDOu+T4KRoapTUXxsFmZ6nURWplTHkZHKQgm0mgoLScCXo05BLlf9ZzS10MOWvwZBrCfQcWSEkOI4Ocix/LtkCN5MOv/gVjxmjTkNqVSh48hIZaAEWg29eJyCaUOPIztTNcZMT1+AH4+8ByMTsY4jI4SUpEvHPMz+IpXbvnI2Cgs/Pg+lkoa31DaUQKuZyKepmDLgCJJeq2Y5EQh58PvjXdjYGuk4MkJIaQ0fko2PJ6Zz2/8cDMfqOYE0RrSWoQRajUS/SMeUAUcQH6vqeMDn87Di135wdrHQcWSEkLL6ZGIGPhhSMLzl9633sG3FLR1GRCoaJdBqIjYqA1MGHMarl6r/cDwesPTnPujQ3VbHkRFCtMHjAXM+T4Nn7xyubOuS62rr95KajRJoNRAZnopJff9GTIRqliEeD/huqzu6eNjrODJCSHkIBIDv/GR0KjRGdIvvdfz4/Q0dRkUqCiVQHXt0NxETPP9GbGRB8ly4qTd6vuOo48gIIRVBLAbWLlNPoj8svYEfll7XYVSkIlAC1aHgq3GY3O9vrsMQjwcs2twb7oOcdBsYIaRCSSQMa79PQme3giT64/c3sX7+VepYVINRAtWRSydfwPvdo8hIUw2yFgh4WLmzH3oPdNJtYISQSiHRA9YsS0LXTgVJdOf6YCyYch4yGY0TrYkogVYxxhh2bwzBzOEnkZujmvZLJOZj08F34dazgY6jI4RUJokesHppEnp2K+hYdGzfY3z2/glkZdCMRTUNJdAqJJUq8N20i1j77RXkt9oYGImw7dhgtGhnpdvgCCFVQk8MrFqSjKGDCubJvXruJT7qfxjxsZlvOZJUN5RAq0hSfDamDjqKv3eHcWX1Gxph9/n34eBsprvACCFVTigA5n+dqjbZwsPgBIzs+j+EBMXpMDJSFpRAq8CNgGh4dfoDN/8tWM/TtWt9/HpqGMwt9XUYGSFEV3g8wHtSBubNSgH/zdy5ia+yMbnfYfxv+wPqXFQDUAKtRAqFEttW3MTHA44iIS6bKx/+UUus2dufJoYnhOD9wdnwW50EUxNVRyK5TIklMy7BZ7o/crJlOo6OvA0l0EoS9zID0wYfxxbf69wk0iIRHz5b3fHpgk60JBkhhNOpQx52/ZSAps4FHYkO7QrFqC7/Q2gILcxdXVECrWCMMRzaFYr32x9A0IWXXHkDB2PsufABeg6gCRIIIZoa1Fdg++ZEDOxX0Fr1/HEKxvb8CzvW3oZCodRhdKQolEArUNzLDEwbchzfTbuIzPSCb5IDRzbBzjPDYG1rqMPoCCHVnUTC4DMvBQvnpEAiUSVMuUyJjQuDMLnfYTwNTdZxhKQwSqAVIC9Xjl9W3cKQNvtw5WwUV25kIsbavf0xe0V3CIR0qQkhJePxgCHvZuO3X+LRqkUeVx58JQ5enf6A33fXuDHkRLfoU70cGGO4ePwFhrnuh99315CTXfBH7THYCQcuj4BrN1pNhRBSdg3tFPjZLxEfT0iHQKDqRyGXKfHLqlt4v8MBXDr5gnrq6hiPleE30LFjR9y6RevZAap5bLf4XsONSzFq5eZWEizY2BuuXevrKDJCKkeDK6bcz5m208GEZroLpo55+lyI1RvNcD9UT628Y68GmL2yO9p2ps8bAKrqe74q+HJBCbSM7t96jS2+19WaagFAKOJj/Iw2GDutDTXXklqJEqhuKZXAsX8MsPUXE2RmqQ+Be2e4Mz5b1BlNW1vqKLpqghJo9aNUMlw5G4U9fnfUetbm69G/Ib5e3g0m5hIdREdI1aAEWj0kp/Cx8zdjHD5hCIVCfTicx3tO8P7WDa5d6+itI0qg1UdWhhQnDz7Bb5vv4vmjFI3nXbvVx1fLusLO0UQH0RFStSiBVi9R0QJs+9UEF/810HjOrWcDjPu0DTyHNYZYXIcmbKEEqluMMYQEvcLfu0Jx+q+nyMnSnAmkbed6+GJJFzg2Mav6AAnREUqg1VPYYxF27zNCwBXNRGpZzwDDJ7XEiI9bwb5RHfiiTwm06jHG8OhOIs7+/RRnDj1F5NM0jX0EQh56D3TElNkdUN/eSAdREqJblECrtxeRQvx2wAhnLupDodDsh+HatT7eHdUUA0Y0gXX9WjomnRJo1cjLleP25VhcPhuFi8df4OUzzaQJAGaWEgwd3xzDJ7eEobG4iqMkpPqgBFozxCfwceyUIY4c10diskjjeT6fhw49bNH7XSe4D3KCc0vz2jO1KCXQypGXK8fD4ASEXI3DrcAY3LgUU+xgZKGQj469G2Dsp63Rsp117fnjIqQcKIHWLHIFcPWaBIeP6+PGbQkUyqJHB9g5GaOrZ0N07m2Hjr3tanYLGyXQ8lMolHj5LA2P7yfh7vVXuBMUh4fBCZDLip9LUijko3VHGwwd3xxd+tjTSimE/Acl0JorNY0P/38lOHtBgjsPJGCs+EpBw8YmaO1WDy4drOHSwQYu7a1hYqZX7P7VCiXQ0svNkSMmIh3RL9Lx/FEywh8mI/xBEp6FJSMvV1Hi8abmemjf3Rb9hjVG++62lDQJeQtKoLVDUjIfQTckuBwkxo3besjOEZZ4TMPGJmje1gqNmpnDqZnZm4c5zCyq2dC9Kk6gJV85HWCMISdbjsRX2Uh4lYXEuDf/vsrG65hMvHyRjpiIdMTHZpXpvCbmemje1gpd+9ijU68GsHUwrqR3QAgh1ZOlhRKDB2Zj8MBsyGTAwzAxgu/p4dZtAR6ESSCVaVYkXj5Px8vn6RrlZpYS2DmZoL6dEeo3NEI9OyPUszNEfXsjWNoYwMJGHyZmerX2NphOaqBBF14i+Eos0lPzkJ6Sh7SUXKSl5P+ch/SUXMik5Vu6R99ACFsHY7i0t0Yn9wZo1d4GptXt2xIhNQjVQGs/mQwIfybCo3ARwh4JERomQES0pMhevaUlFPFhYa2Ppq0t8cuJoRUYbRHqQg308tlI7N54p9zn4fFUK55Y1TdAw8amcGlvjSYuFnBqakbJkhBCykgkAlxayODSQgYMUZVJpcCzFyJERAkR+VKAiAgeIqOEiHmlV2Rt9b/kMiXiY7NgblVD7qOWgU4SqGkpprzj8QA9iRAGRiKYmOnB3EoC6waGsKpnAMt6BrCyMYC5laTIeWdfx2bhdRmbdwkhb9eg0M/pialQ8KTF7ktqF1sz1aNb24IypRJIShEhIUmExGQxEpJEiE8UISGBj4RkEdIzREjLFCM3TzWURsSvfX8vZWrCtbKygpOTU4W9eMST1Bq/ynqePAN6QrqXWhXoWlet4q63gV429IS178NQlzJy8mCsX/tqaIUZWjWAnoHuWwYTEhJgbW1d6v2trKxw+vTpIp8rUwIlmqpbz+TajK511aLrXXXoWledirzWtO4WIYQQogVKoIQQQogWKIGW09SpU3UdQp1B17pq0fWuOnStq05FXmu6B0oIIYRogWqghBBCiBYogRbhr7/+gpeXFxwdHaGvr4/mzZtj/vz5yMjIeOtxt27dwtSpU9GiRQsYGBjAwcEB48ePx4sXL6oo8ppH22v9XytXrgSPx0PPnj0rKdKar7zXOiwsDCNHjoSVlRV3vJ+fXyVHXTOV51pHRUVh0qRJcHBwgIGBAZo1a4ZFixYhK4vGthflzJkz8PT0RP369aGnpwd7e3uMGjUKoaGhJR6bm5uLuXPnwtbWFvr6+ujWrRv+/fffUr82NeEWoWvXrnBwcMCwYcNgb2+PkJAQ+Pr6okWLFrh69Sr4/KK/d8yZMwdBQUEYP348WrVqhZiYGCxbtgzx8fG4c+cOGjZsWMXvpPrT9loX9vz5c7Rt2xaGhoZo2rQpLl++XAWR1zzluda3bt2Cp6cnPDw8MGXKFJiamiI8PByZmZn4+uuvq/Bd1AzaXuusrCy0b98eMpkMvr6+cHBwwM2bN+Hj44OhQ4fi4MGDVfxOqr8DBw4gODgYXbp0gbW1NaKiorBq1Sq8fPkS9+/fh6OjY7HHjh8/HidPnsTatWvRuHFj/PDDDzh16hSCgoLg6upa8oszoiE+Pl6jbM+ePQwAu3DhQpmOi4iIYDwejy1evLhCY6wttL3Whb3zzjts6tSpzN3dnfXo0aOiQ6w1tL3WCoWCubi4sPfff78yw6tVtL3WZ86cYQDYmTNn1Mq//fZbJhAIWFZWVoXHWhs9evSIAWDr1q0rdp87d+4wAGznzp1cmUwmY82aNWNDhgwp1etQE24RipqlolOnTgCAmJiYMh3n6OgIa2vrtx5Xl2l7rfPt378fwcHBWLlyZYXHVttoe60vXbqE0NBQqmmWgbbXWipVzfBkYmKiVm5mZgalUglGDYalYmlpCQAQiUTF7nPs2DGIRCKMHj2aKxMKhRgzZgzOnDmDvLy8El+HEmgpBQQEAABatmxZpuPCwsIQHx9f5uPqstJe65SUFMyaNQtr1qyBhYVFVYRW65TmWuc3iefm5qJr164QiUSwsbHBF198gZycnCqJszYozbXu168fmjZtim+//RahoaHIzMzExYsX4efnh+nTp8PQ0LCqwq1xFAoFpFIpwsPDMW3aNNSvXx9jxowpdv+HDx+iUaNGMDAwUCtv1aoVpFIpnj59WvKLlreqXBdER0cza2tr1q9fvzIdJ5PJWO/evZm1tTVLTk6upOhql7Jc648//pj17NmTKZVKxhijJtwyKu21njZtGgPAzM3N2eLFi5m/vz9bu3Yt09fXp2bdUirL3/Xr169Zz549GQDu8cknnzCFQlEFkdZcbm5u3PVq0qQJCw0Nfev+/fv3Z126dNEoP3fuHAPA/v333xJfs1ouqF2dZGZmYtiwYRAKhdi1a1eZjp05cyauXr2KkydPwtzcvJIirD3Kcq0DAwOxd+9eBAcH19rFeitTWa61Uqla8OHDDz/E0qVLAQAeHh5QKBSYN28eQkND4eLiUukx11Rluda5ubkYPXo04uPj8dtvv8HBwQE3btzA0qVLIRQK8dNPP1VR1DXPb7/9hvT0dDx//hzr1q1D//79cfny5WIXQGGMFfnZwcrSTK5lsq8TcnJyWJ8+fZi5uTm7d+9emY6dN28e4/F4bO/evZUUXe1S1mvdsmVLNn36dJaSksI9evTowbp27cpSUlJYbm5uFURdM5X1Ws+bN48BYMeOHVMrDw4OZgDYvn37KivUGq+s13rr1q0MAHv69Kla+S+//MIAsDt37lRWqLVKSkoKMzU1ZdOmTSt2n1GjRrFmzZpplB88eJABYA8ePCjxdegeaDFkMhm8vLxw48YN/PPPP2jTpk2pj12+fDlWrVoFPz8/TJgwoRKjrB20udZhYWHYtm0bzM3NuceVK1dw7do1mJub0zf1YmhzrVu1agUAGt/W2Ztv6qUZalQXaXOt79+/D3Nzczg7O6uVd+7cGYDq756UzMzMDE2aNHnrfcxWrVrhxYsXyM7OVisPDQ2FWCxGkyZNSn6h8mT52kqhULCRI0cyPT09dv78+TId6+fnxwCw5cuXV1J0tYu219rf31/j0a5dO9a6dWvm7+/PXr58WYlR10zaXuvExESmp6fHZsyYoVa+YsUKBoCFh4dXdKg1nrbX2sfHp8hr+vPPP5f6vhxh7NWrV8zQ0JBNnTq12H1CQkIYALZ7926uTCaTsRYtWrDBgweX6nUogRZh+vTpDABbuHAhCwoKUnvkfzBHREQwgUDAlixZwh134MABxuPx2MCBAzWOe/jwoa7eTrWm7bUuCnUiervyXGtfX18mEAjY/Pnz2blz59jKlSuZRCJhkyZN0sE7qf60vdYvXrxgxsbGrGnTpmz37t3s4sWLbM2aNczY2Ji5ublRR6IivP/++2zp0qXsyJEj7OLFi2zbtm2sefPmzNTUlD1+/JgxVvzf9ejRo5mZmRnbvn07O3/+PPPy8mJ6enrs9u3bpXptSqBFcHR0VOsBV/jh4+PDGFP9oRfeZoyxSZMmFXucu7u7Tt5LdafttS4KJdC3K8+1ViqVbP369czZ2ZmJRCLm4ODAFi9ezKRSadW/kRqgPNf64cOHbOTIkcze3p5JJBLWtGlTNnv2bOrJX4xVq1axDh06MFNTU6avr8+aNWvGpk6dyl68eMHtU9y1zs7OZrNmzWL16tVjenp6rHPnzszf37/Ur01T+RFCCCFaoLv/hBBCiBYogRJCCCFaoARKCCGEaIESKCGEEKIFSqCEEEKIFiiBEkIIIVqgBEoIIYRogRIoIRWEx+OBx+OBz+fj2bNnxe7Xp08fbt/du3erPTd58mTuOR8fn2LPsWfPHm4/Dw8PtecuXbrEPZf/MDAwgK2tLXr37o25c+ciJCSkPG+VEAJKoIRUKKFQCMYYfv311yKfDw8PR0BAAITCt68kKBQKsXPnTigUiiKf3759e4nncHR0hI+PD3x8fPDVV19h8ODByMrKwrp169ChQweMHz8emZmZpXtjhBANlEAJqUD16tVDx44dsWvXLsjlco3nd+zYAcYYBg8e/NbzDB48GNHR0Th9+rTGc2FhYbhy5QqGDBny1nM4OTnB19cXvr6+WLFiBbZv347bt28jJCQEbdq0wf79+zFy5MiyvUFCCIcSKCEVzNvbG69evcKJEyfUymUyGfbs2YPu3btzS4QVZ/z48dDX18f27ds1nssv++STT7SKz9XVFefPn4e1tTVOnz6NI0eOaHUeQuo6SqCEVLCxY8fC0NAQO3bsUCs/duwYXr9+DW9v7xLPYWZmhpEjR+LkyZOIi4vjyvPy8rB37164u7ujWbNmWsdoY2ODadOmAQD27dun9XkIqcsogRJSwYyNjTFmzBicPn0a0dHRXPn27dthYmKCUaNGleo83t7ekMvlah2NDh8+jKSkpFIl4ZLkdz66ceNGuc9FSF1ECZSQSuDt7Q2FQoGdO3cCACIjI3Hu3DmMHz8eBgYGpTpHz5490bJlS+6+KaBKwubm5vDy8ip3jHZ2dgCAhISEcp+LkLqIEighlaBLly5o06YNdu7cCaVSiR07dkCpVJa55vjJJ5/g+fPnuHjxIp4+fQp/f39MmDABEomk3DHmJ2Uej1fucxFSF1ECJaSSeHt7IzIyEqdPn8auXbvg5uaG9u3bl+kcEydOhJ6eHnbs2MHVRCui+RYAYmNjAQDW1tYVcj5C6hpKoIRUkgkTJkBfXx/Tpk1DTEwMpk6dWuZzWFlZ4YMPPsDhw4exc+dOdOvWDa1bt66Q+Pz9/QGoasuEkLKjBEpIJTEzM8OIESMQHR0NQ0NDjB07VqvzeHt7Iy8vDwkJCRVW+4yPj8fPP/8MQDVkhhBSdm+fyoQQUi7ff/89hg8fDmtraxgbG2t1jj59+uDo0aNQKpUYMGBAuWO6e/cuJk6ciMTERAwaNAhDhw4t9zkJqYsogRJSiRwcHODg4FCuc/B4PK2SXEREBHx9fQGoJnFITEzE7du3cfv2bQCqmmd+LZQQUnaUQAmppSIjI7FkyRIAgEQigZmZGZo2bYo5c+Zg/PjxcHV11W2AhNRwPJbfl50QQgghpUadiAghhBAtUAIlhBBCtEAJlBBCCNECJVBCCCFEC5RACSGEEC1QAiWEEEK0QAmUEEII0QIlUEIIIUQLlEAJIYQQLVACJYQQQrTwf89G5xy2w742AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f109c352a474a7e95d7c06092a156c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0caebff0bfe446d296d3bfc6c65375ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18a8597aa2f74420a05914384eb9dbec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ce5965099ad46e5afd01ade150aecb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49b2938df44f41409ed8de075dc10878",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e33178e4206341af9fed257f51149707",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07dfe3e800924de8bbce7b5496fa5a84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ffbd5cff307472faba6000e9cc9d243",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "161e3c6cca0e485ba511d59d8b89bfca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fb5e49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/Real_ERP_data/.DS_Store b/bf/Real_ERP_data/.DS_Store
new file mode 100644
index 0000000..5008ddf
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diff --git a/bf/Real_ERP_data/np_no_data.pt b/bf/Real_ERP_data/np_no_data.pt
new file mode 100644
index 0000000..b8511da
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diff --git a/bf/Real_ERP_data/np_yes_data.pt b/bf/Real_ERP_data/np_yes_data.pt
new file mode 100644
index 0000000..859894c
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diff --git a/bf/bf_model_misspecification_Jul13.ipynb b/bf/bf_model_misspecification_Jul13.ipynb
new file mode 100644
index 0000000..ecf1aaa
--- /dev/null
+++ b/bf/bf_model_misspecification_Jul13.ipynb
@@ -0,0 +1,5231 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/bayesflow/trainers.py:26: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(20, 160, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 30, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()\n",
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param1 = params[0]\n",
+    "    param2 = params[1]\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        \n",
+    "        net_sim = net.copy()\n",
+    "    \n",
+    "        # your code here that updates \"mu\" or mean time of drives using parameter theta\n",
+    "        weights_ampa = {'L2_basket': 0.09, 'L2_pyramidal': 0.02, \n",
+    "                    'L5_basket': 0.2, 'L5_pyramidal': 8e-3}\n",
+    "        synaptic_delays = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                       'L5_basket': 1.0, 'L5_pyramidal': 1.0}\n",
+    "        \n",
+    "\n",
+    "        net_sim.add_evoked_drive(name='evprox1', mu=param1, sigma=param2, numspikes=1,\n",
+    "                         weights_ampa=weights_ampa, location='proximal',\n",
+    "                         synaptic_delays=synaptic_delays, event_seed=RNG.integers(low=0, high=1e4))\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        res[i] = dpl[0].data['agg']\n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=2)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=2, num_coupling_layers=2)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "857a040c-e1a6-4748-ad04-42df69027520",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('data1000/x.pt')\n",
+    "theta = torch.load('data1000/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1519bdafdb8540caa3653b78d496e030",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5104.106445</td>\n",
+       "      <td>0.535374</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Loss   W.Decay\n",
+       "0  5104.106445  0.535374"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(90 , 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[90 ,  15]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77f6909fd4f64d5a91626d6f2c49e55f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[90 ,  15]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abc28186637f4d4385abf19b6b9e6c74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[90 ,  15]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d56fd0398ab437fbc90b27833a98391",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*1\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[90 ,  15]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fd692ea76d84d0abb713e61541f8d30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "74db2cff-0fb6-41cd-ac89-9e3426def7cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,))\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,))\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,))\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,))\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,))\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "94c5e02d-01ff-4007-904a-5941c63e1c03",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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P4EM33G89hubZTDtUJno3u9FBzFlUFrzP/Ll/4Pp7ja7W/XOrnQuBkfwdpOei94U+T/apAmEcdy6Qi47kfTaxUGH0Ltposnb2T/VBQMblHDUGFcMrMJiQCKO40CA5115SE6nAOFSSYfglymDS/bbDGC12XDdZVIdZYKgqKfn9tbc8ge/cnxUYdEyvKqm5LKA0SNC71O/x6NQpoUwcBv/dNKbSrTZf6PH3cdfhRqn+5bbXg91xEDC0AqMdSQ+hMC7DMGJx3lxAMIyStJ0mcN5kIsHQCiO0GFfuZf4NYyAjF5B6gRvT/cQ9BmLMZXpzIcgH6LlkVFJ9br9TV+IybrUKC80TGCWN3kcvGcHOg/0QGIOlbuwUQyswwihSfPGLBAHfnefh1sf24fs/39Vz/4QNo6RKqkzVsZgLjB5VUrLNrk+dN0QWdmWrkdFrapBuVCbdYraLFXWDrNG7v53r1OjdSS4pwKwCqgSdGb3XLB7pC8OI4WwY84J2KOMw2lFxEJ88Nv+4Kz7+I7zpn2/puX/jnTIMoZKyH8MNolwl1cviMoz02OYlZbNl0S12e6dz6TAxiF5S+gLH52g/Fj9qoTTD0K7702178cTeKfWYIobhdeZWe/SSEezqQy2dYc//NrQCg7vVhlHxy0xxB3NlwxipEsMoZ8MgimxTe8RakB7PutvLBBxGlZTJSypO1ZKmWIlO/fyz15u7RZzbliiWZ76RYRjs/34YvjtNuKgH7v3KP9yM5/35d7Rjsu1zlE4NojGMXgVkHMdzkmJmtjDcAkPs/IoZRqf1MHqdGDRJyzKMIpVU1vDIfutBBg6hvDB6SeUZ8eUOtrubnVOjd/osv3XfTrzgz7+D3X1Qg/SKDMNgn/vhWttxHEbcu5dUUDY1CGMYrTDGgZIqZhvK2F8GGUMrMNosNUjiJVXOCFY2wvlQj1GsdL2yXlLipbHcR57BtTcbxvBNXpOXVJizqMvAve4QzaFKiu5j9+EG2lFcev7MJvJsGP3oX6d2mzKeRgoLzVFTFqcGSf4etWQEALDzUG8C3MVhzBO4SiqKi1/mThnGvi6S8oVRjJ2HZtL/k+/KGr1tAVu3PLoXr/rbH2K6ZZ/YPamkhnDyKgJDExSm+xGH96iSmsvAvUFKx28L3AP6IzDEdcqyfxSrF4tsGPRdoZcUU0kBwK6eBYaLw5gXtKNIpMVoR1FhHEYnBXcAYG8XAuP6u7bj+X/+HRxutDtWSdlSddy3/SDueupArgDryeg9hJPX5DIpIteNbrX0t7uBmku3WpnuJnkwgygweJdafUiw1bGXlMhwYD+e/2Ka49RGoZdUFMP3gNUTSXof2hB2g06z8g4ihlZgcN11VMLo3THDmOpcYOw+1MBMK8Jkoy36U9bobVN70OTKi0jtheIO4+RVXSZVVWOeSqpb4TiXbrW6TabTax6aafW/n9xepqmD+pH9uVOVlNgA5Byv2DDyGEYJo3fge5gYqQIADvfAqKRg7LqJecfQCgyeCymMi20Y8gUst2rsnezcuEXXaIVR1wxDn9u8TRsWslutrsIxGr3Tr7qO9J5LGwY975z7sSGOY7zo/30Xn7klP3Nzp9DtAbxH/Uj53o3Ru+h4kw0jjqXRml6nZgmjt+95GKkmS+VMq/v7lYKu/DPdc7iBmx/e0/U1+42hFRi0o4yEO2U5gdFO7QwfufHnxgdHOWb2d8EwuOGVJvNMju2Bw5a/XzKMPIHRC8Po+tR5g0kllWdnGKYCSvp9dJKZoBXG2H24iZ19iBcw9Yn6w9+bA9MtfPrmbT2x3DKMQTm+hBMD7w/1/9v378SF//dGHJhqSZVUiYp7ge8JN/my77MJ3bhn/8ant+L1n/hxT9ftJ4ZWYLS1nXdZlVQYxfju/bvwkRsfxJP7sn7utUoyJN3YMCQbkAKsbA59m1so3VaeSqqXne8g6Mg7hclLKi/7bq+qAKGSmhMbRvK3G5VU2Xeh8z6pAoN//sY9O/B/rrsHj+2ZMp3aUfvlI72Tv3mH899oPJ7cN42ZVoQ9kw1m9C7wkopjBJ6HauAj8D3M9JCxtptSvxSLowcmzheGVmDwxRkokxokzVYbxhlhw0HfdWPDaIu+RGIhK8qGKfpn0eOWETy9rGPD6OJn9JIK7YuO/K67e+WR3v/248e68qArC53FdLIbbefYcXqBsviGatAc1cTgO/W7nzqAL9z6ZAcXSP50VA8D+S6qJhsG7dKnW2Fpo3cYxfBTrcNIxe9NJSUEXfnnc/SSUQDoSSD3E30RGFdffTVWr16Ns846qx/NlQLpTsvuqkKxE42sPvtRFIsFviuGITxcYsWoVmaCWFVSpWwYC0slpSxgwkvKPg86VXnooPHdfbiJP/ny3fj63fnlgHuBzmI6MSo354BhtKJImTO0CPP5+R9bn8D7rr+34/bLx2HI/233qgqM5C+5ps+0ovJG71QlBSTZG3pRDXVqqwGSgEEAeOxIYhhvectbcMMNN/SjqVLgaTJohxBG+QExIVtQIo2dEPjk6Sb3fciEmFLasYTroc2tlprJMy4utNQgSrZaTdDmekl1OU76ebOpT7Z5yZWBEJolz7njif34+HcfVr7bP9XEAW3u8y7p1S1n2pK5y37EHZUAzjvy0zdvwwl/cL3KvNjCa0+lI/+nMSV2MNMKpYYix+h93R1P4b7tBzWB0T3DEIKxgyYoJ93jeya7vm4/0ReB8fznPx/Lly/vR1OlYFuM8xa/UCy80r6gL8K8rW7SoEsWowqvMnYMmdZa+17oWu396WXNH3aVVBjFuP7O7bj5kb0AirykVLTDqFRqC73NfpTqtF5Le5gd2TDaWaF5YKqF3YcbeOu//DQjCL76s6fx0W/9XPlu43v/G+e+95vKd3yOtKNIGchGKjz5uxSX8FpU27f/9t6v3Ju2n1UxxbF98TWxEKGSakqVVCNnM/d7196B2x7fLxIV1qt+TzYM6lJnm4Dk2EFhGHNW03vLli3YsmULAGDXrt7Sh/PJ09IW+UpgPkdhGJbYBr6wdycwGMPgNL7Ebstm+Cujkup00VcW3CEUGLp++rc/c5v43IlK6l9//Bj+8XuP4Md/9Iv519OGvqwjQzfQn0dHAkNTy9391AG88m9/iJXjdew+3MDntj6Ozc8/WblWmebzGEbDwDDKxEWp7duPpfeQO/Pyhdd2pslLitswykZ6AzLv1EglEAKyG3TjrUf9e3xABMacGb03b96MrVu3YuvWrVi1alVPbfHJqEzUvIlHBukwtqovlLKnXQgM7onF13fbpPytf78VX79re3pOel2LW22+SqqzfnLhM4QaKY1haL8Z7ke8qNr3zxxslIrc1RfxftR2tl5Lv58uvKTofqk+PCUwpKqOhGSHbm9/56EZTDXbGsNQhcyMYBjqe9jJRqTMkbw5/jw7sWEoAkPEYRQ/Sz9dJetVvyd2GVve8TyQ1uOZA/11le4WQ+klxSdnoyQr4EJCqI60t7NXhkEvXzuMlRfRJjD++95ncNvj+7T+qceIKnu5KqnO+qoKjOGTGJG2ONl+I9i8U6J0h13E0PTfZ1UlpTOMLryk2hbPQV1gcLZtwgUf+BZe/bGbVIYRRcpuf9pg9KYKmHEc41M3PYpPfP+R/I6XcQphx8jnaRd4/FsSKtTXRisU7ZWxL5JKaqTSo9GbeXeVBY3rXFUKLcJQCgy+q1Amao7qR6YGkVHYLe0hKAtpDwxDV0mZJmUcx2m8hno92wKYN7E7FxhcfVDu3HYY4dkfuBFfvv2pjq41G+BdziywOUZvfZhkBHD+9fShpw3Ao7sn8fw//05fA+X0/nfCMISXlNh8qB33VXkhBKYJNC8e3Hk4kwrEyDDYnKLDwyjGe75yLz7wtfty+92JWixWBIedyZjmCBmsp5nRu4xKSrjVVntzq6U+dbK0cKeeQUBfBMbrX/96POc5z8EDDzyAdevW4ZOf/GQ/mrWCq2d07wwbeCCUMHprKwHtHGuB31XKA6k+SnZu1ZwykHqqkkIvqVwbhqEvUYz3fuVePGbwruhGJTXdCrHrUAN/+MW7yp3QJdphhM/e8niBAwNXSZlVeByx+Gs+tmgXnzV6J4vkQzsP4/G9U33VL5cRgDZQ2V6bF6CvSQzptZO9Bk/vrwfu8QknbBgG1tfP2jMm/X8uwzDMkWlh9I6sY2SCYBg9uNVe8v++i99JbW0mr7s/+tJdRld+zjAGwUGlL0bvz372s/1opjRULyn5AK+/82lsOmE5zjpmifUc1UtKfQC0QxutBV3p9kl4tcJkQo5UArTCtpEdCHuH9tLadph5E9u0SO481MA/3/Qojl8xhjc/d5HyW7OLeuBe+tLkpVnvBz75w0fxwa/fDwB4/QXHGY/Ry7Jy5KUG0fcAfMyrFmcJwK6SIiFeRq1RFj0F7mnzWh8LXSVF4xHFMXyov9nS8odaHAZ1j2+w6PedB8ulAi/zrpn0/2XdaoVHlMHo3bIwDP4cFLfaLu1Xj+yexCO7JzP3ACQbj8/85HG84NRVuGzDUcpvTcVGCwQaS5xrDKVKirMK7m76Z1+5F//yo23mc9hLJFNIawIjnTyj1aArhqEH7o3UAqVdc38g+gWYKuuVUUllvzPplgllVG/fuu8Z/HTb3kw/Zhu0W89TFZgivcVvuW615mOLVVLmedIqIcw7RT8C92zMKdAYRl7adl5ZTgncC812j7ZBzfnw7sOl+s1bs81Hk+NCjE6N3tk4DBsL4mPie/1RSYn+6BuXnHnYUuyqs2c7K4uhFBg2G4bpM4G7p9o8j4TAqAVdpcLm8R1RlAgeW59odyp2uektWb2kOmQYMgI3+1srzL5MOv7ymz/HP35PGivLlIGdbLTxk0d6y6x5OFWFTIzYya+qklJ/My04Uv9t/r5YJaV+1hmGbZfaDWxxOGWgpwbJMgztWjkLlSIwWJ8SY3b2eJMjxcM7zQLjhD+4Hn/2X/eIz7oXlgkmt/Mojq3vKVcJ0xjKSG9p9LYJnNDAMOo9Gr1539TPyV+TyqlsnNlcYSgFhi0OA7AvrCaXV1scxkg16OrhtNmOM4xiITDyGIZe+CerYqG+5jAMQ1/LMowwjvHE3il8675ntP5FHXtT/c5nbsOvbvlxV5l+CVRvgCJcTTD52BPMDCO7OwXsjgY6bDaM1iyopPT+5+no9QVGT5OjP3svo5Ky3/9BK8OIzAzDwPoe3pWoYJaMVjPHc02Aoj4qMGIrP8d2gRrFcqGn+1Tdas2bRgJ/D32mkmr0gWHYtAimO9HjzOYbQykwumIYmkE6aUdjGOm5Y7WgJ7faMEpVUmkOfZMbJgk2XX2QmUyWl185xtBVU44fAl/g4jjGX3zjAbz1mq34yI0y6ldPGV9GYGzdti+9ZvcTm0p+VnKUtfyWsl5l2eOFSspiUC7yFNOvIVRSYfGzseGd//kzfPt+KaTjOMZ/bH0is2GwzcP//YU78bufvV35ThcYeluB7lZLDMtwDZtKysYwVIGR/H1kV5ZhmAMrixmGydMttrRHP1bSAArd6D3VDBWPJdPz54KIpuJI1UdTS/uTh7ufOmBk3Pp8yktd0+oxNqzfGEqBwXcFzbDcC8bpus2QzG0YRQvkdDNLTblbbRzHqOeopGwvt00l1anRmwSGaffb0ozepAb6z61P4rbH9+HHj+xBFHfuTcVZzc0P78Hffeeh4pM0kHdO3hqc5yWVtyDpw2TLEKyjSCXVTeT3dXc8rRTGuW/7Ibzr83carm3u3ON7pzIpr2mO0FTJekmZ2+b3N5Eyu/1MYEyxud4KYzGeVSbU29omBAAOpsKfzyOTSoffot0mkR7LhEviFmw7PhabDnrOxA6mmkm/akEyIKY1w2b0BsoHbn7kxp/jfdffm/FwtKmkTLfea7qifmMoBYbCMNrlGIbRS8oSuDdaC3LdWHcfbuDc934TP3pot/J9pF2jjEoq4yVlUbF0mq2WjHOmgD++kISRTOXejiJ85MYH8eEb7s8wjDIufXRPzXaE13/ix/iLbzyQO44mHG5QRbQcARnx/pvHSzleLDYq6NDiTMfq77TwcBVkpwjjWDlvumUu/ZmnY89slsQmRBp3ObJeUlmGRZscnt6flxluMy+paiCXj3aYfSa0sPLfTAKD36LtfmXQG/suzjd6E8OI4zgdr2RcKCV7vaIyEA5FJSUC9zqrujfdCjHTijIaBpsWwWjDaEeiRo+zYXSJPBuGVWAwI5fNT1y41ValW+3PntiPJ/epO7mdBxtotqOM/72wS6S0VQgMQ59E/IXYFcpdsKKjL6GSMq3lxIAKvaTiGHsOpwIjjNFsh4mXVxQb1Qxl0AojYWB9en9nQW2kksrPPKzuMjnMkd5m9mZjdbbzCdKGUfxsbNAFsi163LZItKPYOvf1BZuQsWFYDMkAsPcwExgsQSN/fyrMit4yuNWSYOUp/im7LffY4qyhkGFofc07nvoXRqqgmkxZLC3EJjuGopLSGEZZw3ezHaHZzgqMjJ1K3Fu2jVYYYyz1tnQMo0vk2TBsRu9QW8wBg8BocxtG8v//uPZ2/O23VNUK7Qb1mASu6opiCBuGiWG0NEFh2zXTv51GepO/uOll0G0Ye9JcQ60wEsIiimPNt778ZG20IxyzLC38sreztMwkMPK8g/K8pIxGb/obm48tujdbpDfNvU4FhmSiTLXaocAIDQKDGAc1q++EszactD+GHT4PIuMG8BYroEQLLgAlywKNJxdYesbYGmMnpVRSQrjz+7E/uxhyoQ/jOFdgGGN32NB5wobRucBohVFGcCfeXTH+6Et34d6nD+amDGmFEUbSjKqdpIyfLQylwFDiMLRB1NN9iHOUbLXUjtnoPVKVbrWTjRCHGmoQ03STdKFmgdGOUoZRy3OrVQUFX+i4IJMqqRwVTQ7DMKVF5/2ZbISYTI8NoxitVFiEUWxN7FhkfGuGEY5ZmgiMTqOgbQZbwu2P78MXb5PV3MqlBkn/Zr7Pv5btGr0G7vF4HYJVYFgWxHYYZ9Sx1J+2RSVF97F/qolv3/+MkXnRs51sSjWUyjAkW+CLPn/vqDkusGj+Up+4/UOJyrYJACH11e9Mz+7Ltz+FPYcb4hpRFCubu8MZhpFtgwtz6jttAMuqpBrEMLTjoyhxKvjMTx7Hd3++01pCOEpZ/qhgGP3zxusWQykw8hmGxUsq/bod5RRQ4jYMlqpcN3CT0Ux/ISWLSRbdeoWMZCYbhqoH5902Bdbl+fqbdiYNbRfMwb/blbKL5YtqaEWxYGAJw8guAgAwVbDDarUjrByvAwAe76C0JM/JZNs5vubvf4Qfp7UvgKzwylNJ6RSjbC4pW6S3CNzLSQxpgonhWlVSOYw5s1kSuaSSzxmBkV7iC7c9hbdes1UsnJFhweYCjNswiD0DQEWxYcjjTSoxEqq02NZYHQKFYVju16Q+i5E1eh+YauF/fu4OPLlvWvQvimNx3cUjFbFByrNh8LWZ7o3e58lmG9f8aFshs2yFEZqhyYYh7SkzTRkTos8zvoEFgJsf2TPvudyGUmAo0j+zyypmGHpZT0KzHSHwPdQCH1GcFoIJo8yOgrvncch2k4k8UsLobXoRWoadfd7uwqTvz7VhsAWO0jesnqgLVkHZRm22gqlG1kCrZOcNI7EIlK1F/MTeKVzwf78lPpfdtNucBJS+WXZwepR92Ws0NYbREothiId2Hirss8mG1jHD0OJkkn6orFWft1xVFMdSSPH7F0n5WNucYTTa8tkqXlKGucLncZuNESAX6+T44vs1RnrHBnUh+0LaMKRKatmimrhHYhimd8TE+Ospw7j54T1493/do2RCMKEZJgwjy/S4x5Z08dVvnfpFNow//tLd+J+fuyP3mrONoRQYeiARR8uysNJhnGFkUoOEEWqBL3WfUeLJotsqaDHWv9eN3hXfQ8X3OnKrpfP1fuu7SQ7TLefFYUwxdQMxjKOWjKQqqSQPlq4j5y/1YYPAONzku1BpJypbKUxvs6zPeZZhZI8RbrWaUqps6Va9K80wUuq/0zi98/N34sV/9X3j+HDIZ85sGFZmbGcYNqM3zUM975GebJA2Ifz2xWLPNhVcYEw32+IY7iXVCiPsOtRIVV3Z/uoqKS4wOjF6Z3JJ6c/fYKyOmA1j6VhN/E6MochLqi1UUsnx9HyLvOOa7YRhmFSDlANvmkWd2+J9RvMSnc0x5qziXj+RZ8OwG72zL6fJ6F2rSIHRjmLjA5dZL3XKn75sqZ3E9z3UKr6ZYWhpHBQhaNqtdaiSmhZxGNnfnjkkk8LtSv8/anFSbL7RiuD7yQtqYxjklsjBy38225EY2wNdRn2XNbLrNisjw0iHrlujt2nRboZyh0879a3pjvPAdCs3Ut0UB9Q5w8gKDD1rQJZh0PWTvyQU9MA8QGMYTCU12QxRTd8PLjDCKMbrP/FjPLTzMDYeuzTTX8nCSCXVmdHbFrinPzv+TpNKKvGSSq67fExGnefGYSgMI1UNpQKGNly0pjTaIR7ZNYkzjl6stNFM2Ziuwo1ilmq9GVozEbQ0QTUIGEqG0Y0Nw0T/TenNq4xh0EKgCwxSRekCg8chAElkba3iW9xqNYHB78mQTbbTinsyDiN73o4DMyJdAwmM1anAmG6FosSmasOQ/5t20DwyuNGOoKdtL0KZFB8m6M8wN34jo5KiZyC/u3/HQdy3/WBu34DkHtsaw1iUComiOuEmVmkTGNbkeCnDMdWOl4FqZoahx/ZIW460T/BziWHUAh+TjTZiJHmpuGtsK4zxUJo7yjRXudoOUIWNKRW5Dqm2UedkhmGwz1XmJUUbqGWMYeR5SSllE0JVJUXvP33/x1+6Gy/76A9EZUMCPQ/y/OP9pjWB3rfkHtV+6CqpQcBQCoy8OAyb6oZPJN1oKc5tR6hXfJFCgadD5qBJr+8c5I4jFRh+8pKZ3WrVlzs07GiSNmnHl7cQZn/LU0ltPzCNtakX097JBmoVH4vTZH8zrTDx8krtN/Ia8nyu0iIc0FwvdWP+vU8fxP075EL86Zu34c4n94vPdAvvuXxDcr2SKimTWsCWIly39UjvFPn9B66/D+/5yj3KcaY9SKMdCvWnEBjpi83ZlgmmYMxOVVKmoEFupwNMRm91cyKz2ya/8+Hh842+Hx+pYLKRqFB8z7NGevPFdkx4CqZ9StVkCsNgfbQFi1ptGDleciTQYqaSWszyWpFazKSVUBm/3ADy4+mYHz6YBPDq7zmN72FNYERxLI6dboWsRrnaBx4XNigYSoGRyzAsO/F2JLNXCl2vQQfMVVIyu6V6HO0wZixutVQn2PM8VAMzw8gmHeT96F0lJQSGYcF55mADxywdSc8F6oEvdnxU75hc+kzXKGIYzba0YdB5L/+bH+ClH/mBOOb/XHcPLv/YTZn2aYEpE9Ua+F5h0BVg94Iy7fSnmmFG5WZkGC3OMJK/xDAOWGpJiPYMxnZ74F5+3/n8JxWTEBham7odQK8BrlSJNKS/GK9XMNVsI4qT2ATOMGybOD1js1BJBdzozRiGhW2YvaSyXlW8H9VAMgg6b1FdLr65gXvcESC9BkV86x6OtE7wuEhu49IZZxjF4nlPMS+pB3YcxEl/eL1I+SKToQ7OMj04PekAqnpJmzA5bnlk5BIqKV3/nRqq6UXgJR05hJeUls5BuhMSw/BQt9kwIvUljSwvnHQH7kwlJfI6GVz6OMMAgCDw2G4sNSbGWhwGa8Zkw1CDuyLGMMoxBTpMvOQlTqv4nvF52wyhNhsG/75tsFnZ0k7TM6ENQVmB0VEchm0DpHloXbnlZnxu6xPpORaGoQmGTAJM/l4ZJNXESAWHG23EcbIZqrDkVPxd4v+PZASGgWGw4eX32zKwHN1An42azjIMLjD4bj0vDoO3S5suut2mJpjpXbONn77BimP5vGda0obxH1ufRBQDX7tre3r/qcBwKqneoE4qs6eIjnYUiwkijN6Z3UniVisFBgW/RcqCPm2xYehJzoQNw2j0JhtDjP/1uTtw+xP72D1kd1x5HhmmBc2mkjow3cJMK1IERsXPqhcSl0UzwzBFunKhqjCMKJuG29RfkW6CBVuZwHdx1cA3q6T069m+N+ysW2FcmMoBSJ4xLRwklIVKqpBhpAt2mUhvCz2SbDY5j8emhOmYm9w5+fWFSsqgQzeNa8IwkgXO99SMwqVVUmmfKkpqEH5f8n++6BoZRpydJ3zOisC9WJY04Abk/DgMJgBDuQFM7kVdP0x1t3nfdRtGFMciRoW71dYDNTBQ2DCqg+ObNDg96QC59TAsDz+O5QSRDCNrMDUJDCDRvY7VkuGyCoxQfRF9P0cllR57YLqFL2nBOLTL/eJtT2UWBhPMXlLqpCPsSIPjFIbhewgMu0XuoswvYVKf8O94CugwjkXWUoJpMaLFu8JcIU2oV2TVs2pgUUlpc0DsTi3H6R4x2VQO2X402qES3AlAROQWMgwD+7JlQLWppNraoq+cE8dKgB1BV3/mMQzTIjoxUsGT+6YRxTE8eGouKYMtBUAm2wG9U7x1k0cSPyc5JvlbxDBMNoww4ipPueQJL6kwsSnw2CmTWy3ZMHRnAdl3ti7xwEdNJRUxhjHNVFK1ig80krmw61ADn/zhowCA0drg7OsHpycdIM+GQbsr5bv0sy4w9IWrramk+K6Z2zGmLMZwmjD08gdeMgmoj3snm8Ig2tLUGRytMMYHrr8Pf/Slu3DrY/vStpPj+EtKMGktGoJhqPe4/UAiMMiGkbTpKwyD+st3cPylNgkvnv6AM4wwioUnlt4+BzVPgiuMY9z2+D68/KM/UAQz130Hvm82WGrNS5dMfV7Q8erikHFHNSyezXbWhsE3ATr+c+sTQtVgYo2dqqRMrrm8v6ba0/ouXbdhFGWeGK9XMNlMVFK6l5QplQZgt2EoGwIuBBRbXpZhKKfF2UWbf+aR3vQM+eJbZ8LhT798N37r328ztkPvqqcZvU0qbUIxw5Dqbl0dO9OK8L+/cCe+dteOpM/O6N0b8uIwgOxLpEd2miJc6XPge2InwRcORXg0zZHeNKnp5ffTqPGpZoh2GOG89/03zn3vN5Vr666PQDJBH9LKW9I9mQoL5cdhqKsAZSFdsagu1DuBr+qj1d2iQWCE2T7PtEPUgkTwNJkNI46RcTc0CQx6FFwl9b6v3ot7tx/EvczNtc5ensAvjtIF7MkHTVUO21FsYBgGlVRbxmE0tR3nfoOX1Ds/f6dYkEzXtXlJmb6OY+mQYMxTFmXVUcl9qPej25mKXJkX1SuYaiQLnK/bMNicUYzeFpWU4g2l9D1rxAeY0Af/Lt8dm9xqo0iyrdFqlmG0oghPH5jG0/unje1QX4VKSnijadoNywZAN3pzGwaPwyC33Zl2qDy/0drgKIKGUmAo0j8nT5P8TAxDTTduYicV3xeLFl84+AMkY3dDs21IhpEKjNSGcfvj+/GSv/6+ci16gUzqnVY7UuoR8Pus6lVwYPYCstkw6N7rVV94fXBWpYPGkstWU5nKRitxSa4FPlpaHIbOMEyLo4geFlXSpAqALwoKw/A8s8HSZvS2XJMfTqlgVJfOzCWSOAxt0SbjqM4w9mgCUzCMEjYMk7Di/TGdF0Wx8RmZVE+8vTwHBd9LBMYkRXrrNgyDKzhgYBhtyV55fwl8k9Y0qKR4F2NI2wRtflSVlGSrNOajtazROwwTBw+ldLGixUgFhqaSymUY7LlkshgwGwYvF0tzu9GKsGyRjBdxDKNHKF5SJpVO2/wg61rumKzRO4bvS/c5rgqx/s8EiWQN0khGC/8ju9U0323LpKN29F0qLS5FDOOxPZM43GhbvaRoItcCGW8SaEZvpZ+CKeSP+Uw7RL0aoJoGKvLdKwkMkknGRS5Wd3FhHItayvxF5CklPM+cdmXvZFOpYSI9bMzzwhRlryxWhmfUaIeSYWiBoLrAuOdpNRCw18A9m55ftB9no8D5fdiM/3kR777nYVGtgplW4h3me+omwxToBthtGLp7LGGyoaaY0fuuBu7J76knqsCQ59J5YwaBQVHzJnd2ZQx89f4yGgp2TkNhGPY4DECuIaSSarRDLGXxIs6G0SPyvKSAbD4pXSWVZ/Su+L6wE3BWwdkGFxi/9Hc3icVJutWSDcPDnU8eMN6DaSGospKSGYYhVFLZR8abuuLjN+MT339Eelpo16HxqlZ8VSVlaBfghlH5XRHD4DYMAHjmUGI3IYOj2egt++J7yUtOAo23xd0xA0uerss+8n1c/OHv4Lo7nkr7XsQw+GKXtMdfeNPiMdUIM7mkRDoUi8Agf3rhKq2oVi0Mo4BBmcaS163nxNHGJHQBZrKT+Z4nYhgmG2HiJaUUUDLfi81LSs8JReDqG5PRm3edG71pk8fHi4RaxASL4lbL4jQSgcEZRmYIhIC0pRay5QbLCgx1fk1qDGSmpb4/LjVIj7B5I4jfLTaMumbDMFHKwPfEznaa2zCa6o6AJt6DOw/jq3duV9prMBvGX73uXADI5BYyGWv5BM64dkaksslnGPunmiKhYDVIFtTYsPOpBUwlFXjGRSK5p6yR0s4wfOEVxic8ZcS1qQJ5+xQQFsWx4kdPqGsCwxZ3w68LsdgUq2La2sIGmGNCDs60WCxEnJ6bfH5096QSLX7P08mmgXT+MvBLjkMnFfd0pw+dOfGgyzpLIy6rTqrt6eNQNW0ePCnwD820kzgMS3pzjqzRmwSGPCaOpTA9bGEY8h5VZkJ9ps0Pf6c9L9m0JSqp5DvFrbYqn0crRyX1glNXAWCBe4xh2NyJ82wYfBwAGdfEswlPsk2prpIqUy55tjCUAkM1FmYHz2SbALLZKU2ChTLMAupDndb+X850jN97YBcA5t8ubBjAa89bh9+9ZL1SkAYwB+JVS9TuDQyqI+4FxA2ei+qVjCcJjQ3PyhswVqVDd73k98fRaCWVwShQkV+TVFK0uJlVUslf30tUTYpKKuaLgOyn75kj2UXfNRWMzejNvyd2yllUlMYdAMmO2fOSQEU9dxhfrD510zbxPwmuxMNI7v51ldS565bgJWeuwfnHL8vcA4duLNcPofT0gFwU6X55oBi/P/6XszhCYsMghtHOMAyb4KbFTqY3j0RfAOC/fvY09k01Ua8EqFV8HGICgwflGm0YbCw9mOeK5+WopDSGwecljd+nr74A//jG89MxyMZh8CBWm4rRxAK5rYYYhthwtiJMs/ViVAvcy5nys46hFBh5cRj678lnaejlMMVwcIYxYzB6Jy9dhBXjUmBsfWwvJhttmd48/UsL8litYsh1b2cYebtmk9GbbpfaFCkFKqo6gH6rpPdIa281RyUVGhZVU8xAI2UYFKjInwEJWxJeNO5cBy4ZRuKllqik0t+iOHMcnW/b2QJykRJ7U8PCyu8xOUfu8oBkjoRhLHbdge9hol7BwZm2NHazBeTMoxfjBaeuUmxCNI/iOPGsoy7z59JoR1g1MYItb9qEFWwzYlKH6QzbFHciEuaxxf+mh3dj43u/iT2TqrpTF2BmgZHYMACkFSg9q1stB3n4NA0qqQd2HML/+OztuO6Op+F5wES9ouRd6sSt1jPMFXL9DSMpQI2R3mnuM1PA7NqlI4KVyMA9Gq9ISetfpr4JgW9AecVLIJl73ANzpKIKjLLZE2YDQykw9B2z58GaCA2QOmN94E06yIAxjIbBrZZefp71shXGSmI9Au1IeP4afi0dtCjpLzSHyeit5xXilQMBVYXUTDPyAmAMI08llWUYxsC9lGEk0deRkdVQX3g2XwHBMLz0JYdRJRUqC0K+SirLMMw765D9LmpJpM/+lD/+Om64Z4cQ5oHvYfFoFQenW1mBEUVYMlrFuccuBc8kq9eTNhu9Q7G4+2xcilVS2apzvE9cJfXkvmnMtKKMm7O+e+eeaDQtfM/DWDqPD88kDIOrrmxZdUerqqMJvT9RDDyyS7qOe0iSG9pUUiZ7U2QQGG1tfpBKir42RXq3o1jU3xZtpyf4GqPl/WpHsSLg8uLDdJhqjPPUQiQwnnvyCiVhIh+Dnz9zSHHumAsMrcDw2MPT3UKzAXnpjltjGKZITR6HwY3bFC9BD5KuT6VI9ZgMQGUYHDc9tBv378hWZqMdzzOsVGm2TRPDUAUGLei6Vxj9X9MWpkrgGQURIIWvWEwqviXSmzGMMFKEtkLP23Inx2+FG2l9j9RAlOwtq5qg/tsKZgFZdqQvabJOhsoMk/tRbQM0Pr7nYfFIFQdn2pnAvVYYoxJ4ws6kCx8g0dHrzwtIi3elz4XLUZPA0Bm26ZimNgf49fTFTPee4psvEgqeJ+1whxthrpcUB819mg/0TsVxjCfYYud7HsZzGIb0dAP7jhv3s0ZvD4kdkcdr8Dkn05snwoIXV9NjL5IxSFg53cveySb+3zcfkGOgqQrzoLjpN1WBMdMKMd0McdmGNfjMb1yUYXx0Ly/568S5Yy4xOBEhHSCMYtQCPw2eilGv+KlB0fJCiMAYdadvenGU5IPtrA2DFstfPH01Tlk9jgtPXIFf//TWTJoQgDEMTQd51T/9xHhftLPLExgm91d6iWiSyiyXqsGRjpECI/ku8H2zoRPZXfqIJTfWTOolNRN4aLVjYQ+iIlT8+kIlxVZGacPwhBpBz90DqAuCzUuKQMLEtNgA3DtI/QwkGwS+0JPKzveAxaMVHJyRDEN6zSTsrcpUHdUgWQBWjtew+3ATk43QwjAi8fwLGYbmXWUKuOPxNmI82ur8IOSppGiD4HseM3q3sHxRTUsNYn4OdP1myrjIgyyMYzy5TwbKkUBSbBgGN9dMttr0ssKtVrNh0FyiecPnHLGvNnMSaEUR6n6Q8b4iBJ4n7Gb/sfUJZQMTahuOPMy0onRjJI3etLGdaSXPlMZbZ/9OJdUh2lGUca/M06fSZBjRJLUpDiPwWXrzJrdhkJ46+TtWq+CPX3EmVi9OGIaeJoT6BWSNVjZUK8nxthKf+n0S5I41+avn0edxKc12nFmYcgP3yOhNY1gNzEbvdoiRaoBaJUAjXcRICOkMh+fa0u/B85J+RczobXNx9Qu8pGhhtZVotaXKoGvyHEDUU8EwmNGbjPntMFbUe3SfM60QKxYl8+Rwo63UficW02xHYnFVGIbRhqGOpy11CaCqpFqROk/ENTQBxjcPNSYouZchj/SuBp51Eaum0f/tMMJ0S7oiRxEUgQF4STZczjDYczc5BkXc6G1wwfa9xEWbq6S4AOAFlOhael10nXn7bJOiez6WiashzLRCoWqSDCP5rdFOGAatG/q76YzeHYIYBkHXwWdUUmQArOo2jKw3VeCDMYysDUNP0SHqSDSzWTjp30U55To5RKSnZbJxV1gOYfRuk0oq6YsImtL8w3WVVOB7RmM6kI30Hq0FRqP3jCEOg66jCizGMHjGUraj81O32oqBYfAFNPDMzgOy76pKSn/RdIHBhc9MKzS6Q5IN4xBTSZHxlWquVIXzAuntpZPEZKOtLPC0yHTEMDSjt2kBIWHF1bA6I5LjoI4H34xJlZSnCHjPk+/ASDWwMowkE3Ji1+LBqFEci7oPQPKujNc7s2EgztowVIGRbj4iGa/BXx+RGiSMZTEszeMtMDAM6oJeb6RM9mHCdCvERFq0bEoYvSXDmGqGGCNju9YHUwboucJQCoxWGGcYBp/M+q5TqKR0hmG0YUiGQTmjAt8TAoEmBb1I1A8SKPwa1KeyJRapTdtkq1V8mIiAzA2kq6QMO/x2JNRa1FbF94zuukA2GnqkEhj1s5JheEIfrKeTp76YjN70KITvfCQD9/h46IFZeXVC9BiSOAZ+9FDiKXRoppW5Ny5YG+0IB1gt67VLR/Grm47FP715EyZGKjgw3VKEL9ltKky91woTVUizHWFFauuabLaVRY1nne3OhiE9gH7vF0/Bn7zijPT75F74xkqoLg01Uvi1+DnEen1PfV68gNJINcgkbBTnB4nLdiuMlYDGSLNheF6R0Vu9B/pOCIz0Oz5e1McwXWDJZZug2jBUliltHprAYJ9pHD/02rMz1y6yYUy3QpG2XGd4060w+T1dN7hHI/WtSOU1W+iLwLjhhhtw2mmnYf369fjQhz7UjyZzQaVURaSylzCMMcOOGmBxGDrDsMRh6DaM1RN17E8jr+kc2v3Sy0UCg3th0Au2qGTyMD05oul3z8AwpFpD7lZ5X5QdPmcYzEvKFBAIyB08XWOkmm/DqAV+6uvPdnDtSKYFCc0MgxsvyUvKNzAMvn4GvpdbiVAwAPFNjG17prB/qoX9Uy0WsKYeDyQCkDOMiu/hw798DjasXYLFI1WxsJF9qtWORbZj2nm3wkg8S3KV5UZvAMLQyjdBvpcdFw6+MH3ga/fhD75wJwBg9eK69IwzqKQI+vMLtd27YsNQGIY8x/ckqx+pmlP4AwkLoYzNxDCWjVUxrdmIEqN3Vaik4jjGLdv2id9N6UtiSJUUfZtVSXlCsOjsXAp7KXT1RJL67t7w+uGsY5YAyHerpRLIhJlmKAojtTQWn9yvmnSQay7COM5Eh88VehYYYRjit3/7t/H1r38d9957Lz772c/i3nvv7UffrGiFiXGRq1QC32MeGTrDSP7WNcOuKcDPVA/jmKWjoo6ESK2hMQxiI4rAIIZhcKs1QaqkzLUREpWU/CxrR6j3I9xqq1m3Who7QLVhFMVhcLdEXaDFcZJMrZ661c5obKsRRrIv7UgYM1WBgbRPiSdLFJsZRhjFuOT01fjRH1ySCAzNCM5hUklx+4Fe41pVSUVKHQ++2HA3xzERZ5CkO68EMi8XD6JcyVRSfNqFoXQKMDEMk41GZ8bfun9ncv+elxkzU3nPrFNI2pf0a84waqnA0RkGFxh5yfGIcbXCSDCMZWO1TL43D0m9jWaY1CP5zgM78ZWfPY2Xn32U0kd+FmcY9INu9Ka5FMVZtlDxExsHzxgtYyyKGQbBVEOjyTZJAATDJMy0I+FyTLY2nS1zzYTyrkTmqpdzgZ4Fxi233IL169fjpJNOQq1Ww5VXXonrrruuH32zotlOa28zgVHxZa6bTByG8JLKV0kJhuGRwEjUN2uWjIhoXTpHt2GQHlJRSXXIMIpUUvWKasMIhMBQ6bTUX0svqTBK6ks0WpESU5D89Y0vAsB3P8QwskbvJHdRsjjVKr5Q33GjN09zTayA3wvtIClwL4xiWRKTLZpxHGPFohrWLh3NeEnVNKHXDlXX2DjmbpNRZmetqqRUhsF313y3OMaS67WjCJXAV+5bxO0IhhFq5T8lCzHZMMwMwzw/fKaapTEzMQx93uuupCrDIJWU6hjhAQjS/ublOiKX7UQllbD0ZYtqGaHlpW61QBLn8ejuRF119S+cmPTREEsTx3HmGWYC99K5xKP15e/JRmlaERjqfNffC51xJPefzdDAN2YAlMwQQGLzFFHwBq85QHWW4ankozjOZI6YK/TsVvvUU0/h2GOPFZ/XrVuHn/zE7DbaLzSJYfgAwuTBH7t8DLXAx2N7pjLpImgS6QsK6TZFYRStROt0K0Q18LFmYgTfPZjs4mhC0QPUbRjcwE1zrWx6Yrp2nkqKFpNqWofb8+TuiwQFBRzSRG62I7z1mp/iuw/sQuB7eO7JKwDInaxeolUZIy354KiBYRA9pvQOVGCKxiaO5cLFk7ypRu/kr1QjMDUBZxgsPqPie4pOu1aRL38t8IUgE9eAfDl5gj5Tje0kAVwZhsEFRpxEzfvSPXrHATluY7UgZRgqY4pjcoFN2uJPwmjDsBj6A0/qukVWYkPUto4ojvHRGx/El25/EoDmJcXUZBmjN7Nh2GAyei8bq+HBUI1DUuM82mJOTYxURR8BLQ4DPIYk+U63Yfh+GrjHbGIE2mhyb0hZblX2n0NnHPz+dbdaHrOkC4yZtvSCMrlZA3aGEUbzp5LqWWCYrPUmPfuWLVuwZcsWAMCuXbt6uiYxDB549umrL8Dje6fwrft3ZvTatCCYXh7ybAHMKqlq4GPN4jommyEON6RnTDXwlL+0UHFXO2rHT+0rpuA+Dj+9dq7R25fMhhbXWDCM5G9Dc6u966kD+G6a74p7mKnJB/O9pLgNg1ItBL6H7z6wE2/51E/Fb7XAFy81H28edS6M3kYbBoSXFL20ehZRbnvRx4dfjzYE4hpMDdUOY1bLOnudhlbEhl9r8YhBJdWOUrdaWb3wjZ+8RRw3Ug2SehKaSioRYCkb6MJLioM2ENQfIOvoYUIUA39948/FZ5OXlN6vsiqpapDWSElVUhU/cZ/VWQ4ZvYEkueFko41axVc2HdRXQszmCLlM6+1SmpkoNsRUpO+biWGQUDfFYXCQQNSv3QwTmx6JxRWawIjjbJyU/lgXWWwYcWx3vZ9t9KySWrduHZ544gnx+cknn8TatWszx23evBlbt27F1q1bsWrVqp6u2UofhlBJeZ6SPVPXBZoWMIKSx183erfCRCW1OCln+szBGdE2XYsWX1pcxpm6gu9GynhKBSmFLmPDqAZ+GrPA3UJ1L6nkmnoqCBqHgO3Uy6YGGWG2CAD40cN7xLHEMHh/CbSo/MN3H8b1aalSfkVpw/AUNQK/FpAsEtRsRmCw6y2qBdDrWnOGETJXSyFE+A6xFSlpqfkmaNkiKTBWTiS66X1TrVQN4SmLLGGk6gu3UbUkbCSLY5FHEjvdHIdhFhgUwwLIRahMamw9jsMkMHw/6yUl3WrtywhXSe2fbmHJaNWY0sWDpzCMw402xusVMd/NKinubqseB0Ckl6F4DX0fSwyDG9+lDUMew6G/JjzTc6i51fL5uGRMTe8ByHfCtgEYtTGMOC7cfM4WehYYz372s/Hggw/i0UcfRbPZxLXXXovLL7+8H32zQqqk1J0mefrYApNM+lyho49ixLGqz2+lSecoOO+ZgzMyDiM9xvOSMqxEayc4w/C4wCgmc346gcuppPx0R+kZd+KAXCz0KHSe7gFI7tlPDYA6hNE7UtskobaKGfPqKcPg/ZV9Sf7/yaN7RVoUvhjqAXlhJBdGPQ6DOztwZBlGpAbrsQWmHUmVl4x0lteZaYdK4B7X2B23fEz8v3ZJspnYeWgmsYFZ0qxQJt+WFpnNo4wp7YvXA8PQY1fKqqQ4TM/QN3hJUX/zVVKq0XvJWBW+l3U79T05RxrtCJONNhbVA5nyw8AwjEbviP8ei/eDp8snBOk9zGgMY7LRFlU1M3YP7Qt6D4GslxQfe9OGTDAMy/O0Gr3jeN4YRs8qqUqlgo997GO47LLLEIYhrr76amzYsKEffbOilUYr+2LBI9WKGjBFoBeiEiSUPdGpJ/pFobMURi714SQqqXRRONgQiy3fRVYDSWs5w+DtlGMYnsh9YwKPw6hXfEStxJAn3Go1QUkTUt+NmHJJJX+zLrMi8tXCMCbY/dYrgeJYwMfItKjwhU8E7vkeAvJs0VwdgUSwS4GhLoYKw6hX0khq+XvMrtlW1FyqSg/IMgzfIvzXLh0FkMwNiuMxMYx6NchkTk36IY2xtMFQmJfJhmETGMzOoOcTy8OkZX7w/4n5ETxPumLnbYYqfhIU2gojHJhqYWnKMHR4nic2dI1WiMONEItqFZmFVrjPqnNGN3pzoZ+oCCHsYfp1/dR2x1VSzTDChnd/I73nrHrdxGpFkkzNLZtvUE33XMQw7F5SMbMbzm0oXV9ySb385S/Hy1/+8n40VQrNMEK1Ih+UvvBlvECYj3/VT3zGJ0aqaBxuYKoRAuM82ZivvBi6SuqodEfJd5GJsTWZqNyGwedImWhvPWKdhBuhXvHFDqsaeGi0ZRoNABnbzagQGOpuxJStFkhebj1P7kwrxJn/5wasXz2utEkLEt/916u+MRsoP49DTVue/OWeLXogIh3Hjd7K/dbUa7dDVWBEsdzNq/WiDUbvdoSD09lIb8LK8Tp2H25g5Xgd1cATbtdV366SotxaahxGxOYxjYHHfjcxDPU5U7s+8/DTPeXyoOcu4w4QNcZGdS+pdcvGUKv4OGfdEnz2FhhRCVLX1gjY32hi1XjdaDj2PDX1yKRQSSXHypov8pwoziZO1BmGVG+aXWQD31MZBptrJs/BjA0jkGPOmeNMK1JUdSa7rsm7CgD++lfPxV/9989x1JJReR2LSmquBcbcXq1PIP2gLigovYWukqL3K/CkumDJKM/tz9xlfdUAXA0S3fNI1ceeyaZou6odQ3EYNobxwlNXYd0yOQFM0Gmzno69VgnEokLqKQpMSu5bV0mpLr8EmUYb4p75X46v3vk0ppqhKDXL1QaAympI7ULgC48pn5ZJJeWn9ii70VvaMPjC+s7LTsPbX3iy+FzxfbQ1lVTMFhhdCAGaW20rVBiGvnAfu3xU3OPK8Tqe2p/kRaoE5mJUIxaGwT/7muDg48JhylAApCqWdMw7MXrvOKAKjFogn5XMCuCJbK1JX4Gz1y3B/e99Kc47bpm17STS20cYJ6nAx0eqRtWnB6kybrQjTDbbWMQEhtA8Kbcux46+DjVhTA4UCTNN+84YXSVj9JbnmwSGvu6Tatz31IV/ppXUuP/Bu16E77/zRcZ7tiX8fOU5a/GDd11idKABkvWMVFIm4TubGE6BEUaoVTwxmTIMQw/IEz7+clFckrpGUgAM0ckkzYg8lyj5eOrhQm1nGUa+DeN3f/EU/O+Xnp57X2Sk068tPjP6S15SiVttVtcPyN2lrnIQi4BuAzJM4B8/slc5RldJ8YW3XvUVGl4rYBi8u/Se006WJ5ZTF3eZlJAvzM89eQVOXrVIfK4EtJuX7caIzXYR5jlF7U5ruaR0R4pjlyV2jEMzbayeqGM7CQwrwwgSIRaqAqPFvLWESqoHG4YeuKfHHpmwQ2cYlewc1NVm4t3zPaxdOmJtm4p1ka2m6nt2lZTYjIRWo7cS6R3LdzuOyWtK9agLPDmXqO/kFk1zbaaZ9ZLi98phc7RINihcJZU45hy7fAzHrRgz3nMlMMc/GY/V4zCohkZOLrXZwHAKjJRhmFQqQNaIRHQ2YC8zTZrDDZluGci6mNLxY7UKppqhaLui0fYpA8PIGsjydwOhpp7QFx6eGoTsGYlbbfK7zqyk0budaSe5hjputuC9pG9J26Oa0Zsv5r7nKTRcNXobVFJaEBa1IVVSWdsCr5PB818F2kJU8b1UJSV33xEzejc0zytAbjSWjFYx00rqKksjsjq2G49dmpwLYNVEHdvTXTqP9OYYSVWo3PsLgPKZph1fL0wqKRJsr33WMcr35Jad9JcYRrFKKsswsvOfSqDSGPM7nBjJegARiHFFzBXbNM18rpJqSaM3XUizbYvvuFozjtVNSBglWXVJJUXvzud/8zl452WnCSGu2zBEn0os5ly9qzMMPufNDMM+FjpsRu+8ejCzgaGshyFSg5B7JduZVYNsjQT66DOVFPnSH9Zy0eveIPTyU9AVLSpcJVWrSE+L8bp8ebI+2/nyOYy58MuqNnikd5V5NtkYBi3uxKJqQWK/IZWDdBrIGvJtqLNgQABohvJlqwUaw2CqjSKjN3erJZ23TX3E3akJeiRy4KuBe77vKdlN1XQjyV/aDEyMJJuD6WaIpWM17D7cyIztW557AlZO1PGKs4/GrY/tw/YDSWAnj/TmGKkGqASJy7QShxFGSnEm/hcwG73pHn7/stPQDCN89c7tYjxoDDpSSWkMg58jvQGTzxQsa9LJm1BJBTl5pSU2DQPDgKeqpBqhopJ66JlDuOHu7Uo+uBgxuByPkXWrFXMplpkD1q+ewPrVE0n/At2ttsCGoX1Hzy7wVVfhZjtS5rxpvGR6I/UZm47lG9QwihPbK+xsc7YwvAyDLZ56CL3NSyrw5aK9eFSmIQDkwJtsGEBitJ5sysA9m0pqXIn0Nk8uG6IoVliTfrxkFcmLwKOigazAoMWd+kbjJDOQqgyjqH8AU0mxmI9a4OOzv3ERzly7WFlsTN42+v2K/1ngXuAn0bl6QjiAksgl/ytGWE8d76RGQ8RKv6YLjIW1AJJhUPryZhhhWeo/r4+t73u4/Ny1CHwPqyeka3HF8NwA1YZhSj7I74evSXlxGHodE595+NGYnXbUBC46abmoDGmC7hlXFWoW6Trra0K6rOqc4hSIYdCc1eF5LC9bK8RkUzV6X3PzY/jNf7tNUcHoDKMdaWVWU5tgqKmklP7pNgxu9C6hklIZBnPLboUwpQnSr53x3LKMq84wKDUILwU8Fxg6gRGRLjTwMzpJACJISDlH2DCkukDaMJKBb3MbBntoUiUVYLIRCgpY1Wi7UNkw4aUTiqIdPOWyAszBdGToJ+8O30NqIE5+191qq2nUMXlJEePQI7111V4ehEqqJQXGaC3Ac9J0I3wHWOSHrhq9k78e7KlBRJ1lgw0j8HWGkez4IqaSihnDMNVaoDmweKSKPWmwI9VuzyvUtEoTGKbnTB5wPP4DSBY5PTNqcaS3jJRXBAZbjGnMVo7Xce3m5+TaGXTwbMbCwUD8lWy+DKppXFM7zd1V8c1qGKqOVw087J9qIo6RMgz1uO2MDcWxOpan/ckN+NRN28TndiqgbG61QHbO87XD7M1ls2F4qpdUW/WSshm99evbxlUvBcDjMOaSZAydwOCZPemBkscTAJGGgIMvGvSASHVEJSHVWA15HAmYRbUKpjjD4MbpwLwwZugr+/xPb9qEj165UfldCUoLslXwamlK9yDd+VFFsdjCMII06pgoN/l1ywUByj3aiihxZBiGlmRNYRiBfSwAcxyGlzInXvQmkxBOE3RAViVVTXNJ0RXIW6atCSHeD/pt8WhFOAosTRlGXn2DpSyKtxr41nEkhsHtEu2QG+bT/pP6x5N9u/Wxffj77z6k9FPfVHC3bOpv0OECD6iZAHQhZmJBAPAL61dk2kmYsJc6MSQGWt+wq2a3jHolwJ7JxLl7Ub2S6fe23ZPKZxMDE7+lNgwKyjUJAF1NzJ+zaZOjk0euklJySbVCxcvRbPT2Ml5XZRhGGMXYOykd4PPKFPcbQycwpDFPBrHx3D41reb0gemWWOR9T+7+qoFadJ5eQp16U1DSWD1hGG3tRQQgajjz83kbBJ5C/MRViwTLIUSpjhfIqsbo3sgoHPCXkXTwmmqB3AYJI1aGocZl5EE3epM3iLyGWQ1VJDAy9TAMKilRZ9nAiHxNJZUwjEjJOhoD5mDAtBtkx+LzqQzD4EZfkyqRQJ40esU9PTMqj+aPUu+fKz7+I/z5DQ+Ic+h4m9DU66aXVSHRdWX76hw0eXIBwD++MbsBqrB2OMMwCS+6TL3ii8VwvB5k+s0FBg/uNIGM7KFgGNljsgxDdeIoOp6r7/Q4nroSh5G9dsWQJdom2PmciuIYuw/JdD9zaccYOoFBwqDKopK5SqpWkcVcDky3cOH/vRE33vcMgGRScgPjonog3dPErk1dPGlRJ4bRipJkhUrlLs4w2IPN85KqGehoyPSs+mIgzvE8IShokeTpLjgC3zOm5zDVw9D7x8HTeZPKbbpJgXtq9UPV6M0EhuFFMKmk6J7CiKmP0ucci2OyKqksw/CUSG/yJqNr6q66dC8AFEG+dJHZhsHBx8eWSwqQu1A1vXnMBIAcA0COn87E2myu6mo4rpLyPCZcu2AYvif7pFeg01sbr1dw8fqVynfc+y6KeEXL7DXJC4sLjEW1SmbRfnSPFBj8eZoQRnIuce86pY/anC8yelu9pFhdcyonUMQwEi8p9Xs7w5CDNtVIPPjILpW3mek3hk5g0Etdq/hCj6cwDCZIDk63MNOKhNtgkmFTCgReQ5jbMDho8RirB5hshqIMJ0etor60hIzRWzOm6y8x7YjoWH0BT9RwyTVWLx7Bqom6Mb25vJ7KqPSiTxm3ZMtCx4u/0OJIgrbZDhXBoKQGKWAY5DtP/wO08EMYSfl9iQA3UtnoAkNnGFGsuOvyNvR0I4Bq9CYsTxlGnsDgDMP03ORvyU5bZRgyt5TuJUXjxxfFRjtSGYbGaGkBbLajjBdZWdQZM6AxjplAt7VnU8EGnidsNYFvPpe+qlelSoobvQkqw8jfXSdu6mmWYpvAyLFhmJAVGPwek3MbhhgYmw0jKzDsc4ewM2UXRy1JBcYcutYOncDgDIMW+yU6w9AKufMKbzxojQsM7iUFSM8i0k8vqlXQbEeYaUVZ7yXLTloXCHwhqRpsFDzS28gwKr5I5vZ/XnkmPvGmTaqXlKaS8hWB4Sv/A3IScz2sCVzlRHEmh4XAiKzxFsq4WNrWEwCSjUZRSWXyfZkZBpfj5C1Hrz91hRZ+PlahxtA4Y1gmBIZ9IeHHkw1MDyoEkucRGgL3dKFGZ4q5yALLGq1IsaPxnafPFuNWGCsCtQN5IQQVF0jUZaHiMqwcpsR8gNx9hynDMKld6DuFYdQrmX5z+RDHqseZjna6AZMV97LH6Co3zjx1b8vkeMs9MhsGudgrcRgm+4khDsMmMAJFYCQb4KPSlEW23GKzgaGLwyC//yT2gXaEzOjNVFLkTUIP3vOYysnzlKLzYjHShIFQSaXusgemW8aAOoKqklL7znfwpihPlWFkbRj1io+rLz4RLz5zjZiMvmePw+CFkbi7J31HL6lu4NfB+1nxfRGTAqg1wqmPhCIbBpCMewVq4J7HhCBdAwBiFk+TtKnqiNV+qiopke/HwDBk4GPyHWcMSy1utRyKl17aJ4o0/9Brz8aVFxyn9IkzhmQhTfuoGahpnj22Z0ocP9MOEUZS3cTnG99kNNpRRqCWBa/ISO3FGZWUgWFkbHZy900CN/A8NYNwCjqzXpEeh4sMDIODe72ZILLVFrjVEmqBrwR0mrLI2oQipaIBJMPgKinTXZi9pMz3wvv5TFr9c808CIwhZBipSoq9KLpKqqExDG4AlDYMme4DYK6K2hOTDCN5+FQEhoMLENqRAvleUjWWnp0QRaoNRT+/XvFx4spFeMGpsp6Imksqa8PgrILa5rmHeL9MRjj9PnwveZEVhsHTYVv+D3wPb3vBSZn0KHoRIy/tFy92JGoUsFiNpE21j4rRO1AT/Xls5039JvDUIIHvKVlCqbRqnp64btgwkKcUV0uIOAzFSypiqjZVJUUCdxvT28+0QlG3hR8LqF5NzXaYUdHlQWW/zEtKU0lJoZZtg/dlvF4Rm63Al0XBeLI+DqGSYotsUjvD3ucYMq2KCWEUi7kURrGR2fAN4mgtUOp7GxmG1kSVsXM6XNS0L0g+aI7DKN607dIZxhx6SQ0hw5ButQTd6E1J4+glbxm8pHzfw6J6JXOsLgykDaOYYYxUfWWxyQvcqxpenJDlSbIF7unwchgGN3onKilPOU5EytOu2PdQr/iZZIW6XWZcExhjYzxDLxOKGsP4w5edAQB4/qkr8eXbn8InfvBoJj0195KidZXUGbo3kaKO0Y3efrJTlSqp5DeRAVersQEkaRYSgcFVUsVutfyehYtyxQcaarQ7ZxgJM1RLxeouq9TWI7ukwPiDL9yFmx/ZIxwY1OzGMvmg7oxQRDBGqwFaYfJMa6KQExMYUDcZpoWNE+Lff8mpePEZa8R9kVedTXBRe3yRnRgpZhh5+nvuJRXH5mtX2XeLaoESxGfaJFi9pAIZuCcERqHR289oIexGb66SShnGEscwCkGLHV+0FRsGM3rrQVpUoAhIJv6EwYZhM3orDMNiw1g5XlcWjyzDSI4j9Ymui1cC9wJT4F42vUbi/WMRGEyXXmFGb3oRqK90zPJFNSUieLQa4LINazKV1jgza7SjjAAV/eUCg7WxYe0SQaelDUO2n/jOQ9P1S2Ox3u9kHJDZUYdRjL+44X7xmV9Pr+JH41L1PSX4cumYWlqzCFz4ZsbA94UKir5Xckl5cgwAYMWi5Fnc/Iisakj/69589D9Pb57ngKGDC0maZ6pKKm0nh2Hw8V++qIZj00JTFd9TbCD8XMFY0s/E1mqVJFV+XrejGDARv2OXj2K8XsHv/eKpylwyLcZ80zFSDdRU5wZhpDMFbsPQjd5FgXsVg5eUNXCPC4yDDUzUK6KEq/OSyoEobh+oOxECt2HQA5QxFuoDphrLiqtixoaRLBj0QplUUvTy63V7bR4VSXlV+TLSS6Ik1vP9jEHOxDDohQC0KFVPNXrXAl8EV524MsnqqgfA/ckrz8SWN50v2rjtTy/FP75xk7JIeB65I4fpNSNrviKbuzG/JgmBOI5F+4GvLqRAYuzVjd6Kl5Sv5iiisf7yHU+LvlN/AbNKKsl15CdJ71Is1WJliiBdlFOVlKauIpUU7T5bYTa9OS0aZ65dDAC45dG9meuYduwBM/xze5h+nAmcGZsC9/SgySIvKf67r/VDT+HCj6dxITWz52WD2wiJSiq7WC4ZreLu91yGi09ZKWx8Ni+p0Zp8P7hdFLAwDP2drsgNqG70LmIYFT/rKWl7TKqX1AxWTtRZ/R/nJWWFSSXFd7iKl5TmEcN3TL7vYWKkinYUY7oVZlwbCdLoLRmGvqOmz/puVH/4eh1w6gv3phALjm9gGAUqKa42obbpnErg4XWbjsXNf3gJzk0zrVLzPOX76gmZQkJP7khjM16viAh53eht668+rmLHz1RSPJo4jCkFTPLdwZlWxlPHZtQ9ZuloRtjSObQImAL3qCb3WDXZHNQrvnhm5OlUBO7GrI8Bj8OoC4YRWRfj9avHxVw54+jFynVaBjdw31ddt1W32vx+m+pHcyFMspuaNLXneebnoUej83NFVHz6HY0Ld2SxsSObW62JadoExgkrks0TZSyYaTOVlKltXSXFmJ5gGK0swzDdQi3wM9/bGMYJKxcJe2oUJ6pSGlfHMHLA3WpNMKmkaHHg2WoDz8PyNChr31RLJDUzRVcDkmFQxk3TMWRL+eBrz8axy0czD18PkKMJfPa6Jfi1XzgBf/v6ZwkddOB7GY+tmuGeVaN3VmBwo7fneTiaVfEyRUyb3IJ13foippLSjd6mcdGvwT//yZfuxp1P7k8r6SW/eamaLYpioSLbO9lkKqlsm/Tvv731Qnzpt56b8fiiY8nzRcklxYzeFd8Xiyftum/6g0vwyTc/23iPOvSYFj3zK8Vh0Ng0WT0MafROPtcCX1Q6fNZxS3OvR//bDN1FqUHGatlNS4W1p9uCzH4/2bmS/G9nGPQu0Tdkw+COLFZhZ3GrVYVovlvtyavGxf9VLXOtCVYvKRa4R+xPTW9uYBgG13rbvf7WC9fji29/rvhcY5sZZ8PIAU8NYkK+SspjOwLp0bRvsik9hyxPjKspdKEiamykqrHXX3AcfvCuSzJt6HEQXH3x7ldtwPErFsndc+ApBjm6Nx2+b84lRX0UbrUWYaP/xm9NV/3QojOuC4wSKqmMwEjbuuGeHbj8Yzchjpmbryd34pTYb+9UM2v0Nqg9Lj5lJVYvHjG8iCmjMZZ9lUbvSiC9pGiTcMzSUWPFQA59M2C2YSTCvZXaqigVv0x5khzHBeLpRyVpuJ+VskLbdWk89Boh8v5zu49RZsMI2IaGFnSbYV6HKXUI76PuGaRnHSA1Dlcz24SdlWFoY5LHMFSB4Ss2DBP0+xYqKd9nKqnsGmUSViYbRp6tSXHqCHzGMJyXlBWcYfzzWzYpZTQBqZK65kfbxELKI4R5apDlqc1hz2RTBu5ZYhG4UVDfvdIkyyskAyQP3POyNbV5ezKdgsGGYWUYcocsv6e+khose1+6Sir5XxrmdeMyHaZ7SdkEBmeBGV2t9ubFsZq2PExjKIhh7JtsIkyNqD4TqnwcOHQBKbykTG616bC1wyQLMuXLGisQEhyj1QCHGm2pdqyYGUZy7RC+7wk2nE1vTosucMGJy3Hjfc8INaIOfTddtQiMQhsG2w1zZkl90dOy2NY1qpeh94v/r6ikAjnfAK6SKmYYcRwbU4PojCaKkZZozTZ0DCubXA18xa3WhIwNgzk5dMowqn75SG/9Ny4w5jKX1NAKjFrFxyWnr8n8TilD3v1f92R+43EJXGDsm2wy3ayFYbDFQ1eHHZxOqvZxvasNVd9X0kcn/eI7EblI6zYMXUUF6OnNGcPQ7CWm4k3SPZW/0OlvBn00/V1Ur6DRTmoPlLVh6H3XX7wojpWqblGcfLdyPHlGeyclw/C1hTX5X722PnZ0jjB687oKgokmwW6+72VcpIswWksEBl2Vrs8Nn/ScKW1HteIrDEPfnQd+Ynd61blrM67Osk11N803FXwIOlFJcWaZcas1jD2HrlYDDAxD2SmrAkgIjJFiG0aMZF5UtZIGqiqOUoOYhaYuVHngnvH+Miop+azaGsMoKqCUFJNSv8t7TL42jrRhMgUYzhaGViVls2HULd8DUFI2BL7KMER68/T391y+AR++4mxxbiWQC4i+ez2Y1n5eXMAwknPlLpAWiKo2Eah/+iJrSrdMenHAbMMQgWRGg3l2py5TP/AXKfnLBQaQFJ9qpbtyE2xutbx/hF2HGkpAHsVeLBurIfA97JtqljZ6m9qnj9KtVi7APPkgPduxWqVQDcVBHk26ms9UE4RcXgXDEAZldbGlRJOL6hVrbW59J+8xocE3CUVutaMGG0bgyfb1iHlrgJnhd31RVtRVukoqXWSLducAsH+qhf1TLWG4Nl7Py89WC0AUwKoGxQIj6yUlPcrCKMJ0MxSR+WoBpWxbpnxyuSopzjAqMhA3nEMvqaFjGA3GMEywfS/ULyxwb/FINVmMJptiR0OT7c3PPSHTxuKRKqaaYca2cPpRyWKhe7KYwBMC0vtsynBb8aUNY9lYFfumWsYd7/JFNTyxN5mgrXbimhrHTBiR3cQwY00qKZN9QI/unUgFxt6pJOdPnj2J37dybe3zfdsPyd2pnwbupa6hy8aq2DvZyiQfzIszyBi9PWIYWS8panffZFPozsdqgfBzL4O/ef2z8JNH9uKoNJiqpjE83t9mOxJBpM0wq5KinvOFdcRSm9vkCVUNPDRDTejnywsjw+BGdBmHQZ0zt2OK08gzzFs3fjythuVaj6UR8CesXIQHdx62Xi/PhgEA333nC9Fqx3jXF36WqT6oI8MwyCaaZhb4X5+7AzfcswNACZVUkM2rlac5VFR5jP0VJUzsJ4ZOYIhstZaJlpdaOvldLog+LUZTTaxLdZl5ZUoXj1aw42D2mDdedDx+Yf0KUSc4DzwJoAy+yi6siQtw8v1fvW4jjlk2arSRrJ6oY+u2vQijGAdnWpioV3Bwpm30ktIhddXc/c/L5GUyqaSAZIEF7M9CUY/o6jXtRXl412GxaFEwItVPWDZWwz6DSirPqKvbf6jvgo21mUoqTvThDzxzCK/euBYAcNqaCaxfM46yWDxSxaVnShUpzRHODJRMsn4SVd9sGyruGe6vGlD9duCCE5bjlecenTmGu1JPNkPwx1LMMFQjs+8l54h4GUtGXR0mlRXvo15xrxaox4tMrzxK3dLnbelO/oQVY8r3/HjpJWUXGGO1ClBLNpN5Ef1JP9XPZPQmG8aPH5VBlkVOB9zNX/Q9z4bBx5GtI64eRg6aXTIMacBVd/fLxmrYe7jYSwqQMRm6Ssr3vVLCIjlXqgwkC8i+XFxHOVYLcOoac/trFo9g31QL920/iKlmKIyjpjgMHaaIaeoX/yrrVpss7PumWso1dNgqESZtqse2I5YWxZOJA33fw7JFtcRLKpN8MIdh6M8x/WhMbx7HePrADA7NtHFayhY/+ZZni1Qm3YDmmYlhNNqRCKpssSJP0ktKHW/6jnbdv7JpHd70nBOS6xgM/zzwjp+fB90pouL7qY5dZRhFXlIm9qc/J9WtVjV6k8FYieGxXIxybB2vqaT4hjsQ9jCzpxKHSeWrIxOHoWWrPfuYJcbzbOOfmaa5DEPdQEiG4QL3rGiFEXzPvrDbFi/urso/i8VIMzyaQDYKk3qnLCq+L3YlMpcTVx3IXb/JKK2D9K//fW9SJOr845el11GFka3OtKl9buzkv/PAPYAxDJsaUNOvK78ZxpmrpLjOfHnKMGSkN9J7zGEw2meqa25LPvjAjoMAINxYe0Wt4qVeeRYbhic9+rK7d/M9USAY99gz7eT1eie8TRuym6CkbeozGb31FOw6TF5UgSIg1MhtYfROPzdMLqk5NoyV4zXFBReAEv3teyhUSYm+lShRnPHGM3hJlTnP9n1pGwbLDecC93KQ55UD2NUjXL8LyJdpxaJkMWpbAvc4FlsYRifgOZ10tREgvYm4DSNvd0g5mb5xzw5MjFRwSsp0siop0wINcS0OnqWUPgNyASOV1B6LSoovupwxKdfIsako9pMgYRj7mFCXgkU9j0MXkDLJpJp8MEhVFvfvOAQAVibXKSq+r+jhk2sxLymf4jAM6c2ZtxgH6cTHLDFBNG56JgH9f3N/swshd6uNMgzD3F6RSopvhIBsHAYtuqr+397v45aPZd4PniqD5mxocavlsKX359DteTUWh9GOYhGf9KevPFM5z87IdIFhv7Ze7yXw1TGbCwydDSMvshgo3u0GQiXFGAaLwzC5rhLIMF5mYtnAjd6mHb5gGIG0YeQyjMUJw7h/xyE875SVmWJIMhI1Oy4m1Q59Nr3wdNgYS8QIZMf8c297Dp7ePy3uJ0T2ZTXfE/WHHeclEfn7prjRm4SQutjktU8Cg1wQZTxPIjAe2nkYRy0eydRZ7xaVwMuMi4zDSIzeQcXLZRg62xUCo5o1UANyTtcq0hZEKFRJaX0l47TuJSUZRP6O2SowdJWUtgD/zxefgmY7whXnrcv0vV7xM15MyxfVM2yHL6DC2aEdFQrNot8B+WyojIJ8l5PrHm608dINR+GtF5+onZcvYIuO03/jFTRNSRJnC0MnMH75/HW46KTl1t9tHjs02LpL69LRKvZPt1id5ByBwfL7d4tfffaxghXkudVyG0aemoznfrropBWK0RxgOY1MAkNcy7BYGBYiGkMKbCOBoe/ml4xWxcJLgVy25INKfzz1enTcktFq8jKmi74uaG3VzExt67uxqu8jihIXYcrV0w8sG6sJt22C8JJK3WoD38OhVjtbetZgwwDk3CaGx9vk42myWxWqpAxzQInD0NSBtikp1axqW/x/VSWlul2tGK/jw798jtqm2M1nBYapLjZ/xiL+JooLU7zr8/j5rO6M3h71RSmgFEaYbITK8yHYrq1/nyfYFecBZsNwDCMHZx2zBGdZDEtAjg1DWxylAElC+lss35QNZMNoFOSbycPm55+s9OlFp63Cs45bxr6TrEKnvybwDLkvOHUVdh9OcuXrWVNNglCopAyLubJD1HaNIynDoIBFm5Dm52TiMPJsGNrudFFVZgoGsrmkTIsh1y3//ktOQzXw8P7r78u8XJUgceGdaUciBqAf+B+XnIK3aK7ZnGGQl9QexjD0560LQuqfOWZCHlsPssyrUCVlmAOcYfSSGkQXGObUIPa+8bQhh6BmdqgEfuZcRWCQd1wY5W689H5++uoL8LxTVmaP0dR+agGlGIdmWhmbCu9H3jWT4+z90wUtufTOpVvt0NkwimCqGQFkF0faCZGAIe+MfIaRTISpgvQBZeF5Hj71axfgRaevFt9Jg66XYQsm8EVlw9rFmV23sGFYUqOb2vc9NQJVX8iIYVDAYp5NyRQIaPqcXFc9h65NL+D+NO5DD9zLU0mN1gK8/YUnG3d9QLLgxHGMmVaIkZz76BRLxqqiHoTeJ2nD8EWdD8+Ti6xnEJwARP94fIgp7sFs9M5fLHU1ayXdsOjJB2190/tjUjtRn0yCrIx3kGljUvWz6c95Mj56n9phCaM3G4NaxVx7nPp7+tETOHXNuPDQouJYk81QyTun30PR950YvUl97gL3eoA9cI8WUHWRpMk805J1km0ghjFtSdPQD0iG4ZcSGEASBzI+UlFqbIjkd7mBe+qxsg/ZRZsfT3ls9k+VYBi+7RrZ/ngG4eJ7nvDKOjBtVkmZdo70nKlvthGspQwzjOOOa190Clm/ICkdSokyQ63etBCc2rCO5DEMk0qKfVekjtFVUhSDQV/rkd629iQbVdvi/aX7SoRH9hgd9JNpniVeV+q5JobRDKOO3Gpt7xy1d8KKRfj3X79IOZ7UZeP17DyyvcIdGb21caT+usC9HlAkMFaO1+F72ZiKmVZY6IdNgXPkojkb4AwjMKgWTHjfL50lz9dYA7nwmtxqzzt+GV5y5pqs0dvzMnaEpB/ymNFaINRftt27em7xTkrmsZLfVXxPjLtUSan3aBoe4WKq+fnrSFRSQKMVoj5RNx/UJ+g5w6qBj1Y7Qhip42EzLNOCaYzKZsfWDPPG5HTAF1adYZyzbglOP3pCCJI4ozYzD6jpeSupZ3xPeX55Ffz0vpve7Urg5xu9fSmkO3GrLc6VlWVkhHEDwygfh2Hvox6455IP9gE2DyaaOBevX4nvvfNFoi4EHT/TKvaiGE9VI3PDMDymcil/fideUi84dRVeYDHs5SUfBBK11J7DiYooL4WGzK1kZxi0eBkD8rhKarqp/J5n9BZZeolhWF7Eip9ElTfakeLKORvQa40Qw4jjWNn9ChuNrpKqBqixCF/APAYmlZQ+t/W4AZ1hfPwNSeXFh3Ym7sa6l5RtTppUVroKitu1ZFxHzkKZwzCqfr7RmxuGiwWGZ/yfg7Mj9XvZt3GDDcPKyLR2ipggocaM3nOZ3rwnpe1//ud/YsOGDfB9H1u3bu1Xn3qCTT1CD8LzPEW3TC/KTDssNIrRzs6WObQfoHWVdMhAZ15ZXKUF5Mdh2NvQA/eSv3x4RmuBiGUw6Wz1c/OM3qSb54F7/DgSGGRk99kYmdoGpJG2VqCSIqeHmVaYq1rrB/QFnJIPhpGqktIZFGGk6mcSIuZ5SfHv9CHSveZsKXHomcgCSml71gJK6XlazADvr1S5eYUqLt4HPa4l6befUTVxG4bN+G5CxSCIM32xMGY+nKYNlE3QdmLD0PtKyVTnsoBSTwzjrLPOwhe/+EW87W1v61d/eobN6G2bAPSiNFph4YQit0s9FUE/QWqoIPDwvFNW4k3POR7HLB0tOIudr9kl8iK989rI85ICpOEbKFBJ2QzrrDvVig80Q7EE6eoMXSWlMx4Te9BTyNgYRjVIAq7mgmHoSSa5DUO12yDts3r+a89bJ1KXEEzPpozRu1rxgQb7HPh4xdlHi9K7os/pg+rUS0phGJoDhc/a0NOhmEA/GVVSvpcRXqY4DN6ODXxTZfVqEvNZ/X75IqnONDEMuclR81VlMyDk95Eg3O+DIRIYZ5zRfa6d2UKRDSNzvLBhRIUR3KsnRvBvb70Q5xxrd+vtFTwO4+glo3jvq88qOEOFyUDN/5ZtIy8OA5AGWHIPtcGUSE//rO8ydXXYWDWA50mBEbCXJTkme12qpvi8U1ZZj6E2mu1o7hmGBxHprRf3oWel9+eik1bgopPU2uKSYcjv9Fxl+v/JNbIqqr+76rxMn2lB14MLrTp5g0DhDINX3OP2jLx1Ms9LSk81AugqKfl9J261NsZls8mddpRMVDlhMHorrJEpKDqxYXCo8R9DIjA6wZYtW7BlyxYAwK5du2btOkXpzXVUhA2jmGEASQnQ2YQpg2xH5zOBA6g1hztpQ1cLAZpKilWlKxNsZNKhE3SBpBeK8f3EU0pnGLaXFwCOXT6G7/x/L8RxqfrR1sWq7yOK23PDMLR7rgVBEgOUpnEnvODUVfir152rlA+1tmkI7qSF1cRaCPoGwrZZkoF71E6+CskcS6P+blJJ5b16QmAYaoJU/Kz7a9vgJQXke0AC6hjYNpgmTz4AOIWllDG71VJ/tY1TRiWV20UB/l63B8mt9sUvfjF27NiR+f4DH/gAXv3qV5e+0ObNm7F582YAwKZNmzroYmfolGFwG0aZbJWzDX3B7/b8QFNFdWLD8D1P8VTiNZ4JpEsfz1FHATkqqRyVl7LApOdN1Csid5VuF7EJ+hNXStWhTedeCbxELRTFc8wwPOHBNtMKM+zttSw1Rm6bhgWsTLZa3YZhdRbJ2DDsQlrpjyIwGMNgkdlceJRSSRmEGqV95yA2pPejaPNexq2Whklf6HnxNLNKyixosvUwyr2ntFGopDms5gqFAuPGG2+ci370DbY8U7YJQC/KVCPMDUCbK+jqlk6hG4JrfVBJyUVJHkMMI89+kZxjXtRNHjz0rpiEycRIFU8fmEmPky8LPy8PtmMqgY/JRrYG82xAN/7Ss2m0ooxOvCxMC7it9jtHhmFYGK14TiW9pGRCyOwzpP+F6tHvzK3WavTWGYah8qTeDxPKeUkVMyLTJiov5sl0XBGqgWxvaLykBhG2nZLVrTJ9YpPNdkeL6myhXwyDzl+7dARHLR4ppd4g+L4t+aC6CwbUWufGtiw7srz2ld8CEhgsfxIxCwPzscH2/GuBh+k0rsZWBrVf0O1CtEGZ1hhGJ6gwN2wCtWuKeCYQuxGfC3T2USaXVP7x/Gd9c8BZSCm32vSadqO3itOZY4AeBJoHU+XLbF/MTAGQpQVGDRsPLiSV9rQ+lZ0GIvVN4A1PevMvfelLWLduHW6++Wa84hWvwGWXXdavfnUNz/Nw/vHLMuVMbbKAIqCnmmFuFty5gozc7c2GQX9XjNfx4z/6xVLlYwk1LdOqydA5Wkt+L2QYFrWRiWHk1QfhNF/PT1XG9mQ7ohr4ItWLrQxqv8A3AVXGMHSVVCcwpXinZ8dTRtCzs3nN2WwYooCS9rnIhmGLAan4vsI6pUrK3B5v0xiHEag2jL/8lXPxiTdJlbfiQFFkwzCkirf1xfT7p6++ADe+4wVGYWpNw1MgQGzgziytQVJJ5eE1r3kNXvOa1/SrL33DF97+XPzZf92Df/nRNvGd7UFQjqWpZmjMtzTXsKXSKIuy6UTy8K6Xnm5e0FmT0uhdYMNIFwX9JVJdPpO/pjoQdBwvT6sLll5VUqTynkuGMT5SES99WYcLE/IYBt94cq+5VhgaVFIWBq4zDAML5DAbvfmizVinr7rY2iDSm5uM3poN47nrV2AJyzqsGtzt1wDMlS912HKjAcnmaf1qM5M3qepMfSq7b5gvldQRF+lN0Cm23eidfB9GcaZM5Xyg1wXfZjPoBM8+YbmxTVMchikNAoduDxFtsrZkBuHkc62SfXHHDSm985IP5l2P3Fnpf4JJR95PcJXHeL0qFvaZVlTaO0aHaSEi5sIZBi2ctYpvZNM2dawI3NMKKNm6a9pc6AxDSQ1SRiVF88IQY1XVvKT0nX/eb9m+dx+4VwRp9Nau2bXRmzEMZ8PoHZmSk5bnwF+UQTB694th9NPjy5SihFKcj5VQSZlePlOcB73ca1mgIh232KCS4jXAi8CPoMVy+aKaIiTmk2EUqUtsyLVhhFlvIbp33amiKO6AUJit1qC+VN2JmSrRKxfpTT+Zng9XayXta0y2E5UUZxhWG436tyzo0lmbRT7jsIEH5rr05n2AvmMqivQ2nTMf6BfD6HYBMsFU42CsWtKt1veML5+aDTf5S10+jqVuoRdswiAwgNTo2aFKihbUoxaPqEJw1m0Ycn5N1CtCJ99oF9dqsMEU50LtcvdSenZ07/pcr9q8pHRVYpGXFFM3ie90GwYT+NKGkccwUpVUicA9U5JF22+ZtrgatiBVSqcMwyZobQK5CPw5OobRB+jpvO3J5xjDGACBIVJ79OhW20+GYdIzUxxGXuJBAJn64LJN9Zik/eQvt4vQfXDBpO8oy7282R34UUtGlL6NzCXDqPfHhmF0qzV4SUm1TvLbS85cg7e94CTxuzWXVHribzzvxPR66Q8FXlL8dhSG4XGPIV4DxHx/yTmqsOPQjd4ZFY/BVmZDJ0bvblVSHhLPwndedpqxT50yjLn2kjpibRi6Sso2AbgOexCM3nLH2KuXVP/uRTIAvrhSHEaBW61vZktmo7r9OG70VnXiZoGU6YeBYaxZPKJcc9ZtGJpKiq493QpL7yxtbaoLNNkwsllb6d6fddxS/MqmY/GP33skOSdng7LtQ68Q/xcZqXX1YnKsZEGep2arLRPXQE0Z4zC0DUOejaAzt1rzMbbMBUWQTAq4570vZdfRVVLl2uVeUofbs1duQccRKzAyRm/LBOCCpT4ADOOUNRO48MTlOP2oieKDDbAlR+sFpl1V2cC9xOht1j3r//N3ZbxeweFGWxq9uUpKO7fMu2takNcsrivG1rlmGKRKSBhGb22axpPvPHWVVCaQr+QGo8hLyqSy0tWswlDPVVK5Ru8ChsGvry/A3EuqyIahqc7y+tKpytfGTIo+21Bh7tFOJdUH6Dpaq1vtgNkwli+q4XNvew7WLB7p6vzA9zBS9QsX8k7bBLLpzQFk4l10JEZvQ5uGnR9/RuuWJYZvehlsNgyewC4P/AiqFLhqoo6jl8px7mdNbxP4gjQxUhELYBSXM9ybYE7FkfwfxnaVVFE8gPV6NBcsv5uMu7ojBzd6l4r0TuePqWokTzWiXxdQWUknKinbsWUYkQnULf0+s15d5drjKX+GJnBvkKFTbKvA4DaMAVBJ9QrP8/D533wu3nDR8X1r0+S+SoLCVPCew/c8426tiGH88vnr0vYTVRTP1cNfsrIqKZ7249BMQuFXjdexdon0yOpnTW8TdIbBbWbdOin4qdFfV9MB5jTfNoZR+noFO2yTW7cuKHgCvzI2gbxrJm668rM+F3jUddE84dmPiyrkdSrg7QzDfFwRaN2qBmq69NnGkauS0hapYfGS6gfOOqa/6ddN+YPOXbcUf/KKM/Dck/Oz9ybBWdnv6YUcqfpGG8ZbLz4Rl5+7FqtTpsWN3voCUWatXbYom3J65URdEUSzzTD4IjQ+kqjcCN0u4EDWsYAWVlPWViEwejSy5/UFMKcGCQJ1HiV1V4qvKeI2DGNU1bykslUK5eQrYqIyGM6+DnTrhWh1Q87YMMq1x1VSjmH0AVmGYT5u0OIwBhGmBb0S+Pj1551UmLDPxjAA4AOvOQtf/d3nGRmG53lCWABaLinOTrxyKqnli2ri/5eddRQA4JTV41jLVFKzzTA46pVALbXai8DQDL8iOlupPJf8pWt2ez2Tyknvi/67EBjaLlsJ3MtTSZGAMRjm9eSDeju8QmHZwL08c06QI7zyYBu3orgMG7hKai5tGEcsw8gG7lkYBlt8BiHSexBh8nwpi8C3C+urLkzUZkWGVEA1eisxHIb01iZQQSUA+NVnHytqVnMUFdDqN5R8XT1cWlfLkf1q9YSsAkdju2ZxHbWKj7HUu+1/vfhUfOXOp0tfq8hLyhRvoOf8MgmMMiop0yKtx+Hoc5SrpIptGMXCQDfcl4VUw5nbk8eVa0+wocCf08C9I1Zg6It/0Y4IcAzDhjJFbmy4eP3KwpK2ZfIJ1StBUtK0HSkvVcX3O7Zh9JI2pZ9QGEYPfdJjUU5eNY6PXrkRLzx1dab9V5y9Fr/9ovVCFfd7Lz4Fv/fiU0pfqygyWw/CpP55HrOFiWhpKejy7l4wjBJGbx0jisDIH2NRjCrnWXQfh5H81b3B9GbKtiu83ubYS+qIFRi6GsQ2CTwvyRraDKMjxobRb+TFSRThjc85obj9ki/h4pEK9k42NR//zvs1KAKDRy73YsNIGIb63as3HqN8puZrlaT0b7coyqVE9S4yySY9L3Ouz2wYeTYBpbyphorvI47tO2xljAvdas0eZBx5wisPnmXT1W3FPYKrh9En6DaMvPeRG5AcsuiWhpduPyin8hqvVzILlalEZ+H1tOOvfPaxWMXUN3OFfgkM3YZhglQD9fYQTWlilOtY+hL4XsbbTon0zrlm3iKdGL2LhQ1vx4YyaXm63TwJhlFgw+i03WrFqaT6grK5pAA5UZxKygxplJ6dnXmZBHRA4mLr+zOZvnVqetI3Ex+64pzOGugTPM8TarZeWA9PE553DNCbcZ23Y2uFswb9PF31qGQyzumWrZYEkOjwwyjsqO82lFJJdSkwbClQikq2FqHqe2hFEeI4nrX3k2PBCIy8B0zHDkIuqUFEt3rbsigbDDVer2QWvAtOXK54QOVh8UgFB2fas3Yf3aAeJAKjFy1ZxfcLBQGxmV6j2YvsTZXAnp1Y2jAMKqkujd5V30OzQ72/DWU8yDop2sVh95IyH1cW1bSeSxjFXeef6wRHrMAoG7jHj3UMw4xuo1vLoqxAmhipZPrwZ5dvKH2dJWNVHJxpz6sN4zO/fqHiLlyv+jjU6E0YrxivYcV4vtB83imr8PGrzrMW+CmLIpvDr5x/LNYbygHzFDFGt9qca9LQ2BiG75VkGIVutapAM+HUNRN47XnH4FnHLS11TYLtPjMR9x3OA/Lsa0cxZjkVWnK92b/E/EAP3MtbIyosatIhi7JG6W5RthBSopLqvg9LRqt4AtPIsZHOOp67Xg10pNQVvQixa37tgsJaHrWKj5edfXTX1yAEBQv8+tXjRqHEDfNCJeWVjcPIU0kV22/IqaWsDSMv0/NoLcBfvW5jfkMG2DZF3dbDIJB7bTOMCmOi+oEjVmDoDCPvhSRm4RiGGSR7Z0uTUyafEACctGqRksqjU5Ar6cGZVtdt9Bv1HiOvAWBZSZVcP9BtTE6SU8xXzvV9mQUgXyWV/DUavUsY8WuVRGAU9dnzvFL2oG5Al9a7kFchsAxokztX0d5H7ApJkpfsEnkPglevcsiioqkS+g1T+nQTfvMFJ+Mrv3tx19e54rwkP9Wxy8YKjpw79Jrbaa7RLdusMOcEI8PIOTePYZRZ4Ekol2FxST/7/yxsTErvUqeXJoExV7EYRyzDEIbsdHeRp22qBI5h5MEUjNXX9ksGBipeNV3givPX4RXnHD0n1L0sKH/VsOxVTGlcysD3vUycQxBwlZS9wcJcUgXXlkK5uJ9lk1l2CqvRu0Mbxuc2X4SDMzIHGWlSnMDoEYoQKDAq6mzEQcXse0nN3S57kIQF0B+V1FyiTFS+CXwhNhVQyrdhJH9NCzkvyGRDJ2NcCcplDugU5eth5Ldz4UkrlM81wTCcSqonUO58GtBcLykXh5GLWY/DoGEfjjWzrxDqkiFTSXVsw/CzwoFnGs7jCXleUkDxItuJ2q9qcQvuFVYbhrbkdO4llRw/V9HeR+wKqauZ8t1qnZdUHkzpzfvafpfBUEcC6kNmwygRZ2eEiWH4nozSzptbeTaMMp0RnmglxrhXtacNnmdOmUL3VulyU0Zr1lzVxDhiV8ilo1W84aLj8KLTVgHISnKOqovDyEUvuaRKtT/LAmmQQYvZ8KmkuvGSUm0RgV8uyj8vl1SZvtQ7smEUB0F2C9/L8ii9OFS3brVz5SV1xNowfN/D+3/pbFx7y+PJZxfp3TXK5NjpR/vDssvuJ6QHzzx3pCS6zSt21UXHY+lo4tZMj5m71ebtrLlXlQlljd5lltRKMDtutQDSRJn6d5JhNNH5O+C8pPqMMhWyyHvDMQwzysZJ9N7+AhQY1eGyYUg32M76+0ZWMpjUM50G7vm+h0+8aRN2HJjGn153T+Z3G0goN9vFi2qFpTDpN0wGetoodJtNgdYuZ/TuE8qoO6ouW20uZjvS26mkhkdYdutWq+Pyc9fighOXlxJANC8qvodLz1yTSZlfGOmdCoxGuziFSNn6Kt3A97LjllG3dTiwtUpyvGMYfUKZLJ3S6D0cL+1co1v9auftL7zx7ySobBDQr83DR698FgDghw/uTtuzH1tkwygiO7VUKDfKMIwSqUa6BTfyE/SSr90yjHbkBEZfUEbdIVxwnUrKiFk3es+yymuQ0YlBdhDQ7zQxZdorigMqdKsNiGEUL6pvuOh4jNdnZ1k0pX73MyqpLr2k2k4l1ReUSUcsCijNVoWgIUe3vvdlsZCN3rRJmcOiaT2BBFy/1LedqqRMKJqXl21YAwDYsHZxYX9ef8FxeNW5awuP6wael71P3a22ay8pxzD6gzJpLaqBj+osekcMO+YqvfkClBfChjFXL3yvOHnVOP7m9c/CC1N39V5Ryuhd4LhSNC9fsuEo3P++l857lL/veRnvMt2ttts4DGfD6BP0B2JCIjAcu7BhrlRSC5FhkJdUO5rHnOsdwPM8XN7HHXhQwq3W8/JTjpeZN/MtLIDU6K0xDJr73bJsmUtqCOIw3vnOd+IrX/kKarUaTj75ZHzqU5/C0qVL+9S1/qDMYvRLzzoGxy0fnAymgwaZOG2W2p9lBjPIIP36XKV2GDSUqel92poJbDx26Zz0ZzZB7sTqd8nfbll8bY4ZRk/b6ksvvRR333037rzzTpx66qn44Ac/2K9+9Q20GOV5SW08dimuvvjEuerS0MHzPLzvl87C5RuPmZX2F7INozrHyeMGDWW8g1573jp8/u3PVb77+6vOw8evOi89dzjmTRK4Z/GS6pphzG09jJ4Yxkte8hLx/0UXXYTPf/7zPXeo3yiTesChGDzwqt+YbaP6IGOu01MPGsqkNzfh5ax64LAwUxPD0F3KO30FqnM8f/qmuP/nf/5nvOxlL+tXc32DiMMYllm1ALGQVVJzXTFt0CDShPTw7Idlo2FiGMJLKujNrXZgbBgvfvGLsWPHjsz3H/jAB/DqV79a/F+pVHDVVVdZ29myZQu2bNkCANi1a1e3/e0YC9mgOiwI+uzbP0wgddyweEn1G91GOHMMy0bDNzEMYcOgyqCdtTlwXlI33nhj7u/XXHMNvvrVr+Jb3/pWrqTfvHkzNm/eDADYtGlTh93sHjJZ2pDMqgWIuSygNGiQ6akXJsMoU6K1CMPCMBbVK1hUU5dcmYki+dzpOxD4iRCaK6eJnmwYN9xwAz784Q/je9/7HsbGBtPLaLZrOTj0DmfDAMIFyzCSvwths7DljedjSZqxlyAD97qP+K8G/pxtOHoSGL/zO7+DRqOBSy+9FEBi+P6Hf/iHvnSsXyiTS8phfjHbNcMHGXOdbXTQ4HVp7B1GnLRqPPOdjBNLPnezaar63nAwjIceeqhf/Zg1zHamVYfeMds1wwcZc51tdNDQD5XUMKPXXFIAUK34w+clNaigB+JsGIML6SEyzx2ZB4ylOm1dVbFQsNA3dDIOo3uV1OlHTWDVRL2f3bLiiE8NMtt5kBx6R7e++EcCTj9qAu//pbOUuIKFhEX1AIHvYfECFZiesGF0Lziv3fycvvYpD0e+wCiRrdZhfrGQ05t7noc3zGJQ5KBjxXgdN77jBQs2Nc+qiTpWTdRx4spFAAb/HTjiVVLrlo3h+aeuwjnrls53VxwsWOhqiYWOE1cuWrAbuiWjVfz0j1+M845bBmDw34EjnmGM1gJ8+uoL5rsbDjlwakOHhY4+xC/OCY54huEw+HDR+A4LHd6QsGwnMBzmHd0Wj3Fw4HjJmWvmuwtdwxsShnHEq6QcBh9lUlw7OOThgfe/VARBDiOGJRbJCQyHeYdTSTn0Cip1O6yQRcoG+x0YXpHscMTAGb0dFjr6keZ9LuAEhsO8ox8prh0chhnekASvOoHhMO9wGYUdFjqG5R1wAsNh3uFsGA4LHTTzB/0dcALDYd5RcTYMhwWObmt6zzWcwHCYd6wYr+GklYuwfnW2XoCDw0LAsHhJObdah3nHWK2Cb/9/L5zvbjg4zBuGpZCUYxgODg4O8wxvSBiGExgODg4O84x6xVf+DioGu3cODg4OCwAbj12Kv7/qPDz7hOXz3ZVcOBuGg4ODwzzD87yhqLroGIaDg4ODQyk4geHg4ODgUApOYDg4ODg4lIITGA4ODg4OpeAEhoODg4NDKTiB4eDg4OBQCk5gODg4ODiUghMYDg4ODg6l4ASGg4ODg0MpOIHh4ODg4FAKXhzH8VxfdOXKlTjhhBO6Pn/Xrl1YtWpV/zo0x3D9nz8Mc98B1//5xjD3P6/v27Ztw+7duwvbmBeB0Ss2bdqErVu3znc3uobr//xhmPsOuP7PN4a5//3ou1NJOTg4ODiUghMYDg4ODg6lMJQCY/PmzfPdhZ7g+j9/GOa+A67/841h7n8/+j6UNgwHBwcHh7nHUDIMBwcHB4e5x9AJjBtuuAGnnXYa1q9fjw996EPz3Z1CnHDCCTj77LOxceNGbNq0CQCwd+9eXHrppTjllFNw6aWXYt++ffPcS4mrr74aq1evxllnnSW+y+vvBz/4Qaxfvx6nnXYavvGNb8xHlxWY+v9nf/ZnOOaYY7Bx40Zs3LgRX/va18Rvg9T/J554Ai960YtwxhlnYMOGDfjoRz8KYHjG39b/YRn/mZkZXHDBBTj33HOxYcMGvPvd7wYwPONv639fxz8eIrTb7fikk06KH3744bjRaMTnnHNOfM8998x3t3Jx/PHHx7t27VK+e+c73xl/8IMfjOM4jj/4wQ/G73rXu+aja0Z873vfi2+99dZ4w4YN4jtbf++55574nHPOiWdmZuJHHnkkPumkk+J2uz0v/SaY+v/ud787/ou/+IvMsYPW/6effjq+9dZb4ziO44MHD8annHJKfM899wzN+Nv6PyzjH0VRfOjQoTiO47jZbMYXXHBBfPPNNw/N+Nv638/xHyqGccstt2D9+vU46aSTUKvVcOWVV+K6666b7251jOuuuw5vfvObAQBvfvOb8eUvf3l+O8Tw/Oc/H8uXq4Xobf297rrrcOWVV6Jer+PEE0/E+vXrccstt8x1lxWY+m/DoPX/6KOPxnnnnQcAmJiYwBlnnIGnnnpqaMbf1n8bBq3/nudhfHwcANBqtdBqteB53tCMv63/NnTT/6ESGE899RSOPfZY8XndunW5E3IQ4HkeXvKSl+D888/Hli1bAADPPPMMjj46Kfh+9NFHY+fOnfPZxULY+jtMz+NjH/sYzjnnHFx99dVCpTDI/d+2bRtuv/12XHjhhUM5/rz/wPCMfxiG2LhxI1avXo1LL7106Mbf1H+gf+M/VAIjNjh05UnQQcBNN92E2267DV//+tfxd3/3d/j+978/313qG4blebz97W/Hww8/jDvuuANHH300fv/3fx/A4Pb/8OHDuOKKK/CRj3wEixcvth43LP0fpvEPggB33HEHnnzySdxyyy24++67rccOS//7Of5DJTDWrVuHJ554Qnx+8sknsXbt2nnsUTGof6tXr8ZrXvMa3HLLLVizZg22b98OANi+fTtWr149n10shK2/w/I81qxZgyAI4Ps+fuM3fkPQ7kHsf6vVwhVXXIGrrroKr33tawEM1/jb+j8s409YunQpXvjCF+KGG24YqvEn6P3v1/gPlcB49rOfjQcffBCPPvooms0mrr32Wlx++eXz3S0rJicncejQIfH/N7/5TZx11lm4/PLLcc011wAArrnmGrz61a+ez24Wwtbfyy+/HNdeey0ajQYeffRRPPjgg7jgggvms6tG0MsOAF/60peEB9Wg9T+OY7z1rW/FGWecgXe84x3i+2EZf1v/h2X8d+3ahf379wMApqenceONN+L0008fmvG39b+v499PK/1c4Prrr49POeWU+KSTTorf//73z3d3cvHwww/H55xzTnzOOefEZ555pujv7t2740suuSRev359fMkll8R79uyZ555KXHnllfFRRx0VVyqV+Jhjjon/6Z/+Kbe/73//++OTTjopPvXUU+Ovfe1r89jzBKb+v+ENb4jPOuus+Oyzz45f9apXxU8//bQ4fpD6/4Mf/CAGEJ999tnxueeeG5977rnx9ddfPzTjb+v/sIz/z372s3jjxo3x2WefHW/YsCF+z3veE8dx/vs6DP3v5/i7SG8HBwcHh1IYKpWUg4ODg8P8wQkMBwcHB4dScALDwcHBwaEUnMBwcHBwcCgFJzAcHBwcHErBCQwHBwcHh1JwAsPBwcHBoRScwHBwcHBwKIX/H2EECxTbuaU2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f02d6815667b4741b8e90c6200e0d99b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43b4bf8139ed48f0bec455f36c69105f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b4cb4c02b2b4ce7b0f9722a3eee5bf8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaefba29c2f74ef59ac0292c20ffd76d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b1982a13fb1480d9fd6cc6a831f274d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66f0c64fb236430da38c55d1b1c6abe6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd48f1a586554e07a040c7aeda09ee8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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CAQMGwN3dXXXcZcuWYcmSJbC2tkbv3r1hb2+Pe/fuYe3atTh58iSuXLmiMWqXVCqFt7c3JBIJfHx8YG5uDjc3N3Tr1g1btmzBrl270LdvX433uHv3bgDAqFGjVGUpKSnw9vbG7du3Ua9uI3zW93NwuMDV6+cxctRwhIeHYenS5artGWMYOmwIjh07Cnf3Gpg0aTKk0jzs2r0TD8IeFON/RmnW7JnYtOknODo6YuxYPwgEfBw7dgw3blyHNE8KoUB9AIyHDyOwZMlieHm1R/fuPWFlZYnnsc9x/MQxBAScxj//HEY3n+4AAE9PTyz6ejFWfLscLs4uGDFipOo4HTp0VP0+7cspqFevPtp7tUeVKlWQJJEgIOAUvhgzCo8fP4K//7Jiv6/SMLcUITszD6lvJ2V4HZsOI2M+xMY0sAwhJcaKoVmzZkXaLjw8vDiHrZC8vb0ZALZly5aPbjt06FAGgC1fvlxVtmPHDgblWAHs2LFjattv2LCBAWDe3t6qsqNHjzIA7KuvvtI4fm5uLktLS1Mtnzt3jgFgbdq0YcnJyWrb5r/uf4/j4uLCALAuXbqwjIwMjdeoXbs2EwqFLDExUa08JyeHWVlZMXt7e5aXl6cqHzVqFAPAZkxbzO6HvmH3Q9+wtOQclpqSwbp29WEcDoddvxbKcnNkLDdHxnbt3MMAsFatWrHUlAxV+etX8czNzZ0BYB3ad1CVf+gnOCiEAWDu7jXY61fxqvLUlAzWqlUrBoC5OLuo7RP/Jom9fBGncayoyGjm6OjI6tSpq7HuYzGFhz3SKEtPy2KdOnVmfD6fPY2KKdL7KcufnKw89vh+our/5PGDRCaTyTX+vz/6N74PBT/6Rjn3jvKHEC3Tze1if39lR0h9/fH3L/VbfP36NQCgevXqH902f5tXr15prPP29kbv3r3VyqZOnYoaNWrg3LlziImJUVtnZKTZKlcoFMLMzEy1/OOPPwIAtm7dCktLS7VtR48ejcaNG2PfvsJvWa5btw4mJpr9V0eNGgWpVIo///xTrfzYsWNITk7GsGHDwOcrb5wkJSVh7969aNSgCcaMmgoAMDbhQ2TEh1gsxv++XQnGGP7c/4fqOLt27wQALFu6AmJxwbCA1tbWWLjg60JjfZ/du3cBAObPWwBra2tVuVgsxvJl3xa6j4WFBWxtbTXKq1WrBl/f/nj06CFiY2OLFUeNGprdhIRCISZNnAyZTIagoHPFOl5Z4HA5cHQ2BfftlSE3R47XzzM+vBMh5L3odrGWsLezmxSl4ciHtu3YsaNGGY/Hg5eXF6KionD79m24uLigY8eOcHJywqpVq3Dr1i307NkT7dq1Q+PGjcHjqc8beuXKFQgEAhw4cAAHDhzQOL5UKkVCQgKSkpJgY2OjKheLxWjUqFGh72HkyJFYvHgxdu3ahSlTpqjKd+1SJrR3bxXfuHEDcrkcCgXD5l/XAAAsbcSquWLz8pRdSB4+fKja586d2+ByuWjXzkvjtd+9BVsUt+/cBgC0b99BY52XV3vVl4H/unz5EjZu+gnXrl5FfEI8pFKp2vpXr17C2dm5yHHExsZi7bo1CAo6h+fPY5Gdrd5H9dUr3XTvEor4sK9qiri3o2+lJOXAxFQAK1vD71ZFSHmjJKsljo6OePiwaLWbFy9eqPb5LwcHh0L3qVKlCgCoGuqYm5vj6tWrWLJkCY4ePYqAgAAAgK2tLSZPnoxFixapJm1ISkqCTCbD0qVLCz12voyMDLUka29v/94vDdWqVUOXLl0QGBiIiIgI1KtXD/Hx8Th9+jQaN24MT09P1baJiYkAgAfhd/Ag/M57Xz8zo6AGlZqaCmtr60Innsg/F0WVf84KO7c8Hk/tPec7cuQwhnw+CGKxGF26fAJ3d3eYGJuAy+UiJOQ8Qi6EFKsB1tOnT+HVvg2Sk5Ph1c4Ln3zSFRbm5uDxeIiJicGevbtL1KCrrJhbipCVkYe0lLfPZ5+nw9hEAJERXTIIKQ7d/MX4+5fJLVl95uXlhaCgIJw9exZ+fn7v3U4ulyM4OBgA0K5dO431b968KXS/uLg4AMrbmPmqVauG3377DYwxhIeH49y5c9i0aROWLVsGhUKB5cuXq/ZRKBTFnhP0Y7XyUaNGITAwELt27cKqVauwb98+yGQytVosAIiEytvNI4ZOwLyZy+BaxxICAa+wQ6pYWFhAIpEgLy9PI9Hmn4uiyj9nb968UWsMBij/P5KSkuBU1Umt3H/pEgiFQly+fA316tZTWzd5yiSEXAgpVgw//LgBSUlJ2LrlN4wcqX5+9u//E3v27i7W8bTBvqoJcrJlkObKoVAAL6LT4FbHSjUnLSHk46gLj5aMHj0aPB4Phw4dQlhY2Hu32759O169eoU6deoUemv4/PnzGmVyuRwXL14EADRp0kRjPYfDgYeHB6ZNm4bAwEAAwOHDh1XrW7dujeTk5A/GVRL9+vWDubk59u7dC4VCgV27doHP52Po0KGqbRhjcKlaH1wuF7fuXIOpufCjCRYAGjduAoVCgUuXLmqsCwnRPEcf0qSx8pxdKCQxXrx4ATKZTKM8KioS9erV10iwCoUCly9fKvR1uFzue6eEi4qKBAD4+vbTWFfchK0tXC4Hjv8Z3zj+FT2fJaQ4KMlqibu7OxYuXIi8vDz07dsX4eHhGtscPnwY06dPB4/Hw+bNm8Hlav53nDt3DsePH1cr27hxI6KiotC5c2e4uLgAAB48eIDo6GiN/fNrwsbGxqqyGTNmAAD8/PwKbWyVmZmJq1evapR/jJGREQYNGoSXL19i/fr1uHv3Lnr27Al7e3vVNumpUhgbWaFX9/4IC7+D33ZveE9Si8KzZ89Uy6NGjgYALPFfjJycgv6bEokEK1f9r1hx5nepWfXdSrXafE5ODhZ/U3gjKhcXV0RGPlE7X4wxrFixDBERmv+3AGBjY6N6FFDY8QDgfEiwWvmZwADs2PFbUd+K1onEfNhVKfjsJL7JLvb8s4RUZvSARYv8/f2RmZmJ77//Hp6enujWrRs8PDyQl5eHy5cv49q1azAyMsIff/wBb+/Cx7jt06cPfH194evri5o1a+Lu3bs4efIkrK2tsXnzZtV2Z8+excyZM9G2bVvUrVsX9vb2ePHiBY4cOQIul4s5c+aotu3SpQtWrVqFBQsWoFatWujZsyfc3NyQkZGBmJgYnD9/Hl5eXjh9+nSx3/OoUaOwbds2LFiwQLWcjzGmqgktnLsSL15FY/mKpfjjz9/Rrm072Nvb4/Xr13j4KAKhoaHYs3sf3NzcAACDBw/Bgb//wvHjx9CkqSf69OmDvDwZDh06iGbNmuPp06gix9i2bTtMnjwVmzdvRNNmnvD17a/qJ2tlZVXos/Evp03H1GmT0ap1c3z2WT8IBAJcuXIZERHh6NWrN06cOK6xT+dO3vjrwH749vsUTZs0BZ/Ph5dXe7Rv3wETJ0zE7t07MXToEPh+1g9VnaoiLCwMZ84EYMCAgThw4K9inXdtsrQRIzMjD5npygZpL6PTIOfRsIuEFElx+vtQP9mSuXbtGhs5ciRzdXVlYrGYmZiYMA8PDzZr1iz2/PnzQvfJ76+6Y8cOduzYMda6dWtmbGzMLCwsWL9+/dijR4/Utg8PD2czZsxgzZo1Y7a2tkwoFDIXFxfWv39/dunSpUJf48KFC2zgwIHM0dGRCQQCZmtryzw9PdmMGTPYjRs31LZ1cXFhLi4uRXq/NWvWZACYtbU1y83NVZWnJueo+l8+CH3DUiQZbP36H1jr1q2Zubk5EwqFrHq16qxTp85szZp17NXLNxp9SL9ZvIS5urop35+zC5s3dz5LS80sVj/Z3BwZy8nOY+vX/8Dq1KnLhEIhc3R0ZBMmTGLxb5KYi7OLRj/Z3BwZ27rlN9aokSczNjZmNjY2rG/fT9nN0Nts0deLGQB2JuCs2vbPY1+xwYOGMHt7e8blchkAtujrxWr9dTt17MQsLS2Zqakpa9umLfvrr4PsTMBZjW11/ZOVIWURd+JV/38XgkOZQqF4/4eA+skSwhhjjMMYK/JMzc2bN0doaOhHt8tvXUpIPsYYnj5MRnaW8tawhaUIDtVMP7IX0SdZGVK8iFbOOfsmIRqvI0UYPtWz8I1/f6dx1FA9mwz+3QZ8Rb/8EVIi9EyWlIuMNKkqwXKgnGmHGBZjUyGsbAsGAlkz7xIe3k3UYUSE6D9KskTrGGNIeF0wIbuZpRA8Pn30DJGtgzHEb6fFk+UpMGdEALJp/llC3ouudETrMtPzkJX5Ti3WgWqxhorD4aBKdTPVHdenD5Oxeo5mtypCiBIlWaJ1CXEFtVhTCyH4RegXS/SXUMSDqUXBnL9/bQ3D2cNFb91NSGVCSZZoVWaGVNX1A6BabEVhZMJH++4uquXFE87hzUsaqIKQ/6IkS7Qq8XWW6ndTMwEEQqrFVgQcADNWtIado/JLU1pyLr4e9y8UCmqtS8i7KMkSrcnOzEN6WsFMNXaOmlPkEcNlZiHCvDVequezV/59jj9+vqfboAjRM5RkidYkxBXUYk1MqRZbEXm2qoIBY+qrltfOv4zI8OJNPEFIRUZJlmhFTlbBNGkA1Ma/JRXL6BlN4F7XCgAgzZVj3ugzkMroCxUhACVZoiXv1mKNTfgQimmY7IpKKOJh/lovCATKy8nDO4nYfLinjqMiRD9QkiVlLjdHhrTkd2ux9Cy2onOrY4Uxs5uqln878QluPnb/wB6EVA6UZEmZS4jLQn4bUyNjPkRGVIutDPqNrofGbaoAABSMiwVbRiIjW/yRvQip2CjJkjIlzZUhNalgvld6Flt5cLkczPmuHUzNhQCAl4k2WLmvv46jIkS3KMmSUhs9ejQ4HA6io6OR+E4tViTmQWws0ElMtWvXQO3aNXTy2pWZvaMJpvm3Ui0fvtgagYdoNChSeenmPt7db4CsWJ28dJEYOwOey0p9GFdXV8TExBS6zsHBAXFxcWplUqkUy5Ytw759+yCRSNC0aVOsW7cOTZs21dg/MDAQPj4+OH78OHr16lXqWMuCVCpHZsY7tVgt9ovt2tUbIRdCkJsj09prkJLx7uOGu3+tx8mrzQEASyb+i8atq1A/aVIp6SbJZsUCJq46eekiyYwus0NZWFjgq6++0ig3NdWcS3X+/PlYv349+vfvj2rVqmHPnj3o3LkzHj58CEdHR9V2GRkZ8PPzw/Dhw/Uiwa5cuRLz58+HmG8FKVMmPaGIB2MT3T2LPXXqjM5emwCLRvyFW49rIE5ihdRkKRaP/xc/H+0DzrtzuRJSCVCLFC2ztLSEv7//R7djjOHXX3/FF198ge3btwMAfH190alTJ+zZswdz585VbTtv3jzk5OTghx9+0FbYxeLo6AhbW3s8uZ+kKlM+i9XdBbVGDbpVrEsWJtn4dtxejF09DQBwISAWf/76AJ9PbKjjyAgpX/RMVk8kJCQgKysLLVu2VJXl//7uLeeQkBD8/PPP2LRpE6ytrUv0Wq6urnB1dUVaWhpmzpwJV1dXCAQCtS8DDx8+xOjRo1G9enWIRCI4ODhg6NChePTokcbxRo8eDaGQj+cvlY8ABEIuTMyUz2KvX7+GIZ8PgrOLE0zNjFCjhismT5mEV69eFRqbRCLBN98sQpOmnrC0MoOdvTWat2iKrxctQGZmJqKjoyES8xFyIQQAIBLzVT9du3qrjvO+Z7K5ublYs+Y7NGveBJZWZrC1s4K3d0f8/fcBjW3zX2vcuDGIjo7G8BFDUdXJAeYWJmjTthVOnDxe9JMO4MjRIxg9eiQ8GtSDlbU5rG0s0LpNS2zc9BMUCoXG9uPGjYFIzMfTp0+xafNGNGveBBaWpmrvUyKRYNGihWjk2QAWlqaws7dGt+5dERioWZNPTU3Fuu/Xolu3T+BewwWmZkZwqlYF/fp/hmvXrhbrvRRF6/qPMarbOdXymrkX8exRcpm/DiH6jGqyWpabm4u9e/ciNjYWJiYmaNSoETp06AAeT31EHDs7OxgZGeHmzZuqstDQUACAi4tytpPs7GyMGzcO/fv3R//+pWu1KZVK4e3tDYlEAh8fH5ibm8PNzQ0AcPr0afTr1w95eXno06cPatasiRcvXuCff/7BiRMnEBQUpPac+L+DwufXYnft2olJkydAJBKhd68+qFa9GiIjI7Fjx284efI4Qs5fgrOzs2q/Z8+eoVu3TxATG4OmTZth/PgJUCgUePLkCX788Qf4jZsAS0tLLPp6Mfbs2Y2Y2Bgs+nqxan8XF9ePvufevXsg5EII6tSpi4kTJyErKxuHDh3EsOGf4+7dO1i+/FuN/WJjY+DVvg3c3NwwdOgwJEuSceDvvzBgQD+cOhmATp06F+mcL1q0EFwuFy1btERVp6pITU1DcHAQZs2agZuhodixY1eh+82aPQOXLl1Ej+490b17d/C4ys9OTEwMuvp0QUxMNLzaecHHxweZmVk4deoE+vTthU0bf8bYseNUx3n4MAJLliyGl1d7dO/eE1ZWlnge+xzHTxxDQMBp/PPPYXTz6V6k91JU0/sfw8XH7RH1TIDcHDnmjjqD3y8MgICmOySVBIcxVuRpM5o3b6668H9IREQE6tWr9/4NrozW/2eybXaW+jDva/jk5uaGHTt2oGPHjmrlX331FX788UcMGDAATk5O2LNnD6RSKSIiIuDk5IRZs2Zh165dCA8Ph729fanj6tKlC44cOQITk4IGKcnJyXB3dwePx0NISAjq1y8YlzYsLAytWrVC7dq1cevWLVX54EHD8NeB33H66A24uLjCvY4lHj95gqZNPVG9ujPOBp6Dk5OTavugoHPo2as7evfugwN/HVSVd+rUHleuXsHyZSswd+58tZgTExNhamoKsVjZ7/JjDZ/ya7GPHxe0bF29ehUWf7MI3bp1xz8HD4PPV37HjI+Ph5dXG8TExiA4KARt2rQFoKzJ1qlbEwCweNE3WLToG9WxzgQGoE+fXujWrTuOHilajTYqKkrjNrZCoYCf31js3bcHF0IuoWXLgpa548aNwZ69u1G1alWc+/e86ktQvq5dvXHh4gXs3rUXgwYNVpWnpKSgq08XPH78CI8fRcHBwQGAsiabl5cHW1tbteO8ePECXu3bwNzcAvfuPijSe3n8+CGszau/d33VSxaq329JF2HMFHvk5SkfH0xc2EKtBXK5e/e5cNEvf4SUCN0u1qIvvvgC//77L+Li4pCZmYn79+9jwoQJiI6ORo8ePXD37l217b/77jvMmzcP169fx2+//QYPDw/8+++/cHJywrVr17Bhwwb88MMPsLW1xdKlS+Ho6Ag+n49mzZrh0qVLxY5v3bp1agkWAHbv3o2UlBQsXbpULcECgIeHB/z8/HD79m2Eh4cDAOQyBaQ5ctU2dg7KWuyWLb8gLy8P69Z9r5ZgAaBzZ2/07t0HJ04cR3p6OgDg1q2buHL1Cjw9G2P27Ln4L1tbW1WCLamdu3aCw+Fg9eq1qgQLAPb29liw4GsAwI4d2zX2c3F2Ua3P59O1G5yrOyM09EaRX7+w58RcLhdTpyqfWxZ2ixcAZs6crZFg7927i5ALIfD17aeWYAFlO4DFi5cgJycHhw7/oyq3sLDQSLAAUK1aNfj69sejRw8RG1v2rf5r1ZBh4pg01fKW70Jx73rcB/YgpOKg28VatGTJErXlBg0a4JdffoGpqSnWrVsHf39/HDp0SLVeJBJh5cqVWLlypdp+UqkUY8aMQc+ePTFs2DBs2LAB/v7+WLJkCdq1a4dvv/0W3bt3R2RkpKrW8jFisRiNGjXSKL9y5QoA4O7du4U22Hr8+DEA5d2K+vXrIykhG/l3i/k8DswslQMR5D/ju3AhBDcLufuREB8PuVyOJ08eo2nTZrh2/RoAoGvXruByy/67X3p6OqKiIuHk5IS6depqrM+/5Xvn7h2NdY08PTVu7wPK5HS1GM8yk5KS8P36tTh9+jSePXuKzMxMtfUv3/OcukXzFhpl+a+bmpqK5cuXaqxPSEwEoHy2/q7Lly9h46afcO3qVcQnxEMqlaqtf/Xqpdot/LIypH8GLl4V4/ZdERRyhnmjAnEwdAiMTXTTj5qQ8kJJVgcmTpyIdevWISQkpEjbL126FC9fvsSZM8qazpo1a9ClSxdVEqxTpw5cXV2xadMmLFtWtP699vb2hXanSEpSthDeunXrB/fPyMiAXK5AUnzBRACWdkbIb1GclKSc7uz779d99DgAkJqSAgCoWtXpA1uXXGpqKgCgShXHQtfnd5FKeRvHuywtLAvdh8/nF9pgqTApKSlo2641oqOfoUWLFhg2bASsrazA5/ORkpqKjRt/hDQ3t9B9HRyqaJRJ3v4//fvvWfz779n3vm7m2/MLAEeOHMaQzwdBLBajS5dP4O7uDhNjE3C5XISEnFfefn9PDKXF4wHfzEvGsHH2yMriIjYqFWvnX8I3P3XSyusRoi8oyepA/vPU/9ZkCnP79m2sXr0av/zyC5ycnJCWloZXr15h2LBhqm2cnZ1ha2uLsLCwIsfwvv6KFhbKZ2l3794ttKb7rsS4TMhlBc+08ofTe/c4CfESmJubfzQeC0tLAMqalDbkx/MmrvDblK9fv1bbrqxt3/EboqOfYdHXi7F4sfodjqtXr2Djxh/fu29h/1fmb+Nct249pk6ZVqQY/JcugVAoxOXL11CvrnqbiclTJqlabGuLo4McM6ekYsUa5bR4+399gM693NC+u4tWX5cQXaJnsjqQf0vW3f3Ds5TIZDKMGTMGnTt3xtixY9XW/bfGkZOTg7LQunVrAMCFCxc+uJ1CwZD4JkutjPNOv9j8BjwXL334OPlavd0+MDCwSLVD7tvbt3K5/CNbKpmZmcHdvQZevnqJJ5FPNNafPx8MAGjSuEmRjldcUVHKBli+vv001l0oQXLLP1+XLl0sRgyRqFevvkaCVSgUuHy5+M/0S6JXtyx09MpWLX/tdxYpSdkf2IMQw0ZJVkvCwsIgkUg0ymNiYjB16lQAwPDhwz94jJUrVyIyMlLt1q25uTmcnJxw+vRpyGTKlrXnz59Heno6PDw8Sh33F198AUtLSyxduhTXr1/XWK9QKBAcHIzkxGzI3tZiC6sUT5o0GQKBAHPmzMbjJ4811kulUly8WJCAmzZthjat2+Du3TtYu3a1xvZJSUlqXyRsbGwAoFgNdUaPGg3GGBYsmKeWnBMTE/G/lcquO6NGjS7y8YrD9W03rPMh59XK79y5jdVrviv28Zo1aw6vdl44fPgQdu7cUeg2Dx7cR3x8vGrZxcUVkZFP1PooM8awYsUyRESEFzuGkuBwgPkzUmBtpTz/SW+ysWzqeRSjkwMhBoVuF2vJgQMHsGrVKnTu3Blubm4wMzNDVFQUTpw4gZycHPTs2ROzZ89+7/5hYWFYsWIFvv/+e1U/2Xxz587F9OnT0b59e7Rq1Qr79u2DqakppkyZUuq4bWxs8Pfff8PX1xetW7dGly5d4OHhAS6Xi9jYWFy5cgVJSUm4e/2Fah+RSLNRUN06dbHl120YP2EcmjRpBB+fbqhVqxby8mR4/jwWly5dhK2tHe7fK7jFvWPHbvj4dMHibxbh0OFD6NChAxhjiIyMxNmzgbh3Nwyurq4AgM6dvHHw4N8YPGQgunfrDiMjIzg7u2DYsPd/cZkxYxYCAk7j2LGjaN6iKbp3746srGz888/fiI+Px6xZs9GunVepz2Fhhg0bge/Xr8Ps2TNx/nwwatasicjISJw8eQKffeaLAwf+KvYxd+3ai27du2LCRD9s2rwRLVq0hKWlBV6+eIn7D+4jLOwBQs5fVD2e+HLadEydNhmtWjfHZ5/1g0AgwJUrlxEREY5evXrjxIniDa5RUlaWCiyclYLZi5RflAIORsL7Dzf0HlqnXF6fkPJESVZLOnfujEePHuH27du4cuUKMjMzYWlpCS8vL4wYMQIjRox473NRuVyOMWPGoFWrVpg8ebLG+mnTpiEtLQ2//PILbt26BU9PT6xfv77ILYs/pkuXLrh37x7Wrl2LgIAAXLhwAUKhEFWrVoW3tzd8PumNvDzlLV0uFxCKCx9YYOjQYWjUqBE2bFiP8+eDcfZsIExMTODoWBW+vv0xcMBAte3d3Nxw9eoNrPt+DY4ePYqff94MsVgMFxdXfDV9hlrf4DFjxiI2NgZ/HfgL675fC5lMhg7tO3wwyQqFQpw8GYAffliPP/f/ic2bN4HP56NRw0ZYu+Z7DB48pAzOXuGUfV2D8fWihbh8+RICA8+gTp26+PGHjfD27lKiJFutWjVcvXIdmzdvxKHDh/Dnn79DLpfDwaEK6tWrh8mTpqBBg4JhDP38xkMkEuGnjT9i797dMDIyQrt2Xti6ZRsOHfqn3JIsAHi1ycGnvTJx5ISyC9nyL4PRrH1VOFY3K7cYCCkPuhmMopLMwlMRKRQMkWFJkEqVSdbG3gjTZ43DgQN/IfrZc7WJDEjFVZzBKNKrzy90m6xsDkb42ePla+V3/Vadq2HbqU/B5Wp5zGsajIKUI93UZCmBGaxUSY4qwXI5gJWtEZ48eQKRSAQ7OzsdR0cMibERwzfzkzFphi0UCg6uBb3Avk33MGKap65DI6TMUMMnUmSMMSTGFbQo/vvIDnw+dBDu3LmNPn36qo2iREhReDaQYsTggr683y+4hMhwzQaDhBgqSrKkyNKSc5Gbq2wVyuEA23f9guvXr2H0qC+waePPOo6OGKpxo9JQu6Zy5CmpVIEFXwRCKi1a1yxC9B0lWVIkjDEkxBUMnmFhJcbjR5F49jQWv/66FZZvB5MgpLgEAmDJ/GQIBMrno+G3E/DLt0UfE5oQfUZJlhRJeqoUOdlva7EAbByMdBsQqVBquMkwaWzBJAJbvwvFnauvdRgRIWWDkiz5KMYYEl4X1GLNrUTg8eijQ8rWkP4ZaOqpHMlMoQAWfBGIrMw8HUdFSOnQlZJ8VEaaFNlZytGlOABsHYx1GxCpkLhcYPG8ZJgYK1uvx0alYe288hnukRBtoSRLPui/tVhTCyF4fPrYEO1wdJBj1rQU1fL+LQ8QcipaZ/EQUlp0tSQflJkuRVZmQS3WrorJh3cgpJR6dM1Gp/YFkwYsHv8vkhNpEgFimCjJkvdijCH+dUG/WFNzIfgC+sgQ7frvJAKJb7KxdEowTSJADBJdMcl7ZWbkIStD2fCEA8DOkZ7FkvJhaaHA17OTVcuBh6JwbN8jHUZESMlQkiXv9e6zWBMzAfiCwicCIEQb2rXOxWe9Cj6DK6afx6vYdB1GREjxUZIlhcpMlyIzvaD7hJ0jPYsl5e/LSamoVlXZJiAzPQ9fjzsLhYJuGxPDQUmWFCrhnWexJqYCCIRUiyXlL38SAS5XmVivB7/Enp/u6jgqQoqOkizRkJUhRUa6VLVsX5VqsUR3GnlIMWJIwSQCGxZdRmRYkg4jIqToKMmWg65du4LD4eD8+fPv3WbixIngcDjYunVrOUZWuHdrscamfKrFEp0bN/KdSQRyFZhPkwgQA0FJthzcunULPB4PzZo1e+82165dAwA0b968vMIqVFZmHtLT3qnF0rNYogcEAsB/QTKEbycRiLiTiJ+XX9dxVIR8HCVZLYuKioJEIoGHhwdMTU0L3SYrKwsPHjyASCRCgwYNyjlCde+2KDY24UMoojliiX5wd5Vh0rhU1fK2NTdx+wpNIkD0GyVZLQsNDQUAtGrV6r3b3Lx5EzKZDJ6enhAIBOUVmobsrDykp9KzWKK/BvfLRLPG6pMIZGZIP7IXIbpDSVbLipJkr169CkD3t4rjXxXUYo2MqRZL9A+XCyyeWzCJwPOnaVgzlyYRIPqLrqJalp9k9+/fj6CgoEK30YfnsVmZVIslhqGKgxyzv0zB0lXWAIAD28LQubcbOvZ01W1ghBSi3JOsh3Bjeb9kiYVJp5Zqf8YYbt++DQAIDAz86Pa6TLLv1mKNTfgQien7F9Ff3T/JRsilbARdMAKgnETgyJ2hsLI10nFkhKij28Va9PjxY6SmpqJNmzZgjBX6I5FIAADGxsaoX7++2v6bN2+Gm5sbxGIxmjVrhgsXLmglzsx0KTLebVFctfAGWoToCw4HmDcjBTbWym48SfHZ8J8cRJMIEL1DSVaL8m8VN2nS5L3b3Lp1CwDQuHFj8HgF/VH379+P6dOnY+HChbh9+zbatm2LHj16IDY2tkxjZIyp1WJNTAUQiqhfLNF//51E4Ozhpzi6lyYRIPql3O8JlvYWrCHJT7JNmzZ97zb5Sfa/t4q///57jB49Gn5+fgCAn376CadPn8bPP/+MlStXllmMmelSZL4z0w49iyWGpG2rXPj2zsSh48rP7bdfnUeLDlVR1cVcx5ERokQ1WS0qSpLNf2b7bpKVSqW4efMmfHx81Lb18fHB5cuXyyw+xhjevFSfaYdGdyKGZtpE9UkEFnxxFnK5QsdREaJESVZLFAoF7ty5A6FQCA8Pj/duV1hNNjExEXK5HA4ODmrbOjg4IC4ursxiTE+VIjtLeXFS1mLpWSwxPMZGDEvemUQg9OIrbP3upo6jIkSJkqyWREREICMjAx4eHhAKhYVuk5GRgSdPnsDU1BR16tTRWM/hcNSWGWMaZSX132exphZC8AX0cSCGqaGHFGNGFMw1u2n5dRoNiugFuqpqyc2bym/SH7pVfPfuXSgUCjRt2hRcbsF/ha2tLXg8nkatNT4+XqN2W1JpybnIyX6nFktjFBMDN3pYOjwbvB0NSs4wZ0QA0lJydRwVqewoyWrJyJEjwRjDtm3b3rtNu3btwBjTmJ1HKBSiWbNmGn1rAwMD0bZt21LHxhhD/DtjFJtbicDj00eBGDY+D1i6MBlmpsrnsa9jM7BsSjB16yE6RVdWPTVz5kzs3LkT27ZtQ0REBKZPn45Xr15h4sSJpT52qiQHuTnK/oUcDmBbxbjUxyREH1RxkGPBzIJuPacOPMHh3RE6jIhUdjSsj54aPHgwkpKSsGLFCrx+/RoNGjTAyZMn4eLiUqrjKhTqz2ItrMXg8ei7Fqk4vDvm4NNemThyQvkIZMWX59G4tSPc6ljpODJSGdHVVY9NnjwZ0dHRyM3Nxc2bN9GhQ4dSH1OSkA2pVHk7jcMBbB1oGDpS8Xw1KRUuzsr+3znZcswZEQBpLk3yTsofJdlKRC5TICGuoBZrY2ek1uCKkIrCyIhhxSIJBO9M8r5h8RUdR0UqI7rCViIJcVmQy5QXHR6PAys7qsWSiqtWDRmmji+Y5H3Xhju4EBCjw4hIZURJtpKQSuWQxGeplm2rGJdZn1tC9NUg30y0a5WjWl44JhCJMNNhRKSyoSRbSSS8yoTibU8GvoALCyuRbgMipBxwOMCiucmq2XokCTlYwBsBBegLJikflGQrgZxsGVKSCr7NV3EyAegiQyoJK0sFlsxPBoej/JZ5mVsPW7lddRwVqSwoyVYCb15kIL87vkjMg7Fp4cM8ElJRtWyWi5FDMlTLG7m9cI1TS4cRkcpCa0mWRlnRD+mpuUh/Z0L2KtVoEgBSOob6t+33RRqaNHo77CKHizm80Wqt7QnRBq0kWR6Ph7y8PG0cmhSDQsEQ96Lg27upmQAiMY0/QkpHJpOBwzG8KRH5PGDZ1xLYsDQAQBLHHHNGnKFp8YhWaSXJmpmZIS0tTRuHJsWQnJhdMHwiAAcnqsWS0ktPT4OAL9Z1GCViZ6vAavkucJgysd44/xKbll3XcVSkItNKkrW2tkZycjISExMhlUoN9vaSIZPJFGrDJ1rZimkSAFJijDHk5eVBIklCkkQCsdBwu8G0Zo8xWXFatbxlVSgunqH+s0Q7tHLvUCQSwdnZGRKJBNHR0ZDLaTiz8pYqyUFmuvKWPYcLMIEJJHRzgZQCh8ODgC+GmZEd+DyBrsMplQmK07jFcccVbl0wBswbdQYHb3xObRZImdPaAzqRSARHR0c4Ojpq6yXIe0SGSzD+kz8glyvvIMxf64W6darrOCpC9AcPDN/Jd6Gf7QokSnhIScrFrKGnseOsL4RCw3veTPQX3T+sYBhj+G72BVWCre5uDu++bjqOihD9Y4MMLF8sAY+r/Fu5czUOq+dc1HFUpKKhJFvBnDkYhctnn6uWF67vQMMnEvIeTRpJMWlcwXOUP36+j0O7wnUYEaloKMlWIJnpUqyafUG13KGHC2rWt9ZhRITov2GDMtClU8G43kunBOPejTc6jIhUJJRkK5BNy6+rWhQLxTzM/LaNjiMiRP9xOMCi2Smo4aZsKJgnVWD6wJNIfJP1kT0J+ThKshXEo3uJ2PvTXdXyxAXNYWJGwycSUhRGRgzfLUuCmamy/2z8q0zMGnoaeXnUM4KUDiXZCkChYFg2LVjV2Kmamzl6f15bx1ERYliqVZVj2dcS1UQCoRdeYe28SzqOihg6SrIVwOHdEbhzJU65wAGWbOpEjZ0IKYE2LXMxcUxBQ6i9G+/hyJ6HOoyIGDpKsgYuIS4Ta975tt29f0241rLUXUCEGLiRn2egc/ts1bL/pHO4ey1OhxERQ0ZJ1sB9Oz0EacnKmUWMTQWYvLiFjiMixLDlT/Tu5qJsCCWVKjC13wm8jKYh00jxUZI1YAEHIxF4KEq1PG9tOxgZG/Zwd4ToAxNjhjUrkmBhrmz4JEnIxuTPjiM9NVfHkRFDQ0nWQKUkZePb6edVyy06VEXbLs46jIiQiqVaVTm+WyaBgK9sCBUZLsHMoachk9HUeKToKMkaqO9mX0RSvPK5kUjMw8L1HXQcESEVT+OGUiycnaxavhz4HP+bEUIzi5EioyRrgEJORePovkeq5Zn/awNTc+oTS4g29OiajbEjCp7H7v/1AfZuvKfDiIghoSRrYFIkOfCfHKRabtTSAd593HUYESEV37hR6fDxLhgB6rvZFxB0/JkOIyKGgpKsAWGMYenkILx5qRw6USDi4pufOuo4KkIqPg4H+HpOMhrWVzZ8YgyYMzwAD27SGMfkwyjJGpDDex7izD8FrYlnr2wHC2uxDiMipPIQCYHvlkngWEUGAMjOkmFin2OIeZKi28CIXqMkayBio1Lxv69CVMutOjvBuw/NE0tIebK2UuD7/yXBzEzZtSc5MQd+vY4i4XWmjiMj+oqSrAGQyRSYP/oMsjKUneNNzYX4egO1JiZEF9xcZPj+WwlEImVXnpfRaZjQ5xj1oSWFoiRrAH793w3cvVbw7GfFVm8adIIQHWroIcX/vpGAx1V25Xl0LxHT+p9Abo5Mx5ERfUNJVs9dPfccv/wvVLXcb3Q9eDS112FEhBAAaNc6Fwtnp6iWb4S8wowhpyCV0vR4pAAlWT0W9yIDc0acgUKh/Lbs5GqG8fOb6TgqQki+Xt2yMMUvVbV8/mQM5o44Q6NCERVKsnpKKpVj1tDTkCQoR3USinhYu9cHPB79lxGiT0YMycDoYQWDVQQeisLXY89CLqdESyjJ6q118y/hztWC6bX8N3WCrYOJDiMihLzPhC/SMaR/hmr5+B+PsXRKsOouFKm8KMnqoZP7H6sN2zZgTH206Oikw4gIIR/C4QDTJ6XCt3dBV56D28OxbCol2sqOkqyeibidgG8mFgybWKeRDT2HJcQAcDjAnOkp6NG1YPjFA9vCsHj8v3TruBKjJKtH3rzMwJR+x5GdWdAf9rudXcHhcHQcGSGkKLhc5fCL7ybaw7sfYsEXZ6kxVCVFSVZPZGZIMcX3hGpcYh6Pg3W/+8DEjGbXIcSQ8HnAojnJ6NO94NbxiT8fY+6IM8jLo+49lQ0lWT0glyswb2QgIu4kqMq+2dgR7nWsdRgVIaSkeDxgwawU+PYpaAwVcDAS0/qfRNbbO1WkcqAkqwe+X3hZbdqs0V81RttPnHUYESGktLhcYO70VAzyLUi0F07HYGy3w0hJytZhZKQ8UZLVse3rbmHn+juq5c693TBsSiPdBUQIKTMcDjBjSiq+GF7Qj/be9TcY0ekfvIpN12FkpLxQktWhv7Y+wLoFl1XLdRrZYN7adjqMiBBS1jgcZT/aWdNSwOEou/M8fZSM4R3/xuP7iTqOjmgbJVkdOf7HIyybGqxadnI1w7p93WhEJ0IqqIGfZWL5omTw+cpE++ZlJoZ1PIjgE88+sicxZHRF14Fzx55h4ZizYG/7qNs6GGPTP70gEvN1GxghRKs+6ZSN9SuTYGyk7M6TlZGHqf1OYOeG22CMBq2oiCjJlrNzx55h5uenIJcr/6AsrET45Whv6qpDSCXRomkutv6UgCoOymnxGAPWzL2EJZOCaAafCoiSbDk68edjfDXoJPKkym+xJmYC/HKsNyysxTqOjBBSnmq4ybB9UwIa1i+Y6P3g9nB80fUQ3rzM+MCexNBQki0nB34Lw7xRZ1Q1WBMzAX452psG/SekkrK2UmDjukR0/6RgdKg7V+IwoOV+XA16ocPISFmiJFsOdm64Df9JQapnsOZWImw71RdVqpnpNjBCiE6JhMCS+cmY4pcKLld5gZAkZMOvxxFs/S6UJheoACjJapFMpsDKWRewZu4lVZmNgxG2n/6UarCEEADKLj4jhmTgpzWJsLZSPqdVKBg2LL6KiX2PIeF15keOQPQZJVktyUiTYmq/E9j7011VWVVnM/x26lN6BksI0dCssRS7fkmAZ4OC57SXzsTis6Z/4OzhKB1GRkqDkqwWvHiWhmEd/saF0zGqsnqNbfHr8T7UipgQ8l52tgpsWpeI4YPTVQNXpCTlYPqgU/hmwjlkpkt1HCEpLkqyZezy2Vh87nUAkeESVVm3/jWwYX8PiI2oHywh5MP4fGDq+DRsXJMIe1uZqvzgjnB82uQPtS/vRP9Rki0jMpkCGxZdwfheRyFJUA7+zeEAkxa1wOxV7cDl0pywhJCia9ZEir3b4vFJp4LWx69j0zGx7zHMHXkGSfFZH9ib6AtKsmXgVWw6RnX5B1tX31S1IBaKePhuV1f0G1VPt8ERQgyWuRnD8kXJWLpQAnOzgoEqTvz5GH0a7cPBHeGQy2kyeH1GSbYUGGM4uvch+rf4E3euxKnKnWtaYE+QL5q0cdRhdISQioDDAbp1ycb+nfFqfWpTJbn4ZsI5DG5zADcvvtJhhORDKMmW0KuYNEzsewwLxpxFWnJBa0DfUXWx9URfWNsZ6zA6QkhFY2WpgP+CZGxYlYgq9gUTv0fcScBI738w8/PTePEs7QNHILpASbaY5HIF9m26i76N/8DFgFhVubGpAKt3d8XkRS3p+SshRGtat8jFH9sTMHZkGkTCglvFAQcj0avBXiybGoy4FzQ0o76gJFsMlwJjMaDFfvxvxgVkZxZ8k2zfzRl/XhxAt4cJIeXCyIjBb1Q69u+MR9dOBYNVyPIU2L/lAXrU24OVsy4gIY4GstA1SrJFEBmWhIl9j2F8r6N4/CBJVW5hLcLavT74ZmMnGJkIdBghIaQyquIgx/LFKfj1hwR4NshRlUtz5dj701341NqNbyaew9OHyTqMsnKjjpsfEBmWhK2rb+Lk/idqY4hyeRx8OrwOxs1pBqGIp8MICSEE8GwgxS8bknD9pgi/bDdDxCMRAGWyPbg9HAe3h6NTL1eMnN4YLTs6gcOhR1rlhZJsIcJvx+PXlaE4e/ipxrrm7ati9qq2sLGnhk2EEP3B4QCtmueiZbNcXLwixo69pgh/m2wBIPhENIJPRMOttiUGjPPAZyPqwtLGSIcRVw6UZN+S5spx5p9I7N/yALcuvdZY71bbEnPWtEOt+jY6iI4QQoqGwwHat82BV5sc3L0vxN79Jrh4taBS8OxxCtbMvYQfFl9Fl0/d0fvz2mjb1RlCId2V04ZKn2SfPkzGkT0R+GdnhGqkpnfV9LDGtG9aon5Tex1ERwghJcPhAI0bSdG4kRTRsen46x8TnAo0QnaOMplKc+U49dcTnPrrCSysRejWvyZ6DKqFpu2qgs+n5jplhcMYK/KEhc2bN0doaKg24ykXr2LTceqvJzi5/zEe3k3UWM/hAA1bOmDKohZwr2utgwgJMWxVL1mofk+vPl+HkWgy67BK9Xt6iH7Fpm1Z2RwEnjPCP8eM8OhJ4bOBmVuJ0LGHKzr3dkM7H2eYmtOkJqVRKWqyCgXDg5vxCDkZjZBT0Qi7lVDodkYmfHQfUAuDx3vQM1dCSIVjbMTwaa8sfNorC0+i+Aj41xgBZ0VISCpIpGnJuTj2+yMc+/0R+HwuGrZ0QOvO1dCmS3U0bOlAt5WLqUImWcYYoiKSEXrhJUIvvML14BdIite8FQwAHC5Qt5EtBoytj3afOINHt0kIIZVArRoy1KqRhsnjgHsPhDhzToyQSyIkSgoSrkymwO3Lr3H78mv8/O0NiI34qN/UDp6tqqBRSwd4tqoCBydTHb4L/VchkmzC60yE3YpH2M14hN2Kx73rb5CcmPPe7TkcZUOmPkProHMfN5rjlRBSaXG5Bc9u50wHHj0R4MJlMc5fFCLymfot5ZxsGW5deq3WOLRKNVM0aG6PWh42qOVhgxr1reFSywICAdV4AQNKsnK5AnHPM/D0YTKePkrG04cSPH2YjGePkj+YUPOJxDw0bOEAH98aaNnJiRIrIYT8B4cD1K2dh7q18+A3GkhO4SL0tgg3bgpxPVSAuASRxj5xLzIQ9yJDrcsjX8CFW21L1Kxvg+ru5nByM0c1NwtUdzOHQzXTStWwSudJljGGzPQ8SBKykZyYjaT4bCS8zsTr2HTEvcjA6+fpeP08HW9eZEImK/qUTkIxDzXrWaO1dzW0aF8V7vWsaUxhQggpBitLBbp2zkbXzsrHbYlJXIRFCPEgXIB7D3h4FClGTq5mjVWWp8CTMAmehEk01vF4HDg6m8HO0QT2jiawczSGbZWC3y1txLCwEsPSRgxjU4HBD5yhkyS764c7OP77I0gSsiFJyIY0V/7xnT6Az+eiSjVT1PW0RZN2VVCnoS2qu1tQUiWEkDJka6NAR68cdPRS3j2UyYFn0Xw8jhQi6hkPTyI5eBYjRKJEs8abTy5nePEsrUgzBvH5XFhYi2BhJYb5239NzAQwNn37YyJU/W5kzFf9XqeRLeyqmJTZ+y4NnSTZpDdZCL9deAvfDxEb82HvaALnGhao42mDGvWs4exuATtHE0qohBBSzvi8/AZUMrXyjAwOnsUIEPOcj5evuHjxkoMXL7mIeyNActr7E/B/yWQKJMVnv7fh6vus2eODnoNrF2sfbdFJkrW2Vx/Ki8vlQCjiwciED1NzISxtxHBwMoWDkwms7YxhZSuGla1RoeMEpybnIvWd+VwJIbpX9Z3fU97E6yyOwpi987u+xVaRVLdV/qCJenlOLgcJiUJIUgSQpAiQlCxAUjIfiYlcSFL4SM/gISOLj4wsIaR5JUtRxqb6M2FLsQajsLW1haura5m9ePTjFMjlRX/OWphcWTpEfLOPb1gJ0LlQR+dDXXmfDy5HAXOjdHA4Rb7ElJv07FyYGRW9RlXRGfb5UACMAWDKfwWmsHZyKvHREhISYGdnp1q2tbXF6dOnS3y8YiVZfVRRRqEqC3Qu1NH5UEfnowCdC3V0PgqU9bmoPO2oCSGEkHJGSZYQQgjREoNPsuPHj9d1CHqDzoU6Oh/q6HwUoHOhjs5HgbI+Fwb/TJYQQgjRVwZfkyWEEEL0lUElWYVCgZUrV8LV1RVisRienp44ePDgR/dLS0vDsmXL0LZtW9jY2MDS0hJt27bF4cOHtR90GXj+/DkGDBgACwsLmJubo1+/foiNjS3Svjk5OZgzZw4cHR1hZGSENm3aICQkRMsRa09Jz0VoaCjGjx+PunXrwtjYGM7Ozhg2bBiePXtWDlFrT2k+G+9auXIlOBwOvLy8tBBl+Snt+YiIiMDAgQNha2sLIyMj1KlTBz/88IMWI9ae0pyL2NhYjBo1Cs7OzjA2Nkbt2rWxaNEiZGZmajlq7Xnx4gWmTZuGNm3awNjYGBwOB9HR0UXat1TXUWZAFi5cyIRCIVuzZg07d+4cGz9+PONwOOzEiRMf3O/+/fvMwcGBzZ8/n508eZKdPn2ajRo1igFgGzduLKfoSyYzM5PVrFmTeXh4sEOHDrHDhw+zBg0aMHd3d5aRkfHR/YcOHcosLCzYli1b2NmzZ5mvry8Ti8Xs9u3b2g++jJXmXMyaNYu1bduWbdq0iQUHB7N9+/axunXrMmtraxYbG1tO76BslfazkS8qKoqZmJgwe3t71q5dOy1GrF2lPR83btxgZmZmrE+fPuzQoUPs3Llz7Ndff2Xr1q0rh+jLVmnORUZGBqtVqxZzdXVlO3fuZOfOnWPfffcdE4vFbNCgQeX0DspeUFAQs7e3Zz169GA+Pj4MAHv27FmR9i3NddRgkuybN2+YUChk33zzjVq5t7c3a9iw4Qf3zcjIYJmZmRrl3t7erHr16mUaZ1nbsGED43K57MmTJ6qyp0+fMh6P99E//jt37jAAbPv27aqyvLw8Vrt2bdanTx+txawtpTkX8fHxGmXR0dGMw+GwxYsXl3ms5aE05+NdPj4+bPz48axjx44GnWRLcz7kcjmrX78+++yzz7QdZrkozbkICAhgAFhAQIBa+bx58xiPxyv0WmoI5HK56vetW7cWOcmW9jpqMLeLAwICIJVKMXz4cLXy4cOH4/79+x+87WdiYgJjY2ON8ubNm+PVq1dlHmtZOnr0KFq3bo2aNWuqytzc3NCuXTscOXLko/sKBAIMHjxYVcbn8zFkyBAEBAQgN9ewhqMszbl4dwSXfC4uLrCzs8PLly/LPNbyUJrzke/333/HrVu3sHLlSm2FWW5Kcz6Cg4MRHh6OmTNnajvMclGacyGVSgEA5ubmauWWlpZQKBRgBtpWlsstWbor7XXUYJJsWFgYRCKR2ocGADw8PAAA4eHhxT5mSEgI6tatWybxaUtYWBgaNGigUe7h4fHR9xwWFgY3NzeNLxgeHh6QSqWIjIws01i1rTTnojARERGIj49HvXr1yiK8clfa85GcnIwZM2Zg9erVsLa21kaI5ao05+PixYsAlM/eWrduDYFAAHt7e3z55ZfIzi7e4PT6oDTn4pNPPkGtWrUwb948hIeHIyMjA+fOncMPP/yAiRMnwsREP2a3KS+lvY4aTJKVSCSwtLTUmFsw/+IgkWjOW/ghW7ZswdWrV7FgwYIyi1EbJBIJrKysNMqtra2RnJxc4n3z1xuS0pyL/5LJZJg4cSLs7OwwduzYsgqxXJX2fMyZMwe1a9fG6NGjtRBd+SvN+ci/ozV48GD4+PggMDAQc+fOxbZt2zB06FCtxKtNpTkXYrEYFy9ehEKhgIeHB8zMzNClSxf07t0bGzdu1FbIequ011GdTdp+9uxZdO3a9aPbdezYEcHBwWCMFTp5b0luXQQHB+PLL7/EiBEjMGzYsGLvX95K+r7L8pzpi7J6P1OnTsXly5dx4sSJQv+ADEVJz8eFCxewe/du3Lp1y+AnxX5XSc+HQqGcqGT48OFYtmwZAKBTp06Qy+WYP38+wsPDUb9+/bINVstKei5ycnIwePBgxMfHY8+ePXB2dsb169exbNky8Pl8/Pzzz9oIV2+V9jqqsyTbtm1bREREfHS7/Cp6/jew/77h/G9lRb3ddePGDfTt2xfe3t747bffShB5+bKysir0m1JycvJHk4O1tXWhTfaLe870RWnOxbsWLFiALVu2YNeuXfDx8SnLEMtVac7HhAkTMHbsWFSrVg0pKSkAlLV7uVyOlJQUGBkZQSQyrFlZSnM+bGxsAEDji7+Pjw/mz5+PO3fuGFSSLc25+O233xAcHIzIyEjUqFEDANChQwdYWFhg/PjxmDhxIjw9PbUStz4q7XVUZ0nW2Ni4WM9DPTw8kJubi6ioKLXnsvnPF4ryB3D//n1069YNjRs3xsGDByEQ6M+cg+/j4eGBsLAwjfKifLP28PDAoUOHkJWVpfY8ITw8HEKhUOP5tr4rzbnI9+2332LVqlX48ccfMWLEiLIOsVyV5nxEREQgIiICv/zyi8Y6KysrrF+/Hl999VVZhVouSvu3AmjW/vJrKyVtNKMrpTkX9+/fh5WVlSrB5mvZsiUA5WenMiXZ0l5HDeaT0717dwiFQuzbt0+tfO/evWjQoAHc3Nw+uP+TJ0/QtWtXuLu74/jx4zAyMvrg9vqib9++uHr1Kp4+faoqi46OxqVLl9C3b9+P7puXl4cDBw6oymQyGfbv3w8fHx+Dq6mU5lwAwI8//ohFixbh22+/xbRp07QZarkozfkICgrS+PH09ESDBg0QFBSEAQMGaDv8Mlea89GjRw+IRCKNeUMDAgIAKHsiGJLSnIsqVaogOTlZo0HPtWvXAABOpZir1RCV+jpaxC5GemHevHlMJBKxdevWsaCgIDZx4kTG4XDY0aNH1bbz9vZmNWrUUC2/efOGubi4MCsrK3b8+HF25coVtZ+cnJzyfitFlpGRwWrUqMEaNGjADh8+zI4cOcIaNWrE3NzcWHp6umq76OhoxuPx2NKlS9X2Hzx4MLO0tGRbt25lZ8+eZf3792cikYjdvHmzvN9KqZXmXPzxxx+Mw+Gw7t27a/z/h4WF6eLtlFppPxv/Zej9ZEt7Pvz9/RmPx2MLFixggYGBbOXKlUwsFrNRo0aV8zspvdKci2fPnjEzMzNWq1Yt1WAUq1evZmZmZqxZs2Zq/U0NzYEDB9iBAwfYxIkTGQC2efNmduDAARYcHMwY08511KCSrEwmY8uXL2fOzs5MKBSyhg0bsgMHDmhs17FjR+bi4qJaDgoKYgDe+1PUUT90JSYmhvXr14+ZmZkxU1NT9umnn2rE/OzZMwaALVmyRK08KyuLzZgxgzk4ODCRSMRatmzJgoKCyi32slbSc5E/wldhPx07dizX91CWSvPZ+C9DT7KMle58KBQKtm7dOlajRg0mEAiYs7MzW7x4MZNKpeX3BspQac5FWFgYGzhwIKtWrRoTi8WsVq1abNasWUwikZTfG9CCj10DtHEdpVl4CCGEEC0xmGeyhBBCiKGhJEsIIYRoCSVZQgghREsoyRJCCCFaQkmWEEII0RJKsoQQQoiWUJIlhBBCtISSLCFlhMPhgMPhgMvlIioq6r3bde7cWbXtzp071daNHj1atW7JkiXvPcauXbtU23Xq1EltXXBwsGpd/o+xsTEcHR3RoUMHzJkzB7dv3y7NWyWEFBElWULKEJ/PB2PsvTM8PXnyBOfPnwef/+G5Ofh8PrZv3w65XF7o+q1bt370GC4uLliyZAmWLFmCr776Cr1790ZmZibWrl2Lpk2bYtiwYcjIyCjaGyOElAglWULKkIODA5o3b44dO3ZAJpNprN+2bRsYY+jdu/cHj9O7d2+8ePFCY8B6QDkLyqVLl9CnT58PHsPV1RX+/v7w9/fH//73P2zduhU3b97E7du30bBhQ/z+++8YOHBg8d4gIaRYKMkSUsb8/PwQFxeH48ePq5Xn5eVh165daNu2rWpqtfcZNmwYjIyMsHXrVo11+WXjxo0rUXyNGzfG2bNnYWdnh9OnT+Pw4cMlOg4h5OMoyRJSxj7//HOYmJhg27ZtauVHjx7Fmzdv4Ofn99FjWFpaYuDAgThx4gRev36tKs/NzcXu3bvRsWNH1K5du8Qx2tvbY8KECQCgMX0kIaTsUJIlpIyZmZlhyJAhOH36NF68eKEq37p1K8zNzTFo0KAiHcfPzw8ymUytcdShQ4eQlJRUpET9MfkNpq5fv17qYxFCCkdJlhAt8PPzg1wux/bt2wEAMTExCAwMxLBhw2BsbFykY3h5eaFevXqq57iAMlFbWVmhf//+pY4xf/LthISEUh+LEFI4SrKEaEGrVq3QsGFDbN++HQqFAtu2bYNCoSh2DXTcuHF4+vQpzp07h8jISAQFBWHEiBEQi8WljjE/cXM4nFIfixBSOEqyhGiJn58fYmJicPr0aezYsQPNmjVDkyZNinWMkSNHQiQSYdu2baoabVncKgaAV69eAQDs7OzK5HiEEE2UZAnRkhEjRsDIyAgTJkzAy5cvMX78+GIfw9bWFr6+vjh06BC2b9+ONm3aoEGDBmUSX1BQEABlrZsQoh2UZAnREktLSwwYMAAvXryAiYkJPv/88xIdx8/PD7m5uUhISCizWmx8fDx+/fVXAMruQoQQ7fjwkDGEkFJZsWIF+vXrBzs7O5iZmZXoGJ07d8aRI0egUCjQrVu3Usd09+5djBw5EomJiejZsyf69u1b6mMSQgpHSZYQLXJ2doazs3OpjsHhcEqUCKOjo+Hv7w9AORBGYmIibt68iZs3bwJQ1mDza7OEEO2gJEtIBRUTE4OlS5cCAMRiMSwtLVGrVi3Mnj0bw4YNQ+PGjXUbICGVAIflt+MnhBBCSJmihk+EEEKIllCSJYQQQrSEkiwhhBCiJZRkCSGEEC2hJEsIIYRoCSVZQgghREsoyRJCCCFaQkmWEEII0RJKsoQQQoiWUJIlhBBCtOT/6SlGaJPIkvoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[90 ,  15]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "    plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e53c16c00d44e58a227a523705b0e84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e50792ad916b4f669214556b60337e05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c841294f4c174479a09ea159b8749b49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7438644d2e6440d5a1a955f8f39b19c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78821bd0f46442998854762512026dae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d2596a8e6dc42c6a718b61e0402f693",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2684bcb1a264407e9f3434ae3e7506a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4cePGDbXysbGxYt68eaJnz57C09NT2NjYiMDAQBEZGSlOnjyp9TOOHz8unnjiCeHr6yusra2Fp6en6Nq1q5g3b544W2UuYmBgoMbczOq0bdtWABDu7u6itLS02nKlpaVi9erVom/fvsLZ2VnY2NiI1q1bi4iICLFixQqNOa2lpaVi4cKFIjg4WPH93n33XVFSUlKneahCCCGVSsXq1atFhw4dhI2NjfD19RUvvPCCyMvLq/a7rl+/XnTt2lU4ODgIDw8P8c9//lNcvnxZzJ8/XwAQUVFRauUzMjLE5MmThbe3t7CwsBAAxPz58xXvnzx5UgwdOlS4urqKFi1aiP79+4vt27eLqKgojbKNopHnoUqEkHdY1l2vXr1w7tw5wyS7iXlx3B4c3ZsEABgzpR2entsNkwf8gvJy2VJr289PQrvOf8/x+lFl4MOUep9uMiL5KE8iMiGq+dSrV41FDfFvnPdQ66Hofhn+OKS8jzDh2VC4uNuh/wjlfC152BIRUfPAQK2H4/uTUVYqG3jk6eOAVoGyFVvCh/oryhzepblwNxERmS8Gaj2otk4HjQ5UvO41oBXk09piL9xDQZ55T2ImIiIlBmo9/HlMuXzWgOHKbl5XDzu07SRbR1MqFbh0um6Ts4mIyHQxUOvobmohbifIlvmysJSgfRf1xaVDeyjnp11koBIRNRsM1Do6f0I5CTogxAU2tupLDXbs7qV4/Wd0aqPVi4iIjIuBWkdXzivXIe3ZX3OydCeVQL16IROVldJGqRcRERkXA7WOrp6/p3jdJVzziRA+fo5w9ZA927D0QSVSEwsarW5ERGQ8DNQ6kEoFrl1UBmq7UM1HIkkkErTp4Kb4+cZfWY1SNyIiMi4Gah0k3cxDcaHsQcH2jlbw8LHXWi64vTJQb17JbpS6ERGRcTFQ6+Dq+UzF66CHXKt9lmIblUC9cjZTaxkiIjIvDNQ6uBqjDMfOD/tUWy64vavi9fXL7PIlImoOGKh1cF3l/mloT+9qywWEuMDCQtZ6vXe3CMWlNg1eNyIiMi4Gah3Exyof3KvarVuVrZ0V/IKcZT8IICFNczQwETVdM2bMgEQi0fqAc2MJCgpSPMSbmiY+YFxH2ZnFyM0qAQBYWkng3cqxxvLB7V1xO1G2olJcait0bpPS4HUkI7r0H6C4Cf8dOwQAXT/U+zBBQUFITk7W+p6Pjw/u3lVfHaysrAwffvghNm/ejJycHPTo0QPLli1Djx49NPY/ePAgRowYgT179uDRRx/Vu66mZsiQIYiOjoYeT9QkI2Og6ki1dert66jo0q1OQIiL4nVyhlcNJcksFKcAjkHGrkX1ipIMdigXFxe8+uqrGttbtGihse3tt9/GihUrEBkZCX9/f2zcuBFDhw7F9evX4eurXBilsLAQs2bNwlNPPdUkwnTx4sV4++234efnZ+yqKBw+fNjYVaBaMFB1pBqoQe1cay2v6PIFkHS3+vutRKbG1dUVCxYsqLWcEALffvstnnnmGaxbtw4AMG7cOAwZMgQbN27Em2++qSj71ltvoaSkBKtWrWqoateJr6+vWuA3BSEhIcauAtWC91B1lKASqB271d7i9FcJ1BS2UKkZunfvHoqLi9G7d2/FNvlr1W7jY8eO4euvv8aXX34Jd3f3en2W/P5iQUEBXnvtNQQFBcHa2lot+K9fv44ZM2agdevWsLW1hY+PD6ZMmYIbN25oHK+me6hnzpzBhAkT0LJlS9jY2KB169aYPXs20tPTNcoCQE5ODt577z2EhYXBwcEBLi4u6Nq1K95++20UFRUhKSkJEokE0dHRAGSLw8j/DBkyROM7VlVaWoolS5agS5cucHBwgLOzMwYOHIj/+7//0ygr/6wZM2YgKSkJkyZNgqenJ+zs7NCrVy/s2bOn5hNdxY4dO/DUU0+hXbt2cHR0RIsWLdCzZ098/vnnkEo1l12Vn9fExESsXr0aXbp0gb29vdr3zMnJwTvvvIOOHTvC3t4eLi4uGDZsGH7//XeN4+Xn52Pp0qWIiIiAv78/bGxs4OXlhbFjx+L06dN1+i6GwBaqjtQGJHWofkCSnF+Qk+J1coYXpFIJLCx4b4RMX2lpKTZt2oSUlBQ4OjqiS5cuGDRoECwt1R8U4eXlBXt7e5w/f16x7dy5cwCAwEDZc4QfPHiAmTNnIjIyEpGRkXrVq6ysDBEREcjJycGIESPg7OyM4OBgAMD+/fsxfvx4lJeXY8yYMWjbti1SU1Px66+/Yu/evYiKitJ6X7eq9evXY9asWbC1tcXYsWPRunVrxMXFYe3atdi9ezdOnz6NgADlIx1v3bqFoUOHIjk5GT179sS//vUvSKVS3Lx5EytWrMCcOXPg6uqK+fPn4/vvv0dycjLmz5+v2L+2QUhlZWUYOXIkoqOj0aFDB7z44osoLi7GL7/8gieffBIXL17Exx9/rLFfcnIyevfujTZt2mDatGnIycnBli1b8Pjjj+PQoUMYOnSoTuf87bffhoWFBcLDw+Hn54f8/HwcOXIEc+fOxdmzZ7Fx40at+82dOxfHjx/Ho48+itGjRyuuneTkZAwZMgRJSUkYOHAgRo0ahaKiIuzZswejRo3Ct99+i1mzZimOc+3aNbz33nsYNGgQHn30Ubi5uSElJQW7du3Cb7/9ht3LlmFUv346fRdDYKDqQAiB+FjlikdBD7nWuo+Tiy2c3WxRkFuK0nIbZOS6wNcjr+EqSdRI7t69i2nTpqltCw4Oxvr16zF48GDFNolEgueffx6ff/458vPz4efnh40bN8LJyQlTp04FALz//vvIycnBl19+qXe97ty5g06dOiE6OhqOjspBg7m5uZg8eTIcHBxw7NgxdOrUSfHe1atXER4ejpkzZ+LChQs1Hv/mzZuYPXs2goKCEB0drXZ/9ciRIxg+fDjmzp2L7du3K7Y/9dRTSE5Oxscff4x33nlH7XhZWVlo0aIF7OzssGDBAhw9ehTJyck6dafLLVu2DNHR0XjkkUewa9cuWFnJfqXPnz8fvXv3xuLFi/HYY4+hX5VQOXr0KBYsWKAW3lOmTMGoUaOwdOlSnQN17969Gl3RUqkUzzzzDH744Qe89NJLCA8P19jvwoULiImJUfyHR+7pp59GcnIyfvrpJ0yaNEmxPS8vD0OGDMErr7yCsWPHwsdHtg5Ax44dkZ6eDk9P9cdopqamonfv3pi3YkWjBiq7fHWQc+8B8nNKAQBWVha1jvCVU+32Tc7gfVQyfc888wwOHz6Mu3fvoqioCH/99Rdmz56NpKQkPPLII7h06ZJa+U8++QRvvfUW/vzzT/zvf/9DaGgoDh8+DD8/P5w5cwYrV67EqlWr4OnpiYULF8LX1xdWVlbo2bMnTp48Wef6LVu2TC1MAeCHH35AXl4eFi5cqBamABAaGopZs2YhJiYGsbGxNR7766+/Rnl5OVatWqUxWCkiIgJjx47F7t27cf/+fQDA+fPn8ccff6Bbt2546623NI4n72rVx7p16yCRSLB8+XJFmAKAt7c3PvjgAwDA2rVrNfYLDAzE+++/r7Zt5MiRCAgIwJ9//qnz52u7r2thYYG5c+cCAA4cOKB1vzfffFMjTC9duoTo6GhERkaqhSkgu2+/cOFClJSUYNu2bYrtLi4uGmEKAP7+/pgwYQKuJyUh5W7jPZeaLVQdJMfnK157+NhXu+RgVX5BToiNkS0GkXzXC3063WyQ+hE1FtUWDQCEhYXhm2++QYsWLbBs2TIsWLBArYVma2uLxYsXY/HixWr7lZWV4dlnn8Xo0aMxdepUrFy5UtFi6t+/P/773/9i1KhRiI+PV7RGamNnZ4cuXbpobD916hQA2S9sba2/mzdl/y6vXbumEbjajhMdHY2zZ89qvJ+ZmYnKykrcvHkTPXv2VNzDGzlyJCwsDN92uX//PuLj4+Hn54cOHTpovB8REQEAiImJ0XivW7duGl30ANC6dWvF99RFdnY2li5din379iExMRFFRUVq76elpWndT/W+upz8c/Pz87X+Pd27J/tdeu3aNbXtJ0+exKpVq3Dq1ClkZmairKxMvQ6ZmQho2ThrATBQdZAcl6t4rTp6tzZ+bKFSMzFnzhwsW7YMx44d06n8woULkZaWphhosnTpUgwbNkzxi7R9+/YICgrCl19+iQ8/1G3+rLe3t9b/7GZny27XrFmzpsb9CwsLa3xffpylS5fqdJy8vDwAaLCpN/n5sv/oVzcaWb5dXg9Vrq6uWvexsrLSOphIm7y8PDz88MO4desWevfujenTp8Pd3R1WVlbIy8vDqlWrUFpaqnXflloCTn5+Dx48iIMHD1b7uap/T9u3b8eECRNgZ2eH4cOHIyQkBI6OjrCwsMDRo0cRHR2N0vJynb6PITBQdZB0M0/xum0n3Uch+qtNneFIXzJf3t6y/zBWbaFoExMTg08//RTffPMN/Pz8UFBQgPT0dMV9VQAICAiAp6cnrl69qnMdqus5cnGRzQm/dOmS1hasruTHyc/Ph7Nz7f+xlodWda00fcnrU3UxDbk7d+6olTO0tWvX4tatW5g/f75Gi/LUqVM1ToHS9nclr+eqVavwyiuv6FSHDz74ADY2Njh37hw6duyo9t7s2bMVI6cbC++h6kC1y1eXOahyvgHKkb7p2ZrPTiUyF/LuujZt2tRYrqKiAs8++yyGDh2K5557Tu29qq2ZkpISg9StT58+AIDjx4836nHk5Q8cOKBTq0/eBVtZWanT8Z2cnBASEoK0tDTExcVpvB8VFQUAOo1ero/4+HgA0Do6uz5BVp+/p/j4eHTq1EkjTKVSKU6cOFHnOuiLgaqD5Lg8xWv/OnT5tvRTDo5Iy3IHVxQjU3b16lXk5ORobE9OTsZLL70EQDaqtSaLFy9GfHy8Wvers7Mz/Pz8sH//flRUVACQ/UK+f/8+QkND9a73M888oxjUom3AjVQqxdGjR2s9zksvvQRra2vMmzdPcd9VVVlZmVoY9OzZE/369cPFixfxySefaJTPzs5W+0+Dh4fsP90pKbovYfnss89CCIE33nhDLYizsrKwaNEiRZmGIJ/SU/XcxcTEaNwz10WvXr0wcOBA/Prrr4qFQKr666+/kJmp8hjNoCDExcWpzQEWQmDhwoW1DjJrCOzyrYVUKpCSoGyh1uUeqpOrLewdrfCgqALFJXbIL3KAawPUkagxbN26FUuWLMHQoUMRHBwMJycnJCQkYO/evSgpKcHo0aPx+uuvV7v/1atX8dFHH2H58uWKeahyb775JubOnYuBAwciPDwcmzdvRosWLfDiiy/qXW8PDw/88ssvGDduHPr06YNhw4YhNDQUFhYWSElJwalTpzTCTZsOHTpg3bp1ePbZZxEaGopRo0ahXbt2KC8vR0pKCo4fPw4vLy9cv35dsc+mTZswZMgQvPvuu9i2bRuGDBkCIQTi4uLw+++/4/r164pgGjZsGLZu3Yrx48dj9OjRsLe3R2BgoMYUJVWvv/46fvvtN+zcuRNdu3bF6NGjUVxcjK1btyIzMxNvvvkmBgwYoPc51Gb69OlYunQpXn31VURFReGhhx5CXFwc9uzZg/Hjx2PLli11PuaPP/6IiIgIPPfcc/j8888RHh4OV1dXpKam4vLly7hy5QpOnTqluMUwb948zJkzB927d0dkZCSsra1x8uRJxMbGYsyYMdi9e7ehv3aNGKi1yEgrRMkD2f+abe0t4exqq/O+EokEPn4tFPdg07I8GKhksoYOHYobN24gJiYGp06dQlFREVxdXTFgwABMmzYN06ZNq/Y+ZmVlJZ599lmEh4fjhRde0Hj/5ZdfRkFBAb755htcuHABXbt2xYoVK3Qe4VubYcOG4fLly/jss89w4MABHD9+HDY2NmjVqhUiIiJ0XlTiqaeeQteuXbFs2TJERUXh999/h6OjI1q1aoUJEybgySefVCsfHByMCxcu4NNPP8WOHTvwxRdfwM7ODkFBQfj3v/+tCAYAmDlzJpKTk/Hzzz/j008/RUVFBQYPHlxjoNrY2ODgwYNYvnw5fvzxR6xevRpWVlbo2rUrVq5cicmTJ9fvhOmgVatWOH78ON5++22cOHECBw4cQIcOHfDVV1/hH//4R70C1d/fH+fPn8fq1auxbds2bN68GZWVlWjZsiU6deqEl19+GZ07d1aUnz17NmxtbbFy5Ups2LAB9vb2GDhwINavX49t27Y1eqBKhB6PNujVq5di5RNzdfrIbTw3aicAwD/YGet//2ed9v/g+SM4HZUKAFjx4lqMWHHRwDWkxnDt2jWN+zRqmsnTZpqLSZMmYcuWLUhPT29ya/pSHajmU69eNRat9d+4DthCrYXqgKTWbXTv7pXz8VfeR03P4sAks8WwMis3b96Era0tvLw4Op90x0CtRZLKHNS6TJmR8/FTPtIqLat+C38TUeNYtWoVjh07hpiYGEycOFFt9SGi2vBqqUVynLKFGqjDGr5VtWSgEpmMlStXKlZxWrZsmbGrQyaGgVqLlPg8xWu/wPp0+SoDlXNRiZq2W7duGbsKZMI4D7UGUqlAalKB4mff1i1qKK2d5lxUTkYlIjJHDNQaZKYXobxMtsKJnb0VHJ1s6nwMJ1dbONjJ5rcVl9ghP8cwq78QEVHTwkCtQeot5f1Tdy/7eh1DIpHA31P5LNW05Pt614uIiJoeBmoNUm+pdPcG1L27V66Vp3K5tjSVLmQiIjIfDNQaqAZqUD1G+Mr5qbZQGahERGaJgVoD1UANaOta7+P4eijnsqYlM1CJiMwRA7UGt1Xuobb0r3+Xb0v3POUx47OrL0hERCaLgVqDND2nzMj5uCtbqHdS2EIlIjJHDNRqPCgux707xQAAiQTw9nWsZY/qqbZQ7919oG/ViIioCWKgViMtSTm9xdnNFpZW9T9VXi75kEhk81nvF0hRVlZZyx5ERGRqGKjVUJ2Dqk/rFACsraTwcpF19QoB3Esv0ut4RMY2fPhwSCQSREdHV1tmzpw5kEgkWLNmTSPWjMh4GKjVUB3h6xdU9zV8q2qpch/1blqh3scjMqYLFy7A0tISPXv2rLbMmTNnAMiem0zUHDBQq6G6hm9we1e9j+ejch/17m0GKpmuhIQE5OTkIDQ0FC1aaB+sV1xcjCtXrsDW1hZhYWGNXEMi42CgViM1UaWFWo+nzFSlOjDpbioDlUzXuXPnAADh4eHVljl//jwqKirQtWtXWFtbN1bViIyKgVoN1XuoLfWYMqM4hlue4nVaUl615YiaOl0C9fTp0wDY3UvNC5+HqoUQQn0d39ZOeh+zpcpqSbfjuLgDmS55oG7ZsgVRUVFay/D+KTVHDFQtsjMf4EFxBQDA2sYCTi51f2xbVT4qLdS7qXziDJkmIQRiYmIAAAcPHqy1PAOVmhMGqhaqKyS5e9lDIpHofUzVUb73MvhMVHMSavOFsaugs6tlL+m1/82bN5Gfn4++ffvijz/+0FomNzcX7u7ucHBwQKdOndTe++qrr7B06VLcuXMHoaGhWLlyJQYOHKhXnYiaCt5D1eJ2omHW8FXl5VoACy7uQCZO3t3bvXv3astcuHABANCtWzdYWloqtm/ZsgVz587Fu+++i5iYGPTr1w+PPPIIUlJSGrbSRI2EgaqF6v3TQD2eMqPKylIKL1dlUGemcXEHMj3yQO3Ro0e1ZeSBWrW7d/ny5ZgxYwZmzZqFjh07YvXq1fD19cXXX3/dcBUmakTs8tVC/bFtLgY7bkv3PGTkugGQ3Uf1D9Z/Og4Zn77dqKZEl0CV32NVDdSysjKcP38er7/+ulrZESNGVNt1TGRq2ELVQnXKTKsA/Uf4yqkPTOJcVDItUqkUFy9ehI2NDUJDQ6stp62FmpWVhcrKSvj4+KiV9fHxwd27dxumwkSNjIGqRarKwviGmDIjpzp1hoFKpubatWsoLCxEaGgobGy0j3wvLCxEXFwcWrRogfbt22u8X3WAnxDCIIP+iJoCBmoVZWWVuHtbGajerfRbGF+Vags1PTm/+oJETdD58+cB1Nzde+nSJUilUvTo0QMWFspfL56enrC0tNRojWZmZmq0WolMFQO1ijvJ9yGE7HULZxvY2FrWvEMd+KpMnUmJ5+IOZFqmT58OIQTWrl1bbZn+/ftDCKHxFBobGxv07NlTY+7qwYMH0a9fvwapL1Fj46CkKm6r3D/18LE36LFV1/PNuM3FHah5ee211zBt2jT07t0b/fv3xzfffIP09HTMmTPH2FUjMggGahWqI3wNOSAJALxVunyzMrm4AzUvTz75JLKzs/HRRx/hzp07CAsLw759+xAYGGjsqhEZBAO1CtVADW7vZtBje7oUwMJCQCqVoCBftriDjY3hupSJmroXXngBL7zwgrGrQdQgeA+1CtVAbd3GcHNQAcDaSgoPN6ni53vpXNyBiMhcMFCrUF3H19cAj22rystLueRgBgOViMhsMFCrMPRj26ryVg1UzkUlIjIbDFQV+bklKMgrBQBYWkrg5mln8M/w9lQJ1DQGKhGRuWCgqlDt7nVxt2uQFVxUW6h3OHWGiMhsMFBV3E5UBqqXr+FWSFKlGqi3E3Ia5DOIiKjxMVBVqLZQW7dpmCfBqHb53knh8oOmRMiX0CIis2Kof9sMVBUNOQdVTrWFeu/ugwb5DDI8S0tLlJeXG7saRNQAysvLYWmp/5oADFQVqssO+gcZfoQvAHh6KAM1P7cCFRXSGkpTU+Hk5ISCgoLaCxKRySkoKICTk/6/8xmoKtIaeMoMANjYAG6uslCVSoHsjOIG+RwyLHd3d+Tm5iIrKwtlZWXs/iUycUIIlJWVISsrC7m5uXB3d9f7mFx68G+VlVKkJStH3bb0N/yiDnJenpXIzZN1L2SkFcLHr+E+iwzD1tYWAQEByMnJQVJSEiorK2vfiYiMKytL+fraNY23LS0t4eTkhICAANja2ur9cQzUv2WmF6GiXNb9amdvBXtH6wb7LG+vStyMl72+m1qILr0b7KPIgGxtbeHr6wtfX19jV4WIdNGpk/J1I/Qqscv3b6oDkty8DL+ggyq11ZLSuPwgEZE5YKD+LVVlQFJDdvcCXC2JiMgcMVD/lnpLef80sK1rg36Wags1LSmvQT+LiIgaBwP1b6ot1OB2rg36WWqBeiuvQT+LiIgaBwP1b6mqj20LaJgpM3Lensq5p5l8hBsRkVlgoP6toR/bpspL5R5qbnYZpFLOaSQiMnUMVAAPisuRdVe2wIJEAni1dGjQz7O3F3B2krVSKyqA3CwuQUhEZOoYqADSkpQDkpxcbGFp1fCnxYsjfYmIzAoDFeoDkjx97BvlM1UD9W4q76MSEZk6BirUH9vmF9Qwj22rSn1xB7ZQiYhMHQMVwO1GeGxbVWqBmspAJSIydQxUqD9lpqEeLF6V2oPGb/OxYEREpo6BisadMiOn2kJNTcxtlM8kIqKG0+wDVQih1uXb0Ov4yvEeKhGReWn2gZqVUYwHReUAAGsbCzi76f9MPF2odvlm3yvlA6uJiExcsw/UlATllBkPbwdIJJJG+VxHRwEHe9niDmWlAgV5pY3yuURE1DAYqPF5ite+AY3T3Qv8vSITR/oSEZkNBmq86lNmGmfKjJz6c1G5uAMRkSljoKp0+bbp0MiBqtJCvcuBSUREJo2BqhKojbVKkpzaer7s8iUiMmnNOlCFEOqBGtg4c1Dl1FqoqfdrKElERE1dsw7U3KwSFBaUAQCsrC3g6mHXqJ+v+qBxLu5ARGTamnWgJquM8HX3tGu0KTNyai3U22yhEhGZsmYdqKojfFs20pKDqlQDNSujpNE/n4iIDKd5B6rK/dOgdq6N/vkuzlLYWMtWSHrwQKrofiYiItPTzAM1T/G6bUf3Rv98iYRr+hIRmYtmHqjGmzIjp7paUmY6F3cgIjJVDNS/tWrkKTNyqqslcXEHIiLT1WwDNS+nBAW5sgXpLa0k8PC2N0o9vLmeLxGRWWi2gaq6KL6bp32jT5mR4+IORETmoRkHqrK717d14z1lpirV5QdTE3KMVg8iItJP8w1U1SkzD7karR6q91DTubgDEZHJaraBmhSXp3gdYoQpM3JqizvcfWC0ehARkX6ab6DeVK6d27qNi9Hq4eYqhaWlbHGHokIpSh5UGK0uRERUf80yUIUQuHUzT/Fz6xDjzEEFAEtLwMuDizsQEZm6ZhmomelFKC4sBwBY21rA1b1xnzJTlRdXSyIiMnnNMlBv3VB293r6OBhtyoyc+lxUrpZERGSKmmegqnb3GvH+qZzaakmci0pEZJKaZ6CqtFDbhXoYsSYyLb1V5qLeyq+hJBERNVXNM1BVRviGdDLelBk535bKQL11PcuINSEiovpqloGapNrlG2y8Eb5yvi2VU2XSUwqMWBMiIqqvZheoD4rLkZ4su08pkQC+AcZ5yowqXx+VxR0ySiGEMGJtiIioPppdoCbHKe9ROrvawsbW0oi1kWnRQsDZSQoAKC8HsjKKjVwjIiKqq2YXqKoDkppC61SupY9Kt28yR/oSEZma5heoKgOSgtu5Gq8iVagOTEq9xfuoRESmpvkFqkoLtX1XTyPWRJ3qfdT0ZAYqEZGpaYaBmqd4HdAEFnWQa+Wr7PK9zbmoREQmp1kFamWlFInXlQ/xDghpOoGq2kJNupFtxJoQEVF9NKtATb1VgNISWXDZOVjBxciL4qtSm4vKLl8iIpPTrAI1/qqyderbuoURa6JJdVDSvbslkEo5F5WIyJQ0r0CNVQZqmw7GX3JQlaODgIuzLFQrKoCsu3zqDBGRKWlWgRp3VXlvMqyHlxFrop1qKzWNc1GJiExKswrUBJUWanAHNyPWRDvVgUlpSbyPSkRkSppNoJaXVyJRZQ5q0EOuxqtMNdQHJrGFSkRkSppNoKbE56OiXLZerqOTNRydbIxcI02qXb4p8bk1lCQioqam2QSq+gjfprOGr6pWKi3UhGv3jFgTIiKqq2YTqKoDktqGNq0RvnJ+rVTuod5ily8RkSlpNoGqOmUmtIe3EWtSPT/fClhYyOaf5mSXo7SkopY9iIioqWg+garSQg1u72q8itTA2hpo+fdIXyGA2wlc05eIyFQ0i0AtLalAiko4NaU1fKtq7adslSbHM1CJiExFswjU+NgcVFbKulKd3Wxh72Bt5BpVTz1Q84xXESIiqpNmEajXL2UpXjfF+aeqVAM16SanzhARmYpmEqjKKShhPZvekoOqWvsrAzXuSqYRa0JERHXRLAL12kVlC7VTEx3hK+ev0kK9nch7qEREpsLsA1UqFbhxWRmoIR2b5hxUuVYtK2H599SZ3OwKPCguN3KNiIhIF2YfqLcT81FcKAslW3tLeHjbG7lGNbOyAnx9VZcgZCuViMgUmH2gqg5I8gt0hkQiMWJtdMORvkREpsf8A/WickBSx26eRqyJ7tQCNS7PeBUhIiKdmX+gqtw/7dLbx4g10V1ggDJQb17hIvlERKbArANVCIGrF5SB1LaThxFro7vgQOVApOsXM4xYEyIi0pVZB+qd24XIzigGAFhbW8A/2NnINdJNmyCVqTO3iiCVCiPWhoiIdGHWgfrXWWXrzi/IGRYWTX9AEgC4ukjh5iob6VteJpB6q8DINSIioto0m0AN69W0F3SoSrWVqvroOSIiaprMOlCvnFcu3dejv68Ra1J3wUHK+6gJsdk1lCQioqbAbAO1slKKqyqB2rFr017Dt6o2KoF686+sGkoSEVFTYLaBeut6rmKFJHsHK3i2dDByjeqmTaCyy/faxbtGrAkREenCbAP1r3PK+6dB7VyNV5F6Uu3yTb1VhMpKqRFrQ0REtTHbQL38pzJQu/dtacSa1I+Ls4CH+98jfcsFUhM50peIqCkz20A9f+KO4nX3fq2MWJP6U72PqrriExERNT1mGah52Q+QcE021UQiATp0NY01fKtq11YZqNdiuAQhEVFTZpaBeuEP5SAeH78WsLO3MmJt6q9DO2WgXjqTZsSaEBFRbcwzUE+mK153DTe9+6dy7R8qU7y+dvEehOAShERETZVZBur5E8pA7fcPfyPWRD/+rSrh6Cgb3Xs/vxJ3UwuNXCMiIqqO2QVqcVE5YlWeMBPW07SWHFRlYcH7qEREpsLsAvXynxmoqJC16tw87eDsZmfkGumng0q3r+qj6IiIqGkxu0A9c+S24nVYL9N4oHhNVAcmnTueYsSaEBFRTcwuUE8eVIbO0MeCjFcRA+kcqmyhXjl3jysmERE1UWYVqDn3HiBWfp9RAvToZ1pPmNHG16cSXh6yFZNKHkgRd4WPciMiaorMKlBPH7kN+cySVq2d4OhkY9wKGYBEot5KvXj6Tg2liYjIWMwqUFW7e/tEmO50maq6hJUqXp8/wQUeiIiaIrMJVCEE/jikHJA05NEg41XGwFRbqGeP3q6hJBERGYvZBOqNy9nITC8CAFjbWKBdZw8j18hw2rcth72dbDDSvYxSpCTkG7lGRERUldkE6qEdCYrXoT28YWlpNl8NVlZAj67KVuqpw2ylEhE1NWaTOod3Jipej37yISPWpGH07lmieH1if5LxKkJERFqZRaAmx+Xh5pVsAICFhQR9hprPgCS53j2VA5NOR91WrAZFRERNg1kE6u+/xitePxTqDntHayPWpmEEBVYo5qMWF1Xi8p8ZRq4RERGpMvlAFUJg1+Ybip8fndzOiLVpOBIJ0Ke3stv38M6EGkoTEVFjM/lAjY25h8TruQAAS0sJhowOMm6FGtDQgQ8Ur3/7vxt8PioRURNi8oG6a9N1xesu4T5m2d0r16t7qeL5qBlpD3DtIp8+Q0TUVJh0oBYXlWPXJmV37xPPhRqxNg3PxgYY0EfZ7Xvgl/gaShMRUWMy6UDd89MNFOTJRr+2cLFBzwGtjFyjhjdsiLLb99fvr3K0LxFRE2GygSqEwOYvLit+HjetAywsJEasUePo17sEHu6y0b4590pxbF+ScStEREQATDhQj+5NQnys7FFmlpYSRD7bycg1ahxWVsBjI4sVP//fmitGrA0REcmZZKAKIfDFwjOKnwePDjKLR7XpauzoIsXr4wdScONylhFrQ0REgIkG6u/bEnD9kixELCwkmPNuLyPXqHH5tarE4P7Ke6nfLjlrxNoQERFggoFadL8Mn7xxXPFzxNhguHnaG7FGxvHMU/cVr3/flsApNERERmZygbrqP6eRkSbr8rSxtcSLH/Q2co2Mo0O7cvTvI2ulCgF8+NJRSKVc6IGIyFhMKlAP70zE5i+VI3tnv9MTLZybz73Tql6ZUwBra1mIXv4zAz9+dbmWPYiIqKGYTKDGxmTi3ecOKX5+KNQdY6a0N2KNjC+wdQWeelLZ9fvpmycQc+qOEWtERNR8mUSgXr2Qiecf3YXCAtlDtu0drbDk++GQSMx/3mltZky9jw7tZOelskLgxX/uwbUY3k8lImpsTTpQhRD49ftYTB/6K3KzZEvuWVpJ8NnGkXB2tTVy7ZoGWxvg4/k5cHGWLfaQn1uKGcO34+B2Po2GiKgxNclArayU4sTvyZge8Ss+eP4ISh5UAJCF6SffD0e7zh5GrmHT0qplJVYuyYZTC1moFhaU4dUnf8MrE/bhyvkMPpWGiKgRSIQev2179eqFc+fO6V2JykopEmJzcPlsBi6fycCx/Um4d6dYrYyTiw2W/zQSQQ+56f15ja3VSRfF6/ut326wz7kZb423/uOOOxlWatvbhXmg/4gA9OjfCh26esK3dQt2lxOR+VP9PdcIDYsmEahvTv8de3++qf1NCRAxJhjzPuoLO3sr7WWauMYKVAC4XyjBii9cse+gQ7Vljqc9B3ev5jd3l4iamUYO1CaRUJ26e2kEqqWVBJ0f9sH4GR3h4e2AlIR8I9VOf6rPwMnLyGzwz3vlmQw8MtgeOw544fhpV5SVWyrec3O3ZpgSETUAvVqonp6eCAoKUtt27949eHl51et4iddyUV5RCSurJnlrV2elFfdha+Wkts3ashxO9oUQwohdrdJK2Ln7wsG5RYN/lD7XgTlo7t8f4DkAeA4A8zsHnp6e2L9/v9b39ApUbQzVDWzKeA54Dpr79wd4DgCeA6B5nQPTbgoSERE1EQxUIiIiAzB4oD7//POGPqTJ4TngOWju3x/gOQB4DoDmdQ4Mfg+ViIioOWKXLxERkQHoFKi3b9/GhAkT4OLiAmdnZ4wfPx4pKSk6fUBJSQneeOMN+Pr6wt7eHn379sWxY8f0qrQx6HMOJBKJ1j8XL15s2EobWGpqKl5++WX07dsXDg4OkEgkSEpK0mlfc7gO9Pn+5nIN/PLLL4iMjERgYCDs7e3Rvn17vPPOO7h//36t+5rDNQDodw7M4To4cOAAIiIi0LJlS9ja2sLf3x8TJ05EbGxsrfuayzVQLVGLoqIi0bZtWxEaGiq2b98uduzYIcLCwkSbNm1EYWFhbbuLKVOmCBcXF/Hdd9+JQ4cOiXHjxgk7OzsRExNT675Nhb7nAICYMWOGOHXqlNqfoqKiRqi94URFRQlvb2/xyCOPiBEjRggA4tatWzrtaw7XgT7f31yugfDwcPHEE0+ITZs2iaNHj4oVK1YIFxcXER4eLiorK2vc1xyuASH0OwfmcB38+OOP4vXXXxdbt24VR48eFT/88IPo1KmTcHJyEklJSTXuay7XQHVqDdSVK1cKCwsLERcXp9iWmJgoLC0txbJly2rc9+LFiwKAWLdunWJbeXm5aNeunRgzZowe1W5c+pwDIWT/iN57772GrGKjUP1lsWbNGp0DxVyug/p+fyHM5xrIzMzU2LZhwwYBQBw+fLja/czlGhCi/udACPO5Dqq6fv26ACA+++yzasuY0zVQnVq7fHft2oU+ffqgbdu2im3BwcHo378/du7cWeu+1tbWePLJJxXbrKysMGnSJBw4cAClpaW6N6WNSJ9zYE4sLOp3y91croP6fn9zom3Fm4cffhgAkJaWVu1+5nINAPU/B+bMw0P2BDBra+tqy5jTNVCdWn9DXL16FWFhYRrbQ0NDa+0zv3r1KoKDg+HgoL5Qe2hoKMrKyhAfH1/H6hqHPudA7uuvv4atrS0cHBwQERGB48ePG7qaTZa5XAf6MtdrIDo6GgDQsWPHasuY+zWgyzmQM5froLKyEmVlZYiLi8Ps2bPRsmVLTJo0qdry5n4NADoEak5ODtzcNB+Z5u7ujtzc3HrvK3/fFOhzDgDgqaeewldffYVDhw7hu+++Q3Z2NiIiInD06NEGqG3TYy7XgT7M9RpIS0vDf/7zH/zjH/9Ar169qi1nzteArucAMK/rIDw8HLa2tmjXrh0uX76MI0eOwNvbu9ry5nwNyOn0tBltz84UOkxfFULUe9+mRp/vsXHjRsXrgQMH4vHHH0dYWBjef/99nDhxwmB1bKrM6TqoL3O8BgoLC/H444/DysoK69evr7GsuV4DdTkHgHldBxs3bkRBQQESExPx2WefYfjw4Thx4oTGA1PkzPUaUFVrC9XNzU3r/xxyc3O1/m9Dlbu7e7X7yt83BfqcA22cnJzw6KOP4uzZs4aoXpNnLteBIZn6NVBSUoKxY8ciMTERBw4cgL+/f43lzfEaqOs50MaUr4OOHTsiPDwckydPxuHDh1FYWIglS5ZUW94cr4Gqag3U0NBQXL16VWN7bGwsOnXqVOu+t27dQnFxsca+NjY2aoN8mjJ9zkF1qvvfmjkyl+vA0Ez1GigvL0dkZCT+/PNP7Nu3D507d651H3O7BupzDqpjqteBKldXV7Rt27bG+6Dmdg1oU2ugjh07FqdPn0ZiYqJiW1JSEk6ePImxY8fWum95eTm2bt2q2FZRUYEtW7ZgxIgRsLW11aPqjUefc6BNQUEB9u7di/DwcENWs8kyl+vAkEz1GpBKpZg6dSoOHz6MnTt3ok+fPjrtZ07XQH3PgTameh1UlZGRgevXryMkJKTaMuZ0DVSrtnk1hYWFIiQkRISFhYkdO3aInTt3ii5duojg4GBx//59RbmkpCRhaWkpFi5cqLb/k08+KVxdXcWaNWvEoUOHRGRkpLC1tRXnz583yLyfxqDPOVi6dKmYOXOm2Lx5s4iKihLff/+9CAsLE9bW1uLYsWPG+Dp62bp1q9i6dauYM2eOACC++uorxQRvIcz7OhCift/fnK4B+fd+7733NBYnuH37thDC/K+B+p4Dc7kO/vnPf4oPP/xQ7NixQxw5ckR88803on379sLFxUXcuHFDCGH+10B1ag1UIYRITk4W48ePF05OTqJFixbi8ccf15jQfuvWLQFAzJ8/X217cXGxmDdvnvDx8RG2traid+/eIioqykDVbzz1PQe7du0S/fr1Ex4eHsLKykq4u7uLMWPGiDNnzjTuFzAQAFr/DB48WAhh/tdBfb6/OV0DgYGB1Z4D+Xc292ugvufAXK6DJUuWiB49eggXFxdhb28v2rVrJ55//nm134fmfg1Uh0+bISIiMgAu/UJERGQADFQiIiIDYKASEREZAAOViIjIABioREREBsBAJSIiMgAGKhERkQEwUIkaiEQigUQigYWFBRISEqotN3ToUEXZ77//Xu29GTNmKN6bP39+tcfYsGGDotyQIUPU3jt69KjiPfkfBwcH+Pr6YtCgQXjjjTcQExOjz1clIjBQiRqUlZUVhBD43//+p/X9uLg4REdHw8qq5icpWllZYd26daisrNT6/po1a2o9RmBgIObPn4/58+fj1VdfxWOPPYaioiJ89tln6NGjB6ZOnYrCwkLdvhgRaWCgEjUgHx8f9OrVC+vXr0dFRYXG+2vXroUQAo899liNx3nssceQmpqK/fv3a7x37do1nDx5EmPGjKnxGEFBQViwYAEWLFiAjz/+GGvWrMH58+cRExODzp0748cff8QTTzxRty9IRAoMVKIGNmvWLNy9exd79uxR215eXo4NGzagX79+CA0NrfEYU6dOhb29PdasWaPxnnzbzJkz61W/bt264dChQ/Dy8sL+/fuxY8eOeh2HqLljoBI1sMmTJ8PR0RFr165V275r1y5kZGRg1qxZtR7D1dUVTzzxBPbu3Ys7d+4otpeWluKHH37A4MGD0a5du3rX0dvbG7NnzwYAbN68ud7HIWrOGKhEDczJyQmTJk3C/v37kZqaqti+Zs0aODs7Y+LEiTodZ9asWaioqFAbuLR9+3ZkZ2frFMq1kQ9m+vPPP/U+FlFzxEAlagSzZs1CZWUl1q1bBwBITk7GwYMHMXXqVDg4OOh0jAEDBqBjx46K+66ALJTd3NwQGRmpdx39/PwAAPfu3dP7WETNEQOVqBGEh4ejc+fOWLduHaRSKdauXQupVFrnluXMmTORmJiII0eOID4+HlFRUZg2bRrs7Oz0rqM8pCUSid7HImqOGKhEjWTWrFlITk7G/v37sX79evTs2RPdu3ev0zGmT58OW1tbrF27VtFSNUR3LwCkp6cDALy8vAxyPKLmhoFK1EimTZsGe3t7zJ49G2lpaXj++efrfAxPT0+MGzcO27dvx7p169C3b1+EhYUZpH5RUVEAZK1pIqo7BipRI3F1dcWECROQmpoKR0dHTJ48uV7HmTVrFkpLS3Hv3j2DtU4zMzPx7bffApBN0SGiuqt5aRUiMqiPPvoI48ePh5eXF5ycnOp1jKFDh2Lnzp2QSqUYOXKk3nW6dOkSpk+fjqysLIwePRpjx47V+5hEzREDlagRBQQEICAgQK9jSCSSeoVeUlISFixYAEC2qERWVhbOnz+P8+fPA5C1TOWtVCKqOwYqUTORnJyMhQsXAgDs7Ozg6uqKhx56CK+//jqmTp2Kbt26GbeCRCZOIuRj5YmIiKjeOCiJiIjIABioREREBsBAJSIiMgAGKhERkQEwUImIiAyAgUpERGQADFQiIiIDYKASEREZAAOViIjIABioREREBvD/pBhApHxPQVsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[90 ,  15]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "    plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/data1000/theta.pt b/bf/data1000/theta.pt
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diff --git a/bf/data1000/x.pt b/bf/data1000/x.pt
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diff --git a/bf/data1000_3ERPDrives_smooth_scale/theta.pt b/bf/data1000_3ERPDrives_smooth_scale/theta.pt
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diff --git a/bf/data1000_3ERPDrives_smooth_scale/x.pt b/bf/data1000_3ERPDrives_smooth_scale/x.pt
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diff --git a/bf/parallel_sim/presim_file_1.pkl b/bf/parallel_sim/presim_file_1.pkl
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diff --git a/bf/v1_Aug16_bf_model_misspecification_1000_data.ipynb b/bf/v1_Aug16_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..bf80ad5
--- /dev/null
+++ b/bf/v1_Aug16_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,6007 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        res[i] = dpl[0].data['agg']\n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb9367cc804e4887ae701f160d128253",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.27825</td>\n",
+       "      <td>0.820758</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Loss   W.Decay\n",
+       "0  9.27825  0.820758"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a299745356214ec79857c1678b74d811",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e233ea6996d4a889ed0b7deb69eabb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "efa068dad5684320b3506f2fb35d8b88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcde3a22cd5c4003902ed985a7745837",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YXz7VvF1if7lV8/47bZ+enta1a9dWfY2qDI5fsVhMmUym3DM+NfaXTzVvl9hfbtW8fz228yU1AIAJggMAMHFPBieRSJR7gi/sL59q3i6xv9yqef96bL8nv4cDALB3T36GAwCwd88FZ3x8XE1NTfI8TwMDA+Wes6qGhga1tLSora1NsVhMknT9+nV1dHSosbFRHR0dunHjRplX/ltvb69qa2u1bdu2hfvutLe/v1+e56mpqUm/+tWvyjF5keX2f//739fWrVvV1tamtrY2jY6OLjxWSfvz+byeeOIJNTc3KxqN6vjx45Kq5/qvtL9arv+HH36onTt3avv27YpGozp69Kik6rn+K+1f1+vv7iEff/yxi0Qi7tKlS+6jjz5yra2t7uLFi+WedUdf/OIX3ezs7KL7XnrpJdff3++cc66/v99973vfK8e0Zf32t791b731lotGowv3rbT34sWLrrW11X344Yfur3/9q4tEIu7jjz8uy+7bltt/9OhR96Mf/WjJ2Urbf/nyZffWW28555x7//33XWNjo7t48WLVXP+V9lfL9f/Xv/7lbt686Zxzbn5+3u3cudP94Q9/qJrrv9L+9bz+99RnOOl0Wp7nKRKJqKamRt3d3UqlUuWe9R9LpVLq6emRJPX09Oj06dPlHfR/fOUrX9HnP//5RfettDeVSqm7u1uf+cxn9KUvfUme5ymdTltPXmS5/SuptP2hUEg7duyQJD3wwANqbm5WsVismuu/0v6VVNr+QCCgz372s5KkW7du6datWwoEAlVz/Vfav5JPs/+eCk6xWFR9ff3C7XA4fMf/oStBIBDQU089pccee0zJZFKSdOXKFYVCIUmf/CO9evVqOSeuaqW91fT3MTg4qNbWVvX29i58SaSS909PT+vtt9/W448/XpXX///ul6rn+pdKJbW1tam2tlYdHR1Vd/2X2y+t3/W/p4LjlvmBvDsVvBKcP39ek5OTGhsb0+uvv67f/e535Z60bqrl7+PQoUO6dOmS3nnnHYVCIX33u9+VVLn7P/jgA+3bt0+vvfaaNm7cuOK5atlfTdf//vvv1zvvvKNCoaB0Oq133313xbPVsn89r/89FZxwOKx8Pr9wu1AoqK6uroyLVnd7X21trZ555hml02lt2bJFMzMzkqSZmRnV1taWc+KqVtpbLX8fW7Zs0f3336/77rtP3/zmNxe+bFCJ+2/duqV9+/Zp//79evbZZyVV1/VfaX+1XP/bPve5z+mrX/2qxsfHq+r63/b/96/X9b+ngtPe3q5cLqepqSnNz89rZGRE8Xi83LNW9Pe//103b95c+O8333xT27ZtUzwe19DQkCRpaGhIe/fuLefMVa20Nx6Pa2RkRB999JGmpqaUy+W0c+fOck5d1u0PFpL0y1/+cuEn2Cptv3NOL7zwgpqbm/Xiiy8u3F8t13+l/dVy/WdnZzU3NydJ+uc//6lz587pkUceqZrrv9L+db3+6/lTDtXg7NmzrrGx0UUiEffKK6+Ue84dXbp0ybW2trrW1lb36KOPLuy9du2a2717t/M8z+3evdv97W9/K/PSf+vu7nYPPfSQCwaDbuvWre6nP/3pHfe+8sorLhKJuIcfftiNjo6Wcfknltv//PPPu23btrmWlhb39NNPu8uXLy+cr6T9v//9750k19LS4rZv3+62b9/uzp49WzXXf6X91XL9//SnP7m2tjbX0tLiotGoe/nll51zd/73Wg371/P685sGAAAm7qkvqQEAyofgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE/8LaDBMDsHNza4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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qII7uLZZ2op5u/9WfeRCvuPW7vHUAbVJ/NVF/guuhial84XvP4LYHnm67OiyM4t152XfbLrAEU8lpt8hucf/J0Y6r0GSZXPPv/b4HIFZRZaFVpkfqHn3n2ymm0rhLcXHVWTuSk9IVOqX+ovWgEoSFBez/+sEeXPDhu3F6rCoN9ZapTA00sljXGtTty11O+reRSg3/58cvFJ6E0vur0OEAgBe4UEmMlLUgShkV2xVH4zoOPNcR+a/ahSJMhZ7NZL+YtTrqr/4JYCol3xzs1ymX4qjhyo/s3Dpzr1X7Uny9+BqtVH4swlSA4mP8Lzv3Aog3fp71/ppaCAy7pyzQ4lbU4JmXpfhz330a/+nrPyvsyiyESpPqL9oZVxOnhGqOvaUZhFEsVHzXaatBGJAvap73V6t67XaBBGqW+qucuLeOd5KpJOoUnQ11S/Cj4o5cp0vtSE7aDqGSz1Tkb0U3NXP6fQDA0FiN2VSsoX5KoJHFulEvpMiwMyPQd88cOZPbRqUWG5ebyZXEBcdosvsNkngPvvi2wwYShBFcJzY6tjvbKvU1795pMZ98oRL/rZdQMk+Qt/o8aJFKCRW2aLXKTpsJYGzGptKOPHJ0iZayFOf0U2EqBa8xp78EIBEqbm8Y6v3J7kCvoBG1EumkTYumSSjlMZWlc+OYjv0nx1K/cbz/nx/H/Jkl/F9XrchsKwv8hR1jdokwUlNKjIwHwOzCzRoRRhE814Hvuu0XKjnjTqh2ifpLMJWM32nhGMuJmWg1eJRsKrrg4otWNYhQ9psP0OTD3EqcSpZAeuCXh+F7DmaUYhtUK1mfhVBpYm7Qe13EntfINWb3xUv0mfGqdHnucqFimUpBNGRTEUa79MMfMagz8uJUSA3CvcVMOHByFAdPjTUVUc8FHe1agyQ6nS8w7WAqYQSm/pp4mwr91mr+pVZBc0Tv6VP7T2HVR+7GrgNxWpzxDKYSRVHLdgSfhEpNV39x3X9r46QIhsI2lSi5dn3V2f/vf/8Yv/d3OwRTofelGdB185wj6p2btz4E7H6KsiFSf50aqQqbSrczlWktVBp5KRuplZ1nUxkx6MHz1F/0gtcTKqSuasZQz+covVC1MESoqb90FUMQRrjtgacbipcIwwiuGxvq281UqoWYSqj8rYfTY9WmFpl6kPNJ/f4j//oUqkGEHye1TPiCz59FGKlMpRlvtXKi/tLtNjWFqbS2gDViFxHHGRhDvfujd7nstS5Uzow1LqyFzbWA9xfQOFM5MSK9v6aFS/G9996Liy66CCtXrsStt96a+v3BBx/E3LlzsXHjRmzcuBF//ud/3o7LtoT/8+MXsOaj3xLxCnuPj+Depw5mHi9tKvXbztsxmxbgvDQt9ILXEypkT2nGpsJ3k7QzDsIIQRQpk18Xwvf/4kV85lu/wifv+kVD13KdWPXSqtvu3T87iL/7wR7xuRGmUnSn+MpPfRdXfuo7LfQyvx/8mZ8eq4oULJ7Ij8brz8j/h1Gk/NaMcd1PDPW6Oy5nCK2qWvijKMqeaUwqTKDWO1cyFa/BHkpQX4fG0+lx6p6bDFPehobPy0btNidHKj2TpqVlm0oQBHj/+9+P++67D4ODg7j88suxdetWrFmzRjnuqquuwje/+c1WL9c2fPOnsQB57tgIzls4C1v++vsYGqvhuVtfbzw+KEBvCTXhhWRgKgaDoqDOhuPpu+FKgEotzKT3QRhXe2xG/cWvK1yKk+/4rkjX4VOw2OHT+fYe5VpRBC9xKW5V/XXH4/vws/2n8K5XXQCAR9S3z/trKFm4n9p/CuvOmdtKd4394E/pEPPCo7EeYrtmviEJo0hJc6PPnbt+ehCbzp+PJQP9mX2QhnotTiXkTKU1wa+qv4qeE/8dV9zZ88+hsehrQf1F1xhqgqkUyTTejFqRzjkxUhXqyinv/bVjxw6sXLkSK1asQLlcxs0334w777yzHX3rKGhHTpOw3kQiQVFI/ZXDFkxMhY4yTUg+EfM8wGoJs2hGqNTCSBg6hforiNsaz2Eqs/ric4YaUn/FddlLbfD+qgQRDg+NCwFRIa+7PGNp0JhQoXgRihdoF0zqL64upXlyJqNSZhSpz4OP5XgtwC1feRz/8uP8Ppc91aWYjNx80WpV8DcSa6Ifx+1JdYMfk7FoTajE1zg12jhToc1mnqG+GaYSCKFSERmtJ9tzsR5aFir79+/H8uXLxefBwUHs378/ddwjjzyCDRs24IYbbsCuXbtavWzLoMVS3/lnqU6KlgsFuPeXwVBvsOPIXU52WwByMySTEKBrNrIWhGGEmWUSKtIuEYSRylQ0lkXqk0Z00KT+8toQp1KpBYgi4OCpWDVYrcm+ZyFgUc1F0J8I27xcYc3AFPfE/08LBxcqKabCbSpsLMcqIaIo/bx06EyF1Cv8ubTTplLc+yv+25hNJb5XuqdmELYgVIq4FDfj/UXjf5JlrJ7yhnrT7kMP6Lr00kvx/PPP48knn8Qf/MEf4I1vfGNme9u3b8emTZuwadMmHDlypNXuZYJeWn0KZqlOGknUKILwDIvmmWQh4JHU1KbRppL8ODh/Bn74TI5Q0Q31DbCAIIrQ57vwXYd5f8V5zvjkH9UEIo1VI15hqktxay8HPcP9JxKhUsCmQiyy6G6Pjmt38stAMF/5nUkYnhmriXkxrAgVdXHh55KNpF4Kd9+rH/xYabEeejPBj4Kp1FRmlgcSKq2E1cj3sIlzC2w6A2VcG2cq0ituiguVwcFB7N0rafa+ffuwbNky5ZiBgQHMnh0HOGzZsgXVahVHj5ozgG7btg07d+7Ezp07sWjRola7lwkyAuqTIGv33IhaKW9xI+8vUjcB+d5fFCz4ygvPwo+ePZY5aSktf7Muxa4b+/qPVqVNRU/TottU6FqNMJUgjDcdvuek7qUahPiPX31S5CI7M17DbQ88nXnPJPD2nVSFShFjaVGXYilU2usBZrKpmNLW1MJIMAnehzBSi5Hx/xMbrpsBITllTHsXqjlMJYoifPmR53DszHh+29RPHqfSYJoW1dutHlOJ77kV549Wcu/l2UUJtSZcimmenBypinViyttULr/8cuzevRt79uxBpVLB7bffjq1btyrHHDp0SOw+duzYgTAMsXDhwlYv3RLGM1QlWQtSI4s17fSMO89EqPQzoZI3IatBBN91se6cAZweq+HYsPllDgKVqTSSUDJI2ENfyRMLEbVVzbGp0P015FLMDPX6y7Fjz3F87bF9+E9f/ykA4NP3/BKf+davcE+GV57OVOiZ5jKVoLj6iypJAq2nmU+1bfD4y9rQkDcSZypRqN4nZxeCqdRxhaZzyHZBzeW5FP/6xTP46J278Ie3P5HbNqGVNC3jmgt1HihNi15gLAgjfP7BZzA0VkCl1cJaXeS9ayb3V8DmH93bZMdY1UPLQsX3fXzuc5/D9ddfj9WrV+PNb34z1q5diy984Qv4whe+AAD42te+hnXr1mHDhg34wAc+gNtvvz0z59FEISulRztsKjJNS/rh026TDMBAvktxEIbwPQezkyCorB0zGepF5cdG1F9hvND3l1yxENUSbzK+ozqjM5WgsV0/wFyK3bRLsdDpJ9/TQpBF94VQEUwlUTvmqNUacSnmgkcXKsPjNbz1iz/Cc0ezU+hn4Zs/PYBHElVmkVQkpxMd/7DmUpzFVEidlReNz88ZqwbKfOH3rQtfOucoYypRFGVWXOS3VNRQb2IqRdO06E4avzh4Gp++95f47i8PF7hu81KFTi0eUV+M+Zo8xrpd/dWWNC1btmzBli1blO/e+973iv/fcsstuOWWW9pxqbYhk6lkPLBm07SMVGo4PVrD2Um6FdrtKzI1R/1VDWIWMatMQsW8Y6ZgxbCBfhLCKFZ/9bsey1IcwoErxmnBrHK6mFMTqoYgUbV5npNa/Emo6GOdVR2RhEgjNpVGXIqVbALave89MYIfPH0UP91/CuefNatuWxyf+davcChxw+Yqoax+P3NkGGfGA3z0TungEkaRMn78WdACW0/9pQgVQ6wSkN5RkyGcX++RZ47hd//uR/j+n7wGg/NnKse3UqRLdSnOP5kErj4naTNQxPgeRnFJ4abmdQGXYjX3V1Gmwp5xME1sKr0KyVR0H/0sm0VxBsATSn7hwWfwps//UPxGk5xfNs9zJAgjlDyX5QDKEipR08GPnKmMVRPhFMX9oXtZMKucUiE0o7+OIpamJYOp6KqELFZLi86+k/EuuYhNhQR+IaaSHDOj5KXGnV7wRhghgdug8gz1F589B64D7DpwGg9oO+0w0tRfbPEfFUKlnvqLhEqoMAHOTvTobXpG/L6PDVcQRcDpUVNgL/9/UaaSnsP1xpnuWR9D2gxw76m865JGoFEUsqmw38YLCgb+XKXNywqVroRIPhhECjvJoq95SR918DQtx4Yrih2EdpEmV1LThKyFCVPpy2YqQRjXvm4++DGOHen3PYxVAxnoydRfC2aV0wsrk4z1PI14X8n7S1dVkB++Hm2elSSQnC0OnoxzntEzpbEwgV7se3cdwvkfuit3B0vtzZ9ZwngtVOZJkUUkC7oai6CPx8CMElYsmo2fHzgtxuLtLz8PQDw2fJEy2VTqCRXBVBLXbAJ/lvoCRqyRXzvv3WiGqZiG1Pgd+5Lcp3VNQyNMJYogNAKALC9cBNSVTnl/8f+36pHXaUwLoXJ6rIorPnk/fvzccfEdt5FwO0WWPl4U3iqi/kraroVxOnrVLpH2UqH/mV66WhDCZ0LFxFS4Y0CtCaESu/nGzgNj1UBRP1WCAJ7rYO6MUipAlN9DUd/+MIrgJKnvdaMqtaanxMlSf1WCELPKHmphhMNDY8o4ZzJO7QHyKHYdpKKYP6sMANo8aU6o1IJQUUvxs3W7QclzsGbpAH5x8DQqQYSy5+LipQMAJFPR2R3AmUpx9VdWYJ5eWtgkTAVrM8y5yLB5qgeTqst0Lp8/PBEqBz2zIkwlQiQ0AoCMwyoC4VKcc495XnVZUDcOlql0DX7ywkkcHhrHX39nd+q3WhgpuX6yFok8Y3qqTaaGqQaxKom+I5uKSS2Qpf7yPUdMdpOhnr/U0l++bjeVa3D1lxLzUAlR9lzM6fMzVUBAnEW1CMj7y3fTLsX0mYRkWJephLhgUWzP+Pd/+yj+/uHnUm3p0L/nDhOp9hNj6oJEqJzhDKPAImJCKks1O536Rvdb8lysWTaA/SdHcezMOHzPEb+Rob4/Cd5tJk6FrjdaDVWbChcq2o7aFI+RF8PFv6P/fviOn+EfH30+s1+mITVmmzAs0lnqr6I2FcoSATTKVJJx6VBEPf//lI+o7wVQbAhFjXPUwlBZqLNc/RrZmXKbiu7CWjXs6vLyBtXC2KWYJrtJ/cX71ixTccmluBao0dm1ACXPwZx+P8VU+AtcnKkgUX/FLsWmnay8h/h7E1OpBSHCCLjgrDj+6QXN+4gzzu/84kUl+7Len68/tg9v/sIjqWvQ4jpvJjGVdFqUevPh6cNn8NCvZRCv/vz4c6I2KYap5LlYNLsPQGy38F1H2JfI5bsvOTYwCJUspnJiuIIvfv9Zcb3xaqDY+BShEqTHC1CFaV7gn2me3/fzF4X3mwlmNVr6OC5URD0T7UCp/qqfESG2qZTE56Jp9KMoMo5Lqr8ZQuVn+05lqirVPGyNe38dPDWKFR++C0/tP1X4nFYxLYQK6Vu5vpRQCyJlB569w1V9+fPAbSpVzTBsegHzgh9rYQjPdTCznK3+CtgLFWi7/CIQTMX3MF4NVf18JUDZ9zC7P2YqXAhwHXG9lCD8Wo7jwEtUC/yedeO3ZCppoULC+YKFM1O/0XWAOBHku760Ex///36uXIPfwwe/+iR2JKpRrqcXTgoz44XGNE/qCZVr//J7eNv/2iE+60xTUX8l4zkjedZlz0UpWdhGKjWUfVfYnaIoft6U68pkqM9iKvf94kV84q5fiAzdKe+vHKFiytggAjnrCAP6eWismpuFwcx48tVfXMjzftRjKg/9+gj+7N92IYpiu+RsxlSKqr/ynC04AoOAOD1WxU3/82Hc8Xg6tVV8TpqpNCJUvverIwgj4MuPPFf4nFYxLYQKqZxmJhOGP5TYpiInOE3Uf3vyAL6y4wV2XPy3iLePwhy0BIZC1aUYOqH8prQVRPCT+iMzSp6ZqQRSYDUST0MgN9++kouxaqCpvwL0+S5m95UQhJGy+60pE77+RKeX3Usi6uM20sZvnW2Z3m0az7kJi9BBbRxLcnbtOzGS6rPps+IFlVxD2lQaFyo69OenxKkkQ0Gs1PccUfNktBLAd10xFqT+IqHSiE2F5j9nNKb7BmSSToKpFrtU36avpTLRuO3xWpib9sbIVAyNK0xF2ewwoVJJe3/d9/MXccGH78KZ8Rre9r924H//8DlxP3zjmaV2HasGuOWfHxdzysTaTFCTfsZjfGashloY4XhGULMpToWeyY49x7Fjz3HjeQRKxdNqnr1GMC3KCdPukCYMz/ZaC81M5QNf+QkA4C1XnJt8X5wBcP0uqb1EtDcTIFs/9wO8/MKFuPTc+Zltk00FAGb1+cYdnhBiTQY/hlGsYit7rlLnHlDVX0Ac3T2jnFa5FIkQ5jYDKjhUCyKQbVTvOzVvcikeD/Ir/clFPx53GVipuZCnmEv6JZ6fCC7uClzEy860c9efXxjFHnZvvO1hXLg4VuVx9RftlkcqQWJTccR5tTDE7L5y6r7qeX9JA318zlhNZyrZ3l8mQ71JwJ4areLI0Jiq/oJ81/LS3phtKunv9M0hoRZGoLIqFLB7kjGVv7zv14giYBdTCRGTmcUM9VmqrO/vPopv/vQgRioB/tc7Lhf36LlOoYh6x5F9p3XBVBJDvy+dqbz5b2OVralcxy8Pncb5C2cJu5DucNFJTAumQokQZwqhoi4OXKhk5/5K/hYy1MsFjf5Pk4cWmiCK8NN9p/C333tWqr8MG8tqGAlV0ew+L9dQz9O0NDKHyIuo7LuoBpFiU4nVX64UKmNpuwK1Ufc6gnk4cgfFd7yaXpynQH/s+RNKW7Sb7vNc3P2Bq/B7V56n/C49ZeK/tDjnCRH9s85UzhhsKnnqjhdPp3efKfVXFLf784Onhd6bbH8lRf0VoOy5QsCGUdzXcg5TqYVRysUWSG8AxjSbSkUz1P/1d3bj3qcOKdfhQkx3AQeAv/vBHty8/VHNIUUK5ryqq/VckwlZ849/T4yoUgtZWYe474+wrN8nR2JGO4fFqWQ92gEq8ZsIIhqKkufku7Mn497ve0IwUJ+yhIoSpxKpQiULZ8ZreMPf/ABff3yfmPdFNAntwrQQKqTvp5dVZypFKujR90VMFXRs1WBT0d1l4zbTLyUhCEOUXMZUclyKgzBqqJiYuEYka5zETEV11VSCLw279fhei6i/4r8U/AiocQX6jpcWkj/7t1140+d/qKREofEs+Q7WLBvAK1equeSCQG1LXC9H/RVFkTJuIpuAwVBfRP317NF0/RvTYkrjTQsMMcGy54id5kilpnh/UZxKX473FwCMGTyF9AWmUguN9w3Ez/Uv7/s13vuPj+HHzx2XqlrWhB6sCsTzZGisljLUnx6jlDPZTKW4+kudOzQ2fFPENwKkAqP+Pn1YPp/jw/Fv/SVPGeM8kCCi/paSjVLWu0fX7Su5YpMqmYpZyJpct+t5f50Zq6EaRDh8ehxUXfmF4yP4px89jyNDxRKBtoJpIVTogY1UAvyH//OESOsBxC9Y1u6bo2jwYxTJKPQgjGSqdbJ7MPWXbDvpS4ZNxWNCJc9QH0bNRXqHYQQvKfFLbtCE0SoxlbSx2hTtmwc6xnPT0fOAqsaL+xV/TylNjiT5pj7/4DO44a++DwAoe/ECvDDxkhLX0nZ1pvQi+vWDUGVpQv01K753U6XFvHHeY8gLpj+/KIrEfdICM6MUC/CS54pCWiNkU9GYSl/JYKjnQsWgAtOZSqjtrrPiVP7p0eeZTYUt6AabShhFCBPjt7xXef8juTaV+t99e9chfIkZn+PyDV6qb3wjQMyiqglxIE4tD8SqKVKTZz1aepdPjVJ4QHwgPausd4HmXp/vCsFej6mYnBHqBbWS+vLMeE0Ir6f2n8ZHvvGUqD3USUwLoUIqh58fPIVv/GS/UpekFkZKGm89wp5/D9RfPPVgJfLzrxjUXwTK/2RquhZGYgc0O8OmotQU1xwCioDUXyVDfY3Raqx2IaYypDAVOU7Hhyv4H/f9OrdSIPfmKhkWed3XXxey1K9fHTotFmBS/yycpRrsA8beAMlU0ml5uKOA2VA/u89HyXNU21sBRvj8MenmbKqJQtfUmYo01LvimYzXQpR8NxWnYlpI+QJlSvaZlbmBwFlntRYKG89jL5ww5pbTvfV4/0Lt+dL8GdGSWHIUiVPZ9g+P4R8flY40YQijKnB4vIazZsdzg5gFzS8ufOk313EEU8x6h2henNbUX7RxqedB2ud7wrZaT6iY2hqtI1TIVhY7AajPnwd3dgrTQqgQUyHBwHNYBUGEo2ekD7vOXAgy+DH/WuruXaqSdPUXb4fmTVaRLk9Rf6UnlEkNFUbFs8LGqgNH2RUTRiuhZlORY8cXou/9+gj+6ju78esXs0se8wh5z2Dj0O0Uev/JVsDdQ2khOWuOylT0tkzXo3sX/Ysi41iWfRf9JU9cPz4vTJ2vQ7fdASaXYslUaIGZqai/5CtaYnEqoab+4v0Yq8NUzJsm5vGlqb+CMI7m33t8VLBGDpNnZJjEbiibpwg4kwQaR1H24qg/d8916toIa2Eo5gJnV2fGA1y4SI1lot/5PD+RqMZcRxrrQzYP1370Xvzvh/cAkIJaah8SppJcP0s9RXOxzJhKPfVXLYhSziiUny8L9MzPjNdSfWk2t1kjmCZChWotpCdTLYxw5Mw45iexCLUwMvq0Fw120+kqsQiKzjb629ekIEhdN0nTAgCzyunEhvo1G6lBQaB6KiVRXlZdlMoZCS35WIjU4zkX5RHyJZNLccqmEn9P90+LkCJUkkV3jrYDI+FBf03MSP8cRZqhPlk0Sp6bipUpwlxNqr3h8VqqQJvMIBB/18+8v3hUN+8H9ZWO1d3AacyMQsXQ56pBuAOx+2otDHHFBQsAADufO2G4z/T8FSrdDKYCZFcM1d+RkufUZd5hJOvTcxXm8HgNa5fNxYySh10HTit9Gq0EYrN0YpgxlWRM6ZKnx2oYrgT4+DfjWCe+UHM73MpEeP3qxSFjH8nuw7NJFGEqZS+9TOeVm6A2h8ZrKVWnZSptgp5sTs0RFeLomXGcPXdGcowqVES0u4Him6DbGVLBj4bT6XqmBSrlUpxjUwF0oVJMqoSk/kpeSl0nX/JcscM5k2F/ol18rkslc73Ua6fw+9DTtNBiQS/LadYH2sXpbseZLsV6MJ8W62Dy/ionaidjEtACLqT8PocrgeK2SrYRDuH95bsKU+GGehFRb2AqI5UA85JNkilWxSRUaMz1BWy8FiCMgPWDcwHErqqp+zS4GdN7oy9qXKiMZLgV8+75rgPPcQrZCKXTgmSRo9VYcKxZNoBdB2LvOpoDo9UAM8seZpY9HB8mm4qDJQN9ST/iax4ZitnZ7MTWoufyo+NemoQGPLn3pLF/lB3DY0KFygxwoaLnsDOli8krGkfOGUNjVUWV6TpqxdlOYVoIFTIKSvdelakcHRrH2clE0pkKj1YH6i/Uet1w3R/ddP4JzYuEgyYiEAuVkUpaF51VprRoYJ5kKolQ0ZgcLW59vptiKvqOOM91ke7PcRyji6/OUOgvCQ6T+quPqQb+4V1X4E+3XCz6DchFrWRwYaZ75/0zMRVy5TWlVMnL9aTY15K2hsdrmNXn4U+3XIzlC2YAUD3OHCd2OQXiBbXE7o8zFSEEDN5fY9VApJYxVX80uc1XNUZHoEWPFiOTaoemvJptgcZf3eQ0ylQoNqfIVC77avwUtT+7z8e6ZQP4+YHTSoAwOT/Mm1ES6i8HwH/7dxvw25cNimseTjymaGPF09YfGRoX6suzZvdhcP4M/HSfOSUK2S4VoZKsRbRGPfrsMbzkP98jkt8GYahsLAh5aWfomcdeYLKvs/v8CSmOOKWFSjUIEUURU3+pCzwQL1Snx2qMqYTKokULS9H4D0WoBGHKDdC00NMuySSvakEobSrJDlbXRXMVUkV7iYuA0rTQLlVvnxaasu8q7dfCUKhfpFDJvg51k3J/6X3Xd/0kPMkYPVpVDaRx3+QUvmrVIqxdNlfcEyDHwzd4m+mfdRuAYCoeMZV034oylcND4zh8egwjlRpmlX1su/pCvPoli2L1F1vk/SSzARCPt6r+ckRxN0p/LlU+bE5XA6HONVV/NLnN1wJVSPG2AMDz4sVQj7CP7zNb/cWZIbepANkqHz6kvuvCcYrNZRoLEpDE6mf1+Vh7zlwMVwLsOTYs1OBjlTiwd2BGiRnqY0/ChbPLjKmMi3YAVbAeHhpX1LobBufhyX0nxe/HhyuiMinPjsFr2QAy0eiDv4rzxFGkfC1UbSq0BpzISeBKTOWMpv6aCNUXMMWFyqv/4gH8844XhHFZ7g7kpHgxMTwuTSoz6kxFFwZ1vb803TQtarzWhw7yn89kKsnCoi/g/BjZX/lbUZtKGEZJnEra+wuIS/8C8eKqM4s+TWWWW8qX1F9OXPlR77vOInQD6Fg1wFg1UJ6fvgjqkfO006br6eqYVJwK+1xN7FluYiDnj6dI5gLe9nX/4yFc8V+/gzMJUwEAB45wvSVwhwnuUgzE3mA6U5HeX1JHP1IJsGSgP/mcr/7SmY6+K6adNKmhTEZ+U2Aw3VNVE9qcqWSpcPh4eMn4h1GEXxw8jX/6UXZ2Y10VSPfeX3IxOC/eNB4dGpdMJVHtzptZEtoCN5k/LnveeUJleLwm2nNdBxuWz8W+E6PCo/SKT96PV9763aRfYSycGeuVTCVQ2ubuyXwOzOxTbUAmjGUxlQkw0gNTWKjUghAHTo3hheMjYvJWhfpLDvShJOr57OQlDDKESlH1lx5pnLapNKb+4iomUkGkmEo71F8OD7TThIoff19K0rjw68oFP7m/XKZC6i8pqBSmkBIq6vlj1UBhKUBaqOiMRK8FHoQhNq9ZgpevWCg+y9/SNhVaZF3HrN7JZSqGXf1IJRCZHRwnTijJF3nfdYQap+S5Ke8vEioio4C2kFJ9mAuSEscmQz3vFwXW0nNNCRUSykneMVOQq6ksBP23FoSCXYVRhDNjNellmGlTYf1ji/BXd+7DnyeJQU0oazYV7r3ns0WabxB9z8Vcrv5ySKjIZ0tChcZK9Y6TsTiu42D94DwAECowfdMkmEqgCr5KEBeBq2jph3hIASDtbXn1YYT6q1JTVP2WqbQIYganR2spKs71zC8mLyHVkK8GobJwCVtIgZ0poFZli9NkFFd/GdO0BDJNC6lF9N1nkKX+KipUwiSi3mCoB2SKE99zlPbJicB3nUJMhedI8rTFjNojxFlj48+yFk0gIrIJumFZdwCgRZEW0loQYXD+DPzFb68Xn2X/kBIq9HK7mk2lCHPN8rKiRcJBukolpcsB4gVVESqeTChJ4yaCH5M2Dibz+fyFsVAxeQlxb0F65jQOupCm84mpmHK86RkQ+HfVQG6KgJipLE7sl9k2Ffl/L2GJYRTPy0qi0jZBF7BcUIr5pj2Tsudg3oyyeAepq5xNkFChzRbfqNTCkM1rYN05c+E4UFRgfExo7tM5XOiPVAPjZoE/E9qQHB9Rmcp3f/kifnVoKGlTakW48OFp/TuJKStUqGofFxD04lXYDob87kmoBGGkLFyi7HBBmwpfVGssOWOe91ee+is21KXVX6dGqthLfvcZTKWw91cUV34k9jCqMRWa1HHCSX3n5Qr1XL1r0r3z4Ec9op23ratQhiu1lLt3mqmobqXCJkYCKtktmgLVdPVXhQkA3VhcxMXc5LQQe9rF/49VampZYN9zmVBRbSpc/UW2DaH+Sj4fOh3r788/Ky4JYLJb8D5LBwaz9xcteqSGqhiYihwLdp/C+yuUWQDCCEPjNaGay4yqZ0MaZxGIn814NRAp/02gsaA5Sn/LnpsZ/Op7rlKozUF8HKk7oygShnraOOnaAJHTzomL6a1aPNtorOfeX2LTw9oaGQ+UoF6aj3wOzBQ2FSlUoijCO//3Tlz/2YeSNtOZAgA1rX8nMWWFCmWx5UFFuiqKew6dNVt6f3GXVV1tVS+nFl90OaPggVJ6Su163l+epv4aqwbYetsPcNVfPBD3SdtdE4rm/yJDvSlOBZAqpZLnKpUARSQ+y02fl2Kb656liy8TKpqRXHjeMc8pXaj0ZdpUVKbCY2B8tnNVI/rTfaD2dWNxYFhIU/drGArytKM2dabiOo64ZinxOpPjL9Vf9BxEvqlksTx0Kl4ALzp7ALPKnth4cPAxJyEivL98zfuLMxXXbFMxudtL9ZdkKhFimx9lfc7K/2Xy/iL34Liv5kHPYiq+JzcROtMqeY6wowCSqfB4IMlU4nVhPKX+ipRz1g/Ow0/rMRVmAyOMVGqKGzvdh1H9NSzfg7SaWPbvBDvOqr9aBD0cPnH1jMG0swFkagw9TYtuU6kXpc7P5WokLpx8bTfIo+DT7cndsmQqoUgBou90lTiVnAWPQxjqM9RfdP2S76hMLIwN2V5BpiJfPpY1OIOpjNdCuTCRYbUS4PSourvVbQA6A9GZSjXps2+y6RhcistMqPBb0+NgTMhiKrT4OHAybCpS/cXvseTJNC1iwUw86aiNQ6dGMaffx+w+HysXz1aSJhL4M9RTm3Cm4joqU9HVX7oHnB5RT9ciIUrjO7PswXedzJoquqGeIurHDEyBI2VTYYI3K0t1yZOFz+J7ljYV6gvlnNON6UD8jOmVp3OXzu3HseFKaq0QLJl7f3GmUpHqL9eRx6jeX4mhnjEQXchyQXV8mDMVq/5qCfRwOFORHkHxoNPOxnMd9PkeHIcmiZwMYsEvoO6Ir8GZSjoHUxhGwuBH4E2GYYQ7n9jPXHTlzpZoOl/0R6uBKgSbcSmONJdi3VCfLG6+6youpdQ3XiFPCoBaKqEi9/6Si7/ZplKpSV01LQQmpuJpY5ny/qpJV+cwjI2qpILg/QXSQmW8Goh7dxNVld7XvDIyJtbGmQqpdQJtESVG2icM9sn4ezJNC81LL1HlUX8OnhoTnowrF8/B7sPp6G7eL1Fvw2CoL/uumLe0uCt5wUL13eDzmNtU6H6jKLGxeA5mls0F5/R2SsylmBu1TdAFJM+I4OewcL4pIvniCkHIkmBWA0RRpNjaqoFU09JU7PNdo5ouSAQsMS9Ate+OVAIlhMEk6KnQ4EmDWp/A3ciPW/VX+0A7Kq5T1r2/yMhJwsV3HVRDdeef9v7Kvuap0Spue+BpAPHLyhdnnqVYZyocjz57DH94+xP4xF1J+dtQpmmZYXApPjlSNfYXUBfppw+fwfkfugs/fOZo6ppCjZUZp8JsKlq0L+289Gu+7x8fx2v+24Pq7jU5lat0qkF6oab7oI88b5bu/aVD9/7i6i9aBMm5ID5O3k8UqX0YSxnq5XVMu3MdWRkSaGfsJLEv3BvL9xxsOm8+PnnTOlx+fhyhLZiLy5lKxO7FlUzl9JiIuVq1ZDZePD2eEsR8vuhBoXxXXPZcIZSNQoWcHwzqL7pELYxtKo5DrDpEyXXRV/KMwsGU98tNIupNNg0OPU6lZrCp6G7MWUyFvqqFoRAiQRiHCIwHoYgXqQVyE0qCyJQDLAwjkXHcZ2lnxmrSO264UlOCpE1u3sRUTjJhoXsZquovJlSsS3FrEExlnKu/aPeqqr+kUHHFDoEe9DjbORCyFpLtDz0jMiD3+56yY+DqL1PaBQLZc/adGEUYxkn5iAmY4lROjlQVppKVpuWhX8dBVd/e9WLqmmEUiXoqQJqp+Ez9pbsU00tCoHF6+OlYeJmYU3xOWv2kqp5kNUISBsOVGs6M11J2FA6dgXBHC2o/K02MianQLjHbppItVEjocnDm6TgOIqSZiu+5+N2XnSfHXVF/pZkKj9A+dGpMZIdYlVSS1FVgXP0lhArLHkDgTIXS7vPbpQ2GTH0vf+RpWhwnNn9HdP+eg5Jr9iTTh9P30uqvrCqjfVpEvWBfvnzeRqHiGpiKo7rXUxbs0URFRV5YtVB1KQbkGCq2l0QDQjYiHlc0d0ZJaZvuQdhU2HyfYQh+rGpq1vFaKLQafH5b9VeLqBgM9XyAXQei/ncfS4tRTaLgZ2ppKXTdezUI8cF/eVIpHDWf1UvvL3sYraRtM7E3R/aw0/wOGGMyBT8Sazk5WlHuS02LIdull8lkrJOGejNTKTPdvpqGJoy9vwxMhfpsEnKxTcXgUswWJW5Tob8jlcC4UHOQAK7UQrzuvz+I7+8+Kq4tq0DKRSbQmJLeB+79pQQ/art0E4JQJnzk3+mGej63+K6ZQH3g5YTTNpXY1fbESAULZsVChbLzPntEEyo5hnqdqdA9c0FM0FXDRptKEMJzpUt2NYjVv77nmo3+GlPxXUcIdJEduKBNRWZTkPE+egZy7vwApG0qtCldMEs6F8RCRQadcpdiAOgzpLSpBlHidBO/LzRW47VQKQLH1wmToHcdB/0lV2Eg/HlGUYSxaoCFs9Ss3YC0G3caU1aomGo/8wWAvyQk1b1ENx2EkdgR6OovIF6sD54cw9cf36fUZpnHhUrJVfSlsp4KlJ29DtKZx0JF7kZ5P8eqoYjuPTVSzfb+Yt8LoaJR4CiK2ZDLDMSpOBW2U9bVVZx18GuKOiCsLZP3VyZTYTYVfg98UTaBfhsaq+GZI1Lg850fxVwA0KLAVSEzrqi/zAkl8+xWsVBRXzFihUBiqNeFiuHefCHUWZqWgBYymaBwtBqgGkRi50vPOi8DA3l70VzTbSqiDwahMq69G5xA0P9rYSTUX2EUCxk/sXGY6qanhYorBDox6HrqL27PAeJFWTIVVRXoe+qmKMVUqvF7s0AwlXjhp8h2vhFxNKail2WmQGaXuxRXAxG3c2q0qniJSpWk6p3WX/K0oEp5neFKnHWCCstxzJkg9dfEXGUSoOt7gfTLS7tayVRi3XQQpYWKvgsjyqmkRWHH9PseRquy9gT3QuKGbR00fWqcqQihIiPq5/T7OHpmHCdHVZtKlvqLdmg6U6FTOVMZSxnqpTdSlakDq0GE/pJmU4k0oWJkKlJllvV8uEsx/268Fhs7H/nwaxV3SQL1RV94gkgKad9z4bpxxt+sNOZxG4EWp5J+kfPjVCLFw5C+4zaVCKrKzbThKOeovwRTYdm1SahQ3/V8XSabip54E1CFiscyJBN0pqJG1MuFPRYqjjBe+4kbuomp6DLaS55TEEZCnZxVh0VkKRZxKlL9RW7vZ1JMxdVciqVqEpD1b0j9RcZ00mRQfkFAskwaNx4vQmW6yYuOM5VFc/rhuQ5OjFQYU8nyyIsdOU6CGerZ8z09WsV4NcTsPl94mV1w1iwsmtOHdUlevE5jyjKVenWcuQcQGez9xA+/FkaYmZR01YMfgWQXYUj5wheksu8q5xS1qYh4mFCWtuVxIr7rYKwaCCob21TMCzNfBImp6KmvpZ1BegJlJZQk9deJ4Qou/NO78cTek0ogIW+vJNRf6VxknpKlOMP7KwhT+nUgVmd6roOlc2dgzbKB1O+e4bqANJQCcjx911WMmmGUZk58d877I/Jd5dpUQjG3TG06QMpLyDNsOKT6i0XUk+tp4r0UhGmhIlVb6rvAx1zaVNK74npMRZTNNrA2rv5yHLrXSOSy8z2ZquSHTx8V8zPFVESW4ojVHjF7jZW1PGh0nyXPFfMibVNR70tXf5GHGqkURyoBxmsB+kux00QtiFIuxX1+ekNVSdTq9L5wm0q/72L+zDhVjBAqClNR3bz1d5jbLYfGahirBegveUJFd/ZAP/7lPS/H4iTotNOYtkKFG5iFoT552EEYol9nKpqRVg+kpO8B4E+3XIzzk9xLvD9x6hHkMhXOsIgNeWyn0l/yMFYNRRuxTcV8r+PVEFd88n7c+9RBnBkzR+0L9lDA+4vUXxRhDJD6Ja3+ov7Rov1PP3oef/2d3cm1zDXjdaZiigniEdomyFT8GlPRDPX0d0wReiprqIWRYI66TaVI5UcTUwkjvnDFcSpKmhbDrQmh7mbZVOINzKkRnakkx+qsjV1Pt0Mohnr2/1hdbI6vMgWC0pQk9RcJhlgFFOfiqgQhRio1/M4Xf4Tf/X8fBQBm9Ia4PylU4kZ1RxL9XgJhU5HsK8v7y3dV7y/6L80REmALZ5fFZ/IGo3GX75DaD77YS5uKo7DesWosAObNLOPkSEWqv5J1iPov++ek7HTjbK5/+I6f4qf7TqHf94QzQSnHsaUTmLpCJTBPPALfeXFDfRz8GCn0FkjbVPQ6KfH38TG/demgohIqeY4odkSf6/Wbx8vwuJb+khfHpiQT7pTmUsxxZGgch4fG8bF/2yVovC5U6FxF/aUtyJwp8YBA+o33L9B2VzQ+H/nGU8Jo7jo89T0TKqxvB06NGe0VlVpYyKaSYiqR3AiInb/rKKo+XajwQEXXVdU70o6Qx1SilKdazFTi/5MBWg1+zGYqPPhR2lQolbos2TAwwxe/OU6aqfDPqYj6LPWX48DTupYuC5F+lvEmIBYS3FGilLxrNC+fTNKaUBsy31zMzka1eA4TZJEuTf3FXMhTTMXXmUr8V1d/LeDqr1qIPt9L2JYs7evq6q8qFyrSpsKDH8eTrA3zZ5ZwfLiiGurD9DNxHOkBRuBr3eMvnIzHouSKmJZyznrTCUxZoVKtqS+7KUjOF0JFBkHqhno9oST9X3dZBZgqyVEn6qw+H+PM8JwXp8J1qjW2cBDIAYB+012KAbnb4v0aSl4mXY2tu9maFmza6ZSSl4gf4WnqA24MB9KLO78WPx6IjeRLBvqwYtEs3PXTA0b1VyWI6jCVtOoBSLsUU985U9GN5rUwErtP3aZSJMFokClUEjdluqbmUqyjzJi0HvzIbSrkjk5MxUk2CrpNhd+jKLPM8uHJ68rFy/eclGdataYJFdYuCWDaBDiOzBvmJ9mXa2GoOBEcGRqXKlKhoox39sPjXKiY1V8pmwqLqBeGet2m4uouxZr3V9UsVGKm4iRMRe0zbVL52kA2QsqhFoSR8NTqL3mYP7OMk0z9xVXsqvrLSam/xg3lDWKmIrNdTyTacrV7770XF110EVauXIlbb7019XsURfjABz6AlStXYv369Xj88cfbcdlcjGurZ78hRbr0qpKDXwvD2FBf0r2/VEZCL/X+k6N43X9/EM8dHVa8mzhTmVX2UWFBUrlMRbyooXGn0l+K41/o+rpLMSB3n9TnWhjJmjK6+ktbaE19IyMnqb/0tCK8f3qfTVlyXUe2qef+8hwHN244Bz/aczwVtAfEC0UeU6GfdI8ndecnFyzVphJp3mCRWGQoU65+n3kuxbXQ4FIcqQklASgFtkyGes4UPU2o8DgV3aYCUBJQnalwoaLu7kuZ6i/VoM3bkZ5w8jf6P7llO8wpopTYVKpBpDynh58+KoQR3TOpi7ggyWIqem0Y8i7zE6Hmu444l24ly1AvvL+SzZgSp5KwdZ+tF7xNEm4VzaYS5/2L+0HzMYyQMJVybKivE1HvOuk5YnzHXEfYhfM2sZ1Ay1cLggDvf//7cc899+DnP/85vvKVr+DnP1drHtxzzz3YvXs3du/eje3bt+N973tfq5etC92m0qe93K7i/SWZSmx4i5K04w57yPLcgO0i7vrpQTxzZBh///AeY3AfEPuHV1jcRd7Ogbtp6vXVgdhIN5rEawAxU9H1+sKlkQypQSRov26n4HXjs/rGDfVcONJ5ijFbV39VwxRbcR0qfKUKawqMu+olZxmLmcX3lC9UaPHQXzS+8yOm4CVOD/z6KabCdq6m4MfcrMxZTCUVtZ3PVPKCH33XFUGKJFTmsBTn3GNP7ztvu2pgKn2amjPFVPLUX8TkialAFYS0geNqrcdfOCEzWbP56DqqIMlKREl9f+z54zg1Etdnp1LQdF2yx1Bkus7AaPiFoT45nhLOHhuuxK7miZ2mxtK0CJdig1CpGpiKYM6eg3mzSoo3IzfU842e4zgiqHXzmiUAzDVz9p0YEeqvvE1sJ9CyUNmxYwdWrlyJFStWoFwu4+abb8add96pHHPnnXfibW97GxzHwZVXXomTJ0/i4MGDrV46F/qLpL/c3GtJeH95seGtltQwKXuuwhwI//yjF/D8MTWvle+5QvB4jspUzl0wC4dOjYk8PHmLotj9hWmXYiBWf41VZZliik3gKGmTuhZGgvbrAiilDzYIFRKQ5WSB4mMbMxXZP/1FGK8FIssrga5VclUPOVpwB3LqPtSzqQDx+OpChcf9+IKpuMoLGWrqryCUGaVJVcV/439NMDEVAEqcCh0n+m4KflTUX/F3VLfHc+N062fGazg9WsWcfl8ZHz1gVb9eWVMZ6RH1ol+uk2IqeTYVof4KZPXMKlN/0YLMmeITe08ym4pkDK6jpjwaraP+uv8Xh/Efv/YkqrVQmZslzxXqLFJv8xQugBT0jsZU5s4oYfXSAex87rjIXk3qL92lWHp/qSmigiRGydeFiuNgwcxyqhaSKaLedRx85PVr8Mx/3YJtV69IrpNmKs8eGRbqL9M73Um0fLX9+/dj+fLl4vPg4CD279/f8DHtRoqpGFKkGw31Ie0okprswsVPnvvZ+3fja4/tU9rzXalzd11VcGy55GzUwgjf+UWcIkUtvKS9qEwQCBdYTf3FDfXxDki9Vz34qsZUafoimMVU+IvG4ySiSNUVe5pOmsaAq790ocK9rxSX4igSi2QWqkFoXHg5yO2aIwyREtKuqzol6PVUFEO903iRLpNNBZCLDw1bTRlPg02Fxp+poPiuf05/LFROjVYV1ReQsEvNvqimaUnaC9NMRSllnMdUjC7F8V/anTuQgrCUMJVqIJnKFRcswC8OnhbCQ86ReOx5Qa8spsKdHF44NhKXg9Y82Oh5UfCwr6W+d9jz5teaUfbwigsXYufzJ0RQLGUF0F2KjUwlMeh7TjyOQRQpmg2ejSMeN7NHHvdOozHi13nby8/DhuXz8Imb1knvrwkWKi0HP5rcPh1t8hU5hrB9+3Zs374dAHDkyJGm+5UWKuqOkacX0dVflE5BESrawq17kfAJqzOVy86bjyUDfbjnZ4cAILV7qjLvDZlVN4upeDgyNC6NkUz4iDZ9mmzpXEm6uob3mZ9b9l3UkhdK7OyFd5jsr6+p+mRqmWyhQo+ee8EAsZrOc53cdBKVWphSZerwXEeJ5AdUlaU0AuczlVooc8ClXIqj+kKlFob5TCVpmz8fk01Fqh9lACIXKrP7fJwZMwuVsp9mKoHJplLP+8t1Un2jFCbUvGpT4fcUq+0UppK479P4X7liIXbsOY5dB07Fx3Cbiib8s4p7cYG8eKBPKbLG2wRkrEdcW0e2ocepECvq8128cuVC/N0P9sRjw7ICpFyKhfpZt6nE7XquqziFOI6D+bM0ocI2OHqcCoHskuRs8uk3XYJ/f/m54vcHfnk4ucceU38NDg5i79694vO+ffuwbNmyho8hbNu2DTt37sTOnTuxaNGipvulZ0DVKwQqTKUkdY9BYssgA7QIftQWbt1YGKu/5KLFffrLvovLzpuPXxw6HR/rml9cQHVhNtlU+ksexmtSBVVNgjX5ZCN9sSkLrP4VyUquwwZUZscj6gFVqHAvurg9UqMk6q9qIOpR8HMAKCnbgfhFch1H9N+ESpJLKg8lTwY1fuwNa/DyFQuV2CJ66XU1Wch2j/SZZ601MhVNSP/w6aN4420PC28fI1MRQoXUXyrzM90PIBdnQM5H33Uwu6+Uw1TSNhWlnLBmU9E3PARKl8JxcjRW59I8PXZmHHc8vi/5TnXm4K7NIraGGeo3DM4FAJFax2VjpF83y1DPj4s3XqHiTssXV6n+0nN/qW0NV+I8e47j4IoLForjyPsrCKKGXIrjFEXxd/R+eg4wf6b63Lj6mz8H3leRXy+5jj5OM3pV/XX55Zdj9+7d2LNnDyqVCm6//XZs3bpVOWbr1q348pe/jCiK8Oijj2Lu3LlYunRpq5fOBWcqZS/tueJ7jlhQJVNxUU10mZ4b58LiuXg49IldStRfjqOmdgcSY6on08b7GS8u73cYsgSI/GUouYqhvioWL15wzE+NASHFVAQFjz/TBCwrQkV9WXiiTF7wCuAqpmymQpPfc9O5xHR1hI4i6q+S5wq1ysVnD6C/FAt8WsCIPehqMj1mhAvrrISSukvxUwdO4Ym9J3F8OPbK0yPqgbTdRLWpmO8n/stzfzGmkqi/ToxUjOqvPEO99BRM74qVHbLLGV48vylTLrX3Lzv34Y/+5Uk8fXhIGSvhUszmPwk7Gn9yLki5fRuECj3bv3jTevzNW14qvvc9B9/6v6/G1g3L8OLpMVSDULFH+K4cR14jiM9fPfX9SKUmFufZfT42LJ8nxiYOfpSZH/JciquJ91ccbkCCnBiOg3MXzFTuMYhkRg1V/cXXFUe5jq46pc1ZllaoU2hZqPi+j8997nO4/vrrsXr1arz5zW/G2rVr8YUvfAFf+MIXAABbtmzBihUrsHLlSrz73e/G//yf/7PljtcDf6BcbUDwmOpFlG91HRF06Ltx0aqfPH8Czxw5k3Id1X3lKVWGJxZM1SYRGypJp2zWW/N+87gKPunJpVjmN4p34HzxEkLFVCQqaXOkUsO3dx2SbtCk/hJMRQop7n0EqIFonqca6rlHCxAzlcNZhnrPgV6ki8ZPT8RIqNTCXKEDxIsL9ZHScFDCxbjt+N507y89TiWKmDrENQf36fOCnvHQWBVRpAYsEmhu6Kwj/i3f+y7l/eU5mJM874MnxzBvpsGmomWxVV2KqQ9p/X1ZW5DlWDgiAhxIp6s/OVJVxorGgHuskfpLeGOJyqvqAmlyEKAN3TnzZ2Bw/gzxves4uOjsObhw0WycGKliuBKobIuNo6xmqqq/HNYWAJwZD5RwhNdcFGtP9hwdlhk4NJdi6aTCvb/kZlUwlZpkGIsH+vH1970cv3XpOfGYcqaSof7S1dG6UBHp7w0ai06iLQklt2zZgi1btijfvfe97xX/dxwHt912WzsuVRh8l17y3dTu0HflgtbHFhlhU/Ec7D85iqGxGt795Z3ixSXoHle+6wjvDkB7IZOI3kqQZiq6+mtc2HAiRW9OiNO0BDKhZaL+4moWqvBmYipBGOGp/afwb08ewPaHnsV/uXGtcg2dlfD+yhQoqk3FFPxIdrTxWijShxBUQ72qbuKxQ3pkP5BUEizAVMi9lqfFIDUB7Tx911HKueouxYBquDWpvw6cHMVvfPYh/M1bXopVS+aIZ0bXJyanOjeou2H+0pvS+nOmYqqnQtmIR6uBCNIj6IXVdAGg58vKcinmi6HnOHGuqmGVqRCGK4EyVrGh3hHCjcakGoRi/GlXzWNLaIz0IaF8XPrco/8vSbL+Hjg5qownd0YRY+qqTMjRNoUj4zWRsgkAbr78XPzNd5/GlkuW4qn9p2KXYmYbob+xm7dapI8yVJueIQBcdt4CXHbeAtz71CFlLvJNm2tgKrw6J4cvGNHECpWJVbZNIBSh4qX1wa4jU2+nc3/FCxd9z4vqZCGKILw7AGDD4Dzl+nwHmuf9JXN/mYMl+/3YXhBFvBhQYKxjbYpm/9GeY/jNv/kBnth7EoBMj+FpTIX04EBaJabaVFzlfmRFRCR9CEU0P0Hu6FSXYir6BaS99QiVoABTYQyEEocqTEXLoEDQ07Twvsa/y/9LN1rgl4eG8KE7fibuAQATam7KUCpdimPwFPCel743+UwMlR9dV/GWW6DV0dALq+kLTG45Yd37SzAsiGA9PhaEkfGaMlZxVLx0HOHqL2Iq5P6qM2dd/eW5koX62ntNiyylkt9/clRV47IgXiWg1OWLtvp3pBIoEexnz+3HM/91C65csRB+oloUjMJVBbLJpsKdeGjMU0w28Q4jWxt/F/gy5jNtAL9/cb9UCbPO2tVuTFmhouc30je3vucInaVe+ZHSc9++7UqcNbsPC2aWc718AFJXyd3Cy1YsEL/FKhgon0XftMWzYvD+4pOeez6JmvWVwGhTMe1QTmpJB6kqoM6wli+YKSapXoGQxww4Wv9oTAPBVAKRzJLgst2gyVAf35vZw6tSC427eQ5e94VsNEEkhYpkKurY6+ov3ldK/ij6qh332PMnlBgeKVQMKYI0t9V6TEWUE/ZlIB9tmjzHUWrknDVbZSq6TSUVKKvHqWTYVDzGkmL1V0nMpUJMxWHqX4/cceN09n2+XNj1OUslCghz+n2RsiWVYZiEypw4G+/JkaoiJOnYsiezQPgZbYg4lUotlRaFICLjDRqIPt9V3hNhqHfA7lUVoLwPPKu2nlBSXl/aLU3tkJOCnlC005iyQkVlKumcVh4LvONxKuJldV2sXDwHa5YNYDyQ0bBZIEMc34VzLx++gPkZu0He72oQsbQWZpUE+aGPVALFBjE7x1BPahhSTz2TCBVa6I4nFeVedsECxfbB+8FtKnEsABMqkab+qoYp92t6canSpjg3MdTr96mjXvAjF9oUXxFyQ70v1Z0cepoWQN25mhJKcvzq0JBYKGjB1Zlc/F38V6q/ZFumvGZqluL4O6E6YTYVACn1l25T0YMchfeXwdNITxzKAxIVppJyYqmlbCrc+0tG1EcYq8S5r+hZ6F6WLmMqjhPPbbJn6h5pNIeJqfCxA+SiH8eY1GMq8X+GK0EqgaNsL3bs0Su0AvH48veEMmrE3l+qWkp/5p7rKO7tqvcXu34d9Zfu2TdRmLJFulRDfVr9xYMVhSdIkk2Y/g9ARNUHURRPlNDszkjpXfiDffg/vRY/P5iol9j3PKtvyvuL9ZteHt2mQiCVwXClhqUD0mCZ5/1F35Gxk1RTdI1dB2K35ysuWCB10K7c1QFIvSzcSYCrhYB4wutJ/OjlT7kUh9lMxXVkm3kJJeN21d0pxcOMVmM1oRBqmqopjNLeXFk2FVMiSSrEBKg2Ff1l1/vP1RMmpjLQX4LnOugve4Y0LSpTMdpU2JwSKpVSbOeRcSppVYue+p7GzXPjtCInR6px5VBtLIbGauBhXZ6ruhSXPJnZemi8Jlx24/6pbVHwI/Wt7Lti7nJvOEDGiSxggYSmOJVY/SWFqmrIlio+ACIjsQnk2COYCld/lTzFQ7SSRNTzbM+VDGHgCvWXSaikhSStWSmbirZhmChMWaEyrttUUkzFSUl4jxVt4rr9Si0Q+cAMOQ4BQHiB8Ad79tx+nD23X2kP0JhKSv0l+000ny80/KWnhXd4PFCMicJQr+1QODPQa6bo47Nm6YCI5RGqMeFtogZ1zWI7ZT3SfLwW4Mx4DXP6fCHA6FLkwk0II1l/vl97kUuem7kj06HHWpA6YUzTj5uYir6o8UqAfKE01bDhiUa5o0DJMPd420pEvcGm8saXnoPVSwcw0F8SKhViRHoGAspRJe/f0YRKfN6MkoehsRrz/qrHVFxFbTdvRjmphxKkxuz0mOr9RZmGhUtxUk8FiLMG95ckW6gJJg1xLVqrZ5Q81c7juQp75Onyab7psTZ0j3wjGUbZTAVIp5oncMceakuMneditCo3U1Wm7SCmQu9nem2KNy36ppf3j+4DkHEq+nwWYzWxMmXqChX+IpX8tFun7zoY1nYY3MDLS4NWgsQwnqOSoboKWbtoVajkMRU5A4SXi6fugAjEVM6M1UT9F8DMVBwnfjnoO73QEd3v19/3ithrJlELqHVhyKYiXxbdxqHngRpL1F/nzJ8hhYpgQKpLsWKo11yKy0yo1GMquh6dDJ+jVVWo6KxAT9MSX0v+NXl/cdQC6a57mnt/pdRfUp0DaBH1BoHZX/JEfAQtsDJwzsGcPulGrKf7KGneX0KoaIFxxjQtmiuraqiPr3lytGpmKinvL1UQ0kI5NFaL1V/JYAjvQUh7g8PYq57kkl+aewUOzCglQiX93pWY+qvsuUr/9TgVIJ3hnED2KlPmC86ogNiuQd5fNB1E9U7tkXtOzOBNpTIcVniCnsc4s69xvG71YvzelefhD1670tj/TmFa2FT0qFlAjVPhrob6d0L9FebXlhdeYxm7aGVxzotTYf0+U5GukwQ+wWlhqAShspsiocLZ2qxynGhQCJUUU4n/XnbefLxhQ5ztwHMcpX+mcsOVmppfSRcqJ0YqCCN1seOGej2FTJb6i/vqe3VmrRp4SllhYwHHbU/6nOB6bHEMEwCmNC0cQQZT0QWFECp0Ho+oryMwufrLdeL+UYzHnH4/tfEp+bpNJXGrpiwSmqHelPONDO2K+it5nieGK6mxOD1aVRZ7Uj9XhPpLrcTYX/KMGZvpfmlMZpS8VNyJEg3Pbp3idYoY6vNS3wPZm0lyNDHFk2UZ6k3Bj/ozJ8cSk/en4v0lhIpZ/VXyXPyXN66bsDLChCktVERQo8Gmwj2P8mIzKP+XHmCooxqEiveSDpMuVL9m3G9WjGg8PVk4U+G7bjVOJe39NaMc7wbpxc5iKkqfXcfIqkaZ+ms8UJkKLQq0Th47Extz5zGhkmVTofrl+v3E107v0LLgKwtPHNwWJkyFCyvdpvL1x/bhHx59XvmOhiUVp2IILA1DuTifJKbipXNmydT3icqHuxTnbFyoHwCUstS+52JGyRM1PzjSNpX4WryGECBryavu7lKo8H67SZwKkBSJS6m/aopaynMdwGEF3DzJ3obGqpjBDfWhqv6KbSrx//tKXjog0zXPC/JuLBvuR4lT8czlhE2uyjqoJgy3bxHKvmaop9xfrhSSdJ4e8e4l6lqxPhki/uk8Xuah3nsxUZiy6q9KEGJ2n4/xWiXxZ1d/NzGVsmHhIqHiOA4WzckeLgqCKsRUDBOd95tA6i/d/50ws2wWKib118xybDik3ZHOVLKKQzlI93VMo/X8XFp4afd6bDiOpp/H0oc4LrXvosYcH7j6UGcqplQaWUjVVk/UCWNV1ZNHX8C/kyTg48h0KTYwlVoYiudHTMV1nNR1aCGkKaGUEqiT/I9PL97s7H4fCzV7CmCwqSTP//q1Z+PKFQuFC3I1GXuu4tKDXsVvbqxeAhL7iS5URtUaP2RT4Z9pkxAzFV58jNRfdI8OmxOu9u44CMJ8oWJiKkqcSqryI8T9i/5mUONSEoJArsKuJlS4+kvm55L9yEqvEqtrWWllblPRuuK5TqZNZbIwZZlKtRYqNZpN3l9/8ab1uH7tEqxdNjc+TtPX0neVJEfRrAyDHRDrpIMo+8F6yo5fqlT0XZCi/iLPLB78yJkKS7xYVpiKWgoZiFkN35HpqeFNC7XrOKmXGIBSgnfJQL+RqdBOlRYJnjBPMBU3nfpeGOp1m4rh2WTB18ZaGOqrgeIAUK8dQC4ujlPfphIXAtO9v1yFZQGMFQpDvbarz4EpTgGIF9FFRqESb4q+/tg+VGrSO23Foln40A0XizZqSU417jZM/dYdC1zHERuakUqQErBDY6r6K85qID/7zPvq9FicW4sWZGmoT9tUZuhMxXOVRZbPb1J/8bnA32mfqb9Ul2KVReptcHheXCqjGqRV47r6i9s99JgcXWbRfKVXI29DFTuwmNVfk4UpzVTOmhG/ZGb1l4uLzp6Dv/29TeI7k+seD6TL8gIBJFPJeq58wovKg046rxG3MdBOJ8v7i6u/ygamwneoM8uxt4/pOnGf0h3XFwM9+PEzv70ev7l+Gf71CVkbh3at+qI7z2BTSam/ApnmZkHK4Mx3a3WEiqvuTrlL8eI5JeW3elCYCrslU5wKN9SfGpE2lXSMVLJwGdoqKuhCbQPz6TetTyWTBKSd8INffRIDM0pYPIfeCZUt1YI4GSptYHg8hQyClWqwGUKo1FJVOk9rhnrfcxUDc4mpVSu1UAh61zHZVOSi2697fzFVEqCOx4CBqchic67wyEvHqaiClN+/jlJiEwzCMCV4yr6n5AekTZzJUJ9Sf2mGekrmGYRR6tg4DKKYA8tEYcoKlfGadHUteW4qO29e5DIAo25/Zo5QIRqcyVQU9ZecuPouxcRU9Hoqpv5wX3qRJZUb6vt8uG46Bb2eFoODjNwEkaU4eUG2blyGPt9T7o12wvqay5kKrfmU/4lAWVwB4PdfsxK+5+IbP9mPPUeHVTVGPfWXr+7mRUR9Jd/7ywQZp1KMqdD9VJievZSp/kp252GYqKmybXLK+Yl9R6/ZYwKf02fGqyKOhQSGKPoVhopTgctYtDiWeUZR4O3QWLq2yelRNZKdp/yJ21MZsJ57T79Xrv5Ss2ezJJeOujiTgNXVcPF50qZT0rQYNKQKs8qwc3mJ+qsapN/7sucq78AYYxONGurjVDVAAECfHdymMtF1U7IwZdVfv/Oyc/GqlWcByDbU61D1r3JXQ5iZU+ejnqHeZFMxVdOr1EIxoU3ZRxWmUjYzFWqzoqm/9L5xO4dpPFzt5dcTSvJU4gQy0OtCnBch4pmcswz1/SUPH3jdKpaXLa0bzwL1y3GkTSVWf6lFs6gdPVkoh1xkzAklOWphBD162TNkc+DJEoGYJdBuvcjCYNpNZ4E/m18eGsKbPv/D+HtdsAWqTcVzHINNBeK6JJz1bAlAvKHTa+6o6iS1hC+1FefY0w31qksxvY/0bLlHGse8GfF843YNHvy47pwBbFw+D7P71PLLUjOZ3gTqIHtVHMOmqb809a2If2PebCJOJbU2qeWE+Rik1fiuyP1Vb7M1UZiyQuXDN6zGGzbENVvKfvaLzaFT6/jcYkylFuYb6k102tVeNiBxD05estFqkOzyMoQKV39x1Z0r2+J91yfdQB2h4jlqWntyPyX1F53CDdGSqRRTf3F1hynOh+693Ij6y2BgDqLEplJKP+Oz5qRtEXpf4UALfoxSiw3P18avkUooSTv+5DP3LCwiKKhLRZgWX+z+6dEXxP91jy6qHeMzVqIb6Ln3l+fGCVeHM6ow6io9VY2qblbombiOY1TL0rn9JU/YSVPOA6m5TSmMZP94nMpVqxbhX9//ypSA48xUnmdeJmn+1hKWx6GHCqjqL3XTpxMhiqjn9ZlMDIr6INRfBebDRGDKChVAjaDVhbgpclkvn6p/lydUiKlkC5W0F5UphQcgDfCjlSD1u2qoZyovQ/yFYqgv+6lJl5VSW/ZZfflFRH0S8EgvIO16HQegNSEIgYuWzBHnquovuehzdUegqXTiPlBfi6u/dFdYsofoGWfpmXBX3A3L5+H/fZu0s3GmwhGGUSp9R6jVKqG+pnN/aSwhjESRqrw5JvuULKgFWA2/9gBL50ILpcO6xpM3uq7MVye9vtR+z9DsdFnwtfdPn1ekpo49MpM5yxgDXb/f97Bi0WwAadWR/p5wRwJ+XSC94NeLU8kS3lz9pWdN0F3ihVBxZD+EoV5Xfzky/kXcn8GBgPpmCr6cTExpoUI7a90XHTAvTCZVT576izdBub8aUX95mjsjYUY5WbwTpsKRZePR3WiBtEtxuqZMvkopK6Jetx3RAtXnuyKQL4oinLdQVrMzLZa+p6a+D5ihnsAdJvL6yiETMKqL4qjmUkw7V57axHPU3WCWTaUWRmLDIWIsAoP6y7BxELeSfF0LIiwZ6MM/vutluGFd/YqogiEWUHfweXEsSRYKMLbMDd2JpxUxC+q3YAXaAj6zVFCouKrdoqSVA5iTCDvXkXOWfnUdLsRcvGTxbKVtajYtVAxMxaCu5fdF14v/1hcqJEjGa2FKwKeFinT71YWKKYUUJTfVBXmaqRR3tZ8oTG2hwjKRmlyKdajlU+szFV5LPS4rms1UXGURppfTPBFoNz1WDVPUm9Kn8ONSfXfUSUt9TzEVgx2Go+y7SrAlfyH0qHUgZlGizK42FiZjp893poBIuMfhGIRK3YSSJEw0l1jqo2g7+buQpYv3XXVXzXeuJFTIw43mFy0g3KVYtOc56Z2xQf3luy5eteqszJT/pvML2VSY0wJnrrpKC2ACNHnOKcYnFrj4+BllD2fGM5LhMXiuowoJV1WrDiQsjVdHFcWyHGnk7/c9vISxX+U+tLGgd5Py5wFmlXZ8XfZ/5owgzstQf9H8GqsGKWcM3QWe20+kUDE7yZD3VxRxhoLkb/Y61i2G+inr/QXInWrZc1J6y7yyrUAxm8rMsicMlbEHSHZVQtMiHBeQSh9LwqIShJjjph9Rv+9iuBIozImrYuhSXL0/s+ynrsUpu8nB5U+3XJxS3VCmZv4Sr1oyGxefPQdz+n0Rn0Gs7Ycfei0Onho1TnjPdZTIdKq4yUGXUTy66rw8JYNNhcAXbcpFxpmK66ovrsteZhpPvUpiXxLoViNViCcXR8918e6rV2DVktn47P27lf7QdYIwzdDy4IgxqS+A9OdHoHtQ1VLxX1Jt6gu2rwmXmWXfaKhP90GqSmWePdkvYiqe46SYnpKmpewpDh90Dv9LuOjsObhq1Vn4j9ddJL7T6wKJ7w2TvxhTkRqFlE1Fq6DJMxJTe1LQaNd2Y/sdBVXSeaZj+btgDfUTAK7+SjGVJmwqMzT110Vnz8E7XnE+1g/OjZlKaF6cAU1v6zrSe8UwEUweShzEHkhNBqRLv+owqr/qpD657LwFWM8qWAJyPPjubXD+TNz7f1+NxQP9LPgxvs9l82bgsvMWGBc3vfIjr5xJcLXFiH+XBRHYxuKBCJzdkQBMMRXD9V0nVulFUYTPP/hMPBZC7eeJ/leDUHFK8F0HG5fPw+Y1S4xtAsRUii8I0jZQgKlo4z6z7OFzv/NSbBicq7QV91UyEx4I6WuMj9tU9LIG5v7KjBbCiUJRf0mmklJ/udLDq8/A4lxN0BHKvot/eNfLRCLO+P7SrBfQ1F+iPfl7nqEeSNTUuveXr2oReFE1oUnISK9CjiV8s2Gy9QC6g44VKh3HjJKH1UsHcNHZcwq5FKveX8mCwQ2KBqbyZ1vXYslAf2xTyVF/8UWD3AqzhArX+5sWY0oqOaOkRtRfuGgWXn/JUjhMZTCz7OG3Lj0Hr1x5lsFQX9z4TejT7AgcvuvI4MdIDQQ13YdimEWGod5oU8nvY5baBlAj9SmTsJLsUnsmUu0QM78Dp8bwP+7/NQDJFMhJglyKlewBBrZEixG1XcuxxZlgsjNlQVe9zZ9Zxm+uX2Z0UeUMz3W4MHGVY+mZcKaeB+7UYVrYuU2FmMp7X30hFs4q45qLFjH1V3zOX/z2erz/NReK83XbX2Y/WGZiDlX9RX/T42K6LyBRU+cwFcrKEV9LegNmFekiQz1XIdP46dOkkUwTE4Uprf7yXAf3/OFVAICHnz6q/GZ6AMaMphlxIQCn07K2fZHU9xTQxW0UriPVVXk1PwC5Y9Nzf33ng9co1wiiCH2+i79888a4rRx9bNFdjmAqhuM9R3qihJp9xMQMSy47PtEh6/2Qqp7iNL+k7Yb5pfnYmoSKHqin21ROjkhjN1d/AYlLcRClmIreZ6EnT/bjtSAtTPNgsjNlQT9mthaTwy/rC2HsKt5fptxfQDpLQxa4W7BkkfLC5Noeq7/i+bD2nAE89v9sTvoomREAvHnT8tQ9FBHKPPhR759sS2WR8XlZTEWqv3ihNEAVXH2+x2JOuM0zw6biJoZ67v3lwnisovbuEqEypZkKh8nDQkc99dcs7YUkm4TvxtXj8gz1KsWO//E8QHzilln9F9NiQ4tYVvAjvx63Helj4LeZqXiMqYShFkBmeDE91xV14Sl/VJb6q9QAzc9aDAFVtXg6WRDncXdnx1FSivCdaxTJcsuAZLE09mEUoRKESlCpST3D0+kDMpq9KOjQkl9EqKjtUpp8Ame10tYXb3pSai9NFdNf8hTjf14faExzmYrrGN1s6f964TYCN37ngd4FfdxMwY+KV1wd7cNYzeClWVLfZ95X3aaSUn8xplJX/dWFTGX6CJUC6i+jS3FGsCHAdl0e5QDKNtRzAzQZHz22K9Z127xtHcRUyp4UPqk6GpqxOr6ufr/NMBU1+IzDczWmouil08fTd7UwlDs53VDvyrbFdYoyFS0VCaBuDD777zfihnVn4/yzZsk+uWqgHhcAYRQpQoUS+dFusZYEP+rMR++zHnMQ5SQiNYHmciGbijYv9I0R7w9XG3KVkm4MFy7FBWJq4uOlR13JMK9nl0n95RgXWvpvlmecyW3bBO4NqpzP1Z2inLCTOk+HtKmEKTajMhVVqKRcirXm4/o/6npi8koztd0NmEZCRf1sVCuZmIoSp5IucQvEu3BaGLMWZ3Vxd4RulWfs5ceWmSoiq58+i0zWA/GIxSiLsc5UXJdFMxu7nXntTKZCqe81VaBJCNF3f3rHU3j02WNxGxlMhbO6egJQt6nwa/Od+obl8/D5t16WrtXCmue2hyiS9WEA4JkjwwCknYZilebNyrepCD0563NTTKWA+ovXNQHS6q+4vWScmNrQcWTgIXeBB+SYmNIWmW6DqxRNzJynWuHpWQgiS3HZfL98buSBjtFjSEwCTJm7WYZ65v2VTihpZiqeKx0PMuNUHFJ/cU9BKH+z2u4GTB+hklpQm2AqKaEiX8JaEKXsCBxqNlXVrqL3jxd2Mu2SePGxsqaCIcgiTKow4/A9B+961QXG87MgbSpZhndiKuo96f71dDwAfP3xfXggqWWivxg8JiMrelqHPnb8vmcZF0J1wVcM9eKYmKmcYDaV1168GIDceBBzGegvpRZPk/qriIrFhEZsKqMVVT1lYir0KEua95f4v1j0yZsuPl5n7kB6c0Nt6ILLNK8Vhuik562pbSDJA1bEppLhUuwYrsWby1Ir0ffjBkO9muDVrP6q1swqX8/l6i/5He+f6TrWpXiCkVZ/pW89z6ZS9tzUQsrTwIhyoRkTUN95kXpBusyqu3o9RQYHCQyeV0r3aKEXPo+plFwX//G6i/D0J2/IfGF11GMqvJxwvXWSv9yjhuSZgFxo4nK28f/r1lPRxo63aVxUNUblGH4jQ/2x4QoWzirj2f+6BVsuWSruw3Fk1HTJc4SnoCnIUA9o03+vByEECgiVZfPiUrLEUAoxFW1e6gkl6TeT+stUHdX3HPEelVzzws7b5X3h181TfxVR32a5FJv64Grvo7G9pJ1KEKbWE5WpqI43dI2shJKu64jS1rr6Ky1U4us4jjXUTzjyPJ8IppK1fZ50G82yScidRfaD1YOUdO8vpZiQ5wp9eZ6h3nOZ+kt7maXgyTbG+0lQWha9N4Gr3nSQgRFQ681ngS/2oxnV6yRTYbv+Ou2Sp5hJMJsXVfZ/bYES/3Vi9nX8TAULZpWF3huAUGPy7M0UeyEDBtP3ze+iSB4v2Sfa8NQ/Z8Wi2djxkdfhfdfELri6oR6Q84LmAalnqf/6LlkGP6bLCOiqpfg3FxcmObuEU0BGMKzep/ic+P9Z9YyKuhRT+1k2EkDmQuPN1YtTMbXJx0FVq8uxkuovrZ+OdF7RnTr02xRrQZewFGAaCRX9YdQz1OtMpb/kpXf63FAvDGtZ11cptuvQriW5nrZL0wP4OPpLnohSpj4UYSp5cSpFQYzGyFQ8JlQKxF7wF5Gy3er3wW0aYqwKMhXfoP7SK0rya8TnZjMVJN5fVJOEYmz8JIebYCq+KxZvuTin51bTTKUB9RcALJ7TL5iTiakJIzrb4NAQ8yqN3P0dUIOBs2x7dB4lFz14aiy5Vj5TMSV57M9Q0XIhmIdSBqvnoFaKRNQrsWcp9ZfZpuI4BpuKkalESjCwid3ytruFpQDTSKjk5cwhmIIfaSL2l1xj1TUgMdQH9Qz13DApddXCSKmliNFjLTj6fPmic/UcR385bVPRF668HVsW6sWpBNymUpepyD5TZHbKNZqpXIoa6nVXWJPRN90X2TY/hu8QY/XXuBQqAWMqriMKMZVcRyzeRby/9D7WgxACDWwKyKhuYmo6W44j4NNMRbdpcZuKWLCNTMXBRWfHQoWyGJieYRZTWTSnD3P6faNABMib0viT1n7yTufYD825v8yNcwGaSmfEmYrmui9cirMi6h1H1FPRWaI+f4XwSpf3mTRM6eBHjtSDM0xq15WlZ+WLFqu9+v18phJG8c4101Cv7bzIYCe9v1SBpnswcZy/cBaWL5iRHBt7iukv6QxDfY6U91dTTCW7X77riHgT3aXYBC6YKDI7leyPMQVehjkPvLQAbyMPrhNX1qsX/HhipGpgKvHCS8WSSp4rFm8prFib5EXFrt+MS3FDQiVhTnmOCjwwkUIaFfWXNp4zDZkfjOovz8EFzG07C3wI+Cbrppeeg82rl7TuUtyITYVNgqzj84KUFaHC2DHva17wY1xPJR1JnxWnwjNTTDamjVARdF4TGjrKvotaRQ1mKvsu+kteSoWmG/4qtewgtpRQcVUDoxqn4rCFMT2h/6+rLhBeWyXPNe4OTTaVlPqrCcqc5/3lurFLKO2y6ufokr8PV8zqL25ToZ+KpmkxRW9nIX55oyT4kV3flb+HEXBipCLqr5Cnm6epv3zPEULFFIAp1V/1VSzGviZ/i8SpEGbmqL9oXpRYX8k1/F1XrcCFi2KBoBuNeVtCDZthUzG5H1+3Zglev16m+ufzU689P5cFqKb7X0woX7BoFs6aXRbOCyaYFu+sZ8NVqWmbirnUN2fc2QklHQShujHTVY9624ZCpJOGaSNUuBtmLUxnFSXEL0eg7JTKnos+P52Usk9TBY3XwkzVjGrbgPCtp5dnoN+H48SBcL4n602YJjSPgi75ZqFi9P7ShWIHmAoQ5/AypVzJOh7IVn/RfTqOk1rU6rVrWtCzwDcdJndW+j2KpC1h7bI4KeOrX3IW7vv5IWGoNzKVnDaBxnTitH40wlQWzoozMS+aU079Ju49aW9Ony8i5WnzAiBVSmB9kpSSf2diKvTbX7/lpcrufjsrhgakBUlRFI1TufjsAez8z5tzj2kkTUte4lc9SzE/ju6zmhNRr6dpKRJR3y2YNkKFe36MVrMniii8xB5e2fcSpiK/+6ubN+KqVYsAyJdxvJat/jIFP8ZMBaKNi5bMwS8PDSlMpZ5XUNlzjC+yYCp14lQaRZ5NhRZG4blSp3n+DOqpv7JYnQk6U3FzFjzTdfgwCfUD4y907xuXz8OTH7sOc2eU8NE7d0mbiudiVlL73HHSfTYZ6hthKqKeSwMLyvrBufj6+16BS8+dl/pNRtTHfz9+41pEhp2vbizuL3m47z9cjU/f+yuEUYT9J0eNhnq6t60bluX2sdm4HT0JaCsQTIVrDjLeEy5U8iLq1VAFstvJLMW6nUSNU1GFnH6beXN6stBSj44fP47Nmzdj1apV2Lx5M06cOGE87vzzz8cll1yCjRs3YtOmTcZjOg16KOUc7yUALJJd/t7nx5Hn/LsbN54jdOs+W0yLqL88Jw46/O3LBqUBDsDqpQPx/x2WUqLubj+DqZTT6VTS6q/GH3/Zy45n4eOQFwhK4AKTdsZZ0c6uU5x5pOqpJP3IckmN22fCS2EV6l9AHce5lAxRU39tOn8+XnHhQnkfJkO9kmOs+KJIardGmIrjOLjsvPlGRwW91sng/JlYvmBm6jhTzM2qJXPwxbdvEqlpsmwqRaC4XTfoDdeIEKrXFv8LZPd/hsGlmlDy5OaEjwkvPiZr7qjnutxQrxnoVeVsdzKVlnp066234nWvex12796N173udbj11lszj33ggQfwxBNPYOfOna1csmnQJFmzbAAvu2ABVi2ZbTzOlDNrYEYJc2eUM3feeWyAoNtUfvdl5+G1Fy9R1CMXJx4ye4+PKCWH81DyXaOLpCnaOa+eSlGQ0TEwbGXp3mtBrP7K8rQS1zcMqC60uI67UaaiG8mzEhLy62TFyaj6/vT5sVAJkntw8Zvrl+Ef3vUy2Q5jJ3rsQZF74iAPu0ZsKnmQbDm/vSwVDCDtXKaaJ1lagXQb2Rug3POcYnEqRWDaRGSqv3gyR4MrvCnbBfegqxiSZ8a/IVVPxeTwAWRnGZhMtKT+uvPOO/Hggw8CAN7+9rfjmmuuwac//el29KvtoBdhyZw+fObfbcg8Tq87DgC3/c5LMbvfz1wk+Y4/a/OoMgb5vdCZusBl581P2pAuw/Xcfs+ZN8P4QvWzGh/yurpQaYapJELFYBkUQV2hWVecPj59/VzvL7bLy21XSwVShKlw92MTU1EEgGHcPNfBSIWCH7M3FvwXxVDfgIAgT59m4oyM/SroTZbn0i1TqWTbVOqBs8VGcOl587F0brbxvRGY6sxkPRvfc0WVT9N7WvZdjNdCZdNndnNXz6PA2jCKWMAvUv2ia3QbWhIqL774IpYujb03li5disOHDxuPcxwH1113HRzHwXve8x5s27Yts83t27dj+/btAIAjR4600j0F9FCK7HK5LhyIo5I5Lj9/vvJZYSoZ7ZuCuQCV1m46fwG2/95leMXKs/AnX3uyUH//y41rjZ4foiSxUpdcPaYZ7y/BVAwXla6SZq8WHfWqb/I2uCtm0dT3+gtcyKbimG0qir7fuFOXTCXL1qE/S/6pkZ120IT6Kw+mVEEm5Hnf0TMxLXJF46GaFSqf+q1LGjq+CPgjzlMT9/keqkHN2Oc+38MQagp7M91jnqG+z1fVX/plutGmUleoXHvttTh06FDq+09+8pOFL/Lwww9j2bJlOHz4MDZv3oyLL74YV199tfHYbdu2CaHTTvuLyY3ThLLv5u6Ev/vBV2PJgLorKlKXRDfUp/sVf75u7dnJ8cXcYbPYBhkROVNJq7/ay1RIVUeJ8vTF/y1XnCsWXiBD/ZXJVMw6/bw+0hhyw3IWhPDy8r2/svrtOQ7GKaI+YxHS+62qv4o/i1oThvo8UD/qzYc89ZewWWawuCIo+nwnAkWYCv/NJOBpwTcxlaz1gI6JnV3kO2QKmAV6lKncf//9mb8tWbIEBw8exNKlS3Hw4EEsXrzYeNyyZbHXx+LFi3HTTTdhx44dmUKlU5BeOPnHEVPJgs5aAHXHn3WumqVWfq8n6eP9AJpb+AGp6qkFkqmk07Q0w1Tids1CJf6blShP31Ea1V96nApjJybVgQm6OzYt9iY7E4GXu+XdNqkdsqLB6b5LGTm5suw1vK9FEATttakIF+WCDNAsVBI2aEooWVBgFtUmTASKPpu8xK9UbM+UHJO/1/qpcaAtkjQtyXcUL6VdoxuZSks92rp1K770pS8BAL70pS/hxhtvTB0zPDyMoaEh8f9vf/vbWLduXSuXbQp5uyyOOAVKY5NanSCNMRVTWgjA7DDQCMgoXQmymUoz6hPBVAyGesFUCqq/sl5EDr6oF1Z/kS0l+UsZkPO9v+R1HOX79LWyCpTJ383j6jrqmCjqr4ZsKu1Vf5GLcl2mkrEB4t+pgX7x307bVDoB5TnlrBn1Er96rqPMB5ENQTjomF2Kgfg90uOcsmLlugkt9ehDH/oQ7rvvPqxatQr33XcfPvShDwEADhw4gC1btgCI7S6vetWrsGHDBlxxxRV4/etfj9/4jd9ovecNIosR6Ch5bkMZYwF9J1J/l2oK8spiKs2+YLSAVgNuU9HUX223qUC5ZiMJJUX7WXEqDRjqS5rqkITQ2TnGXG5XMOf+qs9UCFkJC1PpROqo1LLQbpsKPcp6zFUyxfRvJkN9IxkN1PYnX6jUU5MT8u6xnAgV3fOTn2f2pJNCRRcmaaEyxby/Fi5ciO985zup75ctW4a7774bALBixQo8+eSTrVymLTAZXE0oeY27J+ZlKxXfGxYq3h/9NFmFr7mFQ9hUctVfjbfdV8SmUlCoGJlKRpoW7opbb9FxXQfXrl6Mly6fDwC45iWL8GdvWIM3X748+xy2S673fLIM9YQs1hEfY2asjdlU2uv9JUo51zPUF3EpZkKl5DqoOWYhbEJRbUI3Ie89LXtuyt1Z2FQog7ZhbOj+qwFPKBn/pg9NT9pUpgp4Dqk8lH2v4UldLqD+4gZ5U6U5XVtabnCXp4PsB1Wm/soSXI0gl6kk91KpFVuk9EXRd9MpzPliX7TyIwB88e2Xi/87joN3vPKC3OPpMfAMvfz6bp2Ng5qBIUv9pXmWsd8aiqgvyCyKgkoO11d/1V8IuadTyXeV+VcPctNQ+JRJh86KOfpKcaJXz+AdWtJchTno/msmpqKd0I3qr2kjVIT6q87L25+RSysPPFle1gtB1R71q2clitPTtzcKilNR1F+691cLEfWh0aYiaTvQuE3FNO7c1qHXSm8nuGqH90ruEOsIFfZd1oueF6fSzHNuG1NJnmU9Q71kKunfRG69hOlFEZI6M8Wz59La28y8nCzUYyq8ciYgx1DM5Rz7VCWIUoJcH3vLVCYRRan1tqtX4A11chTpmFGuz1SALLWJ+TxaMJrdjZpsKiKWwHNRCcImvb8aiFOp601UQKiwl6qT6hHJiFSmYooPqGeoL8pU6rVZD+22qdRjKnkuvzybru/KgMBG2LB81oVPmXQ0bFPRvL/y7HPVIJTeX7S5ydBodBOmjVDhOXfysGrJHKxKqtQVxQyFqeQIFddBpFXTyfb+IkN9ky7FBvUX3XufHwuVZuw1PL9X1m9FbSr69U0viMNeqk4acqX6S7d5Jb9zW0gLhnpF/eWovzWKIuWEi4DUX0UN9XnqLypYVg2iuIJpA/flFnxHuwm8WqaOvqQGk6mkt/T+yhcqeg2btEbDCpVJQx51bxUzc1Jgc8RFrNTvsnbfIqFkk0zFVPZXZOsteRgarzW1O6YXxJj7y5W0Hai/OBRTf8mFJstTrh3gTEURKga1Qx5T6fPTFUKzz5Ofm7FvtZ2p1NnA5I2/w8YvbifEgpnlhlRZjdjMugV5JSoWzenD/JnlDO+v7HuVhvow5RxR1CttMjFthIrQzXdgwvL4h7wFz3UdQFMx6xH1hLygqqJ9+g/XvgTXr1uSutbyBTNwarSCRXP6Gm6X1ojAYIAVTEWk9M5vSxeYJqHCI4k7uehQk3GaFif1vcl4b+pnno5bn3u8mWbsCO32/iqaUDLPUSGuRxN/94fXrsJFZw8U7kdR775uQl4xvQ9e9xL8/jUXYt+JUfEd3aOe04tDMpUoxQ57YWimj1Dp4C5XL8KTBd91UI2y1F8aU8mpW1IUf3jtKuUzvfhXrliIL7/zCszpz66ml4V5SYrzLZcsTf3Gd1hAARdVnakYFkmx2LvFI+qbgeJSzL7Xy7lmXV8ylZyklSmbCmMqk2pTaUz9ZXqF+HMilcxAfwnnzJtRuB8mAd7tyHsGM8s+ZpZ9HDw1Jr5LuRTnbFCAtDDphbGZPkKlg7tcxYsnz1DvOggiJ/Vd3D/12JK2o2kH5C7JaUqgAMDsPh9PfHSz8Xx6UQrbVLR7M3lNcUN5Z20qsm1z7i/2jOuov7Lg6gLLcH4jaJeRVkTU11N/5dg8eI6qonn2Uu33IFOhzUBejXgueCSjy9ZEKBuYlPqrtf5OBKaRUIn/dvqh5KnXPNeBF6q/Z+1AhFdJm2IRAOmy2epLS2xFB3eFBOqPdSM2FSX3VwceosjJ5Gbk/nL5sc0JldhdWZ6rqL+asam0yVAvvb/y2+svu5jT72PxQFptKjZtzDjf6DQrmi25m0ACIy8ex+T9VcpjKux43Y6VJajXLC2uZuw0po9Q6eCCxJG3eYy9fzShQrs67dhSi3EqWdcHOjcGYtdWUP2lo16cSifZZv2I+nxVFfUpz6biOY4iSZqNqCe0PU6lTnt9vocf/MlrMbs/vWzQkPhu8w4VWQF+3QwSxHlMxRynQi7F6eNNhn35HqSP//UnbugqW8v0ESod9iyh2I+8Fyl+4dTvslJa5xkAm4XDdpOdQDr4sUGhYrKpKGoV9bt2gqu/stiE6FOOHrwR9RdHc554bWIqQv1Vv725M81qU666Klr2Od1G8rcXdDwJ6P0sylTo/3kuxepmQ/3OdHy3BUB2V286CKn+6syEpcUk70XiAXyETJtKi2laTBBGwg4JVmqf1F9FhMrdH7gKX3jrpQDqqL8cuZvvxKLDDc18eIxMxSCUixrq+fxoNU6lXXM5LMhUivQljqKnjVJjbUym99cn3rgO/+6ywYbPu2FdXP9o/eDczGP4fBHjlBNRbwoD6KSjUbsxbZiKNDJ2pv2+kouh8fwFzzcIFVO9ciCdvr0dkC65ndlLNJqmBQDWLBvAaLUGIM67pkPaNDhTab2v6etw9ZdJ/WBWWxGEUDHUEyGcM1/1hOLttCuPVzMoalPJA7fX5dVdycNkpr5/65Xn4a1XntfwedeuWYKnP3lDbhCiyQEhL6Le7CgSf+4BmTJ9hIrwIurQhKUdar6h3oXrBsp3WYZ6UgXllTJtFFKV1LYmFQihUmvMpkI7ZLNLsVzUi6a+bwbZTEX9S8foEDaVnMXlv/+7DcpnlalMvtKgFVUr9yxs1qbSi8GPQP2odt/AsHk9FR2Kusx6f3UvshbvdoFUN/kuxenrZ9lUzl0wE2fNLuPcBTPb1kehs+5Qage6l0ZtKjR2ebm/PJepvzppU3F0m0r6+eRG1OcwFX3x4dfpBo+nVtgSt0nx5JyNoJdUPI1ABjDK7/LqqfDnYONUuhidTPEBFLOpeK6bEjpZ6q/lC2Zi53/e3NY+dtplM2VTaZCpmIzc3BbGkxa2G8ouuQ5Tycv91UjsSLM2lb+6eSMef/5E4eOLopU8Up5BqDRq8+EeZFMJwnHBYJczrUfcLieYSg8J3GkjVIrWqG8WtCDmqr+ctPDoZE6y1PU77FLcjE0FkAtxvdxfnTTkukxgmQz1dZmK8P4qXomPN9PIQnrjxnNw48ZzCh9fFPVS3+eBCwS/yQVQX0AnG1965xVYNLvxVEY6TIk4Szmsm7PdtPdXy93pOKaNUOk8U0kSOOYa6t3UJJrIHUinddZ6luKiwkuov3LStLhO84tVEXAWZy4nnD6Wo4j6Kw2mO++C1aIVpsIFfpZHY+E2umQ3/uqXLGpLO6Z5S0zFdKucsevqr15IKDn51sEJQqd1kqLOiCF7L0FPAcL7NRFTRRhTO2SpdzWhUvQFyGMq0sGis0KRO3LUdSnugPqrGwpTtTIvZiZJVWeW/VzVTh4Ek55ET7hOwOT9RSpfU7G7LPVXD8gTANNKqHR2l067i0otO7LWFFHfrP65GUwUU6FywkUv01/y4DrArL40ceYMc8GsMgb6/Y56f/muOfiRXzLPptIIU1GC3LpgIW3F03DzmiX453e/DGfP7RcOFY2qsYQHXq+sngVBVV9NG5PAsFyYEtTyjBLdjmmj/uq0Sx7tssdrQeYxsWrA3K+JmDDCbtCha+lMpajwmlH28OV3vgyXGALIuKvvW152Ln5j3dKO6NxbTdNCaCSAkLfSDcbpVphKn+/hFReeFbfTrPqrR12Ki0B/90nVGJmYCtuY0JhcuWIhjgyNd7aTbcL0ESod9BwCJGUdr2YzlWvXLMGpkYryXbPRx82g0y+tblNphH29atVZxu95luI+38PZc4sbwhsBF171hIpp/Gph41Hpqvpr8hfSdvWh6TiVHvJwahS+66hxKokAr6v+Ssbk1S9Z1DYbT6cxbYTK+Qtn4fXrl+LSc+d3pP3lSTyJSYVD+D1DxO5EenXotRzajUbrqTTSZqcXmuzU9+rfrL5QoatG7rmeoJpotEsF26xDRS9mKS4Kz3UUhi0qqBrKcpsM9b2EaSNU+ksebvudSzvW/h+8diUuXDQL169dUv9ghk47EHDo0bnthrCpBI3ZVPKwcFa5pfovRcGj9Xm3xUJbh1XUguJJGU3oBa+eomjV+6sXF9J64JkGALmxM/n1KDaVHpwX08ZQ32mUPBc3bjyn4cXBcRJvowmYO6THbVfKdB16mpZ2CK9rLlqEh/7kNU2VPm4EpB71XbXGvEnomxa9IGycnXXLenHz5cvb2l7raVra2p2ugOe6xlxvJm9RpehfD45FD3Z56mF2n4/Z5c6TxpeeOw8f2bIal5+/oCPtk4BsNvV9VpvLGihJ28p1gFi41HMpNqEm6rw3YlPpDqly65vW47lbX9+29pqvpxL/7Qb36nbD11zV6R5NNhWObpkjjWDaqL+6GV973ytw9tz+jl+n5Ll499UrOnoNz3XaalOZKEh9vs5UiqlymlF/9dDwNARhU7G5vwQ8Tf0lDPXZfj3xeT04FlaodAFesmTOZHehbYiFSvtsKhMFoeZKFkLHifXdjvhcjKk0pP6akJDXiYfXZOr7qaz+Knl6nEoxptJLGzPCFHx8FpMJz3FQIfVXD70QnKkA0sRVlKmQTaURptKDm9BCaNr7q4OVPScbOlPxc1yKOXpxLKxQsWgruPqrl9QYeo6vdHGkDjCV3hmehtB05ccp7FKs5/0jZxlTRD3Q+eSvnYQVKhZthec6zPtrkjvTAKjUr6yfEn9fnKmQTaWRiPoeGqAG0HyN+t5dSOtBT9FEY2SKqOe/96IqsAe7bNHN8FwX1bB4jfpuAa8sCSBTuGRhyUDsaDF/VvF4mh4anobgNZlQUgjwXtqNFITvOUrF1Xrqr1JOEa9uhzXUW7QVniuTavbS4uC66s5at6nUe7c/dMPFuPS8+Xj5ioXFr9mDC0YRtJr7ayqqv1I2lZyIekAKnWlnqP/qV7+KtWvXwnVd7Ny5M/O4e++9FxdddBFWrlyJW2+9tZVLWnQ5uPqnl9QYjuMoi1la/ZV/L/0lD1s3LGsorqB3RqcxLJzVh3kzSw3HWJiKWU0V+HqW4pyI+vj45jI9dwNaYirr1q3DHXfcgfe85z2ZxwRBgPe///247777MDg4iMsvvxxbt27FmjVrWrm0RZeC76x66X1wHfUF1m0pnWAVPSRzG8LvXnkufnPD0obPm6qp7wHgfddcqAjZvHoq8e+9a19qSaisXr267jE7duzAypUrsWJFHHR38803484777RCZYpCESo9JFVeeaGaJZl67hQ01DeDqWqo7/M9LJ7TeDbpTpaLnmy89mI1JyCx4gztV0+rvzpuU9m/fz+WL5e5hQYHB/GjH/0o8/jt27dj+/btAIAjR450unsWbYZn2O33Am64ZCluuETurl1HTavRiVtxrJuMgk4Xkesm1GUqTQaQdgPqCpVrr70Whw4dSn3/yU9+EjfeeGPdC5hc5vJ0rdu2bcO2bdsAAJs2barbvkV3gdP1XqTuAo76QnciB1MPj05HYCq7O1VR1PurF8eirlC5//77W7rA4OAg9u7dKz7v27cPy5Yta6lNi+6F4kHVwztxvXxrZ2wqvbdgdBJF3benAmSalozfvc6pXTuNjr/2l19+OXbv3o09e/agUqng9ttvx9atWzt9WYtJAhcqvcxUHEerS9+BW+nFBaOToPkyFV2KdYjU95kuxUm6oB58h1oSKt/4xjcwODiIRx55BK9//etx/fXXAwAOHDiALVu2AAB838fnPvc5XH/99Vi9ejXe/OY3Y+3ata333KIroQiVHl4cJoSpWAWYgqnsUqyjXomEkpsvdLoZLRnqb7rpJtx0002p75ctW4a7775bfN6yZYsQMhZTGzzvE69g12twgM4b6qf+2tkQREnnaTAw9dgYqb9qWcnBuhi9+9ZbdCVIqPT5bk9Sd4KjGeptnErnMZ0M9fWqr9Lv1R5kKlaoWLQVtMvsLzUep9BNcBxHWfQ7wlSs+kvBdHIppnssZ7B5YjK9yFRs7i+LtoJoe7/f40IF5gj7tl5j6q+dDYHGYzoIFQD4LzeuxZUZueKuXLEQD/zqCJbO7Xwp7XbDChWLtoIW375Sb5Ng3VDfGaZiwXHuwpm49Nx5WLN0YLK7MiH4vZefn/nbtqtXYPOaJVixaPbEdahNsELFoq0g2t7zTMVRDfWdYCrTIR6jEQz0l3DH779ysrvRFXAcpycFCmBtKhZtBqmM+qcAU3Gsod7ComH09ptv0XUgptLX44Z6QGcq7W+/l73jLCyyYIWKRVshmUpvCxXX1XJ/WQuIhUUhWJuKRVshbSq9vV9xoGUp7u3bsbCYMNhXxaKtmDpxKnruL8tULCyKwDIVi7bCm0KG+qjDNhUAWDSnD79/zYWdadzCYhJghYpFW+FNEZuKg4lhKj/+yLUdadfCYrJghYpFWzFlhIoDuMw4b7VfFhbF0Ns6CouuA+3oe95Qr+X+sjYVC4tisEzFoq0IkvKovR6nEhMu7lJsYWFRBL29nbToOlBW1Z5Xf2kuxZapWFgUg2UqFm1FNYiZSq97fzmOGvBoZYqFRTFYoWLRVlQTptLX8wklndzPFhYWZlihYtFWVIX6q8eZCszs5D2vXjHhfbGw6CVYoWLRVtQS9VevMxXXTef7eu7W109Sbywsege9vZ206DpUEqZCFSB7Fbqh3sLCohisULFoK4iplL3enlquY+0oFhbNoLfffIuuQy1MmEqvb/Mdy1QsLJqBFSoWbQW5FJd6PKLedWxsioVFM+jtN9+i60DeXyW3t6eWAytULCyaQW+/+RZdh1etOgsAsHigb5J70hr03F8WFhbFYF2KLdqKP77uIrz95edjyUD/ZHelJfT5LvwedzawsJgMWKFi0Vb4notl82ZMdjdaxkffsMaqvywsmoAVKhYWBlx89sBkd8HCoidh+b2FhYWFRdtghYqFhYWFRdtghYqFhYWFRdtghYqFhYWFRdvQklD56le/irVr18J1XezcuTPzuPPPPx+XXHIJNm7ciE2bNrVySQsLCwuLLkZL3l/r1q3DHXfcgfe85z11j33ggQdw1llntXI5CwsLC4suR0tCZfXq1e3qh4WFhYXFFMCE2FQcx8F1112Hyy67DNu3b889dvv27di0aRM2bdqEI0eOTET3LCwsLCzahLpM5dprr8WhQ4dS33/yk5/EjTfeWOgiDz/8MJYtW4bDhw9j8+bNuPjii3H11Vcbj922bRu2bdsGANb+YmFhYdFjqCtU7r///pYvsmzZMgDA4sWLcdNNN2HHjh2ZQsXCwsLConfR8TQtw8PDCMMQc+bMwfDwML797W/jox/9aKFzn3vuuZbYypEjR7Bo0aKmz59s9HL/e7nvgO3/ZKOX+9/LfQfy+//cc8/VbyBqAXfccUd0zjnnROVyOVq8eHF03XXXRVEURfv3749uuOGGKIqi6JlnnonWr18frV+/PlqzZk30iU98opVLNoTLLrtswq7VCfRy/3u571Fk+z/Z6OX+93Lfo6j1/rfEVG666SbcdNNNqe+XLVuGu+++GwCwYsUKPPnkk61cxsLCwsKiR2Aj6i0sLCws2oYpLVTIi6xX0cv97+W+A7b/k41e7n8v9x1ovf9OFEVRm/piYWFhYTHNMaWZioWFhYXFxGJKCpV7770XF110EVauXIlbb711srtTCKakm8ePH8fmzZuxatUqbN68GSdOnJjkXkq8853vxOLFi7Fu3TrxXV5/P/WpT2HlypW46KKL8K1vfWsyuqzA1P8/+7M/wznnnIONGzdi48aNwtkE6K7+7927F695zWuwevVqrF27Fn/1V38FoHfGP6v/vTD+Y2NjuOKKK7BhwwasXbsWH/vYxwD0zthn9b+tY98WH7QuQq1Wi1asWBE988wz0fj4eLR+/fpo165dk92tujjvvPOiI0eOKN/98R//cfSpT30qiqIo+tSnPhX9yZ/8yWR0zYjvfe970WOPPRatXbtWfJfV3127dkXr16+PxsbGomeffTZasWJFVKvVJqXfBFP/P/axj0Wf+cxnUsd2W/8PHDgQPfbYY1EURdHp06ejVatWRbt27eqZ8c/qfy+MfxiG0dDQUBRFUVSpVKIrrrgieuSRR3pm7LP6386xn3JMZceOHVi5ciVWrFiBcrmMm2++GXfeeedkd6sp3HnnnXj7298OAHj729+Of/3Xf53cDjFcffXVWLBggfJdVn/vvPNO3Hzzzejr68MFF1yAlStXYseOHRPdZQWm/meh2/q/dOlSXHrppQCAOXPmYPXq1di/f3/PjH9W/7PQTf13HAezZ88GAFSrVVSrVTiO0zNjn9X/LDTT/yknVPbv34/ly5eLz4ODg7kTtltgSrr54osvYunSpQDiF/Hw4cOT2cW6yOpvLz2Tz33uc1i/fj3e+c53ChVGN/f/ueeew09+8hO87GUv68nx5/0HemP8gyDAxo0bsXjxYmzevLnnxt7Uf6B9Yz/lhEpkcGbLk8TdgocffhiPP/447rnnHtx222146KGHJrtLbUOvPJP3ve99eOaZZ/DEE09g6dKl+OAHPwige/t/5swZvOlNb8JnP/tZDAwMZB7XK/3vlfH3PA9PPPEE9u3bhx07duCpp57KPLbb+g6Y+9/OsZ9yQmVwcBB79+4Vn/ft2ycSWnYzTEk3lyxZgoMHDwIADh48iMWLF09mF+siq7+98kyWLFkCz/Pgui7e/e53C5rfjf2vVqt405vehN/93d/Fb/3WbwHorfHP6n+vjD8AzJs3D9dccw3uvffenhp7gt7/do39lBMql19+OXbv3o09e/agUqng9ttvx9atWye7W7kYHh7G0NCQ+P+3v/1trFu3Dlu3bsWXvvQlAMCXvvSlwqUGJgtZ/d26dStuv/12jI+PY8+ePdi9ezeuuOKKyeyqEbQoAMA3vvEN4RnWbf2Pogjvete7sHr1avzRH/2R+L5Xxj+r/70w/keOHMHJkycBAKOjo7j//vtx8cUX98zYZ/W/rWPfTs+CbsFdd90VrVq1KlqxYsWEJrBsFllJN48ePRq99rWvjVauXBm99rWvjY4dOzbJPZW4+eabo7PPPjvyfT8655xzoi9+8Yu5/f3EJz4RrVixInrJS14S3X333ZPY8xim/r/1rW+N1q1bF11yySXRG97whujAgQPi+G7q//e///0IQHTJJZdEGzZsiDZs2BDdddddPTP+Wf3vhfF/8skno40bN0aXXHJJtHbt2ujjH/94FEX572q39D2KsvvfzrG3EfUWFhYWFm3DlFN/WVhYWFhMHqxQsbCwsLBoG6xQsbCwsLBoG6xQsbCwsLBoG6xQsbCwsLBoG6xQsbCwsLBoG6xQsbCwsLBoG6xQsbCwsLBoG/7/2/PX0qwWPCEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d1b5b7518a54176a63aea122d480b31",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e30445280840aba704d73345463484",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4ea6cf58c6f4859a1252f1774a75dee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7c01b196a3d4f0daa8aea40a40aacd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6887de0c0822496b8c50e5919cad9d79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bb5fcb79d6248459dcb6daada1cffa3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b86df541372a45608abe078efc786d11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AICQwd558OSbdhVkqXQnBmVK6wwmlH00gIByTzIEAfA4n1/wNMOtwknWk7k04kQ4ZHA7Ai86KWjMujZeuAyfBIQgJeOeUzpaj4AxNh1Bt1ontVtxl+G/IBm+GTtECwtCzVIUDvnA05wo1YL7DWciVKb5Cra3GlPP5F9JeZ8alEg75kuAQhATmxvilzKkvNVzuIFocRtiNWjBMeYpmNvCzcOTJ4eS6BweYdTgP/vAY3ro8Ne9nLk8QBq1KPEYO2mstuDIVABstzUaeJDgEIYGZcBRGrRpVJu288Fq54PLwgqNRq2A3ahVfNMDPwskuh5OMmXCeORzzrFj95HeD834mfAhIN3gtW9rrzIjFuZKdj0OCQxASEJLQmXaOlyIcx/FvbolxxNWm8vs3ZAPHcRnHSwuIgpNic+ZMKCpJuFKh16jx8iO34poV1XjryvxyZZcniJYq+cJpALDB6QAAHLtSmqXRJDgEIQFvIrRSY9aXXUjNHYggGImhOdFl2GFStsMJRmKIxjlJITWtWgWTTp2yaMAXyi+HA/DdnG9dXYcLY755Qi+4TjlZWWdGjVmHI30kOARRtvD7OjSosegw6Wfx7ugMvv1Cb1mU9bvcfIWa8OZWXYaimQ1CgYeUogGAdznJcjiRWBzBSCyvHI7A5rZqAMDv+vk8ToCNYsrPwlklr+AwDIOetirxOqUGCQ5BSGAmxI8rFkJqH/u31/CvL7ybtk9WqeBKjDgW3tyaHYaMY4/LGXE0gYR9OAA/eiCQpNWPP5xfH7W5bHDaodOocDQhBMLrL7fDAXhxuzIVwHiaOT/FggSHICTA9yHTosbMt5sXuhFnGh9cCrgWvLm1OIzwhqKYCZWeWHoCbN7tg4QCAKn9ycx6Dfzs4o7R+fZRm4teo8ZGpx1H+vlQV/8EXxItt8MBgNZqPi+UbvBbsSDBIQgJeBNVTzUWPeZG0Urxj3ohLncQJp0ajsSeE6F4wFWCLueLPzmBD33nt3kVNXgldooWMOnUCIQXOxw5BQfgnceJAQ/+8qcnsP/EEHQaFda12GU591xqkmw2jcU5cZhcMSHBIYgMcBzHVysZtbi2vXrez0bKQXA8gXnlt4LTGZwqPcERxiQ/e3Io53MMJDZU1kqYognwjTmTORxPkH/DllJ8IAUhj/Oztwbx3yeGcE1bdU6jqzMh7OuZmlO+/83nz2Pb7t/Kfq1sIcEhiAyEo3GwsTisBg3WNNqwfs6n0nIJqbXMCd04E6W4pehwhDfLX7yd3eRfjuPw5qVJRGJx/PbCJJZVmyTnR0x6TdIcjuCypEzjlML1K2vw6fe24caOWgDATZ21spx3IcJ65xaGvHphAu+O+oreYYIEhyAysDBE86P7r8Xe+65BrUVXNiG1uW++tRYd9BoVBqYC2H/chZODnuItbgGCYzw5OJ2ypX8yfnTkCj6x5w189Zen8frFCdyYxZu5WacWCwTmIgiOXJ0ADFo1vrqtG1/7vXXY1OrA1vVNspx3ITajBmoVI66fjcZxbpgfL35uZKYg15SKPMFJglAwQoGAEFqxGrS4ZVUdGmyGkg+pBdgo3IHIPIfDMAxaHEb852v9iMU5XLuiGj/+7PVFXCUPx3EY9YZRZdLCHYhgyBPCMgl9xvzhKP75+Xdh1KrxwzevAABuzkJwTLrkDkdwCFUmeUJqAm21Zjzz0A2ynnMuDMOgas7m3ndHZ8AmxPv8iBfXr6wp2LUzQYJDEBkQy2wXJI8bbQa4PEG8MziNAXegYJ9Y82HhHhyB9U47poMROKuMODPkRTzO5TzzRS48gQjYaBy3ra7Dc6dH0T/pTyk4p1zTWFZjgs2gxVuX3Zjys/j+p3sw5g3jylQAt66ul3xds14NPxsFx3Hz2sxM+Vk4TFpo1OUXCKox68SiAaHqT6Niiu5wyu+VJIglZtjDu5i6BUnoBju/n+XDu3+LP/3hsWIsLSODKfZ7fONjG/HGl2/H9muWYSYcxYA7ULA1RGNx/NXPTuD4gCftcaMz/Ot87Qr+E3iqfmAXxmZw9xOv4qv7TwMATg/xc4quXlaN7dcsw199YE1WyXiTTgOOA0KR+SG8KT8ra2PNpWRuC6YTgx7YDBpsbqvGWRIcgihthMqptpr5Q9du6qgVw20AEI4Wv+x0IYOCw1mw30OnUUGrVqE7MQFUeNMuBC+eG8NPjg7iH589m/Y4oQBjvdMOg1aF/snkIrjr4HnE4hx+eWIILk8Qp4am0VptFEcNZItZz4vTwkq1SX9YLDEuN6ots4Lzu343etqq0dlgQd94cYezkeAQRAb6Jvyot+phXrDj/K71TfiPHT3oqOcnNo55S2tnN8dx+H9vDcJZZUSD1ZD0mFUNVmhUDE4P5bfZMh37jvB5Fb02/duN8Po12gxoqzEndThnh7144ewo/uCaZYhzHH7yuwGcdk1jXXPu+1lMOv73unAvTjk7nBqzDpO+MKb8LC6M+dDTVoVl1SZ4Q9GUjUqXAhIcgshA/4Q/5Ujp27sa8Ddb+VnyYyXWSuTVC5M4PuDBg7euTJmfMWjVcFYZcaVAe3L84ShefnccwKxTTIVQ8Vdv02N5jQl9SQaJ/ftvLsGoVeOvP7AaG1sdOPDOMPonA6JTywWzLrnDmfJHUC1TSfRSU23WwRuK4o1LkwCAa9qqxQ4El6eKN7qABIcgMtA34Ud7CsEB+DdIABifKa2Kte+81ItGmwEfu9qZ9jirQZu0LFgOTg95EeeAjU47Bt3BtLvdx31h2I1a6DVqtNdZcHkygMic0uhjV9x45m0X/vDaZXCYdLipsw69Y3yIKJsigYWYEs41MEdw4nEO7gCLarO8FWpLRYONd7T7j7ug06iw3mnHsoTgCJNGiwEJDkGkwRuKYNLPpnQ4AFCfCFeNlkhIbdIXxl/sextv9k3hs7e0Q69Jn0C36DXwhQojOO8kKqR+7z0t4Djg0njqT9cTvjBqLXwIq7PeguicQWIcx+Fvf3EKjTYDPn9HJwDgllW1if/X5dUiRnQ4c0Jq3lAEsThXtg5nU6sDAPD8mVFsdNqh16hJcIil4eSgB1/6+UnE46XfSr/UENqkLK9OvR+kxqyDWsVgrEQczqsXJ7H/+BA+tKEJf3DNsozHm/UazBTK4bimUW/Vi3s/LqRJWk/MsKi18G/wnfVWAEDvKH/865cmcWbYi89vWSWOC9jUWoWdN7fj7z60Nq81ijmcOQ5nUuwyUJ45nFUNVlgNfPWd0FLHrNeg1qLDlRTFGEsBCU4F8PNjLjx9ZADDJb5JsRQRPvWmm4miUjGos+hLxuEEEuLx5a1dksqDrQZNwUJq77imsb5lNpwj7AtKxoQvLPY/W1nPO0ohZPbUa5dRZdJi28Zm8Xi1isGXt3aJRRu5IlapzXE4QgGDEC4tN9QqBlcvrwIwKzgA30n6MgkOUUjODvMlr4NFtNLlipBzMGSosGqw6UumaMCf2DVv1knb123Wq+ErgOBEY3FcmvBjTZMVJp0GFr0mbSugcV8YdQmHY9Jp4KwyonfMBzYaxyu94/jQhuaCNLtM5nAEtyrkQsqRGztqYdSqcVVCeACkrP5bKkhwFAjHcfjp0QF86efvYDoYEXcXD6T5dEkkRxCcTHmQOqsBYyXiIAWHY9RJe3O26LUFyeGMeEOIxTmxWWi9TZ9yKFgoEsNMKDovhNVZb8G5YS9ODHoQYGO4oaMwzS5n9+HMOhxBGMtZcD793jb8+pFb580FWllnxtB0qGCONhPU2kaBPH9mFI/87CQAYGQ6KA6kGizgbnKlEoryVVKZHI5Fn3xqZDHwszHo1CroNNI+T1oNGrCxOMLRWEZhzQYhfCYMGau36lM6HCFnMnekwNXLq/A/58fxqxNDYBjgugWjIeTCoFGDYWaFGuALQMw6tSzTPouFRq1aJJgr6/jwY9+EvyCzeDJBDkeBvHR2DFaDBvfdsAL/c35cfHygBOeflDqzIbX0b8RatWpeCW8xCbBRmPTShSNZlZYcDC7o41ZvNaQMO076+MeFogEAYqHB3tcvo7vZBoepMAl8lYqBSauGLzzf4ZSzu0nFykS+6+ywN6tu3HJRvvJNJIXjOLzSO46bOmvx6F1rAEDc/EUOJ3vCQkgtg8PRauQXnAAbBRuNZ/1GG2BjkvM3AGBJFET4QlFZd9YL83aaE4LD57lCi5pkAnzBAACxLBoANjgd4tefur5NtnUlw2bUipEAgBecci0YSMfyRDPUR352Ej89Ooh9O69b0qat5HAUxtsDHgxPh3DLqjroNCr8rw+vxYG/uAmrG63iJ05COmExpJbeMejUKrBReQXnHw+cxY7vH8n6eQE2Kjl/A/DhQACyFw4MugOos+rF167eakAoEp/Xf05gYiYRUpvjcLRqFW5dXYdaiw73XJV+82q+OEw6eAKzw8lGvWFFOpy5IdMj/VP48dGBJb0+CY6CCEdj+PLP30GdVY8PdM9vle+sMmJ4OogY7cXJCjGkliG3oVUziMTkfW0HpoI5bdLzh2NimEwKFn3C4cgsOC7P/MFvCzsyXBz34VPfPwK3n0X/pB9qFbOoI/eeT/bgt3/9PqgL/Cm8yqSFJ+Fw+Lk8ygypAcAf39CGO7rqsbmtCv96+N203R/khgRHQfzvF3txbmQGX79n/aLOuQ6TDnEOBdtRrlRCkThUDC8o6dCqVYjG5XU408EIpoORrDfsBtioWOorBUtizo8vLG9Tx0F3UCwYAGY7Mgh7XJ546QJeeXcch8+O4tgVN7qbbYucpE6jKkgp9EKqTDq4Ew7HG4wiHI2j3qq8kBoAfOXD3fjejs34wpZVGJsJ4y9/egKvvDue+YkyQIKjEP77xBD+7dcX8ftXO/G+NQ2Lfi6URs6NUxOZCUX4yq2FOYeF8EUDHDhOPpfjDUYQ55B1FwB/OCaW+kphNqQm3yfdABvFlakA2utmN2UKDmfEG8LYTAj/fXIIAPA/58ZwYmAaVy2rSnqupcBh0sKT6KI8nsgnLXRbSuP69hp8oqcVh8+M4tfnl0ZwqGhAAYzPhPGFHx9Hz/JqfGVbd9JjhGmVJDjZEYrGMpZEAxBLkCMxDjqNPOEfYdLodCAyby9FJrJ2OPrZogG5ODvsBccB6+eU3jbbebcz5Ani7SseRGIcVjVYcPDUCACIO+OLQVUihxOPc2LFXE2Z9lGTCsMw+PrHNuAfP7p+ycJq5HAUwMlBD6JxDo98YHXKfQPkcHIjFIlLCukIITe5KtU4jhN/V54gm+Ho+fjZ7ByOWXQ48t0b7wzyTTvnCo5Rp0aNWQeXJygOW9vx3jYAgIoBetqK63AENykMLivXWTjZolYxi2Y9FQpyOArgHdc0GAZY25R6JoiQ0xE+NRPSCEelCo7gcOQRnGAkJhYheLIcmBVkY1k5HKGEWs6Q2qkhL2otOjQsKC12Vhkx6A7CYdJBq2bwB5uX4ebOOrCxOJrsxhRnKzxC6bknwM427rRUhuAsJSQ4CuCUaxor6yxpP6WQw8kNPoeTORAgCA4rk+DM/T15svidcRwHPxuFKYsqNZWKkX1EwTuD0+huti/KfbVUGXFueAa1Fj3qrQaoVIw4GKyYVCU+kLkDEdHhVBVoo2klQyE1BXDK5cW6DBMPSXByIxSJZRlSk6dowBucffOfDkgPqYUicXAcsnI4AD8TRy736/azOD86g54kORlnlQkuTxDD00E02Uun7FhwOO4Aiyk/C6tBI7k1ECEdekXLHE+AxYg3hLUZBMeoVUOrZkhwsiQciUsqGhBDajJt/pzncLIIqQljkrPJ4QD8B5IZmQRH6GwhtKaZS4vDiHA0jtMuLxpKSHAEhyOE1Mp1Dk6pU3GC8/SRK/iLfW8XexmyIWwMbKtJPZES4CtS7AvadxCZCUlsaCl3DifXkFogkYfJ1uHYjBrZ7o3XL03CpFPPa00jIGwEnQlH0VRCGyurxBxOBFP+cMUUDCw1FSc4l8Z9eP70aLGXIRuC4EiJg9sMJDjZwofUipfDYZgcHU4WORyAvzfmhvHy4fWLk+hpq04aklpRN/vBqLGEHI7NqAXDAFN+FpM+tmxHS5c6FSc4Fr0WwUhMMS1ehA7QkgTHqIWXBCcrpJZFC3tv5MrhCILTbDfO6/GVCWGImCnLMle53O/4TBi9Yz5c3744nAbw7fG7E+HfUnIRahWDZjtfQUchtcJReYIjtvFQRouXK1MBVJt1kuZ22ElwsiYcjWXsowbIH1ITfk/OKmN2ITVWCKll6XCMWlmKBl5Pk78R+K8/vga/f7UTN6+qy/t6crK8xoRLE364/SyqqSS6IFSc4Fj1yhKcQXcArVXS9i9QDid7QkUsGrDqNagy6bL6kBBMCI4xy/5jNqMWvnA0675tC3n94iSsek3aqsk6qx7f+P2N8zpDlwJttWacG/YiGufI4RSIihMc0eEopInllamA5H0MJDjZI70sOiE4MoVqhSFqpiwniUqdULoQm0EDjgNm8vy7ODHgwXuWV0GjLr+3lrYakziOYmOro7iLUSjld1fkiVlfmM64xSASi2PIE8xKcLyhqKwNJpUMx3EIR+PQS8nhyOxwojEOOo0KZp1GzMtIQeiJle2oaGGfVj5htXicQ9+EH531lswHlyBCpadBq8IG59KPX64EKk5wLHr523gUiwtjPkRiHNY0WiUdbzNqEItzigknFhrh066kTgMaeXupsbE4tCoVTHo1/Fk4HKkTShdik2Fj8OhMCMFIDCtq05folyptiXX3LK/OWrAJaVSc4FgVFFI75eIbJK5rkfZpjLoNZIc4fC2LkJpcZdHRGAetWgWTVgM2Gpc8f17qhNKFiA4nj3ujb9wPAGgvU8FZVm2C1aDBratLq5hBSVRcLzWLgkJqp1zTMOvUWJFh06fAXMFxFq8xb9kQziIfIobUZCqLjsTi0KgZsWNAIBKDLXENt5+FUadOKipSJ5QuxGbIP6R2cSIhOHXlGVIzaNV45ZHbRLdHyE/FORyhaCDf5GgpcGrIi7XNNqgkjt+1iZ9iy//fvhRk8+Ytd1l0JM5Bo1aJHQMCc0LAv/fdV7H7pQtJnyd1QulCbMb85yX1jfth0qkXdYguJ6rMuoKPs65kKk5whFbs/jLP4URicZwZ8qK7WXpyk0Jq2SHmcCSVRcubw4lE49DNcThCB4FoLI7LkwGxw8RChKq6TBNKF2KX4cNI34QPbTXmrK9NVA6yCM6hQ4ewevVqdHR0YNeuXYt+znEcPve5z6GjowMbNmzAsWPHJD9XbtQqBiadumxCahzHifH73S/14hvPnUMkFsfJwWkEIzFcu6Ja8rnkiNNXEmxCcHQSSnyFMmBWriq1eBwa1WKHI7TOTxX64ieUZp/wNus0UDH5fRhxeYJwStwTRlQmeedwYrEYHnroIRw+fBhOpxObN2/Gtm3bsHbtWvGYgwcPore3F729vXjzzTfx4IMP4s0335T03EJg0WvKolIrHufw0I+O4cVzY7h6WZW4i7tvwi86m2tyEBxyONIQHI6UNvVy53DYGAejTiX2RBMcznhi/HGq32EoEochh7b6KhUDh0kHdxZtdBYy7AnhvStrc34+oXzyFpwjR46go6MD7e3tAIDt27dj//7980Rj//79+NSnPgWGYXDdddfB4/FgeHgY/f39GZ9bCCwGTd45HI7jEI1ziMTiiEQ5RONxMAwDBnzDRYZh+P+D/1rFACqGgVatgirx81T4wlFcmQzgqdf7cfDUCO7sbsBrFybRXmvG3Zta8K8vvIsD74xgVYMFNVns1pbjU2wlwWYhOHKH1KKxOLQqRuyJJuzFmfQlHE6K36HUfUPJqDJpRQeVLd5QBDPhqNgNmiCSkbfguFwutLa2it87nU68+eabGY9xuVySniuwZ88e7NmzBwAwPj6e15qtaRzOpC+Md1zTuDwZwKA7gCFPCFN+Fp5gBN7Ef+FYHJEYP+gqVzQqBmoVA42KgUatEr+PxOJwJ7oDqxhg583t+NJda+BNCKRVr8HlKT9+fsyFO7oasrqmSsXARt0GJCOUOEvZh6NW8R8wZMvhxOLQquc4nERIbUJ0OMnvX6kTSpNRY9aL45WzZcjDN5FtcpROB2ii9MhbcJLtWl/46T3VMVKeK7Bz507s3LkTANDT05PLUkUsBg38CcHxhaP49fkxvHBmFL/rd8OV+MMB+DeaFocRNRYdWhwGrG2ywWbUQK9RQ6fm3YpOo4JWrYJaxf97OAAch8T/ucTXHOIcEItziMV5ZxSLxxGNc4jGhMfiiMU5qBgGzioTWquNWNdsFzej2eeUav7LxzfhHz+yXlJuYSFL0d7mu7++AJ1ahT+5qb2g1yk0szmczI6BSbhXOffhaNSLHY4gOClzOBJb8SSj2qzDhXFfTs8d9oQAAM3kcIg05C04TqcTAwMD4veDg4Nobm6WdAzLshmfWwjMOg36J7z40s9PYv/xIQTYGKrNOlzfXoNPXb8cG5wOdNRbUGvRlWzFTa5vKnaZugKnIsjG8J0XL8CsV+O+G1ZILtkuRbIJqQF8HicqWw4nDt0chyP0UxNCamw0nlRcpE4oTUa1RYep/twcjvBBrdlOgkOkJm/B2bx5M3p7e9HX14eWlhbs27cPP/rRj+Yds23bNuzevRvbt2/Hm2++CbvdjqamJtTV1WV8biGwG7VweYL4xdsubNvYjHuucqKnrboi6u8L7XBefncMwUgMwUgMZ4a9krsglCJsjH+Tlyo4WjUjYw4n4XCEKrWE4AhFAwCfx1koOKFoLOc5MzVmvmggFuey/lsYng5Co2JQZy3fPThE4clbcDQaDXbv3o0777wTsVgM9913H7q7u/Hkk08CAB544AFs3boVBw4cQEdHB0wmE/7zP/8z7XMLzQO3rsTmFdX4wLpGcYd1pWBLiG2heO70KKx6DWbCUfymd6K8BSdLh6NVq+QTnHh8TsiWEUPAE75ZBzIdjKB+wZjmUETa/J5kVJt14DjAE2CzKkYBAJc7iAaboSI+tBG5I0trm61bt2Lr1q3zHnvggQfErxmGwRNPPCH5uYVmZZ0FK8u0/Ua+FHoI24UxH96zvApj3hBevTCBB29dueiYMW8Ib/RN4fY19WL37lIkm304AC84bFSmkFo0LnYvMOk0c0JqYeg0KrDReNLQaDiaR0gt4Yym/NkLzrmRGaws0y7RxNJRcZ0GKh2bgQ+pFWpEwYg3hEabHtesqMaxK+5FTScnfGHc/i8v43NPv40d3z9S0vuh2EQ+Rqrg6DRyOhxOLLU26dSiwxn1hsTmmMlCo3yVWm4Op8bMi0y2lWpBNobeMR82Ukt/IgMkOBWG3ahFJMYhGJG/tU8kFseEL4xGmwE9bdUIsDGcHZ6Zd8zxKx7MhKL4zI0r8PaABw//+HjeUyYLRfYhNflyOHzzTsHh8EPYfOEoJnws1ifClMna0EidUJqMuQ4nG04PTSMW57DB6cjpukTlQIJTYRSy28CELwyOAxrsBmxu49tR/65/at4xZ4a9YBjgC1tW4ctbu/D8mVF8J0UjymJTrBwOx3GIxDhoE/kQs14DP8tvBgaA9Qknkcrh5FrBWGPhBSdbh3NikB+TQQ6HyAQJToUhR5PGVIxM83sxGm0GNNmNaK024tfvzt+ke2bIi7YaMyx6De67oQ0fvYrvnPCD1/tlX0++sLEY1IkNuVLg9+Hk79aiCccn5HDMOr4zhtCwU2hrtDAXl82E0mRUmRIOx5ed4PyubwpNdsOiAgaCWAgJToWRi8OZ8rP45nPn8f/eGkyb+xn18oLTkHjj+ch7nPhN7zgG5nQ2Pj08jbVNNgB8Mck/fmQ97uiqx9/tP42HfnSspHI6bDSe1eZanVoly4hpYS+PEFJbXmPCpXEfrkzx82Y66iwwatWLigaymd+TDJ1GBatBk1U/tZlQBC+dH8Od3Y05XZOoLEhwKoxsBccfjuIP//0N7P6fC/jiT0/gudOjKY8VHY6dF5ztm1vBAPiP3/YB4MttB6aCWNtsE59j0Krx5B9djUfuXI1Dp0Zw7/feFBPkxYaNxiWH0wBAI1MOR+hWIBQNrGqwwh2I4K3LbtiNWthNWpj1arFyTSAcSQhOHuORa8y6rEJqh06NgI3GsW1T4TdsE+UPCU6Fke2grX//zSWcG5nB9z/dgxqzDv99cijlsSPeMLRqBtWJ0Eyzw4hPbF6Gva/340jfFH7TOwEAuK59fodrjVqFh27rwHfvvQrvDHrwlz89gVgJFBKwsewER67WNlFRcPhrr2m0AgAOnxnF8hoTgPml0gKhqPSR2KmoNusw5Q9nPhB8N/Pvv9qP9loz3tPqyPmaROVAglNhZONwpoMR/Psrl3DXuka8b00D7lrfiBfPjqZ0IKPeEOqthnntbP7mg11YVm3Cn/7wLTz1ej8cJi02tSafb31ndyO+vLULB0+N4JGfFV90wtmG1BL7Y/JFGHEgCM6qhODEOWB1A//13FJpAWFCaa7NOwGg2qwX2+dk4tl3hnF22Is/v72jZFtAEaUFCU6FYTVIF5wXz47Cz8Zw/818E8671jUhFInj9YuTSY8fdAfQsmAAl0WvwX/s6AEbjeN3/W7c2FGbNgn/Jze14+Etq/DzYy58/sfHxaaVxYCNxrN689Zp5HE4QlhOkwip1c7ZhCk0RDXrkzgcIaSWh8OpMesklUWfHPTgSz9/B+tabNi2sSXn6xGVBQlOhaFWMbAaNJK6DTx/ehQNNj02JfZXXL28CnqNCq9enEh6fP9kACtqzIse76i34ld/fhM++p4W7Lw5cwfpz93eib/+wBr86uQQfu+JV8UOyUtNJMuQml4tl8NZ3OHgk9ctx73XLsPqxjkOh03ucHItGgD4Bp7uAJu2OORXJ4dwz7+9BqtBgz2f7KF2NoRkSHAqECntbUKRGF5+dxzvX9sohsgMWjU2t1XjtQuLHY4/HMX4TBjLa01Jz7esxoR/+cQmyZsDH7x1JZ667xpcmQrg0/95BOGo/BtVM5Ft0YBeqxIrxfJBKIsWHA4A/MPvrcNjH1kvfm/WacSx0wKC4zHq8nM4kRgnzl9ayDuD03j4xyewqdWBZz93E40jILKCBKcCkdIx+uywF8FIDDd0zB8Z/N6OGpwfncHYTGje4/2TfMluWxKHkys3ddbhO39wFU65vPjGofOynVcqwogAqehkdjjaNNdO5nA8iXJmYT9NLmTqNrDr0FnYjFrs+WRPzl2picqFBKcCkSI4p4a8AGZ3tQvcmBCghXmcy4ld8EIVlVxsWduAT/S04qk3LmNyiUNrcxtoSkH+ooHUoSqTfrbdzdX/cBgvnRvFlAyCUyUKzuLX+q3Lbrx6YRIP3NIuHkcQ2UCCU4EIDTzTcdo1jSqTFs32+bvHu5vtsBk0ePXC/DyO4HCWy+hwBP7kphVgo3Hs+91A5oNlJOuQmkYtS+hvYVl0Msw6fmrtlckAJv0sXr84CU9iNLnDlPvIjZqEkCSrVPuv1/phM2jwh9cuy/n8RGVDglOBOExaeDI6nGmsa7EvKndVqxhcv7IGr16YFBPLHMfhSN8U6qx6WAowbqCzwYprV1Tjmbddsp87HeEsBUenUSHOYVGH7GwRKt00qnQhNQ3C0ThGvPxsowtjPkz5WZh06rz34QCLQ2rjM2EcOjWMj13dKg6FI4hsIcGpQBwmHTxpKpHYaBznR2bEnl0LuXlVHVyeIF67OIkXz47ivv/6HX59fhyfuXFFwdZ8R1cDesd8GCrg8LiFZLvxUzg239LoqISQmlnPi0r/BB/K7B3zwR1g8wqnAbMl2AsrA39ydACRGId7ryN3Q+QOCU4FUmXiRxT42eThnytTfkRiHFY3Jh+odc9VTjirjLj/qaP4zN6jODcygz++oQ07b8pc8pwrt6yuAwC8vKAZaCFho3Hos8jhCHt2hBYzuSKtaIB3GX0TfCjT5QliyBPMO5Fv0KrhMGkx4p0tConFOfzwjcu4oaOmYgcXEvJAglOBCJ+C3SkqkS6O829i7bXJ31wMWjW+fs8GvGeZA3/7wS68/Mht+MqHu+d1GJCbznoLmuwGvHx+aQWnGA4nEltcFr0Q0eEkcmccxyf188nfCDTaDGJfPAB449IkhqZDuPfa5Xmfm6hsKBhbgQhvSu4Ai9bqxVVlF8d9AID2utQFADd01C4qmS4kDMPgllV1ePbkMCKx7KrHciXrkFpiTflWqiXb+LkQYyJP0zfhh17D7/+JxDhZSpWb7AYMzxGc4wMeAFjS3zehTMjhVCBCSas7kLxw4NK4H/VWvdgGp1S4ZVUdZsJRvH3FsyTXi2TZS02YQ5NvpVo0LrS2SVOllijOGHQH8Z5lDrFYI98cDsB3+x6dE1I7OehBW41J7MNHELlCglOBVCUcjifF3JNL47607qZYvDfRh+3X58eW5Hq5Opx8uw1EohL24czpJtBoM4gjH2QRHJsREz5WFM5TLi/W0/hoQgZIcCoQR4YczqUJP9pLMDlsN2qxvsWOo/3ugl8rHufHPGe3D0emkFpcwj6cOeXn9TYDViY+IOTTR02gKbH3aswbxoQvDJcniA0tND6ayB/K4VQgDqOQw1kcUpsOROAJRJI24SwFNrU68OPfDSAai6cNOeWLkPjPRXDydzjSWtsIOKuM4Di+p1kwkv/G04aE4Ix4QwgmKhm75wzNI4hcIcGpQDRqFWwGTdKQ2oCb39fRWl2aTRk3OO34r9f6cWHchzWNhXsTZCUk7heik8nhJGveuRDznM2XH+huhN2kxaSfxR/fkP9eKMHhDE+HMD7D78cRZvIQRD5QSK1CqTLrkjqcwYTgOKvk7YkmFxsTkyVPDkwX9DqCaORUFp13lRovOOnEzqSfdTj1NgP0GjUe3rJKlsS+s8oIFcN3L+gdnUG1WTdvJg9B5Ao5nArFYeLnnixkYIrfyd9aooKzosYMq16Dky4PPr65tWDXEcJi2Qxg02uEKjV5yqI1afY16TVq/N2H1uKWVXV5XSsZJp0GqxqsOD7ggS8UQWd96eXziPKEHE6FUmXSJhccdwBWgwZ2GTYQFgKVikFngwW9o76CXseXmAdj0Ut/HWY3fuZZFh2Lg2GQcbDZZ25cgY4CicF7llXh7StuvDvqQ2cDCQ4hDyQ4FUqD1YCR6cUt6AemAiXrbgQ66i1iN4RCMRPiw41Wg/QggFwhNTbGQatSLWqcupS8p9WBmVAUvnAUqxoof0PIAwlOhdJSZcSELyyOJRYYdAdLtmBAYGWdBRO+MKZTbFyVg5mEw8lGcOSqUuMr8Io7trmnrQoAvxfo9q6Goq6FUA4kOBVKS2I08NzuyxzHYdAdLNmCAQGhgeTFicKF1byiw8khpCZDDmcpWveko73Oguc+fzPe+eqd4r1CEPlCglOhtFTxbyKuOYIz4WMRjMTQWlXabzBC3uLCWCEFh3c4NmMWITW5Og3EubRdBpaK1Y3WvGbrEMRCSHAqFOFTq8s9Kzize3BK2+E4q4zQqVVik9FCIORwbFk4HDk3fhbb4RBEIaC7ukJptBugYuY7nMGE+JS64GjUKrTVmnBxrHCFAzOhKLRqJquyaIZhoFOrZNn4WewcDkEUAhKcCkWrVqHRZpjvcKZ4h1MOMfuVdRZcKqDD8QYjsBq0WVeK6TT5Cw5bAjkcgigEdFdXMC1VRtHVAHyXgRqzbl5jyFKlo96Cy1OBvN/cUzETimZVoSbAz6bJfx+OVkV/moTyoLu6gulssOLciBccx7dSGZgKwlni4TSBlXUWxOIcLk8WJqw2E4pklb8RkMPhRGIctBoKqRHKgwSngulutsEbioouZ8AdKPkKNQGxNLpAYbX8HE7+ZdEacjiEAqG7uoLpbuZnnJwemkY4GsOgO4i2Eh1LsBBhQFyhSqO9oUhOgiOHw4nGuKy6VBNEuUB3dQWzptEKtYrB6SEv+icCiMW5sumbZdZrUG/Vo38yUJDz8w4nx5BaTAaHQ1VqhAIhwalgDFo1OuosOD7gwbujMwCAzvry6ZvVVmPGlYIKTi4hNXXeRQP8xk/60ySUB93VFc77uurx2sVJvHZxEipmNlRVDiyrMeHylPxFA7E4B184R4ejViEckWPjJzkcQnmQ4FQ491zlRCzO4ekjV7Cs2lRWrUyWV5sw6g2LY5DlwhdOtLXJweEYtPkXDUTjVDRAKBO6qyucjnqLOMRrRW35uBuAdzgAcGVK3rCaPyzMwsktpLawA3e28GXR9KdJKI/S3+FHFJzv7ejB86dHsaapfPI3AMSKusuTfqxulG/tAZYXHFMOgiOHw4nE4tBmGL5GEOUICQ4BrVqFD25oKvYysmZ5wRwO71DMuuzDiwatHA6HWtsQyoTuaqJssRu1MGrVGJkOyXpev+BwdLk4nPwFJxqj5p2EMiHBIcoWhmHQaDdgxCuv4AQEh6PP3uHotSqEqHknQSSF7mqirKm36jHmDct6znwcjl6jBhuNIx7ncr5+NFYaA9gIQm5IcIiypsFmwOiMzA6Hzd3hGLSJMdN5dBugHA6hVOiuJsqaRrsBI9MhseO1HAhl0TnlcDS8SOWax+E4LjGAjf40CeWR1109NTWFLVu2oLOzE1u2bIHb7U563KFDh7B69Wp0dHRg165d4uOPPPII1qxZgw0bNuAjH/kIPB5PPsshKpB6qx7haBzeYFS2cwoOx5RjlRoAhHLsNhBNhOJ0FFIjFEhegrNr1y7cfvvt6O3txe233z5PTARisRgeeughHDx4EGfOnMHTTz+NM2fOAAC2bNmCU6dO4eTJk1i1ahUef/zxfJZDVCANNgMAyBpW87NR6DSqnMJaQkgtV4cTSYTiyOEQSiSvu3r//v3YsWMHAGDHjh145plnFh1z5MgRdHR0oL29HTqdDtu3b8f+/fsBAO9///uh0fBhi+uuuw6Dg4P5LIeoQATBkbM0OhCO5dRlAOCLBgAglGMDz0iMdziUwyGUSF539ejoKJqa+A2DTU1NGBsbW3SMy+VCa2ur+L3T6YTL5Vp03Pe//33cddddKa+1Z88e9PT0oKenB+Pj4/ksm1AQjYLDkbE02s9GcwqnAbMOJ9cGnoLDoSo1Qolk/Bh3xx13YGRkZNHjjz32mKQLJEvmMsz8P6bHHnsMGo0G9957b8rz7Ny5Ezt37gQA9PT0SLo2oXzqbXoAwNiMfKXRgXAM5hwKBoC5OZzcHE6UHA6hYDL+Vb3wwgspf9bQ0IDh4WE0NTVheHgY9fX1i45xOp0YGBgQvx8cHERzc7P4/d69e/GrX/0KL7744iIhIohMGLRq2I1a+R1ODiXR/HoSOZwcN3+KORzqpUYokLw+Rm3btg179+4FwAvH3XffveiYzZs3o7e3F319fWBZFvv27cO2bdsA8NVrX//61/HLX/4SJpMpn6UQFUyjzSBvDofN3eHo8yyLng2pkcMhlEded/Wjjz6Kw4cPo7OzE4cPH8ajjz4KABgaGsLWrVsBABqNBrt378add96Jrq4ufPzjH0d3dzcA4M/+7M8wMzODLVu2YNOmTXjggQfy/OcQlUi9TY9RGUNq/rAMOZycHQ6F1Ajlkle36JqaGrz44ouLHm9ubsaBAwfE77du3SoK0FwuXLiQz+UJAgBfqXZhbEK28wXYGMz5VqnlXRZNITVCedDHKKLsabQZMDYTRiyP/mVzCeRVpcY/L5yn4OjI4RAKhO5qouxpsOkRi3OY9MsTVvOHc3c4sxs/8+s0QA6HUCIkOETZU5/YiyNH1+h4nEMwEsvZ4eQdUotS0QChXOiuJsoeObsNCB0CjNrcBEerZqBi8igaiAtFA+RwCOVBgkOUPc0OXnDkGDUthMIMOQoOwzB5Tf2MUlk0oWDoribKnjqLHrUWPU4PefM+VzjhcPSa3P80DFp1Hr3UhI2f9KdJKA+6q4myh2EYrGux4fTQdN7nCufpcABerHItGhD24eg0FFIjlAcJDqEI1rfY0TvmyzmUJRCSy+Hkuw+HHA6hQOiuJhTBuhY7YnEOZ4fzC6sJDkevzf1PQ69R5Vw0IDbvzEPwCKJUobuaUARrm2wAgPMjM3mdR3AmwqjoXDDq1AiyuTkcVigaoOadhAIhwSEUQbPDCK2aweU8K9UEZ5KPw7HoNfCFcxt5HaWJn4SCobuaUARqFYPWahMuT/rzOo/gcPR5OByLXgN/joIz27yTHA6hPEhwCMXQVmNG/4Q8DseQh8Mx5+FwInHah0MoF7qrCcWwvMaE/kl/0imzUpHL4eQsOFEaT0AoF7qrCcXQVmNGgI1h3Jd7TzW5cjj+cDQn4YvG42AYPkRIEEqDBIdQDMtr+Kmx+YTVRMHJw+GY9RrEOSCYw16cUCSWV4UcQZQyJDiEYmit5gXH5cldcMSy6HwcjoEfbZBLWC0Uied1bYIoZejOJhRDs90IABjy5N41OhzlQ1r5DECz6HmH4g/n6HDyaKtDEKUMCQ6hGIw6NRwmLYY8wZzPEY7EoNeowDC551Asei0AwBfKweFE4yQ4hGIhwSEURZPdiOE85uKEo/G88jcAYE44nNxCarG8+rgRRClDdzahKFochrwcjhxv+JbEeOpcNn9SSI1QMiQ4hKJoshsx6A7iN73jOZUlh2UIaQmCk6vDoaIBQqnQnU0oiiaHAb5wFJ/8jyN4e8CT9fPDUfkcTu5VauRwCGVCgkMoihqzTvw6l9CaHG/45nxDarQPh1AoJDiEorh6ebX4dS6CI4fDMenUYJgcHU6UQmqEcqE7m1AUHfUW9D2+FVaDJqf9OHI4HIZhYNHl1k8tFInDqCOHQygTEhxCcTAMgxaHEa4iORyA7zaQa0gt37JsgihVSHAIRdLsMMLlzi2Hk0/jTgGzXoOZHDZ+hqlogFAwJDiEIml2GDA0nZvDkSNpbzNkLzixOAc2Rr3UCOVCdzahSJodRngCkaRhrUlfGAMpRlGHZXI4dqMW3lAkq+eEo0LjUHI4hDIhwSEUidDIM1mbm8eePYvP/uCtpM+TK4diM2oxHcxOcEKRxLRRam1DKBS6swlFUpXYjzMdZBf9bGg6mDTcxnEcQlF5HI7NoIU3S8EJRsjhEMqGBIdQJA4j37E5mcvwBCKYDkYQjcXnPT7pZ8FG42iwGvK+vs2ogTeU3dTPEAkOoXBIcAhFYk8IjiewWHCm/Cw4brEYXRr3AwDa68yyXD8W5+Bnpc/EkWP4G0GUMnRnE4rEYUouOBzHiY9N+eeH2y6O+wAAK+sseV/fZuCvn01YTcjh6MnhEAqFBIdQJFaDFgwDeBa84fvZGNhEKG2h4Fwa90GvUaHFYcz7+raEw8qmUi2ccDhGEhxCoZDgEIpErWKSJu7dc0TGHVgoOH6sqDVDpcp92qeA4HCmk4T0UhGismhC4ZDgEIrFbtTCs0BU5orMZJKQmhz5G+HaAODNYvOnWBZNORxCodCdTSgWh0m7KKTmnuM43AsEZ9QbliWcBvBVakC2OZyEw6FeaoRCIcEhFAvvcFKH1Kb8sz+LxOIIRmKwJkJh+SKG1HIoGqCQGqFUSHAIxeIw6RbncBIhNYtegyl/WHxcaIEjTOvMF6sh4XCyKBqgsmhC6dCdTSgWu1GzOKTmZ8EwQFutCVNz3I/QaNNikEdwNGoVLHoNvEHpORzqNEAoHRIcQrE4jDp4Aizi8dnd/u5ABHajFnUWPSZ9sw5HGJZmlcnhAHzHaE+S1jqpCLBRqFWMLPN4CKIUoTubUCwOkxZxDvCxsy5jys+i2qRDo904r7Gn4HDkyuEAgN2ky6os2h+OJcZT51+WTRClCAkOoVhqLXoAwJh3VlgmfGHUWvRocRgw5WfFvIkvzAuDXCE1AKhKUiWXDn84KlsOiSBKERIcQrF01PMtas6P+MTHJnxh1Fp1aE6UPw8lxlCLORwZ3/AdJu2izaXpCLC8wyEIpUKCQyiWjnoLVAxwfnRGfGzSz6LGrJ8jOLz7EXM4Mjoch0mXtHloKvxsFGZyOISCIcEhFItBq0ZbjRnvjvCCE4nF4QlEUGPRiRs8FzocOQWnyqRdVLSQDn84CrOOBIdQLiQ4hKJZ1WDFuwmHI2z6rLXo0WAzgGEAV0JwfKEoVIy8jTMdRh3iHDCTZMx1MvzhGMx6CqkRyoUEh1A0qxqt6J/0IxSJYcInCI4OOo0K9Va96HB8iYS9nBVisyMSpOVxKKRGKJ28BGdqagpbtmxBZ2cntmzZArfbnfS4Q4cOYfXq1ejo6MCuXbsW/fyb3/wmGIbBxMREPsshiEU02w2Ic3w59ERi301Nonqt2TFbGj0TispaEg0AVSZ+zLXUPA5fFk2CQyiXvARn165duP3229Hb24vbb789qZjEYjE89NBDOHjwIM6cOYOnn34aZ86cEX8+MDCAw4cPY9myZfkshSCSMncQ22SilY1QLl1t0okbM2dCEVnzN3OvLbVSLcBGYaGQGqFg8hKc/fv3Y8eOHQCAHTt24Jlnnll0zJEjR9DR0YH29nbodDps374d+/fvF3/+hS98Af/0T/9Em92IgmA3JlxGkMVkIqRWY+Efsxm1YnNNXwH2wDgSDkdKA894nEuURZPDIZRLXoIzOjqKpqYmAEBTUxPGxsYWHeNyudDa2ip+73Q64XK5AAC//OUv0dLSgo0bN2a81p49e9DT04Oenh6Mj4/ns2yighBcxnQgggkfC51aJbavsRu1Yq8zXzgq66ZPgK9SAxaPQUhGILEBlYoGCCWT8S/sjjvuwMjIyKLHH3vsMUkX4LjFJaEMwyAQCOCxxx7D888/L+k8O3fuxM6dOwEAPT09kp5DEGJILRjBlD+MKrNWdNM2gwbeUATxOAdfKIrlNfIMXxMQhrC5JeRwAolKNioaIJRMxrv7hRdeSPmzhoYGDA8Po6mpCcPDw6ivr190jNPpxMDAgPj94OAgmpubcfHiRfT19YnuZnBwEFdddRWOHDmCxsbGXP4tBLEIIXHvDrDwBCLi9wAfUuMSvda8IflDahq1CjaDRlIOR9h4SvtwCCWTV0ht27Zt2Lt3LwBg7969uPvuuxcds3nzZvT29qKvrw8sy2Lfvn3Ytm0b1q9fj7GxMfT396O/vx9OpxPHjh0jsSFkxaBVQ69RYToQgScQER0PwAsOwE/l9IXlLxoA+Eo4lzuY8bgAK4TUSHAI5ZKX4Dz66KM4fPgwOjs7cfjwYTz66KMAgKGhIWzduhUAoNFosHv3btx5553o6urCxz/+cXR3d+e/coKQiMPET/50B9j5DidRBs038YwXpHGms8qEAXcg43GzDodyOIRyyesvrKamBi+++OKix5ubm3HgwAHx+61bt4oClIr+/v58lkIQKXEY+fJnT3Chw+Fvf2HzZyEcTmu1Ea9dnADHcWkrMQOJEQomcjiEgqFOA4TisZu0cAci8ARYsVQZmE3qDyZCXoVwOK1VJgTYGKYyVKr5w7HEGsjhEMqFBIdQPA6jFsPTQURiHBzGOQ4nEVJzFdThmAAAAxnyOP5ESI324RBKhgSHUDwOkxYDU/wb/twcjj0RXpsNqcnb2gbgQ2oAMDCVPo/jo7JoogIgwSEUjyOJyACARaeZ1zG6UCE1ABkLB6aDETAMxE2pBKFESHAIxVNv1Ytfz3U4KhUDq14jli3L3WkA4B1LrUWHyxOZBcdm0EKlohZPhHIhwSEUT09btfj13Co1YLagACicu2ivteDShC/tMd5gRCxiIAilQoJDKJ51zTbx60WCM+dNvhA5HABorzPj0rg/7THTJDhEBUCCQygejXr2NncYdfN+JuRY1CoGBm1h/hxW1lkw6WfTDmIjwSEqARIcoiL433/wHty1rhE6zfxbXmjYadKqCzYio72Ov8bFNC5nOhgRN6IShFIhwSEqgm0bm/Fvf3T1osfbaniHM5MoSy4E7XUWAMCl8dR5HG8oSg6HUDz0kYqoaOQeSZCM1iojTDo1Tgx6wAHY3FaNFbXzr8s7HBIcQtmQ4BAVzfKEwykkGrUK711Zg58cHcT/feMKAOBXf34j1rXYAQChSAxsNE4Oh1A8FFIjKppGm2FJrnPLqjqw0bj4/cvvzk6tFUZQk+AQSocEh6hohI2WN3TUFPQ6t67mhxPef9MKOKuMODcyI/5MEBxbgcqyCaJUoJAaUfH0PnYX1AWqUBNorTbhJ5+9HutabOib8OPcsFf8mZccDlEhkMMhKh6tWrUkLWWuWVENk06D1Y1WXJrwIxzlRxJQSI2oFEhwCGKJWdNoQyzO4cIYXybtCZDgEJUBCQ5BLDGbWh0AgOdOjwKY7VbdaF+aAgaCKBYkOASxxLRWm3BHVwN+8Ho/vKEILk8G0GgzwKClaZ+EsiHBIYgi8Ke3rcR0MIK7d7+Kk4MeLFuC/UAEUWxIcAiiCFy1rAr//qke9E340Tvmw/JqEhxC+ZDgEESRuG11vdiheik6HhBEsSHBIYgioVIxaHEYAQDLlqCnG0EUGxIcgigiN3XWAVi6FjsEUUyo0wBBFJEvbV2DzW3V2NxWVeylEETBIcEhiCKi16jxwQ1NxV4GQSwJFFIjCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJYDiO44q9iGypra1FW1tbzs8fHx9HXV2dfAtaYmj9xaOc1w7Q+otNOa8/3dr7+/sxMTGR8RxlKTj50tPTg6NHjxZ7GTlD6y8e5bx2gNZfbMp5/XKsnUJqBEEQxJJAgkMQBEEsCRUpODt37iz2EvKC1l88ynntAK2/2JTz+uVYe0XmcAiCIIilpyIdDkEQBLH0VJzgHDp0CKtXr0ZHRwd27dpV7OVkpK2tDevXr8emTZvQ09MDAJiamsKWLVvQ2dmJLVu2wO12F3mVs9x3332or6/HunXrxMfSrffxxx9HR0cHVq9ejeeee64YS55HsvV/9atfRUtLCzZt2oRNmzbhwIED4s9Kaf0DAwO47bbb0NXVhe7ubnz7298GUD6vf6r1l8vrHwqFcM0112Djxo3o7u7GV77yFQDl8/qnWr+srz9XQUSjUa69vZ27ePEiFw6HuQ0bNnCnT58u9rLSsnz5cm58fHzeY4888gj3+OOPcxzHcY8//jj3V3/1V8VYWlJefvll7q233uK6u7vFx1Kt9/Tp09yGDRu4UCjEXbp0iWtvb+ei0WhR1i2QbP1f+cpXuG984xuLji219Q8NDXFvvfUWx3Ec5/V6uc7OTu706dNl8/qnWn+5vP7xeJybmZnhOI7jWJblrrnmGu71118vm9c/1frlfP0ryuEcOXIEHR0daG9vh06nw/bt27F///5iLytr9u/fjx07dgAAduzYgWeeeaa4C5rDzTffjOrq6nmPpVrv/v37sX37duj1eqxYsQIdHR04cuTIUi95HsnWn4pSW39TUxOuuuoqAIDVakVXVxdcLlfZvP6p1p+KUls/wzCwWCwAgEgkgkgkAoZhyub1T7X+VOSy/ooSHJfLhdbWVvF7p9OZ9oYuBRiGwfvf/35cffXV2LNnDwBgdHQUTU1NAPg/0rGxsWIuMSOp1ltOv4/du3djw4YNuO+++8SQSCmvv7+/H2+//Tauvfbasnz9564fKJ/XPxaLYdOmTaivr8eWLVvK7vVPtn5Avte/ogSHS1KQl07BS4FXX30Vx44dw8GDB/HEE0/glVdeKfaSZKNcfh8PPvggLl68iOPHj6OpqQlf/OIXAZTu+n0+H+655x5861vfgs1mS3lcuay/nF5/tVqN48ePY3BwEEeOHMGpU6dSHlsu65fz9a8owXE6nRgYGBC/HxwcRHNzcxFXlBlhffX19fjIRz6CI0eOoKGhAcPDwwCA4eFh1NfXF3OJGUm13nL5fTQ0NECtVkOlUuH+++8XwwaluP5IJIJ77rkH9957Lz760Y8CKK/XP9X6y+X1F3A4HLj11ltx6NChsnr9BRauX67Xv6IEZ/Pmzejt7UVfXx9YlsW+ffuwbdu2Yi8rJX6/HzMzM+LXzz//PNatW4dt27Zh7969AIC9e/fi7rvvLuYyM5Jqvdu2bcO+ffsQDofR19eH3t5eXHPNNcVcalKENwsA+MUvfiFWsJXa+jmOw2c+8xl0dXXh4YcfFh8vl9c/1frL5fUfHx+Hx+MBAASDQbzwwgtYs2ZN2bz+qdYv6+svZ5VDOfDss89ynZ2dXHt7O/e1r32t2MtJy8WLF7kNGzZwGzZs4NauXSuud2Jignvf+97HdXR0cO973/u4ycnJIq90lu3bt3ONjY2cRqPhWlpauO9973tp1/u1r32Na29v51atWsUdOHCgiCvnSbb+P/qjP+LWrVvHrV+/nvvwhz/MDQ0NiceX0vp/85vfcAC49evXcxs3buQ2btzIPfvss2Xz+qdaf7m8/idOnOA2bdrErV+/nuvu7ub+/u//nuO49H+v5bB+OV9/6jRAEARBLAkVFVIjCIIgigcJDkEQBLEkkOAQBEEQSwIJDkEQBLEkkOAQBEEQSwIJDkEQBLEkkOAQBEEQSwIJDkEQBLEk/H/1J812fWeocQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AICQwd558OSbdhVkqXQnBmVK6wwmlH00gIByTzIEAfA4n1/wNMOtwknWk7k04kQ4ZHA7Ai86KWjMujZeuAyfBIQgJeOeUzpaj4AxNh1Bt1ontVtxl+G/IBm+GTtECwtCzVIUDvnA05wo1YL7DWciVKb5Cra3GlPP5F9JeZ8alEg75kuAQhATmxvilzKkvNVzuIFocRtiNWjBMeYpmNvCzcOTJ4eS6BweYdTgP/vAY3ro8Ne9nLk8QBq1KPEYO2mstuDIVABstzUaeJDgEIYGZcBRGrRpVJu288Fq54PLwgqNRq2A3ahVfNMDPwskuh5OMmXCeORzzrFj95HeD834mfAhIN3gtW9rrzIjFuZKdj0OCQxASEJLQmXaOlyIcx/FvbolxxNWm8vs3ZAPHcRnHSwuIgpNic+ZMKCpJuFKh16jx8iO34poV1XjryvxyZZcniJYq+cJpALDB6QAAHLtSmqXRJDgEIQFvIrRSY9aXXUjNHYggGImhOdFl2GFStsMJRmKIxjlJITWtWgWTTp2yaMAXyi+HA/DdnG9dXYcLY755Qi+4TjlZWWdGjVmHI30kOARRtvD7OjSosegw6Wfx7ugMvv1Cb1mU9bvcfIWa8OZWXYaimQ1CgYeUogGAdznJcjiRWBzBSCyvHI7A5rZqAMDv+vk8ToCNYsrPwlklr+AwDIOetirxOqUGCQ5BSGAmxI8rFkJqH/u31/CvL7ybtk9WqeBKjDgW3tyaHYaMY4/LGXE0gYR9OAA/eiCQpNWPP5xfH7W5bHDaodOocDQhBMLrL7fDAXhxuzIVwHiaOT/FggSHICTA9yHTosbMt5sXuhFnGh9cCrgWvLm1OIzwhqKYCZWeWHoCbN7tg4QCAKn9ycx6Dfzs4o7R+fZRm4teo8ZGpx1H+vlQV/8EXxItt8MBgNZqPi+UbvBbsSDBIQgJeBNVTzUWPeZG0Urxj3ohLncQJp0ajsSeE6F4wFWCLueLPzmBD33nt3kVNXgldooWMOnUCIQXOxw5BQfgnceJAQ/+8qcnsP/EEHQaFda12GU591xqkmw2jcU5cZhcMSHBIYgMcBzHVysZtbi2vXrez0bKQXA8gXnlt4LTGZwqPcERxiQ/e3Io53MMJDZU1kqYognwjTmTORxPkH/DllJ8IAUhj/Oztwbx3yeGcE1bdU6jqzMh7OuZmlO+/83nz2Pb7t/Kfq1sIcEhiAyEo3GwsTisBg3WNNqwfs6n0nIJqbXMCd04E6W4pehwhDfLX7yd3eRfjuPw5qVJRGJx/PbCJJZVmyTnR0x6TdIcjuCypEzjlML1K2vw6fe24caOWgDATZ21spx3IcJ65xaGvHphAu+O+oreYYIEhyAysDBE86P7r8Xe+65BrUVXNiG1uW++tRYd9BoVBqYC2H/chZODnuItbgGCYzw5OJ2ypX8yfnTkCj6x5w189Zen8frFCdyYxZu5WacWCwTmIgiOXJ0ADFo1vrqtG1/7vXXY1OrA1vVNspx3ITajBmoVI66fjcZxbpgfL35uZKYg15SKPMFJglAwQoGAEFqxGrS4ZVUdGmyGkg+pBdgo3IHIPIfDMAxaHEb852v9iMU5XLuiGj/+7PVFXCUPx3EY9YZRZdLCHYhgyBPCMgl9xvzhKP75+Xdh1KrxwzevAABuzkJwTLrkDkdwCFUmeUJqAm21Zjzz0A2ynnMuDMOgas7m3ndHZ8AmxPv8iBfXr6wp2LUzQYJDEBkQy2wXJI8bbQa4PEG8MziNAXegYJ9Y82HhHhyB9U47poMROKuMODPkRTzO5TzzRS48gQjYaBy3ra7Dc6dH0T/pTyk4p1zTWFZjgs2gxVuX3Zjys/j+p3sw5g3jylQAt66ul3xds14NPxsFx3Hz2sxM+Vk4TFpo1OUXCKox68SiAaHqT6Niiu5wyu+VJIglZtjDu5i6BUnoBju/n+XDu3+LP/3hsWIsLSODKfZ7fONjG/HGl2/H9muWYSYcxYA7ULA1RGNx/NXPTuD4gCftcaMz/Ot87Qr+E3iqfmAXxmZw9xOv4qv7TwMATg/xc4quXlaN7dcsw199YE1WyXiTTgOOA0KR+SG8KT8ra2PNpWRuC6YTgx7YDBpsbqvGWRIcgihthMqptpr5Q9du6qgVw20AEI4Wv+x0IYOCw1mw30OnUUGrVqE7MQFUeNMuBC+eG8NPjg7iH589m/Y4oQBjvdMOg1aF/snkIrjr4HnE4hx+eWIILk8Qp4am0VptFEcNZItZz4vTwkq1SX9YLDEuN6ots4Lzu343etqq0dlgQd94cYezkeAQRAb6Jvyot+phXrDj/K71TfiPHT3oqOcnNo55S2tnN8dx+H9vDcJZZUSD1ZD0mFUNVmhUDE4P5bfZMh37jvB5Fb02/duN8Po12gxoqzEndThnh7144ewo/uCaZYhzHH7yuwGcdk1jXXPu+1lMOv73unAvTjk7nBqzDpO+MKb8LC6M+dDTVoVl1SZ4Q9GUjUqXAhIcgshA/4Q/5Ujp27sa8Ddb+VnyYyXWSuTVC5M4PuDBg7euTJmfMWjVcFYZcaVAe3L84ShefnccwKxTTIVQ8Vdv02N5jQl9SQaJ/ftvLsGoVeOvP7AaG1sdOPDOMPonA6JTywWzLrnDmfJHUC1TSfRSU23WwRuK4o1LkwCAa9qqxQ4El6eKN7qABIcgMtA34Ud7CsEB+DdIABifKa2Kte+81ItGmwEfu9qZ9jirQZu0LFgOTg95EeeAjU47Bt3BtLvdx31h2I1a6DVqtNdZcHkygMic0uhjV9x45m0X/vDaZXCYdLipsw69Y3yIKJsigYWYEs41MEdw4nEO7gCLarO8FWpLRYONd7T7j7ug06iw3mnHsoTgCJNGiwEJDkGkwRuKYNLPpnQ4AFCfCFeNlkhIbdIXxl/sextv9k3hs7e0Q69Jn0C36DXwhQojOO8kKqR+7z0t4Djg0njqT9cTvjBqLXwIq7PeguicQWIcx+Fvf3EKjTYDPn9HJwDgllW1if/X5dUiRnQ4c0Jq3lAEsThXtg5nU6sDAPD8mVFsdNqh16hJcIil4eSgB1/6+UnE46XfSr/UENqkLK9OvR+kxqyDWsVgrEQczqsXJ7H/+BA+tKEJf3DNsozHm/UazBTK4bimUW/Vi3s/LqRJWk/MsKi18G/wnfVWAEDvKH/865cmcWbYi89vWSWOC9jUWoWdN7fj7z60Nq81ijmcOQ5nUuwyUJ45nFUNVlgNfPWd0FLHrNeg1qLDlRTFGEsBCU4F8PNjLjx9ZADDJb5JsRQRPvWmm4miUjGos+hLxuEEEuLx5a1dksqDrQZNwUJq77imsb5lNpwj7AtKxoQvLPY/W1nPO0ohZPbUa5dRZdJi28Zm8Xi1isGXt3aJRRu5IlapzXE4QgGDEC4tN9QqBlcvrwIwKzgA30n6MgkOUUjODvMlr4NFtNLlipBzMGSosGqw6UumaMCf2DVv1knb123Wq+ErgOBEY3FcmvBjTZMVJp0GFr0mbSugcV8YdQmHY9Jp4KwyonfMBzYaxyu94/jQhuaCNLtM5nAEtyrkQsqRGztqYdSqcVVCeACkrP5bKkhwFAjHcfjp0QF86efvYDoYEXcXD6T5dEkkRxCcTHmQOqsBYyXiIAWHY9RJe3O26LUFyeGMeEOIxTmxWWi9TZ9yKFgoEsNMKDovhNVZb8G5YS9ODHoQYGO4oaMwzS5n9+HMOhxBGMtZcD793jb8+pFb580FWllnxtB0qGCONhPU2kaBPH9mFI/87CQAYGQ6KA6kGizgbnKlEoryVVKZHI5Fn3xqZDHwszHo1CroNNI+T1oNGrCxOMLRWEZhzQYhfCYMGau36lM6HCFnMnekwNXLq/A/58fxqxNDYBjgugWjIeTCoFGDYWaFGuALQMw6tSzTPouFRq1aJJgr6/jwY9+EvyCzeDJBDkeBvHR2DFaDBvfdsAL/c35cfHygBOeflDqzIbX0b8RatWpeCW8xCbBRmPTShSNZlZYcDC7o41ZvNaQMO076+MeFogEAYqHB3tcvo7vZBoepMAl8lYqBSauGLzzf4ZSzu0nFykS+6+ywN6tu3HJRvvJNJIXjOLzSO46bOmvx6F1rAEDc/EUOJ3vCQkgtg8PRauQXnAAbBRuNZ/1GG2BjkvM3AGBJFET4QlFZd9YL83aaE4LD57lCi5pkAnzBAACxLBoANjgd4tefur5NtnUlw2bUipEAgBecci0YSMfyRDPUR352Ej89Ooh9O69b0qat5HAUxtsDHgxPh3DLqjroNCr8rw+vxYG/uAmrG63iJ05COmExpJbeMejUKrBReQXnHw+cxY7vH8n6eQE2Kjl/A/DhQACyFw4MugOos+rF167eakAoEp/Xf05gYiYRUpvjcLRqFW5dXYdaiw73XJV+82q+OEw6eAKzw8lGvWFFOpy5IdMj/VP48dGBJb0+CY6CCEdj+PLP30GdVY8PdM9vle+sMmJ4OogY7cXJCjGkliG3oVUziMTkfW0HpoI5bdLzh2NimEwKFn3C4cgsOC7P/MFvCzsyXBz34VPfPwK3n0X/pB9qFbOoI/eeT/bgt3/9PqgL/Cm8yqSFJ+Fw+Lk8ygypAcAf39CGO7rqsbmtCv96+N203R/khgRHQfzvF3txbmQGX79n/aLOuQ6TDnEOBdtRrlRCkThUDC8o6dCqVYjG5XU408EIpoORrDfsBtioWOorBUtizo8vLG9Tx0F3UCwYAGY7Mgh7XJ546QJeeXcch8+O4tgVN7qbbYucpE6jKkgp9EKqTDq4Ew7HG4wiHI2j3qq8kBoAfOXD3fjejs34wpZVGJsJ4y9/egKvvDue+YkyQIKjEP77xBD+7dcX8ftXO/G+NQ2Lfi6URs6NUxOZCUX4yq2FOYeF8EUDHDhOPpfjDUYQ55B1FwB/OCaW+kphNqQm3yfdABvFlakA2utmN2UKDmfEG8LYTAj/fXIIAPA/58ZwYmAaVy2rSnqupcBh0sKT6KI8nsgnLXRbSuP69hp8oqcVh8+M4tfnl0ZwqGhAAYzPhPGFHx9Hz/JqfGVbd9JjhGmVJDjZEYrGMpZEAxBLkCMxDjqNPOEfYdLodCAyby9FJrJ2OPrZogG5ODvsBccB6+eU3jbbebcz5Ani7SseRGIcVjVYcPDUCACIO+OLQVUihxOPc2LFXE2Z9lGTCsMw+PrHNuAfP7p+ycJq5HAUwMlBD6JxDo98YHXKfQPkcHIjFIlLCukIITe5KtU4jhN/V54gm+Ho+fjZ7ByOWXQ48t0b7wzyTTvnCo5Rp0aNWQeXJygOW9vx3jYAgIoBetqK63AENykMLivXWTjZolYxi2Y9FQpyOArgHdc0GAZY25R6JoiQ0xE+NRPSCEelCo7gcOQRnGAkJhYheLIcmBVkY1k5HKGEWs6Q2qkhL2otOjQsKC12Vhkx6A7CYdJBq2bwB5uX4ebOOrCxOJrsxhRnKzxC6bknwM427rRUhuAsJSQ4CuCUaxor6yxpP6WQw8kNPoeTORAgCA4rk+DM/T15svidcRwHPxuFKYsqNZWKkX1EwTuD0+huti/KfbVUGXFueAa1Fj3qrQaoVIw4GKyYVCU+kLkDEdHhVBVoo2klQyE1BXDK5cW6DBMPSXByIxSJZRlSk6dowBucffOfDkgPqYUicXAcsnI4AD8TRy736/azOD86g54kORlnlQkuTxDD00E02Uun7FhwOO4Aiyk/C6tBI7k1ECEdekXLHE+AxYg3hLUZBMeoVUOrZkhwsiQciUsqGhBDajJt/pzncLIIqQljkrPJ4QD8B5IZmQRH6GwhtKaZS4vDiHA0jtMuLxpKSHAEhyOE1Mp1Dk6pU3GC8/SRK/iLfW8XexmyIWwMbKtJPZES4CtS7AvadxCZCUlsaCl3DifXkFogkYfJ1uHYjBrZ7o3XL03CpFPPa00jIGwEnQlH0VRCGyurxBxOBFP+cMUUDCw1FSc4l8Z9eP70aLGXIRuC4EiJg9sMJDjZwofUipfDYZgcHU4WORyAvzfmhvHy4fWLk+hpq04aklpRN/vBqLGEHI7NqAXDAFN+FpM+tmxHS5c6FSc4Fr0WwUhMMS1ehA7QkgTHqIWXBCcrpJZFC3tv5MrhCILTbDfO6/GVCWGImCnLMle53O/4TBi9Yz5c3744nAbw7fG7E+HfUnIRahWDZjtfQUchtcJReYIjtvFQRouXK1MBVJt1kuZ22ElwsiYcjWXsowbIH1ITfk/OKmN2ITVWCKll6XCMWlmKBl5Pk78R+K8/vga/f7UTN6+qy/t6crK8xoRLE364/SyqqSS6IFSc4Fj1yhKcQXcArVXS9i9QDid7QkUsGrDqNagy6bL6kBBMCI4xy/5jNqMWvnA0675tC3n94iSsek3aqsk6qx7f+P2N8zpDlwJttWacG/YiGufI4RSIihMc0eEopInllamA5H0MJDjZI70sOiE4MoVqhSFqpiwniUqdULoQm0EDjgNm8vy7ODHgwXuWV0GjLr+3lrYakziOYmOro7iLUSjld1fkiVlfmM64xSASi2PIE8xKcLyhqKwNJpUMx3EIR+PQS8nhyOxwojEOOo0KZp1GzMtIQeiJle2oaGGfVj5htXicQ9+EH531lswHlyBCpadBq8IG59KPX64EKk5wLHr523gUiwtjPkRiHNY0WiUdbzNqEItzigknFhrh066kTgMaeXupsbE4tCoVTHo1/Fk4HKkTShdik2Fj8OhMCMFIDCtq05folyptiXX3LK/OWrAJaVSc4FgVFFI75eIbJK5rkfZpjLoNZIc4fC2LkJpcZdHRGAetWgWTVgM2Gpc8f17qhNKFiA4nj3ujb9wPAGgvU8FZVm2C1aDBratLq5hBSVRcLzWLgkJqp1zTMOvUWJFh06fAXMFxFq8xb9kQziIfIobUZCqLjsTi0KgZsWNAIBKDLXENt5+FUadOKipSJ5QuxGbIP6R2cSIhOHXlGVIzaNV45ZHbRLdHyE/FORyhaCDf5GgpcGrIi7XNNqgkjt+1iZ9iy//fvhRk8+Ytd1l0JM5Bo1aJHQMCc0LAv/fdV7H7pQtJnyd1QulCbMb85yX1jfth0qkXdYguJ6rMuoKPs65kKk5whFbs/jLP4URicZwZ8qK7WXpyk0Jq2SHmcCSVRcubw4lE49DNcThCB4FoLI7LkwGxw8RChKq6TBNKF2KX4cNI34QPbTXmrK9NVA6yCM6hQ4ewevVqdHR0YNeuXYt+znEcPve5z6GjowMbNmzAsWPHJD9XbtQqBiadumxCahzHifH73S/14hvPnUMkFsfJwWkEIzFcu6Ja8rnkiNNXEmxCcHQSSnyFMmBWriq1eBwa1WKHI7TOTxX64ieUZp/wNus0UDH5fRhxeYJwStwTRlQmeedwYrEYHnroIRw+fBhOpxObN2/Gtm3bsHbtWvGYgwcPore3F729vXjzzTfx4IMP4s0335T03EJg0WvKolIrHufw0I+O4cVzY7h6WZW4i7tvwi86m2tyEBxyONIQHI6UNvVy53DYGAejTiX2RBMcznhi/HGq32EoEochh7b6KhUDh0kHdxZtdBYy7AnhvStrc34+oXzyFpwjR46go6MD7e3tAIDt27dj//7980Rj//79+NSnPgWGYXDdddfB4/FgeHgY/f39GZ9bCCwGTd45HI7jEI1ziMTiiEQ5RONxMAwDBnzDRYZh+P+D/1rFACqGgVatgirx81T4wlFcmQzgqdf7cfDUCO7sbsBrFybRXmvG3Zta8K8vvIsD74xgVYMFNVns1pbjU2wlwWYhOHKH1KKxOLQqRuyJJuzFmfQlHE6K36HUfUPJqDJpRQeVLd5QBDPhqNgNmiCSkbfguFwutLa2it87nU68+eabGY9xuVySniuwZ88e7NmzBwAwPj6e15qtaRzOpC+Md1zTuDwZwKA7gCFPCFN+Fp5gBN7Ef+FYHJEYP+gqVzQqBmoVA42KgUatEr+PxOJwJ7oDqxhg583t+NJda+BNCKRVr8HlKT9+fsyFO7oasrqmSsXARt0GJCOUOEvZh6NW8R8wZMvhxOLQquc4nERIbUJ0OMnvX6kTSpNRY9aL45WzZcjDN5FtcpROB2ii9MhbcJLtWl/46T3VMVKeK7Bz507s3LkTANDT05PLUkUsBg38CcHxhaP49fkxvHBmFL/rd8OV+MMB+DeaFocRNRYdWhwGrG2ywWbUQK9RQ6fm3YpOo4JWrYJaxf97OAAch8T/ucTXHOIcEItziMV5ZxSLxxGNc4jGhMfiiMU5qBgGzioTWquNWNdsFzej2eeUav7LxzfhHz+yXlJuYSFL0d7mu7++AJ1ahT+5qb2g1yk0szmczI6BSbhXOffhaNSLHY4gOClzOBJb8SSj2qzDhXFfTs8d9oQAAM3kcIg05C04TqcTAwMD4veDg4Nobm6WdAzLshmfWwjMOg36J7z40s9PYv/xIQTYGKrNOlzfXoNPXb8cG5wOdNRbUGvRlWzFTa5vKnaZugKnIsjG8J0XL8CsV+O+G1ZILtkuRbIJqQF8HicqWw4nDt0chyP0UxNCamw0nlRcpE4oTUa1RYep/twcjvBBrdlOgkOkJm/B2bx5M3p7e9HX14eWlhbs27cPP/rRj+Yds23bNuzevRvbt2/Hm2++CbvdjqamJtTV1WV8biGwG7VweYL4xdsubNvYjHuucqKnrboi6u8L7XBefncMwUgMwUgMZ4a9krsglCJsjH+Tlyo4WjUjYw4n4XCEKrWE4AhFAwCfx1koOKFoLOc5MzVmvmggFuey/lsYng5Co2JQZy3fPThE4clbcDQaDXbv3o0777wTsVgM9913H7q7u/Hkk08CAB544AFs3boVBw4cQEdHB0wmE/7zP/8z7XMLzQO3rsTmFdX4wLpGcYd1pWBLiG2heO70KKx6DWbCUfymd6K8BSdLh6NVq+QTnHh8TsiWEUPAE75ZBzIdjKB+wZjmUETa/J5kVJt14DjAE2CzKkYBAJc7iAaboSI+tBG5I0trm61bt2Lr1q3zHnvggQfErxmGwRNPPCH5uYVmZZ0FK8u0/Ua+FHoI24UxH96zvApj3hBevTCBB29dueiYMW8Ib/RN4fY19WL37lIkm304AC84bFSmkFo0LnYvMOk0c0JqYeg0KrDReNLQaDiaR0gt4Yym/NkLzrmRGaws0y7RxNJRcZ0GKh2bgQ+pFWpEwYg3hEabHtesqMaxK+5FTScnfGHc/i8v43NPv40d3z9S0vuh2EQ+Rqrg6DRyOhxOLLU26dSiwxn1hsTmmMlCo3yVWm4Op8bMi0y2lWpBNobeMR82Ukt/IgMkOBWG3ahFJMYhGJG/tU8kFseEL4xGmwE9bdUIsDGcHZ6Zd8zxKx7MhKL4zI0r8PaABw//+HjeUyYLRfYhNflyOHzzTsHh8EPYfOEoJnws1ifClMna0EidUJqMuQ4nG04PTSMW57DB6cjpukTlQIJTYRSy28CELwyOAxrsBmxu49tR/65/at4xZ4a9YBjgC1tW4ctbu/D8mVF8J0UjymJTrBwOx3GIxDhoE/kQs14DP8tvBgaA9Qknkcrh5FrBWGPhBSdbh3NikB+TQQ6HyAQJToUhR5PGVIxM83sxGm0GNNmNaK024tfvzt+ke2bIi7YaMyx6De67oQ0fvYrvnPCD1/tlX0++sLEY1IkNuVLg9+Hk79aiCccn5HDMOr4zhtCwU2hrtDAXl82E0mRUmRIOx5ed4PyubwpNdsOiAgaCWAgJToWRi8OZ8rP45nPn8f/eGkyb+xn18oLTkHjj+ch7nPhN7zgG5nQ2Pj08jbVNNgB8Mck/fmQ97uiqx9/tP42HfnSspHI6bDSe1eZanVoly4hpYS+PEFJbXmPCpXEfrkzx82Y66iwwatWLigaymd+TDJ1GBatBk1U/tZlQBC+dH8Od3Y05XZOoLEhwKoxsBccfjuIP//0N7P6fC/jiT0/gudOjKY8VHY6dF5ztm1vBAPiP3/YB4MttB6aCWNtsE59j0Krx5B9djUfuXI1Dp0Zw7/feFBPkxYaNxiWH0wBAI1MOR+hWIBQNrGqwwh2I4K3LbtiNWthNWpj1arFyTSAcSQhOHuORa8y6rEJqh06NgI3GsW1T4TdsE+UPCU6Fke2grX//zSWcG5nB9z/dgxqzDv99cijlsSPeMLRqBtWJ0Eyzw4hPbF6Gva/340jfFH7TOwEAuK59fodrjVqFh27rwHfvvQrvDHrwlz89gVgJFBKwsewER67WNlFRcPhrr2m0AgAOnxnF8hoTgPml0gKhqPSR2KmoNusw5Q9nPhB8N/Pvv9qP9loz3tPqyPmaROVAglNhZONwpoMR/Psrl3DXuka8b00D7lrfiBfPjqZ0IKPeEOqthnntbP7mg11YVm3Cn/7wLTz1ej8cJi02tSafb31ndyO+vLULB0+N4JGfFV90wtmG1BL7Y/JFGHEgCM6qhODEOWB1A//13FJpAWFCaa7NOwGg2qwX2+dk4tl3hnF22Is/v72jZFtAEaUFCU6FYTVIF5wXz47Cz8Zw/818E8671jUhFInj9YuTSY8fdAfQsmAAl0WvwX/s6AEbjeN3/W7c2FGbNgn/Jze14+Etq/DzYy58/sfHxaaVxYCNxrN689Zp5HE4QlhOkwip1c7ZhCk0RDXrkzgcIaSWh8OpMesklUWfHPTgSz9/B+tabNi2sSXn6xGVBQlOhaFWMbAaNJK6DTx/ehQNNj02JfZXXL28CnqNCq9enEh6fP9kACtqzIse76i34ld/fhM++p4W7Lw5cwfpz93eib/+wBr86uQQfu+JV8UOyUtNJMuQml4tl8NZ3OHgk9ctx73XLsPqxjkOh03ucHItGgD4Bp7uAJu2OORXJ4dwz7+9BqtBgz2f7KF2NoRkSHAqECntbUKRGF5+dxzvX9sohsgMWjU2t1XjtQuLHY4/HMX4TBjLa01Jz7esxoR/+cQmyZsDH7x1JZ667xpcmQrg0/95BOGo/BtVM5Ft0YBeqxIrxfJBKIsWHA4A/MPvrcNjH1kvfm/WacSx0wKC4zHq8nM4kRgnzl9ayDuD03j4xyewqdWBZz93E40jILKCBKcCkdIx+uywF8FIDDd0zB8Z/N6OGpwfncHYTGje4/2TfMluWxKHkys3ddbhO39wFU65vPjGofOynVcqwogAqehkdjjaNNdO5nA8iXJmYT9NLmTqNrDr0FnYjFrs+WRPzl2picqFBKcCkSI4p4a8AGZ3tQvcmBCghXmcy4ld8EIVlVxsWduAT/S04qk3LmNyiUNrcxtoSkH+ooHUoSqTfrbdzdX/cBgvnRvFlAyCUyUKzuLX+q3Lbrx6YRIP3NIuHkcQ2UCCU4EIDTzTcdo1jSqTFs32+bvHu5vtsBk0ePXC/DyO4HCWy+hwBP7kphVgo3Hs+91A5oNlJOuQmkYtS+hvYVl0Msw6fmrtlckAJv0sXr84CU9iNLnDlPvIjZqEkCSrVPuv1/phM2jwh9cuy/n8RGVDglOBOExaeDI6nGmsa7EvKndVqxhcv7IGr16YFBPLHMfhSN8U6qx6WAowbqCzwYprV1Tjmbddsp87HeEsBUenUSHOYVGH7GwRKt00qnQhNQ3C0ThGvPxsowtjPkz5WZh06rz34QCLQ2rjM2EcOjWMj13dKg6FI4hsIcGpQBwmHTxpKpHYaBznR2bEnl0LuXlVHVyeIF67OIkXz47ivv/6HX59fhyfuXFFwdZ8R1cDesd8GCrg8LiFZLvxUzg239LoqISQmlnPi0r/BB/K7B3zwR1g8wqnAbMl2AsrA39ydACRGId7ryN3Q+QOCU4FUmXiRxT42eThnytTfkRiHFY3Jh+odc9VTjirjLj/qaP4zN6jODcygz++oQ07b8pc8pwrt6yuAwC8vKAZaCFho3Hos8jhCHt2hBYzuSKtaIB3GX0TfCjT5QliyBPMO5Fv0KrhMGkx4p0tConFOfzwjcu4oaOmYgcXEvJAglOBCJ+C3SkqkS6O829i7bXJ31wMWjW+fs8GvGeZA3/7wS68/Mht+MqHu+d1GJCbznoLmuwGvHx+aQWnGA4nEltcFr0Q0eEkcmccxyf188nfCDTaDGJfPAB449IkhqZDuPfa5Xmfm6hsKBhbgQhvSu4Ai9bqxVVlF8d9AID2utQFADd01C4qmS4kDMPgllV1ePbkMCKx7KrHciXrkFpiTflWqiXb+LkQYyJP0zfhh17D7/+JxDhZSpWb7AYMzxGc4wMeAFjS3zehTMjhVCBCSas7kLxw4NK4H/VWvdgGp1S4ZVUdZsJRvH3FsyTXi2TZS02YQ5NvpVo0LrS2SVOllijOGHQH8Z5lDrFYI98cDsB3+x6dE1I7OehBW41J7MNHELlCglOBVCUcjifF3JNL47607qZYvDfRh+3X58eW5Hq5Opx8uw1EohL24czpJtBoM4gjH2QRHJsREz5WFM5TLi/W0/hoQgZIcCoQR4YczqUJP9pLMDlsN2qxvsWOo/3ugl8rHufHPGe3D0emkFpcwj6cOeXn9TYDViY+IOTTR02gKbH3aswbxoQvDJcniA0tND6ayB/K4VQgDqOQw1kcUpsOROAJRJI24SwFNrU68OPfDSAai6cNOeWLkPjPRXDydzjSWtsIOKuM4Di+p1kwkv/G04aE4Ix4QwgmKhm75wzNI4hcIcGpQDRqFWwGTdKQ2oCb39fRWl2aTRk3OO34r9f6cWHchzWNhXsTZCUk7heik8nhJGveuRDznM2XH+huhN2kxaSfxR/fkP9eKMHhDE+HMD7D78cRZvIQRD5QSK1CqTLrkjqcwYTgOKvk7YkmFxsTkyVPDkwX9DqCaORUFp13lRovOOnEzqSfdTj1NgP0GjUe3rJKlsS+s8oIFcN3L+gdnUG1WTdvJg9B5Ao5nArFYeLnnixkYIrfyd9aooKzosYMq16Dky4PPr65tWDXEcJi2Qxg02uEKjV5yqI1afY16TVq/N2H1uKWVXV5XSsZJp0GqxqsOD7ggS8UQWd96eXziPKEHE6FUmXSJhccdwBWgwZ2GTYQFgKVikFngwW9o76CXseXmAdj0Ut/HWY3fuZZFh2Lg2GQcbDZZ25cgY4CicF7llXh7StuvDvqQ2cDCQ4hDyQ4FUqD1YCR6cUt6AemAiXrbgQ66i1iN4RCMRPiw41Wg/QggFwhNTbGQatSLWqcupS8p9WBmVAUvnAUqxoof0PIAwlOhdJSZcSELyyOJRYYdAdLtmBAYGWdBRO+MKZTbFyVg5mEw8lGcOSqUuMr8Io7trmnrQoAvxfo9q6Goq6FUA4kOBVKS2I08NzuyxzHYdAdLNmCAQGhgeTFicKF1byiw8khpCZDDmcpWveko73Oguc+fzPe+eqd4r1CEPlCglOhtFTxbyKuOYIz4WMRjMTQWlXabzBC3uLCWCEFh3c4NmMWITW5Og3EubRdBpaK1Y3WvGbrEMRCSHAqFOFTq8s9Kzize3BK2+E4q4zQqVVik9FCIORwbFk4HDk3fhbb4RBEIaC7ukJptBugYuY7nMGE+JS64GjUKrTVmnBxrHCFAzOhKLRqJquyaIZhoFOrZNn4WewcDkEUAhKcCkWrVqHRZpjvcKZ4h1MOMfuVdRZcKqDD8QYjsBq0WVeK6TT5Cw5bAjkcgigEdFdXMC1VRtHVAHyXgRqzbl5jyFKlo96Cy1OBvN/cUzETimZVoSbAz6bJfx+OVkV/moTyoLu6gulssOLciBccx7dSGZgKwlni4TSBlXUWxOIcLk8WJqw2E4pklb8RkMPhRGIctBoKqRHKgwSngulutsEbioouZ8AdKPkKNQGxNLpAYbX8HE7+ZdEacjiEAqG7uoLpbuZnnJwemkY4GsOgO4i2Eh1LsBBhQFyhSqO9oUhOgiOHw4nGuKy6VBNEuUB3dQWzptEKtYrB6SEv+icCiMW5sumbZdZrUG/Vo38yUJDz8w4nx5BaTAaHQ1VqhAIhwalgDFo1OuosOD7gwbujMwCAzvry6ZvVVmPGlYIKTi4hNXXeRQP8xk/60ySUB93VFc77uurx2sVJvHZxEipmNlRVDiyrMeHylPxFA7E4B184R4ejViEckWPjJzkcQnmQ4FQ491zlRCzO4ekjV7Cs2lRWrUyWV5sw6g2LY5DlwhdOtLXJweEYtPkXDUTjVDRAKBO6qyucjnqLOMRrRW35uBuAdzgAcGVK3rCaPyzMwsktpLawA3e28GXR9KdJKI/S3+FHFJzv7ejB86dHsaapfPI3AMSKusuTfqxulG/tAZYXHFMOgiOHw4nE4tBmGL5GEOUICQ4BrVqFD25oKvYysmZ5wRwO71DMuuzDiwatHA6HWtsQyoTuaqJssRu1MGrVGJkOyXpev+BwdLk4nPwFJxqj5p2EMiHBIcoWhmHQaDdgxCuv4AQEh6PP3uHotSqEqHknQSSF7mqirKm36jHmDct6znwcjl6jBhuNIx7ncr5+NFYaA9gIQm5IcIiypsFmwOiMzA6Hzd3hGLSJMdN5dBugHA6hVOiuJsqaRrsBI9MhseO1HAhl0TnlcDS8SOWax+E4LjGAjf40CeWR1109NTWFLVu2oLOzE1u2bIHb7U563KFDh7B69Wp0dHRg165d4uOPPPII1qxZgw0bNuAjH/kIPB5PPsshKpB6qx7haBzeYFS2cwoOx5RjlRoAhHLsNhBNhOJ0FFIjFEhegrNr1y7cfvvt6O3txe233z5PTARisRgeeughHDx4EGfOnMHTTz+NM2fOAAC2bNmCU6dO4eTJk1i1ahUef/zxfJZDVCANNgMAyBpW87NR6DSqnMJaQkgtV4cTSYTiyOEQSiSvu3r//v3YsWMHAGDHjh145plnFh1z5MgRdHR0oL29HTqdDtu3b8f+/fsBAO9///uh0fBhi+uuuw6Dg4P5LIeoQATBkbM0OhCO5dRlAOCLBgAglGMDz0iMdziUwyGUSF539ejoKJqa+A2DTU1NGBsbW3SMy+VCa2ur+L3T6YTL5Vp03Pe//33cddddKa+1Z88e9PT0oKenB+Pj4/ksm1AQjYLDkbE02s9GcwqnAbMOJ9cGnoLDoSo1Qolk/Bh3xx13YGRkZNHjjz32mKQLJEvmMsz8P6bHHnsMGo0G9957b8rz7Ny5Ezt37gQA9PT0SLo2oXzqbXoAwNiMfKXRgXAM5hwKBoC5OZzcHE6UHA6hYDL+Vb3wwgspf9bQ0IDh4WE0NTVheHgY9fX1i45xOp0YGBgQvx8cHERzc7P4/d69e/GrX/0KL7744iIhIohMGLRq2I1a+R1ODiXR/HoSOZwcN3+KORzqpUYokLw+Rm3btg179+4FwAvH3XffveiYzZs3o7e3F319fWBZFvv27cO2bdsA8NVrX//61/HLX/4SJpMpn6UQFUyjzSBvDofN3eHo8yyLng2pkcMhlEded/Wjjz6Kw4cPo7OzE4cPH8ajjz4KABgaGsLWrVsBABqNBrt378add96Jrq4ufPzjH0d3dzcA4M/+7M8wMzODLVu2YNOmTXjggQfy/OcQlUi9TY9RGUNq/rAMOZycHQ6F1Ajlkle36JqaGrz44ouLHm9ubsaBAwfE77du3SoK0FwuXLiQz+UJAgBfqXZhbEK28wXYGMz5VqnlXRZNITVCedDHKKLsabQZMDYTRiyP/mVzCeRVpcY/L5yn4OjI4RAKhO5qouxpsOkRi3OY9MsTVvOHc3c4sxs/8+s0QA6HUCIkOETZU5/YiyNH1+h4nEMwEsvZ4eQdUotS0QChXOiuJsoeObsNCB0CjNrcBEerZqBi8igaiAtFA+RwCOVBgkOUPc0OXnDkGDUthMIMOQoOwzB5Tf2MUlk0oWDoribKnjqLHrUWPU4PefM+VzjhcPSa3P80DFp1Hr3UhI2f9KdJKA+6q4myh2EYrGux4fTQdN7nCufpcABerHItGhD24eg0FFIjlAcJDqEI1rfY0TvmyzmUJRCSy+Hkuw+HHA6hQOiuJhTBuhY7YnEOZ4fzC6sJDkevzf1PQ69R5Vw0IDbvzEPwCKJUobuaUARrm2wAgPMjM3mdR3AmwqjoXDDq1AiyuTkcVigaoOadhAIhwSEUQbPDCK2aweU8K9UEZ5KPw7HoNfCFcxt5HaWJn4SCobuaUARqFYPWahMuT/rzOo/gcPR5OByLXgN/joIz27yTHA6hPEhwCMXQVmNG/4Q8DseQh8Mx5+FwInHah0MoF7qrCcWwvMaE/kl/0imzUpHL4eQsOFEaT0AoF7qrCcXQVmNGgI1h3Jd7TzW5cjj+cDQn4YvG42AYPkRIEEqDBIdQDMtr+Kmx+YTVRMHJw+GY9RrEOSCYw16cUCSWV4UcQZQyJDiEYmit5gXH5cldcMSy6HwcjoEfbZBLWC0Uied1bYIoZejOJhRDs90IABjy5N41OhzlQ1r5DECz6HmH4g/n6HDyaKtDEKUMCQ6hGIw6NRwmLYY8wZzPEY7EoNeowDC551Asei0AwBfKweFE4yQ4hGIhwSEURZPdiOE85uKEo/G88jcAYE44nNxCarG8+rgRRClDdzahKFochrwcjhxv+JbEeOpcNn9SSI1QMiQ4hKJoshsx6A7iN73jOZUlh2UIaQmCk6vDoaIBQqnQnU0oiiaHAb5wFJ/8jyN4e8CT9fPDUfkcTu5VauRwCGVCgkMoihqzTvw6l9CaHG/45nxDarQPh1AoJDiEorh6ebX4dS6CI4fDMenUYJgcHU6UQmqEcqE7m1AUHfUW9D2+FVaDJqf9OHI4HIZhYNHl1k8tFInDqCOHQygTEhxCcTAMgxaHEa4iORyA7zaQa0gt37JsgihVSHAIRdLsMMLlzi2Hk0/jTgGzXoOZHDZ+hqlogFAwJDiEIml2GDA0nZvDkSNpbzNkLzixOAc2Rr3UCOVCdzahSJodRngCkaRhrUlfGAMpRlGHZXI4dqMW3lAkq+eEo0LjUHI4hDIhwSEUidDIM1mbm8eePYvP/uCtpM+TK4diM2oxHcxOcEKRxLRRam1DKBS6swlFUpXYjzMdZBf9bGg6mDTcxnEcQlF5HI7NoIU3S8EJRsjhEMqGBIdQJA4j37E5mcvwBCKYDkYQjcXnPT7pZ8FG42iwGvK+vs2ogTeU3dTPEAkOoXBIcAhFYk8IjiewWHCm/Cw4brEYXRr3AwDa68yyXD8W5+Bnpc/EkWP4G0GUMnRnE4rEYUouOBzHiY9N+eeH2y6O+wAAK+sseV/fZuCvn01YTcjh6MnhEAqFBIdQJFaDFgwDeBa84fvZGNhEKG2h4Fwa90GvUaHFYcz7+raEw8qmUi2ccDhGEhxCoZDgEIpErWKSJu7dc0TGHVgoOH6sqDVDpcp92qeA4HCmk4T0UhGismhC4ZDgEIrFbtTCs0BU5orMZJKQmhz5G+HaAODNYvOnWBZNORxCodCdTSgWh0m7KKTmnuM43AsEZ9QbliWcBvBVakC2OZyEw6FeaoRCIcEhFAvvcFKH1Kb8sz+LxOIIRmKwJkJh+SKG1HIoGqCQGqFUSHAIxeIw6RbncBIhNYtegyl/WHxcaIEjTOvMF6sh4XCyKBqgsmhC6dCdTSgWu1GzOKTmZ8EwQFutCVNz3I/QaNNikEdwNGoVLHoNvEHpORzqNEAoHRIcQrE4jDp4Aizi8dnd/u5ABHajFnUWPSZ9sw5HGJZmlcnhAHzHaE+S1jqpCLBRqFWMLPN4CKIUoTubUCwOkxZxDvCxsy5jys+i2qRDo904r7Gn4HDkyuEAgN2ky6os2h+OJcZT51+WTRClCAkOoVhqLXoAwJh3VlgmfGHUWvRocRgw5WfFvIkvzAuDXCE1AKhKUiWXDn84KlsOiSBKERIcQrF01PMtas6P+MTHJnxh1Fp1aE6UPw8lxlCLORwZ3/AdJu2izaXpCLC8wyEIpUKCQyiWjnoLVAxwfnRGfGzSz6LGrJ8jOLz7EXM4Mjoch0mXtHloKvxsFGZyOISCIcEhFItBq0ZbjRnvjvCCE4nF4QlEUGPRiRs8FzocOQWnyqRdVLSQDn84CrOOBIdQLiQ4hKJZ1WDFuwmHI2z6rLXo0WAzgGEAV0JwfKEoVIy8jTMdRh3iHDCTZMx1MvzhGMx6CqkRyoUEh1A0qxqt6J/0IxSJYcInCI4OOo0K9Va96HB8iYS9nBVisyMSpOVxKKRGKJ28BGdqagpbtmxBZ2cntmzZArfbnfS4Q4cOYfXq1ejo6MCuXbsW/fyb3/wmGIbBxMREPsshiEU02w2Ic3w59ERi301Nonqt2TFbGj0TispaEg0AVSZ+zLXUPA5fFk2CQyiXvARn165duP3229Hb24vbb789qZjEYjE89NBDOHjwIM6cOYOnn34aZ86cEX8+MDCAw4cPY9myZfkshSCSMncQ22SilY1QLl1t0okbM2dCEVnzN3OvLbVSLcBGYaGQGqFg8hKc/fv3Y8eOHQCAHTt24Jlnnll0zJEjR9DR0YH29nbodDps374d+/fvF3/+hS98Af/0T/9Em92IgmA3JlxGkMVkIqRWY+Efsxm1YnNNXwH2wDgSDkdKA894nEuURZPDIZRLXoIzOjqKpqYmAEBTUxPGxsYWHeNyudDa2ip+73Q64XK5AAC//OUv0dLSgo0bN2a81p49e9DT04Oenh6Mj4/ns2yighBcxnQgggkfC51aJbavsRu1Yq8zXzgq66ZPgK9SAxaPQUhGILEBlYoGCCWT8S/sjjvuwMjIyKLHH3vsMUkX4LjFJaEMwyAQCOCxxx7D888/L+k8O3fuxM6dOwEAPT09kp5DEGJILRjBlD+MKrNWdNM2gwbeUATxOAdfKIrlNfIMXxMQhrC5JeRwAolKNioaIJRMxrv7hRdeSPmzhoYGDA8Po6mpCcPDw6ivr190jNPpxMDAgPj94OAgmpubcfHiRfT19YnuZnBwEFdddRWOHDmCxsbGXP4tBLEIIXHvDrDwBCLi9wAfUuMSvda8IflDahq1CjaDRlIOR9h4SvtwCCWTV0ht27Zt2Lt3LwBg7969uPvuuxcds3nzZvT29qKvrw8sy2Lfvn3Ytm0b1q9fj7GxMfT396O/vx9OpxPHjh0jsSFkxaBVQ69RYToQgScQER0PwAsOwE/l9IXlLxoA+Eo4lzuY8bgAK4TUSHAI5ZKX4Dz66KM4fPgwOjs7cfjwYTz66KMAgKGhIWzduhUAoNFosHv3btx5553o6urCxz/+cXR3d+e/coKQiMPET/50B9j5DidRBs038YwXpHGms8qEAXcg43GzDodyOIRyyesvrKamBi+++OKix5ubm3HgwAHx+61bt4oClIr+/v58lkIQKXEY+fJnT3Chw+Fvf2HzZyEcTmu1Ea9dnADHcWkrMQOJEQomcjiEgqFOA4TisZu0cAci8ARYsVQZmE3qDyZCXoVwOK1VJgTYGKYyVKr5w7HEGsjhEMqFBIdQPA6jFsPTQURiHBzGOQ4nEVJzFdThmAAAAxnyOP5ESI324RBKhgSHUDwOkxYDU/wb/twcjj0RXpsNqcnb2gbgQ2oAMDCVPo/jo7JoogIgwSEUjyOJyACARaeZ1zG6UCE1ABkLB6aDETAMxE2pBKFESHAIxVNv1Ytfz3U4KhUDq14jli3L3WkA4B1LrUWHyxOZBcdm0EKlohZPhHIhwSEUT09btfj13Co1YLagACicu2ivteDShC/tMd5gRCxiIAilQoJDKJ51zTbx60WCM+dNvhA5HABorzPj0rg/7THTJDhEBUCCQygejXr2NncYdfN+JuRY1CoGBm1h/hxW1lkw6WfTDmIjwSEqARIcoiL433/wHty1rhE6zfxbXmjYadKqCzYio72Ov8bFNC5nOhgRN6IShFIhwSEqgm0bm/Fvf3T1osfbaniHM5MoSy4E7XUWAMCl8dR5HG8oSg6HUDz0kYqoaOQeSZCM1iojTDo1Tgx6wAHY3FaNFbXzr8s7HBIcQtmQ4BAVzfKEwykkGrUK711Zg58cHcT/feMKAOBXf34j1rXYAQChSAxsNE4Oh1A8FFIjKppGm2FJrnPLqjqw0bj4/cvvzk6tFUZQk+AQSocEh6hohI2WN3TUFPQ6t67mhxPef9MKOKuMODcyI/5MEBxbgcqyCaJUoJAaUfH0PnYX1AWqUBNorTbhJ5+9HutabOib8OPcsFf8mZccDlEhkMMhKh6tWrUkLWWuWVENk06D1Y1WXJrwIxzlRxJQSI2oFEhwCGKJWdNoQyzO4cIYXybtCZDgEJUBCQ5BLDGbWh0AgOdOjwKY7VbdaF+aAgaCKBYkOASxxLRWm3BHVwN+8Ho/vKEILk8G0GgzwKClaZ+EsiHBIYgi8Ke3rcR0MIK7d7+Kk4MeLFuC/UAEUWxIcAiiCFy1rAr//qke9E340Tvmw/JqEhxC+ZDgEESRuG11vdiheik6HhBEsSHBIYgioVIxaHEYAQDLlqCnG0EUGxIcgigiN3XWAVi6FjsEUUyo0wBBFJEvbV2DzW3V2NxWVeylEETBIcEhiCKi16jxwQ1NxV4GQSwJFFIjCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJIMEhCIIglgQSHIIgCGJJYDiO44q9iGypra1FW1tbzs8fHx9HXV2dfAtaYmj9xaOc1w7Q+otNOa8/3dr7+/sxMTGR8RxlKTj50tPTg6NHjxZ7GTlD6y8e5bx2gNZfbMp5/XKsnUJqBEEQxJJAgkMQBEEsCRUpODt37iz2EvKC1l88ynntAK2/2JTz+uVYe0XmcAiCIIilpyIdDkEQBLH0VJzgHDp0CKtXr0ZHRwd27dpV7OVkpK2tDevXr8emTZvQ09MDAJiamsKWLVvQ2dmJLVu2wO12F3mVs9x3332or6/HunXrxMfSrffxxx9HR0cHVq9ejeeee64YS55HsvV/9atfRUtLCzZt2oRNmzbhwIED4s9Kaf0DAwO47bbb0NXVhe7ubnz7298GUD6vf6r1l8vrHwqFcM0112Djxo3o7u7GV77yFQDl8/qnWr+srz9XQUSjUa69vZ27ePEiFw6HuQ0bNnCnT58u9rLSsnz5cm58fHzeY4888gj3+OOPcxzHcY8//jj3V3/1V8VYWlJefvll7q233uK6u7vFx1Kt9/Tp09yGDRu4UCjEXbp0iWtvb+ei0WhR1i2QbP1f+cpXuG984xuLji219Q8NDXFvvfUWx3Ec5/V6uc7OTu706dNl8/qnWn+5vP7xeJybmZnhOI7jWJblrrnmGu71118vm9c/1frlfP0ryuEcOXIEHR0daG9vh06nw/bt27F///5iLytr9u/fjx07dgAAduzYgWeeeaa4C5rDzTffjOrq6nmPpVrv/v37sX37duj1eqxYsQIdHR04cuTIUi95HsnWn4pSW39TUxOuuuoqAIDVakVXVxdcLlfZvP6p1p+KUls/wzCwWCwAgEgkgkgkAoZhyub1T7X+VOSy/ooSHJfLhdbWVvF7p9OZ9oYuBRiGwfvf/35cffXV2LNnDwBgdHQUTU1NAPg/0rGxsWIuMSOp1ltOv4/du3djw4YNuO+++8SQSCmvv7+/H2+//Tauvfbasnz9564fKJ/XPxaLYdOmTaivr8eWLVvK7vVPtn5Avte/ogSHS1KQl07BS4FXX30Vx44dw8GDB/HEE0/glVdeKfaSZKNcfh8PPvggLl68iOPHj6OpqQlf/OIXAZTu+n0+H+655x5861vfgs1mS3lcuay/nF5/tVqN48ePY3BwEEeOHMGpU6dSHlsu65fz9a8owXE6nRgYGBC/HxwcRHNzcxFXlBlhffX19fjIRz6CI0eOoKGhAcPDwwCA4eFh1NfXF3OJGUm13nL5fTQ0NECtVkOlUuH+++8XwwaluP5IJIJ77rkH9957Lz760Y8CKK/XP9X6y+X1F3A4HLj11ltx6NChsnr9BRauX67Xv6IEZ/Pmzejt7UVfXx9YlsW+ffuwbdu2Yi8rJX6/HzMzM+LXzz//PNatW4dt27Zh7969AIC9e/fi7rvvLuYyM5Jqvdu2bcO+ffsQDofR19eH3t5eXHPNNcVcalKENwsA+MUvfiFWsJXa+jmOw2c+8xl0dXXh4YcfFh8vl9c/1frL5fUfHx+Hx+MBAASDQbzwwgtYs2ZN2bz+qdYv6+svZ5VDOfDss89ynZ2dXHt7O/e1r32t2MtJy8WLF7kNGzZwGzZs4NauXSuud2Jignvf+97HdXR0cO973/u4ycnJIq90lu3bt3ONjY2cRqPhWlpauO9973tp1/u1r32Na29v51atWsUdOHCgiCvnSbb+P/qjP+LWrVvHrV+/nvvwhz/MDQ0NiceX0vp/85vfcAC49evXcxs3buQ2btzIPfvss2Xz+qdaf7m8/idOnOA2bdrErV+/nuvu7ub+/u//nuO49H+v5bB+OV9/6jRAEARBLAkVFVIjCIIgigcJDkEQBLEkkOAQBEEQSwIJDkEQBLEkkOAQBEEQSwIJDkEQBLEkkOAQBEEQSwIJDkEQBLEk/H/1J812fWeocQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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XZJfgi/vBye7tfUmU+vx5tu2ivxlGVF5g9KqgSlmQYXB8qlnqeNq97Bmfth5DC1czjNAKo7apPIfYyfbh2OXPsAxhpGM6VknNYgxPtx5dMwG9L3QbeuVmSs2XtmGg+FnyR2Wa8+XjMJL/Br6HPUfsc7Ms+t0Vvm8FRiuKFT1l0WQm28WsMYxUYJRVSZVJ8SztMBGafNHs4JJa83AnWxa8z5whferHD+KmRw5kjo+7DMSYzc2GzBc2f55LVmD0tm/tZqsts+jaxgihvNE7eV0zNpC7mSsLxzDmCJxVzEeGIQRGSYZBzkp5/aNx1oxiRffazQDsxzgMlWHI9x/7/n3471sfzxzfDRPj/5sVlVR3sm1GkLnuHneurSBMOr5IJcXem1hou0bvNUsGsLsHAiOK+zudT98KjCQOI3kfRnGh95OwYRQc91ffuBOX/duOrvs3IhhGSYFBdY1zuqeqpLiOtsNOdvnfuQLfMfJFxlZUKepyEZ5NL6n5GIeRNXr3mGGIdsv2R1VJ/eS+vXhwj+qqGpdkGGWN3j1jGJhfz7Zd9K3RO+QqqRIMo6yX1Gd++lBP+jecpk84XNIwKFRSOdfBo5sbPWIY/Th4Y/YI+WLQimKY5393doHZjcMggTHjpyoNvS/883QrRL0SdNk+PZ+yF62qpF7/6RsAAA9/8KWyTTYOTGO8bKR3yBjG3iPTiKJYbO46QRmD/XxG3zKMMJZCIorjwpQfsx24R0a1sgwjSJ+EzWtJn0ytxayS0jxgxPeROd07d7/uBLNaQCk9x/UP7sPWv/ouDpUcPzMJfezxz73wlGo/DqN8LinAvAmjDVppldTYAJphjINdPo/I2TDmBpxhtNpwqy076Yuoapn+AeUZBsHWPb3fqmtpW6codb75DFUNlbzG6QbCJHBl4F5355uNWEca04/sm8DByea8SN2SxzB6IjAs57EeX8atlr03zXkaJ9MlVVKrU9f4btVS/V5WuW8FRiuKVKN3wUMgVVRZhkEV+joFTfyyRm++8HE8tn8Sl3/7rtx+d+NR04+7nUhjFcl36W85DKNzLyk618wHferqr/nAADNxGOxzWQadh/a9pLL9yB6Tb8Og+1qWYaxOM0l0KzCieH48007RtwIjZEbvKIoLU13Qglz2YXUSpLNnfBr/fv0jSZ9IYJScULYF4gf37Mb/vfbB3IG62GwYipeUYJlp+noTw9CObRdSJdXR39sCDeNm2N4iOpPIc5LqSfRzrL4WH19sk+K/meQ83dcyRu/A97BqLGEYu8enynXS2Kf5Z59qF30rMEQcQZTEYBQxB9odllU1daIK+Pbtu/DnX78d+45Mt62Sshk7xU4op99dqaT6MGOziWHk7cjbTW6nYy4KKM1V7jMT9Khp0/3vBu1HeqevJdq0tVuWYYQREHie8HrsJn+WFIxz/0w7Rd8KDF5xLYrLeEmp/yvCgQ4YBu0Km2EsdorjbTIMfTBRd/MEXTcDcD7sYNtFZPCSygtElAtMZ9cqVFKzEYchnDOi9NztnfOmR/b3JIUFh24v45+bPRAYbcdhiP/Zj+c/Ubt3PHEIv/XP12OqGZZXScUxfB8YqCZL5VSz8x1WJx5wRxshHt032fE5e42+FRh8gQjbYBhlbRj7J9rXzXIWQzvF8ZI7Elu+Imqn2crT17bdVfbf/hMYJi+pvHTnvSqgNJtutcQs2jlnFMV43fYb8PkbHp2RPgHJ2OYLdSuMujcE02vJSy2z8JoYxs2PHsTPHtiHpw5Pie8KI72jGIHnYaCauA53o4LrxFvvXV+5Fed96AfzpnhT3woMroIoY0jiXlI3PXIAF/zdD430sl5JbkknDIMnOBQDsuSDti1KIlgvR3fUnQ2j47/OGUxeUvL5Zo+XbpudnW82swREGmNqxw28GUVohBGONrq3K5j6BCT3gA+3H9yzG8/54Pe7Ehqdq6TaYxhT6X052gbDCNO4i2rgI/A9TLU6v7ft1i4HgBsf2g8AePzg0Y7P20v0rcBoid28SuFt4BPxvqfG8eDeCew1JBOjpJYHOrBh0KLfCqPSA5JgKzkqjHM57Sy2OIw8G4ZRJSX3sB2dbzY9lrLu020IDGIlPWaNvDm90uUTB6eS+AS2wbr3qXF865e72jhB8tJOLinql00txYUJHU/s4GgjFIt2o8BGFKVGbwAYqPhdqaQ6sWGsXTIIIHGzng/oW4FBE4t0+0XGW0nxo4zem4MW+P1dMIwmm1RFlJdg22VFrE0bulkf+tEAZ/aSylNJJa8dq6Q6UCV0CpvTQxm0xFzoscBgi28ripT7SIswn0ufv/6RtsoT5+28D002ccOD+7Tj09cczYIa6Z32NWUHU02+octnDKSSAoCBatCVSiruYBxRaYRH988PO0ZPBMab3/xmrFq1Cps3b+5Fc6XQ0gRGYaQ30w1LJqCpfyLpqtsVw9BjREpMYGGUj/U+Ja8zpZJaKAwjNwdTlwt+nLPB6DUyDKONczbC9ux0ZcGHHle3AkxgsLnUjIpzuynt5xz6hn+5Eb+5/XrF6YPHbdjYlGmMHG1Eos9lNAC/8sFr8PkbHhWpQBKB0QOjdxtNLBuqAgAemSeG754IjDe+8Y246qqretFUKURMj0rG4CjOn1ymBVz3POKDp5NqYpxh8PlSxpU3tAymciqpNjvao//OFUx5gvIZRqrC6PB89Gwmp1t40Ud/hJse2d9hSyXOpfW/LRtGm4by79/1FN7zn79QvvuPnz+Gr968U/mOL76tKNbiMFLVcKQu6O2oxfIOvW3nwcwx3K3WtvjyWyBsGC2DDSNnbpLdgBhGvep3ZcMoKhtrAgneBaWSOu+887B8+fJeNFUKfBLxB573ILgRkRZzfTJygdHJzpsLJa7qKVNoRhY0Muuw81RS3TCMvlRJGTxgyEPNGIdBr9pPX/75o7jwI9cWno+anGiEuPvJcdz5xOH2O10S+rNsb3Exu+LuPjyF13/qhgxrvu6BffjvXzyhfPee/7oN7/wPVYjwW9rSvKRoAeWMIoraE3R549ekDuTJCu33J3u8MHo3QvG/MjZGacMIMN2NSope23imlLpkQTGMMti+fTu2bduGbdu2Yc+ePV21xRcFvnvPW+Qjw2Kup3qYDuVg6EZgcKM3YB+UD+45Iiry8bgSY5s5PLbdRV/xMupHgZHjJWVafGwM46G9k5m02CbozLVXleZM6EYl1dRUUo/sm8CGP7kSv/3pG/GT+/fiKzc9pp4rjkupRxQ32kjN4zTdzKqEy8RFKe2XOYYzDHGeHBtGnH0vbBitsJRtkJDmEcVA1e/q2VOW5XaWFtIs7DrUeYR5LzFrAuOyyy7Djh07sGPHDqxcubKrtvjg5AIjb1fTYgtvaBksCsPoYCEVKinNbmFTSb3mk9fhMz95GADfKavHCG+OHqqk1OJL7f13PkDXT5//dz/E73/hFvFZh807pWy5TF0IzaTA0PvTiUqKFkNyybznqXEAMkMrIY7zd/ev/qef4ePX3KcxDNWtdtrEMGK5Mbtt50H8/OECFV6Jucb7Kd7GdoFqsmGQ+qxthkEqqUp3Ru9OCnHRXO02GWqv0JdeUlaGkae24TYMLTiKwAdPJykZZNBVpAwK26A8eLQpGIatJkAZlVS7bKhX1frmCnyRiOIYD+2dwH27E6Zguk221CDCUF5w//SfZ1ZgdM8wbKn8dYHBMz6bsOORA/i7796r2TDUsU0xHzrDoPZf/vGf4jWfvC633+0IbT4/8o3e2f9SX7nRu8yzlEbvzt1qH9o7gScPT2X6VoR27VIzjb4XGA0WAV3GhtFkXlK651G3RYkUo7fFzkKgdNx6wFknKql2+9pU9M3l/ttoRdjwJ1fiH75/X1vnmgnYKu4B5uuRG1Lt3pY0QmZVUr0NjFP6FJn7WAb64pIVGNq5UoZlUmnynTT/NWEY8htacJUx1aZxt4xKVdoy2P9gH796nRRANXrT/2xutfz/vXCrPf/vfogXf+zHmbaBxCPz4r+/1qgepc3mbNXxKUJPBMbrXvc6PPvZz8Y999yD9evX49Of/nQvmrXCthjnLaqql1T6nYVhDFaDjh4Qd6uNYxk1bmIYus7dluCuTMS4ab41wwhv//zNRgNtJyopus8f/8H95f4wg8irppbnJZXxQMuxe5j+TyC9fZyym15Cf/7t7CwFw6AxpW1U9EpxeUkZeZZl3YahCBCDdxq9LVv8qcwVyr7Gynf2gmPyPd1TYgdTTRmLVcYGEnC32h5sFvTxtPPAUdz71BHBkjmUTew8EBo9ERhf/OIXsWvXLjSbTezcuRNvectbetGsFXwx5+6m7/nP2/Dln5vz6PBdlwzcM7vVDtaCjh4OrxsexjEGa0n+GRPD0HMFSS8p9bgyxjnTgrdnfBpX/nKXUX/cjTfYTKpjkvZD/N9rH8gVkCYvKdtnwJ5KwubKnHc+QD7Pmx89gPP/7oe4f/d4fgNtwMYwy6ATlZTpnIC62CsqKZYnzXRufvyDe8oJU0V9VLCA68Zs+4KfFWAi0rsZimdu2xjydn2ve5WU0jeL67yJaZW10c4W+l4lxW/oD+/ZgxseMhvYJMOIrIswLQSdMgxFKEUxBtOEZabFr6m5gVq9pJhdxAZTV2lymIxlpiAoHR/+7j34t+seZueYncG6/doHcfm377YKfkBdVPTLyzd6a+2IRaiIYaifiWHsO5K4qXaSqNKGjEqqjXGob0L0MRxoDCPv+nnhr2zgnv3cyfHJ+/sNO2YA+LV/+IkythT1kdUmkS6qTOjHsHt5xbGs201t0pyYYkZv2/3l39N969boTbBtcmyaAlOf5gp9KTBscRiAfSfO04HoRXdEW10yDO5WG0WxyHBpVEnpDMPiQUHdyPOSME142gmZ2E1T82gx4Xt3PoUf3iPdn+MSG6unDk/h8zc8UnxgDsgwmHf3FfVHCZ2/za02sghpHbpQFZ5BYuPRO9ZVRsVmQyM0b0IIug3DxmqBHIYRmVP+mYzeD6Q6+eGUaRN++fgh/OUVd4jP/Py26zUJ/TjOFzC00NN1Hm1mA/dakblkKm+XR3pPt8EwosjsXmyb46ZLVxxx5kHxmr4UGKHiVqveZdtOnOtai7ykumUYzfQcJDBMC4peIU4KDvU4YZzLzSWV/Y0mhykturJriWM8uOcIvvXLXRlDIb8HZRjGGz5zI/7sa7cbkzqWBdWIHh2oWI9RXCbb8CrS75NI+FikkorM44TuY9l8YWVQ1uj9gSvvxF98/XblOxrPeop0gufpDMO+yz6k2DD4OSKL6iS7CSGVVK0ilxmzU4I67kww2VviOF/AVANfaZPYwWTqLUUCxdSGwjDS2zZQ9dHQYqzy8Jf/fTve9rkd1ho3rLfp9/n31TGMDqHYMMoyDKqHwXNJ6QtByBhGByoYhWHEsuhKHsPQd7m2RS2fYWS/m85RSTU0ldRHvncvfu/zN+ODV90tzxvHimAucz8eSxOkdaO+IjfjkXrVeozJx158zmMYujCO7f8xHUfQPYPy0rbY8JKP/VhhY3Ec47t3PGndMOj45eOHcNvjh5Tv9MA9fUca6DYMsbOV56DF/RCrac+9wjhD54yFPwcaw7Rx4HPSZANTBFKhDUPdxNjGWhTHqARSJRXHsWDdVNaAnFJM5+T3nRu9k2sop5Z6ZN8kHt0/mdlQ2BiG6UoazobRPZREZ9rDsNE2XnFPRAdbckmVYRj//YsnMvpMbvSOohgDlWKjNy81S/3jKKWSMvRVMAzD/eALXBTHYsf1zV/swr9f/wj++UcPIoriUqor4znDGN/4xRN4++dvLv6TBsrhFeSMTH4rMrs3w22KLROybJ0Lm1stjZ8y0cI67ts9joeYUfjep47gss/dlDkuzyirCyrd6K33y9fuqcl7jEqRHmACg5cZ5oF7VfaQTLp2Eg58/B812ADKGb1Toc++i2GPl4ohbRhxHCuCisYYCUcjw+AqKRG4117VvUYrQjOMM0LS5thiYm6NVoRqYGdCs42+FBh5aTdsA4hX3LMFw3GBkfdwHt47gT/44i34zh1PGs/RTFOok5eUaVell+CkOZXZYYq+tmv0TvtiVEnxHbqsX94MI3z79l345m1PpAwju2vMAx3eaEX4/S/egit/uavtSmGkksrT8uTFYeS61dpUUoUMQxcY9JyLn40NusrvyLTZcG4bh60otrJr6q++E9a9pExuxcQaeAEx7mKbBO4l77mqSVVfJq8yk63sJ33Hu6KmT88XGGrEdz7DkConNa5kopEKjMDOMExG73YZRiOM0GhFGbsH9fnJQ1NCG6FfG6EZRuK8jmF0CFUlpd5E2+SVNgw19TgHV0nRb5/4wf343p1PKcfRoqZntBVJDUPVS8psw1D7oLiKsn7ZdoscuTaMAi+pKI6xb2JanItcgqNInejtjNVGKxKTceeB9pKmSYFhP6Fyf7TDTOol+iarksreexMybrUUTNWhDSNZ6FQ2bHNXti2IoVFgSLUrkN0J6zYM3eGCf7ePJSrkHlO8gBJnGC1tTPFriuKsHaHOhA2/RNv1Wo3eOSqsSkqpwjhWmM3kdPI+l2EY7Hft1vVutJIKiLqAieIYU80QF3z4h/jqLY/nxsM0wwhD6cZzwcRhzDby8jRZdyjMbmEre8q9pOgcn7vuEXzzNjWjp/C20EphKiwmyveSkuoDtX96vzpnGOVsGFEUC/fQZhilLsGdGb1F/8MI65allcLaLPyip0oxIbIIV9NnoFglVTQP9a5Ma0bvdhkGnY+zYRsTy1VJWTZLYsHW1D8246uJsR1UGEYr8zsAoSrR+0nN8V099Y0W21rABUZ5hqEG7tmFfRzHon9RJO0XA1U/o5IyqbHVBJepwKi0V9e70UoYhi5goijZbE42Qjx1aMrqJRXHyTMedAyjO9iSDwLlvKREQRxtwk0zlRRfqHXBMJlS2qzAoHaLjd48LgRQB74pzUL7brVJ38xutfK7iUYobBitKBYuwbpKKs/QrGO6FWHd0kRgPNauwEgnc57AyMu2a2QYpu0pa6cwl5TFhkHPqV21mylOwtaGrW9hFFtdyvVUGKItpgr50o2PGp0BuFqRwD2mmiyOSVFJGcYsV8UIgdGi3b10tVXOX2D01tmI6dn86od/iNt2HlK8oGg+LBuqiXleT/tgUmNHhvlIG8DJRogrbn28cB40wwhNA8PgNpXJZmhVmdLzpfPeteswfnb/3txzzjT6UmDk5WmyuZ/yKnt8YeegQc2N3s0wwpQ2mXnEqKlfxGLy3Gpl/WW1f3q/yqikTIvkVFNd1Dj4YkCeLMcM11K9OKmkVJUHPwXpgG1ohhGWDdcAtJ/Hn85jm4y7Dh3FbTuld1A5G4b6mv0+f+LbVVLFwtwEk7HdptayNd2KYoP9TjV6m3a2APDfv3gcf/LVX+KIQf1H73nbXCXVbEmde5VZ0TkrFm2EXGBQn1KBwdgJv7tt2TCQfd5Hplp4IHUmIJVUxFRSS4dq4tiyKinaoNbTDeD1D+7D//zSrdhRkIWXGIaubgxj+eyONkJ5/Vo36J6RLfT3Pn8zfutTN+Sec6Zhd3afx1BTg6h3uYhhJOnNs+0A0iOhEnhJ6ud0AZ3KMAzVn5vAXWCjKM7PJUXqA4Me2eSdlLco5cdh5DOM3YcTgbFqbAAHnjyMMFWn5TGMyekQYwOq26vuiEDXVVZgxHGMK26Vqj/bIn7e3/5AuT9ZLykDw0hnoh5yZvNM06H/TAsALSTtekmZXKVtDEOv2cLbsNkw9Mhm8R/N2YMWdFNcC1/kxpmX1GQzFPdRtWHEmGy08MTBo0ZdPI13Yh1KbEYJ9ir0/Ow7U7Za/n9yqyWbASBLngJAPcforaZDURkGOQEUpclppCpeff2IYslSjzbsDIPm7pAW+DiX6E+GketWa6fwABngIuOxZKwlf/UwjlOGoT5wWowzE5KzmNRLo1bxRdUsUz9FAB87RDFKGxYXHaY1RXhJGf7HBd2elGGsHqsjipNBTsLC5lZrKl87znehYSSu69H95fIJ3f3kOP7wy7eKz7bL1RfnTGoQo0pKfdWPLUwNoqs9mqqgoMU+jrOLuAmmtBRWgWHpWyuKsgJDCMDksz4+RS1sbUyZVEK8be4lNTHdkgyjwmwYYYS3fe4mXPiRHxlduUk4CYZhMXrnGbGTV75ZyI59fr8qInBPnpeYL++DSSjztYHekw2DGDb19fGDR/EP37/PkBEgaXdcmy8xZxjNUD6XDMOIlPPOB/SlwMgP3CueeDR4MzW9wwi1io8g3Zm0wsTwq9sqjloYBldjxXHixlgP/PxstWT0NuxoeL/bTT4oVFKGCfjUYRmJvWc8eb9mbED8T7AMC8OYMAgMrudutKQnGt+d5sGzpK0oQjbSO3tMLBYb7RyW73XYdMu60fvvv3cvTvyzbxcaRU2Bo+2qpMIwEeh8kaIdKS2AGZWUdr163AYg7yfvDz3bwPcwMZ0scJ4HBL7qVvvj+xL9uskmQOP3qFFgFDMM8QyUnwsYBsslRfdi6SBjGDmBe4pKKlRZEbf5AcBb/vXn+PD37sXOA0eVNmjeH9HmQBTL3yYbMhGizoBJ4Aw4htEdIsPiT7DGYRg8UkypQWoVyTBEsjKdYTTybRjUfuB7qFZ8s1utpj5QPL8UL6n0uza9pPJUUk8eOoqlKTXfM57kblrFBAappLhzQVwgMA6yQC+i4vp15SFvp5gHfXeYZ/S2qa/0xUFXa+qXEKbOAS1NYHx5R1ICdb9WOzvbZ2Ki8jy2HEU29sNrrxB0AWAzeuubEO6BRKdTcxglX47UK5hstMRmqOJzhmHexAlPpDwvKdZHW54wubHi15PdWJgEBmd+IyzljOybQSWlMP7kdzKiyzxwyUHkglzVPL9obeLsO+m3NHpPKUZvtQ/U56GqExhdIS8OwxrprQiZ7A4PYAIjHRii4EpDbXPS6labtEcqKN9LJobZrVZdrFQvqfZUUiYbxnSOSmrXoSnhxbRvooFaxcdYOpGONkMZh6EwDPn/iUZ2B21jGPS/Wx87iF8yY/UND+7Dk6xOcZk05Sbokz3P6K3DFIfw7v+8DX/0H78o7Mt0K2KBe8nrcBolbVLZKeeNs+PPxjDsgajZcdHU7nkmYEywWvW/RiO8YcyO1CuYSHXuHtTst3yTw69rSEvxX6iS4vPA4F6uZKuN850eKixCmv4/yBbfWh7DMLAe2ki2NJU22SgySRrTjzrLjqKYMQyp4rv+gX3Y8CdX4vY05Ytu9J4P6EuBoSQfzKRHsO/IasIInY1ABZJBXfWlwCCBoPuzWxkG0fm0T35qw8h3q1UncdKv7GDNKxmbyzAMC9GTh6ewdkkiMOI4Mf7R7miqmTgFhHGssLK2VFJhlFmMf/0TP8Wvffwn4pjf3H49XvSxH2XaufxVpyf/K8lMivIAAczorR1qSo2x80CS/yevPSB5xnrgHqXVKCocJGoxFORY4n3UoWfK/en9e/FAmk68FamLs2wreZUMIzJ+D5gFxuhABZOpDcP3PDUOI+TjV/5XBq+qrIe71apGbza3FecG9RrofxmVJLdh+NILiprlBuRaYLdhmLykSANHVT51JmcTuFkbBjN6N2Uyx+/fnQQI/yR1neWZJ+YL+lJg5OaSsuqCs15L+mJDCcuIyoqi8RaBYfOSoskfpJPKJMSksTvGLY8eUAaVqRhNXjRxfhyGtnjGccowBsR3QeApu0WKhld2eOz0ZqM363+rnEqKq7HoGio5GUR1eJ75eWcXEfUc+jl1DzV9g2BSc023IulWmz7v4VoqMCbzBQZ/9gS7l1Q+w6BxcemnbhC1YOhZZQWGzjBi7Xt5rGm8jdQriQ0DWRuGGkwr39PuOKuSMrvVKs4frew80AP3dGHO5zSNJZ6k0MgwSsZh0BzRI+qbBo0Fn8N5KqmjjGHITZsaQzUwjwRGX7rV5vmvmxZnqp9drwQYR8tq9G6FMQLfF/nvp1jdg2YYiQdq85KiAUQLTuIlFRh3j9TPiUYLr/nkdRmvjDiO8ci+yY4D92wM4+BkE42WjMQGkollitq1Gb1NRl3+ncIwomy9AZMKTZ80ZTxVq4FvjLsJoxh8jhntpTCrA1tR1m/etMmfboWZcUQqqYNlGQZboduN9M5z6ZU738j4va7OMqtFs+2ODFSwZ3wacZwIa27DUCvDZRkG3SuaG/yy+P21BeWagwzLudWGkfw/X3wpcC+MYnxlx2M4dLSJ/+d5G5N+GFi+rpLKU4cpDCPH6H2UuSnXKz7GkWxGjjZC/DRlGs6ttkvkekkZ6CUdrjMM0wOv+F7G6K2/p8V4sqEPBJVh+F6qksoxek9Mh5lFodmK8K8/exgv+LsfiiA1GsB6ERzAvKDZ3Gp3pXaDY5dKgRH4nqDv/D/cCyfP0QBQVSoNzjDiWEktAeT7vVdYOoci1ALfyDB0AWozepuDJrMMwySQG5xhpK9Uw4On1TDBbMMwe1blRXoDZqcGcgfXx7eu1tHdaovsRonRO3GKyBi9LSrVjEqqmdX38+diYznUvN5F/Rr5/3ngHh02VMsavZtRjG/etgtfvflxdj51EwHIXFwmRqH3xRbHAiTPR0R6N0LRN1KRTTcjvO+/78BHvncvAGfD6Bo2+gvkG8J0gZE1mMfwfameUQWGHADChmFxqxUqKZ/capPjvn/XU/gJuR6KY7MLRTOKcc3du5W2aHJXDHm/c91qtQXlycOJ6x/ZMIBkYlUCvlvkzEJ9Bcy7YZVhyFoaYRRjz5Ep5VgTW6JFgyeMM4EbSyuBV+gSCdiTD5r835NUDuadOcd0S8aa6KoDkw3jEz+4H5+7/hGlvTK5pOxxGHbmyVNhcOgquKxKqlhgTEy3ECP1kgrMDIO/t6mkFIahXJfNhpHduMRxduzz//OKe3TPB2ty/PA4DD2uJTQIQF0lpds+FMaYo5LicRhTLA6jSunTWyEe2ifjl1wcRpfIYxim7+jh1zI2jOxxFUVgyN9NDEO3bYjqeKkQ8L0kuOmOxw/j5kcP4C2f3YHXfzoJ7RcGU0sU+EFND07HVQwUwzTPbXEYVH96xUhNxD7oDMNk8OMT06Rim049zMgrjB5BFMfYPa5W4DOlXJcqKal33jM+jX/64QPKDpRnOa34npHt6CTTVEuBX6d6vdnF1nR/p1thJnCPFhBdYMRxjA995x5RIY/6U0YlZfT6Yh44putPBIadaevXbboPOnwvUblNKl5S5lxSJi8pmpM0Z1RWYR5bTRPDUK5H1rahsawYvbUCSgAwWJUMo85sGM2WGnSpqilVlZReqEr8pw2VFC/CRe9rzIbBI9JdHEaXCAsmmv4gRS4YEhia0Uq2m0Rn615SgCowyNjdDNVBpjMM3/dQC3yMT7fwqn/8mbGPZvtGpNQjoHMBNoGRneg2Gwbdr3olEPURKr66W1T6GWbVOTaGMVDxUUvjThSGoQkM8yKv7uKiKMa7vvIL/M1Vd+OXrLIcFxie5ymeXAR9Vy5sGBZ9t5It1VDwxrTz5gxDX0B0Yc8DJfl5W5aF0nQsR55bOfXXxFxt6dyFIMlRSXmeh+FagIlGC1GUGL2riqNEvkqqoamk+PH8rNwDj48zWy4pejbUEz60KkaGYfKSSmImFGZtuO8kH2UcRlZDYeq77iQSxWoesMn0d7LfTbciLGM5r1wcRpcoYhiZwKv0Ixm59FxAhAzDaGVZBaAKj10HWSyBYBipSsrzcMcThwuvgcALvuiLDl1n1aSS0u5HxHbJtqps1UDaahKGYREYZLxm3TUtRtOtCPVqgGrgKTaMKJbR5Dql56DmqyydAy0efDGtM3oeeF5hamrAnIco+T49PlbvH2XtJVjjMLTkg7SQ6Ebv27VSqqYdfTsqqbBo/EfZ1OdANg5DfG/wktLhe8BQvYI4TjZMXHULqLZD/n4wtRkIlVQry1j5JR4p8Ba0Gb19zSANAB6SPobMhsG9pCiZYCtKglSLyqH67TAMRSVld6sFZFwT/Xu6GSqZD5wNo0uouaSyD9YWzFfX0o3rDKMVRcriyRkGf8+N3ed96Af4eZq10sQw6Ax8Z5ycOzs7q4xC67sSatvEBPi4feHf/wif+elDzOitXiNde63iqyopS01UPUkib4NjuhminjIMHrgHSIExkJOMMdImfhjHMqcXa4vfx8A3uyz/+/WP4K3/tgN3psLaZtSVuaTY9ZLQt6gn5PVKnXdD23Ee0tjh7U8kAmOYCuGQSio0LzBKH3NcsgG70TsUhlr5va6SEt8zBwVAtRMRiGEAyaLuQR2LLcXeIP+nFxGbMnhJRbGMkVLcsw1tZuphEMMglRS7Hb6fbCqiWN5z7nEkU4NEGZUUH+9vP//pAPiGRz5vvvDbNgAmN3SuMqT1JGLrB3fZ191qy1S/nCn0pcAoZBgG5gBkVVK6fp9UUrRo8Z0td1E82gixhOWk+W5aqlXGYaRutZ6HL771XGxZvySTK8m00OUFEhEqemFmqAvhYwcm8dDeCdFeI4xUdRJjKkIlFXiKeoFDj9gGLAKjlZSSrAaJSoovIE8eTlhYXklT6mJy/5PJQ5fKJy9fzHzf3NbHvn8fvnfnU/jZA4mDAf1bn2emHbde6Mf0v+R6w0wcBv33FzsP4VM/flAcKwRX+lmqpOQ5rCqpAobRCKMsY4ikbp8LWJOA5J9FnQvD5oFsGECyAPqexjAsAo+MzDTepzUbxv27j6ARRhio+KgGXjHDUFqX8RUe22gQPM+Dl44lk1stj8NoRpEifKmd7/7ReXj3C09J7wGlBpEbzolpi8DIcYMHVO0FtSEyBTdVgaG71Rb4Jswo+lJgFFFy3ahKD4LUGTSm9F1+GMeKPp+7VypsoxniGJb18tp79yjnkZHewKZVI7jglFUZI6RJN50XSESoGhgGdxtthtLgOVTPVuoSDCOQEe0Bi27XQf/lQse0uE0xhjGtuXRSzY1mKryMNgzBMCDUCEJFZzF6B56Xe6+kWiyrzuDfm/TvfOdoUkmNT7UyyQf5fX7/lXeJ92SPIpdUI8NoRTh7w3L8zrOfhuc8/Rh57hI2DF2otJjOnqvwoijGgYmGNYBRdw7h8D1PuKSOT7XgeXZHCQ76jy6IwyjGzgOTuPAj1+ILNzwK3/cwOlBVEvXxcWJiiRFnGOwaCZS+JEkNknyn2jBkHEZTs2FQu7wOOo1HySgjxeZSpiAWgac8pzZ45Dhfb/RI77L52WYCfSkwTA+Gr3d6LAZNTFJJETJGq1BlGNxuIausJQOLp0m+96kjeOLg0cxunNoZrmXjI03xIqS/P5yT4dXsVpv2X2M45I6nuzxWfA++7wnWU81VSbXHMGqBj2YrUhYxWiRIhUAT0zeoS5JdoYcojqV6ii8CbAL7vme8jwRp1Eb6at5ImHzueR4mfn7q8+GpZsa11RZkxzcLE42W2P3r43hssIq/esVmkWLE1qa+YTIJNN3RAwB+dN8enPO/vy8EOEE3hpsZhofhOlNJaYF7tmvPqKRaUiV1965xcZyHxG1XYRjGSG/Ztkkl1VKeVWKn47VdeJ95LqlWGCtsnM7HN1K+OId83ry/Rc44HJxhyOy3cuxxtfdwXV0/yuZZmwn0pcBQHkwYJYNXK+bCQTe4rvkzm9xvuYupMfYiFSKjA+pDNJUipcFGO30O086YBvDuw1OZ3wh5XlJCp045aMilsaUuTHQeyTA8I3MBzG615sA9ZsPQGIZSKCiUun8+GRWG4XmIIskwbEFefhHD0LOxar/rqeWJoQHqhI5jyeyGapXE822qxbLVEmOIcObxS/HG52xQ+so3Hkmgptxl8/tCizvf1ZqFgSYwDGuT8IZjm6Rdh6bQCCNRw52g5zPjdS5orHieZAsT063kOVniMDjEGKT05g2pktp5QM4Z3/MwUq8U2jBUhhGzcZPdXPhesqng2Qb4mKP7TQwD4FkOkmP4fCMVF42rpw5P489TV2mgXFwNgTMIEjr0HKdbSdnki56xGvd94MWi1DO/bgDY8r7v4PWzXIGvLwVGiy0mzTBVI+XoU0UBFO3Gm7wcktQgyWeTZxRRSXJ7I/XBpCFQKrAwjPdecTv+PQ3i4iCG8WSewMgxepNg0APJmpqApfNwG0aRSkoKXd+YinuqmSx4FIfB1X188jRbMWOFLJ8Qm/iJGgHM6C3Pw5lL4Hm5KVNoYaZ/2FRSJmMwXeM//+hB3PDQPnbPgLHBCg4fbco4DKbTrlV8oa4UKoZmKMbnkemWWBj0inu0OHMzlUn9oKfqN9k5uPs0QVehEXRvMc4w6L0HSIYxldgwqpY4DA6dYVC51ygGHmP1IzwvST1yZFotxEWwMwyI/vHjkjZTexg7jo85zjAamh2KNqW+Ni8CT8b+/NfNO3HTIwfEb+3YMI6mKlxAGr1l0sjEhjFcS2yCut2STnN4qiUSFc4W+lJgRFGs+FDrBjibu5vOMLJeUiR81CRgQJJVEpAD4dkbj8F/vO3ZeNfFJyXHGlJ+02DTjVafve4Rox2Arikv22lgMHrTYivz9ZDAIIOjqpISC5PwkvKN7roAj8NA2mZgrCA43QoVo3eSz4l0vqrAos9cyNMi6nlIJzmzYViSIPq+OdJb9F2zv2S8pMRuMlaOT64nOdEHvnUX9h5piL4Gqa79sNGGkQhjYrsyHUaEY0YSITIx3TLWQJluhXJx9uxjmc5D0Nkc/x5QVVKk4tEXs6wNQ45Xeoa+72EoDXqbaLSU9OaeZ1dJVVNbWTOMMNUMWaR3rLFyD6OaSqooNUhsYBi8Hx6zh3EGS+C1OoR6UWSiTY4JPFVg+L7cpIxpWoa2bBjNSDjOHCGjN7NhTDZC4ZKsb+acDaNNtNhiBECJnQAMxmwrwzCrpGjtNOWSogW0Eng4+4TlQgjpUd+AHMRDBhuGCZQawFZMp1bxjbmkMiopYhiVrEpquhWJhUkJ3LMyDFUlNVgNMrmWAMYwUrfaFhPqCsMIpTcK371RDyXDiDNGRt4PAAgsXlKi76G0nfBz6G3JyGt1ATcVAvI9D2MDCcOQi4xUZ3D1XlPopEMcM1wHkJY4ZQJKCHvGMPiTMBm9FXVfK+slRe0BmsDQ+qu3R/eJZ5LlbFR4rcVIjd7JcYPVwBgPA6QeeEGiOuSlXhOjt2QYPjGMqSIvKaam4vdCuNWqKkvfkyopz1OFMY1PSi4KyLkj4ju0FTLwPHGf9OSOitq2UGCEwlbV0NKjTzcjHG20xEZTn5vOrbZNhFGs7IL8jEqqM4YhA/eyNgwhMNKJQbtIPYMtp/P01mTDMIGK0psC4+h3fccDMKO3lqpiUCteAyT3pqbpyvXUIBx64N5A1ZxMkRhGYsOI02eUZTgNFvBmsmHwXSEJFD75+GLZiZfUzx/ej/P+9gdKbWrptcSfd6To0+leeZ6HscEqxqeaSpJGeq0wtkYL81QrFAzjyHRLuQZ632iZbRgmdVOel9SW9UuUPplUUnqGX10lx9kmZ6OKAdiXc2CwGljL0lYDL80qHCkBjXGcuIATPC9r9ObP3ZT3SzF6Q70G3ucwSu6Rr80dJTWIFoQpMg/oDIN9pP594f85R7QjftPmiG4jnGqGQmWsM9ypVojJZigEBndQoeNtZRxmGn2Z3pwbCAGIdB40OGxxGLq7oMnW4fvZbLUDVV9JBwLItAjUJhmx6mxBzfOSMoHasvnk16t+ZtADcsGj89JANqmkGkz1QYM/LzVIqC26A9XAGogkbRhJ1b6qppqhvgijt8WG4Xke4jjO5O7h/QBU9YAJQp0mz4L7dx/Bo/snsX+iwVRS1DeVYehJ44BkEzA2UMWuQ1NSQKfeNWEUoRrIe0nurc0wxoqRlGE0WgrjbEUxKgFEPfnkHsjzGW0Y7LsPfOsu7E7L7L7/1zcjimPctvOQyH7Ljd506/R7lvGSYvOEq8n48/IgN2kD1UCUKdVBArQZRkLVOjpQwVQrVASyBw8jAxXhIdhoRfjPm3ayPtI1qGxTd2hQBUbKMOLk//riXzVs0PSU77o6SLdpAMCSNO9TXuT+8uGakiLmaDPEMemY0MsJ0CVyF+AKC1INYzX+YzbRE4Zx1VVX4eSTT8amTZvwwQ9+sBdN5qIZJsWQPL7g+X4m0RmBG2w5sraOyJga5Nglg8IVUaqkiGGowkVJXWGxYdggdP4WgVEL/EwAIJBdCIRbbdXkVhsLLxga/HmpQWQKbCkwzMkHQ9RThjHVjFLPIrnTpuYbYaRUJCTQoxCukFEs/6O4V8pz+l5ZG4b8b4vt5iJNGCqBdM1ISctO9/Kk1aMYHSCjt3pfyS1bCspIjAsyhB+ZDtV06mk/TMwPMAsM/Zr/+ccPAaCgR3UMmTKd6uNLz1ZrYxj8eXHGoburc3CVFKW7WT5cy/TB94DRegWNVoTpVohv374LdzxxGH904UlqHznDALs/xJI4I/MSJhTFsch/xUHqQ65O1jP46gLD5BxChn295C5nFctTlSRhqhmJ/9mSP/IcUvy8cZxsPOYCXQuMMAzx9re/Hd/+9rdx55134otf/CLuvPPOXvTNikYrTIxp6Qgg/eqwCBLSKXc5gUE6aJ580POAtUsHWLQyqaQ0hsHYCEF4SdVL2jCCfIaR2DDUSQvIiZ5xqzUIjIbNhmExeuuxJQPVbMnZOE4S9lG0rhSe6fWEcnI0WhajN3erJS8pP8uQFBtGkZdUqEa5x7EUEmEkvYuETUFhGJHw6AGAlaN1fPbNZ+MTl56JscEqDqU2DH6Pm6nRW9gwQpnTi3aT3OgNJB5PdD+kC2u+wLBlAgiYXYHUTqYgvKzRWz2XwjCEXUVlGLxEa14J0YqfePk0GMNYOlTLzFEvdasFEtdjSifzvJNWKH3kfJHnkjIFYfLNB3eiEL+nc32qmWUYIkOtwUtKBzEBPQ6Dq6dXjNSU/yQqKZWB604ZQ2zd0IMkTWWSZwNdC4wbb7wRmzZtwsaNG1Gr1fDa174WV1xxRS/6ZgXtxmjHIxgGRTZbdLT65MkmKcymN68GPlaPDWD3YZVhkEshDQpSWfHUAzTxyzKMSpC4AdpsGMk18+NTTzGb0Vss0jEmGy381007cbQZKi6iQLIwF2WrVYzeWv8SlQwEw6B7wdPJc3986qcab5C8eqnwTyZ48t20ZsN47qYVuOZdzxfeNwR9LodRrHrVQGMY2q6VtzXVVFVSvgc8/6SVGBuoYmygIvrEWW0Yql52rTAShtFlQ1V4XiowuA2CJb2T6h/1GnTY7Da+72XsPvomydQmZXNtGoQMHyuKOtSTAj2vhGgl8NIMxrEoLLV8qJoR9Ilbbeo1NNUSKpfRdNE02TAQS2FvVkkl9yQJ3ENGnRuk0eo8JkJXSen/MamkSKuge0nx+7h8WBUYFOianCvK/B9Q1w1+2ijO5pqbLXRtw3j88cdx3HHHic/r16/HDTdkg0m2b9+O7du3AwD27NnT1TkbrYTu0U30fQ+veuY6NMMI/+ea+3NySWVzskSRNK6aCihVfS8RGONTiJixSWcYtEvhUbqCsld8YV/JA3kIWVVSjGHwbLNywUveTGsMY7oV4oUf/REe2594pTzvxBXivgFFXlI6wwgyxsjbHz8srrMa+NIBgE2YOos6FwKDrWW0IHgexK6QFjs9KdyaJQPYuHIEFa1eei3w1XoKTCjQfyXDiIWLrqkC3nRLVUnxXf8YyyM2VA+wbyINSIziVAWT9PuPv/IL/CKtmDhYCzBcS4y6vE9hFGcWd8WGYTB628ZR4ANepKmkSqTGjuIYL/uHn+CuXclz5DtjYhEkyAm84l4ew6j6fqqSinD4aBOeBywZrGY2a2T0BoDx6SYmGi0MVKUDgTVbbQTldyUrQMqKKB2LvqHwfaQMQ/XiA5jRW2clWhvc/qfbMPIEBsBUWWH22gBVYHANQBRByTU1m+iaYZhcvDwDbbvsssuwY8cO7NixAytXruzqnPQweHru3//VE/HqZyWCq6xKClCD2oSXFDN6Vys+Vo/W0QxjHJhsCNdEmkjCS6qRjQCnZ+x5Xqmc9qQOs6qkAl/cW/Jv9zz5DHSVFFHeWx49KIQFvw/CSyqwe0nR7ofbMKJYsrOv3LQTl/xTUuuDGAbvL4F7bAkvKcXoDdEn309Sg9B3ug2DqyL5JOXnHqwGCKNIcaWNASUGIhSLkHr/gETIcpUU7yt/xhSbkNgwotTImxxLwgJIBOZwPUgZhuxTK2IFdEyR3iVsGAReBY+uxaSS0hFGsRAWgC4w0j752TQZ9Fl3V+eopGOrmXpJLRmsIvD9TOJPD1IldWSqhSPTLQzXKuJe6FH5gFpxT/d4o/eBGEtZlRSp8LgNo0FxGHF2jJo+J4F1BoERRpnxqENUI7Q8T17sSfco7FuGsX79ejz22GPi886dO3Hsscd222wuGmGE0WpFMdoCctdvr4eRT88TG4YvUh4cbYYYqlewemwAQJIKQDCMdIGtpIv2UQPD4BN/qB5gvOAhE0W2uSgmQjJ5Xw38ZEJ4XiYOY1rbXeqBgLQI0OCv5hq9VZXUgGAtESqBj4f3ylKS9YovXIP5eQA5Yb54w6MiGZ8pBQapPrh+Wq+ERrJN73O94oOyEw3VAjRDlWEgVl0Y9V0pV/VMNSNEMXOrZUNnbEAyDJl+JUo9njyYgiAHqj6G6xVM6EbvVJ0HWGwYbTAMfj/zVFI69FOozJDFYeheUgEZvXMYRuChmrpaH5xMBIbvZeeo70nBM91KkvoN1yuZSnpZFWMkP0BdfMMoTopsRTC61QYpO9BtGO/77zvwX6mHlq6C0j/zLAkZlRQbBybvRt3orWNI85IS1xXPnQ2ja4Fx1lln4b777sNDDz2EdevW4Utf+hK+8IUv9KJvVpDPutghe6rA0HcvNNj4REh25mxBTP/DGUYUJ7utVSQwxqfE8RVG1WuBLyLBTSopgAzyatI3Hb6fqNlsO44aq5JHle3IbTC5L+r/aHGf1DwqeH6gpJ++OLd+aplniNqUdonhOoS7KJ3PZDAF5OT47p1Pie/0zKOAaqjkMQqEmE18ffLyCTpUD4w2DC4c6DdzuvFQWUj4hOcqKXre060kpbvNRVkkZmRRxck5ZT/IJqDYMIz1MGwqKSYwRKR3MbPVhVLVxDA8NTjW8+SmKU8lFfg+qn6ikjp0tImlg1VlzMr2PKXAmS4wRPClxiDyVFJJ8Ce535rtERXf17ykIvzrzx4GYM7bprOUGkvdwc/N4yyArCoLkILWFvTI3WoVhsGM3rYccDOFrgVGpVLBxz/+cbzwhS9EGIZ485vfjNNOO60XfbOC0lsEGsMQkZt6pLeI2pSxGkPVABONMIlaHqwqXhH84VQDD6vHkkVx9+Ep4fGkuB4GvkgNMjJgZxhFCDy7txK/Pnofpq6COsMgSIGhMhZTLqnkNesBRa6hdA8HGcMAsgVp+ALFBYapLnGoTX4gTQ2SekkJgaGlnebeXcp1sfMNVSvKYkznaBmFEF2rasPgEff8Wa5kQnJVOjb2HZlGK0pYlynb62A1UILI+PVQy8ImV2jDyAaFNcM4jWGBcn156iLCYY2BGr2kPGS8pKjWxdolA9a2yYmkFcWYaDQxNljNRE8DSeBdXTCMEEemWxipyw2SKYFkHGeN3py9URGuMLVd6Yt2kApBk9EbMBu4daGT1JVJ3nNBPpUGsr7ropMw1QqNanp6NrrwvPxVp+OD375bua+qR6Gs0ldmQ9BL9CRw7yUveQle8pKX9KKpUqAEejxSGZCGoWxyNamPrKSTdmywiolGMjBXQQ3UUQWGj5WjyaLw5KFpPO2YbLh+rSJ3KaMWlVSZ4L0YKm2mNBuEesoqkn55mG4l56A1Rd+pDFoEhilbLZCopvTwqyPTLZzyF1cJt0DpeaV6ZNFvfIFSBIZBNcK7qyYfhFUlFcXZfovzaQyjFUZqKgm2wOhqLkBzq22qbrX8XOuXDYr3xy5J3u8en05S1lhclAeqASrpwqkXbBLXw2wzom+GzafOMHjkvCe+K2/D0JNdGgP3oC6gngc878SV+PhvPRNrlwziH66539h2klvLw3QzwvjRJtYvGzSqZzyPuWE3I0xMJ9HxUmAgfeXPM+tWq6ukyB7G3WpJwFL8Ec9MzDcnJhfajNE7SAJNk82AGsdTr/j4/V89EQDw0avvzd4b3xeaDo5XP2s9Xnf28cp3fPyFc8gwujZ6zwVIPyjdQtUdZzY1SPLKPTtk4i81U6Sel6oaJLvmkXoFh1iwlk7bhQ1jwKyS+p+/eiJe/az1udcVRqog0hdZ7iVF732PLYKaSop2gBmVlCHSW+8vgaJt96YpsYVKKo0k1gUa3/Fwe4apLjGn8EaVlJbqBEi92jyLwNB074mdIvnseYlA1svoJudO7x+b8FOtUNl58zNxgXDs0kRgPHl4CnGcuigb7uNAVXrK6WoT4cLpmwRGVmLkeUmJ/FuGbLU2PKULDJ5LSjPESxaU2GpetuVYRYDqSFzFE0HZjCLUA3O2goxKqpGopOh8NhuGYBgGgdKKpI2Pq6Resy1xjqHN4dEG85Ji48I0H2ybFGJRBGIYBNM1VwLPIpQMxzJaxo3eeYGrM4G+FBjNMELVoJKqCpWU2YbB89+Q4ZJuvJVhpBNmqBZgstEy1tauVaQv90hd6rf5RvM5m1bg/JNX5V4X3wV5XnZ3WGdxGFJgMBuGrpKixIgWhuF55vvHcUdaXlQP0ppqZhlGveIrUb+8PVPEsUkl5XsQBZSsRm9towAAJ64awbkbZaW6iu8rgXsyRQSpuVQhBGQZBk9doQdVrUsFxfLhKgaqPp44mHihUdyBjoRh+GhFasGjJrOlmBaPvDiMTatGlO+5nUHG4pRgGIdUgaGrW6ltQM1QS+AqOh3VVIBGcbIBIFuZDg+MYbRCTEy3MFKriDFqyjjMxwgdo2xC0s0FqaSoz3/9is247X0Xi8zC04bAveSas/00qaSAZNPFmeN0M1LuvamtikV4Gs+reUlNahluZwt9KTCmBcNQqTy5meqqGXqQPDp1bFC68AHyxusxCZQzarhewUQjzHhJAVDSC9hsGIC5lgUHz9CqMx0gWei5W22yuNptGHWrDUNT5WleZiaIZHakktJceIGEJfAdLS/EY2IYfIKJee9BuEKajN5cqPLn9Dev3oLXnS3jgSpBslBQs0FCMUSbTa3fgLR9DdWSZHpcJaXv5I5bnggM3/OwanQATxycEn0yMoxKUIJhQLSp942Djv/XN52Fl25ZK77nmx0aC+UYhuqMwQVeRRMQPAkjwaTrF31KvYhaaZLExKvQvKuuK15SIYbqgVg8//7q+3D6+76TsWHoLFW3D/l+ovrk4ybwPbFh1N1q+RwyqRb1OVlhc0lnGPzem6656ntme46RYahj4gjV0MhJvjkT6EuB0UyTD9LNVhiB7xviMORxtNCTpwvlZOFqAT5haQcxVAswOd2SyQcVhhHIOAyLDUP/jwk0ofS+ivMwIUkTz/c9YxwBkLVh0A6OahnLnTrtkoqHgzB6N7MCY9lQVWEYdB7AHECm7w6TPhm8pBQ2kGVG9D9+vylZW0zqSD9RSZna1LOFjg1UMdloKYJWdwY4btkQgERVt2q0jl2HiGGYa4vUq74QYvy6WyxFia72od91CJarqb/4PWjHrdZmw/A9WVVPZxg5MkIBbXxoA6DPL4LnSTYz1Qwx0WhhpC7jMPZPNDA+1VLcSWMYGIfmIEGZj8M4Ox/petQcUPK9efdvZxiql5TOMEwqqSzDsN1Xmw3D5mE1U+hLgZFEesvAPR5IVUmjSjnkhJQ7gjGWhgDgk1BjGOnkGa5VMNFoZdKbA6rR22bDSNrOv9086rxiSNdBcRg8Oy9XSemCkhZvsmHQLl8PECvDMAgDBoYxVAtw3Z9egI0rR5QFyuZiS7DZMPiukM4hj5MpQ/j9JY8XQsX3U4YhHR74jlRvE5CTb2ywgslGiMlGS9i6dIZx8WlrACQG8JWjdaGSonTeOni0vxqHEWeiihX1g2E9ILtG4GfzO9EzJBvN01eN4IQVw5liP2p76rVVmV5eGOLpnhPDsLamggJMKXNvIkCyx3mph2DF93Bwsok4RmrDUM/0yD6ZEl1XQX3554/h/j1HxOcWV0nFWS8pIOtpxzddpn7aXLkD31czBTRVhmE6d9VgwzAJFr2fEYvDiGIYgztnCn0nMFphlMRHsFxSPJCKUilz8N0rTQZbtSuKSSCQAXC4HmCyEZoZhvKe7Sos9JWwccWw8pkmVNIPs0qKduBBqgv2c1RSlJaBXPAo2rxq2TXagvc4eBwGICNa16beQpxJmFQb+vUSMskH41jQbd2GIeJumAD2PHWyBYGHZhQpgoi71ZoSGtKzHRuoYqLRwlQzwtI0dbW+CbnoGatx3Z9egPNOWolVo3UcSDOxUjCYDio4lCyc6j0QDNiwGNNm57oH9uHD370n6Qvb3ChCk+3e6frWLhnAD/74BdigjbU8kNDnY5D8r0yGeUCNP5LXTG34SVqddMdvZBjs3JQqfbhegaetUA/ukYGiyQZA/vbnX78dP7xHph2iYD1KOmljGByK0dt0vPaVVEmpMTNTrUhh26ZzVw3Zp20CQ4/DoMy/wOwavvtOYMgFW9I5HkiVFGtRbyBPJEYL13A90SlTDWE+CfmrUEnVK5iYbmWSDwLqwqjvejn4zvM7f3ge/uJlz1B+j2I1xkBfZMmG4ftS/UAG4uTeqItaQMVryCc/ZRgiejftjojDaEcl1ZJeUlxIcoah1yzRYWIYXnpdXHVDbCaOEwMxqaTUzL2as0K6mxebBd9TvGrUhIbJKz3bscEqDkwk44Jqt+tjCoAQkhTYmZzXXu7WFIdB5Wzp2vkrxdrEcYzX/fP1wnVVbG4CVTiZvKSkkbosJ2C7Zi+7aQksAuNLl52LC09drXxHcyTwZG2QwDPbMOirejXA/onEpsLjMAgP75MCg9fDMCEMpUrKlK0WyDp6FMVh6G1IlZRkGFGU5AcbUGwY2f6Z7JQ26safcxjHSv2R2VRL9Z3AoInAy5Vyul3XYhe+d+dTgr4luz+pjhmpVzJGb7GDSl9p5zRcSxhGK0pqO/DBxAedolfX7q4eEKgPyChWjWh6/W5yJa74Cc0X0dnp5eoqqcBT1SMUZJcJ3CMbRhsqKW7DqCoCgzEMy32R12uyYUDZFQLSXZjbovT+6rmOAt9P1D3sPzEzipqquQmV1ICs/LbMwjA4uJcQ1X8wgRYVJbgskouebh+gdvgGMmYsqaLt1vmGiISsjO2wdj8DmT9KqkxItSfa0drbvG4J/uaS05XvpJHZFzapIDB7SdF11Cs+9qUu3EkuKfW4h/bqDCNHYMSkkkruoUlQ6eOyyIaht8HVd6Qq1LNF29rim155nPla+FpAee1obXIMIwe8dgAZJTnDSEqEJsfsHp/CW/9tB75x2y4AiZTnHkJJSUhVJaUzDFJJDNUShtEM44z3BF8YdSMkB2cl3AZD0L2kTLmSKPYi8DyxuJp0/XSNpsXcbsMwDwdaNAFZ24NsNtOhWv1wQHGrlf0vr5LypEpKM1BLpgil/wDdD/V8rShiC52ntGEyejcZw5DXXkt/swsM8rgDsvecgxiGnhpEqkPV6yL7Gb9P062IHa+OEUUl1VI3QCb1ig08sJP+T7LdZJjn5+dQ1DWMYdiM3kAyxvenu+cRgw1jP9tZ60ZvHVGUpgaJzJHeQL4NwySMsloDOYdI1uj1YACbW21201jGhrF3fBpxDJGBYjY9pfpXYASeYA6KwAh8UVR9Kg3IIQ8mPsGkwCCVlErhaVKTrWO4Hgi32qr2kKslVVJ67IY+wSiZIJBVN9B/aCc9MlDBUJrNUxq9swKD7Cu1QGZRzdbDUAWHjmUsNTM5GNAOvKmlcVZTg+TvsKI4W+eAbBGhwegdxeoiyPtL0bYE4VYrGEbyKupw57jVcpvYUiEw7JNSt6HZ7iMJMTW4TH72tMVYz4QMJIuRqPqo2dv0OAzdi6wshMBgjEXeR7NKCsiqcOTu2089lRIbnUnVQ9/UK4FITslzSZnAXa9NIJtJnkoqUMaQuukyqXpsKinOMChGSWEYhnNXfD8jSGzPiX+/Oy0utSZNHTKbKqm+q+nNVVK0aPEJy9Np0I3kBXtI/eJ5Xppu2swwqI0l6YIxVKsk7myNVmYnXreqpMy7keR9VmCoDCO78NQqPsYGq1gyWMWfvvhUTDVD/PZnbpAqFV0lxVRwPIuqnhqkwnZJJnAGNZJOYhLWehrnuiU1iGmyApSuXN3B0q4wTP9CmwTOQvQ2iXURhFutZlA2uupqaSU4Y1g+nIytfIYhxx+5O1P6iUvPOR6//sx1or9hqC5yLZZRV/Sf2TAA4NH90jNoqhmJhSlRT6oMg9sw9MWwLBSVlE9qMfXem9rLxB2JzZnMyhv4vpKuRfYvVUlVpVv8sMGGoaBIJRXFLAjUEt/A5uRwraIknDTt3O1C0RPHyxLJ3CnDpJLKeknZLpc/Z6rhvmZs0NrPmULfCQyemkOqEORlcJWUVDUwgUE02fMwMlAVqb9tJRkFw6jJVOG6jtpUA9nUlh7sp2suuMAweUnVKwHecf4m/Pa5TxO7C66S0he1iu8prEJMYM24arIJKO1wW4HnYaQmdfy60Zsb+vh9sgkMumaefJB2hZ6wzajPMzAIjER9wvvsK/YBulYaM42WSSWVfDdqZBh2gaG7dQNIa0CE2HrcUpy1Ybn4jaugACheUzYbxkPM0DvRaOFoMxSCieu2FYERRhmX27KoM4ah/y+PYfDzBb4MxKv4PjPCA1Gcr5IijA1Uc/tdaPROVWBR6jhgGt58Tg7VAmsQn7gurQ2hkgpkHAYxjGK3Wj8jSKxeUuzEkmEkKqnZjPbuO4EhGEagDixCNfAYw1B3jr4n9fSBn3hhkP+8rcLW0lRgUH3dQ0eb2YA6S2WtPJWUyeAVxTFqrM6GKXBvdKCqLGh5qUG4Pr0aSLbR0hZeabfxheun0o6y8KRR71xgVFRBSNC9pH5z23FYPlLDP/3wAeWaAdX12U8nOU3dplBJpX1gQpXfBzUOIynfSlciVY3ZlCYRY2iB7ykZeKVbbTmVlOJA0FRrRYg4jFgXGOruXdgw0ufFa4685pPXYf9Eg7m+yn5wlqUzDJvAJtQrfqaQExdAumHeGK3N+vK28zbi2U8/RlzPtBAYvjG4RBq95f0aS2tn2BCj2OgtAvcsbrV8555E+HOVlIFh2Izenpw3JoZhzSWVsWGYr4X3c08amb8m9c7L28z0Gv0nMIhhsMVoiWL0DnAo1YHqaSAC3xP2B91LqphhMIFhsC0AiXeNsqvQGISesFAXCEUMwxT85nn2OIzAl3mNOMOgRVNGykuhUq/4aGmpRHQ9+XA9kAwjjJRgRb6Q6Gzrb169BQBw5vHL8O3bd+GrNz8unhGPlxCsKb0coZLSjN6qDUOdlLTj0zcCeYF7zShKBQZTSRHDyNETjxgYBm1oVIcIXwgI30tTWfB8V8LonbwSc7n1sYOiDTL6SnWPuijRTlQX5EUEY6gWiEVdCdwTAkPtm6k5vrE4ec0onndiUlmTmFXSJhCDj6fUgyn9LLMR+KhXsjtwDlI16SC19Bnrl2D/RFMEgea5yVbSucLr1ZsW4jyVlM4wiuph8NTo8jgLw9BUUrXAF+x3NhlG/xm9KeUBZxia0XtaYxhUUMnjKinfw0i9KnbK0oah3pIlgmFwlZR6DH1eoSVhs+t0PUWFQpOEB+5xdZK4NoPASFxQk/d5brXchkG7ZU9jGKvHBkT2VSAJTnv3C09WFgLPg+JdpqukOBR3Y9bGRc9YjWesHUuuWUsq5zOVlEw+mKgU9F2uvoPOuNVGMc770A9Eu0lb2ZQmJAtaYZKeXGUYNeWeFV2n7qKssyxiGLSxaLG4DF1VeGp6j350rwxGI0yl/c/YMITqrT2jNxeS3JOQ2uAMMHnNtuEr48R87sD3NcFONsXkM6mxRgcqGScAHXFsjnJ++soRfPt/Pg/vedEpqQqMstVm26BqmjHSrNNss2RiGHYvKZ8JjKyXlC0/lC6AbAJStWFMY8VITZw7zyGj1+g7gWHK88/zN9WZDYO8Xug/3AicCIwARxotxbc9o5Ia0hjGZNNojAayhd6zKim5k+fnEgIjbp9h+BaG4XnJbojbMF6YprOghUi3BfzFy56Bz7zxLNHGP7zumXj7+ZvEoPa8ZECPDKgqqaqhX4C2u9aEHy0akcYwKHAvirI1kmlhteWS4ve7KhY69Xy26PHkt6QAEk+UuGzYnBrEhgq734C6aJCXVBjF4lkqKil2nwHglDWjaTLNOJOZNjSMV0qrQu2qC3W+wODXzNPfyzgMtZ0iLyl+Oj5fktgR+VtVXLOqkuKbQJuws6mkKr6HU9eOCbWvLKCUbefpq5II+DCKUQ18pTaGyTyS7yVFKqnO4zBscn3N2IB4LpONEMeM1MXmxDGMHDQ02gyoA1X1klJVUr4HRSU1NlhFHAOHp1pWGwbZC2jXeXgq6yVFrqvERsSEywwudTGhyfi0Y5JB+/IzjtW8pLI2DB02o7ceqV71fbx0y1rc/dcvwslrRsX94McOVAPFgCu8kTTd+nCtonhJ1S0Mgwu4DNtK7wU9ozjmacvVLK7JtWU9njIqKb7b1gSU7i5tsmE0oxjVQGUYFIdRFlWh3lM90qgPYWrkFgIjlG61+n0eqlVwQjo2th631Hg+nWXxMVOxLOAmDBnKgSqbFk3w2hY28V/OMHz1PR8LtNmgb+pMvUuwnYuzTg79fHoBJY5NK0fF+5rGMEzQ+8IZfEu41ZpsGNm2TPUwbM/pHReciK/+3nPE56FaIMZ4nrq01+hbgWGrJFZjqTD0gKzES0ruCGgxODDRyKQGIdAgG65nKbs4Jw3yVGB88w+ei7/U0n4kbat6bRrYq8fquPuvX4S3Pm+j0EmbvaRMNgxz8sFAExi0QJt2PTZXYN17in4aqVdErQhdV86Rl0uKzn33rnFMNUMl5Qc3VBImplvZADfeVzbxhmuBEiTJzyeM3lpRJiBlGL6PoWpF9L9MtlcOPaZFvwctoZJKvm+GsVCJ6eoe3wdOWZssaFaBobEI/TOhKDUIr8tN45Q7EpQxevP+8N91hsF/q+oqKW0u5Z3LFunNp6fIfGxxq924UubYqgSekjLGBHtwomdQSRUUUPLL55KqVXwlX1c18MUYd15SORBG75xdLe20pQ1DutXyxHvL07Kj+ycbSgZQE4ZrfEKZFz/aFZ20ehQnrR6FDn3Xz72TaCGnwV4xBO6Zrtn3pH5ZybQpPG08+381gcD/R23z66MJl9QGMcdhcCiRrhZB/PpP34Dnn7QSpx07ppwvjhNv/eXDNeyfaODAZEOoBXXbC+/jR39zK7YetxTX3L3beD5SSSl1wtO3rTBGJfCEemaoFsDzPLzk9DX49a3rjNeoQ7/ffNEIfB9xnDwniuRvMlUbyTiR6M/zsHndEnz3jqdKMQxu9ObtJb/l91thGGwRFJHeok1VqOnwxDM09zHwPGVB1ws0kVeZynTN57IF7nGWRQu5za2WbwQD38tkS9ChMwKed0uv5lhX4jCybdUMsVh5tibFC5B5WDkvqRzwPP/XvOv5mR1BreJjqhnhJR/7MU5ftwSAGqUqd0/SAyaPYRCG6twTRl0gKfiP74pMoNxPVFhI7PCVeAXJMPS+mOIkSEcLqLtmnWGY8htJlZSf+R/ZK/h3ARcYljgMDl4PI8Pc2MS49t49OHXtmJJUkIofrRqtY/9EA/snGmLnb6p9Te8pSC5rM0lejUZvFrhXDXyxeJKA+sdLn2W8PhN0O5XCMJgHk+97Qn2qq0PlouvhTc85AeeduFIYZ7PnUxlFRVuc+W954EZvrvKj91JtlhzjWbLkmWwc2XgZdeEDsgxjtF5sw4jipLzscC0QGZkBXVDKtPZFhv8qc5ixwaZmNjEMzuZtQYNlbRj6uSu+zNzgGEYOSO1Sq/g4bvlQ5neqTXHnrsO4c9dh5TffU/WzZKTep/i1WwRGNRAF2/XFlyqzcX98G3iBHbGoa5SdXvXkg6Zssr7HUluwgUMLV00sYNn/mryNdLsF/44OGx2ooBnGmG6FuSopXnEvkzNH+6zaMKTAOGa4DuAIDkw0sXaJuhPni6WeBlu/V3Q+KVzlAiNUUlGy8yf1jKlKoA0Ux6CzSFMg53QrEh5snGHogjDwE7azed0SjLPqf8p18YXZ86wJHwtVUgYbhhLpravNLJo6Y1Cl4hXlGdVVPPmg3h/bQr/3yDQmGiFOO3ZMlBLWz+17SFOD2O/Bly47F7vHp/GNXzyhuNXmXZ+8Hm7DiPGdO57E+6+8S7kW6oeOdnJJ6W3UKvLZuEjvHNBEt6mkbN/TjlmopJjAODDRwMrRxCWWHsJXf+85mdoWo/VKYvTWZsvhNFp8NKdIjeifLye1XLCzqhsTwzCxBFt6cz34y5RYUKh2DBHZqi1DbZPUc4ePJjXOrQLD4lbL2yRMtyIlcC2Kk10tPZcDkw1DahB7+zZG0xReUnKS8XoYlSDJzzRQ9RU1TREojkFEemvxGLxPFFRHWQm4h1hyfVBeAXPFQv06PV96xjXD2LgRyOu/3qYpvbn05DK3p3t6AQZPLvabPl95ihD5nbnPFNC4YcWwJjAsKimLkKNa8FfdvqsEw1A/8wJKURTj7Z+/OfMbYPGSMuaSsp9bTX0jN56zma22/wSGwa2Ww2akFJlZmc5xqBagVvGxf7IhEuzR4D7z+GWZNsYGqzg81cos3K86cz2+ctNO/MqmFYX9NzIMQ1ZXSmEOAB9+zRmYaiWudDpGBxIhBqgJ9cROt0ILV3YkSpWUl/nOpM6gr0jvezANkMyzJ+n9kedRPz+w5wgTBDLt9zEjUqjrqpu81Bc2N16T0Zvk7FOHp0Rm3qFapU2BUcGByaasl0I2DEO0byNMhGPioGGvuKd4EwWyYt/mdWP4tS3HKscC8n7UgiQtico+8vuv7OgVhpF8F2ssyNacbvfi1wWoai66LkAKClOclU3YUapzvRAZv1bykiqjkiIbUx4ykd4VqRFoRTFOXTuGXz5+SLmm5H+GtnyTl5S9jwozCzjDcF5SVpgq3nHY9OnCCMx28J7nYflQDfuPNKyBexyycLx6zLOffgwe/uBLjSoyHVVWL8GWE4le6bh1ywZx6TlPM7a3anQAe9KazFOtKKNa4wVedMjz892cp6juAK6GSF7JW4Oijm1COq/6oL5zvefJcam792WG0cFqgJF6shjrta9VlYt2bVr79Lsp+WAcx2iGEe7eNY7Nqd1rsBooev0ivPOikwBACDihkjLEojRSJkIMQ8+RRT23ecm98Tkn4G3Pf3rSpmHskKDWc4DlYVBjMLSw09goE4dh+133wlMFocqqpME4P0oaSIop+Z6sr07gaz55SdkivTn0LNQmZLykmM0xjCQj1mFlZG3YMPi5q8yG4eph5EDkuymxq+WgB8ELKAGJF86ByYY1cI+DkhyWKTRkA6eSvrao8/NzG0Zen1aN1bHnyDR2H57C/okGTlw9ovxHGL0tLrl0Lo7AV9UGup6ZUmFQGuo8F2e9DXEObWbsHp8WCyV5SVF66mXD1UQlpenRTYslwVZJzZh8MI5x71PjaISREBgnrBjGhmPKlzW95Fnr8fAHXyqEjHCrNTzbRqp+qwYemi2W3py8pAxGfUCqpYYN9gZ+PE9PTiiyYej3i9y6daN3kZeUycahMwzeFRHpnX4mG4Kq/zefbKoZ4dilgwqLA1QjsO+lda/jYqFZZl7bcklRUCalzCn6n/hemzp5z0lR5VXk+uDSm+egGSZeObYba1u89JgC+rx8uIZ9Ew2EYb5bLSAZhk0FUwa0s+TnMjGMSuBlFmoTVo3W0QxjUcv4Wccvw+2PH2bGV5VZcZh26vS9KTaDvhoWDCOx3diEtx5AZftN/45cFON0ki8bSlxrefoQvd/6PdKvidR2RN8b7HmHUYzbUzXC5mOTKPh/fdNZbZU11VGt+EmFRG2xpHOP+BVmwzAbvfV7NGAwCJvGjiiQZVA12mDcNPgyclwGTSav1jiMAoYRWBiGrpJS82DJDYK+mz7ewOoVgUFqmygqVMvpTiYm8MDFOE6Mz3SeMIoxMd3C809aie2/8yztf/ntFR1H5yDw8geugFIOkpKg9rtqXbyESooW6+T7ZcM1HJhoCF/8fIZBKqkuFhJmw5CV7rKTK2A2jLzTkbvld+98CoHvYcv6pcl/dIZh9JKC0g/eB5OXC01cUknZGMbZJyzPXE8RwwDUhZIWqIqfCIyDzOht9u5S28p4smlp7GlhqqTnuv3xwxipVwSrqBh85NtB1fcy94UWpIbuJWVJb66fXjAM7uJtuAc1bXzxtm3QnSLI6UIyDLUdmyz1NaEHZO1hfF3Wi3m96VdOwLqlg3jR5jXZ6zJsBk1V+bhQoXO3wjJutcXPm25TTVP1kjA7Mt3C0qGqEn+TXEO+gC06Tv+tyuK0nNE7B0sGqzhhpV1VYGMY9Fwq2o5m+VAV+ydk4F6eMBA2jC4YRr3ii5QBJtWKKflgrkoq1ZlefddTOHXtmFAXZYIELTEcpvb1XaC+kJG+m2qJ6Pf8s286G/smkhTMgechRHay6qk7ePt6WollQ1U8uPdIJudShS1e+o5X3y2Oa1mJCdUgSRq3Z3waxy4dKNRzl0U18DP3hTMM3wdqXuKKaytOpAtV0usPGqL1fXYPqNKhLRmgub/q71QvnMYNGb1t6jLRH+FRx9rSNkS6MT/tIQBg06oR/PRPLlDbJM8pVpaZ/1/vi4lhNMOo8B7wefDKZ67D6889PnMMtUdJTvVcUhPTLUWgE2yn1vuU10fVPVmmDnICIwd/8Ksn4g9+9UTr71aBIXbzqn53bLCK8elWWzaMbtaUD7xyMzOeJ7YCUwqBZJevChYTeEDXmccvVTLiAvlxGHLhNeuv9eOoH7TTPTRpVkkN1gKsrw2l/wUQZgWWiWHIQEG1L8uGazg40cwsrCZvIgJXwZ2yZhTnn7IK//TDBzJBTpUg8aKZboVW19VO8JxNxxh37YB0q/U9D0dYyhPqsicEhyYw0rGtMAzDpkJXefK2bTCPAXlvhdG7wEvKlBpEZxt5Rm8T8hhGEtehfsefsYyGtrvVyr7IA16zbT2e9bTlmWOE80zFB6alSqqSCozxqZaSDFVeg4VhePpx9v7pzEyqpJwNo2NYbRgGLykgmShxDDRbJQRGutDzqlztgg9C3/fwid86E89kLrycYZSxYXCvjOeftDJTXEimqsjZ0RsWc5NKgb4jHToxDFu2Wv7fbByGvT+63n/ZUA3j0y1Mp3UG9OSDpkdG7Q9UfVz1h+fhizc+auxfNfARxTGmmlHbeaPy8Mpnrscrn7le+Y7HYUi32iijarMxP2KmaqLA7KaiHpgERr7EyAj0dNNAbcSWPuowFbdSGHSgs1dyq7X3Tc9kq/dTf/660RtIFtVit1re53xb6KaVI3jQO4J1S4fEdUw1Q0QxjAzDer8yNgx7HxVB60v257LVdgGbDUNPcyHjMpJXSmts2vkSyIZRlNGyHbz49LWi3CrAJhzzs84TYnxX/JxNKzIMoypcLA0Mw+IlxSN8eVt0a8j4ShHutmy1/BxldLUm/bfveSJHF51PCLqcxauiMSvbHaz6SZBgrxmGCYJhpLUqyIYRRbGiViuyYSgCw3DPTAyjyIaRVUn5ivODnkuq2IbBvlP64SvPryjVSPL/5NUk0BObpPrfliIwUoYRtedWaxvS9IxOXD2CHX9+kZi7FV8mAR0xCgxze9lIb3v/bCopVw+jC9i9pJJXW9DcVDOE75m9dwiUvlzXo/YSCsMQ6oZy/x2pVzJGZj3dNodNYOgRvlkhm3gAHUhVUvWchda04zR95u3rqSRGUmZHjEZv0ygwfNqV5qv1qPZ3rxmG+VxyRxh4MpdUGMeZIlWAyUuKBIaaMI+/Aja32oK+aTvqeiWxwVC7IriQFniriiX7TPT8Vh57zkWpRnhbNpWUPpQig0oqYRj2cyTHFrvymq4PUJ+VSWDY4zDKHZf8Jt9zo3fo3Go7R5GX1MlrRrFxxTDbGSTHTzXD3KA9QKb+6CXD0MG9pCqCrueP9Gvf/QJhCNUHNEWimtxqT1+3BM95+jEWo3e2T/y7gaqPvUcSw7ZpgmT/q5/Dfj16SVi67yQw9EUyTyUlxoPlFlZTG8ZUK8wVfL2AngImYRhJfQydVfFXQr2apFtXVCcGG4CpHovJ7ZirMnSGcfmrTseK0bpYfEUhqpx7zn/np9NT5ovn5rPj8xgGM3rrMBm9eVyCEHhxvvYAUNVydpWU2q48ngkMQ4og2/3KMm97//S08HTOvmEYX/nKV3DaaafB933s2LGjV33qCnYvqeTmnrp2DNf88QsEW5AMIzLuejkoe+lk0xyc0wtwhsBz+uThaccMY1Vq/NZzGeUF7l34jNX4wlvPzQgk3zfrv/nEHKwFItI7L4WGrvrTv9fPC6iGwMCXAuPgZHmVlJ78z3YHyYYx3YzEDn6moHvD1dKEhXEcKztsvgPnGKoFGeFsUvmJSO8clVQmE7K2QJ6z8Rg8feVIJr25NGqbr9G0oOr9EM/PYxuT3IUyeTUyDD/f6N2pp5ht72hztOB9M9owLEy7HS8pDkriqAv+mUZXAmPz5s346le/ivPOO69X/ekadi8p8/G0sEy3wkKBQcbe2VBJBb6HDSuGsXSoKvJclYFcQFRVVDuxIxmjN23SWRNDtYoYqPkMQ7apfi8/62ojXR1Gqa6FSko3ehuuLYbMasz/o6MSeAij5PnrEcO9hu55VgvSehiRqpKiw/Quv+lXTsD/ftXpyne6zQowB+7pbelM3BblTO1nPNQKjLi5cRiefG5F7fHfagaBXjG51cZmgVFs9PbZ+6LrU7/fcIwMIMyzYWTYfMbondtFARlP5s1qxb2uVFKnnnpqr/rRMxTlktJBxtEyDGPVWOKR9GIWVNRr8BiDs09Yjlv/8uK2/i9y29CuzJBmu0wfTIF7fMJxA/FQ3b4zN+XL0tui1OCmCOeAqaQOagJDeghlz0teb3pFNx0V30crDGedYeg2DGUnDPM9MxXmMrEskw0jk9IidQsVnwMf//qmszLZWoVbbUYlVX5B1dkG/ZWiyYFcgiHaMqukvMyfQ6XyJIzvTTAlAdVhG88nrxkT7/NsGEUCoizD4Krm0KU37xxF2Wp1cKN30S58bKCK2953MUbaSErXLkw7xnbAbSBAfvJBaxsZo3d2lz5YpfY9o7sjoYzRW/fC0oMGSWAc1m0YObtTckHdtGpEaVtHLfAxMd1KbRizxzC4l1Qcq0LyrA3L8KpnrsMJK4pzWRmN3gZWqQ8nU6Der2xaZW2fGFvRoiufZfb50nv+/HzxzO1tkgA1zW3y5uIweUnp7/P6nncsz+TLwVOUmG0YKivW25PH5XZRgCc9nFeBexdeeCGefPLJzPcf+MAH8IpXvKL0ibZv347t27cDAPbs2dNGF9tDUeCeDm70LhPlW6ZIUjewDayyyHhJ5cRh5LVRpJKSZUzzh5DNNTjPC0tXYYxoRm+xQw3MkxcATlw9iu2//Sw898Qk5bzNqEqFb5phPAsMQ/XCqVX89Nwqu101NoCP/ObWkm1mhWbdoJLSd7a615wtewG1L3NJ5TMMU9yN/qw5kzQF+umgn0wbE5OXlClwj86Xh2oJlRRNI/1nfrxpQ+lrY1Z+n//Z2ldmo5xXyQevvvrqnpzosssuw2WXXQYA2LZtW0/aNKEoNYgO0t1ONkKrOms2IVxpO+yLvkDr8Qhl2zBNNJVhJJM3z34B2Cm82aiuno+Oq1eSuiW6l1Re4B4AXHyaVB1aVVKBj8m0PvnsMgz5TKaaxUFlNpgYnHAdzzH46vPEtqHwNYFRvqa3XWDQT4l6KhUY5uaUtkxzuxp4mc0At2Go4yznJIZ+5h2T54gybFDR5rmwm44rgsIwnEqqc9gWxiKV1NFm2NYufKZA6p9uGYbuVtpOSvbAVwu7iF0lu7WmIDITrCop1r7ICSV09+z/6XGj9YoUGNout8wks2Y3DjzhxDDQhp2nE+g5lYgJTLXC0qqITJuGBZwW1hhm1Qyg7qaTdvJtf3pGXbuXVPZ3K8NgzhW5ab1zAvd4ICCBMwzPMI5tMCUB1SGCKw2/f+V3n43v3fmUpbqlud1sevPcLgqQ12PCMPrES+prX/sa1q9fj+uuuw4vfelL8cIXvrBX/eoYJjc7IE9gJLdgYjpsyzA8U7DtyMtCt4Ecv3wIm9eN4dS1Y3l/U+B7akZRk2F10JA51dxW2kaOl5Tuu28K+hodqDCVlM4wSggMy/cV3xdxNbMV6Q0kfRYees1iDz0bTI4CNI6VrK3a0Ob11oESDEO0k3/PZYyFefGtMIGRMNnk+7xHmB+H4WWEzWXP2yjPncOydHChaRMueQzjrA3L8f+9xOwIRJuhTK35TlVSQoPgKaWZZxpdMYxXvvKVeOUrX9mrvvQEnufhbec9Hdfeuwd37TosvrdNSHqARxstVMfM1bJmE7q6pdP/0+uSoSq++fvPa6uNJYNVYwEbPuHIhlGkkrKmBsmzYRh+Gx2oSj06XaOwYRRfk10lJY2GM62SqmiLOi3s3aikhAqTe0mlq3Bk2GlTGu72bRhqttqiCnImBgmQB17ynrONvKv3cgRGxfeVZ/uzP7kAxy4dFJ9NbNWGMgwjMAjoMvAtgjErMMq1V2GODU4l1SX+5MWnYLoVKgLD9iBoZzXZnCcMw6LCaff/3dTs+NCrt2TqBwPqPRwsqZKiQK1M+vEclZdJ/80Fk85ayiy2OmshIcHtVjNt9ObPdHSgymwYnTMMo5eUgWFwO0CrEWYEho1h8Ejp5HPyarvlwg7l8e/Ue89zZhW56fK27KlBVOam/pePJespAKj3wBqH0QarNfXDFHHP0U7gHgDhaTdbWJACA8jaMqxeUulxcdxdJb1eQeaS6s7o3U1dh1UsZTpgHuwDZY3emgFdtJmj8jIJjNEBLjDURbJINw2oO1haNJcOVRVWMfMMQ7Y/Wq+IBfBos3MbRl7ywUgJYJO/mRw8ytr+ArbYG/sj1ItcSKhslT8/aqecSior0KtaenN92rQT6a2nbjH3hY7Nbcr6P70LWcZRrj1etmA2Bcbcr5AzBH3HZI30ZU9oppPPlYHNSFwWeurvXkAu6PI74VabE7SX/McsMExul7IAUHbHOMrcmfVddZlNGT+GFtQ1YwOqEJxFhjEyUEGNJ77s2GYlvWUINI5N7qVyoVHPZxsvtriBdgL3+CJe8XnsBTd6G5tT2jIJ9Ipm9M6zlRXNKZ5vzRq41yHDsBWe0vvbPsOYZ3EY/YpMQZgCo7f+fq7As9V2AtLrdypwjG2abBgljd66xxX/nqAvMmuXSB00TQwjw+jQS4oW1DVLVIExmzaMEcYwpppRoWrPBqHG4yqpICswpDA2u1nbbBjU7jlp2d3CAkqGBbWixTeoDCM7tnTk2TB0o3eeraxoSpQK3GtjzJn+53nAO87fhOefvDLTvzJ9JIjSsGlNldnCghUY+g4qz+hp+89cQDfotouyCQvbgcn3XgiMosA9C8PIM3rzqFnJMJjAYIuk75UTjvwIUm2sGRvQduYzzDDYMx0ZqIhnNd0KletrB3oqGMBmw0h/SwXDS05fi2ccO4aPXn0fgPyxf/U7zxNCXHq0FSyoiqFb9sHLqKTyBZDSd5vRmx+rjYW89CiZtgJVsJnQqY1RqKQA/PELT7b2qd3AvVrgd1XQrV3M/ZZ6hqDvmGwPgutyTcnNZhvLhpJEg8uHyicc5BCTsYfCLxATnqlvaiXdan3z5NLrdgNy0eBt5tkw6PcyNF4tGZq8rhqtKzaYGXerZX0YrVfE2DvaCEurIjJtGhawioFhCJVUuuhuXjeGP7zwJPmfHJvZplWj4pkU7bBNmwuxG6YUM2w8mcZWpk2P1C/FRu9MIFw7XlK+vR29jXYJvO2+6e2UFxgyvsoZvXsAfXBZ3WrnGcM4ec0ornnX87Fx5UhH/+9WpWWCabDLSO9iL6nAsBjpvvl6+/pvI3Vmw9AERplL5YdQPMeK0bqqkpphG5Zuw6CAwalW1LYRVW9TNTIn7/NUUplAvpJjv8hLSqbOyC6+uhecEumdc/q8dDnVQHWr1ec53wQUjRNaCzyv2Ojdrs2J+qhfZ9ZLqlx7qpeUs2F0jdIqKbaYzQejN4COhQUgF9Be2mNkISf5nXSrLY7DMHUlMOzmePtDtQCTjdCikuKLYzb5nAmDzEZAlQJXjtQVu8VMMwy+4A3XKsJ/Xk9v3g6E145Bj5+nkurUnbNoh20yihPbFQyDLbrl4jDUc3Poec/0QwbZMy00ehtSqpjOV3SMCTaGkalFU5ZhiOSijmH0BBmjt2WwVBWGMT8ERjfwPA//eOmzsPW4pT1r06Q2oAJUywpUZ77nGdUdqkoq2/66pYO4b/cR4RqqqqSgvC+z21s6lE0auUJTSc1mLqnRgYqie+5UJeV5SaEtkzeQqZBQUX2QIpgYDYe0YbD/MJsFPybweKR3sUrKlN5Gd6vVF3IuMIrdarPqvUxf6BratmGY71tWJVWuPSU1iHOr7R5lKrwlxzEbxgIQGADwos1rRAnaXsC0q9y8bgyfeeM2POfpx+T+N/DVNCOiTZPRmx134TNWA5BCnAsMXV9fZpItZ0WozkiF6cYVwziWeWTNtFstXyyGmQ0D6M6rzfc94/1UbBi6wOjUC08IDMvvhp20vhCbvaTs5xSMxKiy9JXkg/p95Myy2K3WzL442vHM4+BGb1N7hNJutUL17FRSPUEncRimMqYOZldJz/NwwSmrC/9rYxiE97zoZFz/4P5M+++++GS86LQ1OHlNUjCIx2Hoi1GZycuZ0B/+6ok48/hlWDJUFSkvgNm1YVUDXxlv3Xi1VXxP8ZIy2zDkebs5X3mjd7HAUFVSxQzDtJBXAnVDoi+47dgwhEt6CbbT7t5SJi3U2uvQhiFLEDuVVE+QifS2PIjqAmQYvUaRK2UeAt9+7x/+4EsBAD9/6MZM+77vCSYA5HhJeeY4Dx184agGPpakKipPE4KzCT7eOgzsB5B1XZbeSOx+pte2eqyOesUXAZf/+5Wn45q7d5c/l8ELSv0dmd91O4snPnP2YD+nlyMwqoGPOGeDPagIjCKGkZ4nZ+NgUp+WgY0ldepWGygMwwmMrlHWhkFG4ihur4zpYkJROog8PO/EldhQUDlOqDlyjhkxuNrS+3bX+W4W515CERhdCKtAcy09Zc0o3vb8jXj9OU+Tx6T37GVbjsU7zj9RFAL7rXOOx2+dc3zpc0nVimU+ecnz0AUxTxHDsxnomYrzzmnaGFR8T2FSOjhrLLrHZQzanaukzGO8UxsGIbFhOJVU1yirkgJktKRjGGa0k0Zcx+vPfVrhMWV824drFXhekvOLH1dWJcWhLwgnrR7BvU8daauNXoAb2buxYehGb9/38KcvVtNsS5WU15V9q8jmwAPzOALfy2TW5alBysRh2BhGFNt32CprtR4GQNoz8+w7nQfumVl6JjK9zbFcDTw0HMPoHpnAvZwHXPU9NDA/4jDmI6RnyMy0L7Ph5qkCPIzUKjjSaGViDspOXhI4ukPEFW9/7qxGyxJ6xTB8zys0YstFt7uHWJRLyRYXw1WHgjFwYZ+rklLPzVEJPDRKPrqygXt5MUydZqv1LPErugBpVy3aVwWU5jOyDMN+bEUYkOY+0ns+osiVsluUyScEJHaMTPK2NlRSpIbR2xisBYoX1WzB9z0xTrtlGEULmJ5/q1MUekn55sh7Lth51TrhVtup0btkHA5vx4ZSG5cO1bM2JqXvUdtttxIkKrloloTGgmUYmUjvnEEgC6o7hmFCpwnXyqLIkEoYHahi75FG5r9l+zU2mFTt6zR1fK/AA0TrlQDNsNWRfUi0UQ0Kg05F7qEu7XRFY+FFm9cY84v53IbBbBJeiQU4j2EkNb3LoRdutSccM4yzNixrq4IlYDfuZ5MPts8wAKAZRaj7M7/hXbACQ6eVebtXWkCc0duMsgt6x+2XFEgjA5XMzrYSeKXToCQM4yjCPLeaGcY173o+xgali3C94uPIdHfC+G9fvQUrRvKrRZ530kr87SVbcNLqzrMIAMUM48zjl+HM45dlvucMQ3r4lI3DsDOMMhuGWiWxURYNEz9lq3njaclQFV/53efkN2Rq2+I+XJRbqgi0EWiFMQrSuvUEC1ZgZHNJ5Ryb1l9wRm8zOvUMKYuyNpLRgUpm0XjXxScXZswlUHT64TSX1FxAT/tCzKAbldRZG5YXHjNQDfAbZx3X8TkIZdWHmf/5UrC3r5JKXk1aAj2XlAn1VGCU6XPV97sqPlYEvQtFqUKKIBjGLBm+F+wKyROJAfmLnczLsmBvR1dop1BRJxALSYFyYXSgmlk0zj95Fc4+oXjBBIDfTBfMEwrcfGcT9TROYCYXqV6iTNyECRXmQssFQBmbQL4No9iGRWnry9hvbPVbuoXVhqEtOe2euiIEhrNhdAURCRn4mG5FuROy0iP97kJFp1XGyqKMLz6QGr27WFhfsXUdfm3LsfNqca53mdtpthGUFO46koh/dRz53ECec/15gXtlVFLiHpeY3pWgvNddO/Atl9lu8sHXn3s8Htt/VHymqo2zxTAWrsBgdonpAv1lxTGMXJiid3vafkmV12+f+zSctSGrH28H80lYAEwlNb+6ZYWp3kUZ8LoVPL1GmaBQyWqyB3leGYZRXii346bdDuxeUu3ZMN7/66crn2ntmq3gvQUrMIg11Cs+xlHOS8oxDDPmi5fUqWvH2vZOme8gdcl8E2Q2dGrPCjwZuEc7/UQllbwv41ZrylZbpi/tZOitBDNjw7ARKboXFCPUtpdUem2zFby3YFdIoWYKqJZDnkrKV451UKGndJix9vtELdNLULR3v1x7IFf4tv9HQYNK8sES6kguYEwoyzDKMIeqX97rrh0QE7KpoGSerTYFRtrXVuQERlegnRuVEs0bLI5h5KPTYKWymOnAwPkMUdCoT669U7aZGJPVNsoavfNsGECxPYXmdZkeB8HMGL0BSoWS/Q7o3HVdeEm1ZkcltWBXyCWDVXz4NWfg17euA5D/IOimu8A9M2bc6D3DAmk+QzCMPrn4The2DccM42nHDCv/5WlE8gsoyeNNc7SoL7R5LJNCI3GrLTysI/ieoR6GrwuM9m4saVKas8QwFqwNAwAuedZ6fOnGRwHkT0iioI5hmNFN8sEyMFX0WywQLp99MvRs3j5F+ORvP0u8J/WMXvjJfk6pwvr5n12IqWaEcy//vtJeHkgl1WgVL6qBn1+/pRuYEjNyYcg/l0VNMAwnMHqCMsnCnA0jH2XdXjuFqab3YkG/udV2mnxPx3HLhnD88qFSKi5h9PY9DA1kS+0WMoyUxU2XWFRnyugNpAzDopLSgxrLgtau2UpAuOAFRsD0pTbIXFJOYJgw05HeZAxdjDaMfhMYnRpndfzoPecDAH5y3960PfuxRcG3xQwjYXGNsDit7arROo4ZmZlElL6XTcyo2zDava20ds2Wl9SCFxgyA2XOMS6XVC469b0vi5mO85jPoEjvmfD9nwmUKXbVDspEjudFegPFi+xQ6vhSJhr6E5eeOWPj0GT0zpSt7TA1iIvD6BG4/tOGimMYuZitSO9+2WX3EpJhzHFHSqLXMTm04+40l1SZvrznhacg8D28/IxjC/szMoMZ/Dwve53U9UqHm7LZziW14AVGmcWu5rykctErNURR+/2yaPYSJDBmsQZOV1g9VscxwzU87ZihnrRXRhXjMyO5+ff8cywZquKvXrG50y72DEmhK/U74SUVdDbHKi41SG9RRGeB5KbXAn9R6tDLYMbTmy/iOAzh8jmLZTa7wTEjddz0Fxf1rL0ybrWelx8b0W5eq7lCYvS22DA6ZG41l3ywtyizgxmqVTBUd9X2bJhpldRijvQmu1mzXyhGj+GXsIm8ePMajA7Yl6p+GTa+lxVt3QbuVUQ9jD5gGO9+97vxjW98A7VaDU9/+tPxL//yL1i6dGmPutYbkFkib4fypl/ZgAtOWTVLPeo/kDvgTKcG6ZeJ30uQwOgXhtFrlCmgdMZxS3HGcUuV725hLKdfxk1eHEbF78xbrq/qYVx00UW4/fbbcdttt+Gkk07C5Zdf3qt+9QxljHRrlwzi3I3HzFaX+g6e5+HyV56OVz5z3Yy0v5gjvauzrFKYb8jLRJuHZcM1LEvrsPcLMzXFYeh52tp2q/Vnd/x0JTAuvvhiVCoJSTn33HOxc+fOnnSql5jpxHmLBa89+/gZKzw005Hk8xmixOZiVUn1IGizX8aNb2AYMk+WPKYdULXQvmAYHJ/5zGfw4he/2Pr79u3bsW3bNmzbtg179uzp1WkLsZg9cPoF/iI2eks/+sWukur82ffLqMljGDJ4tb02RT2M+RLpfeGFF+LJJ5/MfP+BD3wAr3jFK8T7SqWCSy+91NrOZZddhssuuwwAsG3btk772zZ88UD6ZVgtPixmoU7sarZ2iPMNvQgE7Jd9xrLhGpYNqVHkMsaEPrdrw0iOL5MnqxcoFBhXX3117u+f/exn8c1vfhPf//735+UOcTG7bPYLFrOXlLNhJK/dPPp+mdv/9uazMVBVvTFlLqnOAjg9L6nfMVv1MLrykrrqqqvwN3/zN7j22msxNNSbQJ5eQ/dzdph/mOnUI/MZwi1ylib8fIPnLZ7NwjEj9cx32VxS7d+HauD3h9H7He94B8bHx3HRRRdh69at+N3f/d1e9atnmOmgM4fu0WmmzoWAxc4wep2bqt9A1y9z3rV/JyqB1x+R3vfff3+v+jFjqDgvqXmPXnjK9Cuqs5zaYb6hFyqpfobuVtzJMlUL/P4QGP2AXidLc+g9FrMNg1KDUEbVxQaq57AYnz2Qdcrp5D78n9c9EytHs+qumcCCFxjSbW2OO+JgxWJOb37asWP40xefMmNBkfMdxy4ZwF+94jRc/Iw1c92VOcG6pYO44JRVOPP4pbjm7t0dMa1f2bSi9x2zYMEvo4t599ovWMwFlDzPw9ue/3SsGhuY667MCTzPw+88ewOWDGUr6S0GDFQDfOaNZ+GEFSMA5v86teAFxgkrhvGO8zfheSeunOuuOFgw0xX9HBzmO/rFlrMoVFJ//MKT57obDjnwF7FKysEB6B/34gXPMBzmPxzDcFjs8BzDcHAoh8Wc3tyhN/jk65+Fk1aPzHU3Oka/eHM6geEw53DpWxy6xYs297eXVadp3mcbTiXlMOdw0fgOix2eEBhz248iOIHhMOfoFzru4DBT8Lz+YNlOYDjMORzDcFjs6Jeqk05gOMw5FnPyQQcHwNkwHBxKw3fR+A6LHB76w1PQCQyHOcdirrjn4AA4huHgUBouDsNhsUMavee4IwVwAsNhzrFmyQBesfVYnLVh+Vx3xcFhTuD1CcNwgXsOc45q4ONjr33mXHfDwWHOUK/4yut8xfzunYODg8MiwNbjluIfLz1z3rNsxzAcHBwc5hie5+Elp6+d624UwjEMBwcHB4dScALDwcHBwaEUnMBwcHBwcCgFJzAcHBwcHErBCQwHBwcHh1JwAsPBwcHBoRScwHBwcHBwKAUnMBwcHBwcSsEJDAcHBweHUnACw8HBwcGhFLw4juPZPumKFSuwYcOGjv+/Z88erFy5sncdmmW4/s8d+rnvgOv/XKOf+5/X94cffhh79+4tbGNOBEa32LZtG3bs2DHX3egYrv9zh37uO+D6P9fo5/73ou9OJeXg4ODgUApOYDg4ODg4lEJfCozLLrtsrrvQFVz/5w793HfA9X+u0c/970Xf+9KG4eDg4OAw++hLhuHg4ODgMPvoO4Fx1VVX4eSTT8amTZvwwQ9+cK67U4gNGzbg9NNPx9atW7Ft2zYAwP79+3HRRRfhxBNPxEUXXYQDBw7McS8l3vzmN2PVqlXYvHmz+C6vv5dffjk2bdqEk08+Gd/5znfmossKTP1/3/veh3Xr1mHr1q3YunUrvvWtb4nf5lP/H3vsMZx//vk49dRTcdppp+FjH/sYgP65/7b+98v9n5qawtlnn40zzjgDp512Gt773vcC6J/7b+t/T+9/3EdotVrxxo0b4wceeCCenp6Ot2zZEt9xxx1z3a1cPO1pT4v37NmjfPfud787vvzyy+M4juPLL788fs973jMXXTPi2muvjW+66ab4tNNOE9/Z+nvHHXfEW7ZsiaempuIHH3ww3rhxY9xqteak3wRT/9/73vfGH/rQhzLHzrf+P/HEE/FNN90Ux3EcHz58OD7xxBPjO+64o2/uv63//XL/oyiKx8fH4ziO40ajEZ999tnxdddd1zf339b/Xt7/vmIYN954IzZt2oSNGzeiVqvhta99La644oq57lbbuOKKK/CGN7wBAPCGN7wBX//61+e2QwznnXceli9XC9Hb+nvFFVfgta99Ler1Ok444QRs2rQJN95442x3WYGp/zbMt/6vXbsWZ555JgBgdHQUp556Kh5//PG+uf+2/tsw3/rveR5GRkYAAM1mE81mE57n9c39t/Xfhk7631cC4/HHH8dxxx0nPq9fvz53QM4HeJ6Hiy++GM961rOwfft2AMBTTz2FtWuTgu9r167F7t2757KLhbD1t5+ex8c//nFs2bIFb37zm4VKYT73/+GHH8Ytt9yCc845py/vP+8/0D/3PwxDbN26FatWrcJFF13Ud/ff1H+gd/e/rwRGbHDoypOg8wE//elPcfPNN+Pb3/42PvGJT+BHP/rRXHepZ+iX5/E//sf/wAMPPIBbb70Va9euxbve9S4A87f/R44cwSWXXIKPfvSjGBsbsx7XL/3vp/sfBAFuvfVW7Ny5EzfeeCNuv/1267H90v9e3v++Ehjr16/HY489Jj7v3LkTxx577Bz2qBjUv1WrVuGVr3wlbrzxRqxevRq7du0CAOzatQurVq2ayy4Wwtbffnkeq1evRhAE8H0fb33rWwXtno/9bzabuOSSS3DppZfiVa96FYD+uv+2/vfL/ScsXboUL3jBC3DVVVf11f0n6P3v1f3vK4Fx1lln4b777sNDDz2ERqOBL33pS3j5y18+192yYmJiAuPj4+L9d7/7XWzevBkvf/nL8dnPfhYA8NnPfhaveMUr5rKbhbD19+Uvfzm+9KUvYXp6Gg899BDuu+8+nH322XPZVSNosgPA1772NeFBNd/6H8cx3vKWt+DUU0/FO9/5TvF9v9x/W//75f7v2bMHBw8eBAAcPXoUV199NU455ZS+uf+2/vf0/vfSSj8buPLKK+MTTzwx3rhxY/z+979/rruTiwceeCDesmVLvGXLlvgZz3iG6O/evXvjCy64IN60aVN8wQUXxPv27Zvjnkq89rWvjdesWRNXKpV43bp18ac+9anc/r7//e+PN27cGJ900knxt771rTnseQJT/1//+tfHmzdvjk8//fT4137t1+InnnhCHD+f+v/jH/84BhCffvrp8RlnnBGfccYZ8ZVXXtk399/W/365/7/4xS/irVu3xqeffnp82mmnxf/rf/2vOI7z52s/9L+X999Fejs4ODg4lEJfqaQcHBwcHOYOTmA4ODg4OJSCExgODg4ODqXgBIaDg4ODQyk4geHg4ODgUApOYDg4ODg4lIITGA4ODg4OpeAEhoODg4NDKfz/5h4+Uno3yN4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cfedc6cabfe4646a974156fb484cbb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47a81fd7b1a1464f8296edb899ea6d81",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf664902846747fa9fc84a0933e3363e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "939600ea967c43269765e0223af27e1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37b645137bc04a22b2dae22ffa9c6c09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c177f4708578461aba617ae5110e40a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "036ed98db41545d1b8e3b2a7799c548d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+Hc7evklm7yJS5L5ePYtjZ7yC2LxRV5ir8S6F878eeonFRKWWHkAloHGs8u7fv88mTJjAHB0dmb6+PjMyMmIuLi7su+++Y2/fvi3ynPzxpF5eXuzixYusffv2zNDQkJmZmbFhw4axly9fyh0fHBzM5s2bx1q3bs2srKyYUChkDg4OzN3dnfn7+xf5Hrdu3WLDhw9ntra2TFdXl1lZWbGWLVuyefPmsQefjGV0cHBQGNtZnPr16zMAzNLSkuXk5BR7XE5ODtu+fTvr0KEDMzU1ZUKhkNWtW5e5ubmxzZs3K4yJzcnJYStXrmROTk6y8v3www8sOzu7XONYGWNMIpGw7du3s8aNGzOhUMhsbW3ZrFmzWHJycrFl9fLyYi1btmSGhoasRo0a7Msvv2RPnz5ly5cvZwCYr6+v3PEfP35ko0ePZjY2NozP5zMAbPny5bLX/f39WY8ePZi5uTkzNjZmnTp1YmfPnmW+vr4Kx1aJKh7HymNM+QdYbdq0wcOHD5W9DNEi7yJS8fXQSwgNKmiO0tMXYPJ3n2Hw2MbQ0S3bU4jUpGycO/wCZw++QHpqwRAAIxNdrNzthn4jGqg89pKEhISgiU2hmneNNlX6/oSQCiicn9qUfM/m9/xWBj1jJSr36lk8xnY7JZdUW3e2xYk7wzHMs2mZkyoAmFroY8I3rjjsOwwDRjWU7c9Iy8WCcd5Yt/AWcnOL7kRBCCFcoMRKVOrl03h49jqLuPfSjkk8HjBjSRus9eoFIxNhKWcXz9hUiG9Xt8fm431Q275gpp1DW59gWr/zSEnKLuFsQgipOpRYicpEvU7B1H7nkZIoHfwtEPDw0143eExuqrL3aNamJnadG4COXxRMp/fg3xhMdDuDuPeKnaMIIaSqUWIlKpGWkoOvh15CYpx06jKBgIdfj/RB2251VP5eRiZCLN/ZHZPmF4xzDA1KxLhupxH1uuilsQghpKpQYiVKy8uTYME4b7x5kQRA2vy79sAXaNbGppQzK47P52HMV82x+NfO4AukQ2+iI1IxwY2SKyGEW5RYidI2LvHHbe+CeWPnrmoP1/aK4+UqwxdD6mHV7h6yKRDj3mdiSp9z+BCdXsqZhBBSOSixEqX4XgrHoa1PZNsDRzeU671bFdr1qIOf9/WUJdeYqDRM7XsOCbFFz+xECCGViRIrqbCE2Ewsn3lDtt24ZQ3MWdGOk1hc29fC/3Z0g46O9E86/FUyZg6+iKzMXE7iIYRUX5RYSYUwxrB8pi8SYqWdlfQNdPDz3p7g87mbarBd9zr4YXMXWQzBAXH43tMHEglN4k8IqTqUWEmFnPYKhu+lgonEf9jSBaYW+hxGJNWlrwNmL28r2/7n3BtsWXqXw4gIIdUNJVZSbvEfM/HrYn/Zdrf+DujgVreEM6rWoDGNMMyzYEqyP34NwPnDLziMiBBSnVBiJeW2cYk/0lKk8/aamAmxYK3iItZcm/59a7TvUTCGdsUsX7x4Es9hRISQ6oISKymXR7djcOHIS9n2ovWdoW+gfqsPCgR8/LC5C+ydzQAAohwxvh15BanJORxHRgjRdpRYSZnl5oqxes5N2XbzNjZo76b6mZVUxcBIF8t3doeBoTTxv32Tih+n/AMVLOhEqglPT0/weLwiF1rniqOjo2wxcaKe1K+qQdTWid+ey1as4Qt4+H5TF44jKp29sxm+W9MRP839FwBw42I4ju16irFft6ycN3zyPyAzqvTjuGJoD7RcpfRlHB0dERkZWeRrNWvWxIcPH+T2iUQirFq1CkePHkViYiJatWqFjRs3olWrVgrn+/j4oHfv3rh06RIGDBigdKyapnv37vDz86N/ADUYJVZSJumpIuz++YFse/iUprCxNeIworLr1t8RQQFxOHswBACwYbE/2nargwbNaqj+zTKjACNH1V9XVTIiVHYpMzMzfPvttwr7jY2NFfZ9//332Lx5M9zd3VGnTh0cPnwYPXr0wIsXL2BrWzBLV3p6OqZNm4Zx48apRVJds2YNvv/+e9jZ2XEdisz169e5DoGUghIrKROvTYFITpAuzWZkoosJ37hyG1A5TV3YCk//+4DXIUnIFUmwcII3TtwZAT19ugUqytzcHCtWrCj1OMYYfvvtN0yaNAn79+8HAAwdOhTdu3fH4cOHsWjRItmxixcvRnZ2NrZu3VpZYZeLra2tXOJXB87OzlyHQEpBz1hJqeI/ZuLg1sey7akLW8mmD9QUQj0BlmzqIos79HkiNv1I41urQlxcHDIzM9G2bcH44vzvCzcn//vvv9i9ezd27twJS0vLCr1X/vPH1NRUzJ8/H46OjtDV1ZX7B+DFixfw9PRE3bp1oaenh5o1a2LMmDF4+fKlwvVKesZ6//59eHh4oFatWhAKhahbty5mzJiBmJiYImNLTEzEjz/+iGbNmsHQ0BBmZmZo2bIlvv/+e2RkZCAiIgI8Hg9+fn4AAB6PJ/vq3r27Qhk/lZOTg7Vr16JFixYwNDSEqakpunTpgr/++kvh2Pz38vT0REREBEaNGgUrKyvo6+ujTZs2uHTpUsk/6E+cO3cO48aNQ8OGDWFkZARjY2O0bt0a27Ztg0QiUTg+/+f65s0bbN++HS1atICBgYFcORMTE7FkyRI0adIEBgYGMDMzQ8+ePXHt2jWF66WkpGDDhg1wc3NDnTp1IBQKYW1tjcGDB+PevXvlKosq0L/rpFS/rXmArAzp1ICW1gboN6IBxxFVjEN9c8xc0gbbVtwHABzZ/gQ9BztVytJ21UFOTg6OHDmCqKgoGBkZoUWLFujatSsEAvl/uqytrWFgYIBHjx7J9j18+BAA4ODgAADIysrC1KlT4e7uDnd3d6XiEolEcHNzQ2JiInr37g1TU1M4OTkBAK5evYphw4YhNzcXgwYNQv369REdHY0zZ87g8uXL8PX1LfK576e8vLwwbdo06OnpYfDgwahbty5CQ0Oxb98+XLx4Effu3YO9vb3s+PDwcPTo0QORkZFo3bo1vvrqK0gkErx69QqbN2/GzJkzYW5ujuXLl+PAgQOIjIzE8uXLZeeX1llJJBKhT58+8PPzQ+PGjfH1118jMzMTp06dwsiRI/H48WP88ssvCudFRkaibdu2qFevHsaPH4/ExEScOHECQ4YMwT///IMePXqU6Wf+/fffg8/no127drCzs0NKSgpu3LiBuXPn4sGDBzh8+HCR582dOxe3bt3CgAED0L9/f9nfTmRkJLp3746IiAh06dIFffv2RUZGBi5duoS+ffvit99+w7Rp02TXCQkJwY8//oiuXbtiwIABsLCwQFRUFC5cuIC///4bFzduRN+OHctUFlWgxEpKFB2eir/2Bsm25yxvC4FAcxs6Bo5piPs3o3H/5jsAwNJpN3AucDQMjXQ5jkzzfPjwAePHj5fb5+TkBC8vL3Tr1k22j8fjYfr06di2bRtSUlJgZ2eHw4cPw8TEBGPHjgUALF26FImJidi5c6fScb1//x5NmzaFn58fjIwK+gEkJSVh9OjRMDQ0xL///oumTZvKXgsKCkK7du0wdepUBAQElHj9V69eYcaMGXB0dISfn5/c89cbN26gV69emDt3Ls6ePSvbP27cOERGRuKXX37BkiVL5K4XHx8PY2Nj6OvrY8WKFbh58yYiIyPL1Myeb+PGjfDz80O/fv1w4cIF6OhIP9qXL1+Otm3bYs2aNRg4cCA6fpJcbt68iRUrVsgl8TFjxqBv377YsGFDmRPr5cuXFZqoJRIJJk2ahEOHDmH27Nlo105xHvGAgAAEBgbK/vHJN3HiRERGRuL48eMYNWqUbH9ycjK6d++Ob775BoMHD0bNmjUBAE2aNEFMTAysrKzkrhMdHY22bdti3ubNVZpYNfcTklSJvesfIi9X2pRT28EEnXrbl3KGeuPxePj2pw4wMpEm0ncRqdj6v6pvKtJ0kyZNwvXr1/HhwwdkZGTg2bNnmDFjBiIiItCvXz88efJE7vh169Zh8eLF+O+///DHH3/AxcUF169fh52dHe7fv48tW7Zg69atsLKywsqVK2FrawsdHR20bt0a/v7+xURRvI0bN8olVQA4dOgQkpOTsXLlSrmkCgAuLi6YNm0aAgMDERwcXOK1d+/ejdzcXGzdulWhU5ObmxsGDx6MixcvIi0tDQDw6NEj3LlzB66urli8eLHC9fKbYJWxf/9+8Hg8bNq0SZZUAcDGxgbLli0DAOzbt0/hPAcHByxdulRuX58+fWBvb4///vuvzO9f1HNfPp+PuXPnAgC8vb2LPG/RokUKSfXJkyfw8/ODu7u7XFIFpM/1V65ciezsbJw+fVq238zMTCGpAkCdOnXg4eGBFxERiPqkp3plohorKdbHd+k4d6hgKsBvVrQDj8fdJPuqYlXTEDN/+Bwbl9wBABzd8QR93OujVUf16qSizgrXcACgWbNm2LNnD4yNjbFx40asWLFCrsamp6eHNWvWYM2aNXLniUQiTJ48Gf3798fYsWOxZcsWWQ2qU6dO+Pnnn9G3b1+EhYXJaiel0dfXR4sWLRT2370rfab+5MmTImuDr169AiBtVvw08RZ1HT8/Pzx48EDh9djYWIjFYrx69QqtW7eWPePr06cP+HzV12XS0tIQFhYGOzs7NG7cWOF1Nzc3AEBgYKDCa66urgpN9wBQt25dWTnLIiEhARs2bMCVK1fw5s0bZGRkyL3+7t27Is8r/Nw9X/77pqSkFPl7iouLAyD9PRXm7++PrVu34u7du4iNjYVIJJKPITYW9rVqlblMyqDESorltTlQVlu1tTdGq07ak3j6uDvD70oEHt6KAWPAsmnXcTZgtMZ1ylI3M2fOxMaNG/Hvv/+W6fiVK1fi3bt3sg4pGzZsQM+ePWUfqI0aNYKjoyN27tyJVavKNv7WxsamyH8AExISAAB79+4t8fz09PQSX8+/zoYNG8p0neTkZACotCE7KSkpAFBs7+X8/flxFGZubl7kOTo6OkV2OipKcnIyPv/8c4SHh6Nt27aYMGECLC0toaOjg+TkZGzduhU5OUXPeFariESX//P18fGBj49Pse9b+Pd09uxZeHh4QF9fH7169YKzszOMjIzA5/Nx8+ZN+Pn5ISe36paQpMRKipQYl4WThZ6tzvi+jVbUVvPxeDzM+7kDpvW7gMyMXESEJsNrUyBmLGnDdWgazcbGBgAUaixFCQwMxPr167Fnzx7Y2dkhNTUVMTExsueuAGBvbw8rKysEBQWVcCV5xf2dmplJp7d88uRJkTXassq/TkpKCkxNTUs9Pj95FVdrU1Z+PJ9OypHv/fv3csep2r59+xAeHo7ly5cr1DDv3r1b4tCpon5X+XFu3boV33zzTZliWLZsGYRCIR4+fIgmTZrIvTZjxgxZT+uqQs9YSZEOb3uM7Kw8AIBVLUN0/EJ9Vq9RFRtbI0ya7yrb3vPLA0S9TuEuIC2Q34xXr169Eo/Ly8vD5MmT0aNHD0yZMkXutU9rN9nZ2SqJrX379gCAW7duVel18o/39vYuUy0wv2lWLBaX6fomJiZwdnbGu3fvEBoaqvC6r68vAJSpt3NFhIWFAUCRvbkrktAq8nsKCwtD06ZNFZKqRCLB7du3yx2DsiixEgXpqSIc2/1Mtj11QSutqq0WNmhsI9kMTKIcMX6eS1PJlSYoKAiJiYkK+yMjIzF79mwA0l6wJVmzZg3CwsLkmmVNTU1hZ2eHq1evIi9P+k+dn58f0tLS4OLionTckyZNknV+KapjjkQiwc2bN0u9zuzZs6Grq4t58+bJnssWJhKJ5JJC69at0bFjRzx+/Bjr1q1TOD4hIUHun4caNaR/j1FRZZ8ac/LkyWCMYeHChXIJOT4+HqtXr5YdUxnyhwJ9+rMLDAxUeKZeFm3atEGXLl1w5swZ2YQin3r27BliY2PlYggNDZUbQ8wYw8qVK0vtjFYZqCmYKDjtFYz0VOmDf1MLPXQf6MhtQJVIIOBj7qp2mON+BYwBt69F4drp1+jjUZ/r0NTWyZMnsXbtWvTo0QNOTk4wMTHB69evcfnyZWRnZ6N///5YsGBBsecHBQXhp59+wqZNm2TjWPMtWrQIc+fORZcuXdCuXTscPXoUxsbG+Prrr5WOu0aNGjh16hSGDh2K9u3bo2fPnnBxcQGfz0dUVBTu3r2rkOSK0rhxY+zfvx+TJ0+Gi4sL+vbti4YNGyI3NxdRUVG4desWrK2t8eJFQce/I0eOoHv37vjhhx9w+vRpdO/eHYwxhIaG4tq1a3jx4oUsQfXs2RMnT57EsGHD0L9/fxgYGMDBwUFhaFNhCxYswN9//43z58+jZcuW6N+/PzIzM3Hy5EnExsZi0aJF6Ny5s9I/w6JMmDABGzZswLfffgtfX180aNAAoaGhuHTpEoYNG4YTJ06U+5rHjh2Dm5sbpkyZgm3btqFdu3YwNzdHdHQ0nj59iufPn+Pu3buyRw/z5s3DzJkz8dlnn8Hd3R26urrw9/dHcHAwBg0ahIsXL6q62CWixErkiMUSHN1ZMFRi7KzmGj1utSwaNbfC4HGNcP6wdOad9Ytuo9sAR7VcDk8d9OjRAy9fvkRgYCDu3r2LjIwMmJubo3Pnzhg/fjzGjx9fbAuHWCzG5MmT0a5dO8yaNUvh9Tlz5iA1NRV79uxBQEAAWrZsic2bN5e5R3BpevbsiadPn+LXX3+Ft7c3bt26BaFQiNq1a8PNza3Mk1OMGzcOLVu2xMaNG+Hr64tr167ByMgItWvXhoeHB0aOHCl3vJOTEwICArB+/XqcO3cOO3bsgL6+PhwdHfHdd9/JEgQATJ06FZGRkfjzzz+xfv165OXloVu3biUmVqFQCB8fH2zatAnHjh3D9u3boaOjg5YtW2LLli0YPXp0xX5gZVC7dm3cunUL33//PW7fvg1vb280btwYu3btwhdffFGhxFqnTh08evQI27dvx+nTp3H06FGIxWLUqlULTZs2xZw5c9C8eXPZ8TNmzICenh62bNmCgwcPwsDAAF26dIGXlxdOnz5d5YmVx1TQ7tWmTRvZTCpEs107E4Z5o64CkE4DeObhyGoxn25GmgieX5xDcqK0tjJ7eTt89ePnCseFhISgiU2hjjk1PunsVE1Wt6kuRo0ahRMnTiAmJkbt5gwm5VA4P7UpuYNiSEiIwrPa8tL+T0xSLoe3F9RW+3rUrxZJFQCMTITwnOeKLcukYw73rnuIYZ5NUNNOcaWWElHS0iqvXr2Cnp4erK2tuQ6FaBDtbuMj5fL80UcE+Eu75vN4wJhZzUs5Q7v0HV4f9RpbAAByssXYvJQm6a+utm7dCnd3dwQGBmLIkCFysxkRUhpKrETm0NaC2mqrTraoYWPIYTRVTyDgyzX/Xjz6Ek//q7pp0Ij62LJlC+7du4fJkyfjt99+4zocomHo3zACQLo0nPfpMNn2lAWVM+ZN3bm2r4VOvezh7yN9TvrL/Fs4fstDa4cbkaKFh4dzHQLRYFRjJQCAM17BcpPtN3CpwXFE3Jm+uDV0daW3xrP/PuLyn4pjFQkhpDiUWAnEYgn+2lcwZdzomc04jIZ7tR1MMNSzoFfg5qV3Icop2yw4hBBCiZXg1tVIvI+SLnEl1BPAbVDJ09FVB2O+ag4zCz0AwIe36fhr73OOIyKEaApKrAR//laQNHoOqUcrvEA6/GbMVwW9onf/9AAZaaISziCEEClKrNVcdHgqbntHyrZHz6jezcCFDRzTCDa1pYtlJydm49C2J6WcQQghlFirvZP7niN/7i3nJhawtTfhNiA1ItQTYPyclrLt/RsDIBHTBP2EkJJRYq3G8vIkOHe4YKLw6jYhRFn0+rIe7J2l60NmpuciLbXoBZsJISQfJdZq7LZ3JOI/ZAIA9A100OkLe44jUj8CHT4857nKtjPTcpErpmfQhJDiUWKtxs4eCJF97zbYCQId+nMoSufe9mjUQjqulzEgNsmM44gIIeqMPkmrqYTYTNy8HCHb9pjclLtg1ByPx8Pk7wpmokpON4IojyYtI4QUjRJrNXXx2Evk5UlnWrJzNEXdelQLK0mrjrZwbV8LAMDAQ1yyKccRqYdevXqBx+PBz8+v2GNmzpwJHo+HvXv3VmFkhHCHEms1xBiTawZ2n6Tc2oPVxbjZLWTfS2ut9Kw1ICAAAoEArVu3LvaY+/fvA5Cu20xIdUCJtRp6/igWYcGJAACBgIeeg2mmpbJo2a4WdPWktwwDD/Ep1bvW+vr1ayQmJsLFxQXGxkWvW5uZmYnnz59DT08PzZrRGGlSPVBirYYK11bbdK0NQ2NdDqPRLMYmQtn3SWnG1XoO4YcPHwIA2rVrV+wxjx49Ql5eHlq2bAldXfo7I9UDJdZqJiszV261lhFTXTiMRvPo6glgqCcdy8rAQ/zHTI4j4k5ZEuu9e/cAUDMwqV6oa2M1c/3cG6SnSue8NTXXQ/PPa3IckWbhAbAxT0HERxsAQFJ8FqxrGUJXWP2et+Yn1hMnTsDX17fIY+j5KqmOKLFWM2cPFTQD9x/ZgBbwrgAjg2wY6uUgM0cPjAHxHzKr3VSQjDEEBgYCAHx8fEo9nhIrqU4osVYjsTHpuO8bLdseMq4xh9FoLh4Aa/MURP5/rTUxPgtWtoZwNdrNbWDlECSardT5r169QkpKCjp06IA7d+4UeUxSUhIsLS1haGiIpk3lx0nv2rULGzZswPv37+Hi4oItW7agS5cuSsVEiLqgZ6zVyNVTYbIJ9x0bmMGqliG3AWkwY4NsGOQ/a2VAYmwWxxFVrfxm4M8++6zYYwICAgAArq6uEAgKmspPnDiBuXPn4ocffkBgYCA6duyIfv36ISoqqnKDJqSKUGKtRgp3Who0thGHkWg+HgBrs1TZdkJs9erElJ9YW7VqVewx+Yn102bgTZs2wdPTE9OmTUOTJk2wfft22NraYvduzanxE1ISagquJiJDk/H8YSwAgMcHegx04jgizWdimAU9oQQ5Ij4kEuBm1CRY1zLiOqwqUZbEmv8MtnBiFYlEePToERYsWCB3bO/evYttUiZE01CNtZq4fKKgttr0M2uYmOlxGI124AGwtCgYx5rwMRMSifav1yqRSPD48WMIhUK4uBQ/XKuoGmt8fDzEYjFq1pTvjV6zZk18+PChcgImpIpRYq0GGGO4UqgZmDotqY6ZiRi6OtJkmpfHkJyQzXFElS8kJATp6elwcXGBUCgs8pj09HSEhobC2NgYjRopPnb4tDc6Y4x6qBOtQYm1GnjxOB7hr5IBADq6fHToWZfbgLQIjw9YWEhk2/EfMsCYdtdaHz16BKDkZuAnT55AIpGgVatW4PMLPmasrKwgEAgUaqexsbEKtVhCNBUl1mqgcDNwm861oW9Aj9ZVycJMDAFfmkxFIglSk3I4jqhyTZgwAYwx7Nu3r9hjOnXqBMaYwqo3QqEQrVu3Vhj76uPjg44dO1ZKvIRUNfqE1XISCcOVE9QbuDLx/7/WGp8gHVIS9yEDphZ61LRZjPnz52P8+PFo27YtOnXqhD179iAmJgYzZ87kOjRCVIISq5Z7dDsGH99lAAD0DXTQqqMtxxFpJwtzCRIT+ZAwHrKzxMhIy4WxadHPH6u7kSNHIiEhAT/99BPev3+PZs2a4cqVK3BwcOA6NEJUghKrlitcW+3cxx46utT6Xxl0BAxmZhIkJUtrrQkfMymxlmDWrFmYNWsW12EQUinoU1aLiURieJ8Ok20PGNmAw2i0n6W5BDxIn7WmpYqQk53HcUSEEC5QYtVid3yikJIo7UhjYi5E01Y2HEek3YRCBmPjgh7BCdV4STlCqjNKrFqs8BSGXwxxBp9PnWkqW+EJI5ITspGXKynhaEKINqLEqqUyM3LhezFctt3H3ZnDaKoPQwMGfT1pMpUw6XqthJDqhRKrlvK9GI6sTOkzPqtahqjX2ILjiKoPy0ITRiTEVo9pDgkhBSixaqnCzcD9htenMZVVyNREIjfNYUqi9k9zSAgpQIlVCyUnZMH/WsHalr2+pGZgVSptxkIeTzquNV/Cx0ytn+aQEG2gqvuUEqsWunbmNfLypB/sdeuZwtbehOOItAePJ0CuuPTjzM3E4POkN2l2tnTCCEKIesvNzYVAIFD6OpRYtVDhSSEGjGzIYSTaR1dHH6lZpf9XKxAAZmbytVZCiHpLTU2FiYnyFRFKrFrm/ds0PLwVI93gAT0G0YLmqqQvNEHSxzeIT2MQ5ZXcdKQwYUQWTRhBiLphjEEkEiE+Ph5JSUmwtLRU+po0paGWuXoyVPYMsGGzGrC0NuA2IC2jI9BFzch5SEzzQIRpZ+TqZqOk/0+TUvnIyZG+nhbwDuaW+lUUKSFEJj6+4PuQEIWXBQIBTExMYG9vDz09PaXfjhKrlrn8Z6js+4GjqRm4MujlvYdt7HbYxm5Heu2vwQTFNx3FBwnxzXxr6Xn6fPhGToaZBSVXQqpU06YF31dBR0JqCtYib14kIeRxHACAL+Cha19aLYRrn7UQoYGzCACQky3B2QOK/y0TQrQLJVYtUrjTUvM2NjAyodVVuMbjAcO/zJBtH94eCLGYpjkkRJtRYtUSjDFcLpRYB4+jBc3VRe+emTA1kSbTD9GZ8LsSyXFEhJDKRIlVSzx/FIuosBQAgK6Qj3bd63AcEcmnrwcMGVCo1rotkMNoCCGVjRKrlijcDNymqx309KlfmjoZNjgDfL6008R/fjEIC07kOCJCSGWhxKoFxGIJ/v6roDfw4DHUDKxubGuK0bVTwZzBx3Y94TAaQkhlosSqBR78G4O499KZffQNdfBZh1ocR0SKMvzLdNn35w6GIDU5h8NoCCGVhRKrFrhSaCWbLr3tIdChX6s6atVSBGcn6ZzBOTkSnD1IQ28I0Ub0CazhRDliXDsTJtseSM3AaovHA4YPLai1Ht4WQENvCNFClFg13G3vSKSlSCcgMDEToomrFccRkZL07ZklG3rz/m0mbl2loTeEaBtKrBqu8ILmPQfXowXN1Zy+PsOgfoUnjHjMXTCEkEpBiVWDZaSJ4HspXLY9gOYG1gjuQwqG3ty78Q5vXiRxHBEhRJUosWqw6+ffICdbuup2jZoGcGxgzm1ApExq1xKjc3saekOItqLEqsGuFBq72se9PoeRkPIaMaygE9PZg8FIS6GhN4RoC0qsGioxLgt3fKJk2/2GN+AwGlJerV1FqOcoHXqTnSXBuUMvOI6IEKIqlFg1lPfpMIjF0ud0do4mqFXHmOOISHlIV70pPPQmEBJJ5a8TSQipfJRYNVTh3sADRlKnJU3U94ssmBhLh968i0zHbW8aekOINqDEqoFiIlMReOe9dIMHfDG0HrcBkQoxMGAY2LfQ0Jsd1ImJEG1AiVUDFe605NzYAhY1DDiMhijDY0gGeDxpE/Adn7eIeEVDbwjRdJRYNVDhZuAh4xtzGAlRll1tMToVHnqz+xmH0RBCVIESq4Z59Swer54lAAD4fB669XPkNiCitBFDC5qDT3sFIT1VxGE0hBBlUWLVMJeOF9RWXVpbw9BYl8NoiCp83ioHjvb/P/QmU4xzh2jVG0I0GSVWDSKRMFw+UZBY3Sc15TAaoio8nvyEEUe2P6ahN4RoMEqsGuTR7Rh8eCv9ABbqCdC2mx3HERFV6ftFFoyNpENv3oanwf9aVClnEELUFSVWDXLx2EvZ9593rQ1doYDDaIgqGRrQqjeEaAtKrBoiJzsP104XLGjuMYWagbVN4aE3/jT0hhCNRYlVQ/z7d8GC5kamunBpZcNxRETV7GqL0bkDDb0hRNNRYtUQlwo1A/cY4EQLmmup4V8WNAefoaE3hGgkSqwaICUpG35/R8i2h3k24S4YUqkKD73JoqE3hGgkSqwa4NqZ18gVSXuMWtcyRN16ZhxHRCqLwtCbHTT0hhBNQ4lVAxRuBu43ktZd1XZyQ2/e0NAbQjQNJVY1FxOVhoe3YmTbA0fREnHajobeEKLZKLGqucIT7js1NIeFFa1kUx3Q0BtCNBclVjXGGJNrBv5yIq1kU13Q0BtCNBclVjX28mkCwoITAQB8AQ89BjhxHBGpSjT0hhDNRIlVjV04+kL2fYvPa8LAiFayqU5o6A0hmokSq5rKzRXLNQPTFIbVz6dDbw7TqjeEaARKrGrq1t+RSIjNAgAYGOmgTZfaHEdEuNCv0NCb6PA03LoayXFEhJDSUGJVU2cPFjT79RjoBIGAflXVkYEBw+D+Bc9aD2wO4DAaQkhZ0Ke1Gor/mIl//y6omQyf4sJhNIRrI4ZmQMCXNgH/5xeDkMA4jiMihJSEEqsaunTsJfLypM1/te1NUMfJlOOICJdq1RTDrVuWbNuLaq2EqDVKrGqGMSbXDDyUxq4SAGOGF3RiunoyDB+i00s4mhDCJUqsaub5o1i5sau9h9XnOCKiDpo0ysVnLXIAAGIxw5EdTziOiBBSHEqsaubsgYLaqmv7WjA0prGrRGp0oVrrX3ufISONJowgRB1RYlUj2Vl5uHKiYG7gUTOacRgNUTed22fDvo50woiMtDyc9grmOCJCSFEosaqR6+ffIC1FWgsxMRPCtX0tjiMi6oTPB0Z7FNRaD24JlHVyI4SoD0qsaqRwDaSPe33weDwOoyHqqF/vTJibiQEAH6Iz4HPmNccREUI+RYlVTYS/TMJ932jZ9lDPJhxGQ9SVvh7gPrhgwgivTQFgjKY5JESdUGJVEyf/CJJ937B5DdjYGnEYDVFn7kMyINSVJtOggDgE+L/nOCJCSGGUWNVAdlae3NjV8bNbchgNUXeWFhL07ZUp296/iSaMIESdUGJVA96nwpCaJB2jaGSii7bd7TiOiKi7wp2Ybl6KQFhQAofREEIKo8SqBv7a+1z2/YCRDcHnU6clUjInhzx07VgwzeHv6x5yGA0hpDBKrBx78SQej+99ACBdf9NjKq27Sspm4tg02fdX/grF2zcpHEZDCMlHiZVjhWurLq1tYFHDgMNoiCZxaZyLz1tlAwCYBNi/kZ61EqIOKLFyKCNNhIvHXsq2J35DnZZI+XiOKai1njkQjNgYmpyfEK5RYuXQpeOvkJkunaLO3FIfLWmmJVJOrVxFaNZEOltXXi7DgS2PuQ2IEEKJlSuMMfy555lse6hnY5ppiZQbjwdMLFRr/XPPUyQnZJVwBiGkslFi5cj9m+/w6rl0iARfwMPgsbTuKqmYTu2z4ewkbfnIyZbgyI6nHEdESPVGiZUjh7Y+ln3f6Yu6MDYVchcM0Wh8vvyz1sPbApGanMNhRIRUb5RYORAZmgy/KxGy7cnfteIuGKIV3Lploa5dHgAgPS1P7h83QkjVosTKgcM7nsi+r9/UAnWcTDmMhmgDgQCYPD5Vtn1wSyA9ayWEI5RYq1hKUjbOFZoXmGqrRFV6u2XB0V76rDUzIw/7NwZyHBEh1RMl1ip2en8wsjKlTXYW1gZo06U2xxERbSEQAFMnFjxrPbLjCeI/ZpZwBiGkMlBirUKiHDEObXss2x7zVXMaYkNUyq1rFurXy+8hLMYfvz7iOCJCqh9KrFXo/JEXiHsvrUHoG+ig/4gGHEdEtA2fD0z3LHjW+ueeZzQbEyFVjBJrFRGLJXJzuQ7zbAyhnoDDiIi26tIxG00aSWdjEuVI8Ps6qrUSUpUosVaRf86+QVSYdPURHV0+Rk5vznFERFvxeMC0iQW11pN7n9PKN4RUIUqsVYAxhr3rC2oNfdydYWisy2FERNt1aJuD5i7SSSLy8hi2LL3LcUSEVB+UWKvA7WtRCHkcBwDg83nw/PYzjiMi2o7HA+bMKKi1Xj0Vhsf33nMYESHVByXWSsYYw85V/8m2u/Sxh3kNfQ4jItVFCxcRenYrGG6zbsFtMMY4jIiQ6oESayW7dTUSzx58BCCtRcz84XOOIyLVyaypqdDVlSbTp/99hPepMI4jIkT7UWKtRIwx7Fh5X7bdpa8DrGoZchgRqW7saosx/MuC4TabfvCHKEfMYUSEaD9KrJXI91IEggKkz1Z5fGDWUqqtkqrnOTYNpiYSAMC7yHQc3fmklDMIIcqgxFpJJBKGnasKaqvdBzihhg3VVknVMzVhmDKhoCPT7p8fIDGOJugnpLJQYq0kV068wosn8QCkPYG/+qENxxGR6mzYoAzZsnIZabnY/OMdjiMiRHtRYq0EOdl52Pq/e7Lt3sOcYWFlwGFEpLrT1QW+nZUs2z5zIASBd2n4DSGVgRJrJTi26yliIqWrjOgK+ZhJtVWiBjq1z0HXjgVNwKtn30RenoTDiAjRTpRYVSw5MRu/ry2YZWnMV81hZCLkMCJCCsybnQI9PWkyffksAcd3P+M4IkK0DyVWFfttzQOkJkunkjMxE2LUDJoTmKgP25piTBlfsGbrtv/dxfu3aSWcQQgpL0qsKhQWlIBjOwtqALOWfg4dXfoRE/Uy2iMdDvbSNVszM/KwcpYvzchEiArRp76KMMbw01w/2TMrO0cT9BxSj+OoCFGkqwv88F0yeDxpMr3lHYULR15yHBUh2oMSq4pcPv4KD/6NkW0v29YNPB6Pw4gIKV7LZiKMGJoh214z3w9x7zNKOIMQUlaUWFUgLSUH6xf7y7Z7DHSCcxNLDiMipHQzJ6eitq10bGtaSi5WUJMwISpBiVUFtiy7h4SP0lVE9AwE+HZ1e44jIqR0BgYMS+Yny7ZvXo7AyX1B3AVEiJagxKqk+zej8eeegg5LXy9rS4uYE43xeascDB9aMEn/2u9u4c2LJA4jIkTzUWJVQka6CMumX5dtN3CxRF+P+hxGREj5fT0tBc5O0l7COdliLJrgDZGIVsAhpKIosSph0w938S5COgZQR4ePVb/1oA5LROPo6wErf0iE8P/XbQ15HI+NS2guYUIqihJrBfn7RMk1Ac/8oQ2sahpxGBEhFVe/Xh6+npYi2z6y/QmunHjFYUSEaC5KrBUQ9z4DSyb5yLYbNquBweMacRgRIcobMSwD3ToXzCW8bPp1hAUlcBgRIZqJEms5icUSLPb0QUKs9ANIqCfAT3vdqAmYaDweD1i2MAn2daTPW7OzxPhm+BXZFJ2EkLKhxFpOv699hPu+0bLtpVu70pJwRGsYGzOsXZkI/f+fqD8yLAXzRv2N3FzqzERIWVFiLYd//47ArtX/ybb7j2yADj3rchgRIapXzzEPPy5Mlm3fuxGN1bP9aPIIQsqIEmsZhQUlYME4b0gk0g+XOo4m+GZlO46jIqRy9OqRhemeqbLt017B2Lf+UQlnEELyUWItg6T4LHw97DIy0qTPngwMdbDpz74QCOjHR7TXpHFp6N+7YP7gLcvu4a+9zzmMiBDNQJmhFFmZuZjjcQXR4dL/3vl8Hn490hsWNei5KtFuPB6wZH4yWrsWdF5aNfsmLhx5wWFUhKg/SqwlEOWIMXfE3wi88162b9H6jmjY3IrDqAipOrq6wPpVCWjaSAQAYAz4cep1eJ8K4zgyQtQXJdZi5OVJsGjCNfhfi5LtG/1VM/Qc4sxhVIRUPSMjhs1r42XTHkokDAvGeePsoRCOIyNEPVFiLYJIJMb3E33gc/a1bF//kQ0weX4rDqMihDtmpgzb1sfDvm5Bcl069ToOb3/CcWSEqB9KrJ/IyszFNx5X8PfJUNm+bv0daSk4Uu3VsJRg96Z4NKgnku1b+90tbFxyB2KxhMPICFEvlFgLSUnKxoyBF3HraqRsX+fe9vhxSxeaWYkQSJPrrs3xaO5S0KFp/8YAfONxBRlpohLOJKT6oMT6/8KCEzGq00k8uh0j29fXoz7+t6MbJVVCCjExZti2LgGd2hfMK3zzcgTGdj2F8Je0lishlFgB3LgYjjFdTiIqrGB1j5HTXfDdmo6UVAkpgoEBw/pViRg3Mk22LzQoEcPbn8DZQyE0SxOp1qp1Ys3OysPP3/phjnvB5A88PvDdLx0xdWFrjqMjRL0JBMDs6an43+JE6OpKn7FmZeRh6dTrWDj+GhLjskq5AiHaqdom1uDAWIxo/xeO7SpYU9XQSBc7TvVH3+H1OYyMEM3Sv3cW9u+Mg8P/9xgGgL//CsWgFkdx8ehLqr2SaqfaJda0lBz8Mu9fjOxwEq9DEmX7GzargUM3htLkD4RUQAPnPBzYHYdB/QqmQExOyMb3k3wwqfc5BAXEchgdIVWr2iTW3Fwx/tr7HAOaHcXRnU9lk+nz+TxMWfAZdpzpDzNLfY6jJERzGRgw/LggGZt+iUdN64La6wO/dxjR/i8smeSDqNcpJVyBEO3AYypop2nTpg0ePnyoinhULjdXjCt/hmL3z//h7ZtUudcc6pthxa7uqONkxlF0RBPV9i/4e0mv/TWYwITDaNRTZhYPv3uZ4q+zRpBICjoA8vk89BvRANMWtUaDZjU4jJBUK4U7oVbBowmtTazJCVk4+Ucwju9+io/vMuRe0zfQwdf/a4s+7s7U65eUGyXWsot8q4Mdv5ni1l3FRSs+72aHUdObwW1IPQiFAg6iI9UGJdaKE4nE8L8WhfOHX+Dm5XDkiuRng9HR5WPoxMYYP6clDAx1OYqSaDpKrOX3+KkQfxw2xoMAxQRrZqmHL750Rl+P+mjbvQ50dKrNEypSVSixlk9CbCbu+0bjxsVw3PaORFqK4uwvevoCDBjVEBO+aQkjEyEHURJtQom14oJe6OLIn8bw8zeQayLOZ2Glj+4DnNC+Rx2061EH1rZGHERJtA4l1uKJxRK8fZ2CoIA4PLz1Dg9vxeDNi+JnerGpbYTRM5uh97D6EOpRUxNRDUqsyouN4+P8FSNcuGKAuPjiW4/qNbbA513t0LSVNVw+s4GziyU1G5Pyo8QKZKSJEB2eiujwVLx9k4LwV0l49SwBoc8TkJWZV+K5xqZCdOlrD/dJTeFQ31xlMRGSjxKr6kgkQFCIEP/c1Mc/vnpISCq5RUlXyEf9pjXg2NAcjg2kX/b1zVDTzhg1ahpSMzIpWnVOrN6nwvDTXL9yzdjC4wG1HUzQwa0ueg11hlMjc+qQRCoVJdbKkZ9kHwTq4b8HAjx/YYC8vLInSj6fhxo1DWBjawSb2saoUdMAFjUM4NTIAkPGN67EyInaq+LEqlPp71AOBkY6pSZVfUMd2DmYwLV9LbR3q4smrlbQ01erYhBCKoDPB5q7iNDcRYTJ44DsnGQ8C9JD8AtdBIUI8DJUBx/jih9rLpEwxL3PRNz7TAQFxMn2t+pkS4mVVCm1ykj540l5PEDPQAeW1gaoaWcMe2dT1K1nBjtHU5ia68mdExlGA85J1apd6PuUuCRIeDQnbmVpYCf9GtJTup2WLkBktD7efdDDu/d6ePtOB+8/CpGYIkRqetFJV6iTW+R+QiqLSpqCrays4OjoCACIi4uDtbW1Utf7EJ3O+dqOOXlp0NPR7CY+bSgDoL7lMNLLKNeHdlpWDkwM9Eo/UM1pVjl4MKlpB109+We3qvicUgdUDu5YWVnh6tWrRb6mksRamLqMaVWWNpRDG8oAUDnUjTaUQxvKAFA51BV1oSOEEEJUiBIrIYQQokIqT6zTp09X9SU5oQ3l0IYyAFQOdaMN5dCGMgBUDnWl8meshBBCSHVGTcGEEEKICpUpsb59+xYeHh4wMzODqakphg0bhqioqDK9QXZ2NhYuXAhbW1sYGBigQ4cO+Pfff5UKuqIqWo6HDx9i+vTpaNy4MQwNDWFvb4+xY8ciPDy8CqJWpMzvo7A1a9aAx+Ohc+fOlRBlyZQtQ0hICIYPHw4rKysYGBigUaNG2Lp1ayVGXDRlyhEVFYWJEyfC3t4ehoaGaNiwIZYuXYqMjIzST1ax6OhozJkzBx06dIChoSF4PB4iIiLKdK663OMVLYO63d/K/C4K4/L+BpQvh7rc4xXCSpGRkcHq16/PXFxc2NmzZ9m5c+dYs2bNWL169Vh6enppp7MxY8YwMzMz9vvvv7N//vmHDR06lOnr67PAwMBSz1UlZcrx3XffsY4dO7KdO3eymzdvsqNHj7LGjRszS0tLFhUVVUUlkFL295Hv9evXzMjIiNnY2LBOnTpVYsSKlC3DgwcPmImJCRs0aBA7e/Ysu3HjBvvtt9/Yxo0bqyD6AsqUIz09nTVo0IA5OjqyAwcOsBs3brB169YxfX19NmLEiCoqQQFfX19mY2PD+vXrx3r37s0AsPDw8DKdqy73eEXLoE73N2PK/S7ycXl/51OmHOpyj1dUqYl1y5YtjM/ns9DQUNm+N2/eMIFAUGohHz9+zACw/fv3y/bl5uayhg0bskGDBikRdvkpU47Y2FiFfREREYzH47Fly5apPNaSKFOOwnr37s2mT5/OunXrVuU3njJlEIvFrGnTpuzLL7+s7DBLpUw5vL29GQDm7e0tt3/x4sVMIBCwjIyMSom5OGKxWPb93r17y/whqE73eEXLoE73N2MVL0dhXN7f+SpaDnW6xyuq1KbgCxcuoH379qhfv75sn5OTEzp16oTz58+Xeq6uri5Gjhwp26ejo4NRo0bB29sbOTk55a1gV5gy5ShqRhAHBwdYW1vj3bt3Ko+1JMqUI9+xY8cQEBCANWvWVFaYJVKmDDdv3kRwcDDmz59f2WGWSplyiETSmcVMTU3l9pubm0MikYBVcZ9CPr9i3S3U6R6vaBnU6f4GKl6OfFzf3/kqWg51uscrqtSSBwUFoVmzZgr7XVxcEBwcXOq5Tk5OMDQ0VDhXJBIhLCysnOFWnDLlKEpISAhiY2PRpEkTVYRXZsqWIykpCfPmzcP69ethaWlZGSGWSpky3L59G4D0uV779u2hq6sLGxsbfPPNN8jKqto5e5UpxxdffIEGDRpg8eLFCA4ORnp6Om7cuIGtW7di5syZMDLSjAW+1ekeVyWu7m9lqcP9rSx1uscrqtTEmpiYCAsLC4X9lpaWSEoqfpHx0s7Nf72qKFOOT+Xl5WHmzJmwtrbGlClTVBVimShbjoULF6Jhw4bw9PSshOjKRpkyxMTEAABGjhyJ3r17w8fHB4sWLcK+ffswZsyYSom3OMqUQ19fH7dv34ZEIoGLiwtMTEzQs2dPDBw4EDt27KiskFVOne5xVeHy/laWOtzfylKne7yiyrS6TVHrm5alqYoxVuFzK4OqYpk9ezbu3LmDy5cvF/mhUtkqWo5bt27h0KFDCAgI4HzN2oqWQSKRAADGjRuHVatWAQC6d+8OsViM77//HsHBwWjatKlqgy1BRcuRnZ2NkSNHIjY2FocPH4a9vT3+++8/rFq1Cjo6Oti9e3dlhKty6naPqwLX93dFqdP9rQx1u8crotQaq4WFRZH/dSYlJZX6R2dpaVnsufmvVxVlylHYkiVL8Pvvv2P//v3o3bu3KkMsE2XKMWPGDEyZMgV16tRBcnIykpOTkZeXB7FYjOTk5Cp7HqZMGWrUqAEA6NWrl9z+/N/F48ePVRNkGShTjj/++AM3b97ElStXMG7cOHTt2hULFizAxo0bsWfPHjx58qSywlYpdbrHVYHr+1sZ6nJ/K0ud7vGKKjWxuri4ICgoSGF/Wf5rcHFxQXh4ODIzMxXOFQqFcp0+Kpsy5cj3888/Y+3atdi6dSvGjx+v6hDLRJlyhISEYM+ePbCwsJB9+fv74969e7CwsKiyWpKyf1OAYk0xv4akbMeP8lCmHM+ePYOFhQWcnZ3l9rdt2xaA9HelCdTpHleWOtzfylCX+1tZ6nSPV1SpEQ4ePBj37t3DmzdvZPsiIiLg7++PwYMHl3pubm4uTp48KduXl5eHEydOoHfv3tDTq7o1HZUpBwBs27YNS5cuxc8//4w5c+ZUZqglUqYcvr6+Cl8tW7ZEs2bN4OvrCw8Pj8oOH4ByZejXrx/09PQU1kH09vYGIF1+qqooU45atWohKSlJoXPP/fv3AQB2dnaqD7gSqNM9rgx1ub+VoS73t7LU6R6vsNLG46SnpzNnZ2fWrFkzdu7cOXb+/HnWokUL5uTkxNLS0mTHRUREMIFAwFauXCl3/siRI5m5uTnbu3cv++eff5i7uzvT09Njjx49Us2AoTJSphzHjx9nPB6P9e3bl929e1fuKygoSGPKURQuxrkpW4YVK1YwgUDAlixZwnx8fNiaNWuYvr4+mzhxosaUIzw8nJmYmLAGDRrIJohYv349MzExYa1bt5YbA1hVTp48yU6ePMlmzpzJALBdu3axkydPsps3bxZbDsbU5x6vaBnU6f5WphxF4XIcK2MVL4e63OMVVWpiZYyxyMhINmzYMGZiYsKMjY3ZkCFDFAb6hoeHMwBs+fLlcvszMzPZvHnzWM2aNZmenh5r27Yt8/X1VVH45VPRckycOJEBKPKrW7duVVoGxpT7fXyKqxtPmTJIJBK2ceNG5uzszHR1dZm9vT1btmwZE4lEVVeA/6dMOYKCgtjw4cNZnTp1mL6+PmvQoAH77rvvWGJiYtUVoJDS/sY14R6vSBnU7f6uaDmKwnVirWg51Okerwha3YYQQghRIfV/CkwIIYRoEEqshBBCiApRYiWEEEJUiBIrIYQQokKUWAkhhBAVosRKCCGEqBAlVkIIIUSFKLESUsl4PB54PB74fD5ev35d7HE9evSQHXvgwAG51zw9PWWvLV++vNhrHDx4UHZc9+7d5V67efOm7LX8L0NDQ9ja2qJr165YuHAhAgMDlSkqIQSUWAmpEjo6OmCM4Y8//ijy9dDQUPj5+UFHp+SVHHV0dLB//36IxeIiX9+7d2+p13BwcMDy5cuxfPlyfPvttxg4cCAyMjLw66+/olWrVhg7dizS09PLVjBCiAJKrIRUgZo1a6JNmzbw8vJCXl6ewuv79u0DYwwDBw4s8ToDBw5EdHS0wgTlgHR1E39/fwwaNKjEazg6OmLFihVYsWIFfvnlF+zduxePHj1CYGAgmjdvjmPHjmH48OHlKyAhRIYSKyFVZNq0afjw4QMuXboktz83NxcHDx5Ex44dZUtmFWfs2LEwMDDA3r17FV7L3zd16tQKxefq6op//vkH1tbWuHr1Ks6dO1eh6xBS3VFiJaSKjB49GkZGRti3b5/c/gsXLuDjx4+YNm1aqdcwNzfH8OHDcfnyZbx//162PycnB4cOHUK3bt3QsGHDCsdoY2ODGTNmAACOHj1a4esQUp1RYiWkipiYmGDUqFG4evUqoqOjZfv37t0LU1NTjBgxokzXmTZtGvLy8uQ6OJ09exYJCQllSs6lye/09N9//yl9LUKqI0qshFShadOmQSwWY//+/QCAyMhI+Pj4YOzYsTA0NCzTNTp37owmTZrInssC0uRsYWEBd3d3pWPMX2Q9Li5O6WsRUh1RYiWkCrVr1w7NmzfH/v37IZFIsG/fPkgkknLXNKdOnYo3b97gxo0bCAsLg6+vL8aPHw99fX2lY8xP1jweT+lrEVIdUWIlpIpNmzYNkZGRuHr1Kry8vNC6dWt89tln5brGhAkToKenh3379slqrqpoBgaAmJgYAIC1tbVKrkdIdUOJlZAqNn78eBgYGGDGjBl49+4dpk+fXu5rWFlZYejQoTh79iz279+PDh06oFmzZiqJz9fXF4C0dk0IKT9KrIRUMXNzc3h4eCA6OhpGRkYYPXp0ha4zbdo05OTkIC4uTmW11djYWPz2228ApEN7CCHlV/IULYSQSvHTTz9h2LBhsLa2homJSYWu0aNHD5w/fx4SiQR9+vRROqYnT55gwoQJiI+PR//+/TF48GClr0lIdUSJlRAO2Nvbw97eXqlr8Hi8CiW/iIgIrFixAoB0cor4+Hg8evQIjx49AiCtqebXWgkh5UeJlZBqJjIyEitXrgQA6Ovrw9zcHA0aNMCCBQswduxYuLq6chsgIRqOx/L71hNCCCFEadR5iRBCCFEhSqyEEEKIClFiJYQQQlSIEishhBCiQpRYCSGEEBWixEoIIYSoECVWQgghRIUosRJCCCEqRImVEEIIUSFKrIQQQogK/R94PmgUYjgM9QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "873fa8ea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qNuRXt6GksQt1bT2obetBc5cGD4z0w8oFsVw/ywKxSIjopho6evFOdgE2niqHTrj+mKONHCO8naByd8DoIHdMCPHE/fG+XD/LQrFIiOiGtDoBGUdL8d7uQnT3afH4+CDcEzoMUT4uULnbw4rHQOj/sEiI6CeqW7vx68yzOFHShMnhXvjv+6MR6s2LCunGWCRE9CO7L9Vixbfn0Nuvw7sPj8TC0f6csqKbYpEQEQBApxOwcttlrD9cghg/F3z4yCiEcGkTug0sEiICAHx84CrWHy5ByoQg/HZeFGwVXO6dbg+LhIhw9GoD3t1ZgAdG+uH3yTGcyqI7wgsSiSxcbVsPfvXVGYR4OSFtYRxLhO4Y90iILJhGq8MLX55GV58WXz0zGo62fEugO8efGiIL9s6OApwsbcbqJQkIUzqLHYckilNbRBYq+2IN0g8W44kJQZif4C92HJIwFgmRBSpt6MSKjecwMsANr82LEjsOSRyLhMjC1Lb1YFnGScjlMqx9dBRP8yW98RgJkQUpb+rC0nUn0NjRi0+fHAuVu4PYkcgMsEiILMSVug48tu4EujVa/P2Z8UgIcBM7EpkJFgmRBcivacPST05AJpPh65+PR6SPi9iRyIywSIjMnFYn4NeZZ6GQy/DVM+O5fhYZHA+2E5m5rLOVyK9px+/mRbNEaEiwSIjMWI9Gi3d3FiLO3xXz4nzFjkNmikVCZMY2HC9DZUs3Xp0byTsa0pBhkRCZqdZuDdbsu4LJ4V64N3SY2HHIjLFIiMxUxtFStHRp8J9zIsSOQmaORUJkhnQ6AV+fLMeksGGI8XMVOw6ZORYJkRk6erURlS3deFgdIHYUsgAsEiIz9E1uOVzsFJgVrRQ7ClkAvYukvLwc06ZNQ1RUFGJiYrB69WoAQFNTE5KSkhAWFoakpCQ0NzfrHZaIbq21S4PsvBo8OMofdtZckJGGnt5FolAo8O677+Ly5cs4fvw41q5di0uXLiEtLQ0zZsxAUVERZsyYgbS0NEPkJaJb2HyuEn39OizmtBYZid5F4uvri9GjRwMAnJ2dERUVhcrKSmRlZSElJQUAkJKSgk2bNuk7FBHdhm9yKxDl64IYP66nRcZh0GMkpaWlOHPmDMaNG4fa2lr4+l6/ktbX1xd1dXU3/Jr09HSo1Wqo1WrU19cbMg6RxcmrasWFylYsVqsgk/ECRDIOgxVJR0cHFi1ahPfffx8uLrf/l1Bqaipyc3ORm5sLLy8vQ8Uhskgf7rkCRxs5FozirXPJeAxSJBqNBosWLcLSpUuxcOFCAIBSqUR1dTUAoLq6Gt7e3oYYiogGcfpaM7LzavDzKSPg5mAjdhyyIHoXiSAIWLZsGaKiovDyyy8PPJ6cnIyMjAwAQEZGBubPn6/vUEQ0CEEQkLYtH8OcbLFsYrDYccjC6H0/kiNHjuCLL75AXFwcEhISAABvvfUWXn31VSxevBjr169HYGAgNm7cqO9QRDSIfQV1yCltwh/nx8DRlrcZIuPS+ydu4sSJEAThhp/bs2ePvi9PRLeg1Ql4O7sAwz0dsCQxUOw4ZIF4ZTuRxG0+d/3GVa/MioC1nL/SZHz8qSOSsH6tDqt3FyHSx5k3riLRsEiIJOy7M5UobezCy0nhvHEViYZFQiRRff06fLCnCPEqVyRxcUYSEYuESKK+PVWBiuZuvJQUzqvYSVQsEiIJ6u3XYs3eIowKdMPUcK4IQeJikRBJUGZOOapae/BKUgT3Rkh0LBIiient12LtvitIDPbAvaGeYschYpEQSc22C9Woa+/FC9NCuTdCJoFFQiQxGUfLEOLliImhw8SOQgSARUIkKecrWnC2vAVPjA/idSNkMlgkRBLy+bEyONjIsXCMSuwoRANYJEQS0dTZh83nqrBwtD9c7KzFjkM0gEVCJBHf5Jajr1+HJyYMFzsK0Y+wSIgkQKsT8MWxMowP8UC40lnsOEQ/wiIhkoC9+XWobOlGCvdGyASxSIgk4PNjpfB1tePijGSSWCREJu5qfQcOFTXg0cRAKHjjKjJB/KkkMnFfHCuDtVzG2+iSyWKREJmwzt5+/ONUBebF+cLL2VbsOEQ3xCIhMmHfn6lEe28/HudBdjJhLBIiEyUIAj4/VopYfxeMDnQTOw7RoFgkRCbqREkTCms78MSE4Vzll0wai4TIRP39xDW4OVgjeaSf2FGIbopFQmSCNFod9ufXYW6sD+ys5WLHIbopFgmRCTpV1oz23n5MjfAWOwrRLbFIiEzQvoI6WMtluJc3ryIJYJEQmaD9+fVIDPaAk61C7ChEt8QiITIxlS3dKKhtxzROa5FEsEiITMz+gjoAwNQIL5GTEN2eIS+S7OxsREREIDQ0FGlpaUM9HJHk7cuvh8rdHiO8nMSOQnRbhnQCVqvV4he/+AV27doFlUqFsWPHIjk5GdHR0UM5rFnp6uvHlboOVDR3o769FwDgam8NF3sFXOys4WJvDWc7BRRWVqhq6UZpYyfq23vh5WwLPzd7+LnZQ+lsy1VjJaK3X4ujVxuwaLSKFyGSZAxpkeTk5CA0NBQhISEAgCVLliArK8uoRXKpqg2ZJ6/BWm4FBxs5/NzsMSPKG97OdkbL8ANBEFDV2oOa1h40dvSiqbMPzV0a9Gi00Gh16OvXoU+rQ69Gh/qOXhTWtqOiuVvvcd0drLFidiSWjA2AlRXfnEzZyZJmdPVpMS2S01okHUNaJJWVlQgICBj4WKVS4cSJEz96Tnp6OtLT0wEA9fX1Bh3/fEULHlt3An1aHRRWVujs64cgADIZMDbIA7NjfTAmyB1u9tZwc7CGRiugtbsPrd39cLJVYJiTDdwdbPR6861v78XhK/U4cqURR680oKq154bPU1jJYKOwuv6f3AoejjYYFeiOn6kDEKZ0QqCHI7ycbSGTAW3dGrT19KO9R4O27n609WjQ16+Dj6sdgoc5Qulsh/qOXlS2dKOqpRvfn6nEb7+/gK9zy7HywVjE+rve9b+Hhta+gjrYKKwwIYSn/ZJ0DGmRCILwk8f+fXc9NTUVqampAAC1Wm2wsX8oEVcHa3z1zHio3B0gCAIKazuw/WI1si/W4I9bLt3ydaxkgLezHRKDPTA1wguTw70wzOnWy3nXtfVg7b4r+CqnHH1aHdwcrDEhxBPPTvVEgIcDhjnawsPJBu4O1rBTyO+orG5nfFcHa4R6X59jXzI2AJvOVmLl1nw8uPYIVsyOwDOTQrh3YoL2FdRhQogn7G14NTtJx5AWiUqlQnl5+cDHFRUV8PMb+nWDCmvbsXTdCbg5WCMzdQL83ewBXC+xCB9nRPg449czw1Ha0Inihg60dGnQ0qWBtVwGVwcbONsp0Nnbj4b2XjR09KG8uQtHrjRg87kqyGRAvMoNc2N9MD3SGwHuDgO/9FqdgJySJmy9UIWNuRXQ6gQ8rA7A0nGBiPZ1Ee2NWyaTYcEoFaZHKPHqd+exans+jhU34t2HR8LzNkqJjKOssRPF9Z14fHyQ2FGI7siQFsnYsWNRVFSEkpIS+Pv7IzMzE19++eVQDom+fh1ezDwLW4XVj0rkRoYPc8TwYY639bo6nYC8qjbsK6jD7su1SNuej7Tt+QAANwdr+Lrao769Bw0dfbBVWOH+eD/8akYogjxv7/WNwdXBGh8tHY0NJ67hj1suYe7qQ3jvZwm8etpE7C+4PrXL60dIaoa0SBQKBdasWYPZs2dDq9Xi6aefRkxMzFAOidV7CnG5ug3rnlDftETulJWVDHEqV8SpXPGrGWEob+rCydImVLf2oLq1G9UtPQjzdsLsGB9MjfCCo4lekSyTyfD4+CCMCXTHC1+dxtJ1J/DouED819xIONtZix3Pou0rqEPwHfxxQ2Qqhvzd7r777sN999031MMAuL7Q3cf7r2KxWoWZ0cohHSvAwwEBHg5DOsZQivZzwdZfTsJ7uwux7lAx9ufXYfUjozB2uIfY0SxSj0aLY1cb8eg43pedpMdsLi7o6uvHK9+cha+rPV6/n9ep3A57Gzl+e18U/vHcPbC1luOJ9Tk4XtwodiyLdKy4Eb39Ok5rkSSZTZEU1LSjpVuDdxeP5BTNHRoV6I5vfj4BKnd7PPXZSZxgmRjd/vw62FvLkRjMPUKSHrMpklGB7jj8n9MxPsRT7CiS5OVsiy+fGQ9/d3s89beTOFRk2Gt6aHCCIGBfQT3uGeHJm1iRJJlNkQDgktt6ul4m4xDo4YCUT3Ow7lDxDa8FIsMqbujEtaYuTI3ktBZJk1kVCenP29kO/3juHiRFK/Hm1st4ZeM5dPT2ix3LrO25XAsAmBrOZVFImlgk9BOOtgp8vHQMXpoZju9OV2LqO/vwxfEyaLQ6saOZpf89V404f1dJnwVIlo1FQjdkZSXDizPDsOkX9yLEywmvb7qI2e8dxK5LtWJHMyvF9R24UNmK+QlDv+ID0VBhkdBNJQS44evU8Vj3hBpWVjI883kutl2oFjuW2fhh2Z3741kkJF0sErolmUyGmdFKbPvVJIwMcMN/fnse1xq7xI4leYIgYPPZKowP9oSPq/Fva0BkKCwSum02CiuseWQUZDLgha9Oo7dfK3YkSbtY2Ybihk5Oa5HksUjojgR4OOCdh0fifEUrVm3LFzuOpGWdrYS1XIa5sb5iRyHSC4uE7tjsGB88MSEIGcdKcbW+Q+w4kqTVCfjf81WYEu4NVweuxEDSxiKhu/LL6WGwllth3aFisaNIUk5JE2rbejmtRWaBRUJ3xcvZFotGq/CP05Wob+8VO47kbD5XCQcbOWZGDe0q1UTGwCKhu/bMpGBotDpkHC0VO4qk9PZrse1CDWZFK3lLXTILLBK6ayFeTkiKUuKL42Xo5DIqt+14cRNauzVI5rQWmQkWCenl51NC0NqtwTe55WJHkYwTxY1QWMm4UjWZDRYJ6WVMkAfUQe5Yf7gE/VyL67acLG1CjL8rHGy4WjWZBxYJ6S11cggqmrux7WKN2FFMXo9Gi3PlrUgc7i52FCKDYZGQ3mZGKRHi5Yj/OXCV9y+5hfMVrejT6jB2OO+ESOaDRUJ6s7KS4dkpI5BX1Yb9hbyz4s2cLG0CABYJmRUWCRnEglH+8Hezx4d7irhXchM5JU0I83aCu6ON2FGIDIZFQgZhLbfCs1NCcPpaC44VN4odxyRpdQJOlzVjbDD3Rsi8sEjIYB5WB8DL2RZr9l4RO4pJulzdhvbefiRyWovMDIuEDMbOWo7USSE4erURp8qaxY5jcgaOj3CPhMwMi4QM6tFxgXB3sManh0vEjmJyTpY2wd/NHv5u9mJHITIoFgkZlKOtAvfF+WJvfh16NLzx1Q8EQUBOSTPG8voRMkMsEjK4ObE+6NZocaioQewoJqO0sQsNHb2c1iKzxCIhgxsf4gkXOwWyeaX7gJMl14+P8EA7mSO9imTFihWIjIxEfHw8FixYgJaWloHPrVq1CqGhoYiIiMCOHTv0zUkSYi23wswoJfbk10LD9bcAADmlTXB3sEaot5PYUYgMTq8iSUpKwsWLF3H+/HmEh4dj1apVAIBLly4hMzMTeXl5yM7OxvPPPw+tlvPllmRWjA9aujTI+b+/xC3dydImqId7QCaTiR2FyOD0KpJZs2ZBobi+gun48eNRUVEBAMjKysKSJUtga2uL4OBghIaGIicnR/+0JBlTwr1gZ22FHXmc3qpr60FZYxentchsGewYyaeffoq5c+cCACorKxEQEDDwOZVKhcrKSkMNRRJgbyPHlHAv7MyrhU5n2Uum5PD6ETJzt7whwsyZM1FT89O/KleuXIn58+cP/L9CocDSpUsB4IZrLQ22S5+eno709HQAQH09F/wzJ7NjfLAjrxbnKlowKtByT3s9WdIEe2s5YvxcxI5CNCRuWSS7d+++6eczMjKwZcsW7NmzZ6AsVCoVysv/ece8iooK+Pnd+LaiqampSE1NBQCo1erbDk6mb0akEgorGXbk1Vp0keSUNmN0kBus5TxJksyTXj/Z2dnZ+NOf/oTNmzfDwcFh4PHk5GRkZmait7cXJSUlKCoqQmJiot5hSVpcHawxYYQnduTVWOyKwG09GuTXtHHZeDJrehXJCy+8gPb2diQlJSEhIQHPPvssACAmJgaLFy9GdHQ05syZg7Vr10IulxskMEnL7BgflDR0oqiuQ+woojhV1gxB4PUjZN70umn0lSuDr/L62muv4bXXXtPn5ckMzIpW4vWsi9hxsQbhSmex4xjdyZImKKxkSAh0EzsK0ZDhpC0NKW8XO4wOdEe2hZ4GfLK0CTH+rnCw0etvNiKTxiKhITc7Rom8qjaUN3WJHcWoOnv7ca68FYlcqJHMHIuEhtzsGB8AsLiLE/cV1KFPq8PMKKXYUYiGFIuEhlyQpyMilM7YdalW7ChGtf1CDYY52ULNA+1k5lgkZBRJ0UqcLG1Cc2ef2FGMortPi735dZgTq4TciutrkXljkZBRJEUroROAnZcsY3rrQGEdujVazI31FTsK0ZBjkZBRxKtcEerthL+fuCZ2FKPYdqEG7g7WGMf1tcgCsEjIKGQyGZ6YEITzFa04W94idpwh1aPRYs/lWsyO8YGCy6KQBeBPORnNglH+cLSR4/NjpWJHGVKHihrQ2afF3DhOa5FlYJGQ0TjbWWPhaBW2nK9GkxkfdN9+sRqu9ta4Z4Sn2FGIjIJFQkb1+IQg9PXr8PXJ8ls/WYL6+nXYdakWSdFKrvZLFoM/6WRU4UpnjA/xwIbjZdCa4Q2vjlxtQHtPP+6L8xE7CpHRsEjI6J6YMByVLd3Yl18ndhSD236hGs62CtwbOkzsKERGwyIho0uKVkLpYovPj5eJHcWgdDoBey7XYVqkN2wVvG0CWQ4WCRmdtdwKjyYG4WBhPcoaO8WOYzCXqtvQ2NmHqRFeYkchMioWCYniIbUKALD1QrXISQznQGE9AGBSGIuELAuLhETh72aPkSpXZF80nyVTDhTWI9rXBV7OtmJHITIqFgmJZm6cL85XtKKiWfr3KWnv0eB0WTMmh3NvhCwPi4REMzf2+imy5rBXcuxqI/p1AiaH82wtsjwsEhJNkKcjonxdzKJIDhbVw8FGDnUQF2kky8MiIVHNjfVBblkzatt6xI6il4OFDZgQ4gkbBX+lyPLwp55E9cP0lpRvw1va0IlrTV08PkIWi0VCogpTOiPU2wnbL0i3SA4WXT/tdwqLhCwUi4RENzfWBydKGtHY0St2lLuyL78OgR4OGD7MUewoRKJgkZDo5sT6/N9teGvFjnLHWrs1OHylAbNjlGJHIRINi4REF+3rgkAPB2yX4Nlbuy7VQqMVMC/eT+woRKJhkZDoZDIZ5sb64OiVBrR2acSOc0e2nq8auEqfyFKxSMgkzI3zRb9OwO7L0pneau26Pq01L94XMplM7DhEomGRkEkYqXKFn6sdtl+UziKOOy/VQKMVcB/vzU4WjkVCJkEmk2F2rA8OFjWgo7df7Di3ZeuFak5rEcFARfLnP/8ZMpkMDQ0NA4+tWrUKoaGhiIiIwI4dOwwxDJm5ubG+6OvXYa8E7pzY2qXB4aIG3M9pLSL9i6S8vBy7du1CYGDgwGOXLl1CZmYm8vLykJ2djeeffx5arVbfocjMjQlyxzAnW2RLYHprR14N+nWc1iICDFAkL730Et5+++0f/VWWlZWFJUuWwNbWFsHBwQgNDUVOTo6+Q5GZk1tdP3trz+U6NJjwxYlanYB1h4sxwssR8ZzWItKvSDZv3gx/f3+MHDnyR49XVlYiICBg4GOVSoXKykp9hiILkXLPcPRpdfjbkVKxowxqy/kqFNZ24MWZ4ZzWIgKguNUTZs6ciZqan14otnLlSrz11lvYuXPnTz4nCMJPHhvsFy49PR3p6ekAgPr6+lsGJvMW6u2E2dE+yDhWip9PCYGznbXYkX6kX6vD6t1FiFA6435OaxEBuI0i2b179w0fv3DhAkpKSgb2RioqKjB69Gjk5ORApVKhvLx84LkVFRXw87vxlb+pqalITU0FAKjV6jv+B5D5eX7aCGTn1WDD8Wt4buoIseP8yPdnKlHc0In/eXwMrKy4N0IE6DG1FRcXh7q6OpSWlqK0tBQqlQqnT5+Gj48PkpOTkZmZid7eXpSUlKCoqAiJiYmGzE1mLF7lhklhw7D+cAl6NKZzkkZfvw6r9xQhzt8Vs6K5thbRD4bkOpKYmBgsXrwY0dHRmDNnDtauXQu5XD4UQ5GZem7qCDR09GLjqQqxowz4JrccFc3deGUWj40Q/SuZcKMDGiJRq9XIzc0VOwaZAEEQsOCjo2js7MW+V6ZCIRf32tkejRZT3tmHAHcHbHx2AouELMLtvifzynYySTKZDL+YForypm5sOS/+dSXfnqpAbVsvXubeCNFPsEjIZM2I9Ea40gkf7b8CnU68HWdBELDheBli/FwwIcRTtBxEpopFQibLykqG56aOQGFtB/aIuGzKqbJm5Ne047HxQdwbIboBFgmZtAfi/aByt8dfD1wVLcOG42VwtlVgfgJvXkV0IywSMmkKuRWWTQzGqbJmnL7WbPTxGzt6se1CDRaO9oeDzS0vuyKySCwSMnmL1QFwtlNg/eESo4+98VQF+rQ6LB0fZPSxiaSCRUImz9FWgUfHBWL7hWqUN3UZbVydTsCXJ64hMdgD4Upno41LJDUsEpKEJ+8ZDiuZDOsOFRttzL35dbjW1IXHuDdCdFMsEpIEX1d7PKxW4e8nrqG4vmPIxxMEAWv2XYHK3R5zY32GfDwiKWORkGS8lBQOW4UV3tqWP+RjHbnSiLPlLXhu6ghYi3xVPZGp428ISYa3sx2enxaK3ZdrcfRKw62/QA8f7i2C0sUWD41RDek4ROaARUKSsmxiMPzd7PHHrZehHaKr3XNKmnCipAk/nzwCtgouNkp0KywSkhQ7azlenRuJy9Vt+Hj/lSEZY82+K/B0tMEjiYFD8vpE5oZFQpJzf7wv5sX74s87C/F2dv4N78h5tzYcL8PBwnosnxQCexvujRDdDl6qS5Ijk8nwwZJRcLGzxkf7r6K5qw9vPhgHuZ53LNxyvgqvZ13EjEhvLJ8UbKC0ROaPRUKSJLeS4a0FsfBwtMbafVfR0qXBez9LgJ313e1FHCysx0tfn4U6yB1rl47mmVpEd4C/LSRZMpkMK2ZH4vX7o7H9Yg2S1xzGpaq2O36dM9ea8eyGUxjh5YR1KWPvuoyILBWLhCRv2cRgZDydiOYuDR5cewSfHCyGRqu7ra89VFSPJz7NgZezLT5flghXe+shTktkflgkZBamhHsh+8VJmBLhhZXbLmPan/fjq5xr6OsfvFC+OF6GJz87CT9Xe/x9+Th4O9sZMTGR+eAxEjIbnk62SH98DPbm1+GDPUX4r+8u4N2dBRgX7InRQe5QB7nD3kaOk6VN2F9Qj12XajE90hsfPDIKTrb8VSC6W/ztIbMik8kwI0qJ6ZHeOFBYj+9OV+JUWTO2Xvjxfd+9nW3xwrRQvJQUrvfZXkSWjkVCZkkmk2FqhDemRngDAKpbu3GqrBk9Gh3UQe4I8nTgbXOJDIRFQhbB19Ue98fbix2DyCzxYDsREemFRUJERHphkRARkV5YJEREpBcWCRER6YVFQkREemGREBGRXlgkRESkFxYJERHphUVCRER6kQmGvOG1noYNG4bhw4fr9Rr19fXw8vIyTCAjk3J2gPnFJuX8Us4OmG/+0tJSNDQ03PLrTapIDEGtViM3N1fsGHdFytkB5heblPNLOTvA/JzaIiIivbBIiIhIL2ZXJKmpqWJHuGtSzg4wv9iknF/K2QHmN7tjJEREZFxmt0dCRETGZTZFkp2djYiICISGhiItLU3sOLdl+PDhiIuLQ0JCAtRqNQCgqakJSUlJCAsLQ1JSEpqbm0VO+U9PP/00vL29ERsbO/DYzfKuWrUKoaGhiIiIwI4dO8SIPOBG2X//+9/D398fCQkJSEhIwLZt2wY+Z0rZAaC8vBzTpk1DVFQUYmJisHr1agDS2P6DZZfK9u/p6UFiYiJGjhyJmJgYvPHGGwCkse2BwfMbdPsLZqC/v18ICQkRrl69KvT29grx8fFCXl6e2LFuKSgoSKivr//RYytWrBBWrVolCIIgrFq1SvjNb34jRrQbOnDggHDq1CkhJiZm4LHB8ubl5Qnx8fFCT0+PUFxcLISEhAj9/f2i5BaEG2d/4403hHfeeecnzzW17IIgCFVVVcKpU6cEQRCEtrY2ISwsTMjLy5PE9h8su1S2v06nE9rb2wVBEIS+vj4hMTFROHbsmCS2/c3yG3L7m8UeSU5ODkJDQxESEgIbGxssWbIEWVlZYse6K1lZWUhJSQEApKSkYNOmTeIG+heTJ0+Gh4fHjx4bLG9WVhaWLFkCW1tbBAcHIzQ0FDk5OcaOPOBG2QdjatkBwNfXF6NHjwYAODs7IyoqCpWVlZLY/oNlH4wpZQcAmUwGJycnAIBGo4FGo4FMJpPEtgcGzz+Yu8lvFkVSWVmJgICAgY9VKtVNf1BNhUwmw6xZszBmzBikp6cDAGpra+Hr6wvg+i9gXV2dmBFvabC8UvmerFmzBvHx8Xj66acHpiZMPXtpaSnOnDmDcePGSW77/2t2QDrbX6vVIiEhAd7e3khKSpLctr9RfsBw298sikS4wYlnN2tcU3HkyBGcPn0a27dvx9q1a3Hw4EGxIxmMFL4nzz33HK5evYqzZ8/C19cXr7zyCgDTzt7R0YFFixbh/fffh4uLy6DPM8V/w79nl9L2l8vlOHv2LCoqKpCTk4OLFy8O+lyp5Dfk9jeLIlGpVCgvLx/4uKKiAn5+fiImuj0/ZPT29saCBQuQk5MDpVKJ6upqAEB1dTW8vb3FjHhLg+WVwvdEqVRCLpfDysoKzzzzzMDuu6lm12g0WLRoEZYuXYqFCxcCkM72Hyy7lLY/ALi5uWHq1KnIzs6WzLb/V/+e31Db3yyKZOzYsSgqKkJJSQn6+vqQmZmJ5ORksWPdVGdnJ9rb2wf+f+fOnYiNjUVycjIyMjIAABkZGZg/f76YMW9psLzJycnIzMxEb28vSkpKUFRUhMTERDGj/sQPbwIA8P333w+c0WWK2QVBwLJlyxAVFYWXX3554HEpbP/Bsktl+9fX16OlpQUA0N3djd27dyMyMlIS2x4YPL9Bt78hzw4Q09atW4WwsDAhJCREePPNN8WOc0tXr14V4uPjhfj4eCE6Onogc0NDgzB9+nQhNDRUmD59utDY2Chy0n9asmSJ4OPjIygUCsHf319Yt27dTfO++eabQkhIiBAeHi5s27ZNxOQ3zv7YY48JsbGxQlxcnPDAAw8IVVVVA883peyCIAiHDh0SAAhxcXHCyJEjhZEjRwpbt26VxPYfLLtUtv+5c+eEhIQEIS4uToiJiRH+8Ic/CIJw899VKeQ35Pbnle1ERKQXs5jaIiIi8bBIiIhILywSIiLSC4uEiIj0wiIhIiK9sEiIiEgvLBIiItILi4SIiPTy/wEHtWswSTiTtAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b52ad20864f94373a05f317b99843584",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5b69161e5e04aad86c643ba2314cba5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v1_Aug22_bf_model_misspecification_1000_data.ipynb b/bf/v1_Aug22_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..8bd4af9
--- /dev/null
+++ b/bf/v1_Aug22_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2475 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        res[i] = dpl[0].data['agg']\n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "aa8f2ae3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "68c984d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d98d64742651473f917654de1d8cbc4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.598639</td>\n",
+       "      <td>0.819271</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  9.598639  0.819271"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fad86fa",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "1753771b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "70bcf019",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "id": "2b0a740d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf2e8487d3894168af1a38eae4c2e3bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40b715c9",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "b08a5ed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "8752bed4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "97d2e389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "ad5aafe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "968c776f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x281b44760>]"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "b59dda7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "a9cb5d72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "19b5ddb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3675c17807454ec6b5140817c1ab96e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18bd4ab5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4803a069",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6325e10cf1c24a93b46a19d3e739dae3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AQK4rzFaQTqdVUVGR2Xa73Tpz5kzWmnQ6LZfLdcu5+/v7NTg4KL/fr69//eu6++67lU6n1dTUtGyu99P+l84qef5n7+trAEC+2Fp2l/Z+1vu+v07WKyDHcZbtsyxrzTU36unp0djYmBKJhFwul3bv3r2muUKhkPx+v/x+v2ZmZm75WgCA3JP1CsjtdmtycjKzPTU1pbKysjXX3Gjz5s2ZP3d1dekzn/nMmubq7u5Wd3e3JMnv92dbxi39XyQ9AGCprFdAjY2NSqVSGh8f1/z8vIaGhhQIBJbUBAIBDQ4OynEcjYyMqLi4OOvHb+/cI5KkEydOZL4lFwgENDQ0pKtXr2p8fFypVEoPPPDA7awNAJDDsl4BFRYWqr+/Xy0tLVpcXFQwGJTX69XAwIAk6YknnlBra6uGh4dl27bWr1+vZ599NnN8e3u7Tp8+rdnZWbndbu3fv187d+7Unj17lEgkZFmWKisrdfToUUmS1+vVjh07tHXrVhUWFuqb3/ymCgoK3qflAwBMsZyVbrrkGb/fr3g8broNAIBW/57MkxAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMWFUARaNRVVVVybZtHTx4cNm44zjatWuXbNuWz+fT6OhoZiwYDKq0tFQ1NTUrzn348GFZlqXZ2VlJ0rVr1/TYY4+ptrZW1dXVOnDgwO2sCwCQ47IG0OLionp7exWJRJRMJnX8+HElk8klNZFIRKlUSqlUSqFQSD09PZmxzs5ORaPRFeeenJzUqVOntGXLlsy+73znO7p69arefPNNvf766zp69KgmJiZuc3kAgFyVNYBisZhs25bH41FRUZHa2toUDoeX1ITDYXV0dMiyLDU1NWlubk7T09OSpObmZpWUlKw4d19fnw4dOiTLsjL7LMvSpUuXtLCwoLfffltFRUW666673s0aAQA5KGsApdNpVVRUZLbdbrfS6fSaa2704osvqry8XHV1dUv2f/7zn9eHP/xhuVwubdmyRU8//fSKARYKheT3++X3+zUzM5NtGQCAHFOYrcBxnGX7rr9iWW3N9S5fvqxnnnlGJ0+eXDYWi8VUUFCg8+fP6+LFi/rt3/5t/d7v/Z48Hs+Suu7ubnV3d0uS/H5/tmUAAHJM1isgt9utycnJzPbU1JTKysrWXHO9sbExjY+Pq66uTpWVlZqamlJDQ4PeeustPf/88/rUpz6lO+64Q6WlpXrwwQcVj8dvZ20AgByWNYAaGxuVSqU0Pj6u+fl5DQ0NKRAILKkJBAIaHByU4zgaGRlRcXGxXC7XTeesra3VhQsXNDExoYmJCbndbo2Ojuqee+7Rli1b9PLLL8txHF26dEkjIyO677773v1KAQA5JWsAFRYWqr+/Xy0tLaqurtaOHTvk9Xo1MDCggYEBSVJra6s8Ho9s21ZXV5eOHDmSOb69vV3bt2/XuXPn5Ha7dezYsVu+Xm9vr37xi1+opqZGjY2Nevzxx+Xz+d7lMgEAucZyVrqBk2f8fj8f0wFAjljtezJPQgAAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgxKoCKBqNqqqqSrZt6+DBg8vGHcfRrl27ZNu2fD6fRkdHM2PBYFClpaWqqalZce7Dhw/LsizNzs5m9r3xxhvavn27vF6vamtrdeXKlbWuCwCQ47IG0OLionp7exWJRJRMJnX8+HElk8klNZFIRKlUSqlUSqFQSD09PZmxzs5ORaPRFeeenJzUqVOntGXLlsy+hYUFPfrooxoYGNDZs2d1+vRp3XHHHbe7PgBAjsoaQLFYTLZty+PxqKioSG1tbQqHw0tqwuGwOjo6ZFmWmpqaNDc3p+npaUlSc3OzSkpKVpy7r69Phw4dkmVZmX0nT56Uz+dTXV2dJOkjH/mICgoKbnuBAIDclDWA0um0KioqMttut1vpdHrNNTd68cUXVV5engmad/zoRz+SZVlqaWlRQ0ODDh06tOLxoVBIfr9ffr9fMzMz2ZYBAMgxhdkKHMdZtu/6K5bV1lzv8uXLeuaZZ3Ty5MllYwsLC3rttdf0b//2b1q/fr0++clPatu2bfrkJz+5pK67u1vd3d2SJL/fn20ZAIAck/UKyO12a3JyMrM9NTWlsrKyNddcb2xsTOPj46qrq1NlZaWmpqbU0NCgt956S263Wx//+Me1ceNGrV+/Xq2trUu+1AAA+NWQNYAaGxuVSqU0Pj6u+fl5DQ0NKRAILKkJBAIaHByU4zgaGRlRcXGxXC7XTeesra3VhQsXNDExoYmJCbndbo2Ojuqee+5RS0uL3njjDV2+fFkLCwt65ZVXtHXr1ne/UgBATskaQIWFherv71dLS4uqq6u1Y8cOeb1eDQwMaGBgQJLU2toqj8cj27bV1dWlI0eOZI5vb2/X9u3bde7cObndbh07duyWr3f33XfrqaeeUmNjo+rr69XQ0KCHH374XS4TAJBrLGelGzh5xu/3Kx6Pm24DAKDVvyfzJAQAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARqwqgKLRqKqqqmTbtg4ePLhs3HEc7dq1S7Zty+fzaXR0NDMWDAZVWlqqmpqaFec+fPiwLMvS7Ozskv0//elPtWHDBh0+fHgt6wEA5ImsAbS4uKje3l5FIhElk0kdP35cyWRySU0kElEqlVIqlVIoFFJPT09mrLOzU9FodMW5JycnderUKW3ZsmXZWF9fnz796U+vdT0AgDyRNYBisZhs25bH41FRUZHa2toUDoeX1ITDYXV0dMiyLDU1NWlubk7T09OSpObmZpWUlKw4d19fnw4dOiTLspbsf+GFF+TxeOT1em93XQCAHJc1gNLptCoqKjLbbrdb6XR6zTU3evHFF1VeXq66urol+y9duqSvfe1r2rt376oWAADIT4XZChzHWbbvxiuW1dRc7/Lly3rmmWd08uTJZWN79+5VX1+fNmzYcMu+QqGQQqGQJGlmZuaWtQCA3JM1gNxutyYnJzPbU1NTKisrW3PN9cbGxjQ+Pp65+pmamlJDQ4NisZjOnDmj7373u9qzZ4/m5ua0bt06fehDH9KTTz65ZI7u7m51d3dLkvx+/yqWCgDIJVkDqLGxUalUSuPj4yovL9fQ0JCef/75JTWBQED9/f1qa2vTmTNnVFxcLJfLddM5a2trdeHChcx2ZWWl4vG4Nm7cqFdffTWzf9++fdqwYcOy8AEA5L+s94AKCwvV39+vlpYWVVdXa8eOHfJ6vRoYGNDAwIAkqbW1VR6PR7Ztq6urS0eOHMkc397eru3bt+vcuXNyu906duzY+7caAEDesJyVbuDkGb/fr3g8broNAIBW/57MkxAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGLGqAIpGo6qqqpJt2zp48OCyccdxtGvXLtm2LZ/Pp9HR0cxYMBhUaWmpampqVpz78OHDsixLs7OzkqRTp05p27Ztqq2t1bZt2/Tyyy/fzroAADkuawAtLi6qt7dXkUhEyWRSx48fVzKZXFITiUSUSqWUSqUUCoXU09OTGevs7FQ0Gl1x7snJSZ06dUpbtmzJ7Nu4caNeeuklvfnmm3ruuef0hS984XbXBgDIYVkDKBaLybZteTweFRUVqa2tTeFweElNOBxWR0eHLMtSU1OT5ubmND09LUlqbm5WSUnJinP39fXp0KFDsiwrs+/+++9XWVmZJMnr9erKlSu6evXqbS8QAJCbsgZQOp1WRUVFZtvtdiudTq+55kYvvviiysvLVVdXd9Oa733ve7r//vt15513ZmsTAJBnCrMVOI6zbN/1Vyyrrbne5cuX9cwzz+jkyZM3rTl79qy+/OUv37QmFAopFApJkmZmZm46DwAgN2W9AnK73ZqcnMxsT01NZT4iW0vN9cbGxjQ+Pq66ujpVVlZqampKDQ0NeuuttzLHP/LIIxocHNTHPvaxFefo7u5WPB5XPB7Xpk2bsi0DAJBjsgZQY2OjUqmUxsfHNT8/r6GhIQUCgSU1gUBAg4ODchxHIyMjKi4ulsvluumctbW1unDhgiYmJjQxMSG3263R0VHdc889mpub08MPP6wDBw7owQcffPcrBADkpKwBVFhYqP7+frW0tKi6ulo7duyQ1+vVwMCABgYGJEmtra3yeDyybVtdXV06cuRI5vj29nZt375d586dk9vt1rFjx275ev39/frxj3+sr3zlK6qvr1d9fb0uXLjwLpcJAMg1lrPSDZw8s3HjRlVWVr6rOWZmZvL2o7x87l2if9Pyuf987l361e1/YmIi8387b+VXIoDeC36/X/F43HQbtyWfe5fo37R87j+fe5fon0fxAACMIIAAAEYQQP9fd3e36RZuWz73LtG/afncfz73LtE/94AAAEZwBQQAMOIDH0DZfmoiF1VWVqq2tlb19fXy+/2SpP/+7//WQw89pHvvvVcPPfSQLl68aLjL/7XST3Lcqt8DBw7Itm1VVVXpBz/4gYmWM1bqfd++fSovL8/8P7Xh4eHMWC71Lv3yifO/+7u/q+rqanm9Xv3N3/yNpPw4/zfrPV/O/5UrV/TAAw+orq5OXq9Xe/fulZQf5166ef/v6fl3PsAWFhYcj8fjjI2NOVevXnV8Pp9z9uxZ021l9Ru/8RvOzMzMkn1/+qd/6hw4cMBxHMc5cOCAs2fPHhOtreiVV15xXn/9dcfr9Wb23azfs2fPOj6fz7ly5Yrzk5/8xPF4PM7CwoKRvh1n5d737t3r/NVf/dWy2lzr3XEc5/z5887rr7/uOI7j/OxnP3Puvfde5+zZs3lx/m/We76c///5n/9xfv7znzuO4zjz8/POAw884Pzrv/5rXpz7W/X/Xp7/D/QV0Gp+aiJfhMNhPfbYY5Kkxx57TC+88ILZhq6z0k9y3KzfcDistrY23XnnnfroRz8q27YVi8X+r1vOuNXPidwo13qXJJfLpYaGBknSr/3ar6m6ulrpdDovzv/Ner+ZXOpd+uUDmTds2CBJunbtmq5duybLsvLi3Es37/9mbqf/D3QA3c7PSOQCy7L0+7//+9q2bVvmieD/+Z//mXn+nsvlyvnHF92s33z5O+nv75fP51MwGMx8hJLrvU9MTOjf//3f9Vu/9Vt5d/6v713Kn/O/uLio+vp6lZaW6qGHHsq7c79S/9J7d/4/0AHkrPFnJHLFv/zLv2h0dFSRSETf/OY39c///M+mW3rP5MPfSU9Pj8bGxpRIJORyubR7925Jud37L37xC33uc5/TX//1X+uuu+66aV0uruHG3vPp/BcUFCiRSGhqakqxWEw//OEPb1qbL/2/l+f/Ax1Aa/0ZiVzxTo+lpaV65JFHFIvFtHnz5syv0E5PT6u0tNRki1ndrN98+DvZvHmzCgoKtG7dOnV1dWU+ZsjV3q9du6bPfe5z+qM/+iP94R/+oaT8Of836z2fzr8k/fqv/7p+53d+R9FoNG/O/fVu7P+9Ov8f6ABazU9N5JpLly7p5z//eebPJ0+eVE1NjQKBgJ577jlJ0nPPPac/+IM/MNlmVjfrNxAIaGhoSFevXtX4+LhSqZQeeOABk60u886bhySdOHEi8w25XOzdcRzt3LlT1dXVeuqppzL78+H836z3fDn/MzMzmpubkyS9/fbb+od/+Afdd999eXHupZv3/56e//fyWxP56Pvf/75z7733Oh6Px/nqV79qup2sxsbGHJ/P5/h8Pmfr1q2ZnmdnZ51PfOITjm3bzic+8Qnnv/7rvwx3+r/a2tqce+65xyksLHTKy8udb33rW7fs96tf/arj8Xic3/zN33SGh4cNdr5y748++qhTU1Pj1NbWOp/97Ged8+fPZ+pzqXfHcZxXX33VkeTU1tY6dXV1Tl1dnfP9738/L87/zXrPl/P/H//xH059fb1TW1vreL1eZ//+/Y7j3Prfaj70/16ef56EAAAw4gP9ERwAwBwCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIAR/w8r3n0OLN4OnAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "23a42d1d6da9465481f9ac277f9de3e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5201d5b9a9d646fabcc0bfd5dedff265",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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7xZ/s/k3Ve7V0At0HWm9dunSJTZgwgXl4eDBjY2NmZmbGfHx82P/93/+xJ0+eqN2n9H7M8PBwtn//ftapUydmamrKrKys2PDhw9mDBw+UysfExLBZs2Yxf39/Zm9vz8RiMXN3d2fBwcHs/Pnzat/j7Nmz7N1332XOzs7M0NCQ2dvbM19fXzZr1ix25Y17Ad3d3VXujSxLkyZNGABma2vLCgsLyyxXWFjIVq9ezTp37swsLS2ZWCxmjRo1YkFBQWz58uUq95QWFhay+fPnM09PT+7zffvtt6ygoKBK94EyxphUKmWrV69m3t7eTCwWM2dnZ/bRRx+xzMzMMj9reHg48/X1ZaampszOzo6988477NatWyw0NJQBYBEREUrlX7x4wcaMGcMcHR2ZUChkAFhoaCj3+vnz51mvXr2YtbU1Mzc3Z127dmW7d+9mERERKmW1Qsv3gQoY+3f8eSW0b98eV69erZ1MrgPOHE7Ah0PlTVV+nRrg+z96w1AsKnOfxLhMrFl4GdEX5IMIjIxF+G5dbwwY1axW49W6vxQGcIyt9GXHi9JRmISQekQxP7VvX27Rmvg3Tn2gNSQnuwihH0Zw657NrbE4/K1ykycAuDexxg8b++DLJV1hbCJrUS8sKMHs8cew5eebtRozIYQQzVECrSFrv7uMl09zAQBiYxGW/NkXIoPKnV6BQIA+73jh538GoFFj+dydiz4/i98WUY2fEELqIkqgNeB5cg7+WnuLW/94bgdY2RqXs4d67k2ssWL722jZ1oHbtir0IrauoZooIYTUNZRAa8C6H69BUiQbVevkYoa3RzbV+FiW1kb4YVMftOsiH0X3/ayzOLDtQbXjJIQQUnMogVZTZnoB9mySz3f5SWhApeYOLY+xiQHCfglUqonO/eAUbl1RP6MLIYQQ7aMEWk07/7iLgnzZTdT2TqboGOhSwR6VY2JqiO/WBcG9iWzKLklhCT4JPogXKTkV7EkIIUQbKIFWA2MMOzfIZ/AY+1Hratc+FVlYGWHBr0GwsJJNrJD6PA9fvHcUxcXSCvYkhBBS2yiBVsO1c0/x5JHsqQgGhkL0C25S4+/R0N0Cc1f1hFAoS8zXzz/DL9+V/fQHQggh2kEJtBr2bZEP7OnUyxVio/Lv+dRUuy7OmPCp/FFKvy26gkunk2vlvQghhFQOJVANlZRIcWr/Y259xPsta/X9Rk9vBb/OsmcGMgbMef8Ecl8XVbAXIYSQ2kIJVEM3op4jI1X2RAUTMwO08HOoYI/qEYmE+PqnbrCwlvWHPnuSg6XfXqjV9ySEEFI2SqAaOrlPXvvs2MOF66OsTXaOppgxryO3vv23O7gYQU25hBDCB0qgGmCM4dR++XP++o2o+cFDZek12BNd3mrErc8NOYncHGrKJYQQbaMEqoG4u+lKo299Axpo7b0FAgE+nR/A3dryLOk1VoVe0tr7E6KLJk2aBIFAoPaB3nzx8PDgHlpN6iZ6oLYGFGufPu0cam30bVnsHE3x4dwO+HH2eQDA1jU38c5471rvhyUKbv4HyEviO4qymboBvguqfRgPDw8kJiaqfc3JyQnPnz9X2lZUVIQFCxZg69atSE9PR7t27bB06VK0a9dOZf/jx4+jb9++OHDgAAYOHFjtWOubwMBAREZGogpPlCR1DCVQDUSdlP/h7Dtce823it4a2hjHdz9C9IXnYFJg4SensSVyhFb6YglkydPMg+8oypabUGOHsrKywmeffaay3dzcXGXb119/jeXLlyM4OBiurq7YvHkzevXqhfv378PZWT6/c05ODkJCQvDee+/VieS5aNEifP3113BxqZmZxGrCyZMn+Q6BVIASaBUVFhTj5iX5nLTtuzfkJQ6BQIBPQgPwwaD9KJZIcfPSC+zedA/Bk2v3dhqif6ytrREWFlZhOcYYfvvtN0yePBkbNmwAAAwbNgyBgYHYvHkzvvzyS67sV199hYKCAqxcubK2wq4SZ2dnpQRfF3h5efEdAqkA9YFW0c1Lz1FUWAIAsLYzhq2DCW+xNGpshZFTfbj1JV+dR0ZqPm/xEP326tUr5OXloWNH+Ujx0mXFZuAzZ87gl19+wZo1a2Bra6vRe5X2D2ZnZ+Pzzz+Hh4cHDA0NlRL9/fv3MWnSJDRq1AhGRkZwcnLC2LFj8eCB6pONyusDvXTpEkaMGIEGDRpALBajUaNGmDZtGp4+fao2tvT0dMyZMwetWrWCqakprKys4Ovri6+//hq5ublISEiAQCBAZGQkANmX4dKfwMBAlc/4psLCQixevBht2rSBqakpLC0t0b17d/zvf/9TKVv6XpMmTUJCQgJGjx4Ne3t7GBsbo3379jhw4ED5J/oNe/bswXvvvYdmzZrBzMwM5ubm8Pf3x6pVqyCVqk4xWnpeHz9+jNWrV6NNmzYwMTFR+pzp6en45ptv0KJFC5iYmMDKygq9e/fGsWPHVI6XlZWFJUuWICgoCK6urhCLxXBwcMCQIUNw8eLFKn2WmkA10Cq6EpnCLftpcfBQWcZ82Bqn9sfjeXIOXmcWYvncKCz4NYjvsIgOKSwsxJYtW5CUlAQzMzO0adMGPXr0gEik3Pfv4OAAExMTXLt2jdt29arsgfDu7u4AgPz8fEydOhXBwcEIDg6uVlxFRUUICgpCeno6+vbtC0tLS3h6egIAjhw5guHDh0MikWDw4MFo0qQJkpOT8c8//+DgwYOIiIhQ2y/7pvDwcISEhMDIyAhDhgxBo0aNEBsbi/Xr12P//v24ePEi3NzcuPLx8fHo1asXEhMT4e/vjw8//BBSqRQPHz7E8uXLMX36dFhbWyM0NBQbN25EYmIiQkNDuf0rGjRUVFSEfv36ITIyEt7e3vj444+Rl5eHnTt3YtSoUbhx4wa+//57lf0SExPRsWNHNG7cGOPHj0d6ejq2b9+OoUOH4sSJE+jVq1elzvnXX38NoVCIgIAAuLi4ICsrC6dOncLMmTNx5coVbN68We1+M2fOxNmzZzFw4EAMGDCAu3YSExMRGBiIhIQEdO/eHf3790dubi4OHDiA/v3747fffkNISAh3nHv37mHOnDno0aMHBg4cCBsbGyQlJWHfvn04fPgw9i9div5dulTqs9QESqBVdOWMPIF2fsuVx0hkjE0M8PG8jpg37RQAYNeGGAyf1AJ+nepWcxSpv54/f47x48crbfP09ER4eDh69uzJbRMIBPjggw+watUqZGVlwcXFBZs3b4aFhQXGjRsHAJg7dy7S09OxZs2aasf17NkztGzZEpGRkTAzM+O2Z2RkYMyYMTA1NcWZM2fQsqW8W+Pu3bsICAjA1KlTcf369XKP//DhQ0ybNg0eHh6IjIxU6h89deoU+vTpg5kzZ2L37t3c9vfeew+JiYn4/vvv8c033ygdLzU1Febm5jA2NkZYWBhOnz6NxMTESjWPl1q6dCkiIyPx9ttvY9++fTAwkP0JDw0NRceOHbFo0SIMGjQIXd5IIqdPn0ZYWJhSsh47diz69++PJUuWVDqBHjx4UKVpWSqVYvLkyfjzzz8xY8YMBAQEqOx3/fp1REdHc19wSk2cOBGJiYnYtm0bRo8ezW3PzMxEYGAgPv30UwwZMgROTk4AgBYtWuDp06ewt7dXOk5ycjI6duyIWcuXazWBUhNuFbzZ/6nN21fK0ynIVene0P9+dgZSKY3sI9U3efJknDx5Es+fP0dubi5u376NadOmISEhAW+//TZu3rypVP6HH37AV199hcuXL+OPP/6Aj48PTp48CRcXF1y6dAkrVqzAypUrYW9vj/nz58PZ2RkGBgbw9/fH+fPnqxzf0qVLlZInAPz555/IzMzE/PnzlZInAPj4+CAkJATR0dGIiYlBeX755RdIJBKsXLlSZXBRUFAQhgwZgv379+P169cAgGvXruHChQvw8/PDV199pXK80qbT6tiwYQMEAgGWLVvGJU8AcHR0xLx58wAA69evV9nP3d0dc+fOVdrWr18/uLm54fLly5V+f3X9skKhEDNnzgQAHD16VO1+X375pUryvHnzJiIjIxEcHKyUPAFZv/v8+fNRUFCAXbt2cdutrKxUkicAuLq6YsSIEbifkICkN0aG1yaqgVbBrcsvlPo/7RxNeY5I7sM5HXD17FMUFZYg5vor7N18D8Mm0oAiUj2KNRYAaNWqFX799VeYm5tj6dKlCAsLU6qBGRkZYdGiRVi0aJHSfkVFRZgyZQoGDBiAcePGYcWKFVyNqGvXrvjvf/+L/v37Iy4ujqttVMTY2Bht2rRR2R4VFQVA9gdaXe3u4cOHAGTNgW8mWHXHiYyMxJUrqk9AevnyJUpKSvDw4UP4+/tzfXD9+vWDUFjzdZPXr18jLi4OLi4u8Pb2Vnk9KEjWdRMdHa3ymp+fn0qTOwA0atSI+5yVkZaWhiVLluDQoUN4/PgxcnNzlV5PSUlRu59iv3ip0vfNyspS+3t69eoVANnvSdH58+excuVKREVF4eXLlygqUp5IJuXlS7g10E7lhhJoFSj2f7Zu78hjJKoauJrj3ak+2LrmFgBg6bdR6DOsCcwtxTxHRnTR9OnTsXTpUpw5c6ZS5efPn4+UlBRuYMiSJUvQu3dv7g9n8+bN4eHhgTVr1mDBgsrdv+ro6Kj2+btpaWkAgHXr1pW7f05O+Q+nLz3OkiVLKnWczMxMAKi1W2GysmSTt5Q1Wrh0e2kciqytrdXuY2BgoHbwjzqZmZno0KED4uPj0bFjR0yYMAG2trYwMDBAZmYmVq5cicLCQrX7NlCT0ErP7/Hjx3H8+PEy31fx97R7926MGDECxsbG6NOnD7y8vGBmZgahUIjTp08jMjIShRJJpT5PTaAEWgU3L8ubBhSbTOuKUR/44NiuOLx6noeMV/n49fsr+GJxV77DIjrI0VH2BfLNGog60dHR+PHHH/Hrr7/CxcUF2dnZePr0KdcvCgBubm6wt7fH3bt3Kx1DWQ+vt7KyAiCrgaqroVZW6XGysrJgaWlZYfnSJFVWLay6SuN5c/KKUs+ePVMqV9PWr1+P+Ph4hIaGqtQYo6Kiyr0lSd3vqjTOlStX4tNPP61UDPPmzYNYLMbVq1fRokULpdemTZvGjWzWFuoDrSTGGG5fkfd/etfBWX9MTA0x9Ut/bv3PVTeQ8DCDx4iIriptfmvcuHG55YqLizFlyhT06tUL77//vtJrb9ZWCgoKaiS2Tp06AQDOnj2r1eOUlj969GilanWlTaolJSWVOr6FhQW8vLyQkpKC2NhYldcjIiIAoFKjizURFxcHAGpHT2uSuDT5PcXFxaFly5YqyVMqleLcuXNVjqG6KIFWUnJ8NrLSZf/gxUYiuLhb8ByRer0GecDHX5bcS4oZfvyy6gMzCAFkI1bT09NVticmJmLGjBkAZKNOy7No0SLExcUpNadaWlrCxcUFR44cQXFxMQDZH+DXr1/Dx8enrENV2uTJk7lBKOoGyEilUpw+fbrC48yYMQOGhoaYNWsW12+qqKioSOmPv7+/P7p06YIbN27ghx9+UCmflpam9CXBzs4OAJCUVPkpIadMmQLGGGbPnq2UeFNTU7Fw4UKuTG0ovcXmzXMXHR2t0uddGe3bt0f37t3xzz//cBNvvOn27dt4+fKlUgyxsbFK9+AyxjB//vwKB4XVBmrCraQ7V+W/RDcvqzKbj/gmEAjw0dyOmDH8IBgDIg8l4OzRRHTv5853aKSe2bFjBxYvXoxevXrB09MTFhYWePToEQ4ePIiCggIMGDAAX3zxRZn73717F9999x2WLVvG3Qda6ssvv8TMmTPRvXt3BAQEYOvWrTA3N8fHH39c7bjt7Oywc+dODBs2DJ06dULv3r3h4+MDoVCIpKQkREVFqSQzdby9vbFhwwZMmTIFPj4+6N+/P5o1awaJRIKkpCScPXsWDg4OuH//PrfPli1bEBgYiG+//Ra7du1CYGAgGGOIjY3FsWPHcP/+fS4R9e7dGzt27MDw4cMxYMAAmJiYwN3dXeWWIUVffPEFDh8+jL1798LX1xcDBgxAXl4eduzYgZcvX+LLL79Et27dqn0O1ZkwYQKWLFmCzz77DBEREWjatCliY2Nx4MABDB8+HNu3b6/yMf/66y8EBQXh/fffx6pVqxAQEABra2skJyfj1q1buHPnDqKiorgug1mzZmH69Olo27YtgoODYWhoiPPnzyMmJgaDBw/G/v37a/pjl4sSaCXdvipvvm3TsXKjBPnSrJUd+o1ogiM7ZE0ui2adQaebY2FoqN1J70n91qtXLzx48ADR0dGIiopCbm4urK2t0a1bN4wfPx7jx48v84tkSUkJpkyZgoCAAHz00Ucqr3/yySfIzs7Gr7/+iuvXr8PX1xfLly+v9AjcivTu3Ru3bt3CTz/9hKNHj+Ls2bMQi8Vo2LAhgoKCKj2Jw3vvvQdfX18sXboUEREROHbsGMzMzNCwYUOMGDECo0aNUirv6emJ69ev48cff8SePXvw888/w9jYGB4eHvi///s/LhEAwNSpU5GYmIi///4bP/74I4qLi9GzZ89yE6hYLMbx48exbNky/PXXX1i9ejUMDAzg6+uLFStWYMyYMZqdsEpo2LAhzp49i6+//hrnzp3D0aNH4e3tjbVr1+Ktt97SKIG6urri2rVrWL16NXbt2oWtW7eipKQEDRo0QMuWLfHJJ5+gdevWXPlp06bByMgIK1aswKZNm2BiYoLu3bsjPDwcu3bt0noCFbAqPAqgffv23Mwi+mZC0D+4dk7WbBC2NhBd+7hVsAe/MlLzManPHuTlyEakfbmkGybO9OM3qL8U/tiOrdv3qd67d0+ln0WJnjyNRV+MHj0a27dvx9OnT+vcnLikChTzU/v25Rat8N94JVANtBJKSqSIiX7FrTdvo3ojb11jY2+C92a0we+LZdOqrVlwCYPGNKtT967Wa5ScdMrDhw9hZGQEB4e6NziQ1F00iKgSHt/LQH6urCZnam4Ie6f6kYTeGe8NV0/Z8Pvc1xKsCtX+ZMuE1GUrV65EcHAwoqOjMXToUKXZfQipCCXQSlDs//RsZs1fIFVkKBZh+rfyZoxdG2Jw78arcvYgRL+sWLECFy9exJQpU/Dbb7/xHQ6pZ+jrViXcvS5POn6d6sb8t5UVEOiKDj1dcCUyBYzJHnn2x5GhdXYUMSHaFB8fz3cIpB6jGmglPLydyi23aFv/+kimfe0PoUiWMC9FJOPc0To8+IUQQuoJSqAVYIzh4e00br1xcxseo9GMexNrDBjZlFv/YfY5FBdXbv5LQggh6lECrcDTxNfIyZbN9m9kLIJ9g/oxgOhNEz71hYmZrMU+/kEGdm+6V8EehBBCykMJtAIPFGqfzm4W9bbv0MbeBKNCWnHrK+dFITenqJw9CCGElIcSaAUU+z+bt7bjMZLqC57SEnZOJgCAjNQCbFym+txAQgghlUMJtAIPbskTqI9/3XoGaFUZmxhg8qy23PofP13Hq2cVP46KEEKIKkqgFVAaQORd/wYQvemtdxpzn6OwoAS//6CfUzMSQkh1UQItR36eBIlxmbIVAeDR1JrPcGqESCTElP+T10K3/34HTxOzeYyIEELqJ0qg5Yi9m47SqfZt7IxhZKwb80507OmClu3kzwxd+98rPEdECCH1DyXQcigOIHJvYs1fIDVMIBBgyufyWujeP+8j4WEGjxERQkj9Qwm0HLF307lln3b1bwai8vgGNEC7LrLHNkmlDD8vuMxzRKQu69OnDwQCASIjI8ssM336dAgEAqxbt06LkRHCH0qg5Xh8T55Am/jU71tY1Jk0y49bPvy/WMTFpJddmOi169evQyQSwd/fv8wyly5dAiB7bjAh+oASaDke35c3a7o3seIxktrRws8BnXq5cuvrfqQRuUTVo0ePkJ6eDh8fH5ibm6stk5eXhzt37sDIyAitWrVSW4YQXUMJtAw52UV4npwDABAIgYZuFjxHVDvGfdyGWz70dyyePM7iMRpSF129KvtiFRAQUGaZa9euobi4GL6+vjA0NNRWaITwihJoGR7flzdn2tqbQGSgm6fK29deqS/0jyXXeI6I1DWVSaAXL8oe1k7Nt0Sf6MZ9GbXgkULzbSMv3Wu+VTTmw9a4fuEZAGD3n/cxfU5HNHBV31RH9E9pAt2+fTsiIiLUlqH+T6KPKIGW4ZHCgJr6PgduRXwDnNCyrQNiol+hWCJF+PJofLO0O99hkTqAMYboaNmcycePH6+wPCVQok8ogZZBsQm3SUtbHiOpfQKBAGM/bI25H5wCAPzv9zv48Nv2sLYz4TmyuslH/DPfIVTa3aIZ1dr/4cOHyMrKQufOnXHhwgW1ZTIyMmBrawtTU1O0bNlS6bW1a9diyZIlePbsGXx8fLBixQp0705fzohu0M2OvRqgPALXmr9AtKRjoAs3R25RYQl2rL/Lc0SkLihtvm3btm2ZZa5fvw4A8PPzg0gk4rZv374dM2fOxLfffovo6Gh06dIFb7/9NpKSkmo3aEK0hBKoGvl5EiTH/zs/rABw8bDkNyAtEAgEGDFFXnvYvPomiopKeIyI1AWlCbRdu3ZllilNoG823y5btgyTJk1CSEgIWrRogdWrV8PZ2Rm//PJL7QVMiBZRE64aCQ8yuTlwrW2MITYSlb+Djug5wAPrl1xH+qt8pL3Mx7GdcRg0tjnfYdU51W0WrU8qk0BL+0gVE2hRURGuXbuGL774Qqls3759y2wKJqS+oRqoGo8U+j9dPHTz/k91xEYiDFZImOHLo8FKv0kQvSOVSnHjxg2IxWL4+PiUWU5dDTQ1NRUlJSVwcnJSKuvk5ITnz5/XTsCEaBklUDXiH2Ryy7o4hV95Bo1pBkOx7LK4fzOVu72F6J979+4hJycHPj4+EIvFasvk5OQgNjYW5ubmaN5ctbVCIBAorTPGVLYRUl9RAlUjMTaTW27Ssv4/RLsqrO2M8dY7jbn1TStu8BcM4dW1a7JJNcprvr158yakUinatWsHoVD+58Te3h4ikUiltvny5UuVWikh9RUlUDW4h2gDcPXU7UkU1Bk+ST6Y6NT+x3j25DWP0RC+TJgwAYwxrF+/vswyXbt2BWNM5SktYrEY/v7+KveOHj9+HF26dKmVeAnRNkqgb2CMIUGhBqpPfaClPJpaw69zAwAAkwK7NsTwHBGpjz7//HNs3LgR69evx7179zBz5kw8ffoU06dP5zs0QmoEJdA3pL7IQ16OBABgKBbC2taY54j4MWhMM255+7o7KC6W8hgNqY9GjRqFFStW4LvvvoOfnx/OnTuHQ4cOwd3dne/QCKkRlEDfoNj/6dDATG8HPHTp3Qg29rIvD+kv8xF5MIHfgEi99NFHHyEhIQGFhYW4du0aevTowXdIhNQYSqBvSIyTP87L1VP3J1Aoi6FYhP7vNuXWt/16m8doCCGk7qEE+oaEh5ncsq7PgVuRASOborQCfvHUE3pWKCGEKKAE+oYkhRG4bjr+GLOKNHA1R/seLgAAxoCdf9D8uIQQUooS6BsUR+DqcxNuKcXBRDv+iIFEQvPjEkIIQAlUiVTKkPRI3kypD5PIVySgpwvsnGSPNctKL8C5o/QkDUIIASiBKnn+5DWKCmU1LBNTA5hbqp++TJ+IDITo844Xt/7PRronlBBCAEqgShRH4Do2NOMxkrqlzzB5Ao08mICM1Hweo9EemkifEN1UU/+2KYEqUOz/dGui3wOIFLl5WaGFnz0AoKSE4eDfD3mOqPaJRCJIJBK+wyCE1AKJRKL08HdNUQJVoDgHrlcL/b6F5U19hzfhlnfqwdR+FhYWyM7O5jsMQkgtyM7OhoVF9adppQSqQHEWokZ6OIl8eQIHenAPFo+9k4b7N1N5jqh22draIiMjA6mpqSgqKqLmXELqOcYYioqKkJqaioyMDNjaVr+SZFADcemMRD2fRL485pZidO3TCBEHEgAAe7fcg7dvd36DqkVGRkZwc3NDeno6EhISUFJCt+8QUuelKnyxv3dP5WWRSAQLCwu4ubnByMio2m9HCfRfEkkJkuPlTXYN3SmBvqnv8CbyBLr5Pv5vUVcYGOhuI4aRkRGcnZ3h7OzMdyiEkMpoKX8UI7TQaqS7f/2q6GnCa5SUyE64uaUYJqaGPEdU97Tt0gC2jqX3hBbiUkQyzxERQgh/KIH+S3EErpMr3cKijkgkRK+BHtz6gW0P+AuGEEJ4Rgn0X4ojcD2aWPMWR13Xa5Ant3zsn0coyC/mMRpCCOEPJdB/KdZA6RaWsjVrbcf1DxfkFePM4QR+AyKEEJ5QAv1XksIsRC40iXyZBAIBggbLa6H7/6JmXEKIfqIE+i+lp7DQJPLlUmzGPXM4EdmZhTxGQwgh/KAECqAgvxjPn7wGAAgEgHMjc54jqtvcvKy4h40XS6Q4ufcRzxERQoj2UQIF8ORRFnfLkKWNEQzF1Z8jUdcpNuPu20rNuIQQ/UMJFMojcJ1dqfZZGYEDPSAQyJavRKbg1fNcfgMihBAtowQK5f5Pj+Y2/AVSjzg4m6F1BycAsgk/ju6M4zkiQgjRLkqgUB6B25gSaKUpDiaiZlxCiL6hBIo3RuA2phG4ldWjvxuEIlk77t1rL/Hs34FYhBCiDyiBQrkP1NWdEmhlWdoYw69TA279+G4ajUsI0R96n0BzsouQ+jwPACAUCuDoQvPgVkXPt9255UPbY3mMhBBCtEvvE2jSo0xu2drOGCKR3p+SKunaR96Me/vKC7xIyeE5IkII0Q69zxYJDzO5ZXoGaNVZ2RrDN8CJW6dmXEKIvtD7BJqoMALXo5k1f4HUYz36e3DLh7Y/5C8QQgjRIkqgik9h8aZbWDTRtW8jCIWyZtybl1/g1TOaVIEQovsogSqOwPW04i+QeszGzgRtOv7bjMuAY9SMSwjRA3qfQOkpLDWjR3/F0bjUjEsI0X16nUAz0/KRnSF7FJfIQAA7JxOeI6q/uvZ14+bGvXnxOc2NSwjReXqdQBUHENk5mEJQmgFIldk6mCjNjXty72OeIyKEkNql1wmUbmGpWT0UJlU4+Dc14xJCdJteJ1DFAUSNva15i0NXdFNoxo2+8AxpL/P4DYgQQmqRfidQhQFEjb1t+QtER9g5mqKVvyMAasYlhOg+/U6gCn2gLjQCt0b0oLlxCSF6Qm8TKGOMbmGpBd36yhPotXNPkZGaz2M0hBBSe/Q2gaY+z0N+rgQAYCgWwsrWiOeIdIN9A1O0bOcAAJBKGU7tj+c5IkIIqR16m0AVa5/2TnQLS01SnFThyA5qxiWE6Ca9TaDxDzO4Zer/rFnd+7lxyxcjkpGVUcBjNIQQUjv0NoEqjsBt6kMjcGuSY0NzeLexBwBISxgiqBmXEKKD9DaBxj/I5JYb01NYalz3/vJa6NGdcTxGQgghtUNvE2iCQhMuPYWl5nXvJ+8HvXDiCV5nFfIYDSGE1Dy9TKBFRSVIjs/m1l08aBq/mubsZsE1jRcXSxF5MIHfgAghpIbpZQJNfpyNkhIGADCzMISJqSHPEemm7oqjcXdRMy4hRLfoZQJVbL51cjHnMRLdptiMe/ZIInLz6V5bQoju0M8EqjAC182L+j9ri6unJTdAq1gixZlbPjxHRAghNUc/E6jCY8yat7bjLxA9oFgLPXbFj79ACCGkhullAlWcRMG9mTV/gegBxdtZzt5qibxCMY/REEJIzdHLBKpYA21Et7DUKvcm1nBvIjvH+UVGOHerBc8REUJIzdC7BJqVUYD0V7InhAiFAjg2NOM5It2nOBr32NW2PEZCCCE1R+8SqGLt08beGEIhTSJf2xT7QSNv+qCgiG4bIoTUf3qdQJ3daAIFbfBsbs1NVpFXYIwLd7x5jogQQqpPDxOofABRkxY0ibw2CAQC5WZcGo1LCNEBepdA4xVqoE1bUQLVFsVnhEbcaI2iwhIeoyGEkOrTuwSqWANt1JhG4GpLk5a2cHVIBQDk5Jsg6uQTniMihJDq0asEWlIiRWJcFrfu6kkP0tYWgUCAPu1vcOvHaG5cQkg9p1cJ9FlSDtd0aGxiAAsrmptVm/p2uMEtn9gTB4mEmnEJIfWXXiXQ+Afy5lsHZ1MeI9FPrT0T0cA2HQCQ87oYl0+n8BwRIYRoTq8S6KN76dyyZ3MbHiPRTwKBci2UmnEJIfWZXiXQuBh5Am3hZ89jJPqrr2I/6D+xKC6W8hcMIYRUg14lUMUaaGNvuoWFD75eCXC0zgQAZGdKcPUMNeMSQuonvUmgjDGlBFo6wTnRLqGQKY3GPbT9IX/BEEJINehNAn2enIPc1xIAgKFYCFsHE54j0l9vB1znlo/seEiTKhBC6iW9SaCKtU/HhuYQCGgSeb74NYlHwwbFAIDcnBKcOZzAb0CEEKIBvUmgigOIPOkh2rwSCIC+QXnc+v6/HvAYDSGEaEZvEujje/J7QFu2deAxEgIA/XrLE2jkwXi8zirkMRpCCKk6vUmgcTFp3HJjb7oHlG+e7sVo1qQIACCRMBzf/YjniAghpGr0IoHKRuDKa6DuTa35C4Zw+vXO55b3b7nPYySEEFJ1epFAX6TkIidbVtsxFAth50gjcOuCPr3yIBAwAMCVs0/xIiWH54gIIaTy9CKBxt6RN986udAI3LrC0UEKfz9Z3ydjwOH/xfIcESGEVJ5eJND7N19xy01a0gxEdYliM+7ezTE8RkIIIVWjFwn0wS15DbRNRyceIyFv6tU9H2JDWTPuwzsZSrcbEUJIXaYnCTSVW6YaaN1ibs7QtVMBt753Mw0mIoTUDzqfQAvyi5EQm8mte9AkCnXOwH7ye0L/Cb9DD9omhNQLOp9AY++mQSqVNRFa2RrBxNSQ54jImzp1LICDnSxpZqYX4cyhRJ4jIoSQiul8AlVsvnXzoiew1EUGImCAQi1054a7PEZDCCGVo/MJ9P5NeQJt5e/IYySkPIPfzuWWzx5NpHtCCSF1ns4nUMUaaMu2lEDrKteGJWjn++89oVIaTEQIqft0OoEyxvDwtvwWFq8WNAduXTZkgLwWunP9ba7vmhBC6iKdTqDJ8dncFH5iIxHsG5jyHBEpT2D3fJibSQEAKUm5uHw6meeICCGkbDqdQO9cfcktu3pa0hR+dZyxEdC/j3ww0dY1N3mMhhBCyqfTCfT21RfccpsONANRfRA8RN6MG3EwASkJ2TxGQwghZdPtBHpFnkDbdmnAYySksjzdi9HRXzYzEZMC2369zXNEhBCins4m0OJiKWKuyyeRb97GnsdoSFWMHCavhe5Yfwf5eRIeoyGEEPV0NoHG3U1HQX4xAMDU3BB2jjSAqL7o3LEALs6y311OtgQHtj3kOSJCCFGlswn0zjV5861HU2v+AiFVJhIBwUPltdAtq26AMbqlhRBSt+hsAqX+z/pt8Nu5MDaW3dISdy8DUSef8BwRIYQo0+EEKr+Fxa+TM4+REE1YmDMMUpgf97fFV3mMhhBCVOlkAs3LlSDurnwGoqY+9AzQ+mjsyByIhLKm26tnniI66hnPERFCiJxOJtCbF5+jpET2h9fWwQRmFmKeIyKaaNigBP3ektdCf6daKCGkDtHJBHrlTAq33Ko9TSBfn00YkwOBQPZl6MzhRNy78aqCPQghRDt0MoFePfuUW+7W143HSEh1ebgVo1f3Am593Y9UCyWE1A06l0AL8otx6/Jzbt03gEbg1ncTx77mlo/teqTUv00IIXzRuQR66/JzSIpktz9Y2RjB1sGE54hIdTVvKkHXgH+n92PAyv9c5DkiQgjRwQR69Yy8+dbHn/o/dcW0KfJJ5U/tj8eNizQilxDCL51LoFfOygcQde9H/Z+6olkTCfoGyUfkLvv2As1ORAjhlU4l0MKCYty8KO//9OtMEyjokpBJ2RCJZEnz2rlnOLHnMc8REUL0mU4l0MunU1BYUAIAsLI1gr0TTSCvSxq5lGC4wvNCf5h9lntgACGEaJtOJdDThxK45YBAV/4CIbVm6oRsWFnKviQ9S8rBxuXRPEdECNFXOpNAGWOIVEigfYY15i8YUmusLJnSgKLfF1/Bk8dZPEZECNFXOpNAY++k4VmS7H5BA0MhWvk78RwRqS1DB+ShWZMiAEBhgRRhH0XQgCJCiNbpTAJVrH16+9rDwFBnPhp5g0gEfDUrE8J/J5q/eCoZezff5zkqQoi+0Zkso9j/2W+4F3+BEK3w8ZZg5DD5gKJFn5/B8+QcHiMihOgbnUigz568Vrp9pXPvRjxGQ7Rl2uRsODeQjcLNyZbg2ynHIZVSUy4hRDt0IoEe+OsBSrvAPJpZw8rWmN+AiFaYmDCEfpXBPa3l0ukU/LnyBr9BEUL0Rr1PoIwx7NvygFsfNtGbx2iItvm1KcKE0fKm22VzLuD6BZrmjxBS++p9Ar199SUeP8gAAIhEAvQa5MlzRETbpk7MRsvmslG5JcUMs0YfxqvnuRXsRQgh1VPvE+i+LfLRl74BDWBiashjNIQPhobAf0PTuQkWUp/n4bNRh1FYQLMUEUJqT71OoPl5Ehz8+yG3PuoDHx6jIXxydirBgjny/tAbUc/x7ZQTNKiIEFJr6nUC3bflAbIzCgEA5pZimjxezwW0L8Qn0+SzFB3ZGYefvj5PkywQQmpFvU2gJSVSpRGXI6a0hFAo4C8gUieMGZGDEUPlg4o2rbiBn+df5jEiQoiuqrcJ9PD/YpEQmwlANnXfsIkt+A2I1AkCATDr4yz07JbPbfv1+yv4ef4lqokSQmpUvUygRUUlWLNQXqsYOKopTM1p8BCREYmAhXPS0SWggNv2y3+v4PtZZ6hPlBBSY+plAv1rzS0kxcmewGFgKMSkWW15jojUNWIxsCgsDZ06yJPoX2tvY9aow8jNKeIxMkKIrqh3CTQlIRs/L5DXPkdPawVzSzGPEZG6ykgMLFmYhrcC87htJ/Y+xrjuO5H4b/M/IYRoql4l0OJiKeaEnER+rgQAYGVrhLEftuY5KlKXGRoC87/NwOhg+cCi2LvpCO74N3ZviqF+UUKIxupVAl057yKuRKZw66FrAmEoFvEYEakPRCLgs4+yMHd2BgwNpACA/NxizA05hWmD9yPhYQbPERJC6qN6k0D/XHUDG5Ze59aHjGuO1u3podmk8gb1z8O61alwc5Vw284fS8I77bZhxdwoZGcW8hgdIaS+qfMJVCplWB12CT98cY7b1rSVHT7+T0ceoyL1lXczCTb9+goj3smB8N9ZiyRFUqz78Rr6NNmE5XMuIPVFXgVHIYQQQMCq0AnUvn17XL16tTbjUfLoXjoWzDiNq2efctucG5njtwODac7beqjheStu+bXrV7KbNnl0/6Ehlqyywt17RkrbDcVC9BzggSHjvNH9bXeIqZuAkPpB8W+KFsY31LkEyhjDnWsv8dfaWzi0PRbFEin3mkcza6z639swMaPkWR/VtQQKAFIpcPSkCcK3mCMpWXU0t7mlGB17uqDzW43QobsLPL1tYGBQ5xtuCNFP+pRACwuK8SIlFy9SchAXk46Y6y9x4cQTPE/OUSnbf0QTfLawE0T0x6veqosJtJRUCpy5YIzN28xw937ZD2Q3MhahWWt7eLWwgYu7JZzdLdDQzQJ2jqawtBbDwtoIxiYGENShz0aI3tByAjWo9XdQY+W8KGxfdwdZ6RUP2nBxt8DcVT3QpKWdFiIj+kooBAK7FSCwWwESkgxw+Lgpjhw3wotXyrXSwoIS3L7yArevvCjzWAaGQlhYiWFkYgBDsQiGhkLZ/8VCGBgKYWgohFAkhFAogEAACIQCWNsa48c/+9b2xySE1CBeEihjKDd5CkUCNPa2wTvvecPT2wZSKfDwTpoWIyS1oaHCcubLV7zFURFrI2DMIGD0QCD5mRGib1vg2i1zxD42RnqWSYX7F0ukyEgtqLCcIvsGppqGSwjhSZWacO3t7eHh4VFjb/7wdiokCn2cYiMRhEIBCotfw8jAosbeR9/U9fNnaZINkVBacUEevM4vhIWJUcUFAQAMYFLZj+orYEygtC77j/qmXXPHhjAxN6titHXLq1ev4ODgwHcY9RKdu+qpzfNnb2+PI0eOqH2tSglUW7Q92lfX0PnTHJ07zdG50xydu+rh6/zRiBxCCCFEA5RACSGEEA3UyQT6wQcf8B1CvUbnT3N07jRH505zdO6qh6/zVyf7QAkhhJC6rk7WQAkhhJC6TqsJ9MmTJxgxYgSsrKxgaWmJ4cOHIykpqVL7CgQCtT83btyo3aDriOTkZHzyySfo3LkzTE1NIRAIkJCQUKl9CwoKMHv2bDg7O8PExASdO3fGmTNnajfgOqQ6507fr7udO3ciODgY7u7uMDExQfPmzfHNN9/g9evXFe6r79dddc6dvl93R48eRVBQEBo0aAAjIyO4urpi5MiRiImJqXBfbV53WmvCzcvLg6+vL4yMjPDdd99BIBBg7ty5yMvLw61bt2BmVv49cAKBAJMmTcK0adOUtrdp0wamprp/E/rp06cxatQo+Pv7o6SkBMeOHUN8fHyl7ssdN24cDh48iCVLlqBx48ZYs2YNDh8+jKioKPj5+dV67HyrzrnT9+uuU6dOcHNzw9ChQ+Hq6oro6GiEhYXB29sbFy5cgFBY9ndwfb/uqnPu9P2627ZtG65fv46AgAA4ODggKSkJixcvxpMnT3D79m24u7uXua9WrzumJStWrGBCoZDFxsZy2x4/fsxEIhFbunRphfsDYHPmzKnNEOu0kpISbnndunUMAIuPj69wvxs3bjAAbMOGDdw2iUTCmjVrxgYPHlwbodY5mp47xui6e/nypcq2TZs2MQDs5MmTZe5H153m544xuu7UuX//PgPAfvrppzLLaPu601oT7r59+9CpUyc0adKE2+bp6YmuXbti79692gqj3irv22p59u3bB0NDQ4waNYrbZmBggNGjR+Po0aMoLNT9h0hreu4I1M7u0qFDBwBASkpKmfvRdaf5uSPq2dnJ5kM3NCz7aVzavu609pfl7t27aNWqlcp2Hx+fSrVrA8Avv/wCIyMjmJqaIigoCGfPnq3pMHXO3bt34enpqdLs4+Pjg6KiIsTFxfEUWf1B152yyMhIAECLFi3KLEPXnXqVOXel6LoDSkpKUFRUhNjYWEybNg0NGjTA6NGjyyyv7etOawk0PT0dNjY2KtttbW2RkZFR4f7vvfce1q5dixMnTuD3339HWloagoKCcPr06VqIVneUd95LXydlo+tOWUpKCv7zn//grbfeQvv27cssR9edqsqeO4Cuu1IBAQEwMjJCs2bNcOvWLZw6dQqOjo5lltf2dafVp7Goe0Yiq+QYps2bN3PL3bt3x9ChQ9GqVSvMnTsX586dq7EYdQ1jrFrnXd/RdSeXk5ODoUOHwsDAAOHh4eWWpetOWVXOHUDXXanNmzcjOzsbjx8/xk8//YQ+ffrg3LlzZQ4A1PZ1p7UaqI2Njdrsn5GRofYbQ0UsLCwwcOBAXLlypSbC01m2trZlnvfS10nl6et1V1BQgCFDhuDx48c4evQoXF1dyy1P151cVc+dOvp63bVo0QIBAQEYM2YMTp48iZycHCxevLjM8tq+7rSWQH18fHD37l2V7TExMWjZsqVGxyzr2waR8/HxQXx8PPLy8pS2x8TEQCwWKw3qIpWjb9edRCJBcHAwLl++jEOHDqF169YV7kPXnYwm564s+nbdvcna2hpNmjQptx9T29ed1hLokCFDcPHiRTx+/JjblpCQgPPnz2PIkCFVPl52djYOHjyIgICAmgxT5wwZMgQSiQQ7duzgthUXF2P79u3o27cvjIwq++xLAujfdSeVSjFu3DicPHkSe/fuRadOnSq1H113mp87dfTtulPnxYsXuH//Pry8vMoso/XrrsZvjClDTk4O8/LyYq1atWJ79uxhe/fuZW3atGGenp7s9evXXLmEhAQmEonY/PnzuW1LlixhU6dOZVu3bmURERFs48aNrFWrVszQ0JCdOXNGWx+Bdzt27GA7duxg06dPZwDY2rVr2Y4dO9jp06cZY+rPHWOMjRo1illbW7N169axEydOsODgYGZkZMSuXbvGx8fghSbnjq47xp2vOXPmsKioKKWfJ0+eMMbouiuLpueOrjvG3nnnHbZgwQK2Z88edurUKfbrr7+y5s2bMysrK/bgwQPGWN247rSWQBljLDExkQ0fPpxZWFgwc3NzNnToUJUb2uPj4xkAFhoaym3bt28f69KlC7Ozs2MGBgbM1taWDR48mF26dEmb4fMOgNqfnj17MsbUnzvGGMvLy2OzZs1iTk5OzMjIiHXs2JFFRERoPX4+aXLu6LpjzN3dvcxzV3qu6LpTT9NzR9cdY4sXL2bt2rVjVlZWzMTEhDVr1ox98MEHSvmiLlx39DQWQgghRAM0RQshhBCiAUqghBBCiAYogRJCCCEaoARKCCGEaIASKCGEEKIBSqCEEEKIBiiBEkIIIRqgBEpIDREIBBAIBBAKhXj06FGZ5Xr16sWV3bhxo9JrkyZN4l4LDQ0t8xibNm3iygUGBiq9dvr0ae610h9TU1M4OzujR48emD17NqKjo6vzUQkhoARKSI0yMDAAYwx//PGH2tdjY2MRGRkJA4PynyRoYGCADRs2oKSkRO3r69atq/AY7u7uCA0NRWhoKD777DMMGjQIubm5+Omnn9CuXTuMGzcOOTk5lftghBAVlEAJqUFOTk5o3749wsPDUVxcrPL6+vXrwRjDoEGDyj3OoEGDkJycjCNHjqi8du/ePZw/fx6DBw8u9xgeHh4ICwtDWFgYvv/+e6xbtw7Xrl1DdHQ0Wrdujb/++gvvvvtu1T4gIYRDCZSQGhYSEoLnz5/jwIEDStslEgk2bdqELl26wMfHp9xjjBs3DiYmJli3bp3Ka6Xbpk6dqlF8fn5+OHHiBBwcHHDkyBHs2bNHo+MQou8ogRJSw8aMGQMzMzOsX79eafu+ffvw4sULhISEVHgMa2trvPvuuzh48CCePXvGbS8sLMSff/6Jnj17olmzZhrH6OjoiGnTpgEAtm7dqvFxCNFnlEAJqWEWFhYYPXo0jhw5guTkZG77unXrYGlpiZEjR1bqOCEhISguLlYaaLR7926kpaVVKglXpHTw0eXLl6t9LEL0ESVQQmpBSEgISkpKsGHDBgBAYmIijh8/jnHjxsHU1LRSx+jWrRtatGjB9ZsCsiRsY2OD4ODgasfo4uICAHj16lW1j0WIPqIESkgtCAgIQOvWrbFhwwZIpVKsX78eUqm0yjXHqVOn4vHjxzh16hTi4uIQERGB8ePHw9jYuNoxliZlgUBQ7WMRoo8ogRJSS0JCQpCYmIgjR44gPDwc/v7+aNu2bZWOMWHCBBgZGWH9+vVcTbQmmm8B4OnTpwAABweHGjkeIfqGEightWT8+PEwMTHBtGnTkJKSgg8++KDKx7C3t8ewYcOwe/dubNiwAZ07d0arVq1qJL6IiAgAstoyIaTqKIESUkusra0xYsQIJCcnw8zMDGPGjNHoOCEhISgsLMSrV69qrPb58uVL/PbbbwBkt8wQQqqu/KlMCCHV8t1332H48OFwcHCAhYWFRsfo1asX9u7dC6lUin79+lU7pps3b2LChAlITU3FgAEDMGTIkGofkxB9RAmUkFrk5uYGNze3ah1DIBBolOQSEhIQFhYGQDaJQ2pqKq5du4Zr164BkNU8S2uhhJCqowRKiI5KTEzE/PnzAQDGxsawtrZG06ZN8cUXX2DcuHHw8/PjN0BC6jkBKx3LTgghhJBKo0FEhBBCiAYogRJCCCEaoARKCCGEaIASKCGEEKIBSqCEEEKIBiiBEkIIIRqgBEoIIYRogBIoIYQQogFKoIQQQogGKIESQgghGvh/VBAkbEr1lukAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d3200ffb398473c8e01e5644934f14b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c13dab72613b449da1372413e37c3517",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a0ee580b0a2447f9fd510b6d68e1e98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abfb15c802b04d6a9bc8b37b01746d61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e8eb7fa951fb4d89acd4a0be0718bfb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b58bfd556af482bbfac2159982e38d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a37150084714d85a0f446e254e2f18c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rJwm6dsHzmlMGGtlOOE54Oh5Hf2lLpfUIQ4qLaa/GLBWXaQV87SHZT9ZMRn2WUhiF018Krnaso8gyLXuOhr4wwVIZBfqLv37T9FcwTgcDLp8WvNg8lawhxQnhu3npL+qz6pnz7bmeh0v/74/w2Q3P+n1h0V+jR1XKzEHWucTorwzHx60LdcdDV0k76tsGJlQKWjB5bYEGBJ8EKWtRjWgihCwauJPgGGwGjuuNqeq8WVCkwrAzKONTsgzRUhkF+osPUW/2HTOfykhjlkopJforaWzmddTT3FJZh/zlPA949eBx/OOPXxSuNZqWthz9lVUZSdvmWTjWo/UmmvyoHfVtROGOes46kauj0neeF51cjVCdYbvAo68cwld+8pLymKLpr061VIoQrjuP+EJl1sQu4V2NBv3Fc+bNRuQRnccsFSaAky2VtN0ckzb7Ei2sxvtN96qqQyZbKsJvDYbltgJyuPNILdu4aWQ/lXC8i0py3R09+uvEzKgP/hb1rHk/CvOlcD4VuhZR02m0gwosDNrzcN0//hwA8P5LFwrHuK5XaM2rpOiVsYoic5B2Hvbpr5kTK8JzGB5lS6X56K/ApxJYKmk0LAmVtHGatIEcT714TTy20FJRCRX+OhIbMCYc9eKzy2qpNJJRLz+DckBVCo567VNpPYrem4RPVpJL3ztMuES55YaqFGcYaE6Lor/2Hh3GseFaYe22EkWGFO8KLJWyZY66oz6vxg+EUUARSyVmrSPrIO16SbW/8kZ/MZ+KQpDz7Q1XHeVvo0t/iYEumas+N7A+8M/ckT6zkGId/dV6FO1ToZdWdVw2UeXCkvzEbSZBL0tGvesWmwtA0V/v+fqv8PkfPFdYu61Es5bKo68cwtcffln4bldgqdRcd9TzVITFuaDor7SghrCcS3K7pKSlRX81RX8FE0fpU+Few/GcQmX3kWE8s1Md4tssPDTnU2mE/uKfKW/N8smPejvhNiDUZotpTxWhQpNBFW7ZjDYdN3Hf8vc/xfKP/cDvh+sWdk90rbrr4uBgFfsHoyGdYxHNUIsA8L3Ht+PWB7YI3+095m+NICeBjopQKSD6K5qnkkJ/ZbZUgr/K6C9Oe87hU0mL/ooKleD8jA/ros88iKu/9LOG+5cE+blltXB5P0nqsXxYNe934+ivmi5933p4aA39xQ8a2afiKGiAxvJUohSD53l4esdR9tn1irsnytT14C9GWU330UYzkXWA7xeQFyB6n3VHpBWzOlyLBE9hNHpvruvhX36+lZVnIfqLmolrjhajtMslWdF8v5sZmqq5ReCfw1BNLHnSiE+lVRGOcrMNWyqZ+h5+lnOZQktF018tRxh26uGbv9iKBbfcjeEcCwVNHJ73Df0sgY9FEanSkE+FNC9PHDhhm+LfvBAd007shDg6xnwtIbXZ6HnRJNU65w8To7+yNf7oKwfx1Z9Go/SagWCpNPiSN+86ik98fzN+9NxeAKqQ4ryWClnRKf1uxlJJ9KmEn+PoryzXfHHfQMP9ygLZWm50J81MQiVmPai7LvOptNtSOUGFSqiBffoeP1lq/RM78L47ftVUe+Q/GeYtFS4iDACODddw+LhPIYX7WDROf/HjTCVgispTEUIh6y6qCk1x5+EhvOYT92PT1oOFXLMINJv8qKpzxtd0a4b+Wv/ETnzpwS3pB2bsH6FRxYG0/IFh0VJJ8+1VnWxCJbR4osflTdqkeaQq4slfL06oZMnR2Nii8ds0/eVln8v8861x0Xqu55fXsUyj7T6VE5T+8uG4HoYCC+WvvvtrAL4WaKZkEMtgzkROm5I363r9534MANi69mouWqaRa0TpL1VEkGoB2H1kGLMmVhq6L9msVmlZe4+NwHE97Dg8hP7MLbcWzdb+cjwPNcfPJ6K8DCqoKOf/NMKNFzWfaznoL6pOPBhYKGFIcXJ7zFJJGadJmrXQ7yYeBvUtzVIZkoRKUpa/jOd2HwMAdAcZ6EVBfqx0D7KwGBypo7tksfnZyHbCKkuF/lqmAcs0Evd2agVOSEslqbT0UBM0mMpRL/tUhOs3QX+pollUn+Xxs/vIMC76zIP4uwca27xLHtAq7ZwWDFlLHE2kLZTp54XfsZpujmipZN33vMgCn3lCimtcdCLgWyoe17e4xT7rro1Jtb/4frfSpxIb/ZXhorTYF13ORH5PYd5P+N1Q1cFFn34Q9zy9i33H57alXyP8XJeEkWUaKJlG5vFaFE5IoUKPWDXeKDKmEdQY7xulv5J2w2tIqCgoM5FDVbd5eMin3DY8vTvzteS2ATUfTN/JWuJoIg/9BYgLC91fXQopzhrFVOTWAbUc+R6ycHA9X0lIC7/O6lNJCtHPm6fiKBQ2VXvy3iSNZNTT8yFLtShEHPVkqXDfHR2u4dhInYWvA40lP6oUS9lS0cmPbUBSSO9AE0IljKUX48SBOEtF/JvpGimWSpz1RWXLG7XA5AGtmtSkeTVj3bUKeegvQC2oZforK41TZDSe+K7Vx/z8hf14SeF0VjlqB0fqzLGenqeSfA+hFR39La+jPrRUVD6V8LOs2NBls1gfvL+mSP+DLKDI2hKy7GuiFQk0xmQIeSpSMVvbNFCyTF1Qsh2gl8qbzFSJtRkqp8YGvn9umXuRqgWIHwhZNSPmeIyjv2K0TtI2Gy0tImvjSZZKnsi5otFsXTd6DzUnLKLJa7D8gpnVUnGL9KlkqPb7oe8+hdsfejF6rmK1P151UitLjDBLJblvScl6YiWJ5HZUCH0qyfTXYDBvLSpT0oDfkn8+RWr1EZ+K4nnSmlGtu7j317swUnc4ZSb9Gkk+FdMILBVNf7UPu7nS5lQmuilLJXhptHBXbFMIR5XhCtpbtmvQObyZ7yi0Z3mBoAkTt/DLpSTCfqm1LFXbRdJfR4ZquPpLP206zLPZ0Gp6Bu/4p19g0d/cAy9w3NNvYoBExjZjnm0zUOU5yajWXaXyoBIqg9V6qlVHcyFN8WHPPIX+aoZaSrJU+MvR+LZMA8s/9gN84+dbg/PTXxZvyRW5VXR0DhH95UW++8WLB/CBbz2Gz9zzbEPbCcdZ1kDgU7FMTX+1A/SyqLQ5AJYoFLdvdBJqjP7yB3alZMX6VOqOmPWeVaOmcyiCBwijkwA+DDE8x3E9NknihMp/PbkTF376wSjvLs2tJEvleIGWyvZDx/HMzqMsIqdRNO9T8f8+u/sYXE/UWOuuq8wzytyXAiZ1Fo3fcf3trL9w33N49JVD7HulUBlxUkPbacfStO4nWTx5Q4qJWk6rUszP26PDdWzZOxC5fhwES6VArT7iUyGhorBUKN9ry95jDSXwyvOdv65JPpVOrP21YcMGnHHGGVi0aBHWrl0b+f3ZZ5/Fa1/7WlQqFXz+858v4pK5QC9i9xGVpZI/+qurZMb6VPz6YGoKKwl0HG9JDfJWi7SYuq6Hi9c+iG//8lVlPwivHjiO/QMjEU2wkegvuZhfHtBC16yG36xPRdaij3N0SiT6K2Pfmo1EUyGLb6Lu+tbVl374Aq77x59jcKSO5R/7AX747F52jMVo3tBSUd3PcM2J7BAZhyT6Kwttl4SkTbpUeSoRJS7Du6oKUZvFLcDxPhX+O/96tLvm8aojbE+eBsFSkXxgpuG323GWiuM4uPnmm3Hvvfdi8+bN+M53voPNmzcLx0ybNg1f+tKX8Bd/8Rd5L1cI6BkfHgqzwSn79HhTjnqivwJLxTYjO0ASRmqiUMk6z+gcPjrteIJQGa472HN0BC/sTaaRmHNP0tBk7VpOAAS46K8CLRXmi2pyIc7iU1FG5EnHkxXbHVideRSBZvc/4ZGloKTregJN+eK+ARwdruMHz+xh303q8lPTBkecRAF8ILBSuktWqqWVRH9loe2S2yahkrxHfZxQadRSqRW4AMtNfePnr+DeX+9S0l9EWw5VHSXroMLjrx7CJs4ijdBfhgG7E30qGzduxKJFi7Bw4UKUy2Vcf/31WL9+vXDMrFmzcP7556NUKuW9XCGgl8prJVR8LatPZYhzdNKgDC2VkP5SWSrN5BzQcQOCUAknmpynQgt+Wsn6ERIqXvpklCmIajBYixQqzTra2fkplM7TO45gyUc2sLL2BPl+tx/yf+8KlI24/eqT0IizOA1ZCkrWXU8YE6odGyd3+3NwcKSeGAV5MNgTfsbEcurixoqdKu6TX6SbeaXJPpWwwTi/XjahEtX2k7Dh6d2Z/IgexGsP1Rx84FuPiZZKIExImT1edTKXafncD57DZ+99lv0vK5amacA2O9CnsmPHDsyfP5/939fXhx07djTd3rp169Df34/+/n7s27cvb/eUoJfKDybyqQxmoL+Gaw4u+PQDuOfXfu4HG/i8oz5mYFTrrjCoGuXneUuF76u8GNMkTBOS5JeRHZoq7ToiVFqQpxJmQjd3flruxbaDx1F1XOw5OiJ8L9/ujkPHAYS0KPmmSlb2sheNZEb//MX9iTlSWQpKOq4ntKEUKj1lACL9pbqdA4P+85k+oZI5pFjtU8kXrutIc0u4LvdVnF9PtaBuO3gcn7p7cyTKz/+c3Mdndx/FTf/6KP76P59K7XucMsFfgZXQUQiVtOderbtCYIG8zYZlGAH91WE+FVVER9r2o0lYs2YNNm3ahE2bNmHmzJl5uhYL2cLgkcVRf7zq4NhwHa8cHAQQvkyq/dVVsmK1jZG6bKlk6zMdx2uifGVWmgxyWYujw+ExKme9HNvOrqfomExBsOivVtBfuX0q6vPDmmySEI2xVKh0BwlQ2zQzv7OsDtd9x0bwzq/8En9y5+Oxx8SFksvX499FyYrOQ2apVJ1En8+hoE7djN4y0m6XFTtNif5qKvmRWSopVYpj5q1qHK355qP4yk9fZhGGNcdlvqa0BZju57k96dGJcfcrlvyhKgf+/Q1V65mFiqys1CXWwTT9VIl21/7KLVT6+vqwbds29v/27dsxd+7cvM22FPQuVBM1C/1FL5s0dLlKMQkVVbjuSN0RBkujeSoDMZZKVYqB5+PfCceGo/dGv0e4aEW/ZAqiFZYKc9Q37VMR/8qgRUPWSOXr7ThM9JcoVEqW0VDyI5AuIKlPv94Rv0lUWrkTGmv8mFAdN7HLhmH4dEtSEdIDRH/1qi2VwZE6bvrmo9h1ZCh0+CuOy7sPTNbkxziGoe5Gs+QPDPhWWHc53G+kJ3jPaf4HOidOiMX1jzBvSrfSUU/v4ngtO/2l8nvy55mGT39lLbdTFHILlfPPPx9btmzByy+/jGq1ijvvvBOrV68uom8tA72LusJ0zJL8SIMiFCqipUJUWt2NllOv1uWQ4mx9VtFf/GIu7yeumoSqMvVxlopqQMtttsJSaXY/FHZ+ipZXi8kfkheeOEulbJsNJT/yfYqDGVj2SaXR0wpKyuPDMg3lcWXLRE/JwmDVQRL9dXCwCts0MLmnpPz9xX0D2PDMbjz+6mGu6nd4v0MKx3kzeSp0flpIcdIYlPt/JAjQCWlwFz0Vi31OAtl+gxnWCdXznzahLPBfclKn58UnMkfbF/+XlQTL9OmvjrNUbNvGbbfdhquuugpLlizB29/+dixduhS33347br/9dgDA7t270dfXhy984Qv45Cc/ib6+Phw9ejR355sFc9QrwvGyWCosIz8YELRA0Rgi7VZV+6laby6kmE7hNTI+pDhqqSiEylBUqJAwkrlnFQtA1/jyj17Alj3HUnNgmoGTk/5iORNxFBGjv5KFKLNUyrJPxczcNzlvIO24JKGiqp7Ag97fEJcEqLqubRroqdg4Xq2zMaUagwcHq5g6oQzLMJTCIAxOiWrW3974Kl7/uR8FCaTp+TVJyOqoT6KtVRQ0f37NcdFTtoXrxYF+zuaoj6LmpG9NnZn+kvpak+mvIKO+I3d+XLVqFVatWiV8d9NNN7HPJ510ErZv317EpQpBaKlEF/csPhV5YMkDkSKGfEslOqCbob/YwsNNUpWlkhSCeVRBf8VaKkr6y8Gx4Ro+94Pn8LkfPIfrzu0DUEyV4uGagzfd+hO84YxZyv5kRaitqX8n5UFVZJHHQRZS679LojZLVnZLJavVpXq3kX6n0F/yNeKEQck2MaFsCSHFqv4dGKxi+oQyTEMtnMIiqq6gyDy3+xi2HzzOtkVoJtJRuK9En0r4OakMUdxYoq+rdZdZpGmWCj3TwUzrRHjdNZcuxLO7j2HHoeOSo16V/5Vt3Mi/q8q0lCyz8yyVjoQXncQ0abMkP5KlQ4u6rAlMCLSekZoTWQCqTTrqVQuZ0lJJ0OyUlkqcT0XRMbnv33vcVxSGak5T1Ibct60HjrNM+maVq9Cnom4gLiov7vhuKfqrEcdnVvqL+pSkUfLvM20veCDeUimZBnrKthD9pbr1Q4NVTO0pwzTUz4YPo6d3v/6Jnbj6Sz/F3mMj7H7yVFfm70sV/ZV1zMU53x3uufeUs+3nTs80y6X5YyZ3lzCtpxTx8STtzZM2buISPVn0F+2n0mk+lU6E6l2pfBZp5xP9Je8B3RskmKl43qrTXEixagId50u2MEvF/181CVWDtBaTvau6XjVSYoaOzb4bYhyY1uiIwrHxdkjLU/9O1qmc5BY3gXlHvWUaMGN8Fcl9aWxxUGGk7qAchAgr9y2RFsM4YWBbJiZUfEuF1hrVcUM1Bz1lC4ZhwPOi44Ht0SIpGnXXw/7AEV5zXWFuNKN3qCqAE7IOkbiALp7+Igd8Wp5KI4KRP9Y0DNiWiVrdFS2VBAsrlTaNsVToupbZWAh8UTghhYqclASE2kyWjHpa8CgCRObnJ3b5YZsqXwPPQQON56nwUPtUkugCtaAAopqu0lFfc2MHaF6/Ck2QrHt4xCFtIWdbBKfQXwQSKiN1B5ZhwDKyT1JWWj5F3mZpr1oP9xxXKkUy/WUaykXctgxMYD6VeAFcc1yULJMFEUQt7nCcxTnCa3W3sNL3VceNKBpZ26sLgk3sjxtQ1FktlUYWaL57puEv8DVX3kU0Qaik+XdSfCqGYcDqxOTHToRqLDJLpQH/APkS5O06J1Zs4XcesqM+6zxTjQu+/ahQyabZNRJSLNct45E3AkxORMsbUhx3Omn0siIQN4G7maPeg2n6WcpZ2YSsyY/8orfglrvx8xf3R44Zqbuo2CZMI30veCA++qtkmphQtqXoL7UFWwqupzqmzlkQ8m9MqDjSPjRNvFL+vcg+J/m6FHUpg382/Dh1XI/N3dBRn+ZTydBpRf/Iv1FzXMGDnxickWbhxlkqwV/L8Hd+7Ljkx06EanAzjSgDjcPyVIj+kn0qgVBRRYg0m1GvWvSOKx314XVkJEXxRJMfo32Qray4vjQDeg6yb6hRpIYUk/DKUEEAALpsor+cIJqmcesy1eEqPetvBUVAeVTrLiq2BTPGUlL5VFSXLVkmesqWUKZl37ERPLX9cOR6JcuAwYSK2I7oUxF/C4WKK9xbHkuFrsVDbo4UABn8uz10PPQrel5omYT0V8q7auAeBEuFSqY4njKjXn2t5PblcSNvQ8x2fuy02l+dCBX9FW5bmiX+3P/LHPXc2y9bJov+UpWOGKmLZdSzUykpQoWV1Y63VFTzocbor/RFVrayePAC9CfP78O1//BwQ0mRYeY0CbnMpwpI23jKYfRXNiqlu2yyfjVKf/EVo5MgP3uVxj1Sd1G2zcCno7iWLFQMtaVC9NfgSJ2d8y8/34r3fP1XwnE1x7eMqDqGPGeovM9ILapoHOWECj+O8uSpANG5Kd8fBVXI4N/1oSCqj9quBeOtJyb6a/uh40xIqq6ZBNFSAUq27zTnm8hDf8UF1wjJj3o/lfZANS54KZ/mrJN3jhQLU5ooB4uCalGV6YLMzkalUOF8KrKjnpuA1J84mgPIWqbFjfUP8D6Vp3cewWOvHsZ9m3erD1aAWSoZt7CNQ+h8Vv/OHPWR/WPCE3orYaQ9n/xoNumoT6UxpM5ScVMe1boTob9e2DuAp4MsfHnhiOtnyTLQU7b8GlPc73IofapPhbNU5OtQV/hdNIF035IKdddjAQqyU1t+x7GWCnfgYc5Scbwwj4Z8KvJzfPfXNuKLD2xRXjNNAeWfi2UaKAXZ7XE+lWkTyrHnqxBXpoUVlDQQVCnW9FfLkcZJ8y/6Xx95Bd9/aqdwrGyp8AOxbJuskF+sT4U7PnOZFm5clKzo1scRn0pNFHSAOhmr4ZBiqb+0APN5AmT1fe/x7IVFZdour6MeiKP71EKUv6+JXaFQkaO/GnLUSyGeaccREi0V7vpXfuEhvOXvfxa0IV4kLr/ENk1MqNiou54wzqOJdF4gVPz/Iz4VLvor7lWRpUJbdTfjJ3NdjwkLWavPaqk8/OJ+1l+qaQb444OEY08wjuUF+NDxauQcQlrxWb57RlDc0fWktYZTxuT+5y3TQhn12lJpA9Q+lXAw8f6ID9/1NP7w248Lx3qcVl13XMG8FiyVGPpLFZab3ufwQFro+Eg10rgo/JOfgKT5JkV/Za39JR/XVYpaQTSIf/L8Puw9NowskAsHNhsGmWYFMvorwYfEWyph9JcL0zACR31WSyXaJxUiQqUUnZbVuotyYDmo70v83/epxFsq8nVVWzTwlkrUp0LvK6pohMf444U2oGp2O+EJ5TACj4fcXpxQ+ZvvPY1/+slLAET2wHHD+4iL/qo5nrTtQfjbgCKZmAd/rB/9FWyjwL2s4XpU+QvPb8xSkX14vh9H+1TaAtW74gdA2j7V/OlDEqdcKVnMXFcVnYsmP2bUernjaPLwPht54PMTsCshFDX0qWSjv+SJrBJYfK7Ifz0hWnlxCHMGslFGae3InwnMUZ8QSdTbpaa/LDPeV6FC1sKA8r2SZs9jJAgpjss/kf0ypqG2TMlRL8PzwnfueV4gxHhHfVTo+P1yYp8HRX+VTPX48zwPX7j/eUbhqeC4HrMiIpaKNE3j6C8AeOSlA7js8z/G1gOD4fkc/UXvOVKrz3GFucTfK5V0cl1PeQ/8XLEMgzEM/Njj14iyHfpj/WvF3g67Lo+6JFSswKei81TaAJWjngdRR3GalVhy2xEiiSq2yTSOoWpUOFUdJ3Xhi/TX8wRBSIsC/528iVRVoQEp6aCYaCt5oTONIPor+J6ssbDt8Fjiwc+ZPwX/+Vg2CkzeRrj55Ef+c7QNouY2bT2Et375YeYL4ice5RkBfEhx4KhvyFIRJ3kcIlaCwnnLLJUYC0Tuk22qa5TZlqncZwUI3zn1R/CpyJFGnKUS778SLRX5OYzUXXzpwS1Y/0T8GKm7bmipRHwq2SwVwN/J8+X9g3hmZ1hz0HXDeUIhxbylQrXLRqS5RSChct/m3XjL3/+M1YtTHUshxYD4fuVN1agf1L8kyD+Hu82G17RNIxLp2GqcmEIlZU2oOuLGOTL4dzQwUhfaq/A+lVr0/Ht/vRsP/ibcMzyL0isPni7F5OGtKzeG/lJdK2vyY0/ZFqwsysUpM3+NJ5xrmsCFp07DS/vT950AokKsWeVK9FdFf6cJ9tirh/DEtsNs21z+ehMF+itcCAyiv7JSlm42oSKHpKsigoSQYqWlIikBcWVaLF97VfZDsuKS8lRYSHEtar2yPgchxXQ9uT8UVSVvmMbDdcH5VOToL/HYJEuF5iS/hbjKUc9bEY7rK3O8EOBvlapvbDvoC5Mjx8UySAL9ZYbPXbRUxIAa3opslDaNlL43feWCt0LbgRNUqCQ/YHI6Hz6u3oqXX0DlPUrKnE9lWOGoPzBYxS9eOsD+z2KpRBd4VXSQuJjyC5PK7+Ef58U6rvlDLdNAxTaDIAP/O3Jmk4DjBW3d8WCbJmzTyBzxE1ccr1HIVuDxah3XfPlhRk+EhUPF/WYE+kvhUwkd9dknKC3+acE3spBQle4YqTvMUa+6fCSk2BTHOY0B2zRRUtBrAFeDLBhLZBkBCUIlIXep5nhwPI9dT553FHos+93ELSnCCsKyBZfVpwKAWUtHOKe764WWCUXW8f4H3m8UnhP+TkVH9wVlaYYTfD6mAZSDPozEWiqhv6tkqZUHHvzvZY7mkkvfA9G8rFbihBQqaWsCae9HFAUYAXHBlYVKxbaYUMmSEJhl8YyY+SqhIlsqnL8l9HuI5wh7cydYKjYXSkt9Ib9D2YoKLNfz/AHdQDZvsyU4ZPALuOv5WvCT2w5jc0B7yE5LVfJnryr6K9gdsBH6K6m2lnicRH8ppFA1JaNeFeHDf0WC0k6yVIJnU+UsFSPGUS+GFCub4+gvtVJD82svZ6m8euA4lvzvDXhu9zG4rgfXC5WoaPSXeL0kS4UuzSc/um5oqdiW6dfm4gN2uAg31TVfCfwz+4MCmrIywN8ubZgltzdUc9iuk2XbYgLU32Ih9nZY/wmVkhkG3ZCjPqC/gOaVtGZwQgqVOJ8KvQAaHHwoIQ9+ctDGV+TQFOmv7ImUycfIGlm46JHzryo4E8UJSNFE8mIkCKKgI9W6i+PVuqAFlSyThdLS97RIsUq3XLt114Vt+nWH3Iymd5aQ5iyQhVtdovdkB73KUiErzA5yCwimQaG62fqWPflRpr/UUYN8SHHEBxb5X219lS1TGQgAhIsRPaOyZbBNqeSxQ8K5qgjeIFBZH+ZTkRZJmjtU1RgAdh4ZQs3xsO3gcdafeKEiXldFCxNIweM3qnPcMKS4ZAUlTTilg8aGrLARXj5wHEBoqSQlZ5qc1SBbXOQzKnOWip0hH4qfo10lK5IeQPupAOk1zYrECSlU4uZ4l1TmPJb+EiwV/xgq51Ep8Y76LLtIqjvjeR5+vf2IcktiXiOTt7sFgsnC018xPpUadwwtbO9Y9wuc9b9/ICxatkUaerhAkjObBjZ/H47rBVo9hGOSIL+TQvJU3Gg0WZxVoEp+LFkmLG6f90YtlazJj7KAUNFf5FOhkvYy1aJKXuWfxQTBUomjv/zrMp8K76iXjuXLtCTSXy4f/aW2VAZG6sw/QW0Nc7Qaae//8OMXcNnnfxzeYwP0F/lH+VNcL8yoLwWWCk+98RQfgcb5lJ4Stu4PLJVg6+UkS8qnv6IhxUA4l3ifSto4k4N3uksWuz59b5lhcIC2VFqMuDkuL9Dk1JMHKz+Yif4ia6Bi8yHFjVkqrhvutXDf5j347dt+hv98bEdkwe3h+tNTFgWhf38eRuoOs2IqMT4V0Snpf3781cP+/9yxtmnCCEJZaXCGvhQvch91h4RK9gEdycpukgKWLZWaJDTkkvdMu1NZKoH2SmB5KhkFnqN4NiqoNnKTQZYKvQeZWlXlGfHf9J8yFe+5eAHOPXkqo2Hi+isKFQT3oBbGSSHFcvSXfNjRoZA6DvdgCQMAqD8Tgq1+X9o3iJf3hyHBcnsqXyNBVX3c5XyKlLTMj4+aiv4KPi6cMQFb9w/C88JS/5HaZNwboPBevl0C3R8f/SVvOy4jomgqLBWLs1TamVV/QgoVXu+yuEWDnJmkmRwOHHETOMctIC4S5Gwk64Scm7ZpZKS/As2s5uC8T96PHzzjlzbZfcR3Xj6x7XBEk+UtFbZjXZ1fTP0BPinQgKhvcbw4EB2kBwdC6q8UWCq+TwVCm7Qg8hPc4Xwq/DFJUC2Kj716CAtuuVtYSNLbCT+7nse07zAyRpxc8j40ANBbCTRHyxTGB2XUp9FZgyN1PLPzSChwU46X+6RySFcd8qn415cVFlWeEW89Tu4u4WOrl2JCxY63VCTHtCr5kXJLth30qR8/+kt9XzVGfyVbKgCw96g/3nlLhe6JD7MV7rEB+ouvPk7j0nfUh/dasowYS4WbJ8E1F87sxbGROvYNjOAACZVafHSaweWpROgvjprkqb4ka11WbLrKVkRBMoyQHm9nVv0JKVRUJU8AMdENCC2VKAUdviDaopcGdMk2gnbNSPTXH1++CAtnTBD7EgyA41UHh47X8GowWSd3l1gf5AHEZ1yzjG9FSPGkbhIqAf0lkRhVBf1F2H4ojLm3rbA8CfWXhIqqEq/jeoFPJXASZuBzo5aKh7uf2gUA+O8nsyVQAiIN54HbmpVp4epnINBfnKXCa/WM/kqxVL6z8VVc+w8/j61WIENWImVunha1sm0y+kuu1qASyuI4D+8j1qciPaNKYBkB4fN5fs8AvvTgFvxq6yHWt7jFr8roL7Wzn9+JdA+3WyQgWioyUxC3D4x8XNx9dnMVBXifim3JPhUv+CuyAACwcKY/jx975TDrR7SKctiWmFEvdryXC8/v5mqQJQmVSOJnyWTjhpW+59iCdmbVn5BChV9ceUcsX5IDCH0qES2Qt1QknwotQmXbjEz880+dhsvOnCX2JWhLntA00I4M1SKDq8I5W1l8fV0WKg4msbDfaIIify3++tQen8hVMk0W/SXTXyqhUg98KjbTktJN70hIsedhao8vFEkrzoKoo95jfQKiNIBq/5aeshUU4zMFrd4wjMBSSO7D0eE6Rupu5r3GyVK57tw+nD1vMqtc8MLeAaE+VaUR+ktalPiIr7TkR6VPxaN2xIW66rixWjBV5I1LfjwyVGNOapWlQp9Ltikof3Q5uT2Z/oq7T9rum6dHy5bpF3xMo7+Cn+dN6QYAbN4VJlMm+VR4yz3qqA99ePOn9gDw52ySMiIrNt0lC1XHxX3P7GYKoe9TyT4Hi8IJKVT4d2UnWSpB9FdSRdtjzFLxH2UYHmhGJr5lGizcmCAvyqyGV/D7kaFaZBGjEuiAmPFN8Dxf05vYJVoqcTvFAeGiO6O3AsAv+Q34JnSipcIc9dw9MZ9K9nBG+R4d12Phn8/tOZZ6fnhe+NnzeHqL6C+xL7SA8/23TQPdJYtZBgTL8PM/0u4nuqtkyvFBex++egkWzJiAgZE61nzzUVz5hYfwe195hDmxGf3lqeiv6Bjlu8kvynH0F1Ns6qH2TjoX3UNZsVAPx2yJWw8KSpZi6K+jwzXMm9oNwwipMLb5F2ep2KYhVG6uuy5+tmV/JBO/SxIqcfdJwifqqFfTX3XXi8xTmidbuLGZGP1lGCjZ0ZBiIKS/KraJGy5egL/93XPwexecnJgYrapsPVJzseabj+K2H73Armk1QEEXhRNSqPAvy+IslYrkUyGBIZuO/H9kwtPLI22kbJmR2l+2aUYmJQ28UJMWF7+jCkulbIeWiiwIqc2q42JSdzhY5X779xkNKSbajSwVPwTVZDseUl/OOGkiAODKJbODtjmrx/NgmWZjPhXpHj0PjKv+za6jmbcrlreLZQsDK3kvC1Zx8QX8BaarZMHmtEsgO/0VzfmJP/bhF/Yz/5kZJJluPzSE+zfvQf8pU/HISwdZjk3FtgKfDjAkVWtQWXr8s+CVmThaqK7IUwl9KlHlgRD3boj+Yj4M6bkcGaphSncZk7pKoVBxQkuFBAwl37J7cz3c8YutePiFA0J7XdKWAXF7lYT0VzgeSrY/zvnxwStqYei5/z+VqX+eFypJeSpceLqspPZyjnrLNHDdeX0opdTs4p+lZRqolKJKLFnbgKa/Wg5+siX5VEInb7zmST4Vmnwmb6nU0i0VaiqMThJ5+CNDtcjgKgd5I0B8SPFIzcXk7jIMI/QRJEV/ySUeWFRM4MCkHQ/plIUzevHrj70R77rw5KD/YbuhT6WB6C8FfUMlVGqOJ/h4kuAIQoW7r7iQYkVIrG0ZvlCRHPUmo7/SLBWZLlUf77oe3vXVX7IKurY0Pt641BfYRLEmRX/J1/QVgPB/0aeSFv0Vvns5+VG9WZy6nJG/8yPvqBd/PzpUx6TuEiZ3lxItFcsQhUrd9SILMwCUbVEJmC7tT0LgLRUhT8USk3UFAVN3cfdTuxhNV7FN9FZsbD0QUrMjCt8LgTbporaE/kgljwCfIUhSXvgxZRkGypYZSdYWg2U0/dVS8K+KN5FLwaSVQ/Nqjqj18Q3sCxyMtLjzloo8JmxJ4wLiLRUaBCqfStm2mPBShxT7/0+bUMLX33M+rj23L7iW2B+5zhF/3fBafvw+S34MjjNNP75e1mTpXhqN/pIPcTwP+weqbFGPq8OW1A6fMS0LbYK8aRLgL7qVkhlU6Q3vI7ulko3+GpQWY1kjnz2pC0AYehtHf6lymRzXFX2HvFCJoYVufeB5vP8bm5QhxUk7asa93pD+ivepTOq2JaHiH8Pnv9iWgQrnhHccT6l526YpLMwLpk/ATz90Gc6YPVE4jqLJ+PFRMoOMep7+4hb/fQMjuPnbj+GTd/8GgK9gTOkpwXE9lG0TE7tswWKTn4mQUR+xVEKfCsEyooVDR+oOqy/Gj0HD8FkWeY7wCZea/moxBEvFFF9kxTaZxsG/CLFcfdjWwUFfqHRzSUsAIhYJ/SZ/zzRAyZHM+wDk8cC3EYYUi1aHby2YeMMZszC5u+RH8SRYKqGTVk21kaOerysEhJUE+LMc15V8KulaUqSgpOvhwMAITpnuOy5Ve1eM1J2Ib0EuKFmLOOqjz0Be7EqWgS7bYhq2xQmVuD3ixTajVpcK8iJgSb6DWRNJqISWiir6i7fI+GvyX83oDbX2OEvlmZ1HsXnnUU6oGKCceuYcb2Bx8pMfw+vJ8ujocA2TuiRLhUV/hY56WdjWuagtHrZl4DPXno3fPa+PnTd/Wk9kfxrKC6GCkjS+bdMQ3h0/P2QrwDQMRoHN7K2gi0s+BKIC1AysCSAho567R1Xy4//8+q9wzifu89vnmrBMA2UrGk5tcYJM018tRpyjnrJeRyRLBRAnrRhh5P+VLRWeVqPBUlKUHZfLo9cUi5/s+C3bYVnz7kDrGlHwv/wgVRUilCkz+TsgSABUlGlhdB+LDhIFMG+pZMm7Uu0NcXCwilOn+6Gbx4aj1Q3+z/c3433fEPdWjyvTkuSoj9JfJiZULLaQ0Tsj+ivFUIkI0bh1WK4bx9M8FdtkPjGiv/wqxVH6S470omvSO/nW+y7E60+fyd2f2lIZqjkYqbtCCDNZKv/w4xfw5LbDgvCPcc0wRMq0BOe+tG8A+wdG/CoBJf8+VZYKfZYtfMf1lMl8ZcvENSvmMX8fKTzynKMyR64X7nBJx/Ht8oJLVgAMA5jS4wuVGb3lQBnlrUexb6YRPndZIIZ5KmKkoTxufv5i6EPi34NlRJVV/5qcpdLG5Ed1VtE4h0B/meLCW7YtpaVSc1wmOOjb7pLFNMZuFv0VhhQTqMKvr1HIPhWJ9lLkNpBvgVC2TDaomKWiECo8v0yLEY+qILjIUgnb8QWDHxVjOhL9ZYiWCj8BHNdjTkeguZDiw8erqLseTiGhoqC/dh8ZiZRNdyThH3XUq+gvsd2SZeCvVy1h98hbKtmiv6JWlwq8oDQNn66gcdNdtpjVQvRXOSjw6Lgi/cWHThN4QTNvSjfzjQCidc5juOZEwmzpvPVP7MSsiRW87TV97Pjeis18ijLKwQItlmnxf7v8bx/y79PzF8TJ3SV2jzT+hzlLxZQWzbrrRqxBAErLEhAVPIDzqbh+OSP6vSRtvctfY1BBLU0LQt5nTqxgYKSeaKnwIfay4jZjYgW2aWB6EFEGgPlMXddjVDcPfkwZhnoLar/0vaa/2gL+hZck7YAEACBZKo4XOZ/MaCC0VEhmlDkag1647Ij1ryG2KedVAKHfhlC2w8HfXY5SC6Qx8RqagajmUxMsFf8zv+iaRihYaMdD6idNWDmPgfrOT6Isjnr5GCrbsWBGPP3letHCioJPhaO/4iwVv5y/RH+ZJs49eSpWzJ8CIJyYtPA7noev/OQlVvpchlwKJs4Hwy/I9DxprHTZFgtTDy0VM9gmWKS/eGHPX5NkuWmIi1KcpTJSd1Gtu0KYLb+eyUUq+c3MCDSfertsP7LOVW/SVQ22ITYNA5O6Szg6VIPnefE+FW4+OTGOenpPtjQ2y1JUmBBS7LhsTvo+FTX9dVR2ghsGpk4gS6XCQnoJ8is3Euiv2RO78MM/fwOuWnoS+46eu2rsyD40Fa1OfSRBqx31rYZAf4lVaCt2WEKafxH8Ik/WBTn8qHoswFkq3MSlCaH2qYiLHS1I/ASUF6+yZUXoLx5VtijwAjOaUZ/mUzEMAxefNh0XnDqVcbz0SKyIpRKlv+iZZNGS5LlDQoUsFZWjnqfjwnbE9yRH8KktFZn+Ehddnv6yDAPVuotP3fMbfOg/nlLeS5T+irNUFEIlUE66yxZTVPjoL7I4hb3Wvehz4AtKSjJFiGiTMVJ3lSHFdB9ikcooj//WFfPw4auXYGpPCVUpT0V2PDuBFj652z92uOaGIcW1sEyLZZoZo79EtoAJlYilYrPr1xyX9a+7ZAmRbIJQGZZ9KsDUHk6olET6S22pqEOKy7aJk6f3iJGGZlQQ8/cv0F+KACD6nuZpG9mvE1OoCJnGUh5C2TZZDR++vAgvYOh04kInVmzWJov+4l5yqAnFCxU545u3jGQnYdmO0l88+NpNBJUvgBYP0+B8Ko7LFqFq3cUnrlmGNZeexnY8dKSFihy5sk/FNkMnYSZLRbHgAH5xxy5FZAsQQ/tI/q5wEzL/O9XWvfK1ZQ5+SkBz8MEHADAwoq5iLVMzWegvelbMUimFPh0++ssILEbep+JylgopEnxBSVmoxGWaE6htPvkRiFpEqppcC2f24n2vW8j8E0KeiuIxmEaYG+WHzwchxXUp+ZFztjuup1RUopaK+n5JGFJBSfp9zpQu7Do8LGwDQeCLXwK+wkWWysyJFSHAh9qW7zOu9lecQACiScFAGKrN90XVhr9Tqf9ZVyluMfj3LVBERH9xmbSszIEjLlZAmLTU22UzAcPvW0Ggz7ZpohLxqVCboqXADwKVUKHz1ELFXxRs4d6iixsN7u6ShXoQNl1zXEzpjtIatOMhX1cICCcu33JY+j767B556QAefeVgpP24QV8yTUzsKkWc2tSuykEdfo6GFEeEiuNFJq6cHDhzos91m5z1BcRr/LJTNDb6i7snaooWh+6SGW+puGJuiO+4Fq1nzwvvWaa/kiwVvl8l0xR8MY4nRpSpLJXQP2EGiaXxBSWB0KcC+OOcLHXfUiGlR8qodzyBuiXE+1QkRz2jv3wlivrcN7UHVcdl+6NUUy0Vv9+M/uLzVKS+mUaY/Cg76tVOdgR9jD6zWt1LddTLzyCtqkORKESobNiwAWeccQYWLVqEtWvXRn73PA9//Md/jEWLFmH58uV47LHHirhs0+BpIDn6i+dGHddjWbq8yUoviCZwb8XGn1yxGH+zagne+pp5ACRHPdvKNZ3+oonED5qIUOEmCV/0jzCioL9Mw1DsiUEUmuVTKK5frmSySqiYYvSX7FPhBVbd9YS6Wfyi+n83PIu/u39LpP24fWVsy8DEiq2M/qI+8+D7kaX2V1VREFFedKkkB19K3D9OPX2SasXx4AUlLYi8pWKbBkyDr4RN+6l4wpjg6a9br1+B64K8pFqMUEnDYLUeWCmiEJX3aOE3iyOEkVQGalksFdOQLJXQp0LC3vepiJaK7Leia9LxQHjfslDp4QpK1uoh/dU31a/nRSWK+Mrfsk/FNAycMs2nZhfM6EFXyRSqFHuSzKNtE0wjmumvKn1DfVf5VGquGLGootWJ9grprw4SKo7j4Oabb8a9996LzZs34zvf+Q42b94sHHPvvfdiy5Yt2LJlC9atW4cPfOADeS+bC/zzFSOk/JfDWyrEcYs+Ff8vaWq9FRtdJQvvv3ShUjuiQWNZRmSAU7Nhnko2S4X/bEmLhor+okxsHsxSKVsBv+z/PkkhVCjpjrqVFv1l8pYKZwoM11xlWY9YS8Uy0Ntlq+kvV2WpiO+Jr/0l18MCwBY+/nqGIQsVn+aQ6a+0cieqPvEQo7/EhbC7ZMEw/Mx+ev+VUpj8KAgV14MT7LZ52RmzWAXdOkdvNoJjw3XOjxR+77hiYISqXTrPtky2JwgtpirFwTQMtkXDkaGa5FPx+x/NU3GVPpWSZKmE1S3EjpIwJMuc5tN8JlT86g2iT0WK/jIMnN03Gb/468uxdO5kVGwrUipJON4M+yj3vaS0VPw+Hzleww+f3SP85tNf4f+GAcGSo+/863agpbJx40YsWrQICxcuRLlcxvXXX4/169cLx6xfvx7vfve7YRgGLrroIhw+fBi7du3Ke+mm4QlCJeqoJ/rIcT0WgcMPBJocE8oi5cWDBioVZPSvlW6pVCWfCl9sj1CRhYqZLlRUPpWa47LcHD6hLMlSCSkVBP0LfCrcsaFPJaoljdSdyK6FABCXm2WbfjkMVfSX40W59WhIcWipqHh42VJRJQaSpTJccwTNPc4CkCNt4gSmYKkEz2o4eHdUHLGrZIV0a9n2Q4q96La4dTcMPWUBEmwMNSZVBkZ4oSLSX2KeSrRdvqIEjUPLCJNGZQrWMsPxdnSoFpZpqUtlWkpS9JeK/iLKh2np/vdxPhWq/UW/z5viRxoqhYo0B42gyTmTfUFUsU0po166T85qkn0qKkuF5vSdv3oVN/7LJhzignUi9JciVcGSnkVHWSo7duzA/Pnz2f99fX3YsWNHw8e0E7zGJJc2L3MhxXU3zE1R+VSYH6UrXqiUTJMtVOraX4FQYSHFIf1lGPCL7R1PtlRkjZnlqUjRX6raX5RP4pe+8M9TWipBKC2f5QxwPhVF9JeqQmrVcZXb5SbRX72VeEtFnix8M64XLvCuG0aC8WvhplcO4eP/HVrWqnBbEioHBqvg526cpRJx1MfMZ1VIMe3Bw7anDt51b8X2rb9A4z9yvIaJFSo34t9fWE4m6IfbnKUywFkq/LNyXS+imMggwVayjFComGHyrVwmxzQMzJxYgWH4izmNFTH6S1w06zH0FwlPmZrlKTkg9EM6nif4VLrLFqZPKDOhkuxTER+qH/3FWyrqvpUsIyJU5Dwav33/L/XlMCfU7tu8G//6yCvsf6VPRXoWbZQp+YWKajGQNaMsxxDWrVuH/v5+9Pf3Y9++fXm7pwTfG1mbl/NUwh0OeScc+VRC+ksGLQ6XnzmLTTRVlWIat47E/TuuC8vwF1TZUuEXM9ppkgdZWuUUS6XquMHuhmZQssI/QO2oNwS6SdaKxdpfbmz0V5XL2BafQxz9Fe+od5R5KnGWSpihzVez3T8wgh8+u5f9r9IaZwSO+kOD1UyO+oifJwP9RW299rTpAIB3XXSy39dgAaQtjk3D30jq2EgdUyb474ksNnkxdXJYKhSGS9F9gFgC3m83ei49PtsKLX7T9KOQPEW0nmkYmFCxcer0CXhm5xGm2Mh5Km9cOhvnnTLVvy9XHVJMYD4ViYo+abJf9mbahDKj4/iQYsD3q2w/dBxXfuEhfP3hrWzBl6O/5FcfddTL9xk+F7nvqvdDfd8VVLDmx8q3f/kq/uPR7dz50QgyOp+a/vX2w7j5W481tItqs8gtVPr6+rBt2zb2//bt2zF37tyGjyGsWbMGmzZtwqZNmzBz5kzlMXkRF1Lsh/3xlorHJnWtQUuFBsofXr4ItmnAMNTx5K5kqfB0jWUamNilEirJlgpZAqkZ9cG+53ZAbdXS6C8uT0H2qfBNR30qslCJ0l9xmpQdPIN9x0bwq61i1FjdieZn8P/TouH3KbQ2u2P2Mi9bZoyl4vtUDg5WITrq4yyVjNFfI1H6a/60HmxdezXOPdlfQHlLBfDH1ZGhGjwPmBbkSVCorxzxEzrqlZePxcBInfH8/LlynopKqIS5ISYbh6y6sxcVBtT+snmT8czOo8JYoVwc2zSwvG8K/teqJQD8MZRkMYV5Kgj64n94w+mzcM8fvw4LZ/YyOq4WKFaEqRPKOHy8hhf2DgAIn3uqpZJSpoXVBFTQX8p7CNrfzYRKOFbk6hIqBoTuna6788gw7v71rgiN1wrkFirnn38+tmzZgpdffhnVahV33nknVq9eLRyzevVqfOMb34DneXjkkUcwefJkzJkzJ++lm4bgU+G1edPPAicN2PPCzbfqjodXDgzi2HCN86kEWqTCUvmzlafjux94LZbNmxxUKCWnYQz9JUUnEZ0xqavEsqf/16ozsfqcuUxrBaL7qAOcT0W4liKjPtDSTNNI9an4IcmhZUWDXt7DnO5F9KmEkyjOUonT5m3L96lUHRe/e/svhFBaVxX95Yl7kPO1v8jaVIVhA8CpMyawZEseFFJ88LgoVGId9Ql+Hh6q5EcZ5EsgxcUyw83jKE/CDSwVWxIqdO+5fCpcv1Q1xmSElJPBFlmLCZUoNUh9XTZvEnYcHhKqR1AVZ2qT7k/eUVWG/BzoXiq2ibPmTmL35Xq+f4K3VGRHOkV4ylsNyI+0YltBAU0PhwarePSVQ8rnYluGshimDDMiVGrsfiIlY6SQa/7eaZ7SPaWFkxeB3LW/bNvGbbfdhquuugqO4+DGG2/E0qVLcfvttwMAbrrpJqxatQr33HMPFi1ahJ6eHnz961/P3fE8EOkv0e9AGgwtDPSyaq6L13/uxzhrziSsuXQhAERyU3hM7i7hvFOmAfAXgriY+Uj0F/fXNA3BCjp/wTSsufQ04XyVo55l1EtBCHTnP3puL/7n13+Fi0+bjlKwBwVfBlxpqRhimRZqOoz+4umv+DyVqqLWFn//MkrSJDxedYSMaFVIsR3UcHI57r3uhtSLXLWW8GcrT8eblp0U+Z4sAs8TNVRVTSbVvcT5i3hLJVaoSJaKaRgYDBY43lJxVZZKk9FfgyN1zA0c0GL0l5iMp6q/FS6eJtsR0gwsdV6ws+NJqMydDAB4Ytth9hsJXTmqK23DtrCCdjDnyL9pi/OBLCf+e7n+V6VkBuHRUdqOB42pat3FW/7+Z8J23Px9Wma0LRXoeBr7R4frflFXRPfSkWuj8f0zI2OhA4QKAKxatQqrVq0SvrvpppvYZ8Mw8OUvf7mISzWE31r7Q/zBZafhXReeInzPT3LT8Ac8LRhEBdHCwFsqgL8ndVj7i+iv6CLMw+ac9XHRX2HyY+jPIeqHoFp4qIIwD1b7iwul5PdW/8J9zwMAnt8zgEndNizT35yI4vIT81S4iBxA5NwJzFKRaj65Qdiy4zrwPE/QoGMtFdPEqTNC60HeL9z1ILTlel7wrN1AE+Uc9UR/Bdr/hLLFFmi6R2UfLBOnz+7FOy84OZOlEqG/Yu5tKMO1VT4VwhROqNRdL5KbQItXowtJzfHYQiu/I/5eLjx1Gh74zR5pJ1X/b8kKo6EsI/TpqXwqgE/7ARLNMxxWEvDbbtBSiTjqw7lHVbd5R71/rlipGJ7vg6s5UeuAB/VxpO5EBIp/fHhelu0D5OFwbLjuK3KKW1fR6hFLpS4G2LQS4zaj3vM87Dg8hL/53tOK38LPfI0q0wCjgkijIqcuX5KDxsTCmRPw+tNnoj9wIMaB9y/ICxG1FVYp5n0qpiBUVItD2TZhSX6AsEpxVDMDwPhiz/OC7YLDCQaEO9HJ9yBYKtwzA6LJj36kkuhTqTIqSpEgmGCpvKN/Pj58tc+n89QZCbgI9RY8D75Aoe+oD96pRCnxzygO9/3Z6/Ge3zpVEOBxlopqF0YVeAssTkB1BYvFxEopcs1pgaOeaEBLclDzGemNgvwQQkgxFxL8z+/px3svOTXSb77eFh+9xVsGPOQdTHnwlQQALuxaET0otGmKY5PO5xdeGs+yT4UqAYR9qGNyjypvS/yf+j9YVQs8fr6kbfLG3wPh2HAtojzy9xJx1EuWSpXRX6mXzo1xK1SSlAFhK85gwAOhpcKHqhKnfZgL6+XzVO648QIsmBHl4XnYpsG0objIOBZSzIXAWibQWwkHtGpxqHDJjzQQ2X4YUgkaeiak6R0bqTP6jC/SpwpxJM2O5amY4qLDP2/yB4WbdHlCv4AohaGaaJZpBPWLDMyd0h20IVbnBaJ12XifSlimJRRkZH3KtGWckIg7JqtPRUV/UV2stGvLPhX+sGkTKux6vnUYaPSRPJX4+4kDVSCOOur9z31Te2AYRmTxY8qT5KskR738bKhvKkqSryTAt51Gf9FzYMmPXGl71ifDYJaz6FMxIoUk5W2JDSM6j+cEkWUv71NHV/HJrVkKrE6S2I+B4XrsGDGNKANC+iSd0k76a9wKlaRkH36O+/RXuOCTpSLTXxSBZRrh+Vnfj6mYfHI/2Xa3nKViS5aKqg3eUU+LXFyeigdPyHmp1sM8FWFr1ZhkLCrTwveDRX8F/hqyDiyO8qPFjaeuZGe96nXxizZpYqqNxXianu+fsJ+KG1bAJetT1o7jNEHhGJP/HCdU0qO/6P4pLD3NUuF9KgSqPUVKEDUhR901J1Si1+PL9NC15L1ZVKVRrGCO8UKe/RY01GWrLJVwHxkgFBYjGX0qifSX6Qfk8GVaADB/HKFad1lAhHyPPE4O6LufblGnQdDzMjLSX5eeLka+Hhuux463LHkq9Nzjdv0sEuN2k66kKBXRUuH5Tn9y81FFXZKl0l2yIhRQGn5r0XS2eMh45eBxDIzUI7WpKERUpL+i5/PUGmUxq/ZTIU772d1HhfNLlm+d1VOECkXvuJ64+Ia+DAh/bdNgdIxcLQBQCBXFRBNK3TDOmhMqkpXnXz/U2PnaX44XLvZE78mLfZb3mSmjPqFyMoGE44SKjeNVJ7Yt0uDlRd4fGyV2H35ukKih13Nop6Qp86fW3XA7adYPyaqVrQP6zlfGonkqYQAL79v0x9HRoZoQ8EHXSvWpxOSplCOO+qCgJOd7tE0zkq0/LSJUotecN7UbhgH8+Dm1UOEpuaz014N//np8+5ev4kfP7sWxkWT6S86vkpkE5l/T9FfzSLRUuM8UQ0+fLYN8KqJWS5ZKV8li52edrNee24f/89Zlyt++/ctXce0/PBw66oPrKoVKnKYSjBSaTKqCkhR9I9cwKtsWi3irBs48Jf1lhpy6fNu0YADhwm2ZYUgx86nwloq0MKiUAN7SIgqEz8Z3uWdF8DxwPhVufxruna48azY+fPUSlgzH7iPDbOC1xTjFRdbGVYfRO6Jw9LiNs2j8hZaK//2kLjukFz1/L/iIc9YVBUAjUFkqPC0sh/kSSgr6i4UUu1ErjmcJ6F4ncLkhvK+gcZ9KvKUS0l+iT6Vsm5Gsf4qyk/vMo2JbOGlSF57bc0zZJ4NbYzLIFADAaTN78ZG3nIXJPaXAUR9Pf9lSaoG8aynNPe2oz4FEbUCiv3gnmmWaQiFCor8oN6CLs1SKoief3zMQyVMhoUKhlkA8PUOTjUJtk2p/RTYIsoLcHC44QVk11QzLtMgD0+AmCtvES/Cp+F8m0V8qZzZvqjP6yxE3p/LPDV+oWBU3rBHFhxRP6ynjfa9bKERfARnpL2mRVUFWaFQKDj2LMP9EPRXD6C/ycfjXnze1O6T5XP8Zk2Ci19dsQUkgLNXDPxK+9L0cvvynVy7GB95wGi5a6FcEkKmm0FEvWSrcBSqSr+voUF2wLhqN/god9aElz7fll+YX+2qb0TIq03rTLRUgjGBTgWdDGsXErhILKVaBZyrYd5FIQFf4v5UYt/SXkxALLjvq6Tn7jkdRq6ZJzSrF2iabWEW+Hz6SKaQzDCyePZEdE6dxUt/OPWUqdhweSqz9Fa07FJRpkeiv2975GrG8dqDZOZ4XGZi0YAChJmqbYfTXjsNDeHb3UaGUeNSnoqK/wusw+ovTUh2FpeLTX+F+GULtL0d8LpF4/wYd9SqZwkecsX4q7o0oSipKGmOohHkqZDkE1z/zpElMeFCZFrnScVJBSaKa4jBJEmIAhP106GsS/LMndeH3LjiZHVuS6C+/EKaqTEv0XnlLhS8ZFFoqGX0qEv0lK1nDCprYtszIe5Ud9XHzkCju2ZMq2HNU3AJczp1pBBO7bGw/dFztU+V25+yb2o0tQWQno7/IUiEFQ1sqzYOfyHXHxYfv+jXbJ4EfM2JIscE0xpG6ZKmw8uMWm41FRlIc4KuQOi4cN2z/DWf4Tjtea7vvzy7F127oBwC8etC/r9cGWiLzqZhRS0XO5vXzXIIifUR/2Sbesnwurlkxjx1Hjk3Piw5MPrKMLzhJXPp3Nm7Dm279qWBlNE5/RX0qLhPEnvAd76gPQ7TD2l+0OPHZ+fSM0sALVJWwUEX2qKK/Rhq0VEh7J4v5zJMmhpF3brAZlrSY1jjnvYy0e52oiDbjy/TIC7esaMiOer/2V9RS5scS3SsJlWrdFaLCGo3+Cos4KnwqZjgGBeWF+/zahdPx/T+6hG0bzM6NeXZ0ncvPnB35TQ7BbwSTuuwwT0VCxQqFyn//0SW46fV+crQcUEIWu7ZUcoCnJrYfGsK/PvIqzp43Ge84/2RhEfLpL/ocamAjsqXCHPWcpdJk3/5tzUXoLltYfdvD7LvndodcrB99FtIZt//+efjlywcFH8DpsyfidM6KAYBz5vtUWbXuskWdYEBtqdAuknyeirpqapC85rqRiWEgjP7icxMAf4JTu7yVEaW/FJYKT38F74HvP199gODTGf61Pc8TcmPkDcYWz5qIF7kQ0Cx8s+BTUfRZdR9J0V8kLOKiv2RH/fMBZ+9bKkQv+sLTkrTTuuPGLoCUnR2HkP7ihKgLLvqLfCekgYvni/RXWJEhLk8F4KsHhEEtZYGa8j+n0V80bKhtcrRTXg/1n4Xe26KlQnjj0tlYNm9ypFZd3Lr8iWuW4nWLZ6Bim/jORqlPkhXZCPyCqjVMKHdFfnvd6TOwNKDI+S2oxYASzr/WBktl3AoVXoukhYX4XDkDmH/h9NBpINFLOsLFzDca/SXjwsCi4MELlcEgGoyFW5YsvP70+OKa/adMxc7DQ6Ezu+5GBEOcT6VkmcwBS78l7e/AL1582wcGqvjYfz2D915yqnC8ZRosC3gkKfors6US3bMisusk274WgqVClCgtTp9/+zl40+aT8Kf/9kRwH5EuRCDXwpKhqp6r8hfRfZBQiRNop0yfgJ6yhdkT/QWFhOAZJ03E3mN+XSjyddHiyCedxg3RtKEb56inW5Yd9fJckHNCTMPA+id2RrY94LVvZqmUudp2XKhxaKmIbfzsry4Tal+xKLigC6fPnogHPngpTpvZG7ZlGMzikX0q7HPwfVZLZc7kbrz7tQuECsLhOcnnJqG3YmO45ipVgA9ffRbL4QLU74MXoB1R+2usQi63DoSOy2iZllDborlQ5bQYw1Dv/Fgk/cWXdrjw0w8CQGqmPuHfb3otPA/YesBfcKp1N5I/QD4VlaO+RvRXSp4K4Ato+b5NA2wikVZom+KiQ/0iyBSGvE+8f25KSLHCpzIwUmMLIl9rik9+pGZ7KzZ+a9EM7j6ao78ODIxg7b3P4hPXLIv4DAA1/UXPYmJXslC5dPEMPPG/38ju/w8vW4TbfvQCZk6s4MCgz9tTmZZuiZKqO24sh5+2uExSJD/yWw3Q96wisDRkZEc9dWPDM7uF4/hnLvtU+O8ArqCk5Avrmyo6yGWLDQAWzRKtetM0lAEtvNVCkWzTgyTTsM9IhMrqlKt6NwLqn2psRZJPLSPyfZbSQkVi3PpU+EWKtEJaVKI+Ff+zaRiwLHHxskxTWKCFSq0tfj9ZtQpK2uQz6uUtSsnvwRazYOJS7TDHdZklF5enAvjWnir6i3Aw8A3RYsPnMSRGf6U46mWfisdFItG51bqL4ZrLnLt+sl0oeOi9qYSV3+f0580vntTlX209hH9/dDue3X1U6VNRO+rDPBW/T+pr08ZxhL+46gxsXXu1318jpL94nwpz1OfwqUzqVic/yj6VeEuF918YsdfjvydLpWyZ7N2LfhBfOKl2DuVRZo75+Hs0DXCWCr/oRqkwehaqPqugGkdy3kgjYJuuKUxeuT058g0QFSGdUZ8D/ESmCaymv6SQ4uBzWD/LEGgYPs6+1UI/LnchDnQfI3VHUZMJADxUg2fQE/DWVKbFz1NJKNPChanKA5P/lxzJdDw/oEWh4uAHz+xmmwap6C9+cpYlYc+v3WSp0EZGVBCT30+Fzz1StQs0YakE7dE1RupuJA+DP46HbKk0w3Xz+4/ztGSYRe3F3lParaqSH/mCoqRIsETDBPqL8lRU4N8FKQ62FZZyl2ta2abBNsCLQ3fZwhevX4G3vmZe7DGmYTAaTfDbcP3mSys9/fGrcElg1aa9qyg9zH2WVtxrz43vY9gehdNHx5Z8rZD64wRJjNXSKpwQ9NeIRH/FOer5hD2eg+QXaMdDbp+KjLJlxgyYxmQ+dUeuZwQEyV5euDEXaYUly4RtusynYpuGkjIJLRU3MjH450BRcsxS4ekv7h6/cN/zODBYxdXL5+DL7zxX6fQWy8H4GjtZnXJuChBWtZ3cw+0z4oQRYg4X7kwQLZVIFxL7xFtIQCBUlPRXtB1mqZSTLZXEvnCWirhJl/97oqM+5Xoqh6/jhtsJN2SpGIZS2MrnUTCGbRroKpkYGIkKFcs0MJxhkys+clEFywz3e5Frf6nuobdis9/SXlVcPTT/3PDz9//oEiyZMym5MYTPWM70B6LRXHYK/dUGmTJ+LRVecNBiQxEQsqUi1/4CREuFH1yOG+4HUpQlOUVRBRWIz12IAz9gZWuD96mULZNNVrJUBqsOfrX1oJL6AnifijpPhbS9Q0GUnMqnwnPhFEJNvDW/FscV36zY4W6CKqFyVLJUHDfcE73uhAt+/ITLQn+Fx7iSpVKtuxGKgipAy2CO+hyWCh/9xW+LSwK95rixY1R+h3JwBp8BTuBL3/OKmP9XbF+kv6DcDppvB+AtFZNZKnJNK9s0Iz6VZsBbKuJ+KmqnPZ3D/42DfB4/jvnPPWUrI+UaWp7R38T/ZWvV72/4t5k8mUYxboUKP5Gp2qnaUglfDFUpBsJJb8n0l+MV7qiPqwvWqKUiCpV4n0rZ5iatZbLNon619VBsuCajVOoq+iv05xxh9FcwuLlnJ+eFAKGfi38ntMDJC1/Ftpi1I9f7AsKFi3wqdSfcdtblLMy4idxo7S+5ptlI3YkIENpJVIbs22rGUuHpryNDNRYGzO+nEk9/RQW2+rjwM09/hdFfYk4IQaa/4oQK/y66OEuFQqlVOxqm+VSywBQslbAPvE9F9ktm9YvICoLo0wi/z7rAs0KxGeivkuJ9yCVbWo0Tgv6icGCVxiiXaYlaKqYw0HhnZVFCP846aHSh4Q+3I/RX6GMoWeH+CyXLwJkniZEx6rb9xuuuG5k0phE+W0Z/SYsOoN5rgp4zT3+VbBOoOhEtLM5SIcFEygNZKvwkVCU/ysiUpyJo7uI9jNTciDZZsszE5McJKSHFWfriuP4Yp/um5+YkhBTLl6uUTBwbAV63eIaw3TJ/vuuFBSUZ/RXnU5ECIOKUFX7xU/lUZAvKNrPXzkqC76iP+lQE+ksagGxjupRXpfZn0mfOasnY16SxIT/3MBk1+l07nPTAOBYqvOZLtEjNiWrFcka9yqcyyGnYDjexVLseNoM0yikr+AlatuSBHWTUB6W+mU/FNvHOC07G7/bPxzM7jwjJgPL5gJr+MjjOnKo5k4XCHxndWztc+HkhoXI2AoFQCbRLXgi5kk+F6EQWFm6ZQX2s5ASwbGVaws/hbp3+X5WjvmSZiY56JlSamPDUl2PDNdRdj1loLKTYze5ToUX8E9csE3balC0zktNynoosOIVqDgnPVXTU074pJqtkESnpXpC2zT9veY96ghwok3VxVuVxhZ/D77O+8qSxEXHUK3wqccU/W4VxK1RE+stfbGjCR30q/mfD4BZPrn7Wn115OnYcHsJT2w9j15Fhjv4qpq+ymc33rREI8egy/YUgo94h+ossFdOvEFuycN4p03DeKdNi2vb/1p2opeK3LR7PnIvcIjs44qBsm+zZLprVG1oq3PnlGJ8Kfy5PKcX5VEgxqNgmjtdCaipucmV53IKjnuivekh/ySHFtmlAVYZupO7AMg10B9SnXEI+C0j4Uhj3ZAX9FRdWqxLY1N+443grXfapyIKTLyeftCjyl+Md4dQfVfRXEeDHcJwgUfkl5T6rIO9ZIlS2aCK8NykKNOKoVyhkqrydVmLc+lQES2VItFT44S/SX2qfyg0XL8D/WrUE86b0oO5Es4rzQrYqCHnor7iM+mo9cNSX1JM2vm1aqKJlWlTPIfTBhE/7eLWOCjeBZ/RWOKHC+VRY5JHYpm+pKBz1HgmVOgwjLDFCvqLeLpuVMuH7FulzgyHFoaUSH/0VR39V6y4qdpiPkSf6SxYqfJmWON5ephbLnJIhHMedTkLFMLiQYtqITRYqUvJjHFSLn+t6Yc6KbKnElIVpFHyXypwAjLNa+P6lWyrhZ19RVV83K5Ken9wVVR9VzvtWYtwKFd6nRRqsylEvJD+aXJQTF/0VHtsan4o8cQiNahb8AhJ11HPRX5yjPo56k5FcpkVxPOeDIQxWHeFey5yQcL0wUa+U4KhPCik+OlRDbzncZ2SAQowlH0uso76BSBz+ukyo1Fw2xvjourjaXxXbRMWylPeaBSQYDh2PsVQaSH6ssBBzWTvn7jcoKGkpFqyIpSIVM42DKtu77nrC8+NBQkz2dzQK/rqx9FeTPhU+wIbfVhuQfCoF0F+y0iAXFfWvmd5OkRi3QoVfzMhRz7QpbvzLlgqflQ7I4aem71MJ/i8qPK8VjnqVUPG8MIelEqOZxiGpTIvqOZDJzic8Do74+2NMm1DGOfOnoMLTWW6YW0N/IxFKJfF4Ap+nMqm7xPpHyZCUyJe2UVGjyY+kXNBY+c/Ht+OdX/0lADGSSSlUar5wJ5qo0Ug//xzRUpkk+1QS8lT+x0WnCP+TBakK8CD4finxOckbsREE+iuzpeJf2+EsFVX0F5CcLZ8FcZGStiBsJOUpo6UiKqJGLOWVleloxMIgS85UCDJNf+UE7y89wugvtaXC56lELRVxwPkJYPFRNY3gv//wEvziry8vzFGvmuz8b1SluCwIlWzX4OmvaJmW6PGWghYhofLoh6/E9z5wMcpciLDreRwFQ5NXbLMiWTaEf/n5Vnz7l6/i6LBf94vOI8c9ldmga8Xt051tj3pOqARjjMbVKweOs98ogsq2TGW0UtVxUbEtVGwL0yaUcdLkSvSgFNA7odwgClAIQ43jF673vW4hK/cChNWQVbQpgYJU+MdHykNdCneVqxTHgf+NxqyTaKkEtFtGZSgOcUJF3ltFPCd6bmrbphnrnM+6hjRSWYMsOFX0V7sslfHrqFf4VOoKn4pfpdj/bBqIWCr8BLJMA3XHDaia/C/o7L7JAMKQRrIm+Os1AmEwyzH2hgEP/mI2qVyK5azjIJb+iL8ugWmwnI9hsFpHd8mCYfjBEWXL5EKK+ZpN6uivcoxP5cfP7cNIzYUHD5O6SkxJOBb4VMhSoXDkWEoow6MQ6C8po54Hi16KyVMZqTss8fQnH7pMCOPNCnonBwb8wpIy/dUI4izXaPSXp7QuIpZKRvrLENoKrZ5YnwqzVEzM6K2wud0o4kr1JDnq6ZzUkGI+R8cypDwVjv7KGD3ayFqj8qm0O09lHFsq0TwVVrE2UqYlfBGyo57Xai1mqRQX+QXEWwuNhxRzbSqsidBSCfNUVGXuVRAd9emWShiCHC64x0fifSqOF2qnpDWrwl5ZSLG0UDuuh6ND9RhLpcT6LtMRqj4nwVXQbqpCf12cpRIXUhzuH2I3NeFpsTo6XIdlGuE+9twrTROUq8+Z6/fTVEd/yY+k7oo+Fd664CHTX//63gvxp1cujr0HIFyMeUslLvqrZBn4xV9fjqc/flXyDcZAoIq5vpZjrBb/nGz0l+wnEumv8LjMlor0EpPYBVtBf2lHfUEQt5j1/7IqxZI1wFsq4X4qUf7dDw/1o7+KylEBwsGbN3wyS0Y9c9SXmnPUq+gvpaWiGPgDI3Vh0vo+lTDv5JTpE/DXbz4TVy6ZHfRZPJ/3wciacd11MVitYwK3QA+MkE8l3EkwaWJlmXTCLpNSngoP3qcSl/yYNfIuDvzCManLZotXI1Vpv/D2c/DkR98IyzJQsqICNypUxNIvtHEcFcYkyLW/Llk8A29eNidyfVXJHN9Rry4oScf4kXNmZktbhkgVqy0Vef5lXZzlQIbcjnrpekmKIHPU82NAC5VioNzqVVGl2DREn4qc/Cg73Yr0qRCIqlo8ayLW/Y/z8LrF2aqhykjKUzENAEGeSnOOev+vuvR9cl8I/kLK7epnm4JPxTIN/H+vP41tipSV/gKCKsRBEELoqBctFVX1Zh6ZHPW8T0Vy1PPgk/doLO49OsyCB8hRnxd0P1O4jaQaqWdmWyYmd5f8atwKs0Y+v1YX3/+aSxfic7+zHG+VCjjK8waAkuLjmw+tHpejD9XRX1OkjbMaBV9FI3OZFu6cJIiKqOxTaZz+iljsCVQpy1Phnz+nNLcD41aoqKreEk0hb9LFO+Dk5EfZUqm7fvRXkSUPWK0r08Abl57EuPE80V+yhkdVimv1aEHJbG2H1EREm1UcH9f3Lm7PcfKpeJ4Hx+MdzGKCHX9P5BeRi97WHX9DLp9u8L87NlyHaQA95dBRnzURLw59U3vw+d89B+edMpWFrSfSX6YBxwU+/4PncMGnH8QH/9+TAPxdMOXIpmZAz4wEJ/8d0FjYqsq6jAgVif4sWSZ+t39+NCHWMNhiTXOoqxwda/ICDPjMArNUSmpLhTaDaxbUTk/ZhliJghMqchIjp3wmQfCpmGLZ/2boL1mozOiNv/fQpxJ+p30qBUFpqcRu0hW+CBoQKkvFNIN92p2CLRVp8rFtYRsufS/SIcJvCDPqS7bJyoN0xxSzlCHQFNK9qwRsnNDtKomWiuv578XzPNZuXHJpXEFJIHQi25YhCMAJZTssyFd3EzPXs0663zmvDzN6y0xxUTrqbT76y8NTO44AALYf8nf4HKk5hVgqJCQnc0KlmU2ZLNNQ0iryI6kqKirEQQ64UFkqfP+uXj4H158/H3/1pjM5S0UdUixv8dso6B7k8S/QXxFHPfU5uW3++feULcGyaCakmF+Dbr7sNHzrfRfFH6sq06Jw3rcS4zf6S2GpqJMfRQdcuElXmFFPCGPy4+P/m4Gc7BdXpbcR8JorIFUptky8edkc9JRtzOP2t06CKuGNkNWnAohChaylat0VooqYpSJdZ/akChzXw/N7jinoL7+Yoy1phhMqtuAnS7L+Gsk7sswwqktlqVRK/jbUFIbusG2NA0Hk5PepUD8A0aehSnxLw4IZE4SaXwT5mdQV0X9xkHch7EoRKl0lC2uvWw4gtFTior+mTVBvF5EV1CdZ0CX5VLIuzvzz/+DK0zFncjjHeD0x62jj2+utlDBzYnz4uWp/G5q72lLJCVqYeA1OtfOjaYi1v/gkPzlSKNynomhLJQwp9q/jf2h050ceFEZLMA2f9qPaX91lC29adlLm9lTJVAT61zSAhTP9hUkh0wGItBwtGJd89od4ZudRdg0vhv5689lzYJsGvvvodmX0l79RlchhT6hYgp+sqIllGkZkPxUek4MkTDMQPuTPG647+N7j27H7yHAhlgrdzsSKWqhk5e1vvmwR/uMDF6ceV0uhEHmEFrd/vMp/F/c+4soI0bOempf+Moj+EoUKUV6qoIWs0V+8X+bUGRNY6gAg762Ssa8NKAlJtb+0UMkJmuc891p3Xew6MgTXEx+0KqNeFSnEiiQ6bqHb09Pkk3fVyzMIVPtqhxn1jbeb5PzlN3T65nsvxB9fvghzg6ggGTL9BYTJe3SJOPprRm8FbzhjFu56YkekxlYt8KnIiwFvqTSyIKbBMg2lo/6aFXOxde3VeN3imXjT0pNgBc+dLKuhqoMvPrAFPWUbb1kejYZqph8AWDgxIGrYRTMe1YR6YjIY/ZUYHKH+/pTpE1C2TRZdRqCSS9NzChW6B5n+Mk1/DVAFLWQt0yJYIxHBxP2WcRVRBT3EwWL0F9+fDhIqBw8exMqVK7F48WKsXLkShw4dUh534403YtasWVi2bFmeyzUE0iKncrsqPr9nAK/9zA+xf2CEDRBfmwT7zCc/xpm/tQZ45SygRZ7olCIyYCd2yfSXL1Qd14vw1Fmg0nzC34K/poF5U7rxwTeeEbvwVCRHPQ+5jpSqjYsWTsOeoyOsNAlB3lKX2s7iU/n+H12Cv1m1RNnfOJiGmv6ioICVZ83Gl991LkzD7xv584ZrDgarDlaeNQuXnzm7oWsmoZejv/jKEEXz6Krab3Fg/H5ScERMWyvmT8Hmj1+F2ZMkoRJUHM/rU6Ghp9ogzzZjghYyPlNbSPyU2uAtlYyrbxJLIIPy01TRXx1R+2vt2rW44oorsGXLFlxxxRVYu3at8rj3vOc92LBhQ55LNQya8HGDT22pIJOlUk/YUa8Z0AJImi9dR6Z4GkGU/jKYRs0ne2WFyvFH4OkvGRQeTZBDiuU+AvH0F+BrsADw0r4B4XvepwIAC2b0APDpL2rXVxSiQ37ZvMl4/6ULoxdLgE9/+Z/5BM8JCs2XL0I6XHMxVHXQXSrGnUmbX/GWCsBFARXMRajylOJQskT6S4WkeaQqxUKWSlHRX6rggZJlKoMWmCKa8kyTrHoxoz4bhGChlJNUCqmlEDStRK4ht379etxwww0AgBtuuAF33XWX8rhLL70U06ZNy3OphkHabhz3ygsVVe0vlaXCJwAW+XooHp6sq7jSF41Apr8MI6RpsmbR8xAGaYz2pVogvv6e8/HzWy5n//MhxaqwZyCkv1SL0YLpvrB4ab+4mVi4/02Q8zPb382ST4as1qNl+5sFVawGxPL+E+TF3fBpMqLrqo6fpBm3hXSjoN0LZSWCaawFa6dJ+97LKEvRXyo0qj1TWZa8PpWQ/ooK95IVF15Nf1MoqAT6kf8/K42oShCNg8qnokqKbSVyCZU9e/ZgzhyfF54zZw727t2bu0Pr1q1Df38/+vv7sW/fvqbbIc0wTqPh6YFwsIQPnpy+PASfSguiv2gxjSt90QjkRcYwDIwEWm0zDmKh9EeMT0U1aG3LFKiZrgRLhd5JmKcSbW/+tB4YRtRSkSsgnD5rIusTT3/FFZNsFHHRXxMq0Wgifm93wPedZQ3lzopeKYTcZsEfxS4kqp0/46AKb33qY2/Ep992Nvu/0ddBj3FaXvqLHPUKS8W2TOU4IU0/7Zny636SpZJVwUliCSLHqsq0tDn6K9UGv/LKK7F79+7I95/61Kda0qE1a9ZgzZo1AID+/v6m2yHNMI7+uvi06ZjeW8Yp03tCTVty0MX5VOoJ+1Q0A9LoHFf0qeSxVOSyGWZeSyVhYNO/cXONTyITfSoSVSRZKqr2ukoW5kzqwkvStsdyXtHps3sBALuODLP+VhugbtLgBz5E81R6ylEaqu56kW2Gi7JUCDL9JYfzFoWkcvoywjyV8LtJXSVB8DZrSckh842C+qQS7iXTUAazqBILVUiK8GrGUd9QhYQxQH+lCpUHHngg9rfZs2dj165dmDNnDnbt2oVZs2YV2rk8CC0V9eCbPakLH1u9FAAES0WuSsxDtFSK62spxqfiyGnjDSBapoXzqTQhVFRx7wT6L27Q8lQCb6nI2dJ0+pwg4mfB9GjeBOD7VX7x0oGYa/ltnjbLFyo7jwyFIcU1J1eYttjXcJ+UapKlYpqoO27E6ixcqEQslUCrLvQq/thXUUYq+CVzVBFQ8WMpDafP7sXzewZyKwc0VlXvoRTUFZNhJVjksddJ8qlkbEYoahuc9N0PXIzj1Xrk2LJl4uRpPcynCHDRX+2RKfmSH1evXo077rgDt9xyC+644w5cc801RfUrN2gSx9UIUnGOfpVidXE5gLMgCnbUk1ZEQoVM2DyWigy+u03RX7zZbqp/i5tsvMUnhBTHRH+9edlJ+Nf3XoiLT5uubO/kaT3xQiVo49QZE7BkziR86E1nsP7VCnxvfB24quPXqhquuRFLhZIf664fyk25UvJxeTExYqmk+zOaQc3xMCFjkyXLUC7+zSyshH+/6WIcOd5cuXse5L9UOer96K/oHKG+NkIpxlHFjUBVHPK8U6aqr2f6WymI5wd/O8FRf8stt+D+++/H4sWLcf/99+OWW24BAOzcuROrVq1ix/3e7/0eXvva1+K5555DX18fvva1r+XrdQYQ3y3TAgT+3fIOuGRLhZIfC86ot0X6i1kqiuq3zYI3tZuxVLJEtMRNGL4OlFD7S3bUc5z1JYtnxFo+MrXHgxSBkmXi3j95HS47Y1a4C6WTnFHfCKiWGm2F0FvxLeIJMv1l+fSX63qCE79dlkorHPWNRH+prs+XO2l0kZ3cXcLJ03vSD0wBWe1K+ssyc9FfPJLor8wldCx+7mW/NjuHC0pqB3KpS9OnT8eDDz4Y+X7u3Lm455572P/f+c538lymKZAmEjd5VS83YqlEor/8v9UCF3uADykW+6OqX9Ys+FspNxNSnJCnQvIqaczapoma46SEFGfrS9KCrBIaSTk2zYKiv8jymNRlY//ACHoq0QztemCpTCjbOBxo2YU76mN8KkXIlLtu/i1s3nkU/+t7v0ZdUVA0DiXLTLRU2lWLSgXyg6ksRtsylOOomdyfqFBphv7KN351mZaCQDQ38fJyZU/VvtGGIS5scvQXC/V13ELj/0tWjKVSIP2VtNdKpvMTor/omSUNWqWlItNfGWdZV8KCrCrCmXdSqmCa4fbMQOg4lgt5Ek1Wdz3Bwiqa/pItpLjdM5vBivlTmJ+rVnczc/MladdDAh8YM1oYDmr7KX0qltqnEvY7+3Xi5grQXO2vZt5nu4X4+BUqHvlUSti69mrceMmpwu/846Vn7dcBC3lgWVsJC0q2xqdCQoQWqCIXHr67raK/kp4JXVNVULJRJG29m0RbyJ/zwAzyT8hJ/+ZlJ+Gz152N02b2Csfx4cytpL/kBbro5Ec+gq6R6C+V4CiiYkRe0BYKqiKXVy6ZjcvPjAYdWcz6a96nAoUym4Zmqk4Lfegk+mssww3CfmkAyHsjqCwVOsQyDDiI1sjik+iaqZ8VBzn665oV87B/YAT/46IFDbf1jRsvwNwp0bpbqr3AG4FIf8lt+3+TFjDi+FUFJQlVRWFGFZKESppjuCifihVEf1GOypSeEt5x/smR48jhO1xzBIqqmT3pG4FdoKUCiJGPjZS+V76PFoU7N4IkS+XmyxYpz+F9r1kh36NgqWRsxjT9oreeF517WWBx61o7MG6FiuOJNYrkSK44Rz0QLExO1FLgS99X7OIeHS2uLlf7a82lpzXV1qWnz1R+L9J6zWs78me/7eYsFVmoUHZ4GpL8ESqhUZb2Sy8CPv0VcvNx1h9v3fa20FKRUSp4xeZziLI23VuxY/ZQCf6OolRJslTiwJdzygo5F0UVdZoFtmk0Hb0YlpfRQiUXXImiUm6vyz6Lg4XtVCcNuLCgZGtK32dU1JtCXm1dLNMiC+jsQiWpoORIoD2mIUnLV2VC88EBhTnqg3sljTcuTFvcC6N1PpW46xYZQq36nIQ/uOw0/G5/X7StseCoDyZbI+H1TTnqI+H3mU+VzjMANCdUwjwVLVRyga9YC0Q1N16D4Gt/AeHgkbXJVpVpkS2VVoDvbm5LRRYq7Pv48+nZ8Qu8LOhHCrBUVFWIeUFWXJkW/y9ZV3GWCk+Tkk/FMMSAhTz4yrv7leNGlc2eB0Ip9Yxjf9bELsyaGKVi283xqzAclCxqxK8X+lSyXyd+m4jsbQD+/BlBPuo6adfTIjF+hYon1ihKtlTob7JQEZMfi+urnPzYCoiWSmvKtGSpOJu0mA4XYqlE+8BbnEUtZLQ4vPXLDwOIL33DR6NRtn13ySpMKVl5lrp8ftGWilizKl+bYyGkmPJUGqG/sljkMqI+FVGBzYo8pVbabamM2+gv1/WEFxBxrKsc9RL9Fdm/ukXJj3Ltr1Ygr6Ui77vNI8siUbZ8Z6O8+D7y11fgjhsvAJDdUklaCNT0F1fmorA8FUmwxrTLC7mK7SfVtdqfAoTjvSjhVWQEXda93luJxUEZn6REWhlWE1ZG1KdC3zeGZhIvw2u21zIc35ZKwkQQLBU2yEWJHlk8g+OKLJ/C962FhoowuJvxqfBr9QIpozmM/kq2VLrsqIZ+0uQu7Dk6DEDcQTEJiY56hYlftkwueqZYnwph0axe5XF8fyzTRFfJKjzxUXlds1j6q5nqunGQaebRwN+9YwWe2XkUM3rj93uXYUnrRBbEJT82qpSSxduMtdFMv/Ng3FoqjptM+ah9KsG5nroukLijW/E+lQtPnVZYmzLyRn/xg/kUqdBjFp7YNo1IAUkCfU88dxqSNH3VvRmGwayVwsq0cO18/48uwbwp3an9sU0DXSUrkqjYClgFl2nJm4AntDUG6K+JXSVctFBdWy4OzSRtRn0q9KGhS4dVp/P4VNq02o9fS8UV6zzJ9FdSSHGV1QVS76YHFJdUBvjO6/v+7FL0TVUvTEWAH4zNVOrl7/0UyVKRaUMVyrYpVCjmQd9ntlSSkh9jXkxXycJwrbhtoPlHmOQnEirMmga622SpUGBKUeu2XaRQYQtkrmbajmZCimN9Kg1eO4+PzMwhkJrBOBYqkpaYKaRYFCpx0V9A9r0QsuL0YKfCViGvT4WnreRwWHoWSfy9r6UXY6kk+VTi7q2VlkolRlgCogC3Lf8ZtMOnUnTyo7DpU85nSF0azYz6ZtDMwh5XpqXRWw93qm3sPICzVHRIcT64nidoQvJiosyoD76iGPa46C/+2E6B6FMpVkU0Mzheeyp2pJIuYfbELrzzwpPxzguiGekqJIWBxllhJIiKLNMit63sj6ThX3jq9Nz7q2dBkQUlAXFByttm0ZFp7YLZRL8jPpUm790uwFIpSqFKw7gVKo4rhhTLznX+3dBnEjROzF4LwoLUaRMip6WShCx7YP/VVWdiULGpEOAPen6L2azXUyFu4hRtqfDPMIn+kn0q/+etywq5fhqKLn1faPRXmzO8iwL1u5FHKo9V5r9t8NrNCiP+HE1/5YTjiSHFdSldndfc47jSuDItqmPHOoqof/WhN52hdG5miWgpYg+MLIizwsiaKM6nktVSKT6cOQsKj/4q0KfSbALgaCNLPlbWNhoN9WbbBDfjqNe1v4qBm2KpJCU/ErrL6p0JVceOdeT1qQDAH7xBXWiPWhsLjyQua7iVPpWkqs+yT6VdyEOXqJC3Uq7QVsfTXznaGA1HfZstlQ6Lv8gOuUyLm0B/xZmW3aX46K/Omg75qxQnIUv0V7sQV0gx9KkUM+Sz3qq4l0v7phsLTCnMUuE+5/WpjIE8lWZQhDDM66hvZghZOaycZjBuhYrricXXLl8yC793wcksJ0RFf8nF35Id9Z01IQSfSsF9HwtlNwhxE4esiaLuPesElX0q7UKpaJ9KgUoJS5YdA+OlETRbYoWHXGcwK/LsQdPu/WvGrVCRLZWKbeEz156N2ZP8DNqkPBVCNKQ4fFwdNh/EjOiiFzd6fmNA84zzqdCCXhQF1chGVYR2auZFX0sc+wXRX2NgvDSCcJ3I0wYJlcbOy5P8qOmvguB46odoKTSFOEe9nKQm0F+dNR9a6vdoJimsVYgTGsxSaUH0V9bj2pmXwTZ+K6ikEC+r82Zmj6Xx0giKpL8abSMPGxCWn2r41KYwboWK76iPfq8a0HHVR5NCijvOdG+hCVxEVEyz+Pb7L8THfvss9n/cYh+3RXSzyHqvgk9lFBz1RVW+LnLsm22ORioKRQjDZh31pCzlqf1ltalOy7gVKjL9RaDFlf8lblHMkoXfKWglj03+qXY+k1kTfRrz4tNm4Equ/Huc0Cg6b4Muk7bJEz+G2ulTobHvFFSktMjS92Oh9lczaGaPehlhTlyj1zabOg/gLZX2PO9xG1LseOpd0pgAUWheac/cMAzYpoG6W+zOj+1AWEql+LazZNQXjQf//PU4XvXLumTJBaGaYEX5VOg6cfuoEMTor86lv4pMfgw5/lzNtB1FlJAP16TG2qBh21yeCvW74VObwrgVKq7rKbVIlfn5pmUnwUByvgGhbJuoV51Cd35sB1oZ9tvM5kV5MbGrhIldJQAitRX3XkiYFNVDVvoiRUiJ0V/tDCkOLJWChIptGihZtE96vrbGws6PzaCRfWDmTenGjsNDke+bpdBY6fscQqVd83PcChV5PxWCKizw9NkTMxd09AWP03FOxlaG/VKLo7VIZMk2JoWhVhAfRFRCmiIi7qfSfp+KU5BPxTAMzOytYOeR4dxRRB2b/NhAOPDdf3wJ9g+MKNpA0EZj185Ty01v0lUQ4n0q/t+kl/OTv7wMx0Zqyt9oEems6dA8l5sFzYZJFgVygCdmtgdjoe5mK6+fes3M9Nfo+FToukXRXwAwY2IgVPI66lvo32slGhGGU3rKmNITLRwa1v5qkP4qIk9FC5V8kAtKEqwMLzWpTlW5YIdvu9BKimq0F4kslgoJnqJ27aRbTXPUF+mLaARkIRUV/QUAM4NdEvM6fDs1pLiY6C/xb1bkEQztnp8d5irLDkfao56Qt5hdiTLyO0yotMOnMnr0V3pdL3LU1wunv5LvuTRq9Jd/v0VFfwHAzCDiLneZlg71qeSpFMzaaDKjPqS/mhEqoc+xHRi3QsX1YiyVHNwkENIdHSZTWqodtpJay4Is2fL0m1ytulnQOErzqYxWmRZmqRRIf03vLWYfmLFU1qcRNFP6XkazsRp5ghvaXcFg3AqVOJ+KKqO+EZTYjnrN9200EC78raC/mud7C7m+acAwkqOrmKO+MPoro6N+1ErfF09/TZvgWypHhtT+xqwYbbq0WYSh883322hSMDGKtxlLpYWJz8rrteUqowA3pkwLW1ybbJfor06bEK3cw4KaHM1nYptGsk+FoqEKEiq1wOJJc9RbgcDz+9j+KsVF3S8AzAgslQOD1VztGIb/TDqO/iowo77RuZKHYWl3VehxK1ScmDItecMZyVHfYTKlpdoh45pHcTRZppFIf00PtvCd3F0q5HokVEp2+vNkWmYby7SUChaiANg2yAdzChXAX+g6bQ5N6S7h6rPnoH/BtKbbaDakOI8fqqPor4MHD2LlypVYvHgxVq5ciUOHDkWO2bZtGy677DIsWbIES5cuxRe/+MU8l8yMOEd93vDXcoc66ltZSqWVJWCyomSaiT6L687tw9prz8b7X7ewkOsxoZIhYbboumNZYLWE/ipOqJgpluVYhG2Z+PK7zsWyeZObbiPvJl3NUFh0SkfQX2vXrsUVV1yBLVu24IorrsDatWsjx9i2jb/927/Fb37zGzzyyCP48pe/jM2bN+e5bCbEOerzOpVDn0pnTYhmNaRsbY++49WyjMRNsEzTwPXcfjp5Ua37i3UWoVIy2z9mGP1VYPRX31Q/1P7KJbNTjkyHaXReQcki0KwClif58aw5k7DyrNk4a+6kxk9uArlm2Pr163HDDTcAAG644QbcddddkWPmzJmDc889FwAwceJELFmyBDt27Mhz2UyITX7MuQB2avJjK7NqQ59K4U1nBpURaRdec/IUAMANr12QeizRXu1NfvSv5RVoqUzuLuHXH3sjPrjy9Nxt+fRXp82i/GDrToO3bpoGTKM5hmRKTxlfeXc/szRbjVzJj3v27MGcOXMA+MJj7969icdv3boVjz/+OC688MLYY9atW4d169YBAPbt29d031xPTX/lXVTLHR791Zrkx/ZytipYbaZTZk/qwta1V2c6djR8KvROivSpAGD11vLCp78Kaaqj0Cz9dfqsiW2zNPIiVahceeWV2L17d+T7T33qUw1daGBgANdddx1uvfVWTJoU/3DWrFmDNWvWAAD6+/sbugaPuIz6/JbK6FM9zaDZUMYsGAshorZpMppprCFLcmbRoEdRtFApCqZhdNwcKgIhDd3YvV93Xh+uO6+vBT0qHqlC5YEHHoj9bfbs2di1axfmzJmDXbt2YdasWcrjarUarrvuOrzrXe/Ctdde23xvG0A8/eX/zeuo7zT+i2XUt2Iij3JGPV17rDp+RyODvBWO+iJhmUbHbSdcBFoZ2j9WkEu1W716Ne644w4AwB133IFrrrkmcozneXjve9+LJUuW4IMf/GCeyzUE11Nrzizeu0mp0LmO+tZZWK0MAsgK20oOKR5N2Myn0j5LymoR/VUU3v+6hXjL2XNGuxttB5srnaaVNoBco/yWW27B/fffj8WLF+P+++/HLbfcAgDYuXMnVq1aBQB4+OGH8c1vfhM//OEPsWLFCqxYsQL33HNP/p6n4P/+znJce+68yPe0qDarwXV+Rn3xbY+F6C/bNNpKLzUC6lc7u2cyS6V912wEH3jDabh40YzR7kbbMdoVvduBXI766dOn48EHH4x8P3fuXCY4LrnkkkIjULJiVYwWZOakBVieSodpGq0s+kgtjmaIKL9p11iDbZpBZn0b6a+cypNGa9BsQclOwrgtfR8HWlObFipkqYxNn3As2pJRP4rz5NZ3rECloByUojEa/p5WRX9p5EPeMlGdgBNOqIRcc3PnE/3VaQpge6oUj95UmT8tfg+c0UbJaj81RxWF3/3aU9p6XY1kaPprHMLI61MJaj0VtdlTu0BjuBULP1GBYzX6arQxGpbKhIqdOY9Go33IsvNsp2Ns8gUtBM3tZv085RZUf20HWulTaXY3uxMFdkpdMo0TB2MhqKXVOOGEipUzKoYc9bWCNntqF8wWcrkngvMxD+yUumQaJw5aOQ/HCk64kW7kdGCST6WobWnbhVbuUc+qoGptXAlrDIc7a7QXRuiAHN2OtBAnnFChekPN0l9MqHQY/cUoqha88RMhSzgP0jYQ0zhx0MqAmbGCE06o5A21pNpfdbfT6K/WZ9SPZ544D2zL1EJFA4Cmv8YlmFBp1qfSsY56/29L6C+0ru3xgLGc7a/RXpwI/scTLqSYNMamo7/szvSpEFpSpmUMJD+OZbxp2UlY2iFlyzVaC538OA5BC19uR32H0V8kQ1tSpmUMVCkey7hmRbQGncaJCR1SPA6Rt9AeCZVah1kqlOzZ2irF43eiaGgUgWZ3fuwknHhCJWdGfdnuzJpK1N+WlGnB+Ne+NDSKgHbUj0P89vK5AIA3njW7qfPLlgWg85IfSQa20lI5EbeH1dBoBK3MFxsrOOF8KmfNnZSrJlKpQy0Vr6X01/iPaNHQKAJjYUO7VkPrlg2iU5MfmaXSijdOlsp4nikaGgXgRKhSrIVKgyiZnVn7ywkslVZYEyyiRY8mDY1EhH768StV9DLQIGi/8U6lv1phTeiMeg2NbDC0paIhw2ZlWjpLqLjMUim+7VZm62tojCecCOH3Wqg0CNukjPrOor8oV7OVjnotVDQ0ksF8KqPcj1ZCC5UG0VXyH9niWRNHuSeNoZXJj7pKsYZGNpwIVYpPuJDivOgp2/j2+y/E0jmTR7srDaGn7L/qaRNKhbfNCkqO55mioVEAjBOA/tJCpQlcfNqM0e5Cw3jzspPw8dVL8Y7z5xfetqa/NDSy4USgv7RQOUFgmgZuuHhBa9rWGfUaGpmgkx81NDLgRDDpNTSKwIlQfUILFY3cOBHqGWloFIEToEixFioa+UHCRJdp0dBIhmEYMAxNf2loJCLcTnhUu6Gh0REwDUOXadHQSALV/BrPPLGGRlEwjfFdJ28c35pGu6C3E9bQyA5DWyoaGsnQ9JeGRnaYBsa1pz6XUDl48CBWrlyJxYsXY+XKlTh06FDkmOHhYVxwwQU455xzsHTpUnz0ox/Nc0mNMYjXzJ+K687tw5I5k0a7KxoaYx6mYYzrSMlcQmXt2rW44oorsGXLFlxxxRVYu3Zt5JhKpYIf/vCHePLJJ/HEE09gw4YNeOSRR/JcVmOMYXJPCX/79nMwoaJzaTU00uA76scvcgmV9evX44YbbgAA3HDDDbjrrrsixxiGgd7eXgBArVZDrVbTDl0NDY0TFjqkOAF79uzBnDlzAABz5szB3r17lcc5joMVK1Zg1qxZWLlyJS688MI8l9XQ0NDoWIx3SyWVr7jyyiuxe/fuyPef+tSnMl/Esiw88cQTOHz4MN72trfh6aefxrJly5THrlu3DuvWrQMA7Nu3L/M1NDQ0NDoBpjG+q0+kCpUHHngg9rfZs2dj165dmDNnDnbt2oVZs2YltjVlyhS84Q1vwIYNG2KFypo1a7BmzRoAQH9/f1r3NDQ0NDoKpmHo6K84rF69GnfccQcA4I477sA111wTOWbfvn04fPgwAGBoaAgPPPAAzjzzzDyX1dDQ0OhY6DyVBNxyyy24//77sXjxYtx///245ZZbAAA7d+7EqlWrAAC7du3CZZddhuXLl+P888/HypUr8Za3vCV/zzU0NDQ6ED79Ndq9aB1yxYBOnz4dDz74YOT7uXPn4p577gEALF++HI8//niey2hoaGiMG1RKJsr2+M0714kFGhoaGm3EF69/DWZNrIx2N1oGLVQ0NDQ02ohzT5462l1oKcavDaahoaGh0XZooaKhoaGhURi0UNHQ0NDQKAxaqGhoaGhoFAYtVDQ0NDQ0CoMWKhoaGhoahUELFQ0NDQ2NwqCFioaGhoZGYdBCRUNDQ0OjMGihoqGhoaFRGAzP87zR7kQcZsyYgQULFjR9/r59+zBz5sziOtRmdHL/O7nvgO7/aKOT+9/JfQeS+79161bs378/8fwxLVTyor+/H5s2bRrtbjSNTu5/J/cd0P0fbXRy/zu570D+/mv6S0NDQ0OjMGihoqGhoaFRGMa1UKG97jsVndz/Tu47oPs/2ujk/ndy34H8/R/XPhUNDQ0NjfZiXFsqGhoaGhrtxbgUKhs2bMAZZ5yBRYsWYe3ataPdnUxYsGABzj77bKxYsQL9/f0AgIMHD2LlypVYvHgxVq5ciUOHDo1yL0PceOONmDVrFpYtW8a+S+rvZz7zGSxatAhnnHEGfvCDH4xGlwWo+v+xj30M8+bNw4oVK7BixQrcc8897Lex1P9t27bhsssuw5IlS7B06VJ88YtfBNA5zz+u/53w/IeHh3HBBRfgnHPOwdKlS/HRj34UQOc8+7j+F/rsvXGGer3uLVy40HvxxRe9kZERb/ny5d4zzzwz2t1KxSmnnOLt27dP+O4v//Ivvc985jOe53neZz7zGe9DH/rQaHRNiYceesh79NFHvaVLl7Lv4vr7zDPPeMuXL/eGh4e9l156yVu4cKFXr9dHpd8EVf8/+tGPep/73Ocix461/u/cudN79NFHPc/zvKNHj3qLFy/2nnnmmY55/nH974Tn77qud+zYMc/zPK9arXoXXHCB94tf/KJjnn1c/4t89uPOUtm4cSMWLVqEhQsXolwu4/rrr8f69etHu1tNYf369bjhhhsAADfccAPuuuuu0e0Qh0svvRTTpk0Tvovr7/r163H99dejUqng1FNPxaJFi7Bx48Z2d1mAqv9xGGv9nzNnDs4991wAwMSJE7FkyRLs2LGjY55/XP/jMJb6bxgGent7AQC1Wg21Wg2GYXTMs4/rfxya6f+4Eyo7duzA/Pnz2f99fX2JA3aswDAMvPGNb8R5552HdevWAQD27NmDOXPmAPAn4t69e0ezi6mI628nvZPbbrsNy5cvx4033sgojLHc/61bt+Lxxx/HhRde2JHPn+8/0BnP33EcrFixArNmzcLKlSs77tmr+g8U9+zHnVDxFMFsSZJ4rODhhx/GY489hnvvvRdf/vKX8ZOf/GS0u1QYOuWdfOADH8CLL76IJ554AnPmzMGf//mfAxi7/R8YGMB1112HW2+9FZMmTYo9rlP63ynP37IsPPHEE9i+fTs2btyIp59+OvbYsdZ3QN3/Ip/9uBMqfX192LZtG/t/+/btmDt37ij2KBuoj7NmzcLb3vY2bNy4EbNnz8auXbsAALt27cKsWbNGs4upiOtvp7yT2bNnw7IsmKaJ97///czMH4v9r9VquO666/Cud70L1157LYDOev5x/e+U5w8AU6ZMwRve8AZs2LCho549Qe5/Uc9+3AmV888/H1u2bMHLL7+MarWKO++8E6tXrx7tbiVicHAQx44dY5/vu+8+LFu2DKtXr8Ydd9wBALjjjjtwzTXXjGY3UxHX39WrV+POO+/EyMgIXn75ZWzZsgUXXHDBaHZVCVoUAOB73/seiwwba/33PA/vfe97sWTJEnzwgx9k33fK84/rfyc8/3379uHw4cMAgKGhITzwwAM488wzO+bZx/W/0GdfZGTBWMHdd9/tLV682Fu4cKH3yU9+crS7k4oXX3zRW758ubd8+XLvrLPOYn3ev3+/d/nll3uLFi3yLr/8cu/AgQOj3NMQ119/vXfSSSd5tm178+bN87761a8m9veTn/ykt3DhQu/000/37rnnnlHsuQ9V/3//93/fW7ZsmXf22Wd7v/3bv+3t3LmTHT+W+v/Tn/7UA+CdffbZ3jnnnOOdc8453t13390xzz+u/53w/J988klvxYoV3tlnn+0tXbrU+/jHP+55XvJcHSt997z4/hf57HVGvYaGhoZGYRh39JeGhoaGxuhBCxUNDQ0NjcKghYqGhoaGRmHQQkVDQ0NDozBooaKhoaGhURi0UNHQ0NDQKAxaqGhoaGhoFAYtVDQ0NDQ0CsP/D/2575a/lDs7AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a88a751690334fc684cf5ee707420d2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f945baf1efa948d6b6da78149a96e020",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "502ee11bd4f8400abacfe7cd4f9c68ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c90495aa258416cbb3396a34c42d2ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c56e5c3c4fa411a982af66e7e3cece6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3045f407f0854fe693591e1a8fb00809",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bb63114480b4fccaa418b730077627d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52b68025",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "929f0aa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20a6f47c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b50030c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffa6518a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No preprocessing for the 1000 simulated data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf2e21db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cd06657",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "id": "277bd5b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "yes_data = yes_data / 3000\n",
+    "no_data = no_data / 3000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "97b0b5a2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3804c1b0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jFaz86ATOV7Vg+V0j8NzsSDgqeVZD/RuUpW0yMjIQGRkJrVaL9evXX/d1QRCwZs0aaLVaxMbG4uTJk71fW7ZsGfz9/REdzSmSRNau02DEig+zUXH5Ct5/LAG/v38My4YGzOzCMRqNWL16NdLT05GXl4ctW7YgLy/vmm3S09Oh1+uh1+uRmpqKVatW9X7tscceQ0ZGhrkxiMgCfv/lOeTXtGLD4jswbXSA2HFIYswunKysLGi1WoSHh0OlUiElJQVpaWnXbJOWloYlS5ZAJpNh4sSJaGpqQlVVFQBgypQp8PHxMTcGEQ2xT4+XYduJcvxqqhZTI/3FjkMSZHbhVFRUICTkP8tTaDQaVFRU3PI2RGS9ciub8fu0c7hL64unZvABaXR7zJ40IAjCdZ/78R3EA9mmP6mpqUhNTQUA1NXV3dJriej2NXcY8Mt/nYS3iwobUuKhkHOFALo9Zp/haDQalJWV9X5cXl6OoKCgW96mPytXrkR2djays7Ph5+dnXmgiGhBBELB222lUXO7AWw/fgWFujmJHIgkzu3ASExOh1+tRUlKC7u5ubN26FcnJyddsk5ycjA8//BCCIODo0aPw9PSEWq02d9dENMT+/l0xdufV4LdzozAulNdayTxmF45SqcTGjRuRlJSEqKgoLFq0CDqdDps2bcKmTZsAAHPnzkV4eDi0Wi1WrFiBt99+u/f1ixcvxp133on8/HxoNBq899575kYiokGw40wV/pyRj7kxgVg2OUzsOGQDZMKNLrBYuYSEBGRnZ4sdg8hmbcsuw/Ofn8G4UG988Ph4uDnyHnHq20D/Jg/KjZ9EZDs2Hy7F2s/OYLLWF5uXsWxo8PAniYh6vbOvCH/OuIAkXQD+tvgOriJAg4qFQ0QAgNNlTfhzxgXMiwvCXxfFQangAAgNLv5EEREEQcD69AsY5qrCnx6MZtnQkOBPFRHhgL4eR4ob8KtpWrg78aFpNDRYOER2zmS6enYz3McFD00IFTsO2TAWDpGdSztdgfNVLXg2KRIqJf8k0NDhTxeRHTOZBLzxjR7RwR64P4arf9DQYuEQ2bGjxQ242HAFK+4Oh5yLctIQY+EQ2bFPs8vg7qREki5Q7ChkB1g4RHaqucOA9HPVmB8fBCcH3uBJQ4+FQ2Snvj5dia4eExYlhPS/MdEgYOEQ2alt2WUYHeiOmGBPsaOQnWDhENmhC9UtOF3ejJ8mhNzy03eJbhcLh8gOfXq8HA4KGR68I1jsKGRHWDhEdqa2pRNbsi5hbowaPq4qseOQHWHhENmZDd/qYTCa8PTMUWJHITvDwiGyI8V1bdh6vAwPTRiO0GGuYschO8PCIbIjf9mdD0elHL+aFiF2FLJDLBwiO5FT1oSdZ6ux4u5w+Lk7ih2H7BALh8hOvLrr6gPWVkwJFzsK2SkWDpEdOFrcgEOFDVh170i4OfLJ8iQOFg6RjRMEAa9nFsDf3RGPTOQD1kg8LBwiG3eosAFZJY1YPVXLRTpJVCwcIht29ewmH2pPJ6SM5yKdJC4WDpEN21dQh5OXmvDkNC0clTy7IXGxcIhslCAI+GtmATTezvjpOJ7dkPhYOEQ26tvztThT3ow10yOgUvJXncTHn0IiG/XB4RIEeTphAVeEJivBwiGyQYW1rThU2ICHJ4ZCqeCvOVkH/iQS2aCPjlyESiFHSiKv3ZD1YOEQ2ZjWTgM+O1GO+2PVGObGNdPIerBwiGzM9lMVaO82YsmkMLGjEF2DhUNkQwRBwIdHLiJO44n4EC+x4xBdg4VDZEOOFDWgsLYNj94ZJnYUouuwcIhsyOYjpfB2ccD9sWqxoxBdh4VDZCMqmjqQmVeDlPHDuUgnWSUWDpGN+PjYRQDAwxOGi5yE6MZYOEQ2oKvHiK1ZZZgeFQCNt4vYcYhuiIVDZAN2nq1CQ3s3lnKyAFkxFg6RDdh8+CLC/VwxWTtM7ChEfWLhEEncmfIm5JQ1YcnEUMhkMrHjEPWJhUMkcR8euQhXlQILx2nEjkJ0UywcIglr7jDgq9OVeHBsMNydHMSOQ3RTLBwiCTuor0d3jwkP8pk3JAEsHCIJ25tfCy8XB8SHeIsdhahfLBwiiTKZBOzLr8OUCD8o5JwsQNaPhUMkUbmVLahv68LU0X5iRyEaEBYOkUTtza+FTAZMiWDhkDSwcIgkam9+LWI1XnyqJ0nGoBRORkYGIiMjodVqsX79+uu+LggC1qxZA61Wi9jYWJw8eXLAryWi6zW2dyOnrAlTI3l2Q9KhNPcNjEYjVq9ejczMTGg0GiQmJiI5ORljxozp3SY9PR16vR56vR7Hjh3DqlWrcOzYsQG9lvonCAJqW7tQVNuG6pZOXL5igLODAp7ODvBwVsLDyQEezg5wc1Siq8eISw1XUNLQDkOPCUFezgjyckawlzO8XBx4p7pEfKevgyAAUyP9xY5CNGBmF05WVha0Wi3Cw8MBACkpKUhLS7umNNLS0rBkyRLIZDJMnDgRTU1NqKqqQmlpab+vtQRBEPDJ8TLkVrbARaWAi0qJ2BBPTB7pC5XS8qOO3T0mlDa0o76tCw1t3Whs70ZrpwHdPSZ0GU3o7rn6vw7D1fLQ17ahucNg9n4TQr3x8nwddEGeg/BfQUNp74VaDHNVISaY3yuSDrMLp6KiAiEhIb0fazQaHDt2rN9tKioqBvTa76WmpiI1NRUAUFdXZ27sXoIg4K+ZBfjbnkK4Oymv/lHvMQEA3J2UmBEVgFljAhA6zBVeLg5wdVSirasHTVe60dVjgo+LCr7ujnBVKW777EAQBORWtuBQYT0OFTXgeEkjOgzGG26rUsrhqJDDQSmHs4MCwV7OuD9WjVEB7tD6uyHIyxneLg7oMBjR0tGDlk4DWjsNaOnoQWunAQq5HGHDXBDq6wpHpRyVTR2obOpAUV073j9YgnlvHsSSO8PwzKxRvHPdShlNAvYX1GHqaH/IOR2aJMTswhEE4brP/fgPb1/bDOS131u5ciVWrlwJAEhISLidqNcRBAGvZxbgzT2FSEkMwZ8ejIFcLkOnwYjDRfVIP1uNzPM12H6qot/3cnKQI2yYK6aM8sO9kX5ICPUZ0NnR4aJ6vLa7ACcuXgYAaP3dsChBg7Gh3vBzd4SvmyN8XFXwcHKAg0I24FLzAqAewD9+fd0cEavxAgA8MiEUr+6+gM1HSrHnQi02PnRH79fIepwub8LlKwYOp5HkmF04Go0GZWVlvR+Xl5cjKChoQNt0d3f3+9qh9Pa+Iry5pxCLx4fgjw/E9P5r0clBgWmjAzBtdAB6jCacLm9CXWsXmq4Y0NbVA3cnJTydVXB0kKOxrRv1bV2ob+tCXlULPjhUgtQDxXBVKXBPpB/mxqgxLtQb/u5OvTfnNV8xIPN8DT47UYajxY0I9HDCy8k6zI4ORICHk8X++3/M08UB6x6IwQPxwViz5RQWvnMYz88ejeV3jeC1HSuy70It5DLg7ghfsaMQ3RKzCycxMRF6vR4lJSUIDg7G1q1b8fHHH1+zTXJyMjZu3IiUlBQcO3YMnp6eUKvV8PPz6/e1Q+XExct4bXc+5scHXVM2P6ZUyDEu1GfA79vW1YPDhfXYm1+HzLwa7DxbDQBQyGUIcHeEj5sKF6pa0WMSEOzljN/fPwYPT7CuZ9AnhPlg51N3Y+1nZ7Bux3kcKWrAn38SC19Ov7UKe/PrMHa4N7xcVGJHIbolZheOUqnExo0bkZSUBKPRiGXLlkGn02HTpk0AgF/84heYO3cudu7cCa1WCxcXF3zwwQc3fe1Qu9Ldg2c+zUGQlzPWPRA9qOPgbo5KzNIFYpYuEOseiMaJi5ehr21FVVMnKps7UNfaheV3j8DcaDViNZ5We+bg5aJC6qPjsPlwKf608wJmvr4fLyXrkBwXZLWZ7UFtayfOVjRjbVKk2FGIbplMuNGFFCuXkJCA7Ozs23797788h38eu4gtKyZiYjifkNgffU0r1n52BjllTZg5JgCvL4rjhAKRfHaiHM9uO40da+7ibEKyGgP9m2x3Kw3sL6jDR0cvYvnkESybAYoIcMfnqybhd3OjsPdCLZa8n4XWTvOnYdOt25tfC393R4xRe4gdheiW2V3hHCtuQIS/G57lkMQtUchlWDElHBsfGouz5c1Y+n4W2rp6xI5lV3qMJhwoqMO9kX4c1iRJsrvCeW72aGxfPdmqLtJLyezoQGx86A6cLm/GkveOobG9W+xIduPkpSa0dvZwOjRJlt0VDnD1wj7dvtnRarz10B04V9mC5I0HkVfZInYku7A3vxZKuQyTOR2aJMouC4fMNztajW1P3AmD0YSF7xxGWk7FDW/kpcHz7fkajAv1hgcnbJBEsXDotsWFeOHrJ+9ClNodT23Nwc9SjyKnrEnsWDbpQnULCmraMDdGLXYUotvGwiGz+Hs44ZMn7sS6B6JRXNeGB946hNUfn0RZ4xWxo9mUr3IqoZDLWDgkaSwcMpuDQo5HJoZi39qpWDM9AnvO1yIl9SiarnBCwWAQBAFfna7EZK0v/Ny52gNJFwuHBo2boxJPzxyFrSsnora1E89uO8PrOoPg5KUmlF/uwPw4y60zSDQUWDg06OJCvPDbOVH45nwN3jtYInYcyfsqpwKOSjlm6QLEjkJkFhYODYnHJ4dh1pgArE+/gFOXLosdR7J6jCb8+0wVpkf5czkhkjwWDg0JmUyGV38SBz93R6zbcV7sOJJ1qKgBDe3dSI4LFjsKkdlYODRkPF0csHJKOE5cvIwTFxvFjiNJX+VUwt1JiXsj/cSOQmQ2Fg4NqUUJIfB0dsD/7i8WO4rkdBqM2JVbjTnRgVyKiWwCC4eGlKujEo9ODEXm+RoU17WJHUdS9lyoRVtXD4fTyGawcGjILZ0UBgeFHH//jjPWbkX6uWr4ujnizpF8jAbZBhYODTk/d0csHBuMz0+Wo661S+w4kiAIAo4VN2DSyGFQDOITaYnExMIhi/j53eEwGE348Eip2FEk4VLjFdS2diFxhI/YUYgGDQuHLGKknxtmRgXgo6MXcaWbD27rT1bJ1Vl948NYOGQ7WDhkMU/cE46mKwZ8crxM7ChW73hpIzydHRDh7yZ2FKJBw8IhixkX6oPxI3yQeqAYXT1GseNYteOll5EY5g05r9+QDWHhkEU9OVWLquZOfHGyQuwoVqu2tRMl9e1I5HAa2RgWDlnU3RG+iNN44u19hegxmsSOY5WyS6+uPccJA2RrWDhkUTKZDE9Oi0BZYwe+Ol0pdhyrlFXSCCcHOaKDPMWOQjSoWDhkcdNH+2N0oDve2lsIk4nPy/mx46WNuCPEGyolfz3JtvAnmixOLpfhl1O1KKprx/6COrHjWJXWTgPOV7VwOI1sEguHRDFbFwh3RyXSz1WJHcWqnLh4GSaB99+QbWLhkChUSjmmRfnjm/O1nDzwA8dLG6GQy3DHcC+xoxANOhYOiSZJF4jG9m4cL+UTQb93vOQyooM84OqoFDsK0aBj4ZBo7hnlB0elHLtyq8WOYhW6eozIKW/i/Tdks1g4JBpXRyXujvDD7txqCAJnq50pb0Z3j4kTBshmsXBIVLOjA1HZ3ImzFc1iRxHd9wt28gyHbBULh0Q1I8ofCrmMw2q4OmFA6+8GH1eV2FGIhgQLh0Tl5aLChBE+yDhn34VjNAk4UXqZZzdk01g4JLrZ0YEoqmtHYW2r2FFEc6G6Ba1dPRg/wlvsKERDhoVDops1JhAAsCu3RuQk4uldsJNnOGTDWDgkukBPJ8SHeNn1dZys0kYEeTpB4+0idhSiIcPCIauQpAvEmfJmVDR1iB3F4gRBwPGSRk6HJpvHwiGrkKQLAADstsOznEuNV1Db2oUEDqeRjWPhkFUI93PDqAA3uxxWO1LUAIALdpLtY+GQ1UjSBSKrpBGN7d1iR7GojNxqhPg4Y1SAm9hRiIYUC4esxqwxgTAJwLfn7We2WvMVAw4V1mNutBoymUzsOERDioVDViM62ANqTydk5tlP4XxzvgYGo4A5MWqxoxANORYOWQ2ZTIYZUQE4oK9Da6dB7DgWkX6uCkGeTojTeIodhWjIsXDIqiwcp0GnwYTtpyrEjjLkWjsNOFBQj9kcTiM7wcIhqxIf4oVYjSc+PHLR5h9ZsOdCLbqNJsyNCRQ7CpFFsHDI6jw6MRSFtW04UtwgdpQhtfNsFfzdHTF2ONdPI/vAwiGrMy8uCF4uDvjoyEWxowyZ9q4e7Muvw5zoQMjlHE4j+8DCIavj5KDAzxJCsDuvBlXNtrnUzb78OnT1mDg7jeyKWYXT2NiImTNnIiIiAjNnzsTly5dvuF1GRgYiIyOh1Wqxfv363s9v27YNOp0Ocrkc2dnZ5kQhG/PIxFCYBAFbjl0SO8qQ2HmuCr5uKq4OTXbFrMJZv349pk+fDr1ej+nTp19TJt8zGo1YvXo10tPTkZeXhy1btiAvLw8AEB0djS+++AJTpkwxJwbZoBAfF0yN9MfHWWXo7jGJHWdQdXQbsfdCLZJ0gVBwOI3siFmFk5aWhqVLlwIAli5dii+//PK6bbKysqDVahEeHg6VSoWUlBSkpaUBAKKiohAZGWlOBLJhj94Zivq2LmTY2PpqR4rrcaXbiCQdZ6eRfTGrcGpqaqBWXx2DVqvVqK2tvW6biooKhISE9H6s0WhQUXHr91ikpqYiISEBCQkJqKuru/3QJBn3RPghdJgL/nnUtiYPHCioh5ODHOP5OAKyM8r+NpgxYwaqq6//F+Yf//jHAe3gRvdS3M5NbitXrsTKlSsBAAkJCbf8epIeuVyGhWM1eD2zADUtnQjwcBI70qDYX1CHieHD4OSgEDsKkUX1WzjffPNNn18LCAhAVVUV1Go1qqqq4O/vf902Go0GZWVlvR+Xl5cjKCjoNuOSvZkTHYjXMwuwK7caS+4MEzuO2coar6Ckvh2PTgwVOwqRxZk1pJacnIzNmzcDADZv3oz58+dft01iYiL0ej1KSkrQ3d2NrVu3Ijk52Zzdkh2JCHCH1t8N6Wdt4zrO/oKrw8FTRvmJnITI8swqnBdeeAGZmZmIiIhAZmYmXnjhBQBAZWUl5s6dCwBQKpXYuHEjkpKSEBUVhUWLFkGn0wEAtm/fDo1GgyNHjuC+++5DUlKSmf85ZIvmRAfiWEkDGtq6xI5itgMFdQj2csZIP1exoxBZnEyQ4IJVCQkJvG/HjuRWNuO+vx3E+gUxSBk/XOw4t81gNOGOP2RiXpwaryyIFTsO0aAZ6N9krjRAVm+M2gPDfVyw85y0h9VOXWpCW1cPpkRwOI3sEwuHrJ5MJsOc6EAcLqxH8xXpPifnQEEdFHIZJml9xY5CJAoWDknCnBg1ekwCvpHw46cP6OtwR4gXPJ0dxI5CJAoWDklCnMYTQZ5OSJfosFpdaxfOVjRzdhrZNRYOSYJMJkNSdCAO6OvQ1tUjdpxblpFbDUEAl7Mhu8bCIcmYE61Gd48Jey5cv4SStdtxphJafzeMCnATOwqRaFg4JBnjQr3h6+aIjHNVYke5JbWtnThW0oi5MerbWtaJyFawcEgyFHIZZkcHYO+FOnR0G8WOM2C7zl0dTrs/lg9bI/vGwiFJmROtRofBiP0F0hlW+/eZqv8bTnMXOwqRqFg4JCkTRvjA28VBMrPVals6kVXaiPv4KGkiFg5Ji1Ihx8wxAdhzvhZdPdY/rPb97LT7OJxGxMIh6ZkTrUZrVw8OFdaLHaVf/z5dhQgOpxEBYOGQBE3SDoOHkxLbssvFjnJTOWVNyCptxINjg8WOQmQVWDgkOY5KBR6ZGIqM3GoU1bWJHadPr+3Oh7eLg008OI5oMLBwSJKW3TUCKoUcm/YViR3lhrJKGvGdvh6r7h0JN8d+H6xLZBdYOCRJvm6OSEkMwfZTFahs6hA7zjUEQcBfdufDz90Rj04MEzsOkdVg4ZBkrZgSDgD4+3fFIie51qHCBmSVNOLJqVo4qxRixyGyGiwckiyNtwuS44OwNasMje3dYscB8J+zmyBPJ6SMDxE7DpFVYeGQpK26ZyQ6DEb841CJ2FEAAHsu1CKnrAlrpkfAUcmzG6IfYuGQpEUEuCNJF4B/HC61iscWvLmnEMN9XLBwnEbsKERWh4VDkvfLe7Vo6ezBv45eFDXHuYpm5JQ14fHJYXBQ8FeL6Mf4W0GSFxfihcnaYXj3YAk6DeItd/PPoxfh7KDAgrE8uyG6ERYO2YRf3qtFXWsXtp+qEGX/LZ0GpOVUIjkuCJ7ODqJkILJ2LByyCZNGDoMuyAPvHSyBySRYfP9fnChHh8GIRyaGWnzfRFLBwiGbIJPJsOLucBTWtmG/vs6i+xYEAf88dglxGk/EaDwtum8iKWHhkM2YG6NGoIcT3rXwjaDHShpRWNuGh3l2Q3RTLByyGSqlHEsnheFQYQPOljdbbL//OFQKDycl5sUGWWyfRFLEwiGb8vDE4fBxVWHdjjwIwtBfyymoaUVGbjWWTgrjMjZE/WDhkE3xcHLAb2ZE4FhJI3bl1gz5/t7eWwgXlQKPTx4x5PsikjoWDtmcxeOHI8LfDa+kn0d3j2nI9lNa346vTlfikYmh8HFVDdl+iGwFC4dsjlIhx+/ui8LFhiv48EjpkO3nnX1FUCrk+PndPLshGggWDtmkeyP9cc8oP2z4Vo/86tZBf/+Kpg58frIcixND4O/uNOjvT2SLWDhks15O1sHZQYFF/3sEJy5eHrT37TQY8fQnOZDLZFh5z8hBe18iW8fCIZsV5uuKz1dNgpeLAx559xj25dea/Z49RhN+teUUskob8epPYxHs5TwISYnsAwuHbFqIjws++8UkjPB1xc83ZyMt5/bXWhMEAb/94iwy82rw0jwd5scHD2JSItvHwiGb5+fuiK1PTMS4UG88tTUH/2/7WVzpvvVn56xPv4BtJ8rx1PQILJ0UNvhBiWwcC4fsgoeTAz5aPgFP3BOOLVmXcP/fDuLUpYFd1+kxmvDSV7n43wPFWHJnKH49I2KI0xLZJhYO2Q2VUo7fzonCv34+AVe6jXjw7cNY/o/jyClr6vM1LZ0GLN+cjX8cLsWyySPw0jwdZDKZ5UIT2RCl2AGILG3SSF9kPj0Fmw+X4t2DJXjgrUOIUnsgMcwb40K9ER/ihYqmDhwvuYy0nApcaryCPz0Yg4cmDBc7OpGkyQRLLDg1yBISEpCdnS12DLIBbV092HLsEvYV1OLUpSZc6f7PE0NlMmCM2gO/mxuFSVpfEVMSWbeB/k3mGQ7ZNTdHJVZMCceKKeHoMZpwoboVZ8qbofZ0wthQbz69k2gQsXCI/o9SIUd0sCeig/kQNaKhwEkDRERkESwcIiKyCBYOERFZBAuHiIgsgoVDREQWwcIhIiKLYOEQEZFFsHCIiMgiWDhERGQRLBwiIrIISS7e6evri7CwsNt+fV1dHfz8/AYvkIUxv3iknB1gfrFJOf/NspeWlqK+vr7f95Bk4ZhL6qtNM794pJwdYH6xSTn/YGTnkBoREVkEC4eIiCzCLgtn5cqVYkcwC/OLR8rZAeYXm5TzD0Z2u7yGQ0RElmeXZzhERGR5dlc4GRkZiIyMhFarxfr168WO06+wsDDExMQgPj4eCQkJAIDGxkbMnDkTERERmDlzJi5fvixyyv9YtmwZ/P39ER0d3fu5m+V95ZVXoNVqERkZiV27dokR+Ro3yv/SSy8hODgY8fHxiI+Px86dO3u/Zk35y8rKMHXqVERFRUGn02HDhg0ApHP8+8ovlePf2dmJ8ePHIy4uDjqdDi+++CIA6Rz/vvIP6vEX7EhPT48QHh4uFBUVCV1dXUJsbKyQm5srdqybCg0NFerq6q753Nq1a4VXXnlFEARBeOWVV4TnnntOjGg3tH//fuHEiROCTqfr/VxfeXNzc4XY2Fihs7NTKC4uFsLDw4Wenh5Rcn/vRvlffPFF4dVXX71uW2vLX1lZKZw4cUIQBEFoaWkRIiIihNzcXMkc/77yS+X4m0wmobW1VRAEQeju7hbGjx8vHDlyRDLHv6/8g3n87eoMJysrC1qtFuHh4VCpVEhJSUFaWprYsW5ZWloali5dCgBYunQpvvzyS3ED/cCUKVPg4+Nzzef6ypuWloaUlBQ4OjpixIgR0Gq1yMrKsnTka9wof1+sLb9arcbYsWMBAO7u7oiKikJFRYVkjn9f+ftibfllMhnc3NwAAAaDAQaDATKZTDLHv6/8fbmd/HZVOBUVFQgJCen9WKPR3PQH2hrIZDLMmjUL48aNQ2pqKgCgpqYGarUawNVf0traWjEj9quvvFL6fmzcuBGxsbFYtmxZ75CINecvLS3FqVOnMGHCBEke/x/mB6Rz/I1GI+Lj4+Hv74+ZM2dK7vjfKD8weMffrgpHuMGEvJs1uDU4dOgQTp48ifT0dLz11ls4cOCA2JEGjVS+H6tWrUJRURFycnKgVqvxzDPPALDe/G1tbVi4cCHeeOMNeHh49LmdVPJL6fgrFArk5OSgvLwcWVlZOHfuXJ/bSiX/YB5/uyocjUaDsrKy3o/Ly8sRFBQkYqL+fZ/P398fDz74ILKyshAQEICqqioAQFVVFfz9/cWM2K++8krl+xEQEACFQgG5XI4VK1b0DhtYY36DwYCFCxfi4YcfxoIFCwBI6/j3lV8qx/97Xl5euPfee5GRkSGp4/+9H+cfrONvV4WTmJgIvV6PkpISdHd3Y+vWrUhOThY7Vp/a29vR2tra+/93796N6OhoJCcnY/PmzQCAzZs3Y/78+WLG7FdfeZOTk7F161Z0dXWhpKQEer0e48ePFzPqDX3/xwIAtm/f3juDzdryC4KA5cuXIyoqCk8//XTv56Vy/PvKL5XjX1dXh6amJgBAR0cHvvnmG4wePVoyx7+v/IN6/AdzloMU7NixQ4iIiBDCw8OFdevWiR3npoqKioTY2FghNjZWGDNmTG/e+vp6Ydq0aYJWqxWmTZsmNDQ0iJz0P1JSUoTAwEBBqVQKwcHBwrvvvnvTvOvWrRPCw8OFUaNGCTt37hQx+VU3yv/II48I0dHRQkxMjDBv3jyhsrKyd3tryv/dd98JAISYmBghLi5OiIuLE3bs2CGZ499Xfqkc/9OnTwvx8fFCTEyMoNPphJdfflkQhJv/vkoh/2Aef640QEREFmFXQ2pERCQeFg4REVkEC4eIiCyChUNERBbBwiEiIotg4RARkUWwcIiIyCJYOEREZBH/H8Cwxb4dvFifAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ce0ed98bae1424c81b4ffba499c36b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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gtE8TnQ1s2qDjLDE+o+QXLYjGsWrU1atX2ejRo5mjoyPT1dVlBgYGzNXVlc2aNYs9f/680H3yxpNu27aNHTt2jLVt25bp6+szExMTNmjQIPbo0SOl8vfv32czZ85kHh4ezNLSkkkkEubg4MB8fX1ZyMktijGs/41jzXPx4kU2ePBgZmtry3R0dJilpSVzd3dnM2fOZNeuKZd1cHBQGdtZlPr16zMAzNzcnGVlZRVZLisri23YsIG1a9eOGRsbM4lEwurWrcu8vLzY2rVrVcbEZmVlsSVLljAnJyfu/ObPn88yMzPLNI6VMcbkcjnbsGEDc3FxYRKJhNna2rKpU6eypKSkIs9127ZtzN3dnenr6zMLCwv24Ycfsn///ZctWrSIAWBBQUFK5d+8ecM++ugjZm1tzYRCIQPAFi1axG0PCQlhXbt2ZaampszQ0JC1b9+eHTp0iAUFBamUrWrUvcYFjP3XF14NrVq1wvXr19U9DKkBrl14gWmDTiD1XX5X/haetTBvTUeYWaiOUXxfWOgrbFp6DZHhSdy6ei5m+PV4f9S2NypdEAVruOX488/rFUqqiPgCnz0WrfiLg1Qb6l7jdI+VVJqbl1/hkwHHuaQqFAowe7knftjRo1RJFQBatLPFT4f6YOCY/D/6pw8TMbLzX4i4F18hcRNCSFlQYiWV4u6NN5jS7ygy0hSD4yW6Ivx4oBd8fOuX+VgSqQhTv/4AC9Z1hEis+BN+8yIN430OI6pATZYQQvhAiZVUuNiXqZg26ATSUv5LqlIRfvq7Nxo1U51CrSy69HHCsq3e0DP4b2KA2AyM9zmMl9EpasdMCCHlRYmVVKjMjBxM9zuJuFfpAACxWIgfD/SCYwMzjRy/RTtbfL/FGxJdxcR2r2NS8Un/Y0r3cAkhpDJRYiUVatkXF3D3eiy3vOinznBubF7MHmXn1soGSzZ1hVhH8ecccT8Bc0afQW5u4c//JISQikSJlVSYwMNP8Nfv+YPLR3/mjrZeJT8NpTxadayNL75vxy0Hn4zE+oVlnwSAEELURYmVVIjYl6lY9EkQt9y0lTVGTiv8ySGa0v1DZwz92JVb/n3VTQSfjKzQ1ySEkPdRYiUaxxjD4qlBSIpXTM2mpy/Gkl+6VsoMSeO+aIEPOudPMP7VuAC8ek6dmQghlYcSK9G4M38/QfDJ/AdUL9zYGUYm0kp5bZFIiLk/tIeljT4A4F1iFuaOofuthJDKQ4mVaFRKchaWfXGBW27fvS4+6GhXzB6aZ2KuiwXrOkIoUtSQb1x6hZ3rb1VqDISQmosSK9GoHxdd4YbWSHVFmL1c9RmclcGtlQ1GTG3KLa9beAXhd2lmJkJIxaPESjQm4n4C9v96l1uevqgNDI35e0D58E+aoYGbBQAgJ1uOeRMCkZNDTcKEkIpFiZVozKqvQpCbq5jU3t7ZBD18nXmNR6yjuN+qI1H8mT8Ii8Mfv9zhNSZCSPVHiZVoxKUzUbh4Or/D0ldrOlSJ56Q6NDDFiE/zh/msX3gFb2DCY0RE24wdOxYCgaDQB63zxdHRkXuYOKl66EHnRG1yOcPqeZe55dZd7NCgiQWPESkbMtEV544+Q/STZKSnZWO5yBdrc7dW3Ave/gZIj66446tL3x5w/1btwzg6OiIqKqrQbTY2Nnj9+rXSOplMhm+//RZ79uxBQkICWrZsidWrV6Nly5Yq+wcEBKBHjx44fvw4+vTpo3as2qZLly4IDg6GBp7qSXhAiZWo7czBCDy+o+gYJBQKMOt7T54jUqYjEeGzJW0we+QZAMAZYQtckDdBJ3a/hD3LKT0aMHCsmGNrQlqkxg5lYmKCzz//XGW9oaGhyrqvvvoKa9euha+vL+rUqYNdu3aha9euePjwIWxtbblyqampmDRpEkaOHFklkuqyZcvw1Vdfwc6ucnu3F+fs2bN8h0CKQYmVqCUnR46NS65yyz5+9WFuVbpnq1Ym9za10H2gMwIOPQEA/E80BEdyvkPVi1S7mJqaYvHixSWWY4zh119/xbhx47B1q6K1YODAgejSpQt27dqFOXPmcGXnzp2LzMxMrF+/vqLCLhNbW1ulxF8VODvz23+BFI/usRK1nNj3GM8eJwEARGIBJn6p2qxXVXw81wNGpopeyi8EFvhF2JPniGqOuLg4pKeno3Xr1ty6vJ8LNidfuHABP//8M3766SeYm5fvYQ159x/fvXuHL774Ao6OjtDR0VH6AvDw4UOMHTsWdevWhVQqhY2NDYYPH45Hjx6pHK+4e6xXr16Fn58fatWqBYlEgrp162Ly5Ml4+fJlobElJCRgwYIFcHNzg76+PkxMTODu7o6vvvoKaWlpiIyMhEAgQHBwMABAIBBw/7p06aJyju/LysrC8uXL0axZM+jr68PY2BgdO3bEn3/+qVI277XGjh2LyMhIDBs2DJaWltDV1UWrVq1w/Pjx4n/R7zl8+DBGjhyJhg0bwsDAAIaGhvDw8MCPP/4IuVy1N37e7/Xp06fYsGEDmjVrBj09PaXzTEhIwLx589C4cWPo6enBxMQE3t7eOHPmjMrxkpOTsXLlSnh5eaFOnTqQSCSwsrJC//79ceVK5c4bTjVWUm45OXL8suwat9xveCMYm1bODEvlYWqhi0lzPLBmfigAYLvQC75PkmHvTJ2ZyisrKwu7d+9GdHQ0DAwM0KxZM3Tq1AkikUipnJWVFfT09HDjxg1u3fXr1wEADg4OAICMjAxMnDgRvr6+8PX1VSsumUwGLy8vJCQkoEePHjA2NoaTkxMA4PTp0xg0aBCys7PRr18/1K9fHzExMfj7779x4sQJBAUFFXrf933btm3DpEmTIJVK0b9/f9StWxfh4eHYsmULjh07hitXrsDe3p4r/+zZM3Tt2hVRUVHw8PDAJ598ArlcjsePH2Pt2rWYMmUKTE1NsWjRImzfvh1RUVFYtGgRt39JnZVkMhl8fHwQHBwMFxcXfPrpp0hPT8dff/2FoUOH4tatW/j+++9V9ouKikLr1q1Rr149jBo1CgkJCdi/fz8GDBiAwMBAdO3atVS/86+++gpCoRBt2rSBnZ0dkpOTce7cOcyYMQPXrl3Drl27Ct1vxowZuHjxIvr06YPevXtzfztRUVHo0qULIiMj0bFjR/Ts2RNpaWk4fvw4evbsiV9//RWTJk3ijvPgwQMsWLAAnTp1Qp8+fWBmZobo6GgcPXoUp06dwrFjx9CzZ+V8mRYwDdwdb9WqFXeRkJrj5P7H+HKU4pujjo4QB64OgYERf+NWS0MuZ5jbYAluCesBALoPdMa6/b3KdIwHDx6gcePGRRcIHVv177G22672YYrqvOTk5IRt27ahc+fOSus///xz/Pjjj/Dz84OdnR127doFmUyGBw8ewM7ODrNmzcKOHTtw//59WFtblz6Q+AKfPRatuLi8vb1x5MgRGBgYcJsTExNRr149iEQiXLhwAU2aNOG23bt3D23atEHDhg1x8+ZNbv3YsWOxY8cOPHv2jEtujx8/hpubG+zt7REcHKx0//XcuXPo3r07+vfvj0OHDnHr27dvj8uXL+P777/HvHnzlE7h7du3MDQ0hK6uLoCSOy/lxVGwFr1s2TLMnz8fvXr1wtGjRyEWK+pNsbGxaN26NaKiohASEgJPT09u37wvG4sXL1ZK4v7+/ujZsyd69eqFkydPFv57f8+TJ09UmqjlcjnGjRuHnTt34sqVK2jTpg23Le/3Wrt2bVy6dImLJU+XLl1w4cIF7N27F8OGDePWJyUloUuXLnj06BEiIyNhY2MDQFFjzc7OhqWlpdJxYmJi0Lp1a5iYmODBgwelOpcSr/ESUFMwKRfGGDavyK999B7WoMonVUDRuWqu/G9uOeDQE9wIKbzZjhRv3LhxOHv2LF6/fo20tDTcuXMHkydPRmRkJHr16oXbt28rlV+xYgXmzp2Lf/75B7///jtcXV1x9uxZ2NnZ4erVq1i3bh3Wr18PS0tLLFmyBLa2thCLxfDw8EBISEiZ41u9erVSUgWAnTt3IikpCUuWLFFKqgDg6uqKSZMmISwsDPfvF9+x7eeff0Z2djbWr1+v0qnJy8sL/fv3x7Fjx5CSongAxI0bN3D58mU0b94cc+fOVTleXhOsOrZu3QqBQIA1a9ZwSRUArK2tsXDhQgDAli1bVPZzcHDA119/rbTOx8cH9vb2+Oeff0r9+oXd9xUKhZgxYwYARbIuzJw5c1SS6u3btxEcHAxfX1+lpAoo7usvWbIEmZmZOHjwILfexMREJakCQJ06deDn54eHDx8iOrpyeutTUzApl/MnIvH4vykCRSIBRk935zmi0mvGotBbfh0nha0AACtmXcQfl4dAKOR/3K02KVjDAQA3Nzf88ssvMDQ0xOrVq7F48WKlGptUKsWyZcuwbNkypf1kMhnGjx+P3r17Y8SIEVi3bh1Xg2rfvj2+++479OzZExEREVztpCS6urpo1kz1MYWhoYrbALdv3y6009Xjx48BKGos7yfewo4THByMa9euqWyPjY1Fbm4uHj9+DA8PD+4en4+PD4RCzddnUlJSEBERATs7O7i4uKhs9/LyAgCEhYWpbGvevLlK0z0A1K1blzvP0oiPj8fKlStx8uRJPH36FGlpaUrbX7x4Ueh+Be+758l73eTk5ELfp7i4OABQqYGGhIRg/fr1CA0NRWxsLGQymUoMBZvnKwolVlIuW37Ir6169XOCsZl637Yr28zcowgUuEMm0MG9m3E4uf8x+n7UiO+wqoUpU6Zg9erVuHDhQsmFASxZsgQvXrzgOqSsXLkS3t7e3Adqo0aN4OjoiJ9++gnfflu68bfW1taFTlASH6/4Mrh58+Zi909NTS12e95xVq5cWarjJCUlAUCFDdlJTk4GgCJ7L+etz4ujIFNT00L3EYvFhXY6KkxSUhI++OADPHv2DK1bt8bo0aNhbm4OsViMpKQkrF+/HllZWYXuW6tWLZV1eb/fgIAABAQEFPm6Bd+nQ4cOwc/PD7q6uujevTucnZ1hYGAAoVCI8+fPIzg4uMgYNI0SKymzW1de4dYVxeB/gQAYP7vq9gQuSm0kYpT8PH4XdQcArJl/Gd0+dIauHl0S6sq7P/p+jaUwYWFh+OGHH/DLL7/Azs4O7969w8uXLzFixAiujL29PSwtLXHv3r1Sx1DUrF8mJoqOardv3y60RltaecdJTk6GsbFxieXzkldRtTZ15cXz/qQceV69eqVUTtO2bNmCZ8+eYdGiRSo1zNDQ0GKHThX2XuXFuX79enz22WelimHhwoWQSCS4fv26yv3RyZMncz2tKwPdYyVltnN9/r0zjw61uWefaptJ8jMwNVfUtN+8SMOuH2/xG1A1kdeMV69evWLL5eTkYPz48ejatSsmTJigtO39mkVmZqZGYmvbti0A4OLFi5V6nLzy/v7+paoF5jXN5ubmlur4RkZGcHZ2xosXLxAeHq6yPSgoCABK1du5PCIiIgCg0N7c5Ulo5XmfIiIi0KRJE5WkKpfLcenSpTLHoA5KrKRMXkS+4yZZAFClx62WxAiZGP1Z/r3hX5ffQOLbDB4j0h737t1DQkKCyvqoqChMmzYNADBy5Mhij7Fs2TJEREQoNcsaGxvDzs4Op0+fRk5ODgDFB3NKSgpcXV3VjnvcuHFc55fCOubI5XKcP3++xONMmzYNOjo6mDlzJndftiCZTKaUFDw8PODp6Ylbt25hxYoVKuXj4+OVvjxYWCimBC1LZ5vx48eDMYYvv/xSKSG/ffsWS5cu5cpUhLxeyu//7sLCwlTuqZdGq1at0LFjR/z999/chCLvu3PnDmJjY5ViCA8PVxpDzBjDkiVLSuyMpmnU7kXKZPdPtyGXK4YAODQwhXPj8g3iryp6D22Aw7seIvpJMjLSsrH5hxuY80MHvsOq8g4cOIDly5eja9eucHJygpGREZ48eYITJ04gMzMTvXv3xuzZs4vc/969e/jf//6HNWvWcONY88yZMwczZsxAx44d0aZNG+zZsweGhob49NNP1Y7bwsICf/31FwYOHIi2bdvC29sbrq6uEAqFiI6ORmhoqEqSK4yLiwu2bt2K8ePHw9XVFT179kTDhg2RnZ2N6OhoXLx4EVZWVnj48CG3z+7du9GlSxfMnz8fBw8eRJcuXcAYQ3h4OM6cOYOHDx9yCcrb2xsHDhzAoEGD0Lt3b+jp6cHBwQGjRo0qMqbZs2fj1KlTOHLkCNzd3dG7d2+kp6fjwIEDiI2NxZw5c9ChQ8X8bY8ePRorV67E559/jqCgIDRo0ADh4eE4fvw4Bg0ahP3795f5mHv37oWXlxcmTJiAH3/8EW3atIGpqSliYmLw77//4u7duwgNDeVuPcycORNTpkxBixYt4OvrCx0dHYSEhOD+/fvo168fjh07punTLhIlVlJqaaky/L0tvxfe+FnN+QtGQ0RiIcZ90QJLPj0PANi76V+M/qw5atVRneuW5OvatSsePXqEsLAwhIaGIi0tDaampujQoQNGjRqFUaNGFXmfMzc3F+PHj0ebNm0wdepUle3Tp0/Hu3fv8Msvv+DmzZtwd3fH2rVrS90juCTe3t74999/sWrVKvj7++PixYuQSCSoXbs2vLy8Sj05xciRI+Hu7o7Vq1cjKCgIZ86cgYGBAWrXrg0/Pz8MHTpUqbyTkxNu3ryJH374AYcPH8bGjRuhq6sLR0dHzJo1S2ns7sSJExEVFYU//vgDP/zwA3JyctC5c+diE6tEIkFAQADWrFmDvXv3YsOGDRCLxXB3d8e6devw0Ucfle8XVgq1a9fGxYsX8dVXX+HSpUvw9/eHi4sLNm3ahG7dupUrsdapUwc3btzAhg0bcPDgQezZswe5ubmoVasWmjRpgunTp6Np06Zc+cmTJ0MqlWLdunXYsWMH9PT00LFjR2zbtg0HDx6s1MRKE0SQUjvw+z0s/kRxr8bYTIoDV7RziEptx/wOHC8jk8EYw3S/k3j0r6Inot+EJljys1eR+5c4eLyGPN2mynhvgghNGzZsGPbv34+XL19WuTmDScVQd4IIqrGSUmGMYf+vd7nlgaNdtDKpFkYgEGD8Fy0wd2wgAODvbQ8wbmYLODY0K98Bq1PSInj8+DGkUimsrKz4DoVoCeq8RErlzvVYPLilGJQtFArQf6TqIHRt1rJ9bTRvpxhPJ5czbPy29DPOkOpp/fr18PX1RVhYGAYMGKA0mxEhxaHESkrlz9/ya6utOtau0pPtl9e4mS24n0/9Gc59kSA107p163DlyhWMHz8ev/76K9/hEC1CX8FIiZITM3Hqz/yxcSM+bVpMae3VpIUVPLvVxeXA5wCAHxddwc9H+vEcFeHLs2fP+A6BaCmqsZISHd39CJkZijGFVrb6aNy8+t5rGjuzOfI6s144FYVbV17xGxAhROtQYiXFYowpNQMPnuBa5DCK6sCpoRm8+uU/aYPutRJCyooSKynWtQsv8PRRIgBALBaip199niOqeCOnuXM9nkMDnyMslGqthJDSo8RKilVwiI1n97rQM9DhMZrKUcfJGF79C9Zar/IYDSFE21BiJUVKfJuBwCNPueWRn5b/aSDaZsTUZlyt9crZGNy8TLVWQkjpUGIlRTq5/zFyshVP4qhVxxBOjco5YYIWquNkDO8BBWqtS6jWSggpHUqspEiHd+ZPID5wdPWaEKI0hk9tBqFIUWu9GhSDGyEvS9iDEEIosZIiPL7zFvfD8mda6uFb/Tstva+OozG8++c/U3TjYqq1EkJKRomVFOrI7vzaatMPbGBoLOExGv6M+LQpV2v9J/gFrl98wXNEhJCqjhIrUZGTI8exvfkPbx48oQmP0fDLzsEY3QYUqLXSvVZCSAkosRIVIWeiEf8mHQCgZyBGq461eY6IXyMK3Gu9duElsjJzeI6oaunevTsEAgGCg4OLLDNlyhQIBAJs3ry5EiMjhB+UWImKwzvzH2bu1c8JInHN/jOp7WCEHgOdueWUZBmP0VQ9N2/ehEgkgoeHR5Flrl5V1PRbtdL881IJqWpq9icmUZGUkImg4/mTjw8cXf6H/VYnwz9pCpFYUWuVZeYiLYWSKwA8efIECQkJcHV1haGhYaFl0tPTcffuXUilUri5uVVyhIRUPkqsRMmpP8ORLVOMXbWxM4BDA1N+A6oibO2N0L1ArTX2VRqP0VQd169fBwC0adOmyDI3btxATk4O3N3doaNT/WfuIoQSK1FyZFd+b+ABo2re2NXiDJ+S30M4LSWbaq0oXWK9cuUKAGoGJjUHPY+VcJ49SsSda28AAAIh4FMDx64Wx9Ze+V5r7Ks0OBnVzGFIefIS6/79+xEUFFRoGbq/SmoaSqyEc3J//hAbl2ZWMDaV8hhN1TT8k6ZISleMZU1LyUZaqgwGhjUzuTLGEBYWBgAICAgosTwlVlJTUGIlABQfkif+yE+sA8dQM3BhbO2NkBmVf9nEvUyDQcP8xOoq2chHWOVyTzZNrf0fP36M5ORktGvXDpcvXy60TGJiIszNzaGvr48mTZTHQ2/atAkrV67Eq1ev4OrqinXr1qFjx45qxURIVUD3WAkA4H5YHKIikgEonrvq2a0uzxFVXQZGOsh71Hvqf7XWmiivGbhFixZFlrl58yYAoHnz5hCJRNz6/fv3Y8aMGZg/fz7CwsLg6emJXr16ITo6umKDJqQSUGIlAKBUW23uWQtSXWrMKIpYLIRRgWbyuJfpPEbDn7zE2rJlyyLL5CXW95uB16xZg7Fjx2LSpElo3LgxNmzYAFtbW/z8888VFzAhlYQ+PQlyc+U49Wc4tzxoDI1dLYmFtR5SkrLAAKSmyJCeKoO+oUTt5lVtUprEmncPtmBilclkuHHjBmbPnq1UtkePHkU2KROiTajGSnDj0kvEvlSMy5TqitDS05bniKo+HYlIqdYa+6pm1Vrlcjlu3boFiUQCV1fXIssVVmN9+/YtcnNzYWNjo1TWxsYGr1+/rpiACalElFiJUjNwu251a/wUhqVlYa2Xf6/1nQzpadm8xlOZHjx4gNTUVLi6ukIiKbxXdGpqKsLDw2FoaIhGjRqpbBcIBErLjDGVdYRoI/oEreFkslyc+fsJt/whTQpRaopaa35SiXtZc2ZjunHjBoDim4Fv374NuVyOli1bQijM/6ixtLSESCRSqZ3Gxsaq1GIJ0UaUWGu4kDPReJeYBQAwNJagSQsrniPSLuZW+lytNaUG1VpHjx4Nxhi2bNlSZJn27duDMaby1BuJRAIPDw+Vsa8BAQHw9PSskHgJqUyUWGu4gs3AnXs7UlNcGUmkIhiZ1Mxaqzq++OILbN++HVu2bMGDBw8wY8YMvHz5ElOmTOE7NELURr2Ca7C0VBnOF3iSzYCRqvfBSMnMrfWQkiwDQ36tVd+AJpsvztChQxEfH4///e9/ePXqFdzc3HDy5Ek4ODjwHRohaqPEWoNdOBmFjHTFQ7vNLHXh1MiM54i0k0QqhqGJhHtOa9yrNDjUN+U3KC0wdepUTJ06le8wCNE4agquwfwPRnA/ew+ox2Mk2q9gD+GUZBkyasi9VkKIKkqsNVRaqgwXTkVyy70GN+AvmGogr9aaJ46e10pIjUWJtYYKPhGJrMxcAIralr2zCc8RaT8Laz3u53fJMmSkU62VkJqIEmsNRc3AmieRimFkTD2ECanpKLHWQGmpMlw8HcUt0wPNNYdqrYQQSqw1UMFmYEsbfWoG1iCJ7nu11ho2hzAhhBJrjeT/V8FmYCceI9FejLEit5kXrLUmZVGtlRAtUty1XVqUWGuYtBQZLvpTM7A6BAIRcnJyitwu1RXDkGqthGil7OxsiEQitY5BibWGOf9eM3DdetQMXFY6Yl2kpLwrtozFe7XWTKq1EqIV3r17ByMjI7WOQYm1hjlToDdwtw+pN3B56EqMEJ+QgISEeGRnZxfadPR+rTWWxrUSUmUxxiCTyfD27VskJibC3NxcrePRlIY1SFqKDBeoN7DaxCIdGOlZ4V1yCuLjE8BYbqHlsrPlSIjLAAC8iQMSU/QhkarXxEQKkfY2/+fYB/zFQbSaSCSCkZER7O3tIZVK1ToWJdYaJOj4M8iy/msGrqWPOk7GPEekvcQiHRjqlfyt9rclwbhwSvFlxrNbXWw+OaCiQ6t59jbJ/3m4+h1PCFEXNQXXIGcO5j/QvBtNClEpxsxoDqFQMYvw5cDnuH7xBc8REUIqGiXWGiL1HfUG5oO9s4nSvex1C69opDs/IaTqosRaQ5w/Qc3AfBk13R1iHcWlFnb5FS75R/McESGkIlFirSEKTgpBzcCVq1YdQ/Qakv/0oPXfhFKtlZBqjBJrDZD6ToZLZ/JrST5+1Axc2UZMbQqprqJH8INbbxFw6EkJexBCtBUl1hrg/Pu9gR2pGbiyWVjrY8BIF275x0VXkZsr5zEiQkhFocRaA5z5O7925N2f5gbmy9CPXaFvoAMAePYoEcf3PuY5IkJIRaDEWs29Pzdwz8ENiilNKpKxmS78JuSPudz47VXIZIVPLkEI0V6UWKu58yciuWZgCxs9agbm2aCxjWFsppjV5WVUCv7edp/niAghmkaJtZorODewV19qBuabgZEEQye5cssbv/0HaSkyHiMihGgaJdZqLC1VuRm44JAPwp8Bo1xgVUsfAJAYl4Fta8J4jogQokmUWKuxCyejuEfEmVvp0SPiqgiprhhjZ7bglreuuYnYl6k8RkQI0SRKrNWYf4Fm4K59HfkLhKjwHuCEei5mAICsjFz8tPQfniMihGgKJdZqKj0tGxdPUzNwVSUSCTFpjge3fHDbfUTci+cxIkKIplBiraYunopCZkYOAMDMUhcO9U35DYioaNWxNjw62AIAmBxYPf8yzxERQjSBEms1VbAZuEsfR/4CIcWaNMcDAsVT5XDhVBSuno/hNyBCiNoosVZDGenZuHAqklvuPbQhf8GQYjk3Nke3D5255eWzLtJUh4RoOUqs1dCFU1HISFc0A5ua68KxgSm/AZFijZvZnJug//GdeBzcSpNGEKLNKLFWQwEF5gbu1NuBx0hIaVjZGmDY5Kbc8rqvQ5GcmMljRIQQdVBirWYyM3Jw/mQkt9x3GDUDa4PBE5ugVh1DAEByYhZ++paG3xCirSixVjMXT0chIy0bAGBiJoVjQ1N+AyKlItUV4+Ov8off7PvlDsLv0vAbQrQRJdZqpmBv4E69HCDI63JKqrwOPezRvF0tAIA8l2H5rItgjPEcFSGkrCixViOZGTkILtgM/FEj/oIhZSYQCDD16w8gFCm+DF0JikHg4ac8R0UIKStKrNVIyJlopKcqmoGNTaVwamTKb0CkzJwamqH/8PwvRMtnXURaKj39hhBtQom1GinYDNyxpz01A2up0TPcYWquCwB4HZOKjUuu8hwRIaQsKLFWE1mZOTh/4hm3TM3A2svIRIrJ81txy7s3/It7N2N5jIgQUhaUWKuJkIDnSEtRNAMbmUjg3NiM54iIOrz7O6Fle8U8wnI5w6Ip55CTQzMyEaINKLFWE2eUmoGpN7C2EwgEmLGkLSRSxYxMD269xe6Nt3mOihBSGpRYqwFZVi6CjhdsBqZJIaqD2g5GGDW9Gbe8YdFVvIh8x2NEhJDSoMRaDYQERCP1naLnqJGJBPWbmPMcEdEUv/GuXO/uzIwcLPn0PI1tJaSKo8RaDZw+EM793MGHmoGrE7GOEJ8vbcc9Wi4kIBp/br7Hb1CEkGJRYtVyGenZOHcsvxm4/3BqBq5umrSwwqCxjbnlH768hKjwJP4CIoQUixKrlrtwKoqbFMLETApnagaulsbPagmH+iYAFE3C88YHUC9hQqooSqxa7tSf+c3AXfs5UTNwNSWRivDVqg4QiRXv7+2rb/D7yps8R0UIKQwlVi2W+k6GC6ciuWXqDVy91Xe1wKjp7tzyT0uv4n4YTRxBSFVDiVWLBR17hqzMXACAhbUeHOqb8hsQqXDDPnZD4+aWAIDcHIYvR51BWgrNJUxIVUKJVYudOvCY+7nbh848RkIqi0gsxNxVHSDVU0wcEfk4CQs/PkdDcAipQiixaqmk+AyEnHnOLfcZ1oDHaEhlsnMwxudL23HL/gcjsPNHmpWJkKqCEquWCjj8lOsVamNnANu6RjxHRCpTtwH10K/A0KpVX4XgxqWXPEZECMlDiVVLFewN3Gsw1VZroinzP4BLM8X9VnkuwxfDTyPudRrPURFCKLFqobhXafjnfAy37ONXn8doCF8kUhEWbuwMEzMpAODt63R88dFpZGXm8BwZITUbJVYt5H8wAnl9VerWM4aljT6/ARHeWNsaYP7ajtyUhzdDXmH++EDI5dSZiRC+UGLVQsf2PuJ+7juMxq7WdC3b18akOR7c8um/IrBy7iUeIyKkZqPEqmWePkzE3euKSQEEQqDbQBpmQwC/CU3w4SgXbnnn+tvYvi6Mx4gIqbkosWqZY3secj+7uFvB2FTKYzSkqhAIBJiyoBU6+thz61bOCcHJ/Y+L2YsQUhEosWoRuZzh2L78D0rfcY2LKU1qGpFIiK9Wd4RbK2tu3VfjAnDu6FMeoyKk5qHEqkVuXHqJV9EpAAAdiRCe3nV5johUNRKpCEt+7gp7Z8WTcHJzGD4fdoqSKyGViBKrFjm2J7/TUuvOdtCRiHiMhlRVxqZSrNjeHbUdFJOG5CXX4wU6vRFCKg4lVi2RmZED/4MR3PLgia48RkOqOsta+li920cpuc4dG4A9P9HUh4RUNEqsWuL88WdIfad4iomRiQRNWljxHBGp6ixr6WP1Hh84NDDh1n0/8yKWzbqI3Fx6SDohFYUSq5Y4WqAZ2HtAPXqgOSkVSxt9rNnbk3vUHADs3nAbnw48gaSETB4jI6T6osSqBRLiMhByJppbHjiaegOT0jM2leKHnT3QoUf+UJyLp6Pg+8EfuHn5FY+REVI9UWLVAqf+fMw9ycbW3pC7b0ZIaenqibFwQ2cMm+LGrXv9PBVjvf/G5hXXaQpEQjSIEmsVxxjDX1vvc8sDRroUU5qQogmFAkyY1RJLfukKQ2MJACA3l2Hdwiv4uM9RPH+azHOEhFQPlFiruLs3YvH4TjwAQCgSoCc9yYaoydO7Ln491lepA1zo2ecY0HwvNq+4Dpksl8foCNF+lFiruIMFaqst2tWCgZGEx2hIdWFd2xCr9/hg2BQ37sk4WZm5WLfwCga33k8PTSdEDZRYq7C0VBlO/JE/heHwT5rxGA2pbsQ6QkyY1RI/HuiNei5m3PqI+wkY7fU3pvueQPjdeB4jJEQ7UWKtwvz/ikB6ajYAwMRMiqYfWJewByFl5+JuiU2H+mDyvFaQ6ubP5nXu2DMM9NiHeeMCEPPsHY8REqJdKLFWYX/9fo/7uc+whjR2lVQYkVgIv/FNsPXMh+jSx5Fbz5hiDHUf192YNy6AarCElAIl1ioq/G48bl99AwAQCICBY2jsKql41rYGWLCuE34+3AcfdKrNrc/JkePonkf4sOU+TP3wOG6EvARjNESHkMJQYq2i/tqW32mpcXMrmFro8hgNqWnqu1rg+9+7Yc1eH5VbEMEnIzG669/4qMNfOLn/MbKzqRcxIQVRYq2CsjJzlB5oPuJT6rRE+NH0Axus2dsT6//sBc9uyo8pvHPtDb4cdQY9G+3C76tuIjmRpkgkBKDEWiUFHn6K5IQsAICBkQ5adaxdwh6EVKwmLayw5Oeu+P30APT0qw+xTv5Hx+uYVKyZfxne9Xbgu8+DERWRxF+ghFQBlFiroD9+vcP93HNwAwiF1GmJVA32ziaYtcwTey/6YvRn7jA2k3LbMtKysXfTHfRx3Y3pvidw7cILug9LaiRKrFXM/bBY3AxRTIwuEAB+45vwHBEhqsws9DBqujv2XfTDrO/bwaF+/qPpGFMM1Rnb7RAGt/kTR3c/pNmcSI1CibWK2b3xX+7nph/YwNJGn8doCCmeRCpCz8ENsPlkf6zY3g2tO9spbX9wKw7zxgeiR4Md+G35dSTFZ/AUKSGVhxJrFfL2TTpO7s+faWnC7BY8RkNI6QkEArRsXxvfbfHG76cHoM+whpBI8yebiHuVjvXfXIF3vR1Y8mkQnj5M5DFaQioWJdYq5M/Nd5EtUzwezrq2AZq0oJmWiPaxdzbB50vbYt9FX4z7ogXMLPOHimVm5ODPzffQr9kezBhyEo/vvOUxUkIqBiXWKkImy8X+3+5yyyOmNuUxGkLUZ2ymi+GfNMWeYF/MWdkezo3NlLYHHn6KQa3+wOwR/njyIIGnKAnRPEqsVYT/gQi8fZ0OAJDqidBjED0ejlQPOhIRun/ojJ+P9MWq3T3Q1qsOt40x4NSBcAxovhdzx5xBVHgSf4ESoiGUWKsAxhh2bbzNLfce0kBpnCAh1YFAIIB7m1pY+qsXfj7cB+28lRPs8X2P0a/ZHnwz+RxiX6byGCkh6qFP7yrg1pXXuHcjFgAgENLj4Uj1V9/VAt/+4oWNB3srzUmcm8twcNt99G6yGxsWX0VaiozHKAkpH0qsVcDO9be4n1t1qE3zApMao1EzS3z/ezes/7MXWnjW4tZnpOfgl++voVeT3dj/213k5Mh5jJKQsqHEyrPIx4kIOPSEWx4/i4bYkJqnSQsr/LCjB5Zv6wanRvmdnOLfpOPbaecxsMU+nDv2jGZyIlqBEivPfl91E3mfFc6NzVC/iQW/ARHCI48OtfHzkT74ckV7WNjoceufPkrEdN8TGO9zGA/C4niMkJCSUWLl0euYVBzd84hb/mT+BzxGQ0jVIBIJ0WOQM3YEDsT4WS2gqy/mtv1z/gUGt92PBRMD8eYFdXAiVRMlVh5tWxuGnGzFvaPa9kZwb1urhD0IqTmkumJ8NKUpdp0bhAGjGnEPo2AMOLzzIfq47sbGJVeRlinhOVJClFFi5UncqzQc2Jw/IcSkuR48RkNI1WVqoYtp37TB5pP9lcbAZqTn4OfvrqHPV9/g7wttkSunp0CRqoESK0+2rLyBrEzFEz+saumjffe6JexBSM1m72yCpb964Yed3VHPJb+DU1ySCRZuHQG/RXMRevY5jxESokCJlQevY1Lx5+Z73PLUha0hENC3bUJKo0U7W2w63Aezl3sqDU17/NwOE3sdwdQPj9EUiYRXlFh58NuK65BlKWqrNnYGVFslpIxEIiF8fOtj57mB+PTDk9CTZHHbgk9GYWDLffh2+nnEx6bzGCWpqSixVrKo8CQc/P0+t/zpN1RbJaS89PR1MPXDUzi54lsM7BgKgUAxdi03l2H/r3fRu8nu/2675PAcKalJKLFWsnULQ7lZZOwcjdC2a50S9iCElMTa7B3+N2EvdvwSh1bN8x+mnvpOhrULQtG36R4c3/cIcjlNMEEqHiXWSvTvP69x5u/8WZbmrGhPtVVCNKhh/WxsWJWA1d+9hX2d/HmGX0alYO6YAAxsuQ/+f0VQgiUVihJrJWGM4Yc5Idyym4c1mrSkB5kTomkCAdC+bRb2/h6H2Z8lwcQ4vxk44n4Cvhh+Gr6t/kDAoSeUYEmFoMRaSU7se4ywy68UCwJg1vJ2/AZESDUnFgN+A9JwcFcsxgxPgZ5uLrft8d14fD70FPxa78fZI08pwRKNosRaCdJSZFg1L7+22m1APdRxNOExIkJqDkNDhk8mvMOhvbEYNSwFutL8BPvo37f4bPBJDHDfi4Pb7nO99QlRByXWSvDL99cR90rR7V+qK8K0b1rzHBEhNY+piRyfTlIk2BFDUiAtkGCfPkrEN5PPoXuDHdi84jqSEzN5jJRoO0qsFezBrTjsWBfGLX88rxUMjGhuU0L4YmYqx/TJ7/D3bkWC1dfLT7BvX6dj3cIr6Oa8A8tnX0T0k2QeIyXaihJrBcrJkeObyeeQm6u4f2PnaIR+HzXkOSpCCABYmCsS7NE/3mDax8mwMM/mtqWnZmPXj7fRq/EuTO53FEHHnyE3lx62TkqHEmsF2rn+Fu7/9+xIgQBY+qsXDa8hpIoxNGQYOTQVh/fG4usvE+Fkn6W0/ZJ/NKYNOgGfRrvw2/LrePuGZnMixaPEWkHC78Zjw+Kr3LLvuCaoW486LBFSVenoAH17pmPv1rdY8/1beLZO52ZyAoBX0SlY/80VeNfbjhlDTuL8iWfIzqbOTkSVuOQipKxkWbmYM+YM18PQwkYPE2a35DkqQkhpCASAZ5sseLbJwstX73DouAGOntBDcori4zInW47Aw08RePgpLGz00W94Q3w4qjEauFnwHDmpKqjGWgHWfxOKx3fiASgu0u9/94ZYh37VhGib2ra5+HTSOxz78w0Wz0uAW2Pl3sLxb9Kxfe0tfNhyH4a03Y89m/5FUnxGEUcjNQXVWDXs7JGn2L72Frc8+jN31Gtkzl9AhBC1SSRAz24Z6NktA5HRYpzw18cJf10kJOpwZe7djMO9m3H44ctL6NTTAb2HNkSXvo7Q09cp5sikOhIwxtSecqRVq1a4fv26JuLRalHhSRjS7k+kvlPMUercxAw/H+5LHZaqmNoFJud4GUnDKbRd7ZD89zOl7leV9ro5ucC1G1IcP62HCyF6yM5RbZXSM9CBd38n9B7aEJ7d60JHR1Rp8RH+UI1VQ1KSs/DZ4JNcUtU31MEPO3pQUiWkmhKLgHats9CudRbepSQj8Lwejp3Uw4PH+Q9fz0jLxvF9j3F832OYmEvRY1B99BnWEB4dakMopM+G6opqrBqQnZ2LqQOO43LgcwCK+6o/HugFF3crniMjhaEaa/XCV421KNHPxQgI0oN/oBTRL6SFlrGxM0DPwQ3Qe2gDuLa0pi/g1QwlVjUxxrBoShAObst/ePm0b1pjwCgXHqMixaHEWr1UtcSahzEg/IkOzpzVhf9ZXcTFFz7jmkN9E/Qc3AA9/eqjgZsFJdlqgBKrGhhjWD77EnZvuM2t6zeiET5b3IbHqEhJKLFWL1U1sRYklwP/3pPgzFldnD2vi+SUwjs01Wtkhh6+9eHj60xJVotRYi0nxhjWLgjF76tucus8Othi2dZudDFUcZRYqxdtSKwF5eQC129KceasHoIu6iIjs/AOTfUamcHHrz58fOujvqs5fa5oEUqs5SCXM3z3+QX88csdbp1rSyus3uMDkZjGq1Z1lFirF21LrAVlZgGh/yhqsZdCdZGZVUSSdTGDj2999PSrj/quNBFFVUeJtYwyM3KwYEIgTv8Vwa1r4GaO9ft7QUdCXem1ASXW6kWbE2tBeUk2MEgXIVeKT7I9uZosJdmqiBJrGbyOScVnfidw72Yct65ZGxss39qNkqoWocRavVSXxFpQZqYAl/+RcjXZLFnhny/Ojc3h4+sMH9/6cG5CzcVVBSXWUgo+GYmvJ51FQlz+dGUdezrg6/WdaDyalqHEWr1Ux8RaUGamAJevShF4XlGTLSrJ1nU2Qde+jvDqVw8tPG0hpttSvKHEWoK0FBnWfh2KfT/fUVo/bmZzDJ/ajKeoiDoosVYv1T2xFpSR8V9NNkgXl67qQlZEkjUxl6JzL0d07eeE9j3sYWBY+FAfUjEosRaBMYbAw0+x7IsLePMijVsv1RNh0U9d8EFHOx6jI+qgxFq91KTEWlBekg0M0sWVa0X3LtaRCNGmax106OEAz251Uc/FjJqMKxhNaViIGyEvsXZBKMIuv1Ja37CpBb7f4g0Tc90i9iSEkMqhp8fg3TkT3p0zIZMBN25JcSFEFxdCJIhPzK+hZsvkuOQfjUv+0QCAWnUM4dmtLjy72aOtVx2YWerxdQrVFtVY/5ObK8eFU1HYtiYMNy69VNomkYowdUEr9B7WkL7pVQNUY61eamqNtSiMAQ8f6+DCZV0EX5TgaVTxFYH6Tczh0aE2WnraomV7W9R2MK6kSKuvGp9Ynz5MxKk/H+PQzod4FZ2itE0gADr3dsT0xW1gbFr4nJ9E+1BirV4osRbv5WsRrl6T4so/ElwPkyIto/iGylp1DdGinS3cPKzh0twKjZtbwsSMWunKosYl1rQUGa5ffInQc89xOeA5njxIUCkjEAAt29fGZ4tb07e3aogSa/VCibX0cnKBBw8luHpDistXxHgYrgu5vOTew3aORmjsbgWX5lao52IGp4amsK9vCl09uptYmGr9W0l9J0P4vXg8+vctHv37Fg9vv8X9m3HIyZEXWl4iFaFLX0eMmdEc1rYGlRwtIYRULLEIaOoqQ1NXGSaOVnSAuvdQB7fvSHHzlgj3HhY+McWLyBS8iExB4JGn3DqBAKhV1whODU3h0MAUte2NYGNniFp1DVHLzhBWtQ0gqaHj+7UisTLGkJMjR1ZGLjIzcpCVkYN3iVlISshEUnym0v9fP0/By6gUvIh6h+SErBKPLRQK4NLcEgNGNkKHHg6QSGvmHwIhpObR02No1UKGVi1kmDBaUaONeKKDu/clePhYhIePRHj2XBe5uaq1WsaAV9EpeBWdwj0y830WNvqwsNKDqaUuTM11YWrx3/8t9WBkIoG+gQ70DRX/9Ax0lJalemLo6Ai1sl9LlUusw9ofwLvETGRl5SIrIweZGYr/y+Vqt1hzLGz00KKdLbr0cUSz1jbQ0y/8SROEEFKTiEWAS8NsuDTM5tZlZwPPonTwOEIHjyPEiIwEomN08OatFIwVn/Ti36Qj/k26ejGJhRDrCKEjyfu/SPF/HSHEOoqfhUIBhEJAIBT897OgwM+AUKQoY2qhi1W7fdSKp1QxV/grlFFUeBLeJZVc0ywNgQAwMJLAtq4h6ruaw7mxOeo4GcPIJL8j0vOn7zTyWkR71C7w8+O78bzFQTSj4PuZ9CaWtziqM2sjwLoF0KFF/rrsHAFev5Eg5pUuXryWIi5eB7FxQsTFixGfKEVSihSMqT/7U06OHDk5cmRmlFy2JJa1KmdokUY6L1laWsLR0bHI7XFxcbCysir18dJSZIgMTyp1eYFAALFYUCWeLJOVkwKp2IjvMCpFTTpXoGadr7adq1Agh7FeCgSCsn+cpWRkwUivZvT6rzrnKle0JTMGoOT3jCvBBEWWZtx/FLXo1CwZDKXKM04ZWteGnqHm+s9YWlri9OnTKus1klhLok29htVF51p91aTzpXOtnuhcKwf/VTxCCCGkGqHESgghhGhQpSTWjz/+uDJepkqgc62+atL50rlWT3SulaNS7rESQgghNQU1BRNCCCEaVKrE+vz5c/j5+cHExATGxsYYNGgQoqOjS/UCmZmZ+PLLL2Fraws9PT20a9cOFy5cUCknl8uxbNkyODo6QldXF+7u7jh48GDZzkYDynuu169fx8cffwwXFxfo6+vD3t4eI0aMwLNnz1TKOjo6QiAQqPw7fPhwBZxR8dR5bws7B4FAgFu3bimV0/b3dvHixUWeq66u8uTkVeW9jYmJwfTp09GuXTvo6+tDIBAgMjKyVPtq2zVb3nPVxmtWnfdV267X8p5rlbheWQnS0tJY/fr1maurKzt06BA7fPgwc3NzY/Xq1WOpqakl7c6GDx/OTExM2G+//cYCAwPZwIEDma6uLgsLC1MqN3/+fCaRSNjKlSvZuXPn2Mcff8wEAgE7ceJEia+hKeqc66xZs5inpyf76aef2Pnz59mePXuYi4sLMzc3Z9HR0UplHRwcmI+PDwsNDVX6l5CQUJGnp0Ld9xYAGzt2rMp5pKWlKZXT9vf2+fPnKucYGBjIxGIxGzx4sFLZqvLeBgUFMWtra9arVy/Wo0cPBoA9e/asVPtq0zXLWPnPVRuvWXXeV226Xhkr/7lWheu1xMS6bt06JhQKWXh4OLfu6dOnTCQSsdWrVxe7761btxgAtnXrVm5ddnY2a9iwIevXrx+37s2bN0wikbBvvvlGaX8vLy/WtGnTUp+MutQ519jYWJV1kZGRTCAQsIULFyqtd3BwYCNGjNBM0GpQ53wZU1yoCxYsKLZMdXhvC7Nz504GgB0/flxpfVV5b3Nzc7mfN2/eXOoPJW27Zhkr/7lq4zVb3nNlTLuuV8bUO9f3Vfb1WmJT8NGjR9G2bVvUr1+fW+fk5IT27dvjyJEjJe6ro6ODoUOHcuvEYjGGDRsGf39/ZGUppi709/eHTCbDyJEjlfYfOXIk7ty5U2jTTEVQ51wLm1nKwcEBVlZWePHihcZj1QR1zre0qsN7W5gdO3bAxsYGPj4VP+9oeQiF5es+oW3XLFD+c9XGa7a851pa1eF9LUxlX68lRn7v3j24ubmprHd1dcX9+/dL3NfJyQn6+voq+8pkMkRERHDlpFKp0odeXjkAJb6OpqhzroV58OABYmNj0bhxY5Vtx44dg76+PqRSKdq2bcvL/VVNnO/PP/8MqVQKfX19eHl54eLFiyqvUd3e25iYGAQFBWHEiBEQi1Wn264K7215ads1q2lV/ZpVl7Zcr5rEx/VaYmJNSEiAmZmZynpzc3MkJiaWe9+87Xn/NzU1VXk80PvlKpo65/q+nJwcTJkyBVZWVpgwYYLStn79+mHDhg3w9/fHnj17oKuri4EDB2L37t1qxV9W6p7vyJEjsWnTJgQGBuK3335DfHw8vLy8cP78eaXXqG7v7a5duyCXyzFmzBiVbVXlvS0vbbtmNUkbrll1aNP1qkl8XK+lerpNYc/DY6UY/soYK9W+pS1XGTQVx7Rp03D58mWcOHFC5YNqw4YNSssDBw5E27ZtMW/ePJUmmIqmzvnu2rWL+7ljx44YMGAA3Nzc8PXXX+PSpUvcsarbe7tz5060aNECzZo1U9lWld7b8tDGa1ZTtOWaLS9tu141hY/rtcQaq5mZWaHfUhITEwv9ZluQubl5kfvmbc/7f2Jiosqb9365iqbOuRY0b948/Pbbb9i6dSt69OhRYnmRSITBgwcjJiYGr169KlPM6tDU+eYxMjJCnz59cO3aNW5ddXtv//nnHzx8+LDQb7+F4eu9LS9tu2Y1RVuuWU2qyterpvB1vZaYWF1dXXHv3j2V9ffv30eTJk1K3PfZs2dIT1d+0O39+/chkUi4dnxXV1dkZWXhyZMnKuUAlPg6mqLOueb57rvvsHz5cqxfvx6jRo0q9Wvn/SEX9m2xomjifN/3/jfe6vTeAopOEGKxGMOHDy/1Pny8t+WlbdesJmjTNatpVfV61RTerteSug2vXbuWiUQi9uTJE27ds2fPmFgsZqtWrSp237CwMAaAbd++nVuXnZ3NXFxcWN++fbl1eV28Fy9erLS/t7c3c3NzKylEjVHnXBljbP369QwA++6778r0utnZ2czDw4PZ29uXOWZ1qHu+70tOTmZ169ZlnTp14tZVl/eWMcaysrKYubk569+/f6lfl6/3tqCyDFXQtmv2fWUdlqFt12xB6g5BqcrX6/vKc658Xq8lJtbU1FTm7OzM3Nzc2OHDh9mRI0dYs2bNmJOTE0tJSeHKRUZGMpFIxJYsWaK0/9ChQ5mpqSnbvHkzCwwMZL6+vkwqlbIbN24olZs7dy6TSqVs9erVLCgoiE2ZMoUJBAJ29OhRtU6wLNQ513379jGBQMB69uypMtj43r17XLm9e/eyoUOHsh07drBz586xffv2sQ4dOjAAbN++fZV2ruqe78qVK9nEiRPZnj17WFBQENu+fTtzc3NjOjo67MKFC0qvo+3vbZ6DBw8yAOzgwYOFvkZVem8ZY+zAgQPswIEDbMqUKQwA27RpEztw4AA7f/48Y6x6XLN5ynOu2njNlvdcte16zVPev2HG+L1eS0ysjDEWFRXFBg0axIyMjJihoSEbMGCAyjeHZ8+eMQBs0aJFSuvT09PZzJkzmY2NDZNKpax169YsKChI5TVycnLY0qVLmb29PZNIJKxp06bswIED5T2vcivvuY4ZM4ZB8fx6lX+dO3fmyoWGhrKuXbsya2trJhaLmbGxMfP29manT5+unBN8T3nP9+jRo8zT05NZWFgwsVjMzM3NWb9+/djVq1dVXkPb39s8/fv3Z+bm5iwrK6vQ41e197akv8fqcs0yVr5z1dZrtjznqo3XK2Pl/xtmjN/rlZ5uQwghhGgQPd2GEEII0SBKrIQQQogGUWIlhBBCNIgSKyGEEKJBlFgJIYQQDaLESgghhGgQJVZCCCFEgyixElLBBAIBBAIBhEKhyhysBXXt2pUru337dqVtY8eO5bYtWrSoyGPs2LGDK9elSxelbefPn+e25f3T19eHra0tOnXqhC+//BJhYWHqnCohBJRYCakUYrEYjDH8/vvvhW4PDw9HcHBwoQ9ifv84W7duRW5ubqHbN2/eXOIxHBwcsGjRIixatAiff/45+vbti7S0NKxatQotW7bEiBEjkJqaWroTI4SooMRKSCWwsbFBq1atsG3bNuTk5Khs37JlCxhj6Nu3b7HH6du3L2JiYnD69GmVbQ8ePEBISAj69etX7DEcHR2xePFiLF68GN9//z02b96MGzduICwsDE2bNsXevXsxePDgsp0gIYRDiZWQSjJp0iS8fv0ax48fV1qfnZ2NHTt2wNPTE66ursUeY8SIEdDT08PmzZtVtuWtmzhxYrnia968OQIDA2FlZYXTp0/j8OHD5ToOITUdJVZCKslHH30EAwMDbNmyRWn90aNH8ebNG0yaNKnEY5iammLw4ME4ceKE0oOYs7KysHPnTnTu3BkNGzYsd4zW1taYPHkyAGDPnj3lPg4hNRklVkIqiZGREYYNG4bTp08jJiaGW79582YYGxtjyJAhpTrOpEmTkJOTo9TB6dChQ4iPjy9Vci5JXqenf/75R+1jEVITUWIlpBJNmjQJubm52Lp1KwAgKioKAQEBGDFiBPT19Ut1jA4dOqBx48bcfVlAkZzNzMzg6+urdox2dnYAgLi4OLWPRUhNRImVkErUpk0bNG3aFFu3boVcLseWLVsgl8vLXNOcOHEinj59inPnziEiIgJBQUEYNWoUdHV11Y4xL1kLBAK1j0VITUSJlZBKNmnSJERFReH06dPYtm0bPDw80KJFizIdY/To0ZBKpdiyZQtXc9VEMzAAvHz5EgBgZWWlkeMRUtNQYiWkko0aNQp6enqYPHkyXrx4gY8//rjMx7C0tMTAgQNx6NAhbN26Fe3atYObm5tG4gsKCgKgqF0TQsqOEishlczU1BR+fn6IiYmBgYEBPvroo3IdZ9KkScjKykJcXJzGaquxsbH49ddfASiG9hBCyq74KVoIIRXif//7HwYNGgQrKysYGRmV6xhdu3bFkSNHIJfL4ePjo3ZMt2/fxujRo/H27Vv07t0b/fv3V/uYhNRElFgJ4YG9vT3s7e3VOoZAIChX8ouMjMTixYsBKCanePv2LW7cuIEbN24AUNRU82qthJCyo8RKSA0TFRWFJUuWAAB0dXVhamqKBg0aYPbs2RgxYgSaN2/Ob4CEaDkBy+tbTwghhBC1UeclQgghRIMosRJCCCEaRImVEEII0SBKrIQQQogGUWIlhBBCNIgSKyGEEKJBlFgJIYQQDaLESgghhGgQJVZCCCFEgyixEkIIIRr0fx3a36EcSC2dAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89170e17f8324425af9968654902d108",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "id": "4dfe1766",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6590d065",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v2+v3+v4+v5_Aug23_bf_model_misspecification_1000_data.ipynb b/bf/v2+v3+v4+v5_Aug23_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..8f88185
--- /dev/null
+++ b/bf/v2+v3+v4+v5_Aug23_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2651 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72\n",
+    "\n",
+    "from hnn_core.utils import smooth_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 13.943223\n",
+    "    maxAbs = 221.50381\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v2: scaling\n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "#         temp = dpl[0].data['agg']\n",
+    "        temp = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "        temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scale\n",
+    "x = x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# Smoothing\n",
+    "for i in range(len(x)):\n",
+    "    x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "25f9ecff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.943223"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "f38d976e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "221.50381"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60484a8f29404912b7d4fed9bbbbdcb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.740263</td>\n",
+       "      <td>0.821029</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  7.740263  0.821029"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0deafd4072a64039b21fb56ce691e9ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2d3e39510>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e47763d4454444cb3b31a98cab20607",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea5086c1ead54285b900e9f13eb199c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "765ac02e641845ab94d697fd55f15c0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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6+oZZO/sq09LsN6Xj29v8zty8/JrroqgFjdO9k//FMMytW7eYkSNHMvb29oxUKmX09PQYZ2dn5ocffmBevy7630TeeFlvb2/m9OnTTKtWrRhdXV3GyMiI6devH/P06VOl4x89esTMmDGDcXFxYczMzBixWMzY2dkxnp6ezPXr14v8jKtXrzIDBgxgrKysGJFIxJiZmTGNGzdmZsyYwdy5c0fpWDs7u0JjV4vzxRdfMAAYU1NTJisrq9jjsrKymK1btzKtW7dmDA0NGbFYzNSsWZNxd3dnNm7cqBjz+3GcLnPnDpOVlcUsXbqUcXBwYMv3448/MpmZmaUap8swDCOXy5mtW7cyTk5OjFgsZqysrJjvvvuOSUxMLLas3t7eTOPGjRldXV2mWrVqzNdff83cv3+f8fLyYgAwly9fVjr+w4cPzJAhQxgLCwuGz+czABgvLy/2/evXrzOdOnVijI2NGX19faZt27bM8ePHmcuXLxc6VhNx8XvOY5iS1xu6uroi6GOnAKI9PrxNxdAOf+H96/yelH2G18Wk+a4QiYvvMJWWko0Dv9zHsT2PkPevRCjiY7V3Z3QfWLuiw+ZUXu/VKiuuwO95NVfu4qgMCt4zXel7qUm4+D2nNt1KLjMjF1P7n8tPuDxg5orWmOrV8pMJFwD0DMSYOM8Vq/d2hpGpBICivXf2CF/8+Vvpp60jhJCqjpJuJcYwDBZNuIiHwdHsPq+tHUv9lNqsjRV+PdELto5GH68LLJ8agOP7iu58QQghpGiUdCuxv/c+xrkj+Su5jJreGO262pXpWuZWeth4uCucGuVPI7d44mX4HS955w9CCKnqKOlWUu9ep2Dt7Pyxd609bDB8cuNPnPF5hiZSrPL+Eo71TAAAcjmDOSP/wf3bpRvYTwghVRUl3UqIYRgs+e4yUpMVQ38MjMRYsKmDSq6tbyjGKu8vYeOgWDItO0uGKf3O4t3rFJVcnxBCKjNKupWQ719huOabP1/uoq0dIZGqbki2STUdrNztAUNjReequOgMTOl3FpkZNHsVIYR8CiXdSiY7S4aNCwPZ7Y497NG0deGB8uVlZWuAxds6QiBUzGX7JDQWK2dcUfnnEEJIZUJJt5I5vOMB3rxKBgCIxHxMX96qwj6rccvqmLywBbt9bM8jnNz/pMI+jxBCtB0l3UokKSETO1bmLzM2eGID6BuKK/Qzew2tA/feDuz2sin+CH+WUKGfSQgh2oqSbiVyYGsokhMU61fqG4oxZFLDCv9MHo+H6ctboWYtxRjezIxczP/mAnJz5RX+2YQQom0o6VYSeVM25pkwz+WzM06pio6eCD9ubA+hSPHP6f6tD/Bef1ctn00IIdqEkm4l8b9d/7JPuQZGYnTp56jWz/+ivimGT2nEbm9behtPQmPVGgMhhGg6SrqVQFZmLvZuusduD5vcEAKB+n+0gyc0gFNjxYxVublyzBvjh+ysohfAJoSQqoiSbiVw5vAzxL5PBwBIdYXoPdSJkzgEQj7mrG0LiVRRrf383zhsX36bk1gIKY3Ro0eDx+MhPDyc61BY9vb27ELxpPKgRey1HMMwOLQ9vy3Xc3Q9iCXqacstSs1aRvhmVjNs/0nRi/r3n+/C4+taaOhqyVlMFSJ0MZAe+fnjuKJrCzReVu7L2Dftg4jX74p8z9LSEu/fK08Bmp2djWXLluHgwYOIj49Hs2bNsH79ejRr1qzQ+X5+fujSpQvOnDmDnj17ljtWbePm5oaAgACUYnVVUglQ0tVy926+Z9tO+XwePMfW5zgi4KsRTrhx8TXuBb6HXM5gybeXcSRwIITCSlSxkh4J6NlzHUXx0sJVdikjIyNMnz690H59ff1C++bNm4eNGzfC09MTNjY22L9/Pzp16oQnT57Ayip/kpbU1FSMHz8ew4cP14iEu2rVKsybNw/W1tZch8K6ePEi1yGQCkBJV8sd/jV/XdvmHWvAwEjCYTQKfD4PM39qjXE9TiE7S4YnobE4+Mt9jJrWhOvQSBkYGxtjyZIlnz2OYRj89ttvGDNmDPbs2QMA6Nu3L9zc3LB//37MmTOHPXbu3LnIzMzE5s2bKyrsUrGyslL6o0ATODqqtzMkUY9K9OhR9cR+SIfvsTB2W5OSmpWtAUZMze/NvGXxTURF0qIIlVlMTAzS09PRokX+LGV5ryMiIth9V65cwa+//opffvkFpqamZfqsvPbO5ORkzJw5E/b29hCJREp/HDx58gSjR49GzZo1IZFIYGlpiaFDh+Lp06eFrvepNt1bt26hf//+qF69OsRiMWrWrImJEyciKiqqyNji4+OxYMECNGjQALq6ujByc0PjoUMxb+tWpKWlITw8HDweDwEBAQAUY93zvtzc3AqV8b+ysrKwevVqNGrUCLq6ujA0NET79u3xv//9r9CxeZ81evRohIeHY/DgwTAzM4NUKoWrqyvOnDnz6W/0f5w4cQLDhw9HnTp1oKenB319fbi4uGDLli2QywuPzc/7vr58+RJbt25Fo0aNoKOjo1TO+Ph4zJ8/H/Xq1YOOjg6MjIzg4eGBf/75p9D1kpKSsG7dOri7u8PGxgZisRjm5ubo06cPbt68WaqycIWedLXYiT8eIzdH8Q/dqqY+ajtX4zgiZf3HOuPiqVcIf5aIzIxcrJgWgG1/9wSPx+M6NFIKWVlZOHDgACIjI6Gnp4dGjRqhQ4cOEAiU+w6Ym5tDR0cHwcHB7L6goCAAgJ2dYh3njIwMjBs3Dp6envD09CxXXNnZ2XB3d0d8fDy6dOkCQ0NDODgoZkfz8fFBv379kJOTg969e+OLL77Amzdv8Pfff+Ps2bO4fPlyke3M/+Xt7Y3x48dDIpGgT58+qFmzJp4/f47du3fj9OnTuHnzJmxtbdnjX716hU6dOiEiIgIuLi749ttvIX/3Ds8iI7Hx8GFMWroUxsbG8PLywt69exEREQEvLy/2/M91nMrOzkbXrl0REBAAJycnTJ48Genp6fjrr78waNAg3Lt3DytXrix0XkREBFq0aIFatWphxIgRiI+Px5EjR/DVV1/hwoUL6NSpU4m+5/PmzQOfz0fLli1hbW2NpKQkXLp0CdOmTcOdO3ewf//+Is+bNm0arl69ip49e6JHjx7sv52IiAi4ubkhPDwc7du3R7du3ZCWloYzZ86gW7du+O233zB+/Hj2Oo8fP8aCBQvQoUMH9OzZEyYmJoiMjMSpU6dw/vx5nD59Gt26dStRWbhCSVdLMQyDk3/kz3M8aGIDDqMpmlCkmPt5+iAfAID/2XBcPPkSX35N1Wba5P379xgxYoTSPgcHB3h7e6Njx47sPh6PhwkTJmDLli1ISkqCtbU19u/fDwMDAwwbNgwAsHDhQsTHx+OXX34pd1zv3r1D/fr1ERAQAD09PXZ/QkIChgwZAl1dXVy5cgX16+f3c3j48CFatmyJcePG4e7dT0/g8uzZM0ycOBH29vYICAhQau+9dOkSOnfujGnTpuH48ePs/uHDhyMiIgIrV67E/PnzFTs//uERm5gI/erVIZVKsWTJEvj7+yMiIqJEVfd51q9fj4CAAHTv3h2nTp2CUKi4hXt5eaFFixZYtWoVevXqhTZt2iid5+/vjyVLligl+KFDh6Jbt25Yt25diZPu2bNnC1V7y+VyjBkzBn/88QemTJmCli1bFjrv7t27CAkJYf8oyjNq1ChERETg8OHDGDx4MLs/MTERbm5u+P7779GnTx9YWio6YtarVw9RUVEwMzNTus6bN2/QokULzJgxQ+OTLlUva6kHdz7g5VPFHMcCIU9p/mNN4tzMAj0H12G3f5p2hV3nl2i+MUN64+LFi3j//j3S0tLw4MEDTJw4EeHh4ejevTtCQ0OVjl+zZg3mzp2L27dv4/fff4ezszMuXrwIa2tr3Lp1C5s2bcLmzZthZmaGpUuXwsrKCkKhEC4uLrh+/Xqp41u/fr1SwgWAP/74A4mJiVi6dKlSwgUAZ2dnjB8/HiEhIXj06NEnr/3rr78iJycHmzdvLtTByt3dHX369MHp06eRkqJoNgkODsaNGzfQpEkTzJ07t9D1zIyNIZVKS13Ggvbs2QMej4cNGzawCRcALCwssGjRIgDA7t27C51nZ2eHhQsXKu3r2rUrbG1tcft2yYf1FdXOzOfzMW3aNACAr69vkefNmTOnUMINDQ1FQEAAPD09lRIuoOhHsHTpUmRmZuLYsWPsfiMjo0IJFwBsbGzQv39/PHnyBJGRGjyqAPSkq7VOFHjKdW1XAzq6Ig6j+bRvZjXFjQuRSIjNRMy7NPy64jZmr2nHdVikBLzmjAequbLbDRo0wI4dO6Cvr4/169djyZIlSk96EokEq1atwqpVq5Suk52djbFjx6JHjx4YNmwYNm3axD55tW3bFitWrEC3bt0QFhbGPtV8jlQqRaNGjQrtDwxULG0ZGhpa5FPks2fPACiqKv+blIu6TkBAAO7cuVPo/ejoaMhkMjx79gwuLi5sm2LXrl3B56v+eSYlJQVhYWGwtraGk1Phsfju7u4AgJCQkELvNWnSpFBzAADUrFmTLWdJxMXFYd26dTh37hxevnyJtLQ0pfffvn1b5HkF2/nz5H1uUlJSkT+nmJgYAIqfU0HXr1/H5s2bERgYiOjoaGRnK/8R//btW6Uqf01DSVcLZWXm4vz/nrPbA8c7cxjN5xkYSTBpvitW/XANALB/Syg8xzijlpMJx5GRspo0aRLWr1+PK1dKtoby0qVL8fbtW7ZzzLp16+Dh4cHebOvWrQt7e3v88ssvWLasZOOLLSwsiuwfEBcXBwDYtWvXJ89PTU395Pt511m3bl2JrpOYmAgAFTbsKCkpCQCK7WWdtz8vjoKMjY2LPEcoFBbZAaooiYmJaN68OV69eoUWLVpg5MiRMDU1hVAoRGJiIjZv3oysrKwiz61evXqhfXnfXz8/P/j5+RX7uQV/TsePH0f//v0hlUrRuXNnODo6Qk9PD3w+H/7+/ggICCg2Bk1BSVcLXT79CsmJ+fMsN2yu+RNPdOrtgDOHn+FBUDRkMgarZl7BzrN9qFOVlrKwsACAQk86RQkJCcHatWuxY8cOWFtbIzk5GVFRUWw7LwDY2trCzMwMDx8+LHEMxf3bMTJSrHgVGhpa5JNwSeVdJykpCYaGhp89Pi+xFfe0V1558fx3QpI87969UzpO1Xbv3o1Xr17By8ur0JNpYGDgJ4d/FfWzyotz8+bN+P7770sUw6JFiyAWixEUFIR69eopvTdx4kS2R7gmozZdLXTuSP5Tbue+jlqRuHg8HiYvbgE+XxHrjQuvcenUK46jImWVVzVYq1atTx6Xm5uLsWPHolOnTvjmm2+U3vvvE0lmZqZKYmvVqhUA4OrVq2q9Tt7xvr6+JXp6zKvulclKNj+5gYEBHB0d8fbtWzx//rzQ+5cvXwaAEvXKLouwMMXwxKJ6nZcl2ZXl5xQWFob69esXSrhyuRzXrl0rdQxcoKSrZVKSsnDFJ5zd7j2sLnfBlJJjPVP0HJLfqWrVzCvIzMjlMCLyKQ+fvEB8QlKh/REREZgyZQoARW/dT1m1ahXCwsKUqnoNDQ1hbW0NHx8f5OYqfv4BAQFISUmBs3P5m0rGjBnDdsQpqpOQXC6Hv7//Z68zZcoUiEQizJgxg20HLig7O1spYbi4uKBNmza4d+8e1qxZU+j4uMREpT8sqlVTDPErTcefsWPHgmEYzJ49WylZx8bGYvny5ewxFSFvONN/v3chISGF2vBLwtXVFe3bt8fff//NTqbyXw8ePEB0dLRSDM+fP1caI80wDJYuXfrZjnGagqqXtcylU6+Qk634K9q8ui5s7D9f7aVJRk9vAv+zr5CSmI13r1OxZ/1dfLewcCcLwr2jJy9i9ZZ96NTJHQ4ODjAwMMCLFy9w9uxZZGZmokePHpg1a1ax5z98+BA//fQTNmzYwI7TzTNnzhxMmzYN7du3R8uWLXHw4EHo6+tj8uTJ5Y67WrVq+Ouvv9C3b1+0atUKHh4ecHZ2Bp/PR2RkJAIDAxEXF/fZJ2snJyfs2bMHY8eOhbOzM7p164Y6deogJycHkZGRuHr1KszNzfHkSX6nxgMHDsDNzQ0//vgjjh07Bjc3NzDv3uH569f459YtPHn6lE1eHh4eOHr0KPr164cePXpAR0cHdnZ2hYZnFTRr1iycP38eJ0+eROPGjdGjRw+kp6fj6NGjiI6Oxpw5c9CuXcV0Uhw5ciTWrVuH6dOn4/Lly6hduzaeP3+OM2fOoF+/fjhy5Eipr3no0CG4u7vjm2++wZYtW9CyZUsYGxvjzZs3uH//Pv79918EBgayzRkzZszApEmT0LRpU3h6ekIkEuH69et49OgRevfujdOnT6u62CpHSVfLnD+aX63UxVP7xrsaGkswZkZTbPG6BQDYtSYYX49wQg077frjoSro1M4FT8MiEPLoFQIDA5GWlgZjY2O0a9cOI0aMwIgRI4pt2pDJZBg7dixatmyJ7777rtD7U6dORXJyMnbs2IG7d++icePG2LhxY4l7Ln+Oh4cH7t+/j59//hm+vr64evUqxGIxatSoAXd39xJPzDF8+HA0btwY69evx+XLl/HPP/9AT08PNWrUQP/+/TFo0CCl4x0cHHD37l2sXbsWJ06cwLZt2yAViWBvZYUfhg1jkwcAjBs3DhEREfjzzz+xdu1a5ObmomPHjp9MumKxGH5+ftiwYQMOHTqErVu3QigUonHjxti0aROGDBlStm9YCdSoUQNXr17FvHnzcO3aNfj6+sLJyQnbt2/Hl19+Waaka2Njg+DgYGzduhXHjh3DwYMHIZPJUL16ddSvXx9Tp05Fw4YN2eMnTpwIiUSCTZs2Yd++fdDR0UH79u3h7e2NY8eOaUXS5TGlWOLC1dWVnWGGqF9iXAY61vRGbq7iSXf/5X6oblN40nlNJ5PJMaXfOYQ9igegaJfedKQ7x1Epe/z4caF2IyWVfZWhuAK/5wWGDFVWgwcPxpEjRxAVFaX6OZgL3jNdK//3Upt89ve8AtCTrha5cPIlm3Cr2+hrZcIFAIGAj8mLW2DGYMVMVX7HX+Dmpddo5V6T48hKQQXL5hHN8ezZM0gkEpibm3MdCqnkqCOVFvE7/oJ93a2/9lUtF9TAxQIeX+X3fF0x/QpyckrWi5MQVdm8eTM8PT0REhKCr776SmmWJ0IqAiVdLZGanI2bl96w2x5faXfSBYDxc5pBR09xk3v5JEFpmUJC1GHTpk24efMmxo4di99++43rcEgVQElXS1w5H86uKGReXVdrq5YLqmahi+GT8ycv2Lb0FuKi0zmMiFQ1r169wtu3b/H7778XO2sTIapESVdLXDz5kn3t1tOeu0BUrO+oerBxUPRcTkvJwebF2rEmJiGElAUlXS2QlZmLKz75i4B37qv9Vct5RGIBJv2Y36Pzb+9HeHg3+hNnEEKI9qKkqwVuXX6D9NQcAICBsRj2dYy5DUjFWrrZoKWbYpJ4hgFWTr+CUoxkI4QQrUFJVwtcOp0/R3Frj5paMddyaU1a0BxCkeKf472b73H2cOFp9wghRNtR0tVwDMPgaoGq5a79vuAwmopjY2+IfqPzB6n/PO860lJpsXtCSOVCSVfDhT2Mx/s3ivUkRWI+6jetvIP3h33XCKbmOgCAmPfp2LU6mOOICCFEtSjpariCHajqNTZnq2ArI119EcbNzl+WzHtjCCJfFF7lhhBCtFXlvYNXEld985NuwRmcKiuPr2rBqbEZACA3R451c7RjjUxCCCkJSroaLDU5GyHX37HbLTtZcxiNevD5PExelL/U36XTr3DdT4MXFiCEkFKgpKvBAi++Zhc4sKihh2oWuhxHpB5Ojc3QtcCyhatm0LzMhJDKgZKuBivYa7ldF1sOI1G/sT80g66eCADw6lkiDm2neZkJIdqPkq6GYhhGqT23fTc7DqNRP1NzHQyfmj8v8y/LaF5mLnXu3Bk8Hg8BAQHFHjNp0iTweDzs2rVLjZERol0o6Wqop/fjEB2VBgAQSwSo97FzUVXy9Qgn1KxF8zJrgrt370IgEMDFxaXYY27dugUAcKWF2gkpFiVdDVXwKbeBiwUEwqr3oxKJBfh2QXN2m+Zl5saLFy8QHx8PZ2dn6OsXvbpVeno6/v33X0gkEjRo0EDNERKiParenVxLFGzPde/jwGEk3GrewRqtOtkAUMzLvGJaAM3LrGZBQUEAgJYtWxZ7THBwMHJzc9G4cWOIRCJ1hUaI1qGkq4GSE7NwLzB/qFALt8o/VOhTJv7oyk4KEnrrA04ffMpxRFVLSZLuzZuKqn+qWibk04RcB0AKC7zwGjKZ4mnO0loPJtV0OI6IWzb2hvAcUw9Hdj4EAKydcw1uvRxgaCzhOLKqIS/pHjlyBJcvXy7yGGrPJaRkKOlqoGv/5Fctt+1ctYYKFWfot41w8eQrxH5IR0JsJjYvvolFWzpyEouzeBsnn1sWD7OnlOt8hmEQEhICAPDz8/vs8ZR0Cfk0ql7WMAzDIPDSG3a7Q/eqNVSoOLr6Iny3ML9T1ZHfHuBB0AcOI6oanoVFICkpCa1btwbDMEV+xcfHAwB0dXVRv359pfO3b98OBwcHSKVSuLi44OrVq1wUgxCNQUlXw7x5lYx3kSkAAKGQj7oNq95QoeK062qL5h3zF7tfOtkfMpmc46gqt6B7jwEATZs2LfaYu3fvAgCaNGkCgUDA7j9y5AimTZuGH3/8ESEhIWjTpg26d++OyEia1pNUXVS9rGFu+ec/5daqZ1KpVxUqLR6Ph6mLW2Bcj1PIzpLhcUgM/tzxAMMmN1ZrHOWtstUmeUm3WbNmxR6Tl3T/W7W8YcMGjB49GuPHjwcAbN26FT4+Pvj111+xatWqCoqYEM1Gd3QNc+tyftJt27kmh5FoJitbAwz9tiG7vXnxTcS8S+MwosotKPTzSTevzbdg0s3OzkZwcDC6dOmidGyXLl1w48aNCoiUEO1ASVeDMAyDW/5v2e0WHar2UKHiDBjnrDRT1ZrZtPxfRZDL5bj34BnEYjGcnZ2LPa6oJ93Y2FjIZDJYWloqHWtpaYn3799XTMCEaAFKuhrkxeMExH1QzC8slghQq54pxxFpJrFEgKlL8seMnv/fc9y4QO2Eqvb42SukpqXD2dkZYrG4yGNSU1Px/Plz6Ovro27duoXe5/F4StsMwxTaR0hVQklXgxRsz63XxAx8Pt2citO0tRU8CszUtWyKP7IyczmMqPIJDn0C4NNVy6GhoZDL5WjWrBn4/PzbiZmZGQQCQaGn2ujo6EJPv4RUJZR0Ncjtgu25VWwpv7KYON8VegaKKQdfv0zG7rXBHEdUuYwc1BNM7B3s3r272GPatm0LhmEKrT4kFovh4uJSaGyvn58f2rRpUyHxEqINKOlqCJlMjttX8ttzm7Wx4jAa7WBipoOxP+QPZdm5OhjP/43jMCJS0MyZM7F3717s3r0bjx8/xrRp0xAVFYVJkyZxHRohnKGkqyGe3o9FckIWAMVEELaORhxHpB16Dq6Dek0UY5lzc+VYNPEijd3VEIMGDcKmTZvw008/oUmTJrh27RrOnTsHOzua8IVUXZR0NUTBoUKNmltSZ5MSEgj4+GFlG4g+jmd+cCca+7eEchwVyfPdd98hPDwcWVlZCA4ORocOHbgOiRBOUdLVEAWHCrX+0obDSLSPXW1jDJvciN3evPgmIp4nchcQIYQUg5KuBsjJkSHoahS73bQ1teeW1qAJDVDLyQQAkJ0lw+JJlyCX07q7hBDNQklXA/wbFI2MtBwAgJGJBFY1DTiOSPsIRXzMWt0GfIGiWj7oahT+t+tfjqMihBBllHQ1wM0CqwrRU27Z1XauhoHj82dO+nneDUR9XDyCEEI0ASVdDVBwUoxW7tSeWx4jpjRGzVqKnt8ZaTlYOvkyGIaqmQkhmoGSLscyM3JxL/Adu924VXUOo9F+YokAP6xqjbzO39d8I3HqwNMyXYuSNSGVF1e/35R0OXYv8B1yshXjSs0sdWFmqctxRNrPuZkFvhrhxG6vnHkF79+kluoaAoEAOTk5qg6NEKIhcnJylNZ/VhdKuhwrOD7XpX0NDiOpXMbObIrqNvoAgNSkbCwcf7FUvZkNDAyQnJxcUeERQjiWnJwMAwP1d1qlpMuxgu25Ld1oKT9V0dETYc7atmw1c+DF1zj86/0Sn29qaoqEhATExsYiOzubqpoJqQQYhkF2djZiY2ORkJAAU1P1r+QmVPsnElZqcjb+DYpmtxu3oNVXVKlhc0sMGOeM/+16CEDRm7m1hy07nvdTJBIJbG1tER8fj/DwcMhksooOV7Okxea/jn7MXRyVQWyB7+Vj+l5yTSAQwMDAALa2tpBIJGr/fEq6HAq+FgWZTPEEZVVTH4YmUo4jqnxGTWuCoKtRePkkAdlZMswb44eDVzwhEn2+LUcikcDKygpWVlVwGNeh+vmvh9JTfrnUL/C9pBqTKo+qlzlUsGq5eUeqWq4IYokAc9e1g/Dj3MwPg6Oxc1UQx1ERQqoqSrocKtiJqgUl3QpTy8kEo6c3Ybd3rArC/TsfuAuIEFJlUdLlSGJcBp6EKtp6eDygoSu151ak/t/UR0NXCwCAXMZg3mg/ZKTTkCBCiHpR0uXI7YD8VYVsHAyhqy/iMJrKTyDgY/battDRVXRjiHieiJ/n3eA4KkJIVUNJlyMFl/Jr1YmmflQHq5oG+G5hc3b7zx0PcPnMKw4jIoRUNZR0OXL7MnWi4kLX/l+gbeea7PaCcRcQHVW62aoIIaSsKOlyIDoqFS+fJgAA+AIe6jc15ziiqoPH42HmitbsdJtJ8VmYO9oPMpmc48gIIVUBJV0O3C5QtWxf2xgSKQ2XVidDEynmrW/HzlZ12/8t9vx8l9ugCCFVAiVdDhQcn9vag9pzudC4ZXUM/a4Ru71lyS2E3nrPYUSEkKqAki4HlCbF6EDtuVwZMaUR6jdTVO3LZQxmDfdFSlIWx1ERQiozSrpq9uZVMt6GpwAAhEI+6jY04ziiqksg5OPHDe2hZ6AYrhUVkYKl3/nT4gaEkApDSVfNCj7lOtY3YacnJNywtNbHjBWt2e3zR5/jxB80KT0hpGLQHV/NCk792OZLWw4jIXk6drdHj0G12e2fvr+CVx97lxNCiCpR0lUjhmH+055Li9Zrim8XNIetoxEAIDMjF7OG+yI7q4ot50cIqXCUdNXo5ZMExL5PB6BY/aYk67oS9ZDqCPHjxvYQiRW/Ek9CY7H+R5omkhCiWpR01ehmgarlOg2rQSCgb78mcaxnignzXNntA1tDaZpIQohK0V1fjQpO/diuC7XnaqKvhtdVGjv949gLiIpM4TAiQkhlQklXTWQyOW5fyZ+JyrU9tedqIh6Ph1mr28LcSjFNZHJiFmYN90VODrXvEkLKj5Kumjy9H4vkBMXECzq6QrbTDtE8hsYSLNjUAXyBYp7I0JvvsXnRTY6jIoRUBpR01aTgUKF6Tc3By5v4l2gk52YWGDuzKbvtvSEEAefCuQuIEFIpUNJVk4Lr57bvSu252mDAOGelZRfnj/XD+ze0DCAhpOwo6apBTo4MQVej2G2XdtSeqw34fB7mrm2rtAzgrOG+yM2lZQAJIWVDSVcN/g2KRkZaDgBA31AMq5oGHEdESsrIVIoFm9qz7bshN95hqxe17xJCyoaSrhoUbM9t6GrBYSSkLBq4WmL09Cbs9u51d3HVN4K7gAghWouSrhoUnPqxfTc7DiMhZTVoQgOlYV7zRvvhw1tq3yWElA4l3QqWkZ6DkBvv2O2mbaw4jIaUFZ/Pw5x1bVHNQgcAkBiXiTkj/6H2XUJIqVDSrWAhN94hJ1txYzapJmU75RDtY1JNBz9u7AA+X9G+G3Q1CtuX3+Y4KkKINqGkW8ECL+ZXLdNTrvZr1MISI79vzG7vXB2EGxciOYyIEKJNKOlWsJuXXrOvO/ag9tzKYPCkBmj28Q8ohgHmjPwHMe/SOI6KEKINKOlWoMS4DDy+FwMA4PGAxi2rcxwRUQWBgI9569vB1FzRvpsQm4k5o/6BTEbtu4SQT6OkW4Fu+b8FwyheV7fRh56BmNuAiMqYmOlg/ob2bPvubf+32LHiDsdREUI0HSXdClSwarngdIKkcmjSqjqGT2nEbv+64g4CL77+xBmEkKqOkm4FCryU34mqA43PrZSGftcQTVormg0YBpg9wpfG7xJCikVJt4K8DU/G6xdJAACBgId6Tcw5johUBIGAj/nr2yu1784c6kPr7xJCikRJt4LcLDD1o11tY4glAg6jIRXJ1FxHaf3de4HvsXFBIMdREUI0ESXdClKwba9t55ocRkLUoVELS6X1d/dtuod//g7jMCJCiCaipFsB5HJGaZGDNl/S+rlVwcDxzmjtYcNuLxh3ERHPE7kLiBCicSjpVoBnD+IQH5MBAJBIBajlZMJxREQdeDwe5qxth+o2+gCA9NQcTB98HhnpORxHRgjRFJR0K0DBoUJ1G5mxYzlJ5advKMbibR0hEit+tZ49iMNP3weAyRuwTQip0ijpVoDAAkm3Q3caKlTV1HauhimLW7DbJ/54gr/3PuYwIkKIpqCkq2LZWTIEX41it1vQpBhVUveBtdG5by12+6fvA9gpQQkhVRclXRULuvoWGem5AABDYwmsahpwHBHhAo/Hw/dLW8GhrjEAxR9j0wedR3JiFreBEUI4RUlXxa76RrCvXdrV4DASwjWpjhCLt7pBV08EAHjzKhnzRtPCCIRUZZR0VeyaT/7aqh5fOXAYCdEENg6G+GFVG3Y74FwEtnrd4jAiQgiXKOmq0JtXyXj5NAEAwOfz0KQVLeVHFJ3pBk1wZrd3rQ2Gz9HnHEZECOEKJV0VKli1bF/HGBKpkMNoiCYZM7Op0kpTC8ZfpI5VhFRBlHRV6KpPftJ160lDhUg+gYCPHze0h7W9omNdZnouvu9/jp1EhRBSNVDSVZGszFzc9i+wlF93e+6CIRpJ31CMpb92gs7HjlVRkSm0IhEhVQwlXRUJvhbFDhUyMBLD2s6Q44iIJrL7whjz17cD7+MkZXcC3mLdnOvcBkUIURtKuipSsD23aRsrDiMhmq61R02MmtaE3T74y30c/f0hdwERQtSGkq6KFBwq1KWvI4eREG0w9LuGaN81f/WpZVP8cd0v8hNnEEIqA0q6KlBoqFBrGipEPo3H42H2mrZwrKdYgUouYzBj0Hk8exDLcWSEkIpESVcFClYtO9SloUKkZHT0RPhppwfMqusCANJSc/DtV2cQHZXKcWSEkIpCSVcFlIcK0SxUpOTMqutixS4P6Ogp/lB7/yYV3319Bmmp2RxHRgipCJR0y+m/Q4Xad7P9xNGEFFbLyQSLt7qBL1B0aX58LxYzBvsgO5uGEhFS2VDSLaebl97krypkIqGhQqRMXNvXwLRlrdjt6/9EYt4oP1ocgZBKhpJuOV06/ZJ93cajJoeREG3XY2BtjPy+MbvteywMSyf7g2EYDqMihKgSJd1ykMsZ+J8NZ7e7etJQIVI+w6c0wtcjndjtY3seYcOPgRxGRAhRJUq65fDgzgfEvk8HAEikAtRras5xRETb8Xg8fLugOTr3rcXu27P+LnasvMNhVIQQVaGkWw4XT+ZXLTdpbQWBgL6dpPz4fB5+WNkGbb7Mb67YuuQWtv90m8OoCCGqQFmiHC6ffsW+7j7gCw4jIZWNQMjHgk0d0KzAlKK/LLuNrUtuURsvIVqMkm4ZvXySwM5CJRDw4Nq+BscRkcpGLBFg2W+d4NIuP/HuWHkHWxbfpMRLiJaipFtG//wdxr52amxGs1CRCiGRCrFshzuad8j/o27nmmCsm3sdcjklXkK0DSXdMvI9lp90ew2pw2EkpLITSwRYsr0TWrpZs/v2bbqHH8deoAk0CNEylHTLIPxZAp49iAOg6PTS5kuahYpULLFEAK9f3NC2c/6/tdOHnmJy3zNIS6EpIwnRFpR0y+Cfv1+wr+s2qgZdfRGH0ZCqQiQWYNGWDug5qDa774bfa4zpfByxH9I5jIwQUlKUdMugYNVyz8FUtUzURyDkY9ryVkozVz28G4Oh7Y/i6X1aFpAQTUdJt5TCnyXgSaji5sbjA+26UNUyUS8ej4cRUxtjxk+twPv4G/w2PAXDOv6FCydefPpkQginKOmW0pnDz9jXdRuaQc9AzGE0pCrrMagOlv3qDqmuoud8Rloupg08j19X3KEhRYRoKEq6pcAwDM78mZ90+45y+sTRhFS8Vu422PpXD1Svqc/u27b0FqYPOo+UpCwOIyOEFIWSbik8uPMBr18kAQCEQr5ST1JCuGJf2xi/HOuBJq2rs/sunHiJAa3+h0ch0RxGRgj5L0q6pVCwarlZWyuaEINoDEMTKVbv+VJphaLXL5IwtP1fOLLzX6puJkRDUNItodxcOXyOPme3+42px2E0hBQmEPIxeVELLNjUHjof23lzsuVYNsUfs4b5Iikhk+MICSGUdEvomm8E4qIzAABSXSGatKr+mTMI4YZbTwdsP9kLjvVM2H0+f4Whb7PDuHnpNYeREUIo6ZbQ396P2dduPexpGT+i0WzsDbHlaA+lceQf3qbhm24nsXrWVWRm5HIYHSFVF2WOEoj9kI6Ac+Hs9sDxztwFQ0gJiSUCTF/eCku2u8HQWMLu378lFANb/Q+P78VwGB0hVRMl3RI4deAJcnPlAABrOwPUrGXEcUSElFzbzrbYda6P0oIJLx7HY3Dbo9i1Npj9t00IqXiUdD+DYRgc35tftdxvTH0OoyGkbEzNdbB8pzumLWsFsVQAAMjNkWPTwkAMbf8Xnv8bx3GEhFQNlHQ/4+71d/mL1Qt56Px1LY4jIqRseDweeg2pg99O9UbdRtXY/Q+Do9G/5RH8uuIOcnJoqUBCKhIl3c84tP0++7qlmw109GhFIaLdbBwMsenP7hj7Q1MIRYpbQG6OHNuW3sKg1kfxOITaegmpKJR0P+HD21T4Hc+fQH7UtMafOJoQ7SEU8TFkUkPsONULTo3N2P1P78diUJv/YfOxnsjOoclfCFE1Srqf8L9dDyGTKWbysXEwRC0nU44jIkS17L4wxqYj3TBxvitEYsXtQCZjsPN0N/T3moP7L+w4jpCQyoWSbjGys2Q4uvtfdnv4lEYcRkNIxREI+Og/tj52nu2Dhs0t2P0voqww7KeZWDf3Go3rJURFKOkW4+yfT9kZqHT0hOjY3Z7bgAipYDb2hvj5QFdMXdICOhLFCkVyho+9G++hn8th3A54w3GEhGg/SrpFkMsZ/L7uLrvdZ1hdtsMJIZUZn89Dn2FOOPnTSrRxzh8qFxGWhDGdT2DxxEs0hzMh5UCZpAiXT7/Cq2eJABRL+A2Z1JDbgAhRM2vzeOyctR3LxhyEnl7+MKJj3o/Qu9EhnP/fc1q5iJAyoKT7HwzDYPe6YHbb4+ta0DMQcxgRIdzg8QDPjjfx555ouLXPYPfHfUjHrOG+mNz3DKIiUziMkBDtQ0n3P25dfoP7tz8AUNx0xsxowm1AhHDM3EyO1UvisWZpHMxM8ztUBZyLwFdNDuHAtlDIZDSVJCElQUm3AIZhsHnxTXa7lbsNqlnochgRIZqjY7tM/OkdDc8+qeDxFFXL6ak5WDXzKoZ3PIZnD2I5jpAQzUdJtwD/s+FKT7lTFrfgOCJCNIu+PoPZ05Lw2+ZY2Ntms/vv3/6AAS3/h82LApGVScOLCCkOJd2P5HIGW7zyn3LdetrDooY+hxERorkaOWfjj99iMH5UMkRCRdVybq4cO9cEo2+zw7jlT8OLCCkKJd2PTh14gmcPFCut8AU8fLugOccREaLZxGLgm5Ep+GNnDBo3yGL3R4QlYWyXE5g/xg8x79I4jJAQzUNJF0BqcjY2Lgxkt7v3/wImZjocRkSI9nCwy8WvG2Mxd3oC9HTzhxedOvgUPRscgPeGu8jOptWLCAEo6QIAdqy8g9j36QAAqY4QE+e7chwRIdqFzwf69k7Hn97RcO+QP7woLSUHP8+7AU+XP3HjQiSHERKiGap80n3xOB77t4ay25MWuNLyfYSUkbmZHCu94rFlbSzsauZ3tHr5NAHje5zCtIHn8PplEocREsKtKp10ZTI5Fo6/iNwcRUeQGrYG6DGwNsdREaL9Wrhk4cCuGEydmARdnfyq5QsnXqJXw4NY9cNVJMRmfOIKhFROVTrp/rH5ntIQoUVbO4DH43EcFSGVg0gEDBuYiv/ti0b3zuns/twcOQ5sDUU3p/3YuToI6Wk5HEZJiHpV2aQb9igeW7xusdtfj3TCF/WrcRgRIZWTWTU5vOYlYOeWGDSsn9/LOTU5G5sX30S3un9gz/q7SEvN/sRVCKkcqmTSTU/LwcwhPsjOUlR7VbPUwYS51HmKkIrUyDkbO7fEYs3SONja5CfYuOgMrJ9/A11q/4Fda4KQmkzJl1ReVS7pMgyD5VP88eJxPACAxwdW7PKgpfsIUQMeTzGd5KE9MZg3MwGW5vlVy4lxmdi06CbcHbyx6oeriHxBHa5I5VPlMs3+raE4dfApuz1xnisc65lyGBEhVY9QAHzdMx1/7Y/GvJkJqG6Rn3zTUnJwYGsoetTfjyn9zuLaPxG0oAKpNHhMKRbFdHV1RVBQUEXGU6H++TsMM4f4IK/EzTvUwMrfv+Q2KEI0UI3rRuzrlJrzKvzzcnMBnwu62P+nHiJeF15K09JaD32GOeHrkU6wr2NS4fGoVMHOmbQGcZVXZZLu7YA3mNT7NLIyFe24VjX1setcH0ikQo4jI0TzqDvp5mEY4HawBIf/0sPNO0XPCufsYoEvv6oFj69qaUctFSVdUkCVSLqBF19jSr+zyMxQrH6ibyTG3n++hpGplOPICNFMXCXdgiJeC3H8tB7O+0mQlFz0hDUOdYzRsac9WrrZwKVdDegZFH5K5hwlXVJApU+6fsdfYO6of9gnXIlUgB2ne8PG3pDjyAjRXJqQdPPk5gI3bklxxkcH129JIZMV3RVFKOSjQXMLNGtTAw1cLdDQ1QJWtgbcj72npEsKqLR1qwzDYOfqYKXl+qS6Qmw/0ZMSLiFaRCgEOrTNRIe2mUhO4eHGLSn8r0gQeEeKrGwBe1xurhz3At/jXuB7dp+puQ7qNjJDLScTONRVfNk6GsHcShcikaCojyOkQlXKJ93YD+lYNOEirpyPYPfpG4qx7e8esLajhEvI52jSk25xMrOA4BAJgu5KcDtYiBfhJV8ZjMcDqlnqwtJaH9Wt9WFpowczSz0YmkhgYCSGkYn042vFtlRXCImOECIRv/RPzvSkSwqoVE+6DMPgzKFnWDvnGuJj8ud1rVnLEJuOdIehsYTD6AghqiSVAG1bZaFtK8UsV4lJfITcF+PRYxH+fcTH0zAp0jOKvsUxDBD7Ph2x79PxMDi6xJ8pEPAg0RFCqiOEVFcIqVTIJmQd3fz9Onqi/G1+V+ggGzrIhnT/E+joCaGjK4JUVwhDYwmMTKUwMpVAqiPkviqcVLhK86QbdPUtNi26iZAb75T2u/d2wOw1bWnyC0JKQRuedD9HLgfeRAkQHiFCeKQQL1/xER7Bx/toIZJSxGAYzUpwYokARqYSGJlIFYnYRAJTCx2YWerB3EoX5tX1YFZdF+bVdWFWXQ9iCVWPayOtftLNzZXD/8wrHNgWijtXopTek+oKMX9DO7TxsOUoOkIIl/h8wNZGBlsbGTq0VX4vJweIjRcgJlaA6BgBPkQLkJjIQ2ISg+QkIDmFh5RUPtLSBUhLFyA7h4/sHD7k8or74z07S4aYd+mIeZf++YMBGJpIYGGlB0sbfVjW0IOltT4srPVgWUMfltb6sLTWg3E1KT09axitS7oymRyhtz7gn7/D4PtXGKKj0pTe5/GAzn0dMXlRC+jq07q4hJDCRCLAylIGK0vZ5w8uIDcXyMzkISubh6wsxVdmgf9nZvGQmVng/5k8yHcFIgMSZEKERI9GyMgAMjKBjHQ+UtP4SEkTICVNhNzc0iX05IQsJCdkIexRfLHHiCUCWFp/TMgfE3Pe6+o2iv+bW+lBKKSaQHXR+KQbF52Ox/di8DgkBv8GReN2wFskJ2YVOo7HA5p3sMbkRS1Qw86Ag0gJIZWdUAjo6zPQR8k7RBn8dpZ9nbLIochjGAbIyuIhOYWHpGQ+klP4SErmIz5BgPh4PmJieYiOAeLi+YhPECIxWVSip+7sLBlev0zG65fJxR7D5/NQzVJHkZBrKDqVWdT4+PT88Snawlofunr0EKMKnCXd7GwZkuIykRSficT4TCTGZSI+NgPvIlPxLjIFUZEpeP0yqdCT7H9JdITo/HUtDJ7YAJbW+mqKnhBCVIfHA6RSBlIpAwvzz88zLZMpOo7Fximqx2Pi+IiO4eP9ex6io4HoWCHiEkTIyPx8opTLGbZa+18U36lMIhWwbc2K/ys6gBmZSmFoLIGeoRi6ekLo6omhoyeErr4IOnoi6Oop/i/VEUAkVnwJRXzw+VWz2puTpPvhbSrcHfaW+XwdPRGatKqO3sPqoGkrK+okRQipUgQCoJqpHNVM5ahbO6fY49LSeWy7dUysANHRPLx7zyD6Aw/RcQLEJYiRlFKyUR1ZmTJER6V99kGopIRCPkQSAURi/sdkrPi/UMADj88D/+NX/muUeL9DXRPMX99eJXGqGidJtzTTL/L4gLGJFDUdjVC/mTm+qG+K6jb6bOeAl08TKipMQqqsGgVeJ34o+ZAaUljBxi4uvpfGEsDYBqhjU/T7Obk8xCcIERsvRlyCCLHxIjZJx8aLEJ8oRkKyFLky1faWzs2VIzdXjgzV5HAlSfGZqr+oipRqyJCZmRns7e0/eUxMTAzMzc1LdL2k+Ey8Cc9va+DzeRAI+RAItK/aISs3BRJh1WxLrqplr+zl5gEw1E0Gn1e4ujMlIwsGOlVv3HtVLTfwubIzHyf+KPD/EmDY//CKPaPgMSVlVMMWYqlqfk4xMTGoV68efHx8VHK9UiXdktDksbwVqaqWG6i6Za+q5QaqbtmrarmBqlt2VZebGkMJIYQQNaGkSwghhKiJypPuhAkTVH1JrVBVyw1U3bJX1XIDVbfsVbXcQNUtu6rLrfI2XUIIIYQUjaqXCSGEEDUpUdJ98+YNpk6ditatW0NXVxc8Hg/h4eGfPS8oKAgTJkyAk5MTdHV1YWtri2HDhuHVq1fljVstylru/1q1ahV4PB7atWun+iArSHnL/vjxYwwYMABmZmbQ0dFB3bp1sXnz5ooLWEXKU+7IyEiMGjUKtra20NXVRZ06dbBw4UKkpVXAQEQV++uvv+Dp6Qk7Ozv25zV//nykpKR89tzMzEzMnj0bVlZW0NHRQevWrXHlyhU1RK0aZS27tt/fyvMzL0gb72/lLXt57m8lSrphYWH43//+BxMTE7RvX/JZPv788088fPgQ33//Pc6fP4/Vq1fj7t27cHV1xevXr0t8Ha6UtdwFvXz5EitWrICFhYWKo6tY5Sl7UFAQWrZsiaysLOzevRvnzp3DDz/8AJmsdJPLc6Gs5U5LS8OXX36JK1euYPny5Th79izGjRuH9evXY+zYsRUYsWr8/PPPEAgEWLlyJXx8fPDtt9/i119/RefOnSGXf3pawm+++Qa7du3CsmXLcObMGVhZWaFr1664d++eeoIvp7KWXdvvb+X5mefR1vtbecpe7vsbUwIymYx9vWvXLgYA8+rVq8+eFx0dXWhfeHg4w+PxmEWLFpXkozlV1nIX1KVLF2bChAlMx44dmbZt26o4wopT1rLLZDKmfv36zNdff12B0VWcspbb19eXAcD4+voq7Z87dy4jEAiYtLQ0VYeqUkX9ru7bt48BwFy8eLHY8+7du8cAYPbs2cPuy8nJYerUqcP07t27QmJVtbKWXdvvb2Utd0Haen8ra9lVcX8r0ZMun1+2pt+iZqays7ODubk53r59W6ZrqlNZy53n0KFDuHv3LlatWqWiiNSnrGX39/fHo0ePMHPmTBVHpB5lLXd2djYAwNDQUGm/sbEx5HI5GA3vr1jU72rz5s0B4JO/q6dOnYJIJMKgQYPYfUKhEIMHD4avry+ysgqvCKZpylp2bb+/lbXcebT5/lbWsqvi/qb2jlSPHz9GdHQ06tWrp+6PVquEhATMmDEDa9euhampKdfhqM21a9cAKNr5WrVqBZFIBAsLC3z//ffIyMjgOLqK8+WXX6J27dqYO3cuHj16hNTUVFy6dAmbN2/GpEmToKenx3WIpRYQEAAAn/xdffjwIRwcHKCrq6u039nZGdnZ2QgLC6vQGCtKScpeFG2/v5W03JXx/laSsqvi/qbWpJubm4tJkybB3Nwc33zzjTo/Wu1mz56NOnXqYPTo0VyHolZRUVEAgEGDBqFLly7w8/PDnDlzsHv3bgwdOpTj6CqOVCrFtWvXIJfL4ezsDAMDA3h4eKBXr17Ytm0b1+GV2tu3b7F48WJ8+eWXcHV1Lfa4+Ph4mJiYFNqfdyOOjy9+gXVNVdKy/5e2399KU+7Kdn8radlVcX9T6ypDU6ZMwY0bN3D27Nkif1Eri6tXr+KPP/7A3bt32dWQqoq8TgjDhw/HsmXLAABubm6QyWSYN28eHj16hPr163MZYoXIzMzEoEGDEB0djf3798PW1ha3b9/GsmXLIBQK8euvv3IdYomlpqbiq6++glAohLe39yePZRimyH/jml6dXpzSlP2/tPn+VppyV7b7W2nKror7m9qedOfPn4+dO3diz5496NKli7o+lhMTJ07EN998AxsbGyQmJiIxMRG5ubmQyWRITEzUinausqpWrRoAoHPnzkr7837m2tKjtbR+//13+Pv749y5cxg+fDg6dOiAWbNmYf369dixYwdCQ0O5DrFEMjMz0adPH7x8+RK+vr6wsSlmPbiPTE1Ni3yaTUhIYN/XFqUte0HafH8rbbkr0/2ttGVXxf1NLUl3xYoVWL16NTZv3owRI0ao4yM59fjxY+zYsQMmJibs1/Xr13Hz5k2YmJho1VNPaTk7OwNAob+A8558yts5TVM9ePAAJiYmcHR0VNrfokULAIp/E5ouJycHnp6euH37Ns6dO4eGDRt+9hxnZ2e8evUK6enpSvsfPXoEsViML774oqLCVamylD2PNt/fylLuynJ/K+u/d6B897cKr17esmULFi5ciBUrVmDq1KkV/XEa4fLly4X2TZ8+HTKZDFu3btWaG1FZdO/eHRKJBD4+PujVqxe739fXFwBK1UamTapXr46EhASEhYUp/Xxv3boFALC2tuYqtBKRy+UYNmwYLl68iLNnz6JVq1YlOq9Pnz7w8vLC0aNHMWrUKACKts0jR46gS5cukEg0f+3ZspYd0O77W1nLXRnub2Utu0rubyUdW3T06FHm6NGjzKRJkxgAzPbt25mjR48y/v7+DMMoxqcJBAJm6dKl7DmHDx9meDwe061bNyYwMFDp6+HDh2Ue56ROZSl3UbRtHBvDlL3sS5YsYQQCATN//nzGz8+PWbVqFSOVSplRo0ZxUIrSK0u5X716xRgYGDC1a9dm9u7dy1y6dIlZu3YtY2BgwLi4uCiN/9VEeWVdsGBBod/V169fMwxT/M970KBBjLGxMbNr1y7mwoULjKenJyORSJjg4GAuilJqZS27tt/fyvMz/y9tu7+Vp+zlvb+VOOkCKPKrY8eODMMobjoAGC8vL/acUaNGffY8TVeWchdF2/5RMkzZyy6Xy5n169czjo6OjEgkYmxtbZlFixYx2dnZ6i9EGZS13A8fPmQGDBjA2NjYMFKplKlduzbzww8/MPHx8eovRCnZ2dkVW+68chZX7vT0dGbGjBmMpaUlI5FImBYtWjCXL19WexnKqqxl1/b7W3l+5v+lbfe38pS9vPc3WmWIEEIIUZPK2auFEEII0UCUdAkhhBA1oaRLCCGEqAklXUIIIURNKOkSQgghakJJlxBCCFETSrqEEEKImlDSJURFeDweeDwe+Hw+Xrx4UexxnTp1Yo/du3ev0nujR49m3/Py8ir2Gvv27WOPc3NzU3rP39+ffS/vS1dXF1ZWVujQoQNmz56NkJCQ8hSVEFJGlHQJUSGhUAiGYfD7778X+f7z588REBAAofDT054LhULs2bMHMpmsyPd37dr12WvY2dnBy8sLXl5emD59Onr16oW0tDT8/PPPaNasGYYNG4bU1NSSFYwQohKUdAlRIUtLS7i6usLb2xu5ubmF3t+9ezcYhlGaLL0ovXr1wps3b+Dj41PovcePH+P69evo3bv3J69hb2+PJUuWYMmSJVi5ciV27dqF4OBghISEoGHDhjh06BAGDBhQugISQsqFki4hKjZ+/Hi8f/8eZ86cUdqfk5ODffv2oU2bNuwSYcUZNmwYdHR0sGvXrkLv5e0bN25cmeJr0qQJLly4AHNzc/j4+ODEiRNlug4hpPQo6RKiYkOGDIGenh52796ttP/UqVP48OEDxo8f/9lrGBsbY8CAATh79izevXvH7s/KysIff/yBjh07ok6dOmWO0cLCAhMnTgQAHDx4sMzXIYSUDiVdQlTMwMAAgwcPho+PD968ecPu37VrFwwNDTFw4MASXWf8+PHIzc1V6mx1/PhxxMXFlShxf05eB6zbt2+X+1qEkJKhpEtIBRg/fjxkMhn27NkDAIiIiICfnx+GDRsGXV3dEl2jXbt2qFevHtsODCgSt4mJCTw9Pcsdo7W1NQAgJiam3NcihJQMJV1CKkDLli3RsGFD7NmzB3K5HLt374ZcLi/1E+q4cePw8uVLXLp0CWFhYbh8+TJGjBgBqVRa7hjzEjmPxyv3tQghJUNJl5AKMn78eERERMDHxwfe3t5wcXFB06ZNS3WNkSNHQiKRYPfu3ewTryqqlgEgKioKAGBubq6S6xFCPo+SLiEVZMSIEdDR0cHEiRPx9u1bTJgwodTXMDMzQ9++fXH8+HHs2bMHrVu3RoMGDVQS3+XLlwEonsoJIepBSZeQCmJsbIz+/fvjzZs30NPTw5AhQ8p0nfHjxyMrKwsxMTEqe8qNjo7Gb7/9BkAxPIkQoh6fntKGEFIuP/30E/r16wdzc3MYGBiU6RqdOnXCyZMnIZfL0bVr13LHFBoaipEjRyI2NhY9evRAnz59yn1NQkjJUNIlpALZ2trC1ta2XNfg8XhlSozh4eFYsmQJAMXEHLGxsQgODkZwcDAAxRNu3tMuIUQ9KOkSUklFRERg6dKlAACpVApjY2PUrl0bs2bNwrBhw9CkSRNuAySkCuIxeeMGCCGEEFKhqCMVIYQQoiaUdAkhhBA1oaRLCCGEqAklXUIIIURNKOkSQgghakJJlxBCCFETSrqEEEKImlDSJYQQQtSEki4hhBCiJpR0CSGEEDX5Pw1q11w+5vglAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc816fb13984c6a848798e7898b2e95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YXz7VvF1if7lV8/47bZ+enta1a9dWfY2qDI5fsVhMmUym3DM+NfaXTzVvl9hfbtW8fz228yU1AIAJggMAMHFPBieRSJR7gi/sL59q3i6xv9yqef96bL8nv4cDALB3T36GAwCwd88FZ3x8XE1NTfI8TwMDA+Wes6qGhga1tLSora1NsVhMknT9+nV1dHSosbFRHR0dunHjRplX/ltvb69qa2u1bdu2hfvutLe/v1+e56mpqUm/+tWvyjF5keX2f//739fWrVvV1tamtrY2jY6OLjxWSfvz+byeeOIJNTc3KxqN6vjx45Kq5/qvtL9arv+HH36onTt3avv27YpGozp69Kik6rn+K+1f1+vv7iEff/yxi0Qi7tKlS+6jjz5yra2t7uLFi+WedUdf/OIX3ezs7KL7XnrpJdff3++cc66/v99973vfK8e0Zf32t791b731lotGowv3rbT34sWLrrW11X344Yfur3/9q4tEIu7jjz8uy+7bltt/9OhR96Mf/WjJ2Urbf/nyZffWW28555x7//33XWNjo7t48WLVXP+V9lfL9f/Xv/7lbt686Zxzbn5+3u3cudP94Q9/qJrrv9L+9bz+99RnOOl0Wp7nKRKJqKamRt3d3UqlUuWe9R9LpVLq6emRJPX09Oj06dPlHfR/fOUrX9HnP//5RfettDeVSqm7u1uf+cxn9KUvfUme5ymdTltPXmS5/SuptP2hUEg7duyQJD3wwANqbm5WsVismuu/0v6VVNr+QCCgz372s5KkW7du6datWwoEAlVz/Vfav5JPs/+eCk6xWFR9ff3C7XA4fMf/oStBIBDQU089pccee0zJZFKSdOXKFYVCIUmf/CO9evVqOSeuaqW91fT3MTg4qNbWVvX29i58SaSS909PT+vtt9/W448/XpXX///ul6rn+pdKJbW1tam2tlYdHR1Vd/2X2y+t3/W/p4LjlvmBvDsVvBKcP39ek5OTGhsb0+uvv67f/e535Z60bqrl7+PQoUO6dOmS3nnnHYVCIX33u9+VVLn7P/jgA+3bt0+vvfaaNm7cuOK5atlfTdf//vvv1zvvvKNCoaB0Oq133313xbPVsn89r/89FZxwOKx8Pr9wu1AoqK6uroyLVnd7X21trZ555hml02lt2bJFMzMzkqSZmRnV1taWc+KqVtpbLX8fW7Zs0f3336/77rtP3/zmNxe+bFCJ+2/duqV9+/Zp//79evbZZyVV1/VfaX+1XP/bPve5z+mrX/2qxsfHq+r63/b/96/X9b+ngtPe3q5cLqepqSnNz89rZGRE8Xi83LNW9Pe//103b95c+O8333xT27ZtUzwe19DQkCRpaGhIe/fuLefMVa20Nx6Pa2RkRB999JGmpqaUy+W0c+fOck5d1u0PFpL0y1/+cuEn2Cptv3NOL7zwgpqbm/Xiiy8u3F8t13+l/dVy/WdnZzU3NydJ+uc//6lz587pkUceqZrrv9L+db3+6/lTDtXg7NmzrrGx0UUiEffKK6+Ue84dXbp0ybW2trrW1lb36KOPLuy9du2a2717t/M8z+3evdv97W9/K/PSf+vu7nYPPfSQCwaDbuvWre6nP/3pHfe+8sorLhKJuIcfftiNjo6Wcfknltv//PPPu23btrmWlhb39NNPu8uXLy+cr6T9v//9750k19LS4rZv3+62b9/uzp49WzXXf6X91XL9//SnP7m2tjbX0tLiotGoe/nll51zd/73Wg371/P685sGAAAm7qkvqQEAyofgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE/8LaDBMDsHNza4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35fae86ccc7546ebb5cc8cc89a21897c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fb1de6b5ec14d839f4bf82043c6b2a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86864a29207440d4a6314df30e06075e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b233348c3ec4440398769f101ce99350",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44d5dd1f6ed04dc4a2d35ac1b4300cb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "728187666c6643eb975a366adcf040a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3702ca6f3edc4978b829724ec19ba835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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yySfsyZMnSvcpeR9z69at7NChQ6xLly7MyMiImZubsxEjRrD79+/LlY+MjGTz5s1j3t7ezMbGhonFYubs7Mz8/f3ZhQsXlH7HuXPn2KhRo5i9vT3T19dnNjY2zNPTk82bN49dLfcuoLOzs8K7kRVp3rw5A8CsrKxYfn5+heXy8/PZxo0bWdeuXZmZmRkTi8WsadOmzM/Pj61bt07hndL8/Hy2bNky5urqytVv0aJFLC8vr1rvgTLGmFQqZRs3bmTu7u5MLBYze3t7Nnv2bJaRkVFhXbdu3co8PT2ZkZERs7a2Zm+99Ra7desWW7p0KQPAQkJC5Mq/ePGCjR07ltnZ2TGhUMgAsKVLl3KfX7hwgfn6+jILCwtmYmLCunfvzvbv389CQkIUytaJOn4PVMAYU/nBW6dOnXDt2rXayeT1FGMMM4YE4eIJWTOIsak+tp54C5bW8u+67fn9Djavvs6tf/ljH4ye0bZOY9Vpu8p0DhlX82fJJb0wCSE6pGx+6tSp0qKauMbpGWgtOx30mEueALD8V1+F5AkAb0/3wOAxLbn17xacR1x0Rl2ESAghRA2UQGuRVMrw4/LL3HrvQc5o31n5+14CgQCzv3gNzs1l3dPzXhVhxYehqEYDASGEkDpECbQWnTzwEA9uy164FooEeP/LzpWWF0tE+Oz7Htw7oWGnnuDUQcUZKAghhPCPEmgt2v7DTW65/3A3pU235bXwsMaQcWWacj89j8JC5e9hEUII4Q8l0Fry4HYK976nQABMmeel8r6T53rB1EI2FdHT2Czs3xZVxR6EEELqGiXQWvLX73e45bbedrC2M6qktDwzCwlGTy/tgfvziivIz1N8UZsQQgh/KIHWgoL8Yhz96wG3PvFDz0pKK/fmRHdY2cqafJOf5+Lw7gdV7EEIIaQuUQKtBeeD45CVKZsmyNRcDK8u1ZtpAQAMDPUwcmobbn3L99dprFxCCNEilEBrwdH/RXPLfQa7qDSfojKDRreAkbFs1obY6AycOxZXxR6EEELqCiVQDcvNKcSZw6UDSA8d10rtYxmbivHG6Bbc+pY11yspTQghpC5RAtWwiyfi8SpX1uHH0sYArq0sa3S8EZPdIRTJ7mCvnUtEZERSjWMkhBBSc5RANSz0aCy33Gugc42PZ9fEBH0GuXDrW9dG1PiYhBBCao4SqAZJpQyh/5Y+p+z7ZrNKSqtu5LulnYmO73uItORXGjkuIYQQ9VEC1aA74UlIfZELAJAYiNCqvY1GjtvCwxrunrJjFRVJEbTjnkaOSwiRmTJlCgQCgdIJvfni4uLCTVpNtBPN2qxBZ8s033r6NObGtNWEwaNb4N7NFADAX7/dxuSPvNTu3Us04OaXQG4831FUzMgJ8Fxe48O4uLggLk557+9GjRrh+fPnctsKCgqwfPly7Ny5E2lpaejYsSPWrFmDjh07Kux/4sQJ9O/fH4cPH8bgwYNrHKuu6dOnD0JDacIIXUYJVIPCTpdOW/b6W5ppvi3Re7ALfvn6GnJzCvHk0UuEn09Ep54OGv0OUg258YCxC99RVCwnVmOHMjc3x0cffaSw3cTERGHb559/jnXr1sHf3x+Ojo74888/4evri3v37sHe3p4rl52djenTp2PChAlakTxXrlyJzz//HA4O2nNNnTp1iu8QSBUogWpITlYB7lwt7SHbsZt9JaWrz9BIH75DXXHkvxGO/rfpDiVQUicsLCwQEBBQZTnGGH777Te888472LJlCwBg+PDh6NOnD/788098+umnXNnPPvsMeXl52LBhQ22FXS329vZyCV4buLm58R0CqQI9A9WQa+cSUVQkBQDYNDaCuZWBxr9j8JjSd0KD9z1ERlqexr+DEHUlJycjNzcXnTuXTttXsly2Gfjs2bP45Zdf8NNPP8HKykqt7yp5Pvjy5Ut8/PHHcHFxgb6+vlyiv3fvHqZMmYKmTZtCIpGgUaNGGDduHO7fv69wvMqegV6+fBkjR45E48aNIRaL0bRpU8ycOROJiYlKY0tLS8PixYvRtm1bGBkZwdzcHJ6envj888+Rk5OD2NhYCAQChIaGApDNBVzyp0+fPgp1LC8/Px+rVq1C+/btYWRkBDMzM/Ts2RP/+9//FMqWfNeUKVMQGxuLMWPGwMbGBgYGBujUqRMOHz5c+Q+6nAMHDmDChAlo2bIljI2NYWJiAm9vb/zwww+QSqUK5Ut+ro8ePcLGjRvRvn17GBoaytUzLS0NCxcuROvWrWFoaAhzc3P07dsXx48fVzheZmYmVq9eDT8/Pzg6OkIsFsPW1hbDhg3DpUuXqlUXTaA7UA25FFLafOvdo0mtfEcLD2u08LBC9N00FBVKcWT3fYx/v/rj7BJSHfn5+dixYwfi4+NhbGyM9u3bo1evXhCJRHLlbG1tYWhoiPDwcG7btWvXAADOzrJXul69eoVp06bB398f/v7+NYqroKAAfn5+SEtLQ//+/WFmZgZXV1cAwLFjxzBixAgUFhZi6NChaN68ORISEvDPP//gyJEjCAkJUfpctrytW7di+vTpkEgkGDZsGJo2bYro6Ghs3rwZhw4dwqVLl+Dk5MSVf/z4MXx9fREXFwdvb2+89957kEqlePDgAdatW4dZs2bBwsICS5cuxbZt2xAXF4elS5dy+1fVaaigoAADBgxAaGgo3N3d8f777yM3Nxd///03Ro8ejRs3buCbb75R2C8uLg6dO3dGs2bNMHHiRKSlpWHPnj148803cfLkSfj6+qr0M//8888hFArh4+MDBwcHZGZm4vTp05g7dy6uXr2KP//8U+l+c+fOxblz5zB48GAMGjSI+92Ji4tDnz59EBsbi549e2LgwIHIycnB4cOHMXDgQPz222+YPn06d5yoqCgsXrwYvXr1wuDBg2FpaYn4+HgEBQXh33//xaE1azCwWzeV6qIJlEA15NLpBG655wCnSkrWzBujWiD67mUAwP7AKEqgpNY9f/4cEydOlNvm6uqKrVu3onfv3tw2gUCAGTNm4IcffkBmZiYcHBzw559/wtTUFOPHjwcAfPHFF0hLS8NPP/1U47iePXuGNm3aIDQ0FMbGxtz29PR0jB07FkZGRjh79izatCl9Dezu3bvw8fHBtGnTcP165SN7PXjwADNnzoSLiwtCQ0Plno+ePn0a/fr1w9y5c7F//35u+4QJExAXF4dvvvkGCxculDteSkoKTExMYGBggICAAJw5cwZxcXEqNY+XWLNmDUJDQ/HGG28gKCgIenqyf8KXLl2Kzp07Y+XKlRgyZAi6lUsiZ86cQUBAgFyyHjduHAYOHIjVq1ernECPHDmi0LQslUrxzjvvYPv27ZgzZw58fHwU9rt+/ToiIiK4/+CUmDx5MuLi4rB7926MGTOG256RkYE+ffrgww8/xLBhw9CoUSMAQOvWrZGYmAgbG/k3HBISEtC5c2fMW7euThMoNeFqwMuMfETfSQUgm/uz/WuNau27eg92gZ6+7LRF3UjB4/vptfZdhLzzzjs4deoUnj9/jpycHNy+fRszZ85EbGws3njjDdy8eVOu/LfffovPPvsMV65cwR9//AEPDw+cOnUKDg4OuHz5MtavX48NGzbAxsYGy5Ytg729PfT09ODt7Y0LFy5UO741a9bIJU8A2L59OzIyMrBs2TK55AkAHh4emD59OiIiIhAZGVnpsX/55RcUFhZiw4YNCp2L/Pz8MGzYMBw6dAhZWVkAgPDwcFy8eBFeXl747LPPFI5X0nRaE1u2bIFAIMDatWu55AkAdnZ2WLJkCQBg8+bNCvs5Ozvjiy++kNs2YMAAODk54cqVKyp/v7LnskKhEHPnzgUABAcHK93v008/VUieN2/eRGhoKPz9/eWSJyB77r5s2TLk5eVh37593HZzc3OF5AkAjo6OGDlyJO7FxiK+XM/w2kR3oBpwI+wZSnqi2zUxhuF/A8DXBjMLCXz6OODCCVmT8aFd9/Hhsi619n2kYSt7xwIAbdu2xa+//goTExOsWbMGAQEBcndgEokEK1euxMqVK+X2KygowNSpUzFo0CCMHz8e69ev5+6Iunfvjq+//hoDBw5ETEwMd7dRFQMDA7Rv315he1hYGADZP9DK7u4ePJB1xIuKilJIsMqOExoaiqtXryp8npSUhOLiYjx48ADe3t7cM7gBAwZAKNT8vUlWVhZiYmLg4OAAd3d3hc/9/PwAABERiqOVeXl5KTS5A0DTpk25eqoiNTUVq1evxtGjR/Ho0SPk5OTIff706VOl+5V9Ll6i5HszMzOVnqfk5GQAsvNU1oULF7BhwwaEhYUhKSkJBQUF8jEkJcGpcfVnwFIHJVANuH7hGbfs6VP7J67vm824BLo/MApzlvpo9J1TQqoya9YsrFmzBmfPnlWp/LJly/D06VOuY8jq1avRt29f7h/OVq1awcXFBT/99BOWL1ft/VU7Ozul70KnpspagzZt2lTp/tnZ2ZV+XnKc1atXq3ScjIwMAKi1V2EyMzMBoMLewiXbS+Ioy8LCQuk+enp6Sjv/KJORkYHXXnsNjx8/RufOnTFp0iRYWVlBT08PGRkZ2LBhA/Lz85Xu21hJQiv5+Z44cQInTpyo8HvLnqf9+/dj5MiRMDAwQL9+/eDm5gZjY2MIhUKcOXMGoaGhyC8sVKk+mkAJVAPCL5T2xuvi61jr3+fTxxEmZmJkvyxAUmIOIsKewbt77XRcIkQZOzs7AFC4A1EmIiIC3333HX799Vc4ODjg5cuXSExM5J6LAoCTkxNsbGxw9+5dlWOoaCARc3NzALI7UGV3qKoqOU5mZibMzMyqLF+SpCq6C6upknjKD15R4tmzZ3LlNG3z5s14/Pgxli5dqnDHGBYWVukrScrOVUmcGzZswIcffqhSDEuWLIFYLMa1a9fQunVruc9mzpzJ9WyuK/QMtIby84pw++oLbr1tJ7ta/06xRITeb5QOVE9D+5G6VtL81qxZ5QOGFBUVYerUqfD19cW7774r91n5u5W8PM28ltWli+yRxrlz5+r0OCXlg4ODVbqrK2lSLS4uVun4pqamcHNzw9OnTxEdHa3weUhICACo1LtYHTExMQCgtPe0OolLnfMUExODNm3aKCRPqVSK8+fPVzuGmqIEWkNRN5JRWCC7WMytJLC0MayT7y070tHRPdEoyFftIiREVXfv3kVaWprC9ri4OMyZMweArNdpZVauXImYmBi55lQzMzM4ODjg2LFjKCqSTf0XGhqKrKwseHh41Djud955h+uEoqyDjFQqxZkzZ6o8zpw5c6Cvr4958+Zxz03LKigokPvH39vbG926dcONGzfw7bffKpRPTU2V+0+CtbU1ACA+XvUhIadOnQrGGBYsWCCXeFNSUrBixQquTG0oecWm/M8uIiJC4Zm3Kjp16oSePXvin3/+4QbeKO/27dtISiodoMbFxQXR0dFy7+AyxrBs2bIqO4XVBmrCraE710pPbsu21nX2vR7edmjsaILnCdnIzS5E6NFY9BtOI5cQzdm7dy9WrVoFX19fuLq6wtTUFA8fPsSRI0eQl5eHQYMGYf78+RXuf/fuXXz11VdYu3Yt9x5oiU8//RRz585Fz5494ePjg507d8LExATvv/9+jeO2trbG33//jeHDh6NLly7o27cvPDw8IBQKER8fj7CwMIVkpoy7uzu2bNmCqVOnwsPDAwMHDkTLli1RWFiI+Ph4nDt3Dra2trh3r7QFaMeOHejTpw8WLVqEffv2oU+fPmCMITo6GsePH8e9e/e4RNS3b1/s3bsXI0aMwKBBg2BoaAhnZ2eFV4bKmj9/Pv79918cPHgQnp6eGDRoEHJzc7F3714kJSXh008/RY8ePWr8M1Rm0qRJWL16NT766COEhISgRYsWiI6OxuHDhzFixAjs2bOn2sfctWsX/Pz88O677+KHH36Aj48PLCwskJCQgFu3buHOnTsICwvjHhnMmzcPs2bNQocOHeDv7w99fX1cuHABkZGRGDp0KA4dOqTpaleKEmgNlU2gHevwOaRAIEDfYa7Y+fNtAMDBP+9RAiUa5evri/v37yMiIgJhYWHIycmBhYUFevTogYkTJ2LixIkVPocsLi7G1KlT4ePjg9mzZyt8/sEHH+Dly5f49ddfcf36dXh6emLdunUq98CtSt++fXHr1i18//33CA4Oxrlz5yAWi9GkSRP4+fmpPIjDhAkT4OnpiTVr1iAkJATHjx+HsbExmjRpgpEjR2L06NFy5V1dXXH9+nV89913OHDgAH788UcYGBjAxcUFn3zyCZcIAGDatGmIi4vDX3/9he+++w5FRUXo3bt3pQlULBbjxIkTWLt2LXbt2oWNGzdCT08Pnp6eWL9+PcaOHaveD0wFTZo0wblz5/D555/j/PnzCA4Ohru7O37++We8/vrraiVQR0dHhIeHY+PGjdi3bx927tyJ4uJiNG7cGG3atMEHH3yAdu3aceVnzpwJiUSC9evXIzAwEIaGhujZsye2bt2Kffv21XkCFbBqTAXQqVMnbmQRIjOk7Q48fpABANjwvzfQpoNtnX13wuOXeKf/AQCASCTA2afvwqIWhhDUCbvK/EM+ruazW0RFRSk8Z5HTQGZjaSjGjBmDPXv2IDExUevGxCXVUDY/depUadEqr3EV0B1oDWRl5nPJUyAAmrdRb1xPdTm6msG9vQ3u3UpBcTFD8N8xGD2jbZ3G0GBRcqpXHjx4AIlEAlvbuvsPMNF91ImoBu6Glzbf2jUxhlii+KJybev7ZunoHod2Kg6STQip2IYNG+Dv74+IiAi8+eabcqP7EFIVSqA1cLvM88/WXvz8z7X3IBduEIWIsGd49iSLlzgI0UXr16/HpUuXMHXqVPz22298h0N0DP13qwbuXCt9/7ND17oZOqo8SxtDdOjWGOHnZS9R//u/aEz9pHbeAyOkvnn8+DHfIRAdRnegNXCnTBNuq/aKAxzXFb+hpe+E0qAKhBBSNyiBqinlRS6eP5GN0SjSE8ClhQVvsXTv1xT6YtmpjL6bhodRii+/E0II0SxKoGoq23xr39QUIj3+fpTGpmK5MXiP7lEc5osQQohmUQJVU9kBFDw68t/13XdoaW/coJ33UI3XewkhhKiBEqiayj7/9OKpA1FZPn0cYWQim4c0MS5LrocwIYQQzaMEqqaoG8nccqt2/HUgKiGWiNBzgBO3fuQvxcGvCSGEaA4lUDWkJuUi5XkuANkQek2cTXmOSMavTDPukV33UVys2kS5hBBCqo8SqBru30rhlu0cTCASaceP0bNLY1jayMbCTU/Nw5UztTOxLyGEEEqgaimbQFvU8fi3lRGJhOgzuPQu9PBfNLQfIYTUFkqgarh/K5VbbvuaXSUl657vEBdu+fjfD5GfV8RfMIQQUo9RAlWDtt6BAoC7pw3sm5oAAHJzCnH23zieIyL1Qb9+/SAQCBAaGlphmVmzZkEgEGDTpk11GBkh/KEEWk0F+cV4dC+dW3dtZcljNIoEAoF8ZyLqjUs04Pr16xCJRPD29q6wzOXLlwHI5g0mpCGgBFpND6PSUFQk691qZiGBsamY54gUlR1U4czhx8jKzOcxGqLrHj58iLS0NHh4eMDExERpmdzcXNy5cwcSiQRt29KctKRhoARaTWWbb51bmPMYScWcm1vArbXszriwUIpTBx/xHBHRZdeuXQMA+Pj4VFgmPDwcRUVF8PT0hL6+fl2FRgivKIFW04M7pR2I2vA0B6gqyjbjHt5NzbhEfaok0EuXLgGg5lvSsNB8oNVU9g60lSf/IxBVpM8QV2z67joA4FLIE6S8yIVNIyOeoyK6qCSB7tmzByEhIUrL0PNP0hBRAq0GxphcAnVz164euGXZ2Ruj3WuNcPvqCzApEPx3NMa/78l3WETHMMYQEREBADhx4kSV5SmBkoaEEmg1JD/LQXpKHgBAT1+Ixk2Vd6jQFr5DXHD7qmzataCd9ymBaoiH+Ee+Q1DZ3YI5Ndr/wYMHyMzMRNeuXXHx4kWlZdLT02FlZQUjIyO0adNG7rOff/4Zq1evxrNnz+Dh4YH169ejZ8+eNYqJEG1Bz0CroewACo0dTSAUCniMpmq9BjpDpCeL8c61JMQ/zOQ5IqJrSppvO3ToUGGZ69dljwq8vLwgEom47Xv27MHcuXOxaNEiREREoFu3bnjjjTcQHx9fu0ETUkcogVbD/dulzbfNtWwABWXMrQzQqUcTbj1oxz0eoyG6qCSBduzYscIyJQm0fPPt2rVrMWXKFEyfPh2tW7fGxo0bYW9vj19++aX2AiakDlETbjU8jErjlrVhEm1V9Bvhhsv/DSq/b2skZi/prPV3ztqups2iukSVBFryjLRsAi0oKEB4eDjmz58vV7Z///4VNgUTomvoDrQaHkaWJlBtG4GoIl39msLUQjbYQ1JiDi6HJPAcEdEVUqkUN27cgFgshoeHR4XllN2BpqSkoLi4GI0aNZIr26hRIzx//rx2AiakjlECVZFUyuSG8HNubsFfMNUglojQd2gzbn1/YCSP0RBdEhUVhezsbHh4eEAsVj7iVnZ2NqKjo2FiYoJWrVopfC4QyLd2MMYUthGiqyiBqigxLguvcmUzmxgY6sHC2oDniFQ3YGRzbvn4Pw/xMoOG9iNVCw8PB1B58+3NmzchlUrRsWNHCIWl/5zY2NhAJBIp3G0mJSUp3JUSoqsogaooJrJMD1wtf32lvOZtrEqH9iuQ4t//RfMcEdEFkyZNAmMMmzdvrrBM9+7dwRhTmKVFLBbD29tb4d3REydOoFu3brUSLyF1jRKoih5GlTbfurXW/h645Q0scxf695a7PEZCGoqPP/4Y27Ztw+bNmxEVFYW5c+ciMTERs2bN4js0QjSCeuGqqOwdqLsWD+FXEb+hrvh9VTgKC6WIvJ6M6DupaNHWmu+wSD02evRopKam4quvvsKzZ8/Qtm1bHD16FM7OznyHRohG0B2oisregbq2tOAvEDWZWRqg6+tNufX926N4jIY0FLNnz0ZsbCzy8/MRHh6OXr168R0SIRpDCVQFUinDozLvgDo1185pzKoywL+0GfdAYBQKC4t5jIYQQnQbJVAVPIuX74FraW3Ic0Tq8e5hz83Ikpmej9NBj3mOiBBCdBclUBXElBlAQdd64JYlEgkxwN+NW//r19s8RkMIIbqNEqgKyg7hpwtj4FZm0JiW3FB+V0KfytWNEEKI6iiBqqDsHWirdrrdc9XO3hhd+5Z2Jvrfpjs8RkMIIbqLEqgKyiZQl5a6MQZuZYaNb8kt/7M1CjnZBTxGQwghuokSaBUUxsBtoZs9cMvy6moPR1czAEBuTiGO/PWA54i0E2OM7xAIIbVAU9c2JdAqPIvPwqucQgD/jYFrpTtj4FZEKBRg6NjSu9AdG29RsihHJBKhsLCQ7zAIIbWgsLBQbvJ3dVECrULZTjaNm5rUm5kk+vs3h4GhbCCqh1FpCDv1hOeItIupqSlevnzJdxiEkFrw8uVLmJqa1vg4lECrUPb5Z8mA7PWBiZkYA0aWvtKydW0Ej9FoHysrK6SnpyMlJQUFBQV0h06IjmOMoaCgACkpKUhPT4eVVc3fqKCxcKtQ9g7Uvb3ujYFbmRGT2yBox30wBlw8+YTGxy1DIpHAyckJaWlpiI2NRXExjdpEiNZLSSldjlIcrlQkEsHU1BROTk6QSCQ1/jpKoFUoewfq3MKCv0BqQRNnU3Tv54Tzx+MBAIEbbuCrTX15jkp7SCQS2Nvbw97enu9QCCGqaNOmdLkOWo2oCbcSjDG5QeRd6lkCBYCR75b+wh3adR/Jz3N4jIYQQnQHJdBKlO2BKzEQwcJa93vglufR0Q5tOtgCAIoKpdix8SbPERFCiG6gBFqJ8mPg1pceuOWVvQvd8dMtZKTl8RgNIYToBkqglSjbgcjNXbfHwK1M935OXPN0Xm4R3YUSQogKKIFWouwdqLtn/eqBW5ZQKMDY99px69t/uIGszHweIyKEEO1HCbQSch2IWlrwF0gd6D3ImRveLyerELt+pqnOCCGkMpRAKyDrgVvmFZbmFvwFUwdEIiHGziq9C9267jqyX9Ig84QQUhFKoBV49iQbudmlPXAtbepfD9zy/Ia6orGjbMLwrIwCbN9wg9+ACCFEi1ECrcDDMs8/GznU3x64ZenpCzHxA09ufcua60hLfsVjRIQQor0ogVagbPNts1b1ZwzcqvR90xXOzWVTtr3KLcKm767xHBEhhGgnSqAVkOuB61V/e+CWJxIJ8c7HHbj1XT/fRmJ8Fo8REUKIdqIEWoGyd6CuDegOFAC6vd6Ue22nqFCKn5Zf5jkiQgjRPpRAlSjfA7c+joFbGYFAgHfnd+TWD/55D5ERSTxGRAgh2ocSqBLPE7KRkyXrgSuWNIweuOV5dWkMnz4OAGSTGqycd47mxCSEkDIogSpR9vmnXRPjBtEDV5mZCztBpCer+/WLzxD8dwzPERFCiPagBKqE/PNPC/4C4VnTZuZ4a6I7t776swvIe1XEY0SEEKI9KIEq8ajMEH6tPW15jIR/E+Z4wtxSNnP784RsbFsXwXNEhBCiHSiBKhETmcotu7VuWD1wyzMxE2PyR17c+u/fhuPF02z+AiKEEC1BCbQcWQ/c0jtQp3o+Bq4qBr3dgmvKzn9VhLWLLvIbECGEaAFKoOW8eJrDDaKuLxbC2s6Q54j4J9IT4r3Fr3Hrh3c/wKWQBB4jIoQQ/lECLadsB6KG3AO3vA5d7dFnsAu3vvz9EBTkF/MXECGE8IwSaDllX2FpaAMoVGXWok4wMtEHAMTFZOKP76/zHBEhhPCHEmg5Ze9AW3s17B645VnbGWHqJ6Xj5P628iriYjL4C4gQQnhECbScsgnUrbUVj5FopyFjW6JlO2sAQGGBFF99GEojFBFCGiRKoGUwxuTmAaUmXEUikRBzl3eBUCh7Nnzx5BMc20sjFBFCGh5KoGUkP8tBVuZ/PXD1hbBuRD1wlWnZ1hrDJrTi1r/+KBQZqTTxNiGkYaEEWkbZDkS21AO3UlM+8oJtYyMAQHpKHr5dcJ7niAghpG5RAi2j7AAKzs3NeYxE+xmbijF3eRduPWjHfZw7FsdjRIQQUrcogZZBPXCrx8fXEX5DXbn1pbNDkJNVwGNEhBBSdyiBllG2Cbe5B/XAVcV7i1/jBpt/kZCNdV+E8RwRIYTUDUqg/5GNgVuaQJ1pDFyVWFgbYPaSztz67l9uI/x8Io8REUJI3aAE+p+U57l4mZ4PANDTF3IdZEjVfIe4oIuvI7e+ZOYpmjeUEFLvUQL9j9wYuPbUA7c6BAIBPlzmUzrMX3QmfvnqCs9REUJI7aIE+h+5MXBbWvAXiI6ytTfGjM+8ufUtayMQGZHEY0SEEFK7KIH+h3rg1twbb7eAp08jAIC0mOGLaadQWEgzthBC6idKoP95cCeVW3ZrbcljJLpLKBRg3tddIZaIAAD3b6di06pwnqMihJDaQQkUsh640WUSqGsrSqDqcnA2w5R5Xtz6ryuvIioimb+ACCGkllACBZAYl4WcrEIAgFgigrUdjYFbEyOmtIaHt6wZvLiIYeG7J2jybUJIvUMJFPLNt/ZNTagHbg2JRELMX9UdEgNZU270nTT88vVVnqMihBDNogQK4MHt0gRKIxBphqOLGd6d35Fb37w6HLevveAxIkII0SxKoIDc88+23nY8RlK/vDnRXa5X7qKpJ5GfRwMsEELqB0qgAO7fTuGW3VrTHaimCIUCfLKyGwyM9AAAj+6lY2PAZZ6jIoQQzWjwCTQ/rwhx0RncuksLC95iqY/sm5rKDbCwbV0EIsKe8RgRIYRoRoNPoI+i0lFczAAAZhYSGBrr8xxR/TNkbEt07G4PAGAMWPTuSbzKLeQ5KkIIqZkGn0DL9sBt2syMx0jqL4FA1pRbMlZufEwm1tO0Z4QQHUcJ9E7p8093GsKv1tjZG+O9RZ249R0/3sLFk/E8RkQIITVDCbTMHahHB0qgtWnAyObo4lc67dmid08iIy2Px4gIIUR9lEBv0xB+dUUgEODjr7vCwsoAAJD8LBfL3z8DxhjPkRFCSPU16ASalvwKKc9zAQAikQD2TiY8R1T/WdoY4uNvunLrwfticGjnfR4jIoQQ9TToBPqgzPuftvbGEIka9I+jznTt2xSDRrfg1r+aG4rEuJc8RkQIIdXXoDNG1I3SWUKauVPzbV2atbATmjibAgBysgqxcOpJFBdLeY6KEEJU16ATaGSZabY6dG3MYyQNj6GxPj7/vgeEItnA/dfOJWLr2gieoyKEENU16ARadp5Kd0/qgVvXWnvZYvzsdtz6xoDLNHcoIURnNNgEmpNVgNj/hvATCADXVha8xtNQjXuvPdzb2wAAigql+GzKceS9ogHnCSHar8Em0Hs3U1Dy9oSVrSEkBnr8BtRA6ekL8dmaHpAYyuYOfRiVjnWLL/IcFSGEVK3BJtCyzz+pAxG/HF3M8N6i17j1HT/ewoUTNEoRIUS7NeAEmsQte3Wx5zESAgCDRrdAF1/5UYrSkl/xGBEhhFSuwSbQsp1V2nSkDkR8EwgE+HhlV1hYy0YpSnmeiyUzTtEoRYQQrdUgE+ir3EI8jErn1t2oCVcrWFobYsG33bn1M0disfvX2zxGRAghFWuQCfTB7VRIpbI7GwtrA5oDVIt07u2AEVNac+vfLTiP6DID/hNCiLZokAmUOhBpt3fnd+TOS2GBFPMnBNOrLYQQrdMgE2jZ559ePjQCkbYRS0RYtK4nJAayV1tiItPw/ecXeI6KEELkNcgEGkkdiLSec3MLvLe49NWW3b/cRsjhxzxGRAgh8hpcAs3NKZSbhaV5GyseoyGVGTS6Bbr3c+LWF087iaTEbB4jIoSQUg0ugd4NT0JxsawDkaWNAYxNxTxHRCoim4C7C2waGQEAMtPysejdU1wHMEII4VODS6A3Lj3nllt7UfOttjOzNMDna3pAIJu0BWGnnmDbOpq1hRDCv4aXQMOeccudezvwGAlRladPY4yZVTpry/olYbgT/oLHiAghpIElUMYYbl4uvQNt91ojHqMh1THpA09u1pbiIoYFE48jJ7uA56gIIQ1Zg0qg8TGZSE/JAwDoi4VwdDXjOSKiKj19IRau7Qmj/wa9iI/JxMp553iOihDSkDWoBFr2+aeTmzmEQgGP0ZDqauJsig+W+XDr+wOj8O//onmMiBDSkDWwBFr6/LNDN5qBRRe9/mYz9B3myq0HzA5BYtxLHiMihDRUDSyBlt6BUgci3fVBgA8aO5oAALJfFuDTySdQVNygfpUJIVqgwfyrk/2yQG5Q8pIOKUT3GJuKsXBtTwhFsib4iIvP8PuhATxHRQhpaBpMAr199QVKppa0aWxEM7DouDYdbDHpQ09u/ZeDA3E92rWSPQghRLMaTAKNKPP+Z5sONIBCfTBmZlu07yx7FUnKhPj01yl4mWPIc1SEkIaiwSTQq2efcstd/Bx5jIRoikgkxGff94CpuWw4xmepVlgWOAaM0VB/hJDa1yASaH5eEW6ElXYg6tCVeuDWF3b2xpj3VVdu/diVjti3NZLHiAghDUWDSKA3Lj1HQX4xAMDcSsINTk7qh54DnTGqz3lu/eu5oXIz7hBCSG1oEAn0ypnS5tv2NHxfvfTZ2H/QwjERAFCQL8XH447RUH+EkFrVMBJoaAK33GOgM4+RkNpiKCnEmtlbYCjOBwA8vp+Br+ee5TkqQkh9Vu8TaPbLAty6XDpzR4cujXmMhtQmtyYv8MWk/3HrB/+8hwPbo3iMiBBSn9X7BHo5JAFFRVIAsvc/LW3oNYf67K0eVzCofw63vuKDM4iJTOMxIkJIfVXvE+iFE/Hcchdfen2lIZj/YSacnQoBAHmvivHJuGN4lVvIc1SEkPqmXidQxhjOH4/j1vsMduEvGFJnjAwZvl6SBrFY1vIQE5mG5XPO0PuhhBCNqtcJNPZBBp7GZgEA9PSENAJRA9K8WRE+mZPJrQftuI+/frvDY0SEkPqmXifQ04cec8st2llDXyziMRpS14YNysWQgaXPQ1d+fFZuSEdCCKmJep5AH3HLA/zdeIyE8EEgAOZ/mAH3lrL3QYuLGD4a/S+Sn+dUsSchhFSt3ibQlBe5uFlm/s8e/Zx4jIbwxUACrAxIg7mZbCSqlOe5+GRcMAoLi3mOjBCi6+ptAj1z5DE3fVnTZmYwtzLgNyDCG/tGxVi+OB1CoewXIvx8Ir6ee5Y6FRFCaqTeJtBje2O4Zb9hzXiMhGgDn075mDX1Jbe+d/NdBG64wV9AhBCdVy8TaMqLXFwOKR2+b8AIev5JgIljsjHw9Vxu/fvPLsh1NCOEkOqolwk0eF8MpFJZ85yjqxls7Y15johoA4EAWPRJOtq3lY2XyxiwYGIwoiKSeY6MEKKL6mUCPbL7Prc8eEwLHiMh2kYsBr5dloYmjYsAAHm5RZg9/DCePcniOTJCiK6pdwk05m4qbv43eLxAALz+JjXfEnmWFlKs+ToVxkaykYqSEnMwfdBBpKe84jkyQoguqXcJdN+2SG65TQdbWFhT71uiyNWlCCsD0qCnJ2vqf3w/A7OGHUJOFs0hSghRTb1KoAX5xTi0s7T5dux77XiMhmi7zt75WPp5OgQCWRK9cy0Jc/yP0MDzhBCV1KsEemTPA6Sn5AEAjEz00alnE54jItqun+8rLJibwa1fOfMUs986jNwcSqKEkMrVmwTKGMP29Te49WHjW0EkqjfVI7VoxNBczJ5WOvA8JVFCiCrqTYa5dDoBD+6kAgCEIgHenu7Bc0REl0wamy2XRK+GPsXU/geoYxEhpEL1IoEyxvDTiivceo9+TjA1l/AYEdFFk8Zm4/3ppUn09tUXmNB7HxIev6xkL0JIQ1UvEujFk08QcVE2TZVAAMz43JvniIiumjgmG/M/zOA6FsVGZ2Bcr70Iv5DIc2SEEG2j8wm0uFiKdYvDuPVu/ZzQyMGEx4iIrhv5Zg6+/jIN+vqy90RTX7zC1H77sfPnWzQAPSGEo/MJdN+WSETdkA3FJhAC73/xGs8RkfrAr1ceNn6XCov/pkErKmL45qOzWDDhODLS8niOjhCiDXQ6gSY/z8H6JaV3n2+Od6dxb4nGeLUvwLZfk+HeonRwhX/3RuOtDrtw7lgcj5ERQrSBziZQxhiWzjqNzDTZwODGJvqY9mlHnqMi9U3jRsX47YdkDBuUw21LfpaLWcMOYd6Yf/E0ljoYEdJQ6WwCDdxwA6FHS+8CFq7rCYmBHo8RkfpKIgYWfZKB71akwtKiiNt+/J+HGNJuJ9YsvIiUF7mVHIEQUh/pZAI9fzwOaz6/yK33fsMZPn0ceYyINAS9uuVh1x/J6N+3NFkW5Bdjy5rr6Nc8EMs/OIP7t1J4jJAQUpcErBrdCjt16oRr167VZjxVCj+fiBmDg5D3SnYn0NjRBH8cexNiiYjXuAi/mlww55azmn5e6993664Yazea4V604vvGLdtZY/CYlujR3xkt21lDKBTUejyEEMjeYyxRBz3mdSqBnjr4CJ9OOs4lTwMjPWw9/iZsGlHHoYaurhMoAEilQOgFAwTuNFGaSAHAupERPDs3grunDVq2s4GjixnsnU1hbimBQECJlRCNogSqKD+vCBsDLmPr2ghum1giwq9BQ9C0mXkle5KGgo8EWoIxIPyGGAePGCH0vCEKCqt+MqKnJ4SJuRgmZrI/YokIAgEgEAogFAogEAggFAIQCGTby/2N/5b19AQwMpEdw9hUH8amsmUrW0PY2hvDtrExbO2NYGImpoRN6r86TqBa3eumuFiKk/sfYd2SMDx5WDrEmrGpPjb+PYiSJ9EKAgHQqUMBOnUoQHZ2Js6cN0TYFTGuhIuRla2vdJ+iIikyUvOQkVo375QaGOrBvqkJHJuZw9HVTPbHxQyOruZo2swMxqbiOomDkPpE6xIoYwz3b6Ui9GgsDvwZhfiYTLnPXVtZ4rvAfjRRNtFKJiYMQwbmYsjAXEilwMPHengQI8b9aD08fizA8xdCJKVKkF9Qt8/s814V4fGDDDx+kKH0c0sbAzi4yJKqg4sZHJuVLts7mUIspj4GhJTHWwItKpIi5Xkukp7l4Fl8Fh5GpiH6bioiwp4h+ZniKwF6+kJM/MATY2a2pU4ZRCcIhUALtyK0cCvC4AHynxUUANk5QmTnCJCbK0RRESBlAjAGMCkg/e9vBoCVbP+vRapkmTGgqFiA3FwBcl8JkJMjRO4rAV6+BFJSBEhJFSA1XQ/pGfpVJuz0lDykp+ThzrUkJfUQwM7BGHb2xrC2M4J1I0NY2xnBprERrO2MYGVrKGs+NhHD6L9mZEMjPWoyJvUeLwl05cdnsfOnWyo1UevpC9F/hBumftwB5lZ010nqB7EYsBJLYWUJAMW1+l2MATk5Ajx7IULiMz08faaHhKdCJCQI8PSZCC9SJCgqqvi5rVTK8PxJNp4/yVb5O4VCAYxM9CExEEFPXwh9sQh6esLSZX3Zskgk4J77CoUCCISyfcfMbAffIa6aqD4htYaXBGpkIq40eYpEAjR1M0f3fk7o1LMJxBIRXiTm4EViTsU7kQatSZnljBeKd1EEsDUBbFsAni3ktxdLgbR0fTxPEuN5sgTPk8RIfK6H5y/08CLFAOkvDcBY9e4mpVKG7JcFyFZzoKY+gyl5Eu1XrV64NjY2cHFx0cgXJycnI/25AIWFshkvRCIh9MU6Oa6DnPyiLEj0TPkOQ+N0oV4iYTHMDLOqtU/Wq3yYGta/uWNrv14lbcxMtqy8hKwYBEq3A8qTskAkgY2zk8L25ORk2Nra1iBm7UT10m42NjY4duyY0s+qlUA1ie93SmsL1Uu3UL10R32sE0D10mW6f8tHCCGE8IASKCGEEKIG3hLojBkz+PrqWkX10i1UL91RH+sEUL10GW/PQAkhhBBdRk24hBBCiBrUTqAJCQn44IMP0LVrVxgZGUEgECA2NrbK/a5du4YZM2bA3d0dRkZGcHJywvjx4/H48WOFsi4uLv8Nni3/58CBA+qGXSV16wVAaawCgQA3btyQKyeVSrFy5Uq4uLjAwMAAnp6e2Ldvn+YrU4a69QoICKiwXgYG8gNb1PX5+vvvv+Hv7w9nZ2cYGhqiVatWWLhwIbKyqn6VJS8vDwsWLIC9vT0MDQ3RtWtXnD17VqEcH+dK3Xpp+7VVk/OlzdeWuvXS5msLAIKDg+Hn54fGjRtDIpHA0dERb7/9NiIjI6vcV5uvL41iagoJCWF2dnbsjTfeYP3792cA2OPHj6vc75NPPmHdunVjP/30Eztz5gzbuXMnc3d3Z1ZWViw+Pl6urLOzMxswYAALCwuT+5OWlqZu2FVSt16MyV6ImzJlikK8OTk5cuUWLVrExGIxW716NTt9+jSbMWMGEwgE7MiRI7VQIxl16/XkyROF+pw8eZLp6emxUaNGyZWt6/Pl4+PDRo0axXbs2MHOnDnD1q1bx8zNzZmPjw8rLi6udN9x48Yxc3Nz9vvvv7OTJ0+y4cOHMwMDAxYRESFXjo9zpW69tP3aqsn50uZrS916afO1xRhju3btYvPnz2d79+5lZ86cYdu3b2dt2rRhpqamLDY2ttJ9tfn60iS1E2jZX4xNmzap/A9yUlKSwrbY2FgmEAjYkiVL5LY7Ozuz8ePHqxuiWtStF2Oyi3zx4sWVlnnx4gUTi8Xsyy+/lNvu5+fH2rVrV+14VVWTepW3fft2BoAdPnxYbntdny9lv0uBgYEMADt16lSF+924cYMBYFu2bOG2FRYWspYtW7KhQ4dy2/g6V+rWS9uvLXXrxZh2X1s1qVd52nJtVeTevXsMAPv+++8rLKPt15cmqd2EKxSqt6uykSmcnZ1ha2uLp0+fqhuOxqhbL1UFBwejoKAAEyZMkNs+YcIE3L59W2lzmyZosl6BgYFo1KgRBgwYUHXhWqTsd+m1114DgEp/l4KCgqCvr4/Ro0dz2/T09DBmzBgEBwcjPz8fAH/nSt16afu1pW69VKVr50sZbbm2KmJtbQ0A0NdXPk0foP3XlyZpRSeiqKgoJCUloXXr1gqfHTp0CEZGRpBIJOjSpUuttvlrwi+//AKJRAIjIyP4+fnh3Llzcp/fvXsXEokEzZs3l9vu4eEBACo9X+BTQkICQkJCMH78eOjpKQ6lzPf5Cg0NBQClv0sl7t69C1dXVxgZGclt9/DwQEFBAWJiYrhy2nKuVKmXMtp+bVWnXrp0balzvrT12iouLkZBQQGio6Mxc+ZMNG7cGGPGjKmwvC5eX+riPYEWFRVh1qxZsLW1xbvvviv32dChQ7Fx40YEBwdj586dMDAwwPDhw7Fjxw6eoq3chAkT8PPPP+PkyZP4/fffkZqaCj8/P5w5c4Yrk5aWBgsLC4WpnqysrLjPtdmff/4JqVSKyZMnK3zG9/l6+vQpvvzyS7z++uvo1KlTheXS0tJgaWmpsL38OdCWc6VqvcrT9murOvXSpWtL3fOlrdeWj48PJBIJWrZsiVu3buH06dOws7OrsLyuXV81ool24Jo8U5s5cybT09NjwcHBVZYtKipinTp1Yo6OjmpEWX01fVb48uVL5uTkxLp3785tmzZtGmvcuLFC2QcPHjAAbPv27eqGq7Ka1Mvd3Z116NBBpbJ1eb6ysrKYt7c3s7e3Z0+ePKm07Ouvv866dOmisP348eMMADt79ixjTDvOVXXqVZ42X1s1qRdj2ntt1aRe2nptRUZGskuXLrFdu3axjh07MgcHh0r/7dCl66umeL0DXbhwIX7//Xds2bIF/fv3r7K8SCTCqFGjkJCQgGfPntVBhDVjamqKwYMH4+rVq9w2KysrpKeng5UbvyI9PZ37XFtduXIF9+7dU/o/ZGXq6nzl5eVh2LBhePToEYKDg+Ho6FhpeSsrK6X/uy1/Dvg+V9WtV1nafG3VpF4ltPHaqkm9tPXaAmTN0D4+Phg7dixOnTqF7OxsrFq1qsLyunJ9aQJvCfTrr7/GqlWrsGHDBkycOFHl/Up+2Loy2z1jTC5WDw8P5Ofn4+HDh3LlStr727RpU6fxVUdgYCD09PQwbtw4lfep7fNVWFgIf39/XLlyBUePHkW7du2q3MfDwwOPHz9Gbm6u3PbIyEiIxWLumQyf50qdepXQ5murJvUqT5uurZrWSxuvLWUsLCzQvHlz7jmmMrpwfWmMJm5jq9skuGHDBgaAff3119X6nsLCQubt7c2cnJzUiLL6atqEm5mZyZo2bcp69erFbSvpuh0QECBXtm/fvqxt27Y1CVdl6tQrPz+fWVlZsWHDhqm8T22fr+LiYjZq1CgmkUjYyZMnVd4vIiKCAWDbtm3jthUWFjJ3d3c2ZMgQbhtf50rdejGm3ddWTepVnjZdWzWtlzZeWxV5/vw5MzY2ZjNmzKiwjLZfX5pUowS6d+9etnfvXjZr1iwGgP3888/cS7eMyd5BE4lEbNmyZdw+u3fvZgKBgA0cOFDhpeC7d+9y5Xbt2sVGjx7NAgMD2enTp9nu3btZjx49GAC2e/fumoRdK/VavXo1mzZtGtu5cycLCQlh27ZtY23btmX6+vpcm3+Jzz77jEkkErZmzRoWEhLCZs2axQQCAQsKCtK6epXYt28fA8D27dun9Nh8nK+SeixevFjhd6nk+VNFdRo9ejSzsLBgmzZtYidPnmT+/v5MIpGw8PBwuXJ8nCt166Xt15a69dL2a6smv4eMaee1xRhjb731Flu+fDk7cOAAO336NPv1119Zq1atmLm5Obt//36l9dLm60uTapRAIZtXXuFP7969GWOMPX78mAFgS5cu5faZPHlylfsxxlhYWBjz9fVldnZ2TE9Pj5mZmbG+ffuyY8eO1STkWqtXUFAQ69atG7O2tmZ6enrMysqKDR06lF2+fFnh+EVFRWzFihXMycmJicVi1q5dO7Z3716trFeJYcOGMSsrK5afn6/02HycL2dn5wrrVFKHiuqUm5vL5s2bxxo1asQkEgnr3LkzCwkJUfgOPs6VuvXS9mtL3Xpp+7VVk99DxrTz2mKMsVWrVrGOHTsyc3NzZmhoyFq2bMlmzJgh13Kli9eXJtFsLIQQQogaeH8PlBBCCNFFlEAJIYQQNVACJYQQQtRACZQQQghRAyVQQgghRA2UQAkhhBA1UAIlhBBC1EAJlBANEQgEEAgEEAqFCuN7luXr68uV3bZtm9xnU6ZM4T5bunRphccIDAzkyvXp00fuszNnznCflfwxMjKCvb09evXqhQULFiAiIqImVSWEgBIoIRqlp6cHxhj++OMPpZ9HR0cjNDRU6YTJ5Y+zZcsWFBcXK/1806ZNVR7D2dkZS5cuxdKlS/HRRx9hyJAhyMnJwffff4+OHTti/PjxyM7OVq1ihBAFlEAJ0aBGjRqhU6dO2Lp1K4qKihQ+37x5MxhjGDJkSKXHGTJkCBISEnDs2DGFz6KionDhwgUMHTq00mO4uLggICAAAQEB+Oabb7Bp0yaEh4cjIiIC7dq1w65duzBq1KjqVZAQwqEESoiGTZ8+Hc+fP8fhw4flthcWFiIwMBDdunWDh4dHpccYP348DA0NsWnTJoXPSrZNmzZNrfi8vLxw8uRJ2Nra4tixYzhw4IBaxyGkoaMESoiGjR07FsbGxti8ebPc9qCgILx48QLTp0+v8hgWFhYYNWoUjhw5Ijdhcn5+PrZv347evXujZcuWasdoZ2eHmTNnAgB27typ9nEIacgogRKiYaamphgzZgyOHTuGhIQEbvumTZtgZmaGt99+W6XjTJ8+HUVFRXIdjfbv34/U1FSVknBVSjofXblypcbHIqQhogRKSC2YPn06iouLsWXLFgBAXFwcTpw4gfHjx8PIyEilY/To0QOtW7fmnpsCsiRsaWkJf3//Gsfo4OAAAEhOTq7xsQhpiCiBElILfHx80K5dO2zZsgVSqRSbN2+GVCqt9p3jtGnT8OjRI5w+fRoxMTEICQnBxIkTYWBgUOMYS5KyQCCo8bEIaYgogRJSS6ZPn464uDgcO3YMW7duhbe3Nzp06FCtY0yaNAkSiQSbN2/m7kQ10XwLAImJiQAAW1tbjRyPkIaGEightWTixIkwNDTEzJkz8fTpU8yYMaPax7CxscHw4cOxf/9+bNmyBV27dkXbtm01El9ISAgA2d0yIaT6KIESUkssLCwwcuRIJCQkwNjYGGPHjlXrONOnT0d+fj6Sk5M1dveZlJSE3377DYDslRlCSPVVPpQJIaRGvvrqK4wYMQK2trYwNTVV6xi+vr44ePAgpFIpBgwYUOOYbt68iUmTJiElJQWDBg3CsGHDanxMQhoiSqCE1CInJyc4OTnV6BgCgUCtJBcbG4uAgAAAskEcUlJSEB4ejvDwcACyO8+Su1BCSPVRAiWknoqLi8OyZcsAAAYGBrCwsECLFi0wf/58jB8/Hl5eXvwGSIiOE7CSvuyEEEIIURl1IiKEEELUQAmUEEIIUQMlUEIIIUQNlEAJIYQQNVACJYQQQtRACZQQQghRAyVQQgghRA2UQAkhhBA1UAIlhBBC1EAJlBBCCFHD/wHr34Kx8BhWrwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "819e9dba4b744eebad21a6d2d22bfb53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "215fc7bc56134dd59798cca678055648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaca809d0f53475992af2381199c1cc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0a355cae61a48eabf85f3b96ee23bf8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2da3b8d72ba47b38219eb8221c4d3b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1bf0d6947a34e5f860102d950adf0b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47ee50fa97b94020b3b3aeec7ca62bec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YRvqbFNaf0Ps4k5Eu4fqtlDu6T1XRrVu3mBEjRjCOjo6Mrq4uY2BgwLi4uDD/+9//mDdv3hS6Td79ojt27GBOnDjBtGjRgtHX12dMTEyYvn37Ms+ePVNY/8mTJ8zUqVMZd3d3xsLCghGJRIyDgwPj4+PDXLt2rdDXuHLlCtO/f3/GxsaGEQqFjIWFBePq6spMnTqVuXNH8b04ODgo3btZlJo1azIAGHNzcyY7O7vI9bKzs5mNGzcyLVu2ZIyNjRmRSMRUr16d8fb2ZtauXat0z2t2djazYMECxsnJiX1/P/30E5P1332nxb1PlWEYRiaTMRs3bmTq1q3LiEQixsbGhvnuu++YpKSkIt/rjh07GFdXV0ZfX5+pUqUK8/XXXzMPHjxg/Pz8GABMYGCgwvofP35kBg8ezFhZWTF8Pp8BwPj5+bHPX7t2jfHy8mJMTU0ZQ0NDpnXr1syRI0eYwMBApXXVUVm/5zyGKVvjWNOmTXH37t2yZ3dS4XJzZZgy4AwCT+ZfjnJrURWLt3lDrFv0RYyY6DQsn3YVD+/EsGXN2lXDpiM9YWAkKnI7TZfX+7PSiC/wva7SlLs4KquC59WmdPwrSlm/59SmWkkxDIOlUy4rJNQ+I+ti5e5On02oAGBVzRCrdnfGAN/8cV/vXI7GuJ7HkZmRU24xE0KIuqOkWkmd2v8cB35/xC53/LoGvpvbvFg39QOAQIcP3xnuGDcz/wb1ezc+YNbIAEilhQ+gTggh2o6SaiUUHZmCRT/kd29v1Nwa01co30tYHP3HuuDbn/IvTZ0/9gqrZ18vc4yEEKKJKKlWMjIZg9mjzyMtRT4UmaGxCIt/9y5T792+39SHz+j67PKudfdw6q/nZY6VEEI0DSXVSubo7jDcvZJ/T9r8zZ7QM1AeIq2kxs10R+tO+eOD+n0biIjnyve6EkKINqOkWomkpUiwbu4NdrnbgJpw9aiqkn3z+TxMX9EK1Rzks4Zkpudg6uCzyM5SvgGeEEK0FSXVSuS3pXcQ/98ADnr6Ovh2TjOV7t/ASIR5G9pDKJJ/rJ4/jMemhbdV+hqEEKLOKKlWElEvk7Fn4312ecJPTaGnX/bLvp+qWd8c42fnd1zasSYU9299UPnrEEKIOqKkWklsW3EXuTnyW11sqhui24Ba5fZavYbUQeNW8svK8o5RAcjKpMvAhBDtR0m1EngXkYLje5+xyz8uaVns+1FLg8/n4X9LW0H/vw5QkS+SsW1FyWbcIIQQTURJtRLwXxWM3Fx5LdXOyRhuLZUH1lY1a1tD+BYYGMJ/ZXChM98QQog2oaSq5d6/ScU/O8PY5e/nNa+w1+4+sBbquVkAkI8zvGjSJZqHlRCi1Siparn9Wx6ybanV7I3g3qb8a6l5+HweJi9sAb5Afqn5dtA7nDsUXmGvTwghFY2SqhbLzMjBoe35c6SOmd6kXNtSC+Nczxx9RubP+LB69nVIsqUVGgMhpTFq1CjweLxCJ0nniqOjIzsROFFPNEm5Fjv113MkJ2QDkA9H2LpTdU7iGPZ9IwQceYmUxGxER6Vi36b7+ObHJpzEUq7u/wxkRHEdRdH07QHXhWXejaOjIyIjIwt9ztraGh8+KN5CJZFIsHDhQuzbtw8JCQlo0qQJVq9ejSZNlD8DAQEB6Ny5M06ePIkePXqUOVZN4+npiaCgIGom0WCUVLUUwzDYW+C+1L6j6kEg4ObChKGxCMMnNsKmRXcAAFuW3MHXI+rBzEKPk3jKTUYUYODIdRRFS49Q2a5MTEwwZcoUpXJDQ0OlslmzZmHt2rXw8fGBnZ0d9uzZAy8vLzx9+hQ2NvnNEWlpafD19cWwYcPUIqEuW7YMs2bNgq2tLdehsC5cuMB1COQLKKlqqTuX3+HF4wQAgEDAU7gEy4Weg+vg2N5nePs6BempOdi8+DbmrGvPaUyk9ExNTTF//vwvrscwDLZu3YpvvvkG27dvBwD06dMHnp6e2LNnD2bMmMGuO3PmTGRlZWH9+vXlFXaJ2NjYKCR9deDs7Mx1COQLqE1VS/2zI7/Hb8uO1WFoLOIwGkBHyIfvjPzLfQe2PsLrZ3SLjbaLjY1FRkYGmjfP73We97jgJeTLly9jy5Yt2LRpE8zNzUv1WnntjSkpKfjxxx/h6OgIoVCokPyfPn2KUaNGoXr16hCLxbC2tsaQIUPw7Nkzpf19rk311q1b6NevH6pWrQqRSITq1atj/PjxiI6OVloXABISEjBnzhw0aNAA+vr6MDExgaurK2bNmoX09HRERESAx+MhKEg+JSOPxwOvWTPwmjWD5/jxSu/xU9nZ2Vi+fDkaNWoEfX19GBsbo23btvj777+V1s17rVGjRiEiIgKDBg2ChYUFdHV10bRpU5w8efILR1rR0aNHMWzYMNSuXRsGBgYwNDSEu7s7NmzYAJlMeW7lvOP66tUrbNy4EY0aNYKenh48PT0Vjtfs2bNRr1496OnpwcTEBB06dMC///6rtL/k5GSsWrUK3t7esLOzg0gkgqWlJXr37o2bN2+W6L2oAtVUtVBqcjb+/Se/l+2QbxtyGE2+lh2qo1Fzazy4/RFSKYNfZl/Dpn96ch0WKYXs7Gzs3bsXUVFRMDAwQKNGjdCuXTsIBAKF9SwtLaGnp4fg4PzBP+7evQsAcHBwAABkZmZi7Nix8PHxgY+PT5nikkgk8Pb2RkJCAjp37gxjY2M4OTkBAM6ePYu+ffsiJycHvXr1Qs2aNfH27Vv8888/OHXqFAIDAwtt5/3Ujh074OvrC7FYjN69e6N69ep48eIF/P39ceLECdy8eRP29vkzNr1+/RpeXl6IjIyEu7s7vv32W8hkMjx//hxr167FhAkTYGpqCj8/P+zcuRORkZHw8/MD/kvQjl+oLUskEnTp0gVBQUGoW7cuvv/+e2RkZODQoUMYOHAg7t27h6VLlyptFxkZiebNm6NGjRoYPnw4EhIScODAAXz11Vc4f/48vLy8inXMZ82aBT6fDw8PD9ja2iI5ORkXL17E5MmTcefOHezZs6fQ7SZPnowrV66gR48e6N69O/vZiYyMhKenJyIiItC2bVt07doV6enpOHnyJLp27YqtW7fC19eX3U9YWBjmzJmDdu3aoUePHjAzM0NUVBSOHz+OM2fO4MSJE+jatWux3osqUFLVQmf+foHsLHkPWwtrfdRyqcJxRHI8Hg/jZzfF931OAQAunYzA3Svv0LSt+rRZkeL58OEDhg8frlDm5OSEHTt2oH37/Mv6PB4P48aNw4YNG5CcnAxbW1vs2bMHRkZGGDp0KABg7ty5SEhIwKZNm8oc1/v371G/fn0EBQXBwMCALU9MTMTgwYOhr6+Py5cvo379/Pl/Hz9+DA8PD4wdOxYhISGf3f/z588xfvx4ODo6IigoSKG99eLFi+jUqRMmT56MI0eOsOXDhg1DZGQkli5ditmzZyvsLy4uDoaGhtDV1cX8+fNx6dIlREZGymvX//3x8SWrV69GUFAQunXrhuPHj0NHR35a9/PzQ/PmzbFs2TL07NkTrVq1Utju0qVLmD9/vjyB/2fIkCHo2rUrVq1aVeykeurUKaXL0jKZDN988w12796NiRMnwsPDQ2m7kJAQhIaGsn/05Bk5ciQiIyOxf/9+DBo0iC1PSkqCp6cnfvjhB/Tu3RvW1tYAgHr16iE6OhoWFhYK+3n79i2aN2+OqVOnVmhSpcu/Wujwjifs46+G1eEwEmW1G1RBx69rsMvrf75JPR01zDfffIMLFy7gw4cPSE9Px8OHDzF+/HhERESgW7duuH//vsL6K1aswMyZM3H79m388ccfcHFxwYULF2Bra4tbt25h3bp1WL9+PSwsLLBgwQLY2NhAR0cH7u7uuHbtWonjW716tUJCBYDdu3cjKSkJCxYsUEioAODi4gJfX1+EhobiyZMn+JwtW7YgJycH69evV+rA5O3tjd69e+PEiRNITU0FAAQHB+P69etwc3PDzJkzlfaXd9m1LLZv3w4ej4c1a9awCRUArKysMG/ePACAv7+/0nYODg6YO3euQlmXLl1gb2+P27eLP7tUYe28fD4fkydPBgCcO3eu0O1mzJihlFDv37+PoKAg+Pj4KCRUQN6Ov2DBAmRlZeHw4cNsuYmJiVJCBQA7Ozv069cPT58+RVRUxfXKp5qqlnnxKB6P7sYAAHh8+ahG6mbED64IPPka0lwGIdfe48aFN2jV0f7LGxK1ULBmAwANGjTAb7/9BkNDQ6xevRrz589XqKmJxWIsW7YMy5YtU9hOIpFg9OjR6N69O4YOHYp169axNafWrVtjyZIl6Nq1K8LDw9layZfo6uqiUaNGSuU3bsjnEb5//36hHayeP38OQH4p8dOkW9h+goKCcOfOHaXnY2JiIJVK8fz5c7i7u7Ntel26dAGfr/o6TGpqKsLDw2Fra4u6desqPe/t7Q0ACA0NVXrOzc1N6XI9AFSvXp19n8URHx+PVatW4fTp03j16hXS09MVnn/37l2h2xVsZ8+T97rJycmF/p5iY2MByH9PBV27dg3r16/HjRs3EBMTA4lEohRDwUvy5YmSqpY5vi+/w0UDdysYm5Xtr+DyYFPdCN3618LJ/fIT2bp5N9GyQ/UKH5iCqNaECROwevVqXL58uVjrL1iwAO/evWM7n6xatQodOnRgT6Z16tSBo6MjNm3ahIULi3d/rZWVVaGfo/j4eADAtm3bPrt9WlraZ5/P28+qVauKtZ+kpCQAKLfbcpKTkwGgyF7KeeV5cRRkampa6DY6OjqFdjAqTFJSEpo1a4bXr1+jefPmGDFiBMzNzaGjo4OkpCSsX78e2dnZhW5btWpVpbK84xsQEICAgIAiX7fg7+nIkSPo168fdHV10alTJzg7O8PAwAB8Ph+XLl1CUFBQkTGUB0qqWkQmY3Dm4At2ud/oov/i5tqQbxvi3OFw5EhkeBwcg6DTEfDs4fTlDYnasrKyAgClmkphQkNDsXLlSvz222+wtbVFSkoKoqOj2XZWALC3t4eFhQUeP378mT0pKuoPMxMTEwDymmphNdniyttPcnIyjI2Nv7h+XuIqqrZWVnnxfDrgRp73798rrKdq/v7+eP36Nfz8/JRqljdu3Pjs7VGF/a7y4ly/fj1++OGHYsUwb948iEQi3L17F/XqKd46OH78eLZHdUWhNlUtcu/mB7yPkrflCEV8NGunvh2ALG0M0GNQbXZ5g99NyGTUtqrJ8i7d1ahR47Pr5ebmYvTo0fDy8sKYMWMUnvu0RpGVlaWS2Fq0aAEAuHLlSoXuJ2/9c+fOFav2l3c5Viot3lCeRkZGcHZ2xrt37/DixQul5wMDAwGgWL2aSyM8XH6XQWG9tkuTzErzewoPD0f9+vWVEqpMJsPVq1dLHENZUVLVIqcPPGcfN21rC6FIub1EnQye0BBiXXmMzx7E4/zRlxxHRL7k8ePHSEhIUCqPjIzExIkTAch7u37OsmXLEB4ernAp1tjYGLa2tjh79ixyc+UT2gcFBSE1NRUuLi5ljvubb75hO7oU1glHJpPh0qVLX9zPxIkTIRQKMXXqVLYdtiCJRKKQENzd3dGqVSvcu3cPK1asUFo/Pj5e4Q+HKlXkPfVL0rFm9OjRYBgG06dPV0jGcXFxWLRoEbtOeci7Z/bTYxcaGqrUhl4cTZs2Rdu2bfHPP/+wg4V86uHDh4iJiVGI4cWLFwr3CDMMgwULFnyx41l5oMu/WiI3V4Z/D+ffm9pnpHKnBXVjbqmHr4bVxd/+8st7G+ffQoevanA2nCL5soMHD2L58uXw8vKCk5MTjIyM8PLlS5w6dQpZWVno3r07pk2bVuT2jx8/xuLFi7FmzRr2PtU8M2bMwOTJk9G2bVt4eHhg3759MDQ0xPfff1/muKtUqYJDhw6hT58+aNGiBTp06AAXFxfw+XxERUXhxo0bSgmuMHXr1sX27dsxevRouLi4oGvXrqhduzZycnIQFRWFK1euwNLSEk+fPmW32bt3Lzw9PfHTTz/h8OHD8PT0BMMwePHiBf799188ffqUTU4dOnTAwYMH0bdvX3Rv3Bh6YjEcbGwwvGnTImOaNm0azpw5g2PHjsHV1RXdu3dHRkYGDh48iJiYGMyYMQNt2rQp8zEszIgRI7Bq1SpMmTIFgYGBqFWrFl68eIGTJ0+ib9++OHDgQIn3+eeff8Lb2xtjxozBhg0b4OHhAVNTU7x9+xYPHjzAo0ePcOPGDba5YerUqZgwYQIaN24MHx8fCIVCXLt2DU+ePEGvXr1w4sQJVb/tz6KkqiVuBb5FfEwmAEBXXweNmhevtyTXBvi64MT+Z8hMz8Wrp4k4dygc3QfW/vKGhBNeXl549uwZQkNDcePGDaSnp8PU1BRt2rTB8OHDMXz48CLbNaVSKUaPHg0PDw989913Ss9PmjQJKSkp+O233xASEgJXV1esXbu22D1/v6RDhw548OABfvnlF5w7dw5XrlyBSCRCtWrV4O3tXeyBJ4YNGwZXV1esXr0agYGB+Pfff2FgYIBq1aqhX79+GDhwoML6Tk5OCAkJwcqVK3H06FH8+uuv0NXVhaOjI/73v/+xyQEAxo4di8jISPz1119YuXs3cqVStG/SBMN//rnIeEQiEQICArBmzRr8+eef2LhxI3R0dODq6op169Zh8ODBpTtgxVCtWjVcuXIFs2bNwtWrV3Hu3DnUrVsXmzdvRseOHUuVVO3s7BAcHIyNGzfi8OHD2LdvH6RSKapWrYr69etj0qRJaNgwf0Cb8ePHQywWY926ddi1axf09PTQtm1b7NixA4cPH67wpMpjyniTYNOmTdkRUgh3/L69iEN/yC91dOjthFmr23IcUfHtWBOKP7c8BADUqGuGY/eGgM9Xv57AYWFhSu02CrRtlpr4At/rKkXXlLTVoEGDcODAAURHR3MzBnDB8+pnaqpEtb74Pf8CqqlqAalUhovHX7PLPYeo14APX+Izuj6O7A5ja6sXjr1Cpz4aOHC4CqZVI+rj+fPnEIvFsLS05DoUokGo8UoLhF5/j4TY/Eu/9Rtr1knA2FSM3kPz/xDYsuQ2jbJEOLN+/Xr4+PggNDQUX331lcIoRYR8CSVVLXD+2Cv2cdO21dTy0umX+Iyur9ATOOh0BLcBkUpr3bp1uHnzJkaPHo2tW7dyHQ7RMPQnmIZjGAbnj+Yn1R6D1G9YwuIwq6KHHoNq45+d8uHHNi++g/bdHWmUJVLhXr9+/eWVCCkC1VQ13JPQWIUBH9w81GtS5ZLoP9YFQpH8I/k4OAbXz7/hOCJCCCkZSqoarmAt1dWjKnSEmvsrtbDWR9f++TXtzYupbZUQolk09wxMAECh7bFr/5rcBaIig8a5sH8Y3LvxAXevRH9hC0IIUR+UVDVYdFQqnj2IAyCf5q25Go/1W1xW1QzRqU/+2LGbFxd/XkdCCOEaJVUNVrCWWqO2GfQMhNwFo0KDxzcEXyDvoHT70jvcu/me44gIIaR4KKlqsIJJtcNXn58ZRJPY2BvBu1f+NHBbFitPBk0IIeqIkqqGykjPwa3At+xyu24On1lb8wye0BB5d9Nc/TcKj4I/chsQIYQUAyVVDXXj/BtIsuXTPJlb6cHa1pDjiFTL3tkE7bs7ssu/LaXxpQkh6o+Sqoa6fDaCfdy6Y3XuAilHQ77Nn4ki8MRrtlMWIYSoK0qqGohhGFwLyB8Ywaun02fW1lxOdczQupM9u7x1GbWtEkLUGyVVDRTxPIkdRUlHyEc9N80aQL8khn6fX1v995+XeBmWwGE0pKBOnTqBx+MhKCioyHUmTJgAHo+Hbdu2VWBkhHCHkqoGuhaQP2dn7YZVNHoUpS+p5VIFHp7y+28ZBti6nNpW1UVISAgEAgHc3d2LXOfWrVsA5PMuE1IZaO/ZWIsVTKpePRy5C6SCDP2+Efv49IHniHieyGE0BABevnyJhIQEuLi4wNCw8E5yGRkZePToEcRiMRo0aFDBERLCDUqqGkaSLcWdoHfssoenHYfRVIx6bpZo2rYaAICRAVuXUW2Va3fvyn8HHh4eRa4THByM3NxcuLq6QijUjoFJCPkSSqoaJuT6e2Rm5AIATMzFsLE34jiiijF8kiv7+MT+Z4h8kcRdMKRYSfXmzZsA6NIvqVxoPlUNc+3f/Eu/ebW3yqB+Y0s0aW2DkGvv5bXV5Xex9I+OXIdVaeUl1QMHDiAwMLDQdag9lVRGlFQ1TMH21PbdHLkLhAPDJ7ki5Jp8HOATfz7D+NlN4VDTlNugKiGGYRAaGgoACAgI+OL6lFRJZUJJVYPEfkjPn5WGJ58/tTJp4G6Fxq2qIvT6B8ikDH5ffhdL/NWjtuoi+pXrEIrtsWRimbZ//vw5kpOT0bJlS1y/fr3QdRITE2Fubg59fX3Ur19f4bnNmzdj1apVeP/+PVxcXLBu3Tq0bdu2TDERoi6oTVWD3DifP+CDvbMJ9A0rX+eP4RPz21aP73uGqJfJHEZTOeVd+m3cuHGR64SEhAAA3NzcIBAI2PIDBw5g8uTJ+OmnnxAaGopWrVqhW7duiIqKKmpXhGgUSqoapOCl37ZdtGsA/eJq2Mwabi3kNXSZlMG2FdQTuKLlJdUmTZoUuU5eUv300u+aNWswatQo+Pr6ol69eti4cSNsbGywZcuW8guYkApEl381hEzG4HqBmmqrTto53m9xDJvYCPdufgAAHNvzFONnN4OdkzGnMZX1kqomKU5SzWtzLZhUJRIJgoODMW3aNIV1O3fuXORlZEI0DdVUNcTT+7FIiM0EAIj1BHCuZ85xRNxx9aiKRs2tAQBSKYPfqbZaYWQyGe7duweRSAQXF5ci1yusphoXFwepVApra2uFda2trfHhw4fyCZiQCkZJVUMUHEDfpbEV+Hweh9Fwb8QP+W2rR3eF4V1ECofRVB5hYWFIS0uDi4sLRCJRoeukpaXhxYsXMDQ0RJ06dZSe5/EUP7sMwyiVEaKpKKlqiJsX85OqZ09H7gJRE64eVdGwGdVWK1pwcDCAz1/6vX//PmQyGZo0aQI+P/8UY2FhAYFAoFQrjYmJUaq9EqKpKKlqAEm2FKHX37PLTdtUnkEfPmf4pPwxgf/ZEUajLFWAESNGgGEY+Pv7F7lO69atwTCM0uw1IpEI7u7uSve2BgQEoFWrVuUSLyEVjZKqBrh/6wOys6QA5EMTWtoYcByRenBrUTW/J7CMwa8Lb3EcEfmSH3/8ETt37oS/vz/CwsIwefJkREdHY8KECVyHRohKUFLVALcC37KPXZtXrgEfPofH4+GbH/PvlTx94AWe3o/jMCLyJQMHDsS6deuwePFiuLm54erVqzh9+jQcHCrnLWJE+1BS1QC3C8xK07qLPYeRqJ/6jS3RskP+TD0b/G5yGA0pju+++w4RERHIzs5GcHAw2rVrx3VIhKgMJVU1l5Geg/u38jt2NG5BNdVPffNjY+R1Hg06HYGQAu3PhBBSkSipqrnQ6++RmyMDAFSx1oOZhR7HEakfp9pm8O5dg11eN/cGGIbhMCJCSGVFSVXN3b6U357apJUNh5GotxGTXCHQkVdXg69GKwzpSAghFYWSqpq7dalAe2onak8tSjUHI3QfUItdXvPTdUilMg4jIoRURpRU1VhqcjYeB8ewy3lD85HCDfmuEcS68hlRnj2Ix/F9zziOiBBS2VBSVWN3r0RDJpO3DVrbGsDIRMxxROrNwlof/cfmj0e7bu4NZKTncBgRIaSyoaSqxm4H5benNmtry2EkmmPAWBeYW8o7c8V9yMDONaEq3T91gCJEe6ni+01JVY3dCsxvT23ZsfJO9VYSegZCfDPVjV32/yUEMdFpKtm3QCBATg7VfAnRVjk5ORAIBGXaByVVNZUYl4lnD+SjA/F4QAN3K44j0hyd+jqjRl0zAEB2Zi42+Klm+EIjIyOkpNBsOIRoq5SUFBgZGZVpH5RU1dSdy/m1VFsHY+gbCjmMRrMIBHyMn+XOLh/dHYawe7Fl3q+5uTkSExMRFxcHiURCl4IJ0QIMw0AikSAuLg6JiYkwNy/bXNU6KoqLqFjBW2mae1J7akk1aV0NHp62uHXpHRgGWDn9Krb/+3WZ5u0Ui8Wwt7dHQkICIiIiIJVKVRixGkovMI5yTBh3cVRWcQWOfxgd//IkEAhgZGQEe3t7iMVl6xBKSVVN3S4wiH4Lb7vPrEmK4jvTHXeuREMmZXA76B3O/P0C3QfWLtM+xWIxbGxsYGNTCQbi+LN+/uMhVCuvcPULHH+6KqIx6PKvGop9n45XzxIBAHw+D/UbW3IckWZyqGmKr4fXZZdXTLuKtBQJhxERQrQdJVU1dKvA0ITVnY0h1qULCqU14gfX/FtsPmZg8+LbHEdECNFmlFTV0O0C7akt6dJvmRgYiRQ6Le3ZeB/PH9Kcq4SQ8kFJVQ0VHPTBw5OSall59XKCq4d8iEeZlMHiyUHUc5cQUi4oqaqZdxEpePNKfi+kQIeHOo0sOI5I8/F4PEzy8ygwi817nKBxgQkh5YCSqpopWEt1qm0Koahso3sQOYdapvAZld+bcsX0q0iMy+QwIkKINqKkqmYKtqe28KahCVVp2MRGsKyqDwBIis/C8v9d4TgiQoi2oaSqRhiGUej5S+2pqqVnIMTkhS3Y5ZP7n+PSqdccRkQI0TaUVNVIVHgyPr5LBwDoCPmo5VK24bKIMg8vO3T4qga7vOD7S0hNzuYwIkKINqGkqkZuFWhPda5rBoEO/XrKw3dzmsK0ii4AICY6Hb/MusZxRIQQbUFnbTVSsD21VSdqTy0vxma6mOTnwS4f+uMJbl58w2FEhBBtQUlVTTCMfHzaPM3b0SD65aldNwe07WLPLs8dd4EuAxNCyoySqpp4GZaI+I8ZAAChWIAa9ag9tbxN9POAkYkIAPA+Kg1Lp1zmOCJCiKajpKomCt6fWqu+Ofj80k9RRorH3FJPoTfw8X3PcOqv5xxGRAjRdJRU1QS1p3KjfXdHdOrjzC4vnHgJ0ZEpHEZECNFklFTVgEzG4M7lAu2p7ak9tSJN/Lk5bKobAgDSUiSYOSoAUqmM46gIIZqIkqoaePEoHknxWQAAsa4AjrVMuQ2oktE3FGLW6rbgC+SX3EOuvce2FcEcR0UI0USUVNVAwV6/tRpUAY9H7akVrX5jSwyf6Moub1p0G3evvPvMFoQQooySqhq4XWBowoK3eZCKNXhCA7i4WwKQTxH345BziH2fznFUhBBNQkmVY1KpTKE9tRndn8oZgQ4fc9a2g4mZGAAQ/zED04adQ24uta8SQoqHkirHnt6PQ2qyBACgp68DOydjjiOq3CxtDPDT2rbIuwJ/90o01s29wW1QhBCNQUmVYwUv/dZpZEHtqWqgSetqGDnFjV3esSYU54++5C4gQojGoKTKsYKdlNpQe6raGDyhITw88y/F/zTmPF6GJXAYESFEE1BS5VBurgzBV6PZ5WZtqT1VXfD5PMxY1QZV7eT3r6an5mBi31NISsjiODJCiDqjpMqhJyExSE/NAQAYGAlhY2/IcUSkIGNTMfw2eUKsJwAARL1Mxv+GnEVOjpTjyAgh6oqSKoduFRiasJ6bJbWnqqGa9c0xc2UbdvnmxbdYMe0qhxERQtQZJVUOKdyf2tWBw0jI57Tt6oCRk93Y5f1bHuKvrQ+5C4gQorYoqXJEIpEi9Pp7drlpGxsOoyFfMvT7hvDs4cguL5lyGVf/jeQuIEKIWqKkypGHdz4iMyMXAGBkIoJVNWpPVWc8Hg//W9YKtRpUASAfcWnqwLMIC43lODJCiDqhpMqRglO9NWhqzWEkpLh09XSw8DcvWFUzAABkpOdgQu8TNFUcIYRFSZUjBSclb9eV7k/VFBbW+lji3wEGRkIAQNzHDIzvdQLJiXSrDSGEkionMjNyFNpT3VpSe6omcaxligVbvKAjlH99Xj1NxA/9TiM7K5fjyAghXKOkyoHgq9HIkcgHaTez1IOFtT7HEZGScvWoihkrW7PLd69EY9pQGnyfkMqOkioHrp9/wz52b021VE3l1dMJvjOasMsXT7zGz+MvQiZjOIyKEMIlSqocuFEgqXp2d+QuEFJm/ce6YMBYF3b52J6nWDH9KhiGEishlREl1QoW+yEdzx/FAwB4PKCRB/X81WQ8Hg9jZzRB1/412bK9G+/jt6V3OYyKEMIVSqoV7OaF/F6/dk7G0NMXchgNUQUej4cpi1oojIr164Jb2Lf5AYdREUK4QEm1gl2/EMU+buldncNIiCoJBHzM+qUNmhRoI1865TJO/vmMw6gIIRWNkmoFYhgGNy7kt6fSeL/aRSQWYP4mT9Rzs2DLfhpzHhdPvOYwKkJIRaKkWoFePklA7PsMAIBQxEetBuYcR0RUTc9AiMXbOsCxtikAQCpl8OPgMzROMCGVBCXVCnS9QC21lksVCAR0+LWRsakYy7Z3RDV7IwBAjkSGH/qdVhhFixCineisXoEKXvptT7fSaDULa32s3N2JHSc4O0uK774+idAb77+wJSFEk1FSrSASiRR3Lkezyy297TiMhlQEa1tDrNrdGVWs9AAAmem5mNDrBB6HxHAcGSGkvFBSrSD3b35AZnoOAPlUbzb/XRok2q2agxFW7u4ME3MxACAtRQLf7sfw7EEcx5ERQsoDJdUKUnBoQhrwoXKxdzbByl2dYWQiAgAkJ2RjTLdjePU0kePICCGqRkm1glw/n39/qlcPJw4jIVyoUdcMy3d2gr6hfLCPxNhMjOlyFFEvkzmOjBCiSpRUK0BCbCYeB+e3ozWhQfQrpdoNqmDpHx2gq6cDAIh5n47RnY8gOiqV48gIIapCSbUCXDkbibzx1W0djWFkIuY2IMIZlyZWWLzNGyKxAADw/k0aRnc+gpjoNI4jI4SoAiXVChB0OoJ93L4bjaJU2bl6VMX8LZ7sJOdvXqVgdJdjiI/J4DgyQkhZUVItZzk5UlwLyG9P9ezpyF0wRG00a2uLeRvbQyDgAQBeP0vE2G7HkJSQxXFkhJCyoKRazkKvv0daigQAoG8ohGMtU24DImqjVYfqmL2mLXh8eWJ9/jAe43ocR2pyNseREUJKi5JqOSt46bdJKxvweDzugiFqp313R0xf3gp5H4vHwTGY0PsE0tMk3AZGCCkVSqrl7PLp/IHUO/s4cxgJUVed+jhj8sIW7PK9Gx8wse8pZGXmchgVIaQ0KKmWo6iXyXj1TH6DP5/PQ5NWdCsNKVyPQbXx7Zxm7PLtS+8wecBpSLKlHEZFCCkpSqrl6PKZCPaxUx1TiHV1uAuGqL2+o+phzLTG7PLVc1GYNvQccnIosRKiKSiplqOgAkm1w1c1uAuEaIxB4xti2MRG7PKF468we9R5SKUyDqMihBQXJdVykp4mwZ2gd+wyTfVGimvED67oP6Y+u3zm4AvM9b0AmYzhMCpCSHFQUi0nNy+8RY5EXrswt9SDlY0BxxERTcHj8eA70x1fDa/Dlh3f+wzzvwukxEqImqOkWk4K3krTqmN17gIhGonH4+G7uc3RfWAttuzw9idYOvUyGIYSKyHqipJqOZDJGFw+G8Eud/ya2lNJyfH5PExe2AKd+uTfirV/y0OsmnmNEishaoqSajl4cPsDYt/Lx3EV6wpQ19WC44iIpuLzefjfspbw7OHIlu1adw/r592kxEqIGqKkWg7OH33FPm7cygYCAR1mUnoCAR8zV7VBm872bNm2lcHYsuQOh1ERQgpDZ3sVYxgGAUdesss9BtXmMBqiLXSEfPy0ti1aeNmxZZsW3ob/qmAOoyKEfIqSqoo9vR+Ht69TAMhPhDSKElEVoUiAeRvbw71N/mdq7Zwb2L3hHndBEUIUUFJVsYK11EbNrNnJqAlRBZFYgPmbveDWoipbtmLaVfy19SGHURFC8lBSVbGC7akFb4cgRFV09XSw8DcvuLhbsmWLJgXh8I4nHEZFCAEoqarUi0fxeBmWAADgC3jwKND+RYgq6RkIsWRbB9RtlN+z/OfxF/G3/yMOoyKEUFJVodMHnrOPXZpYQVePBtAn5cfASISl2zuglos5W7bgu0v4c8sDDqMipHKjpKoiDMPgzMEX7HKfEXU5jIZUFkYmYqzY1Qm1G1Zhy5ZMvkydlwjhCCVVFXl4NwZvXuX3+qVLv6SiGJmIsXJXJ9Rzy78UvGLaVWxfHcJhVIRUTpRUVaTgpd8mrapSr19SoQyMRFi2vSMaNLViy1bPvo6ty+5yGBUhlQ8lVRWQSmU4W+DS79cj6nEYDamsDIxEWOrfAa4e1mzZBr+b2Dj/Fg1pSEgFoaSqAjcvvlUY65cGfCBc0TMQYvG2Dgqfwd+W3sHSqZdp2jhCKgAlVRU4ujuMfdymsz0EOnRYCXd09XSwcKsXmrWrxpb9ufkhZo78FxKJlMPICNF+dPYvo5SkbFw4lj/gQ/+xLhxGQ4icWFcHC7Z4Kcxuc/rAC0zsewoZ6TncBUaIlqOkWkZnD71Adpb8r3+Lqvpwrmf+hS0IqRhCkQCzVrdBryH5kzpc+zcKY7ocRXxMBoeREaK9KKmW0bHdT9nHBU9ehKgDgYCPSfM9MGxiI7bswe2PGNL2EF49TeQwMkK0EyXVMgh/koB7Nz8AAHg8oMdASqpE/fB4PIyc7Ibvf24OHk9e9vZ1Coa0O4TbQW+5DY4QLUNJtQwO/J4/M0i9xpYwMdflMBpCPu/r4XUxf4sXxLrye6hTk7Lh2/24Qkc7QkjZUFItpfQ0CY7vfcYuD5/U6DNrE6IeWnWojjX7u8LMQv4HYG6ODHPGXsCK6VeQmyvjODpCNB8l1VI69ddzpKVIAADGZmK4t672hS0IUQ+1G1TBr4e7w6mOKVu2e/19+HY/hoTYTO4CI0QLUFItBYZhcGBr/hRbfUbWBS+vsYoQDWBVzRBr93dF607V2bLbl95hQMu/8SQ0hsPICNFslFRLIeTaezy9HwcA4PN5+GoYzUhDNI+BkQg//+qJUVPc2A5M76NSMbTdYez/7SFoZENCSo6SainsWBPKPm7evhqMTMQcRkNI6fH5PAz9vhEWbfWGvoEQACDJlmLxD0GYumk0ktP1OI6QEM1CSbWEXj9LRODJ1+zy2OnuHEZDiGp4eNlh05EecKpjxpYF3G2Mfj/PxL1wR+4CI0TDUFItoV3r77GPazUwh0MtU85iIUSV7JyM8evh7ug9rA5bFh1fBSOWTsH6n2/SuMGEFAMl1RKI/ZCOY3vyR1DynUG1VKJdRGIBJvl5wG+TJ4z15UMZSmUC/L78Lga1Osj2JSCEFI6Sagn88UsIJNnyv9atqhnArUVVjiMipHy06WyPwwuXo1md/HmCnz2Iw8BWf+O3pXeQk0O1VkIKQ0m1mGLfp+Pv3/Nvoxk/251uoyFarZpFIrbP3IhZQw5BJJIPDJGbI8PG+bfQr9kBhFx/z3GEhKgfSqrF5L8qmJ2NxtJGH227OHAcESHlj89nMLxzEPb+HosG9bLZ8vAnCRjueRh+315EUkIWhxESol4oqRbD+zep+HvbY3b5u7nNqZZKKhX76rnYuj4OP0xIhq5u/nCGh/54gl4N9+HwjieQSmmYQ0IoqRbDurk32LZUa1sDhVFoCKksBAJgSP80/LU9Bu1a5c/HmhCbiZ/HX8TAlgcRfDWawwgJ4R4l1S94cOcjTu5/zi7/uKQl1VJJpVbVWoqVixKxYkE8rCxz2fKwe7EY4f0Pfhx8FlEvkzmMkBDuUFL9DIZhsGLaFXa5npsFmtDA+YQAANq3ycLfO2MwZkQKxKL8S7/nDoejV8N9WDjpEmLfp3MYISEVj5LqZxzdHYZ7N/InIZ/5SxuOIyJEvejqMvAdmYq/d8Wgk1d+As3NleHA1kfoWncP1s29QZ2ZSKVBSbUIsR/SsXL6NXa5Ux9n2DoYcxgRIerL2kqKRXOTsG1DLNwa5ifQrMxcbFsZjE41d2HtnOs0tRzRepRUi7Bs6hWkJMlvITAwEmLSfA+OIyJE/TV0kWDL2nisXRaHWjXyb8HJSMuB/6oQdK61CytnXMXHd2kcRklI+aGkWojTB57j3OFwdnnW6rbQ1dPhMCJCNAePB7Rsno1dW+OwaE4CHO0l7HOZGbnYte4eOtfajVmjAhB2L5bDSAlRPUqqn4h6mYz53wWyy83aVUMLLzsOIyJEM/H5QCfvTPz5RyyW/hyPmgVqrrm5Mpz48xn6NT+A0V2O4sKxV8jNpftcieaj6lcBkmwppg07h/TUHACAobEIc9a14zgqQjQbnw94t8+CV7ssXLmhi30HDHD/kS77/K3At7gV+BbWtgbw+aY+fEa7oKqdIYcRE1J6PIZhmLLsoGnTprh7966q4uEMwzD4acx5HN/7TF7AA9Yf6Ib6jS25DYwQjlS7ZsI+Tq0+S6X7fvxUiP0HDXHxsh5kMsX7vvl8Hlp3tkevIXXg3dsJevpClb62xih4P3zZTtOkAlFN9T/bVgTnJ1QAQ75tSAmVkHLiUjcHi+cl4v3HFBw9aYDjp3SRmCxPnjIZgytnI3HlbCQMjITo1McZXfvXgoeXHUQiAceRE/J5VFMFcHjHE/w8/iK73Ly9LRZv86aRk0ilVp411U/l5ACXr+vin+P6CL6nV+g6hsYitOvmgI5fO6NNZ3sYGInKNSbOUU1VI1X6pHpkdxjm+V5gP7PVaxhj64leENJfxKSSq8ikWlD0BwHOXdDD6XO6ePNOXOg6Ojp8uLaoitad7NGqU3XUb2wJgUDL+l1SUtVIlTqp7tl4HyumXWE/rxbW+vj9VC8YmRT+RSakMuEqqeZhGCDsmRABgXoIvCzGh5iia6aGxiI08rBG45Y2aNLKBg2bWWt+TZaSqkaqlEk1J0eKVTOuYd+mB2yZuZUetp3qDWNTSqiEANwn1YIYBnjxUoigq7oIuipC+GvdL25jX9ME9VwtUc/NArUaVIFjLVPYOhlDKNSQq1CUVDVSpUuq7yJSMGPEv7h38wNbZm1rgE3/9ICJ+Ze/qIRUFuqUVD8Vn8DHnRAxbt4R4fZdERKSilcrFQh4sHUyhkNNUzjUNIGdkwmsqhnIf2wMYGljAJFYTZIuJVWNVGl6/0qlMhz64wnWzrmO1OT8EV5c3K2wYmdHiHUrzaEgRONVMZeha8dMdO2YCYYB3n8Q4P4jEe4/FOHeAwGi3okhkym3sUqlDKLCkxEVnowrhewXAMwsdFHFSh/GZmIYm4phYq6r8NjEVAwjUzH0DHSgZyCEnt5//xsIoWegA109HerkWIlpfSZhGAY3L77F2rk38Dg4RuG5fqPrw3emO/h8+gIQoql4PKCajRTVbDLRrZN8wP5sCfDqtRDPwoV4/kIHryN4ePNWB3GJX74alRiXhcS4ss2qo6evwyZZPX2hwmN9AyH0DRWfK2xZn+cAPUYCI2TCJCOHkrWG0NqkmpsrQ+CJ19iz8T6Cr0YrPGdoIsKCzV5o1Nyao+gIIeVJLALq1clBvTo5CuVZWTy8jRbgzTsdvHmrg/cfePj4EYiN4yM+QYjEFBEYpuyJKzMjF5kZuUBZhjbWmZb/2HQrRGIBTMzFMDHTldeYzf6rOf9XZmapBwtrfVhU1YeFtT6qWOvTfb0c0KqkKpXK8OhuDM4dDseZv18gJlpxgmQeD+g1tA7Gz2qqPu0mhJAKo6vLoGaNXNSskVvo81IpkJDIR1IyHympfKSm8ZGSIv8/OYWH5CQGKSlAahoPWVlAVhYfmdl8ZGfzkS3hI0siQE5O+ZxbJNlSxL7PQOz7jGJvY2IuhoW1AZto2f//S7p5ZaZVdLXvliSOaHRSTUnKxpPQWISFxuDhnRjcuvQWSfHKl214PKCFtx2+n9cc1rY0pighpHACAWBpIYOlRekH95dKgaxsHrKy5D+Z7P98ZGbxkJmZX5aRyZf/nwFkZDDIyOAhM5NBZhYP0kcJyOSJkQo9JOoYIVda8qSXnJCN5IRsvAxL+Ox6fD4P5lZ6Csm2ipVirTcvAZuYieky9GeoXVJlGAYZ6TlITZIgJTELKUnZSEmSIDkhE+/fpCE6MhXRUal4+zoZ7yJSP7svsa4Anfo4Y/C3DWFlY1BB74AQUpkJBICBPgMD/bL12DVq9wv7OOXCLGRn85CSykNyirwWnZIqr0WnpPKRnMxDfAIQF89DQgIfCUk6SCrBpWyZjEHchwzEffhyLVhHyIfJfx23jM3EMDbTzX/83/8GRqICHbh0oKsvlLcz6+d36tLV14FQyNe6BK02STU9VYIudXYjNUlSpimgdPV10Ki5NXoPrQP31tWgI6RLGoQQzcbjyS9d6+oysLIs3vlRKgWSU/iIT+AjIVGA+AQ+4hMEiIvnIy6OQWwcD4mJAiQmC5GWUfxJC3JzZIiPyUR8TGZp344CHSEfOjp8+f9CPgQ6fAjzHgt40BEK/luHB4EOHzwe5In4v//5fN5/ZQB48sfyMt5/ZfL1qtkbYf5mL5XE/Nn3U+6vUEx6BkIkxWeV7HYsHmBsIoadkxHqN7ZCrQZVYOtoxP7l8+pZYvkES0glUK3A46SPMUWuR8qHUYHHpT3+fACWhvIfVC96PUkOD8kpOkhM1kFikhAJyUIkJunIa72JfCQmC5GYLERyqhiZ2aqdNSg3R4bcHBmgmhxdpBp1zcr3Bf5T5sEfLCws4OjoyC7HxsbC0rL0s7ukJGXjzatkpXIeeBAIedDRUf+aZ3ZuKsQ6Rl9eUYvRMdCOY8ADYKyfDD6vdKeJ1MxsGOnRKGWV6zjI/husglEYtCIlKwfGuoUnZHYthoeiPmkM+0/pLhcbV7WDWL/wyRpKw8LCAmfPnlUqL3NS/ZSmjbBUHugY0DEA6BgAdAzy0HGoPMdA/at9hBBCiIagpEoIIYSoiMqT6rhx41S9S41Dx4COAUDHAKBjkIeOQ+U5BipvUyWEEEIqK7r8SwghhKhIsZLq27dvMWnSJLRs2RL6+vrg8XiIiIj44nZ3797FuHHjULduXejr68Pe3h5Dhw7F69evyxp3hSvtMfjUsmXLwOPx0KZNG9UHWc7KegzCwsLQv39/WFhYQE9PD3Xq1MH69evLL+ByUJZjEBUVhZEjR8Le3h76+vqoXbs25s6di/T09C9vrEYOHToEHx8fODg4sL/H2bNnIzX18yOcAUBWVhamT58OGxsb6OnpoWXLlrh8+XIFRK1apT0G2nROLMvnoCBNPicWplhJNTw8HH///TfMzMzQtm3bYu/8r7/+wuPHj/HDDz/gzJkzWL58OUJCQtC0aVO8efOm1EFzobTHoKBXr15hyZIlsLKyUnF0FaMsx+Du3bvw8PBAdnY2/P39cfr0afzvf/+DVCotp2jLR2mPQXp6Ojp27IjLly9j0aJFOHXqFMaOHYvVq1dj9OjR5Rix6v3yyy8QCARYunQpzp49i2+//RZbtmxBp06dIJN9frSfMWPGYNu2bVi4cCFOnjwJGxsbdOnSBffu3auY4FWktMdAm86JZfkc5NH0c2KhmGKQSqXs423btjEAmNevX39xu5iYGKWyiIgIhsfjMfPmzSvOS6uN0h6Dgjp37syMGzeOad++PdO6dWsVR1j+SnsMpFIpU79+febrr78ux+gqRmmPwblz5xgAzLlz5xTKZ86cyQgEAiY9PV3VoZabwr7Xu3btYgAwFy5cKHK7e/fuMQCY7du3s2U5OTlM7dq1mV69epVLrOWltMdAm86JpT0GBWn6ObEwxaqp8vmla3otbGQlBwcHWFpa4t27d6XaJ1dKewzy/PnnnwgJCcGyZctUFFHFK+0xuHTpEp48eYIff/xRxRFVvNIeA4lEAgAwNjZWKDc1NYVMJgOjQf0FC/teN2vWDAA++70+fvw4hEIhBg4cyJbp6Ohg0KBBOHfuHLKzs1UfbDkp7THQpnNiaY9BHm04JxamwjsqhYWFISYmBvXq1avol+ZMYmIipk6dipUrV8Lc3JzrcCrc1atXAcjb01q0aAGhUAgrKyv88MMPyMws5wE/1UTHjh1Rq1YtzJw5E0+ePEFaWhouXryI9evXY8KECTAw0OxZlIKCggDgs9/rx48fw8nJCfr6+grlLi4ukEgkCA8PL9cYy1txjkFhtOmcWNxjoM3nxApNqrm5uZgwYQIsLS0xZsyYinxpTk2fPh21a9fGqFGjuA6FE9HR0QCAgQMHonPnzggICMCMGTPg7++PIUOGcBxdxdDV1cXVq1chk8ng4uICIyMjdOjQAT179sSvv/7KdXhl8u7dO/z888/o2LEjmjZtWuR6CQkJMDNTHtQ876SakPD5OT/VWXGPwae06ZxYkmOgzefECp2lZuLEibh+/TpOnTpV6JdLG125cgW7d+9GSEiI1s0bWFx5nRaGDRuGhQsXAgA8PT0hlUoxa9YsPHnyBPXr1+cyxHKXlZWFgQMHIiYmBnv27IG9vT1u376NhQsXQkdHB1u2bOE6xFJJS0vDV199BR0dHezYseOz6zIMU+h3QJMufRemJMfgU9pyTizJMdD2c2KFJdXZs2fj999/x65du9C5c+eKelnOjR8/HmPGjIGdnR2SkpIAyP86lUqlSEpKgp6eHsRi7Z69okqVKgCATp06KZR37twZs2bNwr1797Q+qf7xxx+4dOkSwsPD4ezsDABo164dTExMMG7cOEyYMAGurq4cR1kyWVlZ6N27N169eoWgoCDY2dl9dn1zc3NERUUplScmJrLPa5qSHoOCtOWcWNJjoO3nxAq5/LtkyRIsX74c69evx/DhwyviJdVGWFgYfvvtN5iZmbE/165dw82bN2FmZqaxNZSScHFxAQClv0rzaihl7QSmCR4+fAgzMzM2oeZp3rw5APnnRJPk5OTAx8cHt2/fxunTp9GwYcMvbuPi4oLXr18jIyNDofzJkycQiUSoWbNmeYVbLkpzDPJoyzmxNMdA28+J5V5T3bBhA+bOnYslS5Zg0qRJ5f1yaicwMFCpbMqUKZBKpdi4caPGnUhKo1u3bhCLxTh79ix69uzJlp87dw4AStQGpamqVq2KxMREhIeHK/zOb926BQCwtbXlKrQSk8lkGDp0KC5cuIBTp06hRYsWxdqud+/e8PPzw8GDBzFy5EgA8hrKgQMH0LlzZ42qnZT2GADac04s7THQ+nNice+9OXjwIHPw4EFmwoQJDABm8+bNzMGDB5lLly4xDCO/10ogEDALFixgt9m/fz/D4/GYrl27Mjdu3FD4efz4sWpvDqoApTkGhdHke7JKewzmz5/PCAQCZvbs2UxAQACzbNkyRldXlxk5ciQH76JsSnMMXr9+zRgZGTG1atVidu7cyVy8eJFZuXIlY2RkxLi7uyvc/6ru8t73nDlzlL7Xb968YRim6M/BwIEDGVNTU2bbtm3M+fPnGR8fH0YsFjPBwcFcvJVSK+0x0KZzYlk+B5/S5HPip4qdVCGfc13pp3379gzDyE8aABg/Pz92m5EjR35xO01SmmNQGE3+AJX2GMhkMmb16tWMs7MzIxQKGXt7e2bevHmMRCKp+DdRRqU9Bo8fP2b69+/P2NnZMbq6ukytWrWY//3vf0xCQkLFv4kycHBwKPIY5L3noo5BRkYGM3XqVMba2poRi8VM8+bNmcDAwAp/D2VV2mOgTefEsnwOPqXJ58RP0Sw1hBBCiIpofw8RQgghpIJQUiWEEEJUhJIqIYQQoiKUVAkhhBAVoaRKCCGEqAglVUIIIURFKKkSQgghKkJJlZByxOPxwOPxwOfz8fLlyyLX8/LyYtfduXOnwnOjRo1in/Pz8ytyH7t27WLX8/T0VHju0qVL7HN5P/r6+rCxsUG7du0wffp0hIaGluWtEkJASZWQcqejowOGYfDHH38U+vyLFy8QFBQEHZ3PD8Wto6OD7du3QyqVFvr8tm3bvrgPBwcH+Pn5wc/PD1OmTEHPnj2Rnp6OX375BU2aNMHQoUORlpZWvDdGCFFCSZWQcmZtbY2mTZtix44dyM3NVXre398fDMMoTDZQmJ49e+Lt27c4e/as0nNhYWG4du0aevXq9dl9ODo6Yv78+Zg/fz6WLl2Kbdu2ITg4GKGhoWjYsCH+/PNP9O/fv2RvkBDCoqRKSAXw9fXFhw8fcPLkSYXynJwc7Nq1C61atWKnyCvK0KFDoaenh23btik9l1c2duzYUsXn5uaG8+fPw9LSEmfPnsXRo0dLtR9CKjtKqoRUgMGDB8PAwAD+/v4K5cePH8fHjx/h6+v7xX2Ympqif//+OHXqFN6/f8+WZ2dnY/fu3Wjfvj1q165d6hitrKwwfvx4AMC+fftKvR9CKjNKqoRUACMjIwwaNAhnz57F27dv2fJt27bB2NgYAwYMKNZ+fH19kZubq9CZ6ciRI4iPjy9WYv6SvA5Ot2/fLvO+CKmMKKkSUkF8fX0hlUqxfft2AEBkZCQCAgIwdOhQ6OvrF2sfbdq0Qb169dh2WECemM3MzODj41PmGPMmS4+NjS3zvgipjCipElJBPDw80LBhQ2zfvh0ymQz+/v6QyWQlrmGOHTsWr169wsWLFxEeHo7AwEAMHz4curq6ZY4xL1HzeLwy74uQyoiSKiEVyNfXF5GRkTh79ix27NgBd3d3NG7cuET7GDFiBMRiMfz9/dkaqyou/QJAdHQ0AMDS0lIl+yOksqGkSkgFGj58OPT09DB+/Hi8e/cO48aNK/E+LCws0KdPHxw5cgTbt29Hy5Yt0aBBA5XEFxgYCEBeqyaElBwlVUIqkKmpKfr164e3b9/CwMAAgwcPLtV+fH19kZ2djdjYWJXVUmNiYrB161YA8tt3CCEl9/nhVwghKrd48WL07dsXlpaWMDIyKtU+vLy8cOzYMchkMnTp0qXMMd2/fx8jRoxAXFwcunfvjt69e5d5n4RURpRUCalg9vb2sLe3L9M+eDxeqRJfREQE5s+fD0A+8ERcXByCg4MRHBwMQF5DzautEkJKjpIqIZVIZGQkFixYAADQ1dWFqakpatWqhWnTpmHo0KFwc3PjNkBCNByPyetDTwghhJAyoY5KhBBCiIpQUiWEEEJUhJIqIYQQoiKUVAkhhBAVoaRKCCGEqAglVUIIIURFKKkSQgghKkJJlRBCCFERSqqEEEKIilBSJYQQQlTk/+nifHjE65XFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9rsPXSDQOVNHt27eZcePGMQ4ODoxEImH09fUZFxcX5ptvvmFevHih9JzC8Zi7d+9mTp48ybRr147R09NjjI2NmcGDBzNPnz5VOD40NJSZM2cO4+7uzpibmzNisZixt7dnvL29mevXryv9jKtXrzJDhw5lrK2tGZFIxJibmzOurq7MnDlzmLvvjQW0t7cvMTayNA0bNmQAMGZmZkxubm6px+Xm5jJbt25l2rdvzxgZGTFisZipX78+4+XlxWzcuLHEmNLc3FxmxYoVjKOjI1u+xYsXMzk5ORUaB8owDCOTyZitW7cyzs7OjFgsZqytrZnp06cz6enppZZ19+7djKurK6Onp8fUqVOH+fTTT5kHDx4wPj4+DADG399f4fjXr18zI0eOZCwtLRk+n88AYHx8fNj3r1+/znTv3p0xMTFhDAwMmI4dOzLHjh1j/P39SxxbLap5HCiPYf7rf14OHh4eCAwM/PCBpNpkpudidJc/8fxJGgBAIOTh261d0eEj5V3R46Iy4TPdH7ERRdN9rdjRHUMmlpymrTYr7IWpNdTQiYgQrVM8P3mU/XtQFb/j1Aaq5b6fFcAmTx4P+H5nj1KTJwDYOhph69F+aNbagt23Yvpl3LhQsQHUhBBS21EC1WKXTkbh1OFn7PbM5Z5w7/ThFQj0DET4wbcHGjWXjxWTyRjMHXMe8dGZaouVEEJqGkqgWiozPRcrZ/iz26061MWAUU3Kfb6+oRjf/eYFM0v5tGkZqbmYPeIs8vKUd1knhBCiiBKoltq5JojtcasjEWDp5pLj7z7EzEIXPtu6QSiU/zcIDU7Crz9QGzchhJQHJVAt9CruLQ7+fJ/dnunjCSMT1WYZatbKApPmtWa3f1sTiIeBymeOIYQQUoQSqBba/v0d5ObIq1otrPXQy7vkiggVMXhCU7RoI59TUyZlsHjiBarKJYSQD6AEqmViIzNwbE/RCg+zVrYr12oZZeHzeZi3pgMkevJ5NZ4/ScO+zfcqdU1CCKnpKIFqmQPb7kMqlQ/dtXMyhmc32yq5rnV9Q4yf5cZu//LdHSTEUK9cQggpDSVQLZKRloO/iz19Tl1UtQPmB41zRgNnUwBAbrYUa+dfr9LrE0JITUIJVIsc9X2M7Cz5Qr2mFhK06fLhMZ8VIRDy8dUKT3bb71gk7t+u2DJLhBBSW1AC1RIFBTIc+uUBuz1mestKt30q49LaEt0+dmC3182/hgrM9kgIIbUGJVAtceVsDF7HZwGQj/vsM7SR2j7rszmtIBTJ/2uE3HwF/1PRavssQgjRVpRAtcTfu4vW6/Ma6AixjkBtn1XP3hD9RzZmt39aeI0W4SaEkPdQAtUCSS+zcOVsNLs9bHJztX/m6OktoacvAgDEhGfg2N6wD5xBiPaaMGECeDye0gW9ueLg4MAuWk00Ey2orQWOH3jCDl2xcTCCraOR2j/TpI4Ew6c2x+4NIQCALd/ewscjGrNJtda7/y3wToNWsMlNLnqtYw7o2QGuKyt9WQcHB8TExCh9z8rKCq9eKXYyy8vLw8qVK3Hw4EGkpqaidevWWL9+PVq3bl3ifD8/P/Tq1QunTp3Cxx9/XOlYtU23bt0QEBBAfQy0GCVQDccwDP4p9vQ3ZGL1rVE5eEJTnDj4BCmvs5GalI1DvzzA5Hnu1fb5Gu1dLKDvwHUURfjiote69YCs6Cq7tLGxMWbPnl1iv4GBQYl9CxcuxMaNG+Ht7Q1bW1vs378f3bt3x5MnT2Btbc0e9/btW0yZMgVjxozRiOS5evVqLFy4EDY2NlyHwrp48SLXIZAPoASq4Z7cT0bUs3QA8sWye3zSoNo+W6IrxLiZrti49BYAYNdPwRj5RQvoG4g/cCapSUxMTLB8+fIPHscwDH799Vd89tln2LVrFwBg0KBB6NatG/bv34/58+ezxy5YsAA5OTnYvHmzusKuEGtra4UErwmcnCo3RSdRP2oD1XBn/xfOvm7Z1gq6etVbhdpzkBOsbPQBABlpuTjy66Nq/XyiPZKSkvDu3Tu0bduW3Vf4ung18JUrV7B9+3b8/PPPMDMzU+mzCtsHMzMz8fXXX8PBwQEikUgh0T958gQTJkxA/fr1oaOjAysrK4waNQpPnz4tcb2y2kBv376NIUOGoG7duhCLxahfvz6mTp2KhIQEpbGlpqZiyZIlaN68OfT09GBsbAxXV1csXLgQWVlZiI6OBo/HQ0BAAACAx+OxX926dStRxvfl5ubixx9/RMuWLaGnpwcjIyN07twZ//vf/0ocW/hZEyZMQHR0NEaMGAFzc3NIJBJ4eHjg1KlTZX+j3/PPP/9gzJgxaNy4MfT19WFgYAB3d3ds2bIFMlnJjoaF39fnz59j69ataNmyJXR1dRXKmZqaikWLFqFp06bQ1dWFsbExevTogX///bfE9TIyMrBu3Tp4eXnB1tYWYrEYFhYWGDhwIG7dulWhslQFegLVYAzD4NyfRQn0kzHO1R6DSCzAyGktsGmZ/D+n77pgjJjWgtpCa5Hc3FwcOHAAsbGx0NfXR8uWLdGlSxcIBIo9wS0sLKCrq4ugoCB2X2CgfHk8e3t7AEB2djYmT54Mb29veHt7VyquvLw8eHl5ITU1Fb169YKRkREcHR0BAOfOncPgwYORn5+PAQMGoGHDhoiLi8Pff/+N06dPw9/fX2m77Pt2796NKVOmQEdHBwMHDkT9+vURHh4OX19fnDx5Erdu3YKdnR17fFRUFLp3746YmBi4u7vjiy++gEwmw7Nnz7Bx40ZMmzYNJiYm8PHxwZ49exATEwMfHx/2/A91GsrLy0Pv3r0REBAAZ2dnfPnll3j37h3+/PNPDB8+HPfu3cMPP/xQ4ryYmBi0bdsWDRo0wNixY5GamoojR47gk08+wYULF9C9e/dyfc8XLlwIPp8PT09P2NjYICMjA5cuXcKsWbNw9+5d7N+/X+l5s2bNwtWrV/Hxxx+jX79+7P+dmJgYdOvWDdHR0ejcuTP69OmDrKwsnDp1Cn369MGvv/6KKVOmsNcJCwvDkiVL0KVLF3z88ccwNTVFbGwsTpw4gbNnz+Lk+vXo06FDucpSFSiBarCHd18jPvoNAEAk5qNNF27aZ3oNdsLBXx4g6eU7ZKTm4H+/PcKEOa04iYVUv1evXmHs2LEK+xwdHbF792507Vq0Di2Px8Pnn3+OLVu2ICMjAzY2Nti/fz8MDQ0xevRoAMDSpUuRmpqKn3/+udJxvXz5Es2aNUNAQAD09fXZ/WlpaRg5ciT09PRw5coVNGvWjH3v8ePH8PT0xOTJkxEcHFzm9Z89e4apU6fCwcEBAQEBCu2jly5dQs+ePTFr1iwcO3aM3T9mzBjExMTghx9+wKJFixSul5ycDAMDA0gkEixfvhyXL19GTExMuarHC61fvx4BAQHo27cvTpw4AaFQfgv38fFB27ZtsXr1avTv3x8d3ksily9fxvLlyxWS9ahRo9CnTx+sW7eu3An09OnTJaqWZTIZPvvsM+zbtw8zZsyAp6dnifOCg4MREhLC/oFTaPz48YiJicHhw4cxYsQIdn96ejq6deuGr776CgMHDoSVlXy1qKZNmyIhIQHm5uYK14mLi0Pbtm0xZ+PGak2gVIWrwc4Uq75t3cFarWM/yyISCzByagt223ddELLf5XMSC6len332GS5evIhXr14hKysLDx8+xNSpUxEdHY2+ffvi/v37CsevWbMGCxYswJ07d/D777/DxcUFFy9ehI2NDW7fvo1NmzZh8+bNMDc3x4oVK2BtbQ2hUAh3d3dcv17xuZfXr1+vkDwBYN++fUhPT8eKFSsUkicAuLi4YMqUKQgJCUFoaCjKsn37duTn52Pz5s0lOhd5eXlh4MCBOHnyJN68kf+RGxQUhBs3bsDNzQ0LFiwocb3CqtPK2LVrF3g8HjZs2MAmTwCwtLTEsmXLAAC+vr4lzrO3t8fSpUsV9vXu3Rt2dna4c+dOuT9fWbssn8/HrFmzAADnz59Xet78+fNLJM/79+8jICAA3t7eCskTkLe7r1ixAjk5Ofjrr7/Y/cbGxiWSJwDY2tpiyJAheBIdjdhX1Tf9KD2BaiiGYXDhn+fs9idjq7/6trjeQxri8I6HSHr1DmnJOTjq+xjjvnLjNCaifsWfWACgefPm2LFjBwwMDLB+/XosX75c4QlMR0cHq1evxurVqxXOy8vLw8SJE9GvXz+MHj0amzZtYp+IOnbsiO+//x59+vRBREQE+7TxIRKJBC1btiyx/+bNmwDkN2hlT3fPnj0DIK8OfD/BKrtOQEAA7t69W+L9xMRESKVSPHv2DO7u7mwbXO/evcHnV/2zyZs3bxAREQEbGxs4O5e8H3h5eQEAQkJCSrzn5uZWosodAOrXr8+WszxSUlKwbt06nDlzBs+fP0dWVpbC+/Hx8UrPK94uXqjwczMyMpT+nJKSkgDIf07FXb9+HZs3b8bNmzeRmJiIvLw8xRgSE2FXt265y1QZlEA11JP7yXgZW1R926o9tz0ExToCDJ/aHNtWyP9a3bkmCMOmNIdEl/4L1UbTpk3D+vXrceXKlXIdv2LFCsTHx7MdQ9atW4cePXqwN84mTZrAwcEBP//8M1auLN/4VUtLS6XzQaekpAAAdu7cWeb5b9++LfP9wuusW7euXNdJT08HALUNhcnIyACAUnsLF+4vjKM4ExMTpecIhUKlnX+USU9PR5s2bRAVFYW2bdti3LhxMDMzg1AoRHp6OjZv3ozc3Fyl59ZVktAKv79+fn7w8/Mr9XOL/5yOHTuGIUOGQCKRoGfPnnBycoK+vj74fD4uX76MgIAA5OZXX+0Y3f00lP/JKPZ1c3dLdm5aLvUd2giHdzxkx4Ue2xuGkdNafPhEUuNYWloCQIknEGVCQkKwdu1a7NixAzY2NsjMzERCQgLbLgoAdnZ2MDc3x+PHj8sdQ2mLKRgbGwOQP4Eqe0Itr8LrZGRkwMjow5OXFCap0p7CKqswnvcnryj08uVLheOqmq+vL6KiouDj41PiifHmzZtlDklS9rMqjHPz5s346quvyhXDsmXLIBaLERgYiKZNFcfET506le3ZXF24vysTpS6eKKq+7avGieMrQqwjwLBJLuy279og5OdLOYyIcKWw+q1Bg7LHJRcUFGDixIno3r07Jk2apPDe+08rOTk5VRJbu3btAABXr16t1usUHn/+/PlyPdUVVqlKpeX7HTI0NISTkxPi4+MRHh5e4n1/f38AKFfvYlVEREQAgNLe06okLlV+ThEREWjWrFmJ5CmTyXDt2rUKx1BZlEA1UEJMJp7cl0/NxuMDbbtpzuwofYc3grGpDgDgVdxbnDr8jOOIiLo8fvwYqampJfbHxMRgxowZAOS9TsuyevVqREREKFSnGhkZwcbGBufOnUNBQQEA+Q34zZs3cHFxKe1S5fbZZ5+xnVCUdZCRyWS4fPnyB68zY8YMiEQizJkzh203LS4vL0/h5u/u7o4OHTrg3r17WLNmTYnjU1JSFP5IqFOnDgAgNrb8U0JOnDgRDMNg3rx5Cok3OTkZq1atYo9Rh8IhNu9/70JCQkq0eZeHh4cHOnfujL///pudeON9Dx8+RGJiokIM4eHhCmNwGYbBihUrPtgpTB2oClcD+Z8qqr5t4GwKfUPNmflHV0+EwZ81Y+fI/W11IAaObgKBgP4Wq2mOHj2KH3/8Ed27d4ejoyMMDQ0RGRmJ06dPIycnB/369cPcuXNLPf/x48f47rvvsGHDBnYcaKH58+dj1qxZ6Ny5Mzw9PXHw4EEYGBjgyy+/rHTcderUwZ9//olBgwahXbt26NGjB1xcXMDn8xEbG4ubN2+WSGbKODs7Y9euXZg4cSJcXFzQp08fNG7cGPn5+YiNjcXVq1dhYWGBJ0+esOccOHAA3bp1w+LFi/HXX3+hW7duYBgG4eHh+Pfff/HkyRM2EfXo0QNHjx7F4MGD0a9fP+jq6sLe3r7EkKHi5s6di7Nnz+L48eNwdXVFv3798O7dOxw9ehSJiYmYP38+OnXqVOnvoTLjxo3DunXrMHv2bPj7+6NRo0YIDw/HqVOnMHjwYBw5cqTC1zx06BC8vLwwadIkbNmyBZ6enjAxMUFcXBwePHiAR48e4ebNm2yTwZw5czBt2jS0atUK3t7eEIlEuH79OkJDQzFgwACcPHmyqotdJkqgGijgbNGsLT0/1bzpvD4Z0wT/2/kIWW/yERuZAb+/I9W6PinhRvfu3fH06VOEhITg5s2byMrKgomJCTp16oSxY8di7NixpbZDSqVSTJw4EZ6enpg+fXqJ92fOnInMzEzs2LEDwcHBcHV1xcaNG8vdA/dDevTogQcPHuCnn37C+fPncfXqVYjFYtSrVw9eXl7lnsRhzJgxcHV1xfr16+Hv749///0X+vr6qFevHoYMGYLhw4crHO/o6Ijg4GCsXbsW//zzD7Zt2waJRAIHBwd88803bCIAgMmTJyMmJgZ//PEH1q5di4KCAnTt2rXMBCoWi+Hn54cNGzbg0KFD2Lp1K4RCIVxdXbFp0yaMHDlStW9YOdSrVw9Xr17FwoULce3aNZw/fx7Ozs745Zdf8NFHH6mUQG1tbREUFIStW7fir7/+wsGDByGVSlG3bl00a9YMM2fORIsWRf0spk6dCh0dHWzatAl79+6Frq4uOnfujN27d+Ovv/6q9gTKYyqwFICHhwc7swhRj+x3+ehg5Yu8XHn1zMGAwbCsV3LSbq7t3hCCQ9sfAgAaN6+Dv4NGlHoz1UZhYWEl2lkU1JLVWGqLESNG4MiRI0hISNC4OXFJBRTPTx4eZR76wd/xcqAnUA1zNyCeTZ6m5hKNTJ6AfKWWv/eEISe7AM8epeDK2Rh07efAdVjVR9OSU0qxG0edsm8cpKRnz55BR0cHFhYWXIdCtAg1XGmYq+eLqm/bdrPlMJKyGZtJ8PGIomrbHavv0rqGROts3rwZ3t7eCAkJwSeffKIwuw8hH0IJVMNcPV9ULejV34G7QMphyEQXiP4bn/rg9mvcCVDP+DdC1GXTpk24desWJk6ciF9//ZXrcIiWoT+3NEhMeDpeRMpnGxEIeWjuUTUdKtTFvK4eeg12wukj8jFpv64OhKcGPzUT8r6oqKgPH0RIKegJVINc+7eo+raRSx3OJo+viOGfNwdfIO88dNs/Dg/uVN9EzoQQwiVKoBrk5sU49nXXfvZlHKk5rO0M0b1/0SoLv/1IvbQJIbUDJVANUVAgw52AogTarnt9DqOpmJFTm7Ov/U9F49nD5DKOJoSQmoESqIZ4FPgaWW/kqwjoG4pg42DIcUTlZ9/IBJ162bHbv60J4jAaQgipHpRANUTx6tvmHsqXadJkI78omi3k3J/hiAlP5y4YQgipBpRANcStSy/Y1920cEKCxs3rwKNzPQAAIwN819FTKCGkZqMEqgHeZeXj3q2i3qvunepxGI3qRhV7Cj2+/wkS/lsQnBBCaiJKoBog+HoCCvLl6wfWsdSFqbkuxxGppkUbK7TwkE+WLZUy2LMxhOOICCFEfSiBaoC7xWbwcWtfl8NIKm/k9KKn0KO+j5H8+h2H0RBCiPpQAtUAgVeLFoft+JFdGUdqPo9O9dCouXyh4LxcKfZtvsdtQIQQoiaUQDn2LisfjwKLVlxv2Vazp+/7EB6Ph5HTisaFHt7+EBlpZS9cTAgh2ogSKMfu33qFgoKi9k9jMwnHEVVex552sG9oDED+B8LBnx9wHBGprJ49e4LH4yEgIKDUY6ZNmwYej4edO3dWY2SEcIcSKMfuXilq/2zpqd1Pn4X4fB5GTCtqC9235R6y3uRxGBGprODgYAgEAri7u5d6zO3btwEAHh9YyJiQmoISKMeKt3926KE90/d9SPePHWBdX74Y+Jv0POzfep/jiIiqIiMjkZqaChcXFxgYKF/g/d27d3j06BF0dHTQvHlzpccQUtNQAuVQTnaBwuolLdtqdw/c4gRCPkZNb8lu71ofTG2hWiowUL5AgKenZ6nHBAUFoaCgAK6urhCJRNUVGiGcogTKoQd3XiE/T97+aWougZmFdo7/LE3PTxugfgMjAEDWm3zs2UDjQrVReRLorVu3AFD1LaldaEFtDgVeKaq+1fbet8oIhHyM+8oV38++CgDYt+U+xsx0RR1LPY4jIxVRmECPHDkCf39/pcdQ+yepjSiBcuju1aIORO1rUPtncV36OuDwjkd4/iQNOdkF2Lk2CAt/6sx1WJXiIt7GdQgfcIt99ThvRqWuxDAMQkLkNQd+fn4fPJ4SKKlNqAqXI3m5UtwvNv+taw1q/yyOz+dh/Gw3dvuPHQ/xKu4tdwGRCnn27BkyMjLQvn17MAyj9Cs1NRUAoKenh2bNmimc/8svv8DR0RESiQTu7u64evUqF8UgRC0ogXLkYeBr5OZIAQBGpjowr1tzqzXbe9nCuaU5ACA/T4YdP9zlOCJSXoXVt61atSr1mODgYACAm5sbBAIBu//IkSOYNWsWFi9ejJCQEHTo0AF9+/ZFbGyseoMmpJpQFS5Hird/Nm9twWEk6sfj8fDZ125YMOECAODPXY8xZoYrGjYz4zgy1VS2WlQtUgKLXtepumrUwgTaunXrUo8pTKDvV99u2LABEyZMwJQpUwAAW7duxblz57B9+3asXr26ymIkhCv0BMqRwGLtnx16avf8t+XRqoM1WnewBiBfL/Snhdc5joiUR3kSaGEbafEEmpeXh6CgIPTq1Uvh2F69euHGjRtqiJSQ6kcJlAMFBTKE3Cxq/3RrVzPbP4vj8Xj4fKE7eDz59tVzMbhxgaryNJlMJsO9e/cgFovh4uJS6nHKnkCTk5MhlUphZaXYu9zKygqvXr0CITUBJVAOPH2QjOysfACAvqEIVjbKZ3epaZyamqG3d0N2e+28a5BKZRxGRMoSFhaGt2/fwsXFBWKxWOkxb9++RXh4OAwMDNCkSZMS7/MK/2L6D8MwJfYRoq0ogXLg3s2X7OvG/y39VVuMn+0GiZ686T38cSr+2feE44hIaYKCggCUXX17//59yGQytG7dGnx+0e3E3NwcAoGgxNNmYmJiiadSQrQVJVAOhNwouql4drflMJLqZ26lh2GTi6oDNy+7iay3NNG8Jho3bhwYhoGvr2+px3Ts2BEMw5RYpUUsFsPd3b3E2FE/Pz906NBBLfESUt0ogXIgpNgTaE2cgehDhkxqhjpW8mkLUxKz8dvqwA+cQbTR119/jT179sDX1xdhYWGYNWsWEhISMG3aNK5DI6RKUAKtZgmxb9iJBIRCPho0MeU4ouqnqyfCxK+LqgX3bLyHyLBUDiMi6jB8+HBs2rQJ3333Hdzc3HDt2jWcOXMG9vb2XIdGSJWgBFrNird/2jU0hkBYO38EH33aAM3+G/9aUCDDqq8CwDAMx1GRqjZ9+nRER0cjNzcXQUFB6NKlC9chEVJlaufdm0PFq29bd7TmMBJu8fk8zFrRDnyBvEfm3YB4nDr0jOOoCCGk/CiBVrPiHYhqcwIFgAbOphg0vim7vWb+NWSm53IYESGElB8l0GqU9SYPTx8ks9vN3Gr2FH7lMW6mK8yt5PMApyVlY/O3tz5wBiGEaAZKoNXo4d3XkMnk7XwWdfWgb6h8cHptomcgwvSlbdjtI78+xL1bL8s4gxBCNAMl0GoUfKMoMTT3sOQwEs3Sqbcd2nSpBwBgGGDxpAvIyS7gOCpCCCkbJdBqVLwDUZuuNhxGoll4PB5mrWwHPX0RACAmPANbfLivyqVewYTUTFX1u00JtJpIpTKFBbSbu9MTaHFWNgaYusid3d63+Z7CE3t1EwgEyM/P5+zzCSHqk5+fr7B2raoogVaTiMepyHojvyHr6gtR17Z2TCBfEX2HNYJ7p/+WPPuvKjf7HTdJzNDQEJmZmZx8NiFEvTIzM2FoaFjp61ACrSbFq28budShFSmU4PF4+PqHDtAzkFflvojMwKZl3FTlmpmZIS0tDcnJycjLy6PqXEK0HMMwyMvLQ3JyMtLS0mBmZlbpawqrIC5SDiHFqiPbdqP2z9JYWuvji8UeWL/4JgDgwNb76NTLDp17V+/0bzo6OrCzs0Nqaiqio6MhlUqr9fMrLKtoeBQSw7iLgxAuJRf7PQgr+XsgEAhgaGgIOzs76OjoVPrjKIFWk3vF2j9d29b8BbQro/eQhrj2byxuX44HACyeeAF/B42ARV39ao1DR0cH1tbWsLbWggkvDjUrej2KnpZJLdWs2O9BNdQaURVuNUh6mYW4KHl7Gl/Ag1PT2jeBfEXweDzM/bEjzCzkK7akJmVj8aQL7BhaQgjRBJRAq0Hx3qS2jkYQiSvf+6umM6kjwYKfOqGwqfiG3wvs3RTCbVCEEFIMJdBqULwDUav2VH1bXq07WGPYlKLFtzcuvYmHga85jIgQQopQAq0G924WtX96dKIORBUxYXYrOLc0BwBICxjMHn4WacnZHEdFCCGUQNUu+10+wkKS2O3CNTBJ+QhFfCze2Bn6hvKhLa9evMX88f9CKpVxHBkhpLajBKpmjwITUVAgv9mbmktgZFL5rtO1jbWdIeav68Ru3/B7gZ9X3uEwIkIIoQSqdsXHfzZrRU+fqurQoz5GfdGC3f51dSD8T0VxGBEhpLajBKpmxZfm8uxmy2Ek2m/cLFeFRcgXTvBDTEQ6dwERQmo1SqBqJJMxCCnWgahFGysOo9F+AgEfizd0hmU9+YQKbzPzMHv4Wc7myyWE1G6UQNXo+ZM0ZKblAgB0JALYOFR+8uLazthMgm+3dYVQJP+v++xhClZMv0xz1RJCqh0lUDUqPv7T0dmUJpCvIk1amGPmck92++Shpzj48wMOIyKE1EaUQNXoXvEFtDvX4zCSmqffsEboM7Qhu71m3jXcvRLPYUSEkNqGEqgaFe+B27ojJdCqNtPHk51kQSZlMGfEWSTEvuE4KkJIbUEJVE1SEt8hJiIDAMDn89C4eR2OI6p5xDoCfLutK0zqSAAAack5mDXsDHKyCziOjBBSG1ACVZP7xZYvs7I1gFiHJpBXBwtrfXy7tSsEQnn7cmhwElbOoE5FhBD1owSqJsVXYHFtS8NX1KlFGyt8saQNu318/xMc2v6Qw4gIIbUBJVA1Kd4D17M7TSCvbgNHN0GvwU7s9pq5VxF4lToVEULUhxKoGuTmFOBxUCK77eJuyWE0tQOPx8Osle3QuIW8rVlawGDOiHN4+YI6FRFC1IMSqBqEhiQhP08+gbyRiQ5M6+hyHFHtINYRwOfnbjAxk3cqSk3KxuzhZ5GbQ52KCCFVjxKoGhQfvtKkJfW+rU6W1vpYuqUL26noUWAiVs0MoE5FhJAqRwlUDYonUM/uNIF8dXP1rItpizzY7WN7w/DHr484jIgQUhNRAq1iDMPgXrEhLG6edTmMpvb6ZKwzeg5qwG6v/vqKQscuQgipLEqgVSw2IgOpSdkAAJGYj/pOxhxHVDsVdipq1LyoU9E3o84hLTmb48gIITUFJdAqFlzsKce+oQn4fJpAnis6EiF8tnWFobEYAPA6PguLJ12ATEbtoYSQyqMEWsWKt3960ATynLOyMcC8NR3Z7StnY7BrfTCHERFCagpKoFXsXrEFtCmBaob2Pepj6KRm7Pbmb28h6HoChxERQmoCSqBVKD01B5FhqQAAHg9o8t9KIYR7E79pjWatLQDIV275ZtQ5tq2aEEJUQQm0ChWfQN6ynj4kukIOoyHFCUV8LNnUBUamOgCApJfvsPAzP2oPJYSojBJoFSre/tmSJpDXOJbW+liwrhO7ff3fWOxcE8RhRIQQbUYJtArdu0UTKGi6tl1tMGJqc3Z728rbuH/7VRlnEEKIcpRAq0henhQP7xabQL41TSCvqSbMdoOLe1F76Pyx/yLrTR7HURFCtA0l0CoSGpyInGz5pOWGxmKYW+lxHBEpjUDIx8KfOkPPQAQAiIvOxA9zrnAcFSFE21ACrSJ3rxQNi2jayoLDSEh51LU1wKwVnuz2P/ue4NzRcA4jIoRoG0qgVaT44s0dPqrPYSSkvLwGNkCPgY7sts90fyTE0vqhhJDyoQRaBQoKZAo9cFu3t+YwGlIRM5d7wspGHwDwNiMPiyb6QSqVcRwVIUQbUAKtAk/uJyHrTT4AQM9AhLr1DTiOiJSXvqEYC3/qxM5ZHHglAbvWh3AcFSFEG1ACrQKBV4vaPxu3qAMejyaQ1ybNPaww8osW7PbW5bfwKOg1hxERQrQBJdAqEFisAxG1f2qnsTNaoqmbfOpFaQGDhRP8kP0un+OoCCGajBJoJclkjMLE5O4daQJ5bVQ4tEWiJ59+MeppOjYuuclxVIQQTUYJtJLCH6UgMy0XACDRFaJ+AyOOIyKqqmdviC8We7DbB39+gOt+sRxGRAjRZJRAK6n48JWGzcyo/VPL9R3WCO2KTcO4ZPIFpKfmcBgRIURTUQKtpOIdiNr3oPlvtR2Px8PXP7SHcbFVW777KoDjqAghmogSaCUwDIOga0UJtE0XGw6jIVXF1FwXc75vz26f/V84Tv/xjMOICCGaiBJoJTx/koaURPmizGIdAewbmXAbEKkyHXvaoc+Qhuz2qpmX8fIFzVJECClCCbQSij99OjU1ZQfjk5rhiyVtUNdWPinGm4w8LJ1ykRbgJoSwKIFWwt0rRR2I2nvR+M+aRs9AhAXrOqKwX9itS3E4+PMDboMihGgMSqAqkskY3PaPY7c9utD4z5qouYcVhn9etAD3hsU3EBGaymFEhBBNQQlURc8epii0fzo1NeM4IqIu475yRQNnUwBAXq4UCyf8i7w8KcdREUK4RglURbcuvWBfO7c0p/bPGkwkFmDh+k4QieW/LmH3krF91R2OoyKEcI0SqIpuXCxKoF362XMYCakOjo1NMWlua3bbd10wgostYUcIqX0ogaogN6cAQcUmUCg+cw2puQaNbwq3dnUByNvAF33mh6w3eRxHRQjhCiVQFdy79Qo52QUAAEMTMaxsaP3P2oDP52Hemg7QNxQBAOKiMrF2/jWOoyKEcIUSqApuXiiqvm3ZxorDSEh1s6xngJk+nuz2n7+Hwv9UFIcREUK4QglUBcXbP7sPcOQwEsIFr4GO6Fqs3fvbqZeQkviOw4gIIVygBFpB6ak5CA1OZLdp/c/ah8fj4asV7WBmqQsASE3KxvIv/MEwNEsRIbUJJdAKuu0fh8L7pJWNPgyMxNwGRDhhZKKDuT92YLcvnYzCsb1hHEZECKlulEAr6Gax6tu2XWn1ldqsTWcbfDK2Cbu9es5VvHiewWFEhJDqRAm0gopPoNC1nwN3gRCNMHmeO+o3MAIAvMvKx6KJF1BQIOM4KkJIdaAEWgEvnmfgxfNMAIBAwEOzVhYcR0S4JtEVYsG6TuAL5DNRhdx4iW3Lb3McFSGkOlACrYAbxYavODqbQiQWcBgN0RRNWppj3Feu7PbOtUEIOBPNXUCEkGpBCbQCit8Uu/al6ftIkZHTWsCjc1GP7IUT/JAQk8lhRIQQdaMEWk452QUKy5d16ePAXTBE4/D5PCz8qRMs6uoBADLTc/H1qHO0agshNRgl0HK6GxDPTt9nYiZBPXtDjiMimsbYTIKlW7pCIJS3hz68m4i182iqP0JqKkqg5XS5WPWtZ3cavkKUa9bKAlPmu7Pbh7c/xNHfH3MYESFEXSiBlgPDMLhyNprd7vFJA+6CIRpv8ISm6FKsjXzVzMsIvBrPYUSEEHWgBFoOkaGpSIh5AwAQivho4UETyJPS8Xg8zP2xAxo2MwMASAsYzBp2FvHR1KmIkJqEEmg5XDj+nH3d1M0cQhF920jZdPVEWLGjO0zNJQCA9JQcfDn4NK0fSkgNQpmgHC4WS6B9hzbiMBKiTSyt9bH8l+7sH1zhj1Iwe8RZ6plLSA1BCfQDEmIyERqSBADg8YAOH9XnOCKiTZq1ssCc79qz2zf8XmDJpAuQyWjlFkK0HSXQD7h4omix5AbOptA3pNVXSMX0GuykMFPRmSPhWDPvGi1/RoiWowT6ARePR7Kve3s35DASos3GzGiJAaOLVm45sPU+dq4J4jAiQkhlUQItQ0riOwRde8lu0+orRFU8Hg9fLmuDzn2Khrds/vYW9mwM4TAqQkhlUAItg9+xSLatysbeEGYWuhxHRLSZQMDHwp86wa19XXbfugXXsXMtPYkSoo0ogZbh7P/C2df9hlPvW1J5Yh0BVm7vjhZtisYSb1p6Ezt+uMthVIQQVVACLcXr+LcIupbAbn/0qROH0ZCaRFdfhO99veDWruhJdOvy29i87CZ1LCJEi1ACLcX5vyJQeC9zaGRC1bekSunqibDqNy+07mjN7vttTRAWT7pA40QJ0RKUQEtRvPq2/8jGHEZCaiqJrhCrfvVC265FixOcOPAUXww8iTcZuRxGRggpD0qgSkQ9TcODO68ByCdP6D7AkeOISE0l1hFgxfbu6DO0aIjUrUtxGNvtL1qQmxANRwlUieP7n7CvnV3NYWSiw2E0pKYTivj4+vv2mDDbjd0X/jgVQzyP4Nq/MdwFRggpEyXQ90ilMpw4WJRAh0124TAaUlvweDyM/rIl5q/ryC7InZGai2kDTmL793dp6j9CNBAl0PfcuhSH1/FZAAAdiQDtvGjuW1J9en7qhPUHe8PMUt5pjWGAbStu48tBp5CS+I7j6AghxVECfc+xvWHs60697WnpMlLtXFpbYsfx/nD1LBoreuVsDAa1PoyAM9HcBUYIUUDZoZiUxHfwO1Y09+3QSc04jIbUZqbmulizp6dCE0JKYjamf3oKK770x7usfA6jI4QAlEAV/L07FAX5MgBAXVsDODU14zgiUpsJhHxMWeCOH3b1gKl50Tjk/+18jMHuh3Hz4gsOoyOEUAL9j1Qqw/98H7PbI6c15zAaQoq06WwD3zMD0Lm3HbvvxfNMTO57HEsmX0B6SjaH0RFSe1EC/c/VczFIiHkDQD42j6buI5rEyFSCZVu7Yv7ajtA3FLH7/9n3BANaHsI/19pCJuNxGCEhtQ8l0P/s33qffe01wBFiHQGH0RBSEo/HQ89BTth1/lN07Ve0LFpqUjaW+I7FmO/n4FEU9RonpLpQAgUQGpKIW5fi2O3RX7bkMBpCymZmoYulm7ti5Y7uqGNV1DZ6P9IRI1bOxbdTL9GQF0KqASVQALvWFy1q3LKtFeraGnAYDSHl075Hfew+/ylGTGsOkVDeK5dh+Phrdyg+djmAvZvvIS+XJqYnRF1qfQKNi8rE+T8j2O2pi9w5jIaQitHVF2HSN61x/Psf0NX1Ibv/TUYe1s67hv4tDuDU4ac0kxEhalDrE+jOtYHszcW+kTEaNzfnOCJCKs7eKhm/zPkN2+dsR/16Rb1y46PfYMF4Pwxr9z/cuBDLYYSE1Dy1OoHGRmbg2J6imYemLvTgMBpCKq+LaygOb3+I2dPTYWRYwO4Pu5eEKf1OYEq/4wgLSeIwQkJqjlqdQLd/fwdSqfzps34DI3h0rsdxRIRUnljEYIR3Fv4+kIjxo95ARyxj37tx4QWGeB7B/HH/IiY8nbsgCakBam0CjQhNxalDz9jtWSvbgcejcXSk5jAwYPDFpEwc3fcaA/tlgc8ragc9/ccz9G9xEIsm+iEmIp27IAnRYrUygTIMgx+/ucq2fTo2MYGrZ12OoyJEPSwtZFj8TToO+iaiS4ei4S0yGYMTB55iQIuDWDzpAiVSQiqoVibQSyeiFOYRnb+2E4fREFI9HB0KsHZVGn7bkoQ2rXPY/VIpg+P7n2BAi4NYMvkCYiMzOIySEO1R6xJoTnYB1i24xm536mWHhs1o0nhSe7R0ycPWdSnYsSkJHq2KeuxKpQz+2fcE/ZsfwMIJfgi7R52NCClLrUugW5ffwovnmQAAoYiPOd+14zgiQrjh1iIP235KVZpITx56iiFtj2By3+O49m8MGIbGkRLyvlqVQENuvsTeTffY7YnftIKRqYS7gAjRAGwi3ZgEd7cchfduXnyBqf1PYrD7Hzi2Lww52QWlXIWQ2ofHVOBPSw8PDwQGBqozHrXJepOHoZ5HEBMhb99xaGyC304NoJ63pEaod92YfZ1lOQYyHVuVr/X4iQiH/mcA/yu6kDGKvx9Gpjr4ZKwzhk1ujgbOpip/BiFqUfx+Xg21JrUigTIMg7mjz+Pcf1P2CQQ87L04CFY2NOctqRmqMoEWSngpwJG/DXD8tC5yckuuTtSqgzX6j2yM3t4NFRb8JoQzlECr3oFt97H666vs9szlbTFwtDOHERFStdSRQAtlvuHhn1P6OHZSFy9fi0u8LxTy0bFXfXz0qRO69HWAuZVelX02IRVCCbRq+Z+KwldDzrBjPtv3sMXKHV4cR0VI1VJnAi0kkwF3g3Xw9wk9XL2pq3QBbx4PaNHGCl36OqBtVxs097CEjkRY5bEQolQ1J9Aa/T/7/u1XmDv6PJs8Laz1sHRzV46jIkQ78fmAp0cuPD1ykZqWgYsBujj7rw5CnxZV3zIM8ODOazy48xoAINYRoGVbK7RoY4UmLczRpGUdODqbQiSiBeuJ9quxT6BB1xPwxcCTyHojXydRz0CEPX6fUlsNqZGq4wm0NHEJAly+qosr18R4FCYp0fHofUIRH/XsDWFjb4R69oawrm8IMwtdmNSRwMxCF/qGIuhIhJDoCqGjK/9XLBFAJOJTpz9SNnoCrbyAM9H4ZtQ5ZL+Td7kXifnYcrQvJU9C1MC2nhRjhr/FmOFARgYfN+/qIChEjOB7QsS/KjlMrCBfhtiIDMRGVHzGI4GAB6GIX+xLnliFIv5/7wnY9wr3S/SEMDaVwNhMByZmEhibSWBiJoGljT6s6xvCytYAYjE9EZOKq1EJlGEY7Fofgo1LbrB/fIh1BNhytC/sG5pwGhshtYGxsQx9PspGn4/kEzOkpPJx/5EYzyKEeBYuQESkEIkpqo+9lkoZSKVS5OZIqypk8HiAZT15MrVxMIJDYxM4OZvBsYkJHBqbQqxDyZUoV2MSaGLCWyz9/BKu/1u0aLCuvhBbjvaFQyMar0YIF+qYyeDVJQdeXYr2vcvm4dVrAV6+EiDhlRBJSXykpjFISwPS0/l4l81HXh4PuXl85OUXfclk6pn3hWGA1/FZeB2fhXu3Xim8x+fzYONohMYudeDsas5+WdsZUnUy0f4Emp8vxaFfHmL7d3fwJiOP3V/PzhCb/tcHpnWo2pYQTaKny6CBQwEaOBQAyC33eQwDSKVAQQEPBQVAgfS/fwt48v3FtgukgPS/7exsHjLf8JGRyUdmJh/pGTykpDBITOLjdZIIaRliMKW028pkDF5EZuBFZAYunnjO7jcy0UGTwoTa0hzOrhZo0NSUqoJrGa1NoLk5BThx8Cl81wYhLipT4b2PPm2AOd+1p6oXQmoQHg8QCgGhsLBzSNV0EsnPBxKTBHj1WoC4BCGiY4R4Hs1DdKwQick6SpNrZnou7gbE425APLtPKOLDqakZmrSsg8bN66BR8zpo0sIc5nX16Gm1htKqBCqTMXgU+Bqnj4Tj5MEnyEhV/OvV0EQHizZ0QpvONhxFSAjRNiIRYFNPCpt6Uri3ylN4LycXiH0hQnikCM8ihHj6jI/w5zrIelfy1lmQL8PTB8l4+iBZYb9JHQkaNZcn1YYuZrBvaAI7J2NY2RiAz6fEqs00ehiLVCpDxONUBF1PQPC1lwi8Fo+kl+9KHCcS8zF0kgvGzGgJEVWhkFqIy2EstQ3DAK8TBXgWKUJ4hAhPngnwLEKA10kV6xylIxGgfgNjeUJtaAw7J2PUtTWAZT0DWNnow6SOhJ5cK6q2DGPJz5ciPTkHKYnZSEvORmqS/CvpZRZiItIR/SwdsZEZyMstvbedvpEYn4xughFTm0NXX1SN0RNCaiseD6hrJUVdKym6dChavebNWx7CI0WIjBIhIkKAZxF8RMXqICdX+W02N0eKiNBURISmKn1frCOAZT19mNfVg7GpBCZ15ENx5ENy5ENxjEx1oGcggkRXCD19ESR6Quj+9y+1x6ofJwl09TdXcWDrfZXOFekI0Kp9XXw61hnunepRFQghRCMYGjBo7ZqH1q5F1cAyGfDqtQARUSJERgkRFcVH7As+4l+K8Sar7D/683KliIvKLNHHo7yEQj5E4v/GyAr/GzcrVBwzKxDyIPrvfb6ABx6PBx5P3vuYxwPA47GvC9/j/bc9dJILPvrUSaXYagpOEqi+QfmfFsU6AljbGaKFhyVc3C1h42AIgUDenb20v9wIqW3qFXudmZaNAl4iZ7EQRXp8oKWT/Ku4N28FeJkoRvwrCRJeiRH/UoTkFAFS0kRITZcgO7dytWoFBTIUFMgqdY2ydPjITm3X1hYVagM1NzeHg4NDuY5NSkqChYVFmcc8fZiMgvyiHzCfL59lRBueKnML3kBHaMh1GGpD5dNOPB4DI91MZOXkwFBXh+tw1OZNdm4tLh8DMLL/2vjKvn2z7zK8Uo8sygAVuO8KxLCwty//8e8pT37QFObm5jh37pzS9yqUQCtCm+bNVQWVT7tR+bQblU+71ZTyqWdqD0IIIaSGowRKCCGEqEBtCfTzzz9X16U1ApVPu1H5tBuVT7vVlPKprQ2UEEIIqcmoCpcQQghRQYUSaFxcHGbOnIn27dtDT08+QXJ0dPQHzwsMDMTnn38OZ2dn6Onpwc7ODqNHj0ZUVJSqcauFquV73+rVq8Hj8dCpU6eqD7ISKlu+sLAwDB06FObm5tDV1UWTJk2wefNm9QVcQZUpX2xsLMaPHw87Ozvo6emhcePGWLp0KbKystQbdAX8+eef8Pb2hr29Pfv9X7RoEd68efPBc3NycjBv3jxYW1tDV1cX7du3x5UrV6oh6vJTtXzacn+pzM+vOE29v1S2fJp+f1GKqQB/f3/G0tKS6du3L9OrVy8GABMVFfXB87755humQ4cOzM8//8xcvnyZOXjwIOPs7MyYmZkxsbGxFQlBrVQtX3GRkZGMvr4+Y2lpyXTs2FE9gaqoMuW7e/cuY2hoyAwYMIA5duwYc+nSJebXX39l1q9fr96gK0DV8r19+5Zp1KgR4+DgwOzZs4e5dOkSs2bNGkYikTDDhg1Tf+Dl5OnpyQwdOpQ5cOAAc/nyZWbjxo2MsbEx4+npyUil0jLPHTVqFGNsbMz89ttvzIULF5hBgwYxEomECQkJqZ7gy0HV8mnL/aUyP79Cmnx/qUz5tOH+okyFEmjxb8LOnTvLfYNKTEwssS86Oprh8XjMsmXLKhKCWqlavuJ69erFfP7550zXrl017j+4quWTSqVMs2bNmE8//VSN0VWequU7f/48A4A5f/68wv4FCxYwAoGAycrKqupQVaLs92jv3r0MAObixYulnnfv3j0GALNr1y52X35+PtO4cWNmwIABaolVFaqWT1vuL6qWrzhNvr+oWj5tub8oU6EqXD5ftSZTZTNO2Nvbw8LCAvHx8UrO4Iaq5St06NAhBAcHY/Xq1VUUUdVStXyXL19GaGgovv766yqOqGqpWr68PPncpUZGRgr7TUxMIJPJwGhIPztlv0dt2rQBgDJ/j06cOAGRSIThw4ez+4RCIUaMGIHz588jN7f8i1qrk6rl05b7i6rlK6Tp9xdVy6ct9xdlOOtEFBYWhsTERDRt2pSrEKpUWloa5syZg7Vr18LMzIzrcKrUtWvXAMjb0dq1aweRSARLS0t89dVXyM7O5ji6yvvoo4/QqFEjLFiwAKGhoXj79i0uXbqEzZs3Y9q0adDX1+c6xFIFBAQAQJm/R48fP4ajoyP09PQU9ru4uCAvLw8RERFqjbEyylM+ZbTl/lLe8mnr/aU85dPm+wsnCbSgoADTpk2DhYUFJk2axEUIVW7evHlo3LgxJkyYwHUoVS4hIQEAMHz4cPTq1Qt+fn6YP38+fH19MWrUKI6jqzyJRIJr165BJpPBxcUFhoaG6NGjB/r3749t27ZxHV6p4uPj8e233+Kjjz6Ch4dHqcelpqbC1NS0xP7CG3FqqmYuylDe8r1PW+4vFSmfNt5fyls+bb6/cLIay4wZM3Djxg2cPn1a6S+2trl69Sr27duH4ODgGrkArkwmn/B/zJgxWLlyJQCgW7dukEqlWLhwIUJDQ9GsWTMuQ6yUnJwcDB8+HImJidi/fz/s7Oxw584drFy5EkKhENu3b+c6xBLevn2LTz75BEKhELt37y7zWIZhlP6/1JSqaWUqUr73acP9pSLl08b7S0XKp833l2p/Al20aBF+++037Nq1C7169aruj1eLqVOnYtKkSbC1tUV6ejrS09NRUFAAqVSK9PR0jWljUlWdOnUAAD179lTYX/jzu3fvXnWHVKV+//13XL58GWfOnMGYMWPQpUsXzJ07F+vXr8eOHTtw/75qa9eqS05ODgYOHIjnz5/j/PnzsLW1LfN4MzMzpU+ZaWlp7PuapKLlK04b7i8VLZ+23V8qWj5tvr9UawL9/vvv8eOPP2Lz5s0YO3ZsdX60WoWFhWHHjh0wNTVlv65fv45bt27B1NRUI59gKsLFxQUASvz1W/gEU9nOV1x7+PAhTE1N4eSkuGBj27ZtAch/vpoiPz8f3t7euHPnDs6cOYMWLVp88BwXFxdERUXh3bt3CvtDQ0MhFovRsGFDdYVbYaqUr5A23F9UKZ823V9U/f8JaOf9pdqqcLds2YKlS5fi+++/x8yZM6vrY6uFv79/iX2zZ8+GVCrF1q1bNeoGpYq+fftCR0cH586dQ//+/dn958+fB4AKtU9porp16yItLQ0REREKP6vbt28DAGxsbLgKTYFMJsPo0aNx8eJFnD59Gu3atSvXeQMHDoSPjw+OHj2K8ePHA5C3Ex45cgS9evWCjo5mrKupavkA7bi/qFo+bbm/qFo+rb6/VHTcy9GjR5mjR48y06ZNYwAwv/zyC3P06FHm8uXLDMPIx18JBAJmxYoV7DmHDx9meDwe06dPH+bmzZsKX48fP66S8ThVRZXyKaOJ47QYRvXyLV++nBEIBMyiRYsYPz8/ZvXq1YxEImHGjx/PQSlKp0r5oqKiGENDQ6ZRo0bsRApr165lDA0NGXd393IPcle3wjItWbKkxO/RixcvGIYp/ec3fPhwxsTEhNm5cydz4cIFxtvbm9HR0WGCgoK4KIpSqpZPW+4vlfn5vU8T7y+VKZ+23F/eV+EECvki5yW+unbtyjCM/GYEgPHx8WHPGT9+/AfP0xSqlE8ZTfwPzjCql08mkzHr169nnJycGJFIxNjZ2THLli1j8vLyqr8QZVC1fI8fP2aGDh3K2NraMhKJhGnUqBHzzTffMKmpqdVfiFLY29uXWr7C8pRWvnfv3jFz5sxhrKysGB0dHaZt27aMv79/tZehLKqWT1vuL5X5+b1PE+8vlSmfttxf3kersRBCCCEq0NzWWUIIIUSDUQIlhBBCVEAJlBBCCFEBJVBCCCFEBZRACSGEEBVQAiWEEEJUQAmUEEIIUQElUEKqCI/HA4/HA5/PR2RkZKnHde/enT12z549Cu9NmDCBfc/Hx6fUa+zdu5c9rlu3bgrvXb58mX2v8EtPTw/W1tbo0qUL5s2bh5CQkMoUlRACSqCEVCmhUAiGYfD7778rfT88PBwBAQEQCsuehlooFGLXrl2QSqVK39+5c+cHr2Fvbw8fHx/4+Phg9uzZ6N+/P7KysvDTTz+hdevWGD16NN6+fVu+ghFCSqAESkgVsrKygoeHB3bv3o2CgoIS7/v6+oJhGIVJs5Xp378/4uLicO7cuRLvhYWF4fr16xgwYECZ13BwcMDy5cuxfPly/PDDD9i5cyeCgoIQEhKCFi1a4NChQxg6dGjFCkgIYVECJaSKTZkyBa9evcKpU6cU9ufn52Pv3r3o0KEDu4RTaUaPHg1dXV3s3LmzxHuF+yZPnqxSfG5ubrhw4QIsLCxw7tw5/PPPPypdh5DajhIoIVVs5MiR0NfXh6+vr8L+EydO4PXr15gyZcoHr2FiYoKhQ4fi9OnTePnyJbs/NzcX+/btQ9euXdG4cWOVY7S0tMTUqVMBAAcPHlT5OoTUZpRACalihoaGGDFiBM6dO4e4uDh2/86dO2FkZIRhw4aV6zpTpkxBQUGBQkejY8eOISUlpVxJ+EMKOx/duXOn0tcipDaiBEqIGkyZMgVSqRS7du0CAMTExMDPzw+jR4+Gnp5eua7RqVMnNG3alG03BeRJ2NTUFN7e3pWOsXCh8KSkpEpfi5DaiBIoIWrg6emJFi1aYNeuXZDJZPD19YVMJqvwk+PkyZPx/PlzXLp0CREREfD398fYsWMhkUgqHWNhUubxeJW+FiG1ESVQQtRkypQpiImJwblz57B79264u7ujVatWFbrGuHHjoKOjA19fX/ZJtCqqbwEgISEBAGBhYVEl1yOktqEESoiajB07Frq6upg6dSri4+Px+eefV/ga5ubmGDRoEI4dO4Zdu3ahffv2aN68eZXE5+/vD0D+tEwIqThKoISoiYmJCYYMGYK4uDjo6+tj5MiRKl1nypQpyM3NRVJSUpU9fSYmJuLXX38FIB8yQwipuLKnMiGEVMp3332HwYMHw8LCAoaGhipdo3v37jh+/DhkMhl69+5d6Zju37+PcePGITk5Gf369cPAgQMrfU1CaiNKoISokZ2dHezs7Cp1DR6Pp1KSi46OxvLlywHIJ3FITk5GUFAQgoKCAMifPAufQgkhFUcJlJAaKiYmBitWrAAASCQSmJiYoFGjRpg7dy5Gjx4NNzc3bgMkRMvxmMK+7IQQQggpN+pERAghhKiAEighhBCiAkqghBBCiAoogRJCCCEqoARKCCGEqIASKCGEEKICSqCEEEKICiiBEkIIISqgBEoIIYSogBIoIYQQooL/Ax8gDiq/z0yjAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# scale\n",
+    "ori_x = ori_x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# Smoothing\n",
+    "for i in range(len(ori_x)):\n",
+    "    ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "# yes_data = yes_data / 3000\n",
+    "# no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e575fa4ffbd54571928997a0f863a410",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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m+ruLnJrI9Hh0I67rFs6ePXuGfC8oKAgajQZSqRQajQaBgYGDlpHJZKiqqhr4urq6GiEhIQPrX/bQQw9h+fLlIwpPQ+vo7Uf2SQ3+ebQa+eXNkEiAeZP88PN54Vg8NQhSb9crljcYBGz57gLSd57B0tf349W74/lcdxpzcr8/unmFRzeiMOqUWlJSEjIzM5GWlobMzEwkJycPWmbmzJlQq9UoLy9HaGgotm7dik8//RQABsoKALZt24bY2Fhj4hCA5s4+/Dn7NHac0KBbp0eEvzt+tzQKd04LRYiP65Dr2dlJsGb2BCRGB+HhT47hd/88DnmgB6KlXmZMTzR6BOHSfTcT/NxwB49uRGFU4aSlpeGee+7B+++/j/Hjx+Mf//gHAKC2tha/+MUvkJ2dDQcHB2zcuBFLly6FXq/HunXrEBMTAwB46qmnUFRUBIlEgvDwcLzzzjvG/x/ZsLqLPfjZ+9+isrkLq6bLcNcMGaaP9xnRNZlALxe8df90LH/jINZ/fBSf/2o+vFwcRzE1kXnsOd2A4to2vHyXgkc3IpEIV7vIYuFUKhUKCgrEjmFRShs68MCH+Wjp7MP7D8zEnAg/oz4vv7wZ972bh8XRgdh0/wwOJCCrpjcIuP2NA+jW6bH3iZtZOCY23N/J3OtjwOHSRqx86xC6+/T49KE5RpcNAMya6Iunb5uCXcX1ePfAeROkJBLPtsIanKlrx2+XRLFsRMQ9b+U+K6jC2g/yEejlgu2P3IT4MB+TffaD8yfitthg/CXnLL6raDbZ5xKZU49Oj7/tPguFzBu3x0nFjmPTjLqGQ6PvSFkTztS1IdzfHZP8PWBnB1Q1d6O6pQtHK1uw9bsqzJf748010+HtatprLRKJBH+9S4HiNw7iN1uLkP3rBSbfBtFoyzxcgdqLPXjlnnjY2fHUsJhYOBbqVM1F/CXnDA6oG4dcxk4C3D9nPJ5dEQPHUTpN4OniiA2rlbhr0xH8fvspvLFayes5ZDUudunw5teluHlyAOZN8hc7js1j4Vig9w6cxws7TsPHzRG/vz0aK+JDcKG5C2UNHRAAhI1zg2ycK0J8XOHkMPpnRaeNH4ffJETi1dxzWBQVgJXTZaO+TSJTeOubUrT39iPttiliRyGwcCzOkbIm/Dn7NJZMDcIr98QPDEkO8nLBzHBf0XI9vEiOA+pG/DGrGHMn+Q26cZTI0tS0duPDwxW4c1oo7yezEBw0YEEa2nvwqy2FCPd3x9/uVVrU/S/2dhK8cnc8enR6vP1NmdhxiK5JEAT8179PQgLgySVRYseh77FwLES/3oDHthSio1eHt9fMgIez5R18jvdzw90qGbbmV6HuYo/YcYiG9NGRSuw7p8Uzt0cj9BozbJB5sXAsxN9yzyHvfDNevCMOUcGeYscZ0sO3yGEQBLz9TanYUYiu6lx9O/6cfRqLogLwszkTxI5DP8DCsQDfVTTjrW/KsHpmGFbNsOwL8mG+blg1XYYt3/EohyxPb78ej20phIezA/56VzxHVFoYFo4FyNh/Hr7uTnh2RYzYUYblkUVyGAwCNu3jtRyyLK/sOoszde34610KBHg6ix2HfoSFI7Kq5i7sPV2P1TPD4OpkL3acYRnv54aV00Pxaf4F1LfxKIcsQ0FFM949UI7754xHQnTQ9Vcgs2PhiOyTby8AANZY2bnmRxdFQm8QOGKNLILBIOD5L0oQ7OWC/1oWLXYcGgILR0Q9Oj3+97sLWDI12OpG0oz3c8PKaaHYkn8BDTzKIZFtK6zByZqL+M/bouDmZHkjPOkSFo6Ivjhei5YuHdbOs66jm8sevVWOfoOAt3kth0TU1dePv+46g3iZN5LjQ8WOQ9fAwhGJIAjIPFKByEAPzDXB4wTEMMHPHXdOC8Wn3/Ioh8Szad951Lf14o8rpnJyTgvHwhHJsQutOFXThrXzwq166Oajiy4d5Wzax2fmkPmd13YgY38ZliukmDFBvKmfaHhYOCL56EgFPJ0dsHKadZ8CCPd3xx3KUHz8bSWqmrvEjkM2pEenx8OfHIOroz2euZ0DBawBC0cEDe09yD6pwV0qGdwtcAqbkXpyyWTYSYCXdp4WOwrZkOe/KMaZunb87V4lJ5O1EiwcEWzNr4JOL4yZaTdCfFyx/mY5sk/WIe98k9hxyAZsL6zBlvwqPHzLJCyKChQ7Dg0TC8fMdHoDPvm2EgsnByAiwEPsOCaTujACId4ueP6LEugNgthxaIzq7O1H+s4z+N0/j2NWuC+eSJwsdiQaAaMKp7m5GYmJiYiMjERiYiJaWlquuty6desQGBiI2NjYG1p/LNldXI/6tl48YKVDoYfi6mSPp5dF47SmDZ8VVIkdh8YYQRDwxfFaJLy6D5v2lSFZGYp3fjYDDqP0pFsaHUb9aaWnpyMhIQFqtRoJCQlIT0+/6nIPPPAAcnJybnj9sSTzcAXG+7rh5slj7zTAcoUUM8PH4ZVdZ9HWoxM7Dlm5Hp0eB9WNSN95BsveOIhfbSmEn4cT/rV+Ll65Ox7j3J3EjkgjZFThZGVlISUlBQCQkpKC7du3X3W5hQsXwtd38JDF4a4/VvyjoAr5Fc1YO3cC7Mfg/QISiQR/XB6D5q4+vPkVH19AI1fd0oU3vy7FT9/Ng+L53bj//W/x/sHz8HR2wJ/vjMPnj87n8GcrZtQQqfr6ekilUgCAVCpFQ0PDqK2fkZGBjIwMAIBWq73BxOI5Ud2KZ7afwrxJfnhgXrjYcUZNnMwby2Kl+KygCr9dGgVHnvKgYWho78GbX5Xi0/wL0OkFREu9kDJ3AubJ/TEr3HdMjOakYRTO4sWLUVdXN+j1F198cVQCDSU1NRWpqakAAJVKZdZtG6uxoxf/sfkoAjycsfGn08f8eeckZQh2nNTgSFkTFk4OEDsOWbCuvn68sbcUfz9cjn69gLtVYXj0VrnVzS1Iw3PdwtmzZ8+Q7wUFBUGj0UAqlUKj0SAwcGTXJYxd3xr06w149NNjaOrsw7/Wz4OvDZx3vnlyADycHbDjhIaFQ0Pq7dfjl5uP4mBpI5LjQ/CbxZMR7u8udiwaRUb9VTspKQmZmZkAgMzMTCQnJ5t1fUun0xvwx8+LkXe+GS+tjENsqLfYkczCxdEeiVODkFNch75+g9hxyAL16w34zdYiHFA34q+rFHh99TSWjQ0wqnDS0tKQm5uLyMhI5ObmIi0tDQBQW1uLZcuWDSx33333Ye7cuTh79ixkMhnef//9a65vDQRBQG1rN2pbu9HQ1oOmjl706PQD75c2tGPV24fx6bcX8MuFEVg53bIfHW1qyxVSXOzW4VBZo9hRyMIIgoD/2nYSO0/V4Q/Lp+JuVZjYkchMJIIgWN1deiqVCgUFBaJtv7ShA89sO4lvy5uveF0iAUJ9XBHu5478ima4O9njxTvjsCxOKlJS8fT266F6YQ+WTA3Gq/fEix2HLIQgCPhz9mm8e6Acj90qxxNLosSORCYw3N/JHPoxAjq9Af+zV42395XB1dEe//mTKfB1d0S/QYDeIKC5sw/ntZ0439iBpTHB+MPyaAR6uogdWxTODvZYGhOMXcV16O2PhbODdTw+m0bXW9+U4d0D5UiZOwGPc5YAm8PCGYHXcs/hrW/KcOe0UDxzezT8PZzFjmTRbldI8c+j1ThwrhGLp/IZ87Zu85EKvLzrLO5QhuDZFTFW/VgOujEsnGH6rqIZm/aV4V5VGP5yl0LsOFZhvtwf3q6O2HmqjoVjw/LON+GNvWocLmtCwpRAvHx3PB+UZqNYOMPQ3qPD4/9bBNk4N/xhxVSx41gNR3s7LIj0xwG1FoIg8G+0NqS9R4cjZU1472A58subEeDpjN/fHo2fzZ3Am4FtGAtnGP70ZQlqW7vx2S/nwoN3PI/IwskB+PKEBmfq2hEt9RI7Do0Snd6AwgutOFjaiEOljSiqaoXeICDIyxnPrZiK1bPGw8WR1/FsHX97Xseu4jp8VlCNRxZNgiqccziN1MLISzd+HlBrWThj0Kmai3j/YDl2F9ehs08POwkQJ/PB+psnYe4kP6jCx3HACA1g4VyDtr0XT//7JGJCvPDrBI6ouRHB3i6YHOSB/ecakbpwkthxyAQMBgH7zmnx7oHzOFzWBDcneyQrQ3Hz5ADMjfCDt5uj2BHJQrFwhiAIAtL+dQKdvf14/V4lnBx43vlGLYwMwEd5leju08PViX/btVbdfXpkFdXgvYPlKG3oQLCXC9Jum4L7Zo2HtytLhq6PhTOErd9VYe+ZBjy7YioigzzFjmPVFk4OwHsHy5FX3sTHAVsZQRBwtLIF/zxajS9PaNDR24+pUi+8dm88bo8L4V/EaERYOFdR0diJP31Zgvlyf6TMDRc7jtWbNdEXzg52OHCukYVjRQovtOC3/ziOMm0n3JzscXucFHfNkGHWRF+OOKQbwsL5EYNBwJP/OA4HOwlevlvB+wVMwMXRHrMm+mK/2vqeY2SrPv32Ap77vBhB3s545e543BYbzGfSkNF4PPwjOcV1OFrZgt8vnwqpN5/JYSo3Tw5AaUMHalu7xY5C19Cj0yPtXyfwX9tOYs4kP3zx6HzcNUPGsiGTYOH8QL/egFd3n0VkoAdW2djszqPt8nNx9p3jUY6l0hsE3P/et9j6XRUeXSTHhw/MhI/b2H9+E5kPC+cHthXWoEzbiSeXRMGep9JMKjLQA6E+rthTUi92FBrCruI6FFS24M93xuG3S/kzQKbHwvleb78er+9RQyHzxtIYzvtlahKJBEtjgnGgtBEdvf1ix6EfEQQB7+w/jwl+brh3Jp9PQ6ODhfO9rflVqGntxm+XRHEEzihZGhOEvn4DvjnbIHYU+pGCyhYcr2rFL+ZP5JENjRoWDi4N/3x511nMnuiLBZH+YscZs1ThvvBzd8KuYp5WszQZ+89jnJsj7prBoxsaPTZfOKdqLmLtB/nwdXfC66uVPLoZRfZ2EiRODcLXZxrQ26+//gpkFmXaDuw5XY+fzQ3nTBA0qmxurOP/fncBh0qbIBvnigBPZ2zYq4aXiyM+fWg2h0GbwdKYYGz9rgqHS5uwaApvArUE7x0oh5O9HdbOnSB2FBrjbK5wmjr7UFTViuyTGvQbBEi9XbDloTmQjXMTO5pNmCf3g4ezA3YV17FwLMDl+dHuUIbyCbY06myucB6+RY6Hb5FDbxBQ19YDP3cnPqfDjJwd7LFoSiByS+rx4p0CL1CL7KszDejq0yN5WojYUcgGGHUNp7m5GYmJiYiMjERiYiJaWlquuty6desQGBiI2NjYK15/7rnnEBoaCqVSCaVSiezsbGPijIi9nQShPq4sGxEsjQlCU2cfCiqaxY5i8748UYsAT2fMnugndhSyAUYVTnp6OhISEqBWq5GQkID09PSrLvfAAw8gJyfnqu89/vjjKCoqQlFREZYtW2ZMHLISt0QFwsnBjqPVRNbR24+vzjRgWWwwjzTJLIwqnKysLKSkpAAAUlJSsH379qsut3DhQvj68mmZdImHswMWyP2xq7gOgiCIHcdm7T1dj95+A5bH83QamYdRhVNfXw+pVAoAkEqlaGgY+Q19GzduhEKhwLp164Y8JQcAGRkZUKlUUKlU0Go5H5e1WxoTjJrWbhTXtokdxWZ9cVyDYC8XzBg/TuwoZCOuWziLFy9GbGzsoH+ysrKM3vj69etRVlaGoqIiSKVSPPnkk0Mum5qaioKCAhQUFCAgIMDobZO45n9/g+3RyqH/kkGj52K3DvvPaXG7QspHcJDZXHeU2p49e4Z8LygoCBqNBlKpFBqNBoGBIxvmGhT0/3OWPfTQQ1i+fPmI1ifrJfV2gb+HM05UXxQ7ik3KLalHn96A5Qqp2FHIhhh1Si0pKQmZmZkAgMzMTCQnJ49ofY1GM/Df27ZtGzSKjcYuiUQChcwbJ6pbxY5ik7KKahDq4wplmI/YUciGGFU4aWlpyM3NRWRkJHJzc5GWlgYAqK2tvWLE2X333Ye5c+fi7NmzkMlkeP/99wEATz31FOLi4qBQKPD111/jtddeMyYOWRmFzBul2g50cvZos6ps6sQBdSPuVsk4lROZlVE3fvr5+WHv3r2DXg8JCbninpotW7Zcdf3Nmzcbs3mycgqZNwQBKK5tw6yJHMVoLp9+ewH2dhKsnjle7ChkY2x+8k4ST1yoDwDwtJoZ9ej0+KygConRQQj2dhE7DtkYFg6JJsDTGSHeLhw4YEY7T2nQ0qXD/XM4USeZHwuHRBUn88bJGhaOuXycdwET/d0xbxKnsiHzY+GQqBQyH5Q3duJit07sKGPeaU0bjla2YM3s8bz3hkTBwiFRKWTeAC49CI9G18d5lXB2sMNdM2RiRyEbxcIhUcWFXiqc4xw4MKrae3TYVliDFfEh8HFzEjsO2SgWDonKx80J433dcJIDB0ZVVlEtuvr0HCxAomLhkOguzTjAwhlNOafqIA/0QPz3pzCJxMDCIdEpZN6oae1GU0ev2FHGpM7efuSXN2NRVABnFiBRsXBIdAM3gHLgwKg4XNaEPr0Bi6JGNrkukamxcEh0saFekEjA6zij5JuzDXBzsocqnNMHkbhYOCQ6TxdHRPi7c4qbUSAIAvartZg3yQ9ODvxxJ3HxO5AsQrzMhwMHRkFFUxeqmruxcDIfWkjiY+GQRYiTeaOhvRd1F3vEjjKm7D936XHsCyNZOCQ+Fg5ZhNjvbwAtruVRjikdUGsx3tcN4f7uYkchYuGQZYiWXho4cKqmTewoY0ZfvwFHypqwcLK/2FGIALBwyEJ4ODtgor87TvEIx2SOVrags0/P02lkMVg4ZDFiQ7xRzHtxTGa/WgsHOwnm8lEEZCFYOGQxYkO9UHuxB82dfWJHGRP2n9Ni+oRx8HRxFDsKEQAWDlmQ2BAOHDAVbXsvimvbcDOHQ5MFYeGQxYgJufxsHA4cMNbBUg6HJstjVOE0NzcjMTERkZGRSExMREtLy6BlqqqqsGjRIkRHRyMmJgYbNmwY0fpkO7zdHBHm68qBAyaw/1wj/NydEBPiJXYUogFGFU56ejoSEhKgVquRkJCA9PT0Qcs4ODjg1VdfxenTp5GXl4c333wTJSUlw16fbAsHDhjPYBBwQK3F/Eh/PkqaLIpRhZOVlYWUlBQAQEpKCrZv3z5oGalUiunTpwMAPD09ER0djZqammGvT7YlTuaNiqYuNPJRBTesRNOGxo4+nk4ji2NU4dTX10MqlQK4VCwNDQ3XXL6iogKFhYWYPXv2iNfPyMiASqWCSqWCVqs1JjZZsHmTLt2keKi0UeQk1mu/+tLPxwLe8EkWxuF6CyxevBh1dXWDXn/xxRdHtKGOjg6sWrUKr7/+Ory8Rn5eOTU1FampqQAAlUo14vXJOsSFesPb1REH1Y1IVoaKHccq7T+nRbTUC4GeLmJHIbrCdQtnz549Q74XFBQEjUYDqVQKjUaDwMCrP+BJp9Nh1apVWLNmDVauXDni9cl22NtJcJPcDwfUjRAEgU+oHKHO3n4crWzBuvkTxY5CNIhRp9SSkpKQmZkJAMjMzERycvKgZQRBwIMPPojo6Gg88cQTI16fbM+CyADUtfWgTNshdhSrc0CthU4v4GZevyELZFThpKWlITc3F5GRkcjNzUVaWhoAoLa2FsuWLQMAHDp0CJs3b8ZXX30FpVIJpVKJ7Ozsa65Ptm2+/NK1hwNqXscZqayiWvh7OGHWRD7dkyzPdU+pXYufnx/27t076PWQkJCBUpk/fz4EQRjR+mTbwnzdMNHfHQfUjfj5TTw1NFwXu3XYe7oBa+aMh4M97+kmy8PvSrJI8+X+yDvfhL5+g9hRrEbOKQ369AbcwcEWZKFYOGSRFkT6o6tPj2MXOPvEcG0vrEWEvzsUMm+xoxBdFQuHLNKcSX6wt5PgIK/jDIvmYjfyypuQrAzlyD6yWCwcskheLo5QhvnggJo3+Q7H50W1EAQgWRkidhSiIbFwyGItiPTHiZqLaO3i83GuZ1thDaaN90G4v7vYUYiGxMIhi7Ug0h+CABwqbRI7ikU7U9eGM3XtHCxAFo+FQxYrXuYDT2eHgWe70NVtL6yFvZ0EyxVSsaMQXRMLhyyWg70d5k7yw/5zjUPey2XrDAYBnxfVYGGkP/w8nMWOQ3RNLByyaAsmB6CmtRsVTV1iR7FIRdWtqL3YgyQOFiArwMIhi7bg+2luDnK02lXtLq6Hg50Et04JEjsK0XWxcMiiTfBzg2ycK/bzfpyr2l1ShzkRfvB2dRQ7CtF1sXDIokkkEiyIDEBeWRN0ek5z80OlDR04r+3Ekhge3ZB1YOGQxVsQ6Y/23n4cr2oVO4pFyS2pBwAsjmbhkHVg4ZDFmzfJD3YSPq7gx3JL6hAX6o0QH1exoxANCwuHLJ6PmxPiZD44WMrCuayhvQeFVa1InMqjG7IeLByyCjdN8sPxqlZ09PaLHcUi7D3dAEEAr9+QVWHhkFW4Se6PfoOA/HJOcwMAu4vrEObriqggT7GjEA0bC4eswowJ4+DkYMd51QC0dPbhUGkTlkwN5qMIyKqwcMgquDjaY06EH3aX1Nn8NDf/LqxBn96Au2bIxI5CNCIsHLIayfEhqGrutumngAqCgK35FxAf5oNoqZfYcYhGhIVDVmNpbDBcHO2wvbBW7CiiOXahBeqGDvx0VpjYUYhGzKjCaW5uRmJiIiIjI5GYmIiWlsF/86yqqsKiRYsQHR2NmJgYbNiwYeC95557DqGhoVAqlVAqlcjOzjYmDo1xHs4OSJwajC9P1NrsrANb8qvg7mSP5QpO1knWx6jCSU9PR0JCAtRqNRISEpCenj5oGQcHB7z66qs4ffo08vLy8Oabb6KkpGTg/ccffxxFRUUoKirCsmXLjIlDNuAOZQhaunTYf872JvO82K3DlydqkaQMhbuzg9hxiEbMqMLJyspCSkoKACAlJQXbt28ftIxUKsX06dMBAJ6enoiOjkZNTY0xmyUbtnByAMa5OWJboe19D31eVIMenQH38XQaWSmjCqe+vh5S6aWnDEqlUjQ0NFxz+YqKChQWFmL27NkDr23cuBEKhQLr1q276im5yzIyMqBSqaBSqaDV2t7fbukSR3s7LFeEILekHu09OrHjmE1fvwF/P1yBqVIvxIV6ix2H6IZct3AWL16M2NjYQf9kZWWNaEMdHR1YtWoVXn/9dXh5XRpds379epSVlaGoqAhSqRRPPvnkkOunpqaioKAABQUFCAgIGNG2aWy5Y1oIevsN2HmqTuwoZvPWN6Uo03biySWTee8NWa3rngjes2fPkO8FBQVBo9FAKpVCo9EgMDDwqsvpdDqsWrUKa9aswcqVK69Y/7KHHnoIy5cvH0l2slHTx4/DlGBPvLzrLBKmBI75RyufrWvHm1+XIlkZggTODE1WzKhTaklJScjMzAQAZGZmIjk5edAygiDgwQcfRHR0NJ544okr3tNoNAP/vW3bNsTGxhoTh2yERCLBa/cqcbFbh9/+4zgMhrF7I6jeIOCpf52Ap4sjnl0RI3YcIqMYVThpaWnIzc1FZGQkcnNzkZaWBgCora0dGHF26NAhbN68GV999dWg4c9PPfUU4uLioFAo8PXXX+O1114z8n+HbEW01Au/vz0aX5/V4sPDFWLHGTUfHCzH8apWPJcUA193J7HjEBlFIljhPCEqlQoFBQVixyCRCYKA1M1H8c3ZBmx7+CbEjrGL6RWNnfjJhv2YLw/Au2tn8NoNWazh/k7mTANktSQSCf66SgE/d2f8akshOsfYowue/6IYjvZ2ePHOWJYNjQksHLJq49yd8PpqJSqbOvHYlkL0j5EZCPLLm/H1WS0eXSRHkJeL2HGITIKFQ1ZvToQf/js5FnvPNOCZbaesfjZpQRDw8q4zCPR0xtq54WLHITIZzo9BY8L9cyagoa0Hb3xViiAvZzyxJErsSDfsm3NafFfRgj/dEQtXJ3ux4xCZDAuHxozHEyejvq0Xb3xVil69AU8tnQJ7O+u69iEIAl7LPYcwX1fcq+IUNjS2sHBozJBIJHjxzlg4Okjwzr7zUNd3YMNqJTxdHMWONmxfn23AieqL+MuqODg58Iw3jS38jqYxxcHeDi/cEYc/Jcdg3zktVr51GBeausSONSyCIGDDHjVk41yxcjqf5kljDwuHxqSfzQ3H5nWz0NDei6Q3D+JwWaPYka7rm3NaHK++iEcWyeFozx9NGnv4XU1j1jy5P7IeuQn+Hs5Y+34+Ps6rFDvSkC4f3YT6uGIVj25ojGLh0JgW7u+Ofz88Dwsi/fH77afw4o4Sixw2vV/diKKqVjy8aBKv3dCYxe9sGvO8XBzxXspM3D9nPN49UI5/HK0WO9IVLh3dnEOItwvunsGRaTR2sXDIJtjbSfB8UizmRvjhj1mncK6+XexIA/arG3HsQivWL5Lz6IbGNH53k82wt5Ngw31KeDg74uFPjqGrT/y51/QGAX/ecRphvq64R8VrNzS2sXDIpgR6umDDaiXKtB14+t8nRX+Wzv9+V4Wz9e14+rZoODtwVgEa21g4ZHNukvvjt0uikFVUi5d2nhZtEEF7jw5/yz2LWeG+uC02WJQMRObEmQbIJj18yyQ0tPXg3QPl8HV3xvpbJpk9w5tfl6Gxow8fPBDNxw+QTWDhkE2SSCR4dkUMWrp0+EvOGfi6O+LemePNtv2q5i58cLAcK6eHQiHzMdt2icTEwiGbZWcnwSt3x6O1W4en/30S3q6O+Ems1CzbTs85A3s7CZ5aOsUs2yOyBLyGQzbNycEOm+6fjmnjx+HRTwux86Rm1Ld5quYidpzQIHVhBIK9+XA1sh0sHLJ5bk4O+PvPZyI+zAePbinEjhOjWzof51XC1dEeDy6YOKrbIbI0LBwiAJ4ujshcNwvTx/vgsa2F2H9OOyrbaevRIauoFsnKEHhZ0WMTiEzBqMJpbm5GYmIiIiMjkZiYiJaWlkHL9PT0YNasWYiPj0dMTAyeffbZEa1PZC4ezg74+89nIdTHFW/sVY/KNrYdq0G3To81syeMyucTWTKjCic9PR0JCQlQq9VISEhAenr6oGWcnZ3x1Vdf4fjx4ygqKkJOTg7y8vKGvT6RObk7O2Dt3AkoqGzBqZqLJv1sQRDwybeVUMi8ESfzNulnE1kDowonKysLKSkpAICUlBRs37590DISiQQeHh4AAJ1OB51ON3DPwXDWJzK3u2eEwdXRHpuPmPZxBgWVLThX34E1s803/JrIkhhVOPX19ZBKLw0jlUqlaGhouOpyer0eSqUSgYGBSExMxOzZs0e0PgBkZGRApVJBpVJBqx2d8+tEAODt5og7poVie1ENWrv6TPa5H+dVwtPFASviQ0z2mUTW5LqFs3jxYsTGxg76Jysra9gbsbe3R1FREaqrq5Gfn49Tp06NOGhqaioKCgpQUFCAgICAEa9PNBJr505Ab78BnxVUmeTzGtp7sPNkHVZOC4WbE29/I9t03e/8PXv2DPleUFAQNBoNpFIpNBoNAgMDr/lZPj4+uOWWW5CTk4PY2NgRr09kLtFSL8ya6IvNeZV4cH4E7O2Mm3rm/YPl6DcY8MBNHApNtsuoU2pJSUnIzMwEAGRmZiI5OXnQMlqtFq2trQCA7u5u7NmzB1OmTBn2+kRieWBeOKqau40eIn2xW4dP8i7gtjgpJvq7mygdkfUxqnDS0tKQm5uLyMhI5ObmIi0tDQBQW1uLZcuWAQA0Gg0WLVoEhUKBmTNnIjExEcuXL7/m+kSWIHFqEHzcHLG9qMaoz/k4rxIdvf1Yf7P5JwglsiRGnUz28/PD3r17B70eEhKC7OxsAIBCoUBhYeGI1ieyBI72drgtVorthTXo6uu/oWsv3X16fHCwHDdPDkBsKIdCk23jTANE15CsDEG3To89p4ceQXktnxVUoamzDw+L8PgDIkvDwiG6hlnhvgj2csHnRbUjXre5sw9vf1OGGRPGYdZE31FIR2RdWDhE12BnJ8FyhRT7zjXgYpdu2Ov16w341ZZjaO7swx+XT+UD1ojAwiG6riRlCHR6ATtPDX8W6Zd3ncWh0ia8cEcs4sN8Ri8ckRVh4RBdR1yoN8L93PD58eGdVvvieC3e2X8e988Zj3tmho1yOiLrwcIhug6JRIIkZSiOnG9CZVPnNZc9rWnDU/88gRkTxuGPy2PMlJDIOrBwiIbh3plh8HB2wPqPj6G7T3/VZVq7+vDLzUfh6eKAt9dMh5MDf7yIfog/EUTDEOrjijdWT8PpujY89a8TEAThivf1BgG/3loEzcVuvH3/DAR68dHRRD/GwiEapkVTAvG7pVED12h+6G+5Z7HvnBbPJ8VixoRxIiUksmyctpZoBNbfPAnFtW34S84ZhPi4wsXBDt9VNOPdA+W4b9Z4/JTPuiEaEguHaAQkEglevkuB89pOPLbl0pRN9nYSLI4OxHNJU0VOR2TZWDhEI+Tm5IC//3wm9p5uQFSwJ6ZKveDqZC92LCKLx8IhugFBXi48fUY0Qhw0QEREZsHCISIis2DhEBGRWbBwiIjILFg4RERkFiwcIiIyCxYOERGZBQuHiIjMgoVDRERmwcIhIiKzkAg/frCHFfD390d4ePgNr6/VahEQEGC6QGbG/OKx5uwA84vNmvNfK3tFRQUaGxuv+xlWWTjGUqlUKCgoEDvGDWN+8VhzdoD5xWbN+U2RnafUiIjILFg4RERkFjZZOKmpqWJHMArzi8easwPMLzZrzm+K7DZ5DYeIiMzPJo9wiIjI/GyucHJychAVFQW5XI709HSx41xXeHg44uLioFQqoVKpAADNzc1ITExEZGQkEhMT0dLSInLK/7du3ToEBgYiNjZ24LVr5X3ppZcgl8sRFRWFXbt2iRH5ClfL/9xzzyE0NBRKpRJKpRLZ2dkD71lS/qqqKixatAjR0dGIiYnBhg0bAFjP/h8qv7Xs/56eHsyaNQvx8fGIiYnBs88+C8B69v9Q+U26/wUb0t/fL0RERAhlZWVCb2+voFAohOLiYrFjXdOECRMErVZ7xWu/+93vhJdeekkQBEF46aWXhKeeekqMaFe1b98+4ejRo0JMTMzAa0PlLS4uFhQKhdDT0yOcP39eiIiIEPr7+0XJfdnV8j/77LPCyy+/PGhZS8tfW1srHD16VBAEQWhraxMiIyOF4uJiq9n/Q+W3lv1vMBiE9vZ2QRAEoa+vT5g1a5Zw5MgRq9n/Q+U35f63qSOc/Px8yOVyREREwMnJCatXr0ZWVpbYsUYsKysLKSkpAICUlBRs375d3EA/sHDhQvj6+l7x2lB5s7KysHr1ajg7O2PixImQy+XIz883d+QrXC3/UCwtv1QqxfTp0wEAnp6eiI6ORk1NjdXs/6HyD8XS8kskEnh4eAAAdDoddDodJBKJ1ez/ofIP5Uby21Th1NTUICwsbOBrmUx2zW9oSyCRSLBkyRLMmDEDGRkZAID6+npIpVIAl35IGxoaxIx4XUPltaY/j40bN0KhUGDdunUDp0QsOX9FRQUKCwsxe/Zsq9z/P8wPWM/+1+v1UCqVCAwMRGJiotXt/6vlB0y3/22qcISrDMi7VoNbgkOHDuHYsWPYuXMn3nzzTezfv1/sSCZjLX8e69evR1lZGYqKiiCVSvHkk08CsNz8HR0dWLVqFV5//XV4eXkNuZy15Lem/W9vb4+ioiJUV1cjPz8fp06dGnJZa8lvyv1vU4Ujk8lQVVU18HV1dTVCQkJETHR9l/MFBgbizjvvRH5+PoKCgqDRaAAAGo0GgYGBYka8rqHyWsufR1BQEOzt7WFnZ4eHHnpo4LSBJebX6XRYtWoV1qxZg5UrVwKwrv0/VH5r2f+X+fj44JZbbkFOTo5V7f/LfpzfVPvfpgpn5syZUKvVKC8vR19fH7Zu3YqkpCSxYw2ps7MT7e3tA/+9e/duxMbGIikpCZmZmQCAzMxMJCcnixnzuobKm5SUhK1bt6K3txfl5eVQq9WYNWuWmFGv6vIvCwDYtm3bwAg2S8svCAIefPBBREdH44knnhh43Vr2/1D5rWX/a7VatLa2AgC6u7uxZ88eTJkyxWr2/1D5Tbr/TTnKwRrs2LFDiIyMFCIiIoQXXnhB7DjXVFZWJigUCkGhUAhTp04dyNvY2CjceuutglwuF2699VahqalJ5KT/b/Xq1UJwcLDg4OAghIaGCu+99941877wwgtCRESEMHnyZCE7O1vE5JdcLf/9998vxMbGCnFxccKKFSuE2trageUtKf+BAwcEAEJcXJwQHx8vxMfHCzt27LCa/T9UfmvZ/8ePHxeUSqUQFxcnxMTECM8//7wgCNf+ebWG/Kbc/5xpgIiIzMKmTqkREZF4WDhERGQWLBwiIjILFg4REZkFC4eIiMyChUNERGbBwiEiIrNg4RARkVn8Hwd9vImxj5MoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a07d72b6c35e46a8a8132892f6555674",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ac3a1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e2a8705",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "716ee1bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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x7//+73zve9/jhhtuwGQy8dd//dcAPPHEE/I+11xzDf/3//5fzj33XL75zW9y0003ydepXC6zbds2Hn74YW666aZDKlDXqXOiEQgECIfvw2AQGRnpRZKU8ntabZ6mpgUcjiggUShoKRS0lMsKVKoSVmsCp7P6ACeKSvJ5HVZrnK6uCUZGenkrz+zlcpmJiQkaGxs55ZRTMBqN2Gw2IpEIiUTipNJKPKmNyvHiBz/4Affddx8As7OzjI6Ocvrpp9PV1cXzzz9Pb28vw8PDnHXWWfz3f/83H/zgB9Hr9QCyJP1rOeuss/jkJz/JVVddxRVXXHHQbS655BLUajUrVqxAFEUuvvhioCpvPzU19abXPzc3x0c/+lEWFhYoFot0dna+7vaJRIJ4PM65554LVAeQXXnllfL7tfWeeuqpb2kddeocTwqFAps334XRGGJ+voV8/tWmQ4cjTFtbddhcIOAmFGqgWDxwjpFSWcJozGC3x7HZYmg0Rbq7R1EoRIaGBt7SeqLRKKOjo3i9XjweD06nk1QqRTQaxWKxoFarD++EjxEntVF5I4/iaPDkk0/y+OOP89xzz2EwGDjvvPNkGfmPfvSj/O///i/9/f1cfvnlh5yZcjBuueUWtm7dykMPPcTq1asP6oHtL2evVqvlOva3Km//hS98gS9/+cts3LiRJ598km9961tv5RIccl1KpfKkc9XrvDuRJIndu19Grd5GLqcnEGisvYPXO4vbHSSVMjM52Um5fOg+FFFUk0zaSCZtzM624nKF6e/fS3//IDpdjpGRt5ZjqamY22w2dDodTqcTn89HLBaTR2mc6NRzKm+RRCKB3W7HYDAwNDTE888/L793xRVXcP/99/PLX/6Sj370o0A11PTggw+Sz+dJp9OHnHo5Pj7Oaaedxj/+4z/icrkWzV95K9Tk7QHuueeeQ55DTd7+zjvvlF8/lLx9Teyuli+5++67Za+lTp2TkVgsxuDgz1Gri0xPt1O7FdYMSjDYyOho7+salNdSqSgJBht5+ulzWFhopqXFx8DAHlpa5hCEN6dqnE6nGRsbw+/3U6lUsFqtGAwGYrHYSVNiXDcqb5GLL76YcrnMypUr+fu///tFY4LtdjsDAwNMT0+zfv16oDpvZuPGjaxatYorrriCtWvXHrSp6Stf+QorVqxg+fLlbNiwgVWrVr2t9f3DP/wDX/ziFznnnHNkef3X8q1vfYsrr7ySc845Z9GgtA984APcd999cqJ+f+68806+8pWvsHLlSnbs2ME3v/nNt7W+OnWON+Vymc2bf4/FMkMk4pI9CZcrKBuUublW3u7tUZJU7Ny5ioWFJlQqkcZGP0uWDKFWvzmjEAgEGBwcJJ1Oo1QqcblcFItFuTjmRKcufX8MSKfTmEwmstksGzZs4NZbb2XNmjXHe1knNCfj91zn5GDPnj289NK/YbUm2L17OeWyBpMpSV/fKImEhfHxHuDwJVLc7gBe7yzRqAOrNYEkCYyPd5PJvHGJsMPh4IILLmDJkiUIgsDU1BTFYpGurq7jOoa4Ln1/gnD99dezd+9e8vk81157bd2g1KlznEgkErzwwkM4nTEWFpoplzUolSU6OyfJ57VMTnbyWoOSJMkEE8wyS44cIiJGjFix4sZNM81YOTD6EAy6sVoT2Gxxxse7aGubo69vhOnpdqLRA0epv3adu3fvpqGhgYaGBlwuF9PT08RiMRobG1933+NN3agcA147XrlOnTrHnnK5zJYtW9BodlIuq+XkfHv7NCpVmbGxXiqVV2+Jfvw8zdPsZS8SEjp0mDChQMEcc2TIyNs20MBSlnIKp2DHvu9VgampdgYG9tLUFGBoaAldXZN0dEyhUokEg4c2DqIosrCwwPj4OFarFZPJhNFoJBaL4XA4TuhKsLpRqVOnzjuearXXbiYnn6O9PcncnJdKRYnDEcZmizM/7yWXq5YUV6iwiU1sYQsaNJzBGaxiFQ00oNgvz1KkSIAAc8wxzDBP8zSb2UwPPaxnPb30UippmZtrpb19CocjythYD52dk3i9swhChUCg6ZBrzmQyjI+Py532TqeTmZmZE74SrG5U6tSp845nfn6eF154AYdjgnJZTSjUgFJZxuudI5MxyV5LgQL3cA8jjLCa1VzERejRy5+Ty+VIp9Pk83mUSiUGrYF11nWcoTqDBAle3vffL/gFTTRxHufRF+nDZovh9c6TTFqYmOiko0OgpWUeUVQSDh/cQJRKJSKRCPPz8zidTsxmMwaDgWg0isPhOGFnrpyYq6pTp06dI0QqlWLbtm0kEsN4PCnm5lqRJCUtLdOoVCIjI22AQJ48d3EXCyxwCZdwGqdRKBTYPbqb4eFhZmdnD6p2IQgCLpeLvr6+avVm0wZ2sYvNbOaX/JIWWvjA9AVctExBR8cUw8P9TE11oFSKtLXNUC6ricftB3wuVIt8fD4fLpeL3t5e2VtJJpPy1MgTjbpRqVOnzjsWURTZuXMn4+PjNDUFKJdVhEIuDIYMLleIYLCRfN5AgQL/j/+HHz8f5aM0JZr4w/N/4KWXXqJYLGIwGOjo6GDt2rVYLBZ0Oh2VSoV8Pi97E88++yzPPPMMHR0dbNiwgb/s+kt2spNNbOKW8l1MzfTwyc42GhttBAJNTEx00dc3QkfHJEND2kUd/TVKpRKhUIhwOIzdbqehoQGdTkc4HMZms6FQnHhdIXWjUqdOnXcsw8PD7N27F1GMYLXGWVhoQpKUeL2zlMtqfL4mRER+za+ZZ54Pix8m9GyI3z71W0RRZPny5Zx66qm0tbW94Q08m82yY8cOnnvuOe666y6WLl3KxRdfzHLrcp7hGR6PPQP2SS5qDqBNXEohb2R8vJulSwfp7h5naGgponjgLTmdTuP3+3G5XJjNZhwOBz6fj1QqdUIO8jrxzNxJxv5Kvgfj/vvvZ+/evUfss/dXIRYEgf/zf/6P/N5//Md/yJIr3/rWtzAYDASDQfl9k+mtSXLXqXMyEwqF2Lt3L36/n8bGIJKkIBRyY7HEMZnS+HxNVCoqHuVRJpjgPan38PStT/PEE0/Q29vLjTfeyIc//GE6OjrelEdgMBg488wz+eIXv8h73vMeRkdH+fGPf8zUyBTncz5f4AvMTXfysriHiY7bSRCjXNYwPt6NRlOUtcZeS6FQIBQKEYvFSCQSmM1mVCoVkUjkhJy3ckSMyqOPPsqSJUvo6enhO9/5zgHv//znP2flypWsXLmSM888k507dx6Jw54UHI5ReSO0Wi333nuvbGRei8vl4nvf+95ROXadOicy5XJ5X7XXJApFEaczQiTioFxW0dIyT6GgJRx2sZWtvMAL9IX62PyDzSSTSa6++mo++tGPYrcfPM/xRqhUKjZs2MDnPvc5bDYbv/jFL9i8eTMWycKHxT/DMPM+VIY4W5t+zCCDZLMmFhaasdujh5wgmclk8Pv9pFIpstksdrudTCZzSH2/48lhh79EUeTzn/88jz32GF6vV5YlGRh4VaGzs7OTp556CrvdziOPPML111/P1q1bD/fQfOnRL7HDv+OwP2d/VntW8/2Lv/+623z729/mrrvuorW1lYaGBk499VT+53/+h1tvvZVisUhPTw933303O3bs4He/+x1PPfUU//zP/8w999zDn/70pwO2MxgOjKW+GVQqFddffz3/9V//xbe//e0D3r/uuuu44447+NrXvnbCJvXq1DkajI6OMjY2RjabxeMJ7SvfbcThiKLX55iY6GKcSR6VHsUVcTHyoxFava1ceeWVWCxvfd78wXA4HHz605/md7/7HX/6058oFAq8973vpSl+JvaIEWPTn/h56tdMptfzPv97sdnitLXNkE6bKZcX96HkcjnC4TCxWAyTyYTFYiESiRAOh2ltbT0i6z1SHLansm3bNnp6emT5gKuvvpoHHnhg0TZnnnmmbPVPP/105ubmDvewx42XXnqJX/3qV2zfvp17772XF154AaiKSb7wwgvs3LmTpUuX8tOf/pQzzzyTjRs38t3vfpcdO3bQ3d190O1ej9rQrNr/Pp9v0fuf//zn+fnPf04ikThgX5PJxHXXXcd///d/H7kLUKfOCU48HmdwcBC/348gVHC7gyQSVgoFHR7PQnUYV1zFPdI96NI6wreGWbF8BZ/5zMfp6cnS2TlOf/9eli7dS0/PKE1N8+h02be1FrVazeWXX87atWt55plneOyxxwBIzC5nbf4C/qKzh12qbfwPP2X7lBWlUqSl5cD7oyRJZDIZQqEQ2WyWQqGA2WwmmUyecMrgh+2pzM/PL7KUXq/3db2Qn/70p1xyySWHe1iAN/QojgZPP/00l19+uexdbNy4EYDdu3fzd3/3d8TjcdLpNBdddNFB93+z29X4q7/6K3loFlRzKvtjsVi45ppr+MEPfiDPbNmfG2+8kdWrVy/KvdSp806lUqmwe/dupqamqFQqOJ0RVKoygUAjNlscnS7P+GQH90r3ka/kqdxV4dwz1vPJTy7F6dzL/sO4JElArS7R1OSnqWmBTMbEwkITyeRbS44rFAouu+wyBEHg2WefxW63s27dOqYmejhlaZmbOlr41thWbs7/HFvgfSzzRAiFGg6QzN/fWzEajRgMBhKJBLFYjIaGhiN4FQ+PwzYqB0sU1eZ8vJZNmzbx05/+lC1bthzy82699VZ5RG8oFDrc5R0VDnZ+n/zkJ7n//vtZtWoVd9xxB08++eRB932z270VvvSlL7FmzRo+9alPHfCezWbj4x//OD/60Y8O+zh16pzozMzMMDExsW+Eg0RjY4Bs1kA6baG/fy+Fgo5HYsNMCpPwEFx53il8/ONNKBRRgkE3oZCLQmHxw5lKVcJuj9LYGKCnZ5Rk0sr0dDul0psXdhQEgUsuuYR4PM7DDz+M3W6np6eH2dlWlrZV+PvGjXwn8Cj/5P8933eeQVvbDENDS9lfh6xUKpFKpQgGgzidTlQqFTqdjlgshsvlOuR991hz2OEvr9e7aPbH3Nwczc3NB2y3a9cuPvOZz/DAAw+87mjM66+/nhdffJEXX3zxhLK+NTZs2MB9991HLpcjlUrx4IMPAtUGq6amJkqlEj//+c/l7V87o+RQ2x0ODoeDq6666pChtC9/+cv85Cc/OeHc5Dp1jiT5fJ7BwUH5fmSxJNDp8gQCjZjNCQyGLIMLOh6vPAFDcM2Kfj7xiRZyOT179w4wN9d6gEEB9nXgN7J793Lm5loxmVIMDOzBbj94Uv1QKBQKPvKRj+B2u7nnnntIJBKEww3EYnaWt+T4kvFqdBUz/zX3PCVDbYzxYgqFAtFolEQiQalUwmAwkM/nyWQyBzni8eGwjcq6desYHR1lcnKSYrHIr371KzkkVGNmZoYrrriCu+++m76+vsM95HFlzZo1fPSjH2X16tV8+MMf5pxzzgHgn/7pnzjttNO48MIL6e/vl7e/+uqr+e53v8spp5zC+Pj4Ibc7XP7P//k/r1sFdvnll580Q37q1HmrSJLE0NAQk5OT8sNTY2OAUklDLGansTFIsaTiZt+jVAoVPmZs4orLuwmFGhgZ6TuoMamhVCrRarVotXqi0Rb27h0gn9fT2TlBW9s0giC+6XVqtVquvPJKRFHkt7+t9sJMT7dTKGhY1RXi06qP4YvpeDi3FXPTKLB4uFc2myWTyRAIBCgUCqhUKhQKBdHoWzNwR5MjMk/l4Ycf5ktf+hKiKHLdddfxjW98g1tuuQWAG264gc985jPcc889tLe3A9WqpTfS5Id3zjyVOm+d+vdc560QCoV48skn5fJ9vT7D0qWDzM15SSRsLFu2m4cngtwS38Z7/Aa+cNG5BIOtLCwcGFV5c1Robvbh8fjJ5fRMTHS9rmF6La+88gr33HMPGzZs4D3veQ86XY7+/iFyOR3Pjbh41HIb7+9W0zL9cVKRxWs0mUx4PB6WLVtGQ0MDuVyOfD7PkiVLjrp68TGbp3LppZdy6aWXLnrthhtukH++7bbbuO22247EoerUqVNnEeVymT179jA1NSW/1tgYoFJREg67aG72USiXuCvwAgMq+My564lGmw7DoAAoWFhoJZ0209ExydKlg8zMtL3hnJQaK1asYGxsjKeffpqlS5fS1NTE1FQHXV3jrG7VE5u5isns3Siafo8p8hn2DyrVvJVwOIzVakWr1ZLNZk8Y9eJ6R/0JwLe//e1FZcOrV68+aN9JnTp1DmRycpLJyUmy2WrZr1pdwG6PEQ5Xb/AuV4Rf7tmGziZxXcdSKmILs7OLezsEoYJaXUSpLPHakNOhkCSJZNLK3r0DZLNGOjqmaG2dedP7X3TRRRgMBh544AFEUSQet+P3N+FyhTnNZcbg20BC48Pn+tOi/SqVCrlcjkgkQjqdRqFQoFQqicfjJ0SHfV376wTgG9/4Bt/4xjeO9zLq1DnpSKfTDA8PMzPzqsRJY2MAgEDAjdMZYTY0wRZFjA/ZTXTa+tm7twMQUKuLNDQEsVqrCX1BqN6QJUkgl9OTSlkIh51vGNYqlzWMjS2hqWmWxsYAOl21uVIUXz8UZTAYeP/738+vf/1rnnnmGTZs2IDP14xen6W1dZb86FrG0mOkm54hEBmgUXrVs8pkMqTTaSKRCGazGbVaTS6XI5vNYjQa397FPEKclJ7KiWCN6xw96t9vnTdDpVJhcHCQ8fFx+TWlsoTLFSYadVAqabDZ5vhTeA/dDfBe91omJzsRRTVut5/ly3fj8QT2TYH0MDPTzuxsG8FgI6KoxO0OsGzZHrq7R9Hpcm+wFpifb2VqqhOTqZrP0evfuGFy6dKlDAwM8NRTT+1roRCYnOykUNDS3T1BV+Qi7GoNOxy/ocyr1ZulUol8Pk8oFCKTyaDVapEkiVgs9rav55HipDMqOp3uhBVSq3P4SJJEJBJBp9Md76XUOcHx+/2MjIwsmnHidodQKCr4/R4slgSD81uZc1a42N5CNtxFNmugq2sMr3eORMLCnj0DBAJuFAoRmy2GwxHBaEzL44ZDIRcmU5qBgb20tMy+YaVXPN7A8PASBAGWLBk6pJbX/lx66aWo1WoefvhhJEmiUlExNtaDJEGnJ0Vvfi2tnhhPs3nRfqlUilQqRSKRoFKpoFQq5Z+PJydd+Mvr9TI3N3fCNkbWOXx0Oh1er/d4L6POCUyxWGT37t2Lwl6CIOJ2B4nHbRQKehTK55myBOmxCPQKaxhf8NDTM4bJlGF+vhlJUtDXN4JaXUKSFGSzekRRiUIhYTBk5D6UbFaPJAk0NgawWhNMTXUc0O1eo1KpkM0aGRzsr3oaXRPMzIiEw4fuuTOZTJx//vk88sgjDA0NsXTpUopFHePj3fT1jdCOg5jQzIu2pwnGl+OmmowvFArkcjkCgQB2ux2tVksmkyGZTGKz2Y7cxX6LnHRGRa1W09nZebyXUadOneOEJEmMjIwwNjZGsViUX3e5wiiVZfx+D4KQYCb3HJVuWK/sxz/RTVfXFCZTBp/Pg8sVRavNk0pZmJ7uIJUyI0mLAzdqdQGHI4bbHUCtLlEsqlGri/T3D7Ow0MTCQhP7d7zvT7msYWSkl66uai+LQiESDHoOeU5r167lpZde4g9/+AM9PT2o1WoyGTNTUx10dk5yhrmdPY1hHow/yKf4FIp9QaZ0Ok0ikSCdTuNwOBBFkWg0elyNykkX/qpTp867m5pg5P7RCkGo4PEESKfNZLMmZucfQdFfxK3SYo6disMRw2xOEok48HgCKBQVRkf7GB3tI5m0HmBQAEolLYGAh927VzA350WlElEoJEolFU1NPnp7R1CpigfsV0OSlIyPdxOL2fF65/B4Fg65rVKplGVcnnnmGfn1WMzJwkITKknDFU0dlEyzvMRL8vu1bnq/30+xWEStVpNKpSiVSm/1sh4x6kalTp06Jw3lcplXXnmFycnJRa+7XGHU6iILC03MzU0gNg9hdEBPZRWVkhaHI0I6bcTpjJDP6xkc7CeVenMS95KkIBj0sHfvMjIZE2p1iUJBsy/XMojZnHydvRVMTnYSjTppbp7H6Tx02L6zs5Nly5axZcuWRXmihYVqCXSvxcA1PS4e53EyvCrLkk6nicfjZLNZNBoN+Xz+uHbY141KnTrvYCRJolgsUiwW3xHFLdPT0wwPD5PLvVqNJQgiHs8CqZSZaFTP4Nj9NJ0GurIVQ7wfjydAPq/FaMySSpkZHu6jXD5QDFKlUmEwGA7ZlV4sahkZ6cXv96DVFikW1VQqAr29IzQ1zXPo/hQFU1PtJJNW2ttnsFjihzy/Cy+8EKiK7+7P1FQXwYCbK9qdtNsL/IlXe1fy+TyJREKe8ioIwnHtWakblTp13oEUi0VmZmbYs2cPg4ODDA4OMjQ0RCgUOmmNSyqVYufOnSwsLA4jNTSEUKtL+HzNPPXUk7S/L4nDAp7UGqyWFJIkoNUWyOf1jI93I0nKAz5bpVIhSRL5fF6upDo4Cnw+L5OTXWg0ZQRBIpUy09S0QH//0OuUHiuYmOgim9XT1TWBwZA+6FY2m43TTjvtgPOUJAXbt5+Csmzi66ubGdW8hB+//H46nSYcDpPP51Gr1SSTSbkZ9FhTNyp16rzDiMVijIyMkEgk0Ov1OBwOrNbqDBCfz8f4+PiiBPfJQC3sNTU1tcgoKhQiHo+fZNLK2FiaufQzLD8TNDkPtooLnS6PUlmmVNIyOtpDpXLw2qRyuYwoilQqFURRRBRFBEFAqVSiVqsPMDKxmIPh4T4EAQyGLIGAG42myNKle2ls9AMHGu5KRcnYWC/lspru7nHU6oN/B2effTZ6vZ7HH3980evFop7h4SUsNzm5vE/NJuWj8nuFQoFYLEYoFEKtVlMsFo9bz0rdqNSp8w4iGo0yMzODQqHA4XCg0WhkA2I2mzEajaRSKcbHx08a1WpJkvD5fOzevXvRGAkAtzuISlVmdraRBx54gDM/LdBgFHAkVmM0ZlGpypRKGkZHew8IeWk0GiwWCzqdDq1Wi0aj2adGrEWtViMIApIkyQbntWSzJgYH+ykWtTQ2hggG3SQSVlpa5liyZBit9sD58eWymrGxbpRKka6ucQThwJCZXq9nw4YNjI+PMzY2tui9uTkvsXAj73W20d01xSQT8nu1yZClUgmlUkk4HD4uPSt1o1KnzjuETCbD/Pw8CoUCQRDYvXs3zz33HM8++yybN2/m+eefJxAIoNVqSafTjI2NnRSGJZ1O88ILL+D3+xe9rlKV8Hj8xOM2Hn98J5VWP+vXSmjS7VjUSgyGDIWChsnJLgqFxc20Gk3VwOTzeTQajaz629/fT3t7O42NjZjNZlla/lCUSlqGh5eQSFhpbvZRKqmZnOxAp8sxMLAHr3d2n57Yq+TzBqamOjEaM7S1zRz0c9etW4fNZuOxxx5bZBjyeQPhcAPOQjs9Zi3hpj8g7fOKyuWyPBlSpVKRy+UOOmb8aHPS9anUqVPnQCqVCrOzs5TLZTKZDOPj46RSKQqFghzOqW3T1NREV1cX6XSayclJent7XyeHcHwpl8vyeODX5oKam30IQoVdu/RsemoTn/iZCpdWwBrrwWjKkM9r8fm8i8b/ajQa1Go1oigiSZLsodTCRzabDbfbTaVSoVAokE6niUajZLNZOTRWKpUWraVSqZYOt7TM09joR6stMDhYLRBwu4M4nWECAQ+BgFvO58TjdhYWmmhqWiCbNRAKLVYXVqlUvOc97+Hee+9l9+7drFy5Un4vGHRjNqdoKQyQa9pONLMTZ3L1vsbLLH6/H7PZTKlUIhKJYLfbj8ZXc0jqRqVOnXcAgUCATCZDLBZjeHiYbDZLuVymXC4vetJNJpOkUimSySQdHR1kMhnK5TLt7e2Lwj4nApIkMTs7y/bt20mnFye29fosLlcYv9/Fr3/9KI7z4ayBMrZ8L2pl9YYfDrvx+5vkfVQqFSqVikqlgkqlWhQerBmQTCaDTqfD4XDgdDppaWlBFEUCgQCRSIRSqYRCoSCdTpPNZikWi/uur8D8vJd8Xkdb2zS9veNMTXUQDDbS3DxPc/M8DQ1BFhaaCYedVKXzmzEYcrS2zpLL6UmnzYvOcfny5TzzzDNs2rSJgYEBVKrq7TqRsFEsavGWu9lTGUbXsQn14FJKJS3lcplgMIjH40Gj0RCNRikWi7Jndiyoh7/q1DnJKRaLhEIhIpEIQ0NDpFIp8vn8fjc80GgKtLTM0d09RmvrJJnMIFu3Ps+ePXt4/vnneeKJJ9i+fTvDw8OEw+ETIiwWj8fZsmXLQSSZJFpbZxFFJb//vY/ZyCyXfxY8GgP6XDNGY5ZwuIGpqQ55D5VKJZcKq9VqHA4HKpUKURRRKpVoNBpZby6bzTI7O8srr7zC9u3b5WmSBoMBpVJJuVxGq9Wi0+lQKpWLwmORiIvR0T4EocKSJUM4nWEmJzsZHl5CsailrW2apUtrVWICk5MdFAoaOjomUSgWj/tWKBRccMEFxGIxtm/fvt87AsFgA2ZjHs38maQUCdSdTwMSlUqFYrHIwsICgiCQy+WOecK+blTq1DnJCQaDxGIxxsbGSKfTFAqFRd6JwxFm2bLduN0BNJrqrJGenjH6+vaQycwTDoeZn59nbGyM4eFhdu/ezeTkJMFgkEwmc1xKkPP5PJs2bWJubu6A95zOMCZTisFBC48+uonOTxo5q6OCq+RFr88TjToYH++iUqmGmmo3/lpFl8lkQqFQvKaKrJqH0mg0ciiwFhKbnp5mYmKC+fl54vE4iUSCRCKBKIrodDo0Go3sRQCk02b27h0gFGqgsTHAwMBeVKoyw8P9TEx0o1YXWbp0kIaGAJWKisnJTjSa0kHzK729vbS1tfHUU08tqtiLRFxIkoLTbS3smbERMO3E3bggrzsSiZDNZhFF8ZjrJNaNSp06JzHlcplIJMLExATJZPIAD8Nuj9DRMUU6bWL37hUMDi5j585VTE117iuBHcJgWJD3rYV25ubmmJmZkctU8/kDK5mOFsVikccff5zR0dEDSp9VqhJe7xyplImf/nQLolfkA+/P0GOwIxSsZDLGfYn56gwUpVKJUqmkUqmg0+loaGjAZrPJxkCn02GxWHA6nTQ0NNDQ0IDb7cbtduN0OrHZbJjNZrRarezR6PV6NBqNHCbUaDTyZ9W8oUpFxexsOyMjfUiSQHf3OD09I+TzWvbuXUYyaaa1dZaurnHyeR0+XxMOR/QAVWNBELjgggtIp9Ns3bpVfl0UVUSjDlyOGB3xc9kRS6Fofhm9PkOlUiGfzxMMBpEkiXg8TiaT4VhRNyp16pzERKNRZmdniUQiBxgUnS5HR8c06bSZ0dFeSqVaXF1BNOpkcHCAbFZPZ+cEDsc0c3NzpFIp9Ho9oiiSSqWYnZ0lm80SjUblnMLRQpIkQqEQDz/8MENDQwc1ZFVxxgoPPBBlfGqCUz+v4dxWFZZSw74beRuxmFPeXhAEKpUKJpMJp9OJVqulUCjIISy73Y7NZkOv18shsprhsFqtOBwO7HY7VqsVs9ksJ/prRqaWpzGZTBgMBrksuRYSS6ctDA+vwO/vwmLJMzAwSEvLPDMzbczNebFa4wwMDJJMWshmDbS2zqJQLC5fbm9vp7e3l2eeeWaRkkAw2IBCUWGD08OeGTuvlEfp7JxEEKrFBLFYjEKhQDabJRwOH6Vv7UDqifo6dU5SJEkiGAwyPT19kBuwRHv7NKKoYGKik4M9P5ZKGkZG+ujomKK5eR6VqsToqEQmk6GxsVEeBJXNZuVRBKFQCJ1OJ08bPBKIoih7R4ODg8zPzx/0ybqhIYDNFmfvXgv33fcoLR+y8/5TY/Tq3ZQSeubnmxeNCa4ZlFofSqlUolwuYzQacbvdaLVawuEwPp+PQqFAqVSSe1NqobGaodHr9ej1ekwm076ZJxU5b1UuV3MhtSKHfD4vV9tVz0/C53MQCJhpbg7icvlxOKIEgw2Mj3fT1jbDkiXDRCIOXK4sTU0+5ucXjzu+4IILuOWWW3jmmWd473vfC0AuZySTMdLYEOH00LncP3U/q3qnaGmxMj/fRi6XIxwO43Q68fv9tLa2vm559JGiblTq1DlJSafTTExMkEqlDmjOa22dprNzglTKxMqVuyiV1BQKOlIpE6mUhXTaSNXQKJia6qRcVuF2BymVNPh8VYPl9Xpl+ZKpqSmcTicej0cuDDhc41IoFOSxuPPz88zPz8tz11+LwZDB650jFrPwwx8+jWATOOXyBOc2G9CKZuaDjYyP98glu4IgoFAoZM+jhslkwmQysbCwQDweX5R0dzqdaDQaKpUKpVJJXl8ulyOTyaDRaGRvpJabAeQqu5oxkiRJriTL5XL7GRc1s7Mt+P0umpt9NDYGcLnCRCIOTKYMLlcEhaJCY2OQSMRFPv/qGGOPx8OKFSt4/vnnOe200zCbq5VioZCbjo5JzjR3sznl4PehCT7j9hCN2imX1SQSCcxmM/F4nHg8jsPheFvf1VuhblTq1DlJCQQCzMzMLAp7KZUlOjomGRgYpFjUEA67cDiieL2zcsVROm0ikbAxP99CNOognTYzN9eKWl2ipWWOfF7LwkI1ee31emXDkclkmJmZweFw4HA43pZxkSSJbDYrlzKn02kWFhYIh8MkEgn5Rr8/Wm2enp5RSiU1v/ylj9nZWU77ZxeX9kZoUVsJ+BoYHe1bNEu+lnjXarUYjUasVisGg2FR02ctbFXrqq81Ota8E6VSiSAIcqd6bcqiXq/HYDDIXkqpVJK9oP0LAhQKBVqtFlEUFxmdUknL9HQngUAjzc0+3O4wIKHRFNFoCiiVIi0tc4yP9y66Dueffz579uxh8+bNXHbZZQD7ZPVn8TREOCd1Dg/PP8CF1ggdHdMMD5soFAokEgkUCgULCwt1o1KnTp2DI4oiExMTizqmNZoCfX3DWK1xkkkTwWAjjY1BrNYEuZyBRMKGVpvHZEqj0y1gs0WJx+1ks0bSaTPhsAONpkBn5yQjIxo5DNXU1ERLS4s8BCoYDJJMJuU8Q61nw2AwYDKZDtoTUWvKrD25C4JALBbD5/PJzYWRSOSAMJ5aXaC3dwSAxx7T8Ic/PEXfB1pYu3qesxqtlPNGRkb6iEZdBxyzZhgqlQrJZJL5+XlyuRxKpRKz2YzVapWrtmoFATXPrLZGAK1WS0tLC6lUSq6Iy+fzcsd9zVjUQl5qtVo2aqIoygUQlUpFDqtVxSsNTEz0oFYXcTrDuFxh3O4MDkcUrbZIINBIOv2qPL/D4WDNmjW89NJLnHHGGTgcDiRJQTjswuMJsFY9wObSZu6cmeCbPU7c7nnC4Q7S6TQ6nQ6/309vb+9R71k5Ikbl0Ucf5Ytf/CKiKPKZz3yGr3/964veHxoa4lOf+hQvv/wy3/72t/nrv/7rI3HYOnXetSQSCcbHx+Wn+ppBUSorZDJGbLY4jY0BBEHkD38w8uCD1c5wl0vNWWfZOOss8HgK6HR58nk9Ol2Wzs4klYoCkynFkiWD7N69Qi6hDQQC2Gw2TCYTOp2OaDTK3NwcGo0Gq9WK0WiU1X31ej1msxmdTieHkWp6VEajUZ7cGAwGKRQKFAoFQqHQAYUGJlNqnz6WxIsverjrrrtwehysvjrKpZ0arIKJXSN9zMy0L9pPoVCgUCjk0uB8Pi/nObRaLW63m4aGBgwGAwaDAaPRiEqlkkcElEqlRd6GSqVCEARcLhdtbW0EAgH8fj/lchmHw0F7eztqtZpSqbSoAk8QBIrFIul0mlgsJocpJUlCEATZIJVKGvz+Zvz+Jmy2VlavfpmOjknOOutpnnjiwkWaZeeeey47duxg06ZNfPjDHwYgHG7A4/HT2BDlHN85/C75O3ZFc6zw+EmlXBQKWrLZLLlcTp65cjQ5bKMiiiKf//zneeyxx/B6vaxbt46NGzcyMDAgb+NwOPjBD37A/ffff7iHq1OnDjA2NiY3tSkUZXp7R1EqRebmvKxatQO1ukQ4XOSnP53ggQeSaLU6rFYrc3Npnnkmg80GH/uYh0suacDhkPD5mggEWjCb02i1BVpbZ7HbYwwODhCL2YlGJbmSyG6343K5KJVKcj5EkiQMBsMBT/q1cttat/7IyAiBQIB8Po9CoaBQKBxQmVRVHl6gsTFAoaBleLid22//BZVKhfd8pYEzu4ZYYnYR9Deyd++KRVL2tUT0/kalXC6jVCppbGyks7MTjUaDJElyYr3WJForC95fSLJ2HjW14v29lrGxMbkqbtWqVbjdborFIn6/n4WFBUqlEna7ndbWVkRRxOfzsbCwQDqdliX2F/cACcTjdp566lyA6hjhM55l165VJBJVqRWz2czpp5/Oli1bOOuss/bluLQkEjZcrjCrF1awWdrM7bODfNeyHq93nIkJI9lsFrVafcSKK16PwzYq27Zto6enh66uLgCuvvpqHnjggUVGpVb3/dBDDx3u4erUeddTLpcZHByUy3vb26fRagsMD/fh9c5is8WZnCxz++2jPPmkkSuvvIr+/n654S8cDrNnzx7uvnsbL77o58MfdnDqqRl0ugJjY73MzLTR3j7FkiUjrFq1k1CogXJZRS6nB2wIgoNEwoHN1orX60GrtcjihbU11QxL7cacTCYJh8Ok02n5RppOpxcVGAiCiMsVxuPxo1aXiESczMx4+c1vHmBhYYHL/3ID/as3c0aDmXLWxssvr12UzAZkCRatVovBYKBUKqHVamlqaqKjo0M2KKIoyvIskiTJRqNW5bW/cdnfSNa8sVpPy8TEBOPj4zz33HN0d3fT2tqKyWSipaWFcDgsqxsIgoDT6cRgMBCJREilUqTTaXK5nOwZ1ZAkFVu2nIPRmKGlxUc2qycQaGJ+3oskKTjrrLN48cUX+dOf/sTHP/5xoFpe3Nsbx2lLck7sHB4UH+TpmQrv6cricPhIp3UkEolFTZpHi8M+wvz8PK2tr5a/eb3eRU06b5Vbb72VW2+9FeCYd4LWqXMy4PP55Cl/NlsMuz2Gz9dCoaClp2eMTCbN/fdPs3t3E3/xF5/AYDDI+wqCQENDA+eddx5nnXUWL7zwAnfd9STT07s488wAnZ1F5uf7GBtbQjTqpLt7DFFUEI3aMRjyaLUB1GrfvgqolwEdpZIehcKMw2Eln4dyWYEgaAENhYJEPJ4lmcyTz0uIokCpJJDPV8N2CoWIwZDFbo9ht0dRqcqk0ybGx7vJZk08+uij7N69m/dddi5LLt3G+iYlTsHO0Fgvfn/zAddGrVZjNpsxmUzkcjnK5TIWiwWlUsnMzIzskdQMj1qtRpIkWfurds+phb1qSfuaB1STwa8Zy5pYZyAQYHZ2FqPRSENDg+wR1IxWLZRWC6tpNBq576XmuexvWMplDdu3r+G007ZiMmWQpCAmU4axsW70ej1nnXUWTzzxBDMzM7S1tZFKWSgUtLjdIVbFVrGZzfw2/jJrEufi8cwxPt5INBolGo3S1NR0wHU7khy2UTmYhMPhCNJdf/31XH/99QCsXbv2bX9OnTrvVHbu3Ek2m0UQxH1ihAb8/kaWL9+NVhvj0Udn2bPHwZVXXivrWR0MtVrNmWeeSTa7mj17HqNU2k5f31M0NiYRhDWEQo0oleK+hjyJ4eElgIBCIaLRFDAYyhiNIiZTBZMpicWSxGrVoNEgq/zm83m02gIOR0m+GQNIkkClokSprP2uIB63Egy6yWTMSJLEU089ua+Edj1nfnqWnpYcbSoPoaCH7dtPOej51CRYYrEYkiRhMpkwm80oFAo5f2I0GtFqtfKNvhYmqyXTa3meWnVXLRRW81hqRqL2u06nw+12k0wmyefzxGIxOjo6MJvNVCoVuUChFiKseXPlchmdTicbrNf25vh8Xvz+aVyuEJOTHTgccfr7hxgb6+W0005j69atPP7443zqU59CEARCITde7yxmfZFzcufwe37PUzNlPrAMGhvHSCRcJ0f4y+v1Mjs7K/8+NzdHc/OBTxB1jjw1jZ9a7NZisbzxTnVOampjggHc7hBqdZHJyQ70+hxe7wwzM362bxc4/fT/73UNyv4YDAbWrfsgyWQfc3MPks1uRasNYLO9l1DIi1Ip0tzsQ5IEZmbaqVSU5PMG8nmIRpGfvNVq9b4nexWVSoFiMY0o5hCEEiqViEIholQu/r9UUpPL6UmlzPJURkmSeOKJJ9iyZQurV6/mE1/WYuueoFllRUy72bHjFERxcbK51o9S8yQ0Gg0NDQ10dXVhMpkwGo1y6fD+Ia2ap7H/1MeaFwEs8kpq/SaSJMnFALWbdKlUIpFIMD8/TzAYZGJigvb2dtra2nC73ZTLZZLJJOVyWV5nTQS0JhVTUy/Yn1deWc6GDU/T1jbLzp2r6OycZsmSYUZGejn33HN56KGHGBsbo7e3l0jEKashr55ZzWY288fSs5zquwyvd45CwX9MJn4etlFZt24do6OjTE5O0tLSwq9+9St+8YtfHIm11TkEhUKBTZs28corr8j/CJVKJZ2dnVxwwQV4PJ7jvMI6R4vx8XESiQSCINLY6CeZtJBOW+jrG0KSZnnllRxm88Vvqx/BYlmKweCiXP49icQUweAv0Osvwe9fgUJRwePxo1BITE21s3+Hfi18dHBlY92+/98coijy0EMP8fLLL7N27Rq+/LculN1PIEhqLEUvExPdBAKLwze1fIhKpUKv16NWq3G5XHR3d2OxWOQ+lTczM+ZgxmZ/D6Xmpez/f83AeL1eent7mZmZYXh4GL/fT6lUore3V+7qTyaTVCoVzGYzTqcTn8/H/Pw8giCwcuVKduzYsaisOp22MjXVQXf3OI2NQYaGltDXN0Jf3wiVSj/PPvssjz322L6cdlUPzOGIMjfn5ZzKOTzEQzwfTHOZU4/bPYpWexJ01KtUKm6++WYuuugiRFHkuuuuY9myZdxyyy0A3HDDDfj9ftauXUsymUShUPD973+fvXv31p+s3wZ+v5//9//+3wGusiiKjI2NMTY2xsaNGznllAPDA3VObiqVCnv27KFUKuFyRVCpyvj9TZhMKZzOOYLBED6fi46O0972MbLZBhoaLsXjeZnR0R0Eg/cQiYxRKFxKpaKkuXl+n3fUecB43sMlm83y29/+lomJCS677HRu/KKebOPThEsi3eUlhOMtDA31L9qnlhfR6XSyFL3NZqO3txer1YrNZluUU3ojapVeb3domUajoa+vD7vdzujoKKFQiMHBQXp6erBarZhMJpLJJPF4HKPRSFtbGwaDgZmZGfL5PF1dXQwNDS1SmZ6ebqehIYTXO0ck4mRkZAl9fcP094/x4Q9v4LbbHuCFF17g9NNPJxRqwOWq9rycEjyFp3maJ9nM8umr6OkZIxodw+k8ug+dR6QU4NJLL+XSSy9d9NoNN9wg/+zxeA4qYV3nreHz+bj99tsPOi97f373u99RLpdZt27dMVpZnWNBJpOR/47c7iCZTLVpsbd3mFxujGi0gsFw4WEP2QqFGvF6l7F+vY3R0UGGh3fyu99NsHr1Rk47rYO2thmWLh1kerpj0VTFw2F6epp77rmHcjnF1752Nhs3iiSMu5jI5GgpLEEsNzE62keppJX3MZlMcr9HrSPearUyMDCAw+HAZrMdk2qn16JSqfB4PBgMBiYnJ5menmZ4eJiuri66urpwuVxEo1ESiQTJZBKHw4EgCMzMzFAqlbBarYtmoGQyZubnW+juHqe9fZrdu5czPLyEJUtGuPTSMi++2MGmTZtYvnw5YCKdNtPYGCQYdHM2Z/MwD7M7GyT3ygqam49+TqWuUnySkEwm35RBqfHwww8zPT19lFdV51gyPj5OOp3GaEyh0+UJhxvQ6XLo9VGy2QC5nAu9vu+IHGturpV0uoGlS5dzzjln8t73SjzxxM/50Y8e5+mnnYiiip6eUTo6JlCr336cPpVK8cADD3D//T/j9NMlfvzjs7n88iIJVYCtsQjKZBv2Yhs+XwuRSLVrXhAEOflek2JRqVTYbDZWrFhBS0sLLpfruBiUGoIgYLVaWbp0KStWrECtVjM8PMzLL79MMpmkoaFBLj8GaG5upq+vD4vFIkvr78/8fAuxmA2bLU5b2wylkpaRkV5A4MYbe1EqizzxxBMA+P0e1OoiDkeUUzgFM2ae4ikkSSnn444mdaNyknDbbbe9aYNS44477jioOF+dk49SqcTw8DCiKNLQEEYUlUSjdlyuEInEKJWKRKm0Fnh9L+XNq9QKTE52ksmYcDgcbNhwLn/1V/2k0xN873s/4+//fjvPPVfEao2yfPluvN4ZDIY3ntmhUhUxGpNI0iCTk79k797/or9/B1/7Whef/ezpeL0qooUyz6WnyCbtdJWXEovZ9+Vxql5ArStfFEW5p8TlcnHqqafS0dGB0Wh8k+d49NFqtXR0dLBu3TrcbjfBYJCXX36ZyclJSqUSNpsNhUJBqVSira2NdevWsWTJkgOMSi5nJBxuoFJR4HBEsNliFIs6xsZ6cTj0fPGLfbzyynbm5uZIJq3kcoZqvw8qzuZspplmksljMlelrv11EvD444+TSqXe1r4333zzAbI5dU4+UqkUgUAApbKM3R7bN+e8OtXR55ulsdFEOv36ebRanqCWXK5UKnJD5P4x/BqVipKxsR6WLh3a17W/hP/4jy5+/vMYmza9wr/+6xhNTSY++ME21q8P09trRZJ05HI6ikUNkiQgCOwrG86QyfhIJEL4fAHS6QwajYLly9tpb1+JTucAoqAK8mJxkNmEiovVaykVjIyPdyNJSnlIVm2tZrMZo9FIU1MTp5xyCk6n84BzOBFQKBQ4HA65qGl6epqhoSESiQRNTU3yjJdIJILdbue0004jk8mwa9euRUl7n68Zmy2GTlegtXWGZNJMNmtkYqKLtWuLXHbZNL///YP8+Z9fj9/vobNzAqs1zprEGp7maZ7iKU5JHP1ca92onOCk02meeeaZt71/oVDg0Ucf5eKLLz6Cq6pzLJEkiZmZmX3x9yiCUNmnPhwjHg9gMJQoFJZQLmtf93NqxqTWVV6bt16rYqop7u5PuaxhbKyHJUuGqVQUqNUaPv3pBs4//1q2bg2zfft2/ud/hvnZz0Q6OgSWLrXidmuxWlUIgkSxWCIWyxIMZkgkIJVSYDK10NJyNp2dKzAaTVQqYdzuSez2GFvS47wcz/F+3VmoShaGR6p5lNpALFEUF6kLe71e1qxZI4eRTmQ0Gg3Lli2joaGBPXv24Pf7yWazeDwedDoduVyOYrGI0+nkvPPOkyvDar2A+byBWMyByxVCqy3g9c4zM9NOMmnD7+/kssvCzM6+yJYtWzj33HNobtbi8fhJJJZyNmfzKI8ylBs66udZNyonOLfddtthf8bWrVtZtWrVUe+krXN0KBQKjI6OIooiDkeUXM5ALmektXWWqakZ2tsVFIvL3vBzJEmSb8g6nU6WKak18NVGCdc60WvkcgYmJrro6Rkjn9dSKgn09ExjsbgZGLiafL7E5OQk8/PzDA4GefHFjCxNotFosNmqAo59fR20tLTIvR0qVYnW1nHc7gAmU4adiRgvpIOcaxzAnGtmeKxHNig1GXqNRoPJVA3JdXZ2MjAw8Kb7cU4U3G43VquVwcFB/H4/c3NzNDY2yt316XQap9PJunXrSKfTxONxed+FhWbs9hiVioDLFSIScZDJmAmFGmlvX87GjT7uvPNJli5disvVSFvbDGZzklNTp6JAQaui9dALO0LUjcoJzPz8/CJp8wORUChEKhUlbxRLv/XWW/nGN75xXJOXdd4eiUQCv9+PWl3AaEwzP9+CSlVEq42RTvuxWBwMDbUccv9a2EilUlEqlSgWi7Ls+/6NgDXRxXg8TjKZRBAESqXSPu0uK+PjXXR1TVIsqojHrbjdQczmFJOTnfT399Pf33/INSymgssVpqVlHputWuU0HVWztTxMv6EBe3QtQ9PtSNKr/Sc1T8VqteLxeOju7qajo+OYdIgfDbRaLStXrsTpdDI1NUUwGMTlcskzX+bm5jAajTQ2NlIoFOQxwvm8nmjUgd0eo1xW0t4+zeDgAJKkYGamjY6O9bzvfQ+zadO9uFyfpqnJT3Ozj9RwP+tZj1Q6UAHlSFO/w5zA3HXXXQd93WKJ09AQQKOJolYLKBRaUikzwaCbVOrQvT8PP/wwGzduPFrLrXOUeDX0Vb0Bx2J2bLY4Pp8Pm01CrW4mkzl0+KcmT1KpVNBqtVQqFeLxODabjcbGRqDaA5NKpeSnYqvVSqFQQK1Wy9L1iYSd4WE1PT3jWK1JEgkrRmOGpUuHmJvzEgq53/BczOYEXu8cZnMSozFLNqsnm9eyXbEZk0qJdfYSpkJd+7wStay9ZTAYZMXf1tZWPB7P2+4lOVGoNUwajUYmJiaIx+NIkoTL5SKdro4qqOmYFYtFuVBnYaEZhyNGoaDDaMzg8fhZWGhGkhTMzQ0wMDBPPP4Czz23Bbd7OW1t01gscZJJ2wED0I4GdaNygjIxMXGApIJSWcbpfIVE4kX27vUTj4tks2A0Wujr66Snx0siYWd6uh1RPPAJbvv27axZs0aeN17nxCebzTI8PLxvdkeMbNZIsajDbp9h1645Bga05PMHn0EPoNPp5GFVKpUKu92OQqEgHA4Tj8cplUrodDry+TwGg0HWvqr1f+RyOVlcURRFslkTe/YM0N4+g80Wo1RSI4oCra0zWK0J5udbyOUObDY0m5M0NfkwmdJotXkUCnFfMl/BhHKIkXQCx/QHyRW6ZK9KqVTKEiYej4eWlhaam5uxWCyH3YtzoiAIAg6HA51Ox+TkJNFolEqlQktLC2azGaVSid/vR6/Xy5WchYKOSMSBwxEjkbDg8SwQi9nJ5/WUyxqKxQ0sWTLPtm1P8fLLHTQ2amlpmd/XU3T0r1vdqJyg/OpXv1r0u0pVQqP5I/H4i5RKAnp9E3a7E1EsEYn42LVrJ2Njs5xzzhr6+/OMjPQuahSr8Zvf/IYvfOEL9TDYScLU1BSRSASNJo/BkGFuzotSWUKjCROLxWhsdDM11bhoH7M5gcMRQ6MpIgh6CoUGBKEHu70Bq9WKQqHA7XYzNzcnj8EVRZGFhQVEUZSHa9UUdaGaZK5NMRRFNRMT3VitMbzeebTaEmp1EY9nAYcjTCJhI5k0Uy6r0euzOBwxTKY0IKHX51CrS4hiVVJkKlXkR/5RrPFVnKU7A0H/6hheq9VKY2MjLS0tuN1uHA7HO/bfrcFgYMmSJczPzzM3Nydrhw0MDJDNZtm7dy/5fF72NBYWmnA6o4iiikpFSVvbNCMjVcHPTMaMzXYp/f2/YMeO3zIx8XGWLl3A4YgSjR79Crl35jd0khOLxRZV4QiCiCA8iFa7C1G04XJdSDLZTTZbTVC6XFkMhmcIBp9n06ZnOP/8M+jtHWVoqF8W6auRTCZ5/vnnOfvss4/pOdV56xQKBSYnq70Frw19BYMBbDbQaBpIJm0ACEKFjo5qFZUoqiiXjej1WXS6SVSqMJK0AbDKirtdXV2ysGFN1VelUmEwGFAqlSSTSbmEt5bQr5UiAyQSdhIJK05nFLc7gMGQwWxO09AQXtQQWSxqKRQ0GAxZBKHayOfzNROOm/jP/F2I2PiU4cOoFNVQl9PppLm5mZaWFmw2Gzab7ahPKzwRUKlUtLW1YTKZmJycZHx8HLfbzfr164nH41QqFSKRCADFoo5w2InTGcHna6KlxYfLFSIcroYgk0kvLS0XkMv9nj/84THa29fT0jJPLGY7+udx1I9Q5y3zP//zP6955Sms1l1ks240misJhRoWvZvPG1AoLsThaCEUupcXX9zK2WefSXf3BKOjPbw2NPLEE0+wfPlybDbbUT2POodHIBAgFApRqVSwWhNkswZKJS1Wa4I9e/w0N2uABrJZPSDR3T2OxVINQQUCjeh01XG5jY0ibvcYJtPTFIvLUCpPIxyuqlsbjUbMZjOpVEpuGszn8/LoX5VKRaVSoVgs7purnieXy8lGBhREIi4iERd6fRaDIYNKJaJSVR+KCgUten2WpiY/0Wgjs7MryeWq+Z+HxPuISlG+aP0iXquXhoYGOjo6sFqtshDkO9UzORS1YV56vZ7p6Wn8fj+ZTIaenh75utfCYFVvJYJWWyKVstDSMk8iYaNUqhrgbPYUmpvnKBS288gjjVx+uQeDIft6hz8ivLu+sZOAUqkkV3pUmcBq3YIomhCEq0ilXIfcV6MZQKWCQOB/GRzcxfLla2hpmWd+/sAywj/+8Y9cddVVR+EM6hwJcrkcMzMz+5R/85hMGRYWPEAFvT7O7GyI9eudRCJOQEFTkw+LJcHMTJv8tCoIAjqdDlE04fe7cDqHcToHUakKOBynUyxKJBIJKpUKDoeDgYEBWZi0lmPR6/UolUokSSKZTMqz5ms5lprkfXXWuo502i6HzARBoqVlDpcrSaHgJRhciVarR6kss7e8l2czz/KR5o/wyfWfxO12YzAY0Gq18qyTdzMGg4He3l6MRqM8WsRqte7La2WpVCqUSlrC4QYaGkKMjXXT3T1Ba+sMExM9QHVGjUp1MU1NYaamtnLvvR+kq8t81NdeNyonGLWplwCCkEWtvhedDpLJD5DNHtqg1GhsHGD37g2MjGzG6ZygsVFJImElnV5cFTY4OMjc3Fw9aX+CEolEiMViFItFLJYkIJFI2DAas8RifiSpgstlYWjIgVabw+NZIBp1yAalJgcPyHIgqdQp5PNjWCy7qFRmUCo/QFNTE/Pz82QyGYLBIJlMRh75W+s1KZfLpNNpksmkPHekpm3lcrkwGo2Ioign1pPJJIVCAo9nNxpNkmJxNRbLe2hvN1EqlYjlY/zDs//A8obl3P3Ju9GpT64+k2OFSqWitbUVs9ksV4cplUo8Hg8+nw8An68JhyNCQ0NoXxhsHqs1Js+0L5e1mEwfobn5DqanH8Tvb3i9Qx4R6tpfJxjhcFj+uVB4kKamNNns6WSzb14ocNmy8/H52ti+fRRRjNHRMYVCcWAp4SOPPHLQyZ11ji+FQoF4PE42myWXy2GzJSiX1WSzBszmJIFAgMZGBSqVk0zGSGvrHJWKgrm5Vz1SrVaL3W5Ho9FQLBZJJBL78hJL8ftXIwhJyuV7eeWV51hYWKBQKBAMBllYWKBcLqNQKMhkMiwsLOD3++VhcPvPcnc4HLhcLjo6Oli6dCmrVq3C5XJhMuVZsmQPDkcJrfZ9rFr1WdauXY/H48HlcvGzuZ8RyUe48/I76wblDRAEAbvdzrJly+js7JQHhNXk/EVRjd/fhNWaIJMxkMsZaGubWfT3XipZsduvZOnS5n0h06NL3aicQOw/3KxUmqC1dZBMxk0mcz5vpRRQEAT6+69gelpg9+5hDIYMLS3zB2zn8/nYvXv3kVh6nSNILBaTvZRcLoPFkiCRqJaDWixJpqfD9PTYKRb1SBJYLAn8fg/lctUzqT3NOhwONBoNer2eQqGAz+cjFAohCG0olR/AatXg8WyjWAzL8frajHW9Xo8oinLFkVarxWazYbVa0ev1uFwuli5dymmnnYbH48FkMhGPx4lGt9Hc/AIGg55K5f20t1+Iy+WSZ7C/EHqBXw7/ki+f8WXWNK05vhf6JEKn07FkyRJZ8dhoNMp9OsGgm2JRg9c7z/R0G2p1+YC/93K5GY3mWiqVN+4lOlzq4a8TiNHR0X0/iej1v0OjURAKXXbQnhOQEIQKknTwBrCq+un5jI4+TkeHj4YGDeGwi1xusYLrI488Qn9//0nbmfxOozaWNp/Pk89XcylKpUgiYUWhEIEgsViOlpZGkknLPsVi1aLGw1oFlSRJOJ1OlEolpVKJbDYrd8vncmoUii48npfp6trD1NRS0ulqfiSRSMgerMFgkJWNDQYDarUam83G6tWraW9vJxKJEAwGSSajwPN4PPOo1W2k06fjcrVis9nk8FlRKvK1LV+j09rJt8771rG/uCc5SqWSZcuWkUqlGB0dpVAokEwmkSQF8/MtdHZOotPlCQbduN0BotGqhEuNg99Hjjx1o3KCsGnTJvnnYnEzHR1xIpHV5PPt8uuCIOJyhXE4ohiNeQShQrGoIhaz73tSXezannLKmTzxxHYaGsZ473sbaGubZXi4WsteI5fLsWXLFs4///yjfo513phUKkWhUKBYLO7rek8gSQLJpBmzOUUkEkKSwOEwMjtrwmZL4Pc37pPqqVKbw14Tj7Tb7eh0OjkmXysGSad1TE2toLt7iK6uPSwsLKdQsKBQKNDpdNjtdux2O5lMhlQqRaVSIZ/Po1AoSCaT7N69m0QigVqdxG5/CZUqTaVyKrncCszm6lx4rVZLOBxGEAT+e/t/M5mc5I//3x8xqN/8NMY6r6JQKFixYgWlUglBEOQxzrGYA7c7iNc7z+BgP1ZrnK6uSfbuXXrMjEmNulE5Qdi8eTMACkWWlpbnyWYN5HIXye9bLNXhPFptmULBRCTSjCgq0enSNDSEcDojzMy0E4u9OptcoVDQ0XER4+O/oL9/Do9Hd9AGqM2bN7N+/foTag7FuxFJkojH45TLZZLJJNlslra2FJmMkUpFhdmcYno6gsulQaMxodEUAYlweHHy1WazyYl2k8mE2+2W5T4MhmqZcS0cBmAynYrF8gx9fRlEcSUq1XJZjr0mvW61WikWi6TTadxuNy6Xi0QiSltbGJNphExGRTL5HkSxCZ1Oh8lkwmw2k81mKRaLRCtRfrT9R1zVfxUXdl94rC/tOwqz2UxbWxu5XA5Jkpibm6NcLjMz08bSpUM0NfmZmOiiv3+Yzs4pxsZ6OBad9DXqOZUTgL1798o/q1R/wGgs4PefTbmsByq0ts7Q0zNGpaJicnIZY2MrWVhoJRBoYmqqh8HBFeRyejo7J/B4FhZ9dk9PL7OzbezYMbHPYM0hCAcO+7rvvvuO9mnWeQNqPSCZTIZQKIRSWcZgyMh6bgZDitHRMMuW2SkUNOj1OZJJK8Xiq8oJgiCgVqsJh8MolUqWLFmC0+mkWCwSiUTQ6/W0tLTIuZa+vj56ek7Bar2WlpZ1eL0T2GzPUanMolBUSKfTlMtlNBoNOp2OxsZGent7sViSNDU9g90+Ti7nIRQ6l1KpEb1ej8lkkqcx1hoo//nZf0YhKPjOhd85Xpf3HYXX66WxsRGz2Uxra7VAI5czEgy6cblCKBQSs7OtWCyJg+ZTjyZ1T+UE4De/+Q0ACkWUxsbdhEI2FIrTEQSRrq4JrNYEwWAjfn87gqBEpRLkUapV1VkdIyN9dHRM0dw8jygq5Ri7IAiceurF7N17K/39M3R16fcJ0C1WtR0fH6+XGB9naj0jwWCQbDaL2ZwEIJk0IwgiohggnS7S3GxBkgQkSSAadSz6DJ1OJwtA6nQ6QqEQfr9ffs1qtTIzM8Ps7CwWiwWbzUY0GkUQBCKR9YABjWYPBsMM+XwRrbaMwWDHZGomm1ViMAgolXsoleaozkPfQDisQRSLGAw6uRvfZDLti/dLTOQm+PXgr/nimi/S6eg89hf2HYharaajo0PuHers7GRyclIe5NXZWQ196fVZGhv9FItqQqHGN/7gI0DdUznOhEIh+We9/mEEQSQer4YHqgYlycxMG3NzrZTLFTkeXtNmUqlU+xrFFExNdZBI2GhtncFmi8qf29zcTDq9hN27pymX83g8AdTqwgFr+d///d96ifFxQhRF+QYRDocpFApYLCkqFSWZjBGjMUMkEqJSgcZGA5WKkkpFSTxuW/Q5giCQTCblRkKfz0ckEqFYLKLX68nn8ywsLCBJEna7nWg0KifmVSo1avUaisUPk8mcg9/vRhSNOBwqLJZZjMYdmEwj5HJ58vlTyWYvI522kcvlMBqNWCwWlEolWm3Vc8rlcphMJv5u099h19r5+jn1CaRHErvdTmdnJxaLBY1GQ1tbG5WKksnJLjSaIm1tM8zOthGP22htncXhCL/xhx4B6kblOPOjH/0IAJVqjoaGcXw+D2r1MtraqqqvMzOtckMbVCtAFAoFoihSLBYpFovyACNQMDHRRSZjoqNjGo3m1VGk69ZdwK5dImNjU6hUxYO6xKlUiq1btx71c65zILlcjmw2i9/vl2U4zOYkqZQZUGAyZYhEolitRjQaDUqlSDJpWZSgh6qEvUajkUfrqtVqFAoFsViMZDK5T/LFKs8jMRqNOJ1OXC4XlUqFXC6HKCqIRIzEYr0YDB/CZvsM8fgVJBIfIB6/gkzmfYhiP5WKQDweR6fTyUKVgiBgMplIJBKoVCr2Jvby2ORjfG7152i0Hpsn5XcTLS0tNDU1odfrMRgMNDU1kcmY8PmacTiiNDQEmZzsJJWy0NExJXu/R5O6UTmOvCoaKWE2P0K5LJHNXorLFcLlCrOw0LRIcsNoNOLxWPB67ZjNJlncr1wuyyXBkqRgYqITSRLo6ppAEKrif263m1JpGXv3VsMa1Qqy9AFr+uMf//gamZg6x4JaYj4QCJDP59FoCmi1BZLJakmo0ZhicjLKkiXVfpVKRdgnZb6Y2hNrrdu99nNNR2tqaoqpqSni8TgLCwtyqK1mcGrTBxOJhKzFJUkSuVyeQkGJQqGRxxInEglEUcTtdss9E7UEf7lcxmq18u3N38amtfG5dZ9710uvHA2USiW9vb00NlbzWUajkebmZvx+j+yh2GwJxsa6mZ9vIZU6+mOXj4hRefTRR1myZAk9PT185zsHJuIkSeLGG2+kp6eHlStX8vLLLx+Jw570/Mu//AsAavUgNts8MzNdmExuvN5ZUikLCwvNABiNIuecA5ddFmH9+lFWrNjFOedMsGZNGp2uOpmvFgoDKJW0TE21YzBkaWryycc7/fTz2bFDZGxsAoWiQmvrLLA43CVJEr/4xS/qYbBjSKVSIRqNyj0klUoFszkFsC9JLwFBwuEi7e1mBKFCqaQmkVgsvSMIAg0NDUiSJMup1Oae7z+oS6/Xk8lkGBsbIxAIyLPpU6kUCwsLZDIZ7HY77e3tKJVKcrkcsVhM/velUCjkkueGhgb5gaamNZbJZDAYDAzFhnhw9EE+s/wzNNrqXsrRwmg00tHRQUNDAzabbV9BhYfJyU7SaTMdHZNYrQkCgSYMhqNvVA47US+KIp///Od57LHH8Hq9rFu3jo0bNzIwMCBv88gjjzA6Osro6Chbt27ls5/9bD3Msg9BqGC3P042q0AUL6WzcwpJEpia6gCgtTXCunUCuVyeyUkzuVwDIKBSRXE4gpx1FgwNKfH5WlDvJ3mRSNiJRFx4PAFiMQe5nAGXy4UgrGLv3l10dnZjMCgOWmI8NzfHCy+8wPr164/hlXj3ks1miUQiRCIRyuUylUoFkymNKKrI53XodDmSyTCiCG63cV/oy3rAvJxaxVUul5Nv9rXJjVarlUwmg06no7Ozk5aWFmZmZmRJ9Wq/iRqHo5r4r0ngVyoVfD4fpVJJHp6l1+uZnZ2VpVoKhQKSJKHVaslmswiCgMVi4V8e+xdMahOfO/VzJ/2UxhOdhoYGstksGk3Vk6x6hc1MTiro6hqmq2uCubkSBsNpR30th21Utm3bRk9PD11dXQBcffXVPPDAA4uMygMPPMA111yDIAicfvrpsuvd1NR0uIc/afnhD38IgEbzAiZTlL17V9DRUcJoTDM52UmlIrBs2RRer4KJCSOBwFLUais6nQ5BEMhmS8zP+/F4fHR2LmA2DzE21oNWa96nbAtzcy1YrXHa2qYZHu4HBM466zyeeGIXfX2jLF++kpaWOeJx2wGx+UceeQSPx0NbW9uxvjTvOsLhMNFoVG4sBDCZ0qTTJkDAZMoQjUbRaNTYbCokqXRAgh6qRqVUKmG32+no6MDlcpHNZtFqtRgMBkKhEAaDgVwuRygUwmg0yj0stRtRPp+Xx9iKokgoFCIej6NQKNDr9dhsNqampigWi/T19clDo2r5lGKxiMPhYCQ6wm/2/obPr/48zY7mY3g1350olUqcTieCIMgPFyqVCoVCwfS0mpaWEbzeWazWoz9H6bDDX/Pz83KdNFTrp+fn59/yNjVuvfVW1q5dy9q1axdVRr3TCIfDKBRlXK6nicVUWK3n0dTkIxZzkMkYGRgYw+ksMzzsIRI5hcbGTjweD1arFbPZjNVqxePpZn6+i7m5AYzGEkuWjAA5OUwhimpmZ1sxGjO4XNVrWe2uPoVXXpkll8uhVhdpbPQfdI133303gUDgWF2SdyWSJOHz+eSwV1Xtt4RWm99nVMBozOD3x2hpcaHRlCmVNIfMp9TUg/P5PFNTU/JY6nQ6jU6no7u7G4vFwsLCAgsLC2g0Gtkg1OTtazej2vCuUqkkj/SNx+Ny2Ks2DVKSJJRKpVxhptPp+Jen/wWdSsdnV39Wrgarc3Qxm82yLltHRwfNzc00NzfjdjcTDK5kfn4pDQ2nHPV1HLZROVjs/bUJuTezTY3rr7+eF198kRdffJGGhqMv03w8+N///V8A9PpNaLVp5ufX0d29QLmsIhBooL9/GJNJydhYP4LQj91ek7Euy9eylqB3OBwkk3YWFtagVhfp7Bxi/yF5sZiTdNpMc7NPVi4955xz2bZNYHR0BFFU4vEEFlWK1SiXy9x5553Mzc0d1vmWy2WCwSCjo6MMDw8zMzOzSF/q3UwqlSIYDMrJ72KxuG/0LqTTVYUDlSrG+HiK3l4LGk2JQkEnGxyAChWCQpDx0jjoXp27kc/nSSQShMNhJicn940CFjGbzRgMBjKZDD6fj1QqhSiKcqk6gN/vl6VZtFotLS0tFAoF2RB5PB65214QBHn0sNVqZTw6zi9e+QX/X///R7urnTrHBoVCgdlslmfct7e343K5aGlpweNpArqOiYE/7PCX1+uVh8hANR7f3Nz8lrd5NzE4OIhKlcXpfAG/X09PzxJ0ujDT0210d0+g0WgZH1+OTudBoVCQz+dll7ZmjNVqtRx/1+v15HIwPz9Aa+sempoGmZtbLofBZme9LF06SFOTn/l5L1arlYaGdWzbtpXu7h6MRiNtbTOMjR0or5/L5bjjjjv40Ic+xPLly9/yuSaTSbZv38709DSFQkGeKNjc3Exrayter/ddN91vf2peilKpRBRFSqUSJlMaSVKQzRoRBJF8foFcDrxeEwpFnnjchiRVnwenmOJBHiQiRSAO6oSaTys+zbcv+DY2saoqnM1myWQyOJ1OOe9RqVQwGAzEYjHK5TIqlYpoNIokSbJwpMViIZlM4nA4UKvVzMzMUCwWaW1tlQ2JJElUKhVUKpVcVvxvz/wbKoWKz676LHq9/vhe4HcZtfBmKpXC6/ViNpuJxWI4HA4ikcgxeVA/bE9l3bp1jI6OMjk5SbFY5Fe/+hUbN25ctM3GjRu56667kCSJ559/HqvV+q7Npzz44INAtdFRoSiSy52OxxMmFrPT1ORHpVIwPb0chcKBJEmy7EalUiGTyZDNZimVSigUCmw2GwaDQd6uXG5idtaL1ZqgoeFVuZZczkgk4sTtDqDRVA3N2WefzbZtSkZHRygWtVgsSez2yEHXLIoi99xzD48//jiieKDEy6GIxWL86U9/YteuXXJ3dblcJhaLMTo6ysjICLOzs/LM83cboigyPT0te6CpVLXiy2RKk8kYkCQFBkOWeDy2L0mvRRCkfdMeYYQR7uZuKlS4UnMlf9P6N1zUchG3vHILf/bgn5HOpWUJe7vdjl6vl+dzmM1mvF4vRqORbDZLOp1GFEWsVitut1suRYZqriYQCJDNZuUKo2w2K1eZ1QyRXq9nNjHLHTvu4OolV9PV0CXniOocG2reYk1Hzm634/F48Hq9LFu2bN9MnaPLYT8iqlQqbr75Zi666CJEUeS6665j2bJl3HLLLQDccMMNXHrppTz88MP09PRgMBj42c9+dtgLP1l5+eWXUatDuFyDzM/bWLbMRrEooNfnUKlE5ubWIIoWlErksa21JKlCoaBUKsmqsQaDAbvdjiiKBIPBfYank3g8TUPDJOm0iXi8+o/I52vBbo/R0jLH5GQ3ZrOZ7u7T+NOfnqW7O4IgOGhtnSWZtCKKB/9n8cwzz7B3714++MEP0tbW9rp9B9lslqeeeoqpqSlUKhVNTU2L1hqPx5mZmUEURXQ63bvyISORSBAMBimXy7LUvSCIGAxZAoFqCa7RmGVuLo7TacdszlMsqkkkrMSJcw/34MbNNVxDk6GJbnc3n131WTYGN3L976/nH7T/wLfP+jalUomOjg5Ziysej6PRaGhsbMTlcuH3+4nH43R3dy96kq3lU2rqDUajEavVKodeaw24tZnyAN999rtISHxu5efkQVJ1ji01LzORSJBOp+VcS6067GhzROIOl156KZdeeumi12644Qb5Z0EQ5GqndzO33XYbAHb77ymXRWy2tfti5Fp0ugJzc8vJ501oNNUKDrPZjN1ul+Uvat30NYXZSCRCPp+nsbGRfD6/bwCTwNRUOwMDGVpbx0kk+pAkJaWShkDAQ1OTj2AwTSZj4qyzzuLWW19g794RVq8+A41GxOudY3q645DnEIvFuOOOO7Db7axatYqBgQGcTueiJ9JSqcSzzz7LxMQESqVAZ2cJi2U3kiRSLDrQ6VowmUxks1lCoRDDw8Oyiu67BUmS2LVrF9FoVPY2oZqUFwRJzpkYDBl8vjhtbW1oNCXyeT2ZjIlH+F8kJK7kSvRUm97sdjsmk4lPej7Jtslt3LbnNs5wnMF5redhsVQl7ZVKJRMTE7JRqJUIR6NR2euoJe0TiYQ8MdJisWCxWORwSrFYJJ/PY7FYsNvtKBQK/Gk///Py/3Bl35V0ObvqM3qOI0ajUe49UiqVGAyGY/b3VfdNjyHz8/MYjbswm6eJRj14vToqFQGttoDP10U6bUelUmE2m3E4HJhM1a75WsirNmhJkiSam5vxeDwUCgXm5+fl6XuSJKHRmJiebketztDa+moFnd/fSLmsliVajEYjK1eezgMP+CgWQ2QyepzOMBZL/A3PJRaL8eSTT/KTn/yE22+/ne3bt8trGxwcZO/evSgUGTo7d6JQbCYUeoVQaJBK5UmUyt+TSgUpFArkcjmi0SgTExPvijCYJElks1mGhoYYHR1FqVTicrlkdYRakj6TMe3bPsDcXImODhNqdYlk0sxMZYFhhjmbs3HgkKVRjEYjxWKRQCDAn3f9OV2mLr6x7RskS0k5v6ZWqzGZTKhUKsLhMJlMhmKxKMu01MKUiUQCn89HoVDA6XQu+vxCoUA2m0WtVuNyuWTj8b1nv0dRLPL5lZ+vj1E4AbBarWi1WuLxOJlM5pgdt25UjhE33XQTCkWJhobHyOUUdHQsR6kUUSgq+yQVmuQ/eLPZjFarlUe7Njc3Y7PZZFVZrVZLpVLB6XTS1NREqVTaN7e8EaVSSaVSIZWyEom4aGz0YzBU/0FJkpKFhSZMphQWSwKAM888k7ExDc8/P4ZGU6RQ0NLRMY1KVXq905ERRZH5+Xl+97vfcfvtt/Pggw+yefNm8vkI7e27qFTijIy08corK9i9ezmTk91oNBna2l6hXM6RSCTkWejv5BJyQBaLnJ2dZWpqClEU5d6C2h+9yZQhl9MjiiqUyjL5fJBUClpb9SgUFeJxO1vYgh49p1FtZNNoNFitVnK5nDxfvtHZyL+s+xdipRg3772ZWCxGPB4nn8+jVqtpampCqVQyPz9PLBZDp6s2zi4sLDA6Osr8/Lw8O0Wv18sjbOPxOOFwGEmSaGpqkvcLZ8P8+MUfc0XvFfQ4euTX6xw/BEHA4XCg0+lkr/NYVFzWjcoxoFamabM9hFKZQq3uxWRSIEnVkt9wuBOVSoXJZMJiscgifw6Hg3K5TCaTkacBiqKIwWCQY9t6vR6n0yk/PdZCESqVitlZL/m8QFfXLFD1AkIhF8WilubmeUBCr9dz5pln8atf+Ugk/ORyOpRKkY6OKV4r4fJGRCIRtm/fTiQSoq1tGIWixMhIH4VCtbyxoaGBeNzByEgHKlWK5uZqD0UwGCSfz8s5lnci2WyWcDgsG9FisYhKpUKr1VZH7RarA7eMxvSi0Fc8HkcQFLjdAqKoYDqiYZhhTuVUtFTLQ2uTHpVKJQ6HQ/ZuB+wD/MUpf8Fde+9iIjsha4vVJjtaLBa5c77myWi1WsrlMqFQCLVaLUvpa7Va/H4/CwsLKBQKWltbF4VT/vO5/yRbyvK5FdVcSl3n68SgVphRCze/qjd49HjHGpWfPtDCt7774eO9DKCq8aXXj+Nw7KRSMeLxtCEIkExamZ3tRql81aBYLBaam5sxmUxyMrW7u3vfMKUempubZR2nlpYWrFar/Hsul0On06HVahEEgUpFxcxMOwZDHo+n1uCowOdrxmDIYrPFgKq3Ui7b+O1vB7FYYoTDDiyWBF7v7KFP6nXweucxGjOEww4aGiL09MxiMEyhVEqYTCYyGTs+XyM2Wwi9Pi7fcGOxGMFg8Ahd9ROHTKZqHEqlEoVCgXQ6TTKZRKFQYDQaEUURURTR63MolaIc+jIYcsTjcVwuF2ZzjnJZzRPRGSQk1rBG/vxa2W5tLn3t8zUaDX939t/hNDj526f/FovFQjab3adEXJXar5aTt9HU1ERTUxMej0eeS6/VakkmkxSLRbmfRqlU0tpanT1fI5QJ8YOtP+CKvivod/bXE/QnGDXZnFrD6tHmHWlUvvUv13Hp+T7+4i/u5c6H3cc1Vv+DH/wAhSKP2/0AGg2YzatQqURSKQvj492oVFpMJhM2m43GxkZaWlrQarVotVp0Oi2VSoxUaoRYbCfR6BDFYhyTySTLtXg8HjweD0ajEaVSKY+QrcnhJxI2QiEzTU1+ucExGnWQy+lpafEBFdRqNZdccgl//GOCPXtmsVpThEINuN1B3O6Dd9sfCqs1RnPzPHp9Drc7tK9MeR6zeSdNTVtQKMKIoojP5yafV9Ha6pMHU9UEDd9J3ko2myWRSMhFCQsLC6RSqUWz3mvDuV5teqwaFa02jc+XoL3diVpdIpcz8GJpiA46cPDqcK7ag4TT6SQWizEzM0O5XK56MDmJr639Gk/PPM19o/fJkixzc3P7ZrZUE/i1kuZYLIZCocDhcMhd9NUiAQ1KpRKPxyPrg9X47rPfJVfOcePqG9Hr9XWdrxOUY9UP9s40Kn97O7ffeT6xNFx6boids6rj5rXEYjFcrvuxWpNAF3a7ilTKwuhoDwqFQc6beL1eHA4HglDCYJjBbn8eu/13OByP43Q+i92+FZvtSbTa31As/hJ4GYOhmj9pbGzE6/Wi0+kQRRG1Wo1KpZL/uGdnW1Eo1LS3z+xblYDP14JWm8fprPamLFmyhK6uPn70o70UixEUigqxmB2vd47m5jfXUa/RFOjrG8bhiJJOV4sFdu5cxSuvrGLv3i5KpRKtra+g1Wb35XcaMRozmEzVp2G/3y97LO8Eakq+yWSSSCRCNpvFaDTKeQ+DwSAPXYNqf0qppJHHA5dKAYLBCu3tVtTqEr4UhAmzjGXyMWqhzlpJ79jYGJIk0d3dTWtrKy6Xi2uWX8Ny53K+9sTXKAtlnE4nyWSSXC4nS61EIhHS6TRGoxGdTidrhjU0NKBQKIhEIuh0Otxu96JKv0A6wM3bbuaq/qvosfbUE/R13plGBeAbX/gTv/zJ13jqJQtNDom/+It7ufvRhmPqtdx0001YrZtxuYYQRTMej4dEwsrYWA/lsgGz2UxTUxOtra2YzSoslr20tj5FY+MwNpuAwbACtfoCVKqNqNVXotN9CLP5vZhMdlSqHcAv0emGEMWyrPNT68zW6XTyH3+5rGFmxoPZ/GqDYyJhI5Mx0dy8IM+sf//73080quHOO1/Bbg+RThsJhxvwePx0dY2jVJZf52wr9PUN09gYYGHBw+DgUiKRBmr/xNJpC8PDfVQqCjo7J4AKkYiLYlFJU1N1Il0ymSQUCuHz+Q59mJOAmobWxMQEs7Oz+P1+ec57TQnBbDbT2NiIVquVPbNXRSSr6tWFQoBoFNraNCiVFXYnEwgILGWpfKxauaher2diopo36eys6sQpFAo0Gg3uBjffe+/3WMgs8MPtPySXy2G32zEYDPh8Pvx+vyxEaTabCYVCSJIkFxHUPJ+WlpYDyoT/7Zl/oygWuXHVjfL51Xl38441KgD/dNN3+MiGBLff8R5iabj4nDA7Z1X803++/6gf+6abbkKvH6G5+Sn0ejWNjf0kk1YmJrrJ5czYbDY6OjpobXXhco3R2rqF5uYIotjG7Oxadu0aYPt2E3v2lBkbyzMzkycY1BGPtyNJH8BovAal0otC8SJ6/Z+QpDTd3d24XC5KpZKcLK0lTKsJ+urQnppxmJtrQa0u0tRUDXFZLBY2btzIH/4QYdOmSVpb50kkrPukXeIMDOzZZ5QONMxtbTN0dk4SDrsYHBygWDyw+qdU0jI724Zen8PlCiNJCsJhF2ZzFLW6IMfuZ2dn5emHJxu5XI5AIMDs7CzRaJRsNrvovUwmIzcSiqJIOp2mVCqhVhdQq4uyUdHp8qRSCUCF2w2iqGBbPEArrZh4NUGu0Wj2yfTkWFhYkAdrvZb39r2X93nfx092/4TJ0KQsvRKPx0mlUlgsFrlfJZlMYjab0Wg0LCwskMvlaGpqOsAL8aV8/PjFH/Px5R+n3dxe91LqAO9wo1Ljb7/wBH2NWZ58oeq1XH/9Qzy01cBN//yFo3K8m266CZ1ugvb232K3i1gsfaRSTsbGekgmbTidTnp6vPT0RGlp2YxON8r0tIKHHjLyyCN5nn12lN27dzM0NMTg4CC7du1idHSUmZkZQqEQ4XCYSASUyktRKN4DhNBqH0ahSNDb24ter6dcLmMwGPaLbwtMTnpRqURaWqrhrEzGTCTipLHRj1ZbDcEsXbqUtWvXcvPNg+zZU/VQikU1Q0P9lMsqOjsnWb58D83NcxiNKaCC1RpjYGAv2ayBXbtWHdSg1IjH7aTT5n2GrEIo5AIEXK6qt5JOp4nH44u04k4WajmJ/eeLmEwmmpubsdvtuN1uUqkUyWRSTtgnEtXS7tfmU/T6ai7G4XBhMOTIixIvxGL00rvomFqtFkmSmJubQ6vVsmLFioNKo2SzWT7f/3mKlSLffeG7hEIhRFGkpaUFu91OIpFgZmaG2dlZWaOttk6Hw3FAHgXgO1u+Q7lS5gsrv4BKpaqXEdcB3iVGBUCl1HPleQluu+0DzAQUrF+e41Ofv5m7/+DkWzd99Ygdp+qhjNHd/Ss8niJ6fTfpdCvDw0uJxx20tDhYu7ZMV9fzFIvb2Ls3y6ZNLl56yUIqVQ1XuFwuWXDR4/Fgt9vJZrPMzMwwNTVFMpkkn88Tj8cpFjuQpKrWmlb7R2y2Mp2dnVQqFTmZXyOT0RGJtOByhfcZBJif91KpKGhre/Umfskll9DW1sk//MMLvPJKkM7OSWy2BIOD/YyP91AoaPB4AixZMsy6dS9w/vl/wmhMMTTURzb7xl27fr8Htbo60rhU0pJMmnE6o4Aky5XMzMwck/LHI0VNOkej0VAul+WO9VpoqibAmMlk5Oq8crm8r5S4alQqFSW5XLWSS6fLEoul8Hic6PU5goU8wQwHGBWNRrOv7Figv79/kQptqVQiHo/j9/sZGxvDWrby8d6Pc//M/YxER3C5XDgcDnlaZG2ksU6nw2QyyXPPbTbbAYZqJjHDT176CZ9Y/glaDC1yAUCdOu86edi/+6vfAfCDn/dzyYXDXHx2lMy673L3H37KxAsf5x/+7v++7c++6aZv4nL9ic7O53A6K5TLXYTDA4yM9KNUqlm7Nk9Dwx6KxRzDw3Z8wVamSnHCqp0ECTITmyGwECBWiFGhgkpQ4dK5aDY0s8a1hpXmlajyKoaGhmhra6O5uXnfjdeIJF2IVvs4avVjtLZegN/vIBwOo9fr5TkdAPPzHszmIO3t0wwODlAuq5mfb6GtbYaGhiChUHXe+Mc+9jF++ctf8s1vbuUzn+njkktErNY4s7OtjI4uQaEoY7XGWbPmZSyWJMGgm8bGEBZLmlCogXDYxaGeWZJJK/m8joaGENGoi2jUSUfHJEZjmkzGLD/BBwIBvF7v2/4+jhWlUolkMolOp6NcLpNOp8nn8xgMBiwWi6wckM/nqVQqWCwWWVhz/yR9Veq+emMulwOEQiIrV1pRqyNMxvIYMdLIgWN5K5UKra2tsvJ3uVwmkUjI0vT5fB5RFGltbeV7a7/HA//3Ab6747uc2n4qJpNJNkq1HE+tlD0cDqPRaA4a1vrbJ/4WhaDgC6vqXkqdxbzrjEqNG/9siEqlwm33dXHeOdNcfHaU0hk38/iOn/Dis6fx1RueetMKqzfddBM22yDLlz9Ga2sUlUpJKrUcn28Z8biNzk4fNluRfKXE5skSWxYy7M0/x2RxkrJUzW9Y1VZ6LD2sb1iPy+BCrVQjIhLKhxiLjfGTwZ9QoUKrsZVL3Zdy/vT5pNNp+vr6UKlUFApm0ulzMRofx2x+mt7eU+UO2lpfAkC5DDMz7fT0jNLSMs/cXCvhsBubLYHXO0cqZSaf1/P/t/fmYXLc5b3vp6q36qre1+nZV0kjybJsyzYGTORFmC02BkLgQuJgwFxunpOcsCS+8NwQTuDBOSck4V44N3HCCQYuewIiwdjYLAa8ybsty9pGs2890/tW3dVddf9oVUnjmZFtJFsaUZ/n0SOpVdP1Tk2r3vr93vf9ft1uN+9973v52c9+xu23389DD83zR380xPBwiUbDR6EQIhpN4/OV2b9/lAce6MfhWCQYnMbl2o+mNXjiCYlWq5uRkRFisdiKa7a8HKe7e/r4E3mI3l6RcDhHpeKnVqtRq9WYmpqyJr/PVUw1WFP0c2Jiwuqq8vl8lnuiuZ1kGAZOpxPDMKjX6+i6jig28Xpr5HJh6301bYFsFrq7ZZyuBQ4Ui/QzjMDK1YCp6WSa4C0uLlq2xObKQRRFayLe7/Dzscs/xl/+8i/5zpPf4fX9r7eGbavVKs1mk1AoRLlcxjAMS6n4ZB6ZfYT/75n/j49d/jGSUtJepdis4Lc2qUD7P9stb59A13W++M2tXPGqI+wY1rhw5NdM5R2MTbs4dKSTpclLoLyDT33qU9bXfvrTn8bpLOHz7ePii5+hqyuPJEEuFySX20kmH0WTF8B/iCfUCvcfyfJQJkO9BQ4cDHmHeHP0zYwqo2z2byYuxS0LULPA7nA4cLlc7el5t87d43fzzUPf5J/G/4nve77Pu5Pv5nebv8u2bduQZZlyWadUejU+3y/o7Hyazs4OJidnrbZRs/OtVouxtFQgkVikWPRTLIaYmOhn69YDDA4e4+DBzeh6uyV5z549DA0N8dOf/pSPf/xJRkf3c9llAXbu1Egm68zPt7jrrqeZnIRsFpaXwTBg1y4ng4MiDzzwLHfffTdDQ0Ncd911JBIJADKZCF1dM8Riy8zM9FIqBQiFCszMYIkVZjIZisWiZVJ2LmIaW4XDYZaXlymVSlaCaTabViLp6OhgeXmZYDCIoigUCgVrbuD59RSns0GtlqNYFOjt1WnoLQ4V6vSx0vDK9Njxer1W91e5XLYm5s12Y2jXe+r1OtlslvcOv5evP/11Pv3Yp3lV6lV0d3fjcrmOu5G2H6Sq1So+n29VN5dhGHz0Jx8loSS4ZestVqOAjY3Jb3VSMRFFkT95z0EA/tvfvoPNF/+ULcN5tg9p7BieBCapa//Owfm/oqYCLYG3/4GB1w1OR7sXKlNy8cSywkTJQUZ7jIJUZiwHzy1DvuJmyD3EG5Ur2apsZYt/C7JbtpKGmUDMpz1zxsT0q1hYWEAQBK5NXMuNQzfyy8lf8vlnPs8Xpr7AQ4WH+HPtz3nNRa/B7/eTzcYply/H73+QzZsN0mmvJQJoJhVN05iZ6cbnK9PfP8GBA1tpNt2Mjw8wPHyE4eGjjI0NWxL4g4ODDAwMMD4+zsGDB1lamkEUWxw7JvHIIxFEMcgVV7iIx50oigev14uqhnA4Wlx5ZZaf/KTMN77xNP/0T//Em9/8Zi6++GJarbaEezicZ2amh3w+SDCYR5KqqKpMqVSiUqmwsLBAKBQ6J5+EzYFBt9ttrVgajYY1M5LP53G5XPT09KDrOpqm4ff7LY0uc3uqbcolUKm0t5m83hrLywUCgSg+X5VSq85EHq6hf8X5zfZkn8/HzMwM1WrVkvDxer1WUsjn89aMibli/crbvsJr//W1fP6Zz/P38b9HURTq9TpOpxNVVfF6vfj9/lXf8788/i/8aupX/P3Vf4/P5VtzJWPz242dVJ7HX37se9afP/XXf0yo9z66+6fpTlXwKS18PhBEA02HTANm8+3EMZnXyFeqNGo+apVOpOYwl4W3cfPW7WyObyYcDFuT8KZPSltKpX2zaTQaVsupqqpomoYgCJY2kyltn8vluDRxKV+/6ut87cjX+L/3/9/c8vgt3Na6jbdd8TYikQgLCw0EYTuBwDNs2hTm6ac9lpWsKShnGCLHjg0wOvocAwPjHDkyQqkUYHx8gIGBcbZsOcjERL8lGSIIAps3d3HNNQYdHQF8vgpHjgwTjQ4B7W21+XkDr7dGKJQnFls+3ibr5IYb4gwO/gFf/vK9/PCHP6RWq/Ga17yGfD5MKJRHUSoUCu2bUyhUYGGhrUVVqVRYXl6mVqudk9If1WqVVqtFKBRiYWHBGhjUdZ18Pk8kEkFRFGRZZmxszFqBmsV5s57i95epVmUMo73N5/VWKRaLxGLDSFKdpVaNdF4izkrXPnMVYq6IzKK7qUJsUq/XLS96cyVyRc8V/Nmr/ozPP/h5Xtv5Wt4y8BZUVcUwDEtG//mJ/OnFp/mzu/+Mq/qu4h2D70BRFHsuxWYVdlI5BZ/+v9b2gKk36ixm5tEqWXppcPFQB/FQApfLZXme/Kbouo6qqpRKJeuX+R9dlmWr3TYcDnPTyE1cFr+M/3L/f+GPH/tjMq0Mf3z1HxONRllcbOJ05unpOcLiYoxazWUJUkI7SdTrXqameunvn6Cvb5LJyQHy+QhHjrgYGBhn8+aDVKsKjYYbt7uOLFdxOFo4HC1mZzuZmup/XvQCtZpMrSazsNBBV9csyeQ8ilJm27YF3ve+d/Ctb/2Ie+65B5/Px0UXbcMwBEKhHLOzPVQqCsFgnoWFlCWkWSgUyOfz51xSMTW23G43uq5TKBTQNA2n00mr1SIQCJBMJqlWqzidTmvVYhbrDcOg0WgcN+WqkE6fKMDr+jK5nEZPTxivN8dkqUpHa3U9RRRF6+eZSCTo6emxVrgn3EDbXWhreWl89urP8sD0A3z0lx8lqkfpdHaSSCSIRqOr6lgT+Qne8o23EPQE+bsr/w6P27PmSsbG5rempfhM4nF76E31s2PTxVwy+ir6OvstX+/TtU815TtM6RVT9t5MVtFoFL/fTyaTIZvNssm/iW9f822GfEN84olP8MX7vmg58WWz2xHFOCMj04RC4gqPe3PFks3GmJvrJBrN0NXVbisul/08++w2Zma60XUBSarRajlYXo5Rr7splQIcOzaMrq9fQDcMkZmZHmZnu1FViUgkw9DQNDfe+Fb6+/v5z//8TxYXc5TLPgKBdntzoRBCUSo4nSee5Gu1tjy++XR/rmA2P5gT6NVq1bIdcLlcdHV1Ae3kbbZJQ3tV4XA40DTt+MNCFUEwKJVO3PQbjQUyGejt9WM4qkyUtFX1FMBSTujq6iISiZDNZpmfn2dhYWGF5I05M/N8PE4P33vn9/C5fPzJg3/CVGGKUCi0avXx+PzjXPmvV1JulLnjDXfQoXSsuZKxsQE7qZzTmPvgsVjM8hgXRZFgMEgsFqNcLrO4uEjQGeTLv/Nltge284lHP8EX7vsCnZ2duN0y+fzlKIqf4eGjeDzCihuBmQAXFjpZWkqQTC7S338MUWyi6w7S6Q4OH97CgQPbWV6OEQ7nAYEjR0ZQ1RdXnF1Y6GR2tgdNc9HZOUtnZ4a3v/3tuFwufvCDH1Ao+PB6qzgcGvl8ewusrZPWLtjncjlyudyKyfRzgUqlgtPpPF6fmiGXy1EstuOOxWL4fD5UVUWSJEumBbAcPE19M5+vdPz9TsizNJtLZDLQ3e1Cc1aZLbFmUpEkiWAwiMPhoFKp4HA48Pv9VjOAYRhkMhlyudxJ8von0HUdP36++NovUmwW+a/P/Fd+MvYTq2uwoBb4y5//JVd8+Qow4Ltv+S7bo9uJRqOvmDihzcbD/mSc45iy1eaeeL1et4YCOzo6mJ+fZ35+nlQqxZde/SX+5IE/4S8e/AucbicfuvRDHDumUS5fQTj8I0ZGZtm/P0Wj0X5KPlkHbXq69/iNf45AoEguF0FVJZzO9jyKLFepVBTGxwdOOTG/FqZ45aZNhxkYGCebvYTrrruO73//+zzwwCTvfKebQKBELheh2XTi95fIZGLHzcZKlnOdqah7tmk0GmiahsfjYWxsjGq1am1FaZpmed3ouo7H42F5ua0WYOqymQV+aBfpTVMuaMuzFIt5RDFENFqm0mqwUHRwxfPmU8zOLtMpNBgMrnmjN+sktVrNitNsaW42m+i6zvbwdr569Vf5+L6P85673kPnrzqJy3EOZg9Sb9W5YegG/vqKv6Yz1LnueWxsTOxPxwZBkiSi0SjZbHZF63EymWR+fp6ZmRl6enr420v+lo89+jE+ft/H6Qh0cG33tczP6+j6LuLxX9DVlWZqKmY9OZ/MwkKKQiFAKjVPNLqMKLaTTrUqMznZRybTllT5TZic7CMYLNDVNUtn5xyatoNHH32UvXsf4MYbr8LvbyeyUsmP31+yvs4cLCyXy9RqtXNCX8os0JfLZctW1+Fw4PF4kGWZWq1m1TQajYblQ+92uymVSsfrWXVAx+erkMmckEDxeqvMzhaJRHqQ5RrFVp1qLoH4vE0Fs1swlUodV7de/XPRNA2Hw0EoFEKSJKsBxJxhURTFelC5OHUxj3/gcf7t8L9x15G7yFazvK/zffze5t9jV9cuJEl6Rbw4bDY+dlLZQLjdbqLRKJlMxuoMg/aKZXZ2lunpaXp6evjMjs/wkSc/wvt+9D7ufPedDAYGWV7eQqs1RU/PAYpFD8vLa29f1WoKx44NAzpOZ4tWS7S6kl4qgUCAYDBodXCNjQ0Riy0zOHiMublOdu/ezde+9jUef3yBiy5q7+OXSn7C4Rxut0qjIVk3ZVOq/WwnFV3XLQe9er1uFd8dDgc+n4+BgQHK5TLpdJp4PM74+DjNZtOarDdXM4bR7pQTxRbl8omCtyDkKJdVOjpieKQKuaYKxZ5VcTgcDrxeL6lUat3axslujmat7vmY5mHBYJCgEuTmi27m5otuPkNXy+a3kbO/l2DzknC5XEQiEastORgM4vP5iMViqKrK7OwsiVCCz4x+hqQ7yVu/81YyzszxPf5LEcUog4MTyPILGWGJNJuu3yihiKJoxTQwMMDu3bsJBoOUSkGmp7sJBgt0dMwzODhIT08Pd9/9LE6nitutUiq1b7DmasUwDKrVKplMZsXW39nC3EZyOBw0m02r1uN2uwkEAvj9/uNzOu0OvsXFRWRZRpIky0I4k2nbD/j9K4ceAer1ObJZ6OsLI3qKzJUgaayWqnG5XMTj8VMOHprnW0+RwDAMKpUKgiCcc911NhsXO6lsQNxutzVlbt7MTE97Uym3K9LFX2/+a7yilzd/683U5TqhUIxi8VU4nU6Gho7hcp35H7+ZULZs2cIb3/hGdu/ezSWXXMIb3vAGACYnB1BVieHhMURRZ/fu3Rw+XGVqappAoEi97qXZdK3aAjNl5M3ZjrOF6S8vCII1XyQIApIkkUy26x6iKForrFqtRiQSoV5vS/ubQ5LQrqc0Gh407cS2UrO5eFyexUfLVWGuDJ10rorD42m39K5X3zBlYNbq+jIxu9LMbTsbmzPBad1Vstkse/bsYWRkhD179qzr2HfzzTeTSCTYvn376ZzO5iQ8Ho8lTOj1egkGgyQSCavdWFVVBiID/PWmv0bVVH5v7+/hCrpQlBSFwkUoSo2+vjMrLy+KIuFwmFe/+tW88Y1vJBaLWWoBW7Zs4ZJLLkFV27MxwWCBRCLN4OAgPl8nzz3XFpQE1qyrmA6KZzOpmL7ubrcbVVWpVCqWl7vpRwKQyWSsYwB8Pp+VVJrNprUt5fOVVrQSO50NVDVPo+EjFNJoOVTSJccK62AT06FxvaRibrGdqg5iSsx4vV67+G5zxjitpHLbbbdxzTXXcOTIEa655hpuu+22NY/7oz/6I+66667TOZXNGiiKYslrKIpCKBSy3PnS6TSCIDASGuFTmz7FdHGam396M+FkGJdrmFxugEhkie7u7BmJxeFwEIvFuOKKK7j00kvX7NK67rrrUBSFiYl+NK3tzSIIApdddhlHj5apVI4B7aTicml4PO2bstm9tLS0tOKm/EpTrVatJ3tVVanVagiCgNfrtfxGTNOrWCxmSeCLokilUqHZbFpJ0eOp4XQ2V2x9eb3tmRyXK0EkkqPcqqMW4quK9IIgWOoM63XDnVxPWQtd162keLbrVDbnF6eVVPbu3ctNN90EwE033cQPfvCDNY973etet6bJj83pEwgE8Hg81Go1AoEA4XCY7u5udF1nYWEBh8PBheEL+cSmT/D4wuP8xSN/QTQepdXaSbEYpbt7hliseFoxOBwO4vE4F198sWUSZe7XZ7NZy1isWq3y+te/nlpNYWkpTiy2jCTV2L59O8WixOLiEZxOzXp6N4UWoX2TNFuLz9ZqpVAoWK3DpnCj2+3G4/EQDAYtgzFJkiwPHFMFIZ/P43Q6LUfL54tIAjidBcrlCuFwB5JcodxqoBVWb325XK4X3LJqNBqnHMY1B0tNMUobmzPFaSWVxcVFUqkUAKlUinQ6fdoB3X777ezatYtdu3axtLR02u93viMIAuFwGIfDgaqq1jZYMpm0hgcFQeA10dfw0dGP8pOJn/DFyS+STHawvHwBxaKXoaFJIpH2TfKlTkmLokgikWDz5s2Mjo7idrup1+uk02kKhYI1QS4IAtVq1RrknJrqw+nUSKVmcTqddHRcyMLCIs3mHPW6RKvlQFEq1nlMPS3ToMxUBHilMH1SJEmiWq1aMx+yLBOLxajX6xSLRURRtEQjza2lubk5AILB4Ip6SrPpol4/cUOv1+eoVKCnJwqePNUmuIr9q2JxuVy4XK51k4opAXOqeoppa+zz+c6J2R+b84cX3Ei99tprWVhYWPX6Zz/72ZcloFtuuYVbbrkFgF27dr0s5zjfMGsZy8vLlh9Gb28vqqqSzWat7ZLrIteR35Tnnw//MyExxJt63sThw2X6+w+wefMkR450Uyq1penNovKpcDgc1spoy5YtBAIBKpUKxWIRh8NBNBpdcWMzNbKuuOIK7rpriVpNpqdnmvHxYYaHX0Wh8DCzs4/T1dVHpaJYNRbAck0sFApWp9sr+YRdrVZpNBrIsmzNqZiGVj6fj1wuZw2oCoJg+aqEw+EVBXOzzuL3l1esUgA0bZFMBnbuDCJKJeYLkFyjSO9yuU4pOW/+7NZLKo1Gg3K5bBfobV4WXjCp3Hvvvev+mzl4l0qlmJ+ft7wybF55XC4XoVCIXC5nWcCaicXcqvH7/by7493kmjn+6eA/ERgNsD2xnSNHmmzePM7Q0AQLCxWWlgbw+/04HA7q9fqaEikulwufz0cqlWB0NEYiUaZcfphqVUWS4gSDmxDFlR8vURQJhUIMDQ3h8fiZn++gv3/yuLVxmFotxdLSITo6WlQqPlKpeUSxZWmMmRbKZhfYK5lUisUiTqeTRqNhJU1THj6Xy1nJYmlpCVmWmZubw+PxWDMioiiSTqePF8/ruN11FhdPTMm35VmWqVQ8xGJOBFeNdElYs0gvSRKSJK2rEFyv1xEEYd0ifaVSsbznT7WasbH5TTitde/111/PHXfcAcAdd9zBDTfccEaCsvnN8Hq9KIpCtVrF6/WSSCTo6urC4/FQLBYplUo4HA7+dOhPubr7aj5/8PMc8hxCUcIcPDhEqdRHIrHIwMDjeDzjVKvtWovb7ba2xVyuOvF4iaGhNJdcMs2uXc/Q3f0IqvqfNJv34fM9STj8a0TxG8CTtN1mTiAIAqFQiCuuuILJyT4EQaenp92FFo1uxedTGRs7ctxbxECWT2yBtVot8vm81XllKvS+3LRaLcs3xRx8NLvayuUyy8vLlg6YJEmWem93dzelUsmaFTG3vvz+9nUtFk8MPUqSSqlUxOlM4PVW0MU6pZJ/VZG+fax0ys4vcyhzra1MU/rGHNa0sTnTnFZSufXWW7nnnnsYGRnhnnvu4dZbbwVgbm6ON73pTdZx7373u7niiis4dOgQ3d3dfPnLXz69qG3WxSzcF4tF/H4/3d3dhMNhnE4npVKpbRPbMvjszs9yefJybjt4GzPRGZxOD0ePJiiVXovDIdHbO8Ho6CN0dz9Od/cBRkaeY8eOp9i58wCjo7MMDdWJRGJEo7upVl9DoXAtDscfIcvvB14PJIB9wF5gZWFdFEV27txJoRA+viJp1xzC4S0oipuxsUcsw6qT6yqGYVi+7mYn1iuBmUjMP8OJm7OmaSSTSbq6uiwNrnK5bGm2tbu5XFZxHyAQKKFpK+spHk+ZUqmI250iHM5R1TVqpdWrFHOFZIqLPh/Tn2e9FYgpLSPLsl2gt3lZOK3m9Gg0yk9/+tNVr3d2dnLnnXdaf//mN795OqexeQmYhfulpSXLuKmvr88q2ptdSC6Xi//3yv+X//2X/zt/e/Rv+XDqw/Tmepmehs7ON1CpzOBwTON0FvB4mui6m1YrBvSgaV0Ui0l6e3egqu0tskAgcNKTrw/oB8aBnwP/AdwAnLjR+f1+enp6WVhIMjQ0htdbplYL0N3dzc9+NkY+X0NVJXy+CouLJ74/syDentAvvSLtsIVCAYfDYdUiarUafr8fWZbp6uoiFotZsXk8HpaWlnC5XGiaZq1ecrnccZVgA7+/SLG40jGxXp9H0wyi0RSyf458q0U937EqFrPjbL1ayKlaic2aliAIBINBu0Bv87Jgf6rOQ8zCvalT1dHRQSqVIhgM0mq1WFpaaoseNgy+8vqvcHnicv7n/P/kcelxWq0WmUwGSRokEHgDLtfbqNXeTKv1Jny+NyOKO4A4Q0MjAFZHlCiKqKr6vC2pAeCNQBH4KbCy8H/11VczO9uNIBh0dc2iaR5SqQFiMdi/f//xYn1lxdc0m00WFhaslcJawphnEl3XrZpUvV63BCHNGpZpp6tpGrquW7L2fr+f+fl5awvKNFuTpPZ8yslbXwCq2pZn6e+PIEh51CYIpa5V8ZgPBKcq0pvxPZ9yuUylUrHmm2xsXg7spHKe4na7CQaDljd9b28voVCIUChEs9lkcXGRfD6PQ3fw3Ru/y+u7Xs938t/hx+KPqdarLCwssLi4SLFYtCbGTcfCZDJJpVJhcXHRcqrM5/Nks1kWFxfJZDInaXSlgFcDM8CBFTH29fWRy4VRVS+pVLvD0OFIMjIS4plnnqFSUXA6NdzulcOOS0tLtFotq7bycqKqqtVNlU6nrZu22Sq9tLRkmWKZ9sfmaiGXy9FsNslms9ZWnWlIZmqcmeh6mlLJSSrlxSGVWapCqJVaFY/T6USSpHW3t8zV0vPrKfYqxeaVwv5kncfIsowsy9Zqore3l0AgYGlRLS4uMjs7i6EZ/Nu7/40Pbv0g91Xv4x+1f2S5tWzdTBuNBplMxjKCyufz5HI5S4Lf4XAgSZJleaxpmjXs2GYr0EO7xnKik0wQBIaGRlhcTBAOZ3E661SrMlu3plhYmGN6um0qdXJrMZwYhDy5+P1yYa4wTKfN9mpDorOz01r9nSwrPzU1ZU3bq6pqFfDNFZzfX6Rel9C0E0nB6WxQq+UQxThebx3RXWWmCGHCq+Ixhy0labWnjTnBv1bCMe0DfD6fvUqxeVmxk8p5TjAYxO12UygULBdJv99PJBKhWq0yPz/PxMQEpWKJ/3nj/+Qrb/gKWSPL3xT/hgd5EI/XQygUor+/n+HhYbq7uxkYGGD79u2kUilcLhflcpmZmRkOHz7Mc889x+zsLAsLC0xNTVEoFI5H8mqgBTy6Ir6rr76amZluRFGnq2uOalWhu7uTaBQeeeQoui6u2gLTdZ3x8XHr3C+nbEupVKJYLJLJZCyxzEgkQnd3t5VITaXoVCpFo9FAURRrjmRlUtHx+8urtr48nsrxFWEKRSmjiw3KJT8OVqoLi6KI1+vF4/Gsub21Xj1FVVWrQy0cDturFJuXFfvTdZ5jFu5FUSSfzzMwMICiKPj9fpLJJLVajampKY4ePUomk+EPLv0D9t28j9d2v5avLn6VT05/kmdazxAItgvxgUAAh8NBJpNhYWGBpaUlq9tJlmV8Ph+tVotarcbs7CyHDh0ik8lgGAFgFDgMnFh5dHR0sLycQNNcdHTMU622u5J27Ejx9NP7qVTkVUkFIJ1Oo+u61Wb8cqBp2qoViqIohMPhFU/75orObD32er2WTIrZIaZpGopSQRRblEqBFedpNOZptXS83m5CoTx1o0G1sLrzy6yVyLK8ZmKo1+vW6tGk2Wxa23aRSMQedrR52bGTym8BDoeDSCSCYRiUy2VGRkZwuVz4/X66urpoNpscO3aMZ555hsXFRYZiQ9z1h3fx9eu/jugUufXxW3nHve/g3w//O4vLi5axkyn3Pjw8zOjoKNu2beOCCy5g165dbNmyhUQiQaFQ4MiRIywtLaHrF9Au1j9jxdaWeUmRyUSJxTI0Gi5aLSe7dvWzvLzM1FQdWa4iCCvnXer1OrOzs8iybN30zzTLy8vkcjlEUUSSJHRdx+fzEQ6HV9QszGHD+fl5NE3D7/dbisTlchnDMDAMw1Jefn49RVXb8iyJRCdKIEe11aJRWmkfDFjilWslhrWk7s2mhmKxSDgctuwSbGxeTuyk8luCae6l6zr1ep2+vj6gPTDZ39+P2+1mamqK+++/n2PHjtFoNHjPRe/h4H85yDfe9g0cbge3PnorN/78Rn6U+xHJ3iT9/f0kEgkURVnxdCyKIpFIhC1bttDd3U2lUmFmZoZMpo6u9wOHgBNdW5dffjmLi0lcrgbR6DKVisy2bQlEUeSRRyYRBANZXj3Vf/jwYWtVcKZXK81mk/n5eVRVRVEUq1YRDodXdV7V63UcDgczMzOWB7ypAmwmF4BAoEi1Klt+9CaatkA+L9LV5Uf05ik1wFlZXaQ3VyprTcqfLM1iinlOT0+Tz+eJRCIkEgl728vmFcH+lP0WYdoRG4aB0+m0rGidTicDAwNWV9dDDz3E/fffz6FDh8jn8ry57808+N4H+eb136Qv0Mdf/PQv6Pn7Hj6898M8Mf4EhULB0sM6GZfLxaZNm4jFYpZSb6HQCTSACeu40dFR5ufbGlep1ALVqkwwqDMyMsj99x/BMFZO1ptks1kqlQput9tyUzxTZLNZ8vm8peVlypqYPiYm5rBhsVi0Bk7N9uJisT053+4Ua+LzVSgUVs6nCIKOrmcRxRiy3ECUShTqoNRXa35BW2JlYmKCZ599lsOHDzMxMcHc3Bxzc3MUCgVyuRzj4+NMTEygqiqJRIKOjo513R9tbM40dlL5LcPlchGLxSxzKbO9tNVqEY1G2bRpk6Vd9dRTT/HUU08xPj5OLptjd2o333vL9/jJ23/CGwbewL88/S/s+touPnbvx5hJz1jKxLp+YqvKNOiSJOl4220AVXUDB61j2ltLIUqlAPF4mmpVRhAMrrhiM4uLJRYWSmvWVTRN49ChQwSDQUs5+ExgClfW63Wre87pdBKPx5EkadXWV6VSIZPJ0Gw2URSFVqu1Qg9M13UCgQJgrEoqHk+VUqmA09nRnmFxV5krQtiIrYrL42k3TXR2dlq1rVqtRiaTYWZmhmw2y/LyMpqmEQqF6OvrIx6P2ysUm1cU2+7ttxCn02mtHnRdp9FoWE6BACMjIywvL1tdT5VKxRr0i0QiXN57OddsvYb58jyf+eVn+KfH/om7p+7mf73xf7GNbdZTvbkl5vF4GBwcPF60zyJJ3bjdRxHFAtC+yW7ZsoWlpX0MDh6j2Ww/Ve/Y0YXD4WD//jSve118ze9lfHycXbt2IYoiS0tL9Pb2vmT5/pNpNpuWTlqr1UKSJGq1Gh0dHWt6j5haZNlsFl3XSaVSHDx40HJBFUURXdcJBgs0m06q1ZXtvI3GPM1mC6+3m0CgSFNoUCj6CD2v8+tkM7De3t4VK49Go8HCwgI+nw+/348oivbKxOasYT/C/JZi1j3i8bhVxNd13RpmjEaj9Pb2Wk+6S0tLHDt2jCeeeIJHHnmEffv2UV2s8pkrPsN9N92Hy+HiLd97C3fP342u69aTu0kymSQYDB6fl+hEVRu0ayttdu3axfx8CjCIxZbRdZFAoMnIyAiPPjqF263icGirvo9CocDMzAx+v9/StTodTA8Ys/iuqiqCINDV1WWt7kzMZCKKIrlcjlAohKZpzMzMoGkaDofjeH2lRTBYPL5KWZnwVHWWZhOCwR4CgSINGlSLa3d+eTweFEVZlTAajQZOp5NgMIjL5bITis1ZxU4qv+XIskxfXx/9/f04nU6azSaaplk3aFPi3ryZaZrG0tISY2NjPPzww9xzzz2Unyvzv674X+zq2MX7/uN9/GD6BxiGYT29Q/umaK4i8vkG1WqYVutEUuns7GRpKU6z6SKZXKRW8yLLNbZt28b4eJVcLrfmFpiu6xw6dMhS5c1kMiu2314K1WrVUiAwt7yq1SrBYBCv17ti68u0N261WiwuLqKqKn6/n6eeespSJpZl+XirdQmHo7lq6wug2VwglxOJxxMogSxVvYVWWm0hIQgCkiSt2fmlqiput9tOJjbnBHZSsUEURVKpFBdccAGJRAKXy2XdFJ1Op/WEHAgErC2wSCRCKBTC4/G0VwuHZ/hI/CO8Ov5q/o87/w9+vvjzVTMkwWCQUCiEqqqUSgnq9QzQdgt1OByEQhGrtbhW8+L11ti0aRO5nMjs7NyaSQVgZmaGYrGI1+ulWq1aasAvhVarZfmkaJpmTcg3m006O9tFc3Prq1qtsri4SC6Xs2Z1XC4XzWbT+rMsyzgcDgzDIBDIYxgCxWLgeWc1MIxlDCOMJLVwyllKdXCrq9uJTXXi5w82tlotGo3GmhP2NjZnAzup2FjIsszQ0BBdXV34fD7cbrdVGB4eHmbLli2Mjo4yODhIZ2enlVzMLTRa8OHoh9nq38of/fCPGKuOWZIlcMLLXhAEKpU4qqqh60es82/bts1qLXa768c7pgQGBjZx4MACXu/ayaJer/Pss89aBelCoXBcEfjFUywWMQzDGhI1twEVRbEaG9xuN8VikYmJCcrlsuWqacqmmEKXiqJYNZBarUYolKdc9qHrK0uYLpdKpZLH4ehAklREb4lcDWR1deeXeZ61puUBO6nYnDPYScVmBW63m66uLrq7u/F6vdTrdXK5nKXlJQgC8XiczZs3c+GFFzI4OEgoFMLr9bYHA31hPtr5UfwOPzf98CZUXV3REWYeq6o61WqEev05TPXiCy64gLm59g3VFF70eqts27aNqSmVen163bgnJyepVCo4nU5UtX3OFzsQaSY+SZJoNBqW+rFhGMTjcQzDwOv1ks/nmZycxDAMms3m8cHNhCXlUigULEthaMv7C0IBSVLJ5VYPHjYa82haE1nuRpbLiK4amSr4tNUrFbfbvaaQZK1WWzVFb2NzNrGTis0qTHfG/v5+enp68Pl8lkHWwsICMzMzLCwsUC6XCQaDbN26lZGREcsgLBlI8sedf8x4cZxP/vKT1mQ5YCket1crHdTrOaBtmBKLxWg0fFQqCn5/WzPM3ALLZh0sLc3g8axtzFUulzl48CCtVstKLKVS6QW/V8MwKBQK1k25Wq1anV8ul4uOjg5rWn1mZsZqaDAMg1gsxtTUFNls1jLGcjqdtFotYrEYrVYLWW6rL+fzoTVinsQwIBjsJxgs0BLrZIs+HM9ryjRniZ7vS99sNmk0Grb0is05hZ1UbNbFFCAcHBxkaGiIzs5OZFm2agczMzNMTk4yPz+Px+NheHiYWCyGx+Ph0tilXB+7nm8d+RYPzDywwv7XdKJU1SSq2kDT2gV7h8NBOBwmm40SDJbQNAeyXMPj8eD3DzE/P7/uFphhGMzOzpLP563p8hcjNmkmEJ/Ph6qqlMtlVFXFMAyrQG/K/Jvxm/Lzk5OTjI+PWyKSZg3F7/cTCoVYWloiHM5RLvtoNteagp+nXHYSjSYJBgvUDY1yYfWKxtz6crvdK4QkzZWj7eBocy5hJxWbF8QUi+zo6GDz5s1s3rzZktE3u7wmJyfJ5XIkEgni8Tgej4ff7/h9oq4of/ngX1KulC3vE1mWj9cAXNRqEer1E3WVLVu2kE7HEcUWLpeG19uux3R1badcrlMuH1s3zmKxyOTkpNXF1e40y6/bDaZpGpVKBVmWaTQa6LrO0tKSpTjQ2dlJLpejUCigqqo105PL5XjmmWcYHx9HVVWCwSDBYNBaNcTj8eMyLXN4vbU1t74AWq00gtCu1/gCeap6E6G2eh7HTCo+n8/qPjMMg1qtnXDtri+bcwk7qdi8ZEwhyZGREUZGRkilUsiyTLFYJJ1OI0kSkUgEv8fPe+Lv4VDxEF8/8HXLm8TlclnFbE3rRFXTGEYWgB07drC4mAQEFKWGJKkIgs6mTZvJZkXy+SPrxmUq8i4sLFgrEF3X1xScNAyDfD6PIAgoikKtVqNcLlsCkKae2fLyMoVCgWw2y9LSErOzs5ZAZrPZRJIkurq6rHpKR0eH1fV2qq0vw6jRahVwu1M4HE1cvgy5GnjU9duJT16R1Ov149tr9taXzbmFnVRsTguv10tXVxe9vb2WaGGpVLKUka+KX8UW7xb+n/3/D0u5JasTzKyraFrbg0RV21tg0WiUVstHuew7bs7VtuBtd1j1UqtNYRjrWwibiS2TyVgeImsJTlYqFTRNsyRezFVKs9lEEATcbjdHjhxhbGyMdDptve/CwgK1Ws0yQPP5fJbMfSKRsFY/6XSacHiJctm3wpDLpFSaQNcNZLkHWa4geMrkVJDUtX3pzTmZk+N3OBzrOkDa2Jwt7KRic0ZQFIVUKkUymUSSJFqtFqIoIssy70y8k5yW49sHv23Jl5gzF4Lgo9EIWEmlPa8SIpOJ4vXWEMWWtQUWjY7SaNTJZtdfrZiDm+bKYmFhwZJaMQ3Dms0mpVIJSZJwu92Wbpgp6W/aI8/PzwPtifX5+XkymQyCINDR0UEqlbKaAszkYr5Xu0FgEY9HJZNZreEFUKlMARAIDBAO52iJKtkqeOurk4o5K2QmFU3TqNfr1mrPxuZcwk4qNmcMU3QxmUzidrutbZuLQhcx6Bnkq2NfZSmzZD3ZK4pyvD23i0ZjGl1vS6wMDw+TTscxDAGfr2wllXh8Kw6HSDq9/5RxmDWQUqnE7Ows6XQaURSpVCrk83lrviQYDFIul2m1WszNzVGpVKxCfalUsmyUl5aWAEilUvT19VGr1Th27Bj1et2S/hcEgXK5bDUKhMNpdF1ct57SaMyh61683rDV+ZUrygit1fMmZueXuSoplUpWwraxOdc4raSSzWbZs2cPIyMj7Nmzx3oKPZnp6Wmuuuoqy8TpC1/4wumc0uYcx9QUSyaTOJ1Oy4L3+uj1zKlz/GjsR9bnxO83zap60bQmqnoYaA9BptMJDENAkurIcjupCIKfQKCDYnHslFIsmqZZxfpKpcLY2BjFYpF6vc709DSFQsEy2qpUKszOzpLNZimVSpY0jbl9ZkqgKIpCPp/n4MGDzM/PUyqVEATB0kXLZrOk0+nj5mUOAoEl8vkour52Eb3VWkIUE8evQ5FyS6NVi655PV0ul9X9Va/XraFMW33Y5lzktD6Vt912G9dccw1Hjhzhmmuu4bbbblt1jNPp5POf/zzPPfccDz30EF/60pc4cODA6ZzW5hzHTCymj4dhGFwZv5IOVwdfP/b14/bChuW1bhhRDMNDrdbe1komkzSbXsplP5JUs1YqAD7fIH6/ytTU1CljSKfTVqG+XC5z+PBhK3GY6stzc3NMTEwwOztLqVSiUChYFsKmRXIkEjk+xNhu3U0mk3R0dKAoCh6Ph+npaWZmZqhUKhiGcdwZMocgtMhm11ZWLhYzeDxVPJ72oKfiL1LUNBzq2kKS5jadOe3vdDrx+Xy/6Y/HxuZl5bSSyt69e7npppsAuOmmm/jBD36w6phUKsXFF18MtJ9MR0dHmZ2dPZ3T2mwAzBmXaDTa3rpxebg2eC0Hywd5Yu4JyuWyNSWu6watVgeNxji6rlvyMMvLUTyeOh5PDaezLbsSDm8mEhE5fPiZF4igvUo2p82XlpY4evQoDocDv9/P3NwczzzzDBMTE1YR3lzdmPMf5tCnKIqEQiG6uroskzNZlhkdHaWjowOfz2fVVYLBIIHAFNWqm1JJWTOuQuEo0E6QDkcDh5yjrIFbXbud2Kw/5XI5dF0nFArZtRSbc5bTSiqLi4ukUm3b01QqRTqdPuXxExMTPPHEE1x++eWnc1qbDYIsy5ZboiiK7I7sxik4+e7Yd626hs/nOy550kOrVaZanQGgr6+PdDpJqyXi9xfxetuT9PV6kGQyydLSgVVOk8+n0Whw8ODB41tSEqqqMjk5yfT0NOl0msXFxRWeMab0vyiKxGIxuru72bp1K5dccgmbN28mFotZSs7BYBC/349hGIRCIXw+Hw6HA5crja4vkU4n15WJqVYncLmceL19RKNZmo4ay1WQ6qvlWQDLRM0031rLTtjG5lzhBQWDrr32WhYWFla9/tnPfvYlnahcLvP2t7+df/iHfyAQeL5a6wluv/12br/9dgCrQGqzcQkGg8TjcUqlEhEpwkXei/jF8i9YSC/Q3d2NJEk4HA50PYUoClQqh/H5ehkdHWX//n1omhu/v12sL5UCVKsy3d3d+HzzHD58mNHR0VOev9FocOzYMfr6+hBFkUwmw+zsrDXMeLLni0k0GqW7u5tQKAS0jbgcDoe1/WQ6PJqzMObwpCRJJBLHWFoSyWajmJpmz0fTZnG7YxiGm3A4S0ussVSBqNrB8x1jzLqJ1+u1Vn02NucyL5hU7r333nX/LZlMMj8/TyqVYn5+nkRi9eAWtAunb3/723nPe97D2972tlOe75ZbbuGWW24B2sZNNhsbl8tFOBwmFApRLBZ5lf9VPLL4CA/MPsDI0AjBYBC3202r5cLtDlOvH0PXdbq6umg23RSLQZLJBauuoutO/P5e+vqe4YknnnjBpALtz9/Ro0etltxms7lmMgGsbSxN06xkIggChmGgaRrLy8uW2rKpGWb6rMhyA49nkXQ6znqLqEqlgsdTxOEYAiAUylPVG9BQaNZXd3O5XC4ikQipVMpOKDYbgtPa/rr++uu54447ALjjjju44YYbVh1jGAbvf//7GR0d5SMf+cjpnM5mg+Lz+YjFYu32Yt9FuAU398zdY92gFUWh0Wggij0IwjyVSslSPV5ejuF0NgkE8tb71Wp+du3q5ciRIxSLxRcdh6qqqKq6bkIRBMHS1jJrO8FgkEQiwfDwsOULn0gkcDqdVmHe1OSKRMZR1SZzc6F1t74WF48hyyDL/YBOIFAi02ggNoIYxmoLYbfbbf2ysdkInFZSufXWW7nnnnsYGRnhnnvu4dZbbwVgbm6ON73pTQDcf//9fO1rX+NnP/sZO3fuZOfOndx5552nH7nNhkEURcLhMOFwGL/kZ6e8k/uz97OcXUbTNEt+pJ1UdMrlMURRpLu7m8XFBLouEo8vYW4nVSoKQ0Pd+HwGTz755BmL09zi8nq9K+okDoeDfD5PJpPB7XbT09NDq9WyOrN0XcflKuByTTE5GULT1tfiKpXGcDhEZLkfWa7hdKtkGw2EdTq/zIRiS9vbbBRO65MajUb56U9/uur1zs5OK3G89rWvfdG+FjbnL4qi0NHRwezsLJcpl7Gvso8HZh9g88hmFEXB5XJRq4Xxet1UKmMYxkVs2rSJO+98mkbDQzBYxO2u02hIVCoyvb0yl1zSzWOPPcZrXvOaMyKqaEr0T05Oks1mj3dyBYjFYgiCQKFQwO/34/P5WFxctD7XtVqNSGQ/rZaT2dkIur62QyVAvT5NIBCi0fATi6XRXWVKVXDVV8+omEnLlNS3sdkI2NNTNq8I5sxHKBTiIt9FeAQP987fy/LysvU03miAKCaBWarVKn19fTSbbnK5ILJ8YrK+VvOi6yJXXrmFQqHAc889d8biNLW7FhcXGR8f5+DBgzz88MM8+uijNJtNQqEQc3NzlEolFEVB13WczkU8niWmpqLk89V137tSqSCKGdzuTgxDJBLJUTMaVDSQ1rAQNt0mTa97G5uNgJ1UbF4xZFkmmUyiuBUukC5gX2Ef+UK7m8rr9R7X0urH5cpRKCxZA4YLCync7gbhcOb4O4lUqzJbtkSJRCI88MADZ2Q1fPKEumEYNBoNKpUKpVKJ5eVlSqUSuq6Ty+XI5/MUi0UqlRKS9BjlssCzzxqnjOPYsTHicVCUAQDC4RzFZp2atnY7scPhQJIkFGXteRcbm3MRO6nYvGKIokhnZ2e7YC9fRK6Z49HZRy1xR4B6PXZcmPEoHo+HeDzO4mIHhiGSSp1oba9UFHy+Gq9+9auYm5tjcnLytONbS/pF13U0TUPXdarVKocPH7YMu9qy+L+iWp3jwIEwxeL6qxSApaXDeL3t+RRJqiLLVQpNFUGTaaqrJ+Tbcy8uW+PLZkNhJxWbVxRFUYhEIlwoX4iAwC/TvySbzVpbYLVaALfbi2G0t8CGh4fJ5cLU69LxYn2bSkVBEHRe9apNyLLMr3/965c9dtNSeXFxkfn5ebLZSbze50inZebmTl3zMAyDSmWceDxOteojECjicmnMVVVkI0Cjsbq76/nqxDY2GwE7qdi8orhcLlKpFBFvhBH3CA9lH7K85D0eD41GE4ejG7c7TaFQYGhoiFbLRT4fJBgsIIrt8cBKpb0lFAo1ePWrX83Ro0cZHx9/Rb6HVqtFuVwiFtuPpjUZH0+t26Zssry8jCxXCIWSNBoSwWARHZ1cS0Wsh4GVNRNTSPL5FsI2Nuc6dlKxecUxBRkv9F7IVGOKw+nDqKqKJEkYhkGrlcTrrVMqLeL3+3E6nSwuJnG5NGKx9mpF0zw0my4UpcLll19OIBDgnnvuOaV68enidDpxuVw4HA6SySUUpcjMTC+C4H9BxeCxsTGSSfB6+xCEFn5/kaLWRAec6tqdX+YqxbYLttlI2EnF5hXH1AS7WGkLjf5i7hfkcrnjpl0CtVrkuNHXFKIoEggEmJ7uBqC7+4QYabuuUsHlcnH11VczNzfHs88++7LF3Ww2MQwDr7dOX98impbC5dpOMBh8wUaBqanDdHfLGEYHfn8Jt7vOktpAANz1tYUkTXViu53YZiNhJxWbVxyzYN8j99Dh7OCB7AMUi0Vrol1VFVwuP273EtVqlZ6eHjKZGJrmIpFYtN6nVPLh8ai4XA127NhBR0cHd911F5XK+nMip0uz2aC7+yiq2uLo0c7jumX6KZNKrVajVhuno6ODcrldT3E6m+SaVVRtbV96s53Ydne02WjYScXmrBCPx1EUhR2eHRyoHGA+N0+r1cLj8aBpTQwjhSznKBQKDA4OousuCoUg4XAep7NdVymV2sKkfn/bCfHGG29EVVV++MMfvmwDt8lkGlkuMzXVS60mkMvlaDQap/yaw4cPk0gYJJOdVKsKwWCBZtNJTazgbK3d+WUW6U21ARubjYKdVGzOCoqiEA6HuVi+mBYt7pu5j0qlYtVV6vUoigKatmypBafTCTwelVAoC7SHIFstJ35/u9CfTCa55pprOHToEPv27TvjMXs8Kp2dc+TzIfL5tqxKpVJ5Qf2x5557jsFBCZerC0mq4fHU0TQ3Bb2MjwD1+uruLnNGxe78stlo2EnF5qwgiiKpVIpN8ib8op9fL//aUv0VRZFqNXLcGbLt4KgoCgsLKUCgq8usqwiUSn78/hM39Ve96lWMjIzw4x//mGeeeWEjrxePQV/fJLouMDXV+6K/ql6vMzFxhO3bO6hUAkQiOQxDQNMNCnoVqRWg2VzZ3XWy5pfd+WWz0bCTis1ZI5FI4Pf5ucBzAY8VH6NQKmAYBi6Xi0ZDQhD8yHIOTdMIh8NksxEaDTcdHQtAu8urVPLjdjdwu9smXqIo8nu/93v09fXx7//+72dMcDIWW8bnKzEz002z+eIVg48ePUo02qKzM0W53LYartW8VIUKTR0c9fCqrxFF0bJa9ng8ZyR+G5tXCjup2Jw1AoEAgUCAC6ULqepVHpx9EE3TcLvdNJtNNC2Bz1dE0xr09fVRq7V96/3+En5/GYBi0Q9gbYEBuN1u3v3ud9Pb28sPfvADvv/971Otnnra/VS4XA26umYolfxkMmv7zq/H/v37GRiQiESiGAa43XVU1UOZdrzrWQh7PB48Ho8teW+z4bCTis1ZQxRFkskkF8gX4MTJfQv3USwWrbqKqkaRJAFByBIOh9E0D7lcCLe7QSzWtq6u171ommtFUgGQJIk//MM/5Hd+53d4+umn+fu//3vuvvtu5ufnX3IRv7d3CkEwmJzse0lfVy6XOXToEK9+dR+1mkIoVMQwBJpNJ2VK7XbidTq/zKRiz6jYbDTsBnibs0oqlSIoBxlxj/BY/jEqlQqhUAhRFFHVCMGgE1nOU6/7cTgcZDJRurpm6eycZ3x8GIBiMUAoVKC9JXbiOcnhcHDVVVexbds2HnzwQR5++GEefPBBAoEAfX199PT00NPTQzKZXHd4MRJZJhjMMzPTTaPx0ormTz75JA6HzoUXpigWfcRiWQqFIJJUJ9Ms43Wur/klSRJer9duJ7bZcNhJxeasEg6H8fv9bPNs43ul73F06SidnZ3H6yo6uh7E58uTz/cSCoUoldrdUoFAAVmuUK0qFApBotEMilKhUvGvOkcikeCtb30rN954IwcOHGD//v2Mj49bhXzTeGtgYIDt27db3WYuV53e3mnKZT/p9GoV4VPRbDZ5+OGHueyyLvx+H5UKOJ0ay8txBgbGyTRLJJ1+VHV1y7DD4bCL9DYbFjup2JxVXC4XsViM7TPb+V7pe/xq7ldctvkyXC4XlUqFRiOOLB9GFA2i0SjZrEyhECAUyhOLpZmaGqBYDGAYAqFQYc2kAm1BR8Mw2L59O6Ojo+i6Tj6fZ3p6mqmpKaamprj33nu59957GR4eZvfu3+Gqq9p1mImJfp6vzfVCPP3005RKJa677gp03YHPV6HRcKOqHgRHg5xWZogOaurKQrwoijidTmRZtov0NhsSO6nYnHVSqRR9h/oIi2EeXH6QSqVCONzuilLVKMGggM/X3habmZGpVtvyLF1dc0xN9aPrTsplP8FgntnZ7lOeyxR+FATBsjjesWMHANlslqeffpp9+/bx0ENfpqsrhaK8iUbjpd3cG40GP//5z+ns7GR01I+mGfh8Zaane5DlKmXK6AY41CjPL2ua8iymmKSNzUbDLtTbnHVisRh+v5+tnq08VXqKfCkPtG+w9XoEUXQgyzkURaFW89JoeFBVD4FAAZ+v3QWWzweRJNVqLX4xmJ70JpFIhN27d/MXf/Eh/vAPN7Nv3yJ/9Vff5MCBAy/p+7nvvvsolUq89a1X4/HU8XprNJtOlpdjbQ8Vo4RDONH5dXLdxOz8MudUbGw2GnZSsTnrKIpCMBhkm2cbNb3GI3OP0Gq1LIvhZjOMouSPT5gr1GoSpZIfj6dONNpWLS4UggAEg4UXfV6Xy0UwGHzeqwabNs0zMjLKyMjNBAIBvvOd7/C9733vRWmKHTt2jPvvv5+LL76YrVuDyHIVUdSZn+/EMBzIcpXZWpWgR1i380uWZUumxcZmo2EnFZuzjiAIdHR0sF3ejojIr+Z/RbVaxeVy0Wq1aDTiuN1FfD43wWCQalWm0XBTq3np7p5BEFo0GhKq6iUczr/o8+q6vkpiJZFIoyhlpqZ6iES6+eAHP8ju3bs5cOAAX/rSl3jiiSfWldefm5vj29/+NrFYjOuuu45wOIPfX6RUCrC0FAMMFKXCglYkIPhoqIr1/ZuIoojP58PpdNorFZsNiZ1UbM4JkskkMV+MAecAj+QeQVVVa2tKVeO4XA4CgSKBQIBaTcYwRHK5MIFAkWi0rQWWzYbx+Uq4XKcWeDRpNBq0Wi3r7x6PSlfXLIVCiFyu7XHicDjYvXs3H/rQh4hEIuzdu5d//Md/5KmnnqJerx+PT+XBBx/kX//1X5Ekife+973Issjg4Dj1usTExAAgIkk1HI4Wi60CfgLUau0WZXNuRhAEq53Y7vyy2ajYhXqbc4JQKISiKGz1bOU/Kv/BVGaKSCSCIAg0GiEMw43fnyMYDDI31/Zsz2ajJBJpOjtnWV6Ok81G6OycIxzOkk53vMQIdPr7J9B1gcnJ1dpeyWSS97///Rw4cICf//znfP/730cQBGRZplqtYhgGQ0NDvPWtb8XvV9i69Wm83irPPnsptVo7Xp+vgoZGoVUlQC8ldWVSgfaWnCRJ9taXzYbltJJKNpvl93//95mYmKC/v5/vfOc7VteOiaqqvO51r6Ner9NsNnnHO97Bpz/96dMK2ub8w+VyEY/H2T61nf+o/Af3zdzHaO/o8bpKk0YjiSTNEQhsodFotw03Gm5KpQDR6LK1zVSpKMRiyy85qaRSiwQCNY4e7VtX20sQBLZt28bo6ChTU1OMj49TqZQIBhW2bOlnaCiKLJeIRifp6pplaSnOzMyJBKUoZTLNKi4HuBsRDMOBw+Gg1WohiiKCIOD1eu0ivc2G5rS2v2677TauueYajhw5wjXXXMNtt9226hiPx8PPfvYznnrqKZ588knuuusuHnroodM5rc15SmdnJ8PKMD7BxwPpB6jVajidzuN1lQ5crhbRqI4kBajXPbjdGgsLHchyjY6OeQCWluJIkorPV3qBs51Alit0dMyxtBQkl4uc4kidSCTD0NA4b3pThT/7syB/+Zed/OmfBrnuuhzDw0fp7JxDEHRKpQBjYyMrvtrnqzBVrhP2gmudzi9Zli2ZFhubjchprVT27t3LL37xCwBuuukmdu/ezd/8zd+sOEYQBHy+thSFpmlommZLT9isSSwWQ5EVRt2jPFF6gppaIxQKYRgGtVqMYNBBPF5DURSqVRlFqTI52UtX1wwdHfPMzXWSy4Xp6ZkhmVykXF57EPJkBKFFf/84zaaL6en1Je09nhpDQ2NIkoqmualWZYrFAM2mA1130Go5UFUPquqlo2Met1sjkznhPe9waHg8KseWS8SDTsRiDMAq+huGgSiKeL1eq65iY7MROa2VyuLiIqlUCmgPsKXT6TWPa7Va7Ny5k0QiwZ49e7j88stP57Q25yler5dIJMI2zzZKrRKPzz9Os9nE4XCgaSLNZhRFyRCJRKhUFNzuOrWal8XFJIpSobt7GsMQWVxMEAzm8XpfuAW4t3caSVKZmOin1Vr7GcvvL7Bly0GczhZHjw7zzDM7GBsbZmamh4WFTtLpJJlMjErFj2G0ZfJzuTCadmILy+drxzKr5QkJflS1XWfRdd1qSBBFkWAwaPmp2NhsRF4wqVx77bVs37591a+9e/e+6JM4HA6efPJJZmZm2LdvH/v371/32Ntvv51du3axa9culpaWXvQ5bDY+pmrxDmUHAgK/mP0Fqqoer6s0aDRSSFKZjg4FVW3PlyhKhYWF1PFaSoZodJl0OkGr5aS3dxpYX5E4Hk8TjS4zP5+yrImfTyBQYHj4KI2Gm+ee20KxGDrl95BILLU7vBZXaoUpSnuKfslYIkiQalVCFEXrlyAIuFwuAoGAJdViY7MRecFP7r333rvuvyWTSebn50mlUszPz5NIrB7mOplQKMTu3bu566672L59+5rH3HLLLdxyyy0A7Nq164XCsznP6OjoIOaN0efsY192H6qq4vP5qNVq1GopZPkZUqkauh7BMAR8vjKzs13kchHi8TQ9PVOUyz5mZnrp6ztGZ+ciCwudq2ZLAoEC3d3TFApB5uc714zF5ysxNDSGqno5dGgTun7q/y5Op0ZHxwKFQohqVXnee5VZrLaQvU2ChMjUZQzDWLEVfPIkvb1FbLNROa3tr+uvv5477rgDgDvuuIMbbrhh1TFLS0vk83kAarUa9957L1u2bDmd09qcxwSDQQKBANs82zhSO8J8Yd6aJanX3RhGnFisgKIEqFYVFKUCiMzOdlEu+5HlKsPDRymXAxSLSTo7544X8c0Vi0EslmZo6Ciq6mV8fIC1xCJdrgZDQ2PU624OHx55wYQC0NMzjSDozMx0rXhdFFsoSoWjpSoRb1uexTDaqxMzeZj1FHvry2ajc1pJ5dZbb+Wee+5hZGSEe+65h1tvvRVoTxa/6U1vAmB+fp6rrrqKHTt2cOmll7Jnzx7e8pa3nH7kNuclbrebSCTCBdIFGBj8fOrnNBoNnE4nmqbRbPYhSRWSSYlyWUFRqoBOLhelUAjRbDrxemts2nSATCZBqZSkq2uObdv2MzAwxvbt++ntnaJUCpxy9dHXN4Eg6IyNDdFqvfAgYiSSIRzOMj/fSb2+Us5eUcoIgsHBUpG4V8RT77S2vE6up/j9fkRRtIv0Nhua09q4jUaj/PSnP131emdnJ3feeScAO3bs4Iknnjid09j8FiEIAqlUipGjI/hzfh5YeoA/UP+AQCBApVJBVTtRFCd9fU1mZ4MIwiKyXKVa9TE+3s/WrW25epdLZHj4AKVSjFIphcNRRFFU6nUvMzNd5PPrtw6Hw1kCgSLT0z2rEsRayHKF3t5JymU/CwurfVcCgRKGIXCousSN7iBqVkbXdRyO9pyKmTQDgQC6rtsrFZsNjV0NtDnnSCQSSB6Jre6tPFZ8jKpaJRwOo+s69boTv7+LWOwQuh4HDuPzVahWfWiah2PHhhgePoKmOdG0LkKhNLquIgggim4URUCWl+joWKDVclKpeDEMAVE08HpVXK4Gvb1TNBouFheTBAJ5isUA6y3qFaXM8PBRmk0Xx44Nrnmc31+iXJGpSAsE6aZalaytL7Ou4nQ68fv9lkqxjc1GxU4qNucckiQRjUbZnt7Ow+rDPDTzENfHrsfhcFCr1dD1YXy+Z4nFXDQabhSlDLRXCO2hw2EGB8dwueapVAZpNBREUcXl0mi1yuh6Ho9Hxe8vEwrlcDqbGIaDXC5Mrda+oefzIRKJNMnkIq2Wk1wuRC4XoVKRj8eoEolkiceXaDTcHDkyQrO5eptMFJvIcpUD8xJ+b5OwEKagytYqpdVqYRgGHo8HWW6/but+2Wxk7KRic85hukFeKF+Is+DkZ3M/47rR63C73dTrdVS1C7fbT1/fMmNjAfz+PO1CfLvoXSwGOXBgKz09M0SjhwiF3AiCm2azSbNZp9lsC05Wq0HS6S7qdQ+iqBEMZununiWdjvPEE7sQBB2/v0gkkiMSyRGLLT8vUoHl5Sizs92nmHEpAwaHS2WiYfC1wmSaXkSxXU8xt8FMEUmn02m3E9tsaOxPr805h1lXCXgCjHpG+XXm11SrVTo6OqjVaqhqE0W5gGh0L4cPh3A6l/F6q9RqJ6TkGw2JsbFh5udbhMNlFKU9RNlsQi7XolqVqNVk3G4JwzBoNBokEhLJ5CIuV5PBwaOMjw9SLIYoFkMIQotAoITH0zYBazQ8lEq+Fyzi+/1FDEPkuUqajk4H7kZbk8zpdGIYBq1WC6fTiaIoiKKI2+1eYRxmY7PRsD+9NuckwWAQn8/HTs9O0lqaJxaeQNM0RFGkUqngdO5AkjyEQu2PcCBwwhfFMAzcbvdx3TAZVR2kVttJvX4x6XQfuVyCalXBMIST5O914vFlJif7OXJkhFAoz9DQUQShdfw9HRQKIdLpDtLpDvL58IvqCgsGC5RKfmaMWYbkENWKD1EUcTgcljqxWU8B7HqKzYbHTio25yQej4doNMqF0oWIiNw9fTeNRgOPx0O9Xqded+NyDZNIlGk0vCuSiiAINJtNXC4Xuq5TLpepVqssLy9TKBSsuRezQG4YBpFIFo+nzvx8J0tLKZaWRonFVIaHJzjVVP6pkKQaHk+d5byPmjRPwhGmUvHicrkQBMGSaHE6nYRCIVqtlt1ObLPhsZOKzTmJ0+mko6ODiBRhk2cTv15ub4F5vV40TTuuYHwpiuJAktqT9eaq4uStJa+33RJcKBQol8sr3j8YDOJwOND1JqnUPNWqTKEQwuFwsLgYZnFxhFRKo7d38Tf6HkKhPAAHC1Uiik6YCM1mGLe7Xd8xRSRPtjW2i/Q2Gx07qdics8TjcSRJ4iLpIqbVafYv7kfXdZxOJ9VqFbe7G+glENBwOpv4/W25e6fTicvlstp1TfkTs1bh8Xis91ZV9aRVSlscVdd1ms0m6XSMfL6Tnp4MsVj+JccfiWSoVPyMaQvEZQiTAIIrWolNmXtzu84u0ttsdOykYnPOIssy4XCYndJOAH4y9RPq9TperxdVVREEAU27BIcjQCRStrbAms0mkiThdDrRdR2/32+tWMxOq0qlwsLCAqCTSs1Tq8kUCmFEUcQwDOvX3Fw/zWaMkZEFJKn6omNXlDKK0pa/n2aaIZ8PUUvicJxIGmZSMa0h7KRicz5gJxWbcxaPx0MikSDuiTPsGea+9H2Uy2UkSULXdVRVRZI6aDS24XbrdHe3jbrMWoW5zVStVmk02m3E5qxLsdhOQOYqZXGxG6/Xi9/vt2oe9XqdSqXG9PRmnE4vW7ZMIYrNFxV7PJ6m1RLJ56PMClOMKFFU1X88EWorJurNoUc7qdicD9hJxeacRRRFEokEbrebizwXMVYb47n0c1Z3V61WQ5Ik6vXtVCpddHYuEIstWS3CsiwjyzKtVsuqYRiGQbNpJgadzs55dD2CIAwQiUSIRCL4fD5kWT7emtwgl6szOTmC19ticHACUTz11LvHoxKPF0mnYyy3CkjeKnExSqMRtGIwJe8dDodlRObxeGx1YpsNj51UbM5pAoEAfr+fy32XIyCwd3wvtVoNWZbRNA2Hw4HH42V5+TpaLSc7dx4gHF6m2Wxa3WIul4tWq2XVV8zaSjy+jCzr1GrbCQaDDA8Pty2Nh4fp6+uzVhCNRoN0WmRhYYBQqEhn57Q1+e52u1GUlTL3/f0L6LrA4mKSaWGauAJRMUqt1m4nbrVaOBwOBEFAkiTrPLbml835gJ1UbM5pPB4PyWSSsCPMdu92frL4E/KFvFUzAY5vdSWZnd2B291kZGSa4eFnCIencbly6HoVh0O0EhGA0wldXYsIQopKJUIg0Dbp0nWdiy++mC1bttDR0YHb7cYwDDRNY2EhSrHYTSKxQDQ6gd/vx+l0WrL1brebrq4K4XCR+fkuBEFmRpyhW3Eh6zGczpA1m2J2eUmSZK1QHA7HWbjCNjZnFnsD1+acxu12W51ar/K+in/O/jO/nv413V3dyLJMuVzG4XAc7wjbSi6Xo1LpxO3OEQgcxemcOC7PomMYHlotF5rmxOMpEonUyWYvJBwO09HRQT6fp9lsoqoqiUSC5eVlcrkczWbT2kKbmOgilaqSTM5QLjtZWOhFVTXcbhcDA3UikWmWl2XS6QSS5OZo5SjvC8TRGhFEUURVVVwulyV9HwgEcDqdVk3FxmajY3+Kbc5pBEEgEong9/u5qHoRXtHL3om9XLf5OpLJJI1Gw9IEU5StGMYTOBwNFhauRFWzyHIJqOBw1PF6QZIEXK5lBgYmcLs7cbv3Iwhb8ftHLMmUfD5PsVi05kfa0jAqrVYLXTeYnR0CXHR0TOPzLVKr+YhERAIBmJvzc+xYFyCyUF+g7sow6N1Jq9qO1WwgMAwDl8uFoihWQrFnVGzOB+ykYnPOI0kS8XicbDbLpcql/CrzK+Yyc6RSKbxeLz6f7/jchwNNG0VRnsTjKQARymWJej2Ay+UinxdQ1RqbNpXJ5y+i0bgUj2eWVGoCTZvF4biYYHDr8dmViHXDb7VaZLNZWq0WqqrSbDaZmemlXI6TSCzh8zXRNB+Tkynm5ryYJZZHS4/SGYBOVyeFcgDDMKxVVaPRwOv1Wg0BdueXzfmCXVOxOec5eVjxKv9V1PQa/3b038hkMoTDYQKBAB6Ph0qlgmGMAhKRyBEajbpVnJdl+bho5DzxuE6jsYvl5TC53GVkMr9DvQ7d3U/T2akRCoWo1Wp4vV62b9/O9u3bLd/4ZDJpdX5Vq0FKpctJp1/HxMQo6XQAj0ciFovRarU4xjE2BRS8rQj1uozb7bbqJyfL3Z/cCWZjs9Gxk4rNOY+5BRYIBOh39TMijfDdqe+SyWVoNpvEYjGrLdfnC1OtXkAgUCEUmqNer2MYBvV6nWi0yNBQDq93O63WEIqi0NXVhcuVwjCuJxrdhCT9mlZrgXq9TjgcRlEUZFkmlUpZK4xEIoHT6bRcG82ai9kEkM1mqak1jraOcnm8B79/E/F4HKfTuWLrS5IkayjTXqXYnC/YScVmQ+D1ekmlUrhcLq4LXMdcfY69R/eytLSE2+1mcHAQj8eDruv4fJfQanXR2ztDMpnB5WoRi82yZcsssdgo5fLF1Ot1gsGg1RHm90dpNvdQLrcIh/cRCrktEUpZltm6dSvxeNxKUm6329IWMyf2nU4ntVqNcrnMsnsZh6tKvydOrRZG0zRrhWKKSHo8HqvF2a6n2Jwv2EnFZkPg8XiIxWL4fD52Kbvo8fTwL0f/hXQmTaVSIRgMEovFcDgc9PX1oaqvoVwOMDKyyKWXPsfmzXmSyUtxu2/A5Wp7xAOUSiWr1lEsNmi19iDLTuAeFhbmrAHMZDLJ4OAgsiyjqqo1VFkoFKjX65bcisvlIhAIkI1k6fRDl6eLbFayhi9Prp+Yx5t1Fhub8wE7qdhsCARBwO/3E4vFcDldvDPyTmZqM3z14FdZWFhYMZneThROZmYuZG5uB+XyNuB6ms2rqVTaK4aOjg4kSWJhYYHJyUkef/xxHnvsMR55ZIxHH/VRqRzD4XjSkmzx+Xwkk0mSySROpxNZlnG5XNTrdarVqjXEGIlE2LJlC/ct3cfl0U4UVxzDCFqJQ1EUa7WiKIq1QrFXKjbnC/bjkc2GQVEUOjo6WF5e5sLWhVykXMTtR27ndanX4fV6iUQiLCwsMDs7S71eR5IkymU3TmeIQCBp+cF7vV4Mw+DXx37NE4tPYDgN+iP9bA5sZktoC7oeo14vAA8zNhYkHr8QSZIsna5MJmO9T7VatabkS6USwWCQNGmm1Amu7b2KeHwXzWYXpVLJ6hwzDANJkqwivdnKbGNzPmB/km02DKaZVSQSQdM0PtDxAW6duJWPPfIx7pDvoDvaja7rZLNZDMOwZj8SiYS1QqhqVf796L/zvZnvMVOfOfHm6fZvA6EBPnHlJ3jXwHvJZG6nUnmYdDqK1xvE7/cTDoeZn59f4e3SaDSoVCoEAgHi8Ti3j99Ot1/gqs1X0qoMWLGLokij0UAQBKuduO3notsWwjbnDaf1Sc5ms+zZs4eRkRH27NlDLpdb99hWq8VFF13EW97yltM5pc1vOX6/n+7u9jR90pvkT1N/ykx1hvff934e2P8ACwsLVKtVKpUKLpeL4eFhlKDC/fP388kHP8kb7nkD/zD2D7gEF3+++c959H97lNxf5Nj/4f185YavkPQl+eB/fJDf2/tu8F1FIqGQTE6g6zq5XA6n00k0GrV8UCRJsnTFQqEQhsvg3yb+jd/ffBkhKUom03ZyNAzD8mlxOBzIskwgELBXKTbnHaeVVG677TauueYajhw5wjXXXMNtt9227rFf+MIXGB0dPZ3T2djgdrsJBoNWTeTy5OV8pPMjTFen+dBTH+Lb6W9zuH6YY9oxfpz9MR9/5ONcufdKPnT/h7hr/i6uiFzBly75Et/4nW/wgUs+wGjXKCEpxLbENm7aeRMP3PwAX3zjF/nFxC+47F/fwjPLIpHIIlu2KFZbsSRJGIZBLpej1WohyzLxeJxms8lX9n+FaqvM24Z2kk4rlMs1a5XSbDYtyXu/348sy5btsY3N+cJpPSLt3buXX/ziFwDcdNNN7N69m7/5m79ZddzMzAw/+tGP+OQnP8nf/d3fnc4pbWwIBAIkEglUVaVYLHJ1z9WkPCm+svAV9ub2YpzkKR9zxXht5LVcmbiSy+OXE/aFcTgcuN3uFeZdJoIg8MeX/TFXDVzF2779Nq777if42ltu5B07QiQS7ySRSNDf348gCBw5csRyonS73dSEGt+Y/QZv7b+ILjnOwkLEOs/S0pIlJul2u63OL3NmxcbmfOG0ksri4iKpVNuCNZVKkU6n1zzuv/7X/8p//+//nVKp9ILvefvtt3P77bcDsLS0dDrh2ZynmFtQ9Xodp9OJpmlcEbqCrcmtjKXHOFI4gugU2RrbSqfSiSzLSJKEz+ezbIUdDgeBQGBd/5Kt8a3s++A+3veD9/Ghu/+NdH2M918iI7t+D4/Hw+joKOVymYWFBUvU8p/z/0xdr/PJ19yAw6HgdvfT0dHJ9PQ0qqpabcw+n49oNGr5uthJxeZ84gWTyrXXXnvcdnUln/3sZ1/UCf7zP/+TRCLBJZdcYq1qTsUtt9zCLbfcAsCuXbte1Dlsfvvwer10dnYCkM/nLVOui0cu5urQ1WiaZnVoKYqCJEk0Gg2rsC7L8qpVyvMJeAJ8753f47/d+9/44iP/jaXan/K+nQYD4XcRCATo6elB0zRUVeWu5bv4VfFX/PmO9xFzaej6Vnp7+1FVlUKhgNPpRFVVRFG0WqMrlYo1CGljc77wgp/me++9d91/SyaTzM/Pk0qlmJ+fJ5FIrDrm/vvv54c//CF33nmntV3x3ve+l69//eunF7nNbz1er5e+vj4ikQjVatUaPJQkiUKhYK2MDcOgVqsBbb8Ur9dLKBR6US6LgiDw8dd8nG3hbXz18Af5u9pNbEs8xf92wf9JOBzGH/Dz0OxDfLP4TS7xX8LvD/RSqdRpNBI4HCrLy8uWwnGz2bSGOM0Vlr1KsTnfOK1C/fXXX88dd9wBwB133MENN9yw6pjPfe5zzMzMMDExwbe+9S2uvvpqO6HYnDGcTifhcJiuri4SiQSS1O62MkUmzS4tr9eLy+XC6XQSCARe0s3c6/Xymq7XcNvrfklC2cpDM7ex9Uvd3HTvTdz06E18Nf1Vdkg7+MTI9QT9NRyOVyNJfiqViqULZsq7hMNhOjs7LeMvO6nYnG+c1rr71ltv5Z3vfCdf/vKX6e3t5bvf/S4Ac3NzfOADH+DOO+88I0Ha2LxUBEEgGo2Sy+Wo1+vWa8FgcJX974t5L6/XC7UYn7zy50wW/oEruu/j688s0x/o5wNDH+AyOYIi7yOX24zT2Y3TqZHNZsnn81QqFTRNw+12k0wmCQQC1vacbSFsc74hGGZLyjnIrl27ePTRR892GDYbHE3TaLVaVpH+N6HRaLC8vEwoFEKWy0D7galUipFOjyEIC0xO1pmb20EoFMftdtNsNi0fFrfbTSQSYfv27XR2dlIqlSiVSnR0dNiDjzYbhhdzT7YrhDbnPS6X67S3mdxuNy6Xi0qlgiwngLcBD6MoM3g8LarVixHFGC7XEo1Gw2ofliQJt9uNw+EgFosRjUaBdpIybYVtbM4n7KRiY/MiURSFfD5PvV7H4wkAexBFcLkWqVbniUadaFp7GNLn8yGKIqVSiXK5jMvlIhKJ4PF4MAzD6kCzsTnfsB+TbGxeJF6vF4fDQblcXvF6LBazVkPhcJharWZN0JvHhkIha6bLXMmYDpI2NucTdlKxsXmRCIKAoijU63XL5RGwtrYajQbxeBxFUVhcXGR2dva4AZifzs5OK4nU622bY7tIb3M+YicVG5uXgClX/3x1iFAohM/no9VqkUqlrJVKOBxmcHBwxQxXvV4/raYBG5tzGbumYmPzEjAdHovF4vHaSnv14Xa7kWXZWoGkUikGBwfp7u7G4XBYX99sNtE0jWAweLa+BRublxX7UcnG5iWiKApOp5NCoWB1eZmzLKYRWCAQoLOzc0VCAazJfnNI08bmfMNOKjY2LxFziLLZbJLP563XFUWh2WyyvLxs2Q0/n1qtZrUY29icj9hJxcbmN8Dj8RAIBKjVauTzeXRdxzAM6vU69Xp9zan9er1Os9m0W4ltzmvsmoqNzW+Iz+fDMAxKpRK1Ws3ynvd4PFQqFUKh0IrjTVXiF1JHtrHZyNhJxcbmNPD7/UiSRK1Ws1qOy+Uy5XIZr9e7oo1YVdVTerjY2JwP2EnFxuY0eb4MjN/vp16vk8vlLIl909/+pYpZ2thsNOykYmNzhhEEgXA4TDabJZvNAu0ByUgkYq9SbM577KRiY/My4HQ6icfjqKoKYHm72Nic79hJxcbmZcLyYbGx+S3CfnSysbGxsTlj2EnFxsbGxuaMYScVGxsbG5szhp1UbGxsbGzOGHZSsbGxsbE5Y9hJxcbGxsbmjGEnFRsbGxubM4adVGxsbGxszhh2UrGxsbGxOWPYScXGxsbG5owhGKYf6jlILBajv7//N/76paUl4vH4mQvoFWYjx7+RYwc7/rPNRo5/I8cOp45/YmKC5eXlU379OZ1UTpddu3bx6KOPnu0wfmM2cvwbOXaw4z/bbOT4N3LscPrx29tfNjY2NjZnDDup2NjY2NicMc7rpHLLLbec7RBOi40c/0aOHez4zzYbOf6NHDucfvzndU3FxsbGxuaV5bxeqdjY2NjYvLKcl0nlrrvuYvPmzQwPD3Pbbbed7XBeFP39/VxwwQXs3LmTXbt2AZDNZtmzZw8jIyPs2bOHXC53lqM8wc0330wikWD79u3Wa6eK93Of+xzDw8Ns3ryZu++++2yEvIK14v+rv/orurq62LlzJzt37uTOO++0/u1cin96epqrrrqK0dFRtm3bxhe+8AVg41z/9eLfCNdfVVUuu+wyLrzwQrZt28anPvUpYONc+/XiP6PX3jjPaDabxuDgoDE2NmbU63Vjx44dxrPPPnu2w3pB+vr6jKWlpRWvffzjHzc+97nPGYZhGJ/73OeMP//zPz8boa3JfffdZzz22GPGtm3brNfWi/fZZ581duzYYaiqahw7dswYHBw0ms3mWYnbZK34P/WpTxn/43/8j1XHnmvxz83NGY899phhGIZRLBaNkZER49lnn90w13+9+DfC9dd13SiVSoZhGEaj0TAuu+wy48EHH9ww1369+M/ktT/vVir79u1jeHiYwcFB3G4373rXu9i7d+/ZDus3Yu/evdx0000A3HTTTfzgBz84uwGdxOte9zoikciK19aLd+/evbzrXe/C4/EwMDDA8PAw+/bte6VDXsFa8a/HuRZ/KpXi4osvBsDv9zM6Osrs7OyGuf7rxb8e51L8giDg8/kA0DQNTdMQBGHDXPv14l+P3yT+8y6pzM7O0tPTY/29u7v7lB/YcwVBEHj961/PJZdcwu233w7A4uIiqVQKaP9HTKfTZzPEF2S9eDfSz+SLX/wiO3bs4Oabb7a2MM7l+CcmJnjiiSe4/PLLN+T1Pzl+2BjXv9VqsXPnThKJBHv27Nlw136t+OHMXfvzLqkYazSznSoTnyvcf//9PP744/z4xz/mS1/6Er/85S/PdkhnjI3yM/nwhz/M2NgYTz75JKlUio9+9KPAuRt/uVzm7W9/O//wD/9AIBBY97iNEv9Guf4Oh4Mnn3ySmZkZ9u3bx/79+9c99lyLHdaO/0xe+/MuqXR3dzM9PW39fWZmhs7OzrMY0YvDjDGRSHDjjTeyb98+kskk8/PzAMzPz5NIJM5miC/IevFulJ9JMpnE4XAgiiIf/OAHrWX+uRi/pmm8/e1v5z3veQ9ve9vbgI11/deLf6Ncf4BQKMTu3bu56667NtS1N3l+/Gfq2p93SeXSSy/lyJEjjI+P02g0+Na3vsX1119/tsM6JZVKhVKpZP35Jz/5Cdu3b+f666/njjvuAOCOO+7ghhtuOJthviDrxXv99dfzrW99i3q9zvj4OEeOHOGyyy47m6GuiXlTAPj+979vdYada/EbhsH73/9+RkdH+chHPmK9vlGu/3rxb4Trv7S0RD6fB6BWq3HvvfeyZcuWDXPt14v/jF77M9lZcK7wox/9yBgZGTEGBweNz3zmM2c7nBdkbGzM2LFjh7Fjxw5j69atVszLy8vG1VdfbQwPDxtXX321kclkznKkJ3jXu95ldHR0GE6n0+jq6jL+5V/+5ZTxfuYznzEGBweNTZs2GXfeeedZjLzNWvG/973vNbZv325ccMEFxu/+7u8ac3Nz1vHnUvy/+tWvDMC44IILjAsvvNC48MILjR/96Ecb5vqvF/9GuP5PPfWUsXPnTuOCCy4wtm3bZnz60582DOPU/1fPldgNY/34z+S1tyfqbWxsbGzOGOfd9peNjY2NzdnDTio2NjY2NmcMO6nY2NjY2Jwx7KRiY2NjY3PGsJOKjY2Njc0Zw04qNjY2NjZnDDup2NjY2NicMeykYmNjY2Nzxvj/AZAZ3ubTvo8sAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c950ec5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v2+v3+v4+v5_Aug30_bf_model_misspecification_1000_data.ipynb b/bf/v2+v3+v4+v5_Aug30_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..e9473fc
--- /dev/null
+++ b/bf/v2+v3+v4+v5_Aug30_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2770 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/bayesflow/trainers.py:26: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72\n",
+    "\n",
+    "from hnn_core.utils import smooth_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array = np.random.uniform(-4, 0, size=(3,))\n",
+    "#     random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "#     random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "#     combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "#     combined_array = np.squeeze(combined_array)\n",
+    "    return random_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 2.7467337\n",
+    "    maxAbs = 196.12401\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    params = 10**params\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v2: scaling\n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "#         temp = dpl[0].data['agg']\n",
+    "        temp = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "        temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms..."
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/x.pt')\n",
+    "theta = torch.load('Aug29_data1000_3ERPDrives_no_preprocessing_exp_transform/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scale\n",
+    "x = x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# Smoothing\n",
+    "for i in range(len(x)):\n",
+    "    x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "25f9ecff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.7467337"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f38d976e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "196.12401"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e3459f5bb9448d19244c08852685bec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15.586629</td>\n",
+       "      <td>0.818901</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Loss   W.Decay\n",
+       "0  15.586629  0.818901"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "63800080",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=np.array((math.log10(0.142300), math.log10(0.00865), math.log10(0.684013))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33ef55142e38440ab7cc3e5d3e66377b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=222):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(-4, 0, size=(10,3))\n",
+    "#     random_array2 = np.random.uniform(0, 1, size=(10,))\n",
+    "#     random_array3 = np.random.uniform(0, 1, size=(10,))\n",
+    "#     combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "#     combined_array = np.squeeze(combined_array)\n",
+    "    return random_array1.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 10)"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors(999)\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 341)"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x179da9540>]"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10, 1, 341)"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13fadc285ee04d8d850b08edc3b83938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea5086c1ead54285b900e9f13eb199c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AQK4rzFaQTqdVUVGR2Xa73Tpz5kzWmnQ6LZfLdcu5+/v7NTg4KL/fr69//eu6++67lU6n1dTUtGyu99P+l84qef5n7+trAEC+2Fp2l/Z+1vu+v07WKyDHcZbtsyxrzTU36unp0djYmBKJhFwul3bv3r2muUKhkPx+v/x+v2ZmZm75WgCA3JP1CsjtdmtycjKzPTU1pbKysjXX3Gjz5s2ZP3d1dekzn/nMmubq7u5Wd3e3JMnv92dbxi39XyQ9AGCprFdAjY2NSqVSGh8f1/z8vIaGhhQIBJbUBAIBDQ4OynEcjYyMqLi4OOvHb+/cI5KkEydOZL4lFwgENDQ0pKtXr2p8fFypVEoPPPDA7awNAJDDsl4BFRYWqr+/Xy0tLVpcXFQwGJTX69XAwIAk6YknnlBra6uGh4dl27bWr1+vZ599NnN8e3u7Tp8+rdnZWbndbu3fv187d+7Unj17lEgkZFmWKisrdfToUUmS1+vVjh07tHXrVhUWFuqb3/ymCgoK3qflAwBMsZyVbrrkGb/fr3g8broNAIBW/57MkxAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMWFUARaNRVVVVybZtHTx4cNm44zjatWuXbNuWz+fT6OhoZiwYDKq0tFQ1NTUrzn348GFZlqXZ2VlJ0rVr1/TYY4+ptrZW1dXVOnDgwO2sCwCQ47IG0OLionp7exWJRJRMJnX8+HElk8klNZFIRKlUSqlUSqFQSD09PZmxzs5ORaPRFeeenJzUqVOntGXLlsy+73znO7p69arefPNNvf766zp69KgmJiZuc3kAgFyVNYBisZhs25bH41FRUZHa2toUDoeX1ITDYXV0dMiyLDU1NWlubk7T09OSpObmZpWUlKw4d19fnw4dOiTLsjL7LMvSpUuXtLCwoLfffltFRUW666673s0aAQA5KGsApdNpVVRUZLbdbrfS6fSaa2704osvqry8XHV1dUv2f/7zn9eHP/xhuVwubdmyRU8//fSKARYKheT3++X3+zUzM5NtGQCAHFOYrcBxnGX7rr9iWW3N9S5fvqxnnnlGJ0+eXDYWi8VUUFCg8+fP6+LFi/rt3/5t/d7v/Z48Hs+Suu7ubnV3d0uS/H5/tmUAAHJM1isgt9utycnJzPbU1JTKysrWXHO9sbExjY+Pq66uTpWVlZqamlJDQ4PeeustPf/88/rUpz6lO+64Q6WlpXrwwQcVj8dvZ20AgByWNYAaGxuVSqU0Pj6u+fl5DQ0NKRAILKkJBAIaHByU4zgaGRlRcXGxXC7XTeesra3VhQsXNDExoYmJCbndbo2Ojuqee+7Rli1b9PLLL8txHF26dEkjIyO677773v1KAQA5JWsAFRYWqr+/Xy0tLaqurtaOHTvk9Xo1MDCggYEBSVJra6s8Ho9s21ZXV5eOHDmSOb69vV3bt2/XuXPn5Ha7dezYsVu+Xm9vr37xi1+opqZGjY2Nevzxx+Xz+d7lMgEAucZyVrqBk2f8fj8f0wFAjljtezJPQgAAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgxKoCKBqNqqqqSrZt6+DBg8vGHcfRrl27ZNu2fD6fRkdHM2PBYFClpaWqqalZce7Dhw/LsizNzs5m9r3xxhvavn27vF6vamtrdeXKlbWuCwCQ47IG0OLionp7exWJRJRMJnX8+HElk8klNZFIRKlUSqlUSqFQSD09PZmxzs5ORaPRFeeenJzUqVOntGXLlsy+hYUFPfrooxoYGNDZs2d1+vRp3XHHHbe7PgBAjsoaQLFYTLZty+PxqKioSG1tbQqHw0tqwuGwOjo6ZFmWmpqaNDc3p+npaUlSc3OzSkpKVpy7r69Phw4dkmVZmX0nT56Uz+dTXV2dJOkjH/mICgoKbnuBAIDclDWA0um0KioqMttut1vpdHrNNTd68cUXVV5engmad/zoRz+SZVlqaWlRQ0ODDh06tOLxoVBIfr9ffr9fMzMz2ZYBAMgxhdkKHMdZtu/6K5bV1lzv8uXLeuaZZ3Ty5MllYwsLC3rttdf0b//2b1q/fr0++clPatu2bfrkJz+5pK67u1vd3d2SJL/fn20ZAIAck/UKyO12a3JyMrM9NTWlsrKyNddcb2xsTOPj46qrq1NlZaWmpqbU0NCgt956S263Wx//+Me1ceNGrV+/Xq2trUu+1AAA+NWQNYAaGxuVSqU0Pj6u+fl5DQ0NKRAILKkJBAIaHByU4zgaGRlRcXGxXC7XTeesra3VhQsXNDExoYmJCbndbo2Ojuqee+5RS0uL3njjDV2+fFkLCwt65ZVXtHXr1ne/UgBATskaQIWFherv71dLS4uqq6u1Y8cOeb1eDQwMaGBgQJLU2toqj8cj27bV1dWlI0eOZI5vb2/X9u3bde7cObndbh07duyWr3f33XfrqaeeUmNjo+rr69XQ0KCHH374XS4TAJBrLGelGzh5xu/3Kx6Pm24DAKDVvyfzJAQAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARqwqgKLRqKqqqmTbtg4ePLhs3HEc7dq1S7Zty+fzaXR0NDMWDAZVWlqqmpqaFec+fPiwLMvS7Ozskv0//elPtWHDBh0+fHgt6wEA5ImsAbS4uKje3l5FIhElk0kdP35cyWRySU0kElEqlVIqlVIoFFJPT09mrLOzU9FodMW5JycnderUKW3ZsmXZWF9fnz796U+vdT0AgDyRNYBisZhs25bH41FRUZHa2toUDoeX1ITDYXV0dMiyLDU1NWlubk7T09OSpObmZpWUlKw4d19fnw4dOiTLspbsf+GFF+TxeOT1em93XQCAHJc1gNLptCoqKjLbbrdb6XR6zTU3evHFF1VeXq66urol+y9duqSvfe1r2rt376oWAADIT4XZChzHWbbvxiuW1dRc7/Lly3rmmWd08uTJZWN79+5VX1+fNmzYcMu+QqGQQqGQJGlmZuaWtQCA3JM1gNxutyYnJzPbU1NTKisrW3PN9cbGxjQ+Pp65+pmamlJDQ4NisZjOnDmj7373u9qzZ4/m5ua0bt06fehDH9KTTz65ZI7u7m51d3dLkvx+/yqWCgDIJVkDqLGxUalUSuPj4yovL9fQ0JCef/75JTWBQED9/f1qa2vTmTNnVFxcLJfLddM5a2trdeHChcx2ZWWl4vG4Nm7cqFdffTWzf9++fdqwYcOy8AEA5L+s94AKCwvV39+vlpYWVVdXa8eOHfJ6vRoYGNDAwIAkqbW1VR6PR7Ztq6urS0eOHMkc397eru3bt+vcuXNyu906duzY+7caAEDesJyVbuDkGb/fr3g8broNAIBW/57MkxAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIARBBAAwAgCCABgBAEEADCCAAIAGLGqAIpGo6qqqpJt2zp48OCyccdxtGvXLtm2LZ/Pp9HR0cxYMBhUaWmpampqVpz78OHDsixLs7OzkqRTp05p27Ztqq2t1bZt2/Tyyy/fzroAADkuawAtLi6qt7dXkUhEyWRSx48fVzKZXFITiUSUSqWUSqUUCoXU09OTGevs7FQ0Gl1x7snJSZ06dUpbtmzJ7Nu4caNeeuklvfnmm3ruuef0hS984XbXBgDIYVkDKBaLybZteTweFRUVqa2tTeFweElNOBxWR0eHLMtSU1OT5ubmND09LUlqbm5WSUnJinP39fXp0KFDsiwrs+/+++9XWVmZJMnr9erKlSu6evXqbS8QAJCbsgZQOp1WRUVFZtvtdiudTq+55kYvvviiysvLVVdXd9Oa733ve7r//vt15513ZmsTAJBnCrMVOI6zbN/1Vyyrrbne5cuX9cwzz+jkyZM3rTl79qy+/OUv37QmFAopFApJkmZmZm46DwAgN2W9AnK73ZqcnMxsT01NZT4iW0vN9cbGxjQ+Pq66ujpVVlZqampKDQ0NeuuttzLHP/LIIxocHNTHPvaxFefo7u5WPB5XPB7Xpk2bsi0DAJBjsgZQY2OjUqmUxsfHNT8/r6GhIQUCgSU1gUBAg4ODchxHIyMjKi4ulsvluumctbW1unDhgiYmJjQxMSG3263R0VHdc889mpub08MPP6wDBw7owQcffPcrBADkpKwBVFhYqP7+frW0tKi6ulo7duyQ1+vVwMCABgYGJEmtra3yeDyybVtdXV06cuRI5vj29nZt375d586dk9vt1rFjx275ev39/frxj3+sr3zlK6qvr1d9fb0uXLjwLpcJAMg1lrPSDZw8s3HjRlVWVr6rOWZmZvL2o7x87l2if9Pyuf987l361e1/YmIi8387b+VXIoDeC36/X/F43HQbtyWfe5fo37R87j+fe5fon0fxAACMIIAAAEYQQP9fd3e36RZuWz73LtG/afncfz73LtE/94AAAEZwBQQAMOIDH0DZfmoiF1VWVqq2tlb19fXy+/2SpP/+7//WQw89pHvvvVcPPfSQLl68aLjL/7XST3Lcqt8DBw7Itm1VVVXpBz/4gYmWM1bqfd++fSovL8/8P7Xh4eHMWC71Lv3yifO/+7u/q+rqanm9Xv3N3/yNpPw4/zfrPV/O/5UrV/TAAw+orq5OXq9Xe/fulZQf5166ef/v6fl3PsAWFhYcj8fjjI2NOVevXnV8Pp9z9uxZ021l9Ru/8RvOzMzMkn1/+qd/6hw4cMBxHMc5cOCAs2fPHhOtreiVV15xXn/9dcfr9Wb23azfs2fPOj6fz7ly5Yrzk5/8xPF4PM7CwoKRvh1n5d737t3r/NVf/dWy2lzr3XEc5/z5887rr7/uOI7j/OxnP3Puvfde5+zZs3lx/m/We76c///5n/9xfv7znzuO4zjz8/POAw884Pzrv/5rXpz7W/X/Xp7/D/QV0Gp+aiJfhMNhPfbYY5Kkxx57TC+88ILZhq6z0k9y3KzfcDistrY23XnnnfroRz8q27YVi8X+r1vOuNXPidwo13qXJJfLpYaGBknSr/3ar6m6ulrpdDovzv/Ner+ZXOpd+uUDmTds2CBJunbtmq5duybLsvLi3Es37/9mbqf/D3QA3c7PSOQCy7L0+7//+9q2bVvmieD/+Z//mXn+nsvlyvnHF92s33z5O+nv75fP51MwGMx8hJLrvU9MTOjf//3f9Vu/9Vt5d/6v713Kn/O/uLio+vp6lZaW6qGHHsq7c79S/9J7d/4/0AHkrPFnJHLFv/zLv2h0dFSRSETf/OY39c///M+mW3rP5MPfSU9Pj8bGxpRIJORyubR7925Jud37L37xC33uc5/TX//1X+uuu+66aV0uruHG3vPp/BcUFCiRSGhqakqxWEw//OEPb1qbL/2/l+f/Ax1Aa/0ZiVzxTo+lpaV65JFHFIvFtHnz5syv0E5PT6u0tNRki1ndrN98+DvZvHmzCgoKtG7dOnV1dWU+ZsjV3q9du6bPfe5z+qM/+iP94R/+oaT8Of836z2fzr8k/fqv/7p+53d+R9FoNG/O/fVu7P+9Ov8f6ABazU9N5JpLly7p5z//eebPJ0+eVE1NjQKBgJ577jlJ0nPPPac/+IM/MNlmVjfrNxAIaGhoSFevXtX4+LhSqZQeeOABk60u886bhySdOHEi8w25XOzdcRzt3LlT1dXVeuqppzL78+H836z3fDn/MzMzmpubkyS9/fbb+od/+Afdd999eXHupZv3/56e//fyWxP56Pvf/75z7733Oh6Px/nqV79qup2sxsbGHJ/P5/h8Pmfr1q2ZnmdnZ51PfOITjm3bzic+8Qnnv/7rvwx3+r/a2tqce+65xyksLHTKy8udb33rW7fs96tf/arj8Xic3/zN33SGh4cNdr5y748++qhTU1Pj1NbWOp/97Ged8+fPZ+pzqXfHcZxXX33VkeTU1tY6dXV1Tl1dnfP9738/L87/zXrPl/P/H//xH059fb1TW1vreL1eZ//+/Y7j3Prfaj70/16ef56EAAAw4gP9ERwAwBwCCABgBAEEADCCAAIAGEEAAQCMIIAAAEYQQAAAIwggAIAR/w8r3n0OLN4OnAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "765ac02e641845ab94d697fd55f15c0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2cc816fb13984c6a848798e7898b2e95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YXz7VvF1if7lV8/47bZ+enta1a9dWfY2qDI5fsVhMmUym3DM+NfaXTzVvl9hfbtW8fz228yU1AIAJggMAMHFPBieRSJR7gi/sL59q3i6xv9yqef96bL8nv4cDALB3T36GAwCwd88FZ3x8XE1NTfI8TwMDA+Wes6qGhga1tLSora1NsVhMknT9+nV1dHSosbFRHR0dunHjRplX/ltvb69qa2u1bdu2hfvutLe/v1+e56mpqUm/+tWvyjF5keX2f//739fWrVvV1tamtrY2jY6OLjxWSfvz+byeeOIJNTc3KxqN6vjx45Kq5/qvtL9arv+HH36onTt3avv27YpGozp69Kik6rn+K+1f1+vv7iEff/yxi0Qi7tKlS+6jjz5yra2t7uLFi+WedUdf/OIX3ezs7KL7XnrpJdff3++cc66/v99973vfK8e0Zf32t791b731lotGowv3rbT34sWLrrW11X344Yfur3/9q4tEIu7jjz8uy+7bltt/9OhR96Mf/WjJ2Urbf/nyZffWW28555x7//33XWNjo7t48WLVXP+V9lfL9f/Xv/7lbt686Zxzbn5+3u3cudP94Q9/qJrrv9L+9bz+99RnOOl0Wp7nKRKJqKamRt3d3UqlUuWe9R9LpVLq6emRJPX09Oj06dPlHfR/fOUrX9HnP//5RfettDeVSqm7u1uf+cxn9KUvfUme5ymdTltPXmS5/SuptP2hUEg7duyQJD3wwANqbm5WsVismuu/0v6VVNr+QCCgz372s5KkW7du6datWwoEAlVz/Vfav5JPs/+eCk6xWFR9ff3C7XA4fMf/oStBIBDQU089pccee0zJZFKSdOXKFYVCIUmf/CO9evVqOSeuaqW91fT3MTg4qNbWVvX29i58SaSS909PT+vtt9/W448/XpXX///ul6rn+pdKJbW1tam2tlYdHR1Vd/2X2y+t3/W/p4LjlvmBvDsVvBKcP39ek5OTGhsb0+uvv67f/e535Z60bqrl7+PQoUO6dOmS3nnnHYVCIX33u9+VVLn7P/jgA+3bt0+vvfaaNm7cuOK5atlfTdf//vvv1zvvvKNCoaB0Oq133313xbPVsn89r/89FZxwOKx8Pr9wu1AoqK6uroyLVnd7X21trZ555hml02lt2bJFMzMzkqSZmRnV1taWc+KqVtpbLX8fW7Zs0f3336/77rtP3/zmNxe+bFCJ+2/duqV9+/Zp//79evbZZyVV1/VfaX+1XP/bPve5z+mrX/2qxsfHq+r63/b/96/X9b+ngtPe3q5cLqepqSnNz89rZGRE8Xi83LNW9Pe//103b95c+O8333xT27ZtUzwe19DQkCRpaGhIe/fuLefMVa20Nx6Pa2RkRB999JGmpqaUy+W0c+fOck5d1u0PFpL0y1/+cuEn2Cptv3NOL7zwgpqbm/Xiiy8u3F8t13+l/dVy/WdnZzU3NydJ+uc//6lz587pkUceqZrrv9L+db3+6/lTDtXg7NmzrrGx0UUiEffKK6+Ue84dXbp0ybW2trrW1lb36KOPLuy9du2a2717t/M8z+3evdv97W9/K/PSf+vu7nYPPfSQCwaDbuvWre6nP/3pHfe+8sorLhKJuIcfftiNjo6Wcfknltv//PPPu23btrmWlhb39NNPu8uXLy+cr6T9v//9750k19LS4rZv3+62b9/uzp49WzXXf6X91XL9//SnP7m2tjbX0tLiotGoe/nll51zd/73Wg371/P685sGAAAm7qkvqQEAyofgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE/8LaDBMDsHNza4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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tVZ2zVCeGV1Kr41pQTavwZnlf+b1469cexaW3PTwsx64GtCiOJNvwCz98AZ++55mK9yOywnC8f64L5FIsoloFQbHsoOw4ieNPcstrssJpjkpjRMMuiKqMWb16qA+7jvTVeO6adg809LqTFTK+hOVmtIjYOI731zcZsm6CaASTnY73F6u6D8NvESUnY9cqCIoZLKLk6tvaIjqtUQ5ccxm3H2btsdoJbztOzTGDWt2B5FJIagPxyx2HsfGZaIv4JJTtbPeEKwkj4dp0HBdf/tF27D7aH7/NMA6j1kskl+xIdra1Hbeq4rTDXVkhLaG11vMXyw5KjloQfe3+l/DEq0cSwwRh0zwtiE5LVMrGGSnXHFAZYcF2sy2+JdvBl360XVnOp1Zt08kg1D/4z0/gjyt0zWSNm3HW3Ei0fH5pfy+++fOd+KN/eyp2G7qng6X6U6RHo2vOE0SVn284WXOO6wY1AePuaVaL5Fe7jmLJlx/CSYmUULJd2I4TCH+OOx7dgQ+sfzzxHEGtueZJlasaWhApUK7UNTdCZAWgMq3edpxML2n3gVP41s934hevHE48dzWg09ebNZdVGxVZc8O/uOYsb/EaSBAydC/2HR+o+/lrnYqhIBphi6gKwTdyFpF6GxIAae/Y3z3wGxw+NYRnd58AEJa9KpY911zJdmMVzCTWXCNITMMFLYgYHMfF957cE7yMWRfP4WYY8UlaifZXTpjgwnb+G6VyEdRsESX4uGsBrVtp11caYUFELR4SA9D+MPYcGw5BVKNF5I9bpaUPF6rNxRnu6ttpCa1ZY1R0HNre8iVRyQ6JCnExuSRCxOmU0FqXDq2nC/5j2278+fefQ8FfTLK+iyPqmqvIIspWfoTcMKrFp9YYE52+3q+Kk1EbFunbdR6EAsEik0TJ9QfCBVHZdpCrQ5+ierkfR9wiqiZG5E+u4XAjZrGIsroGAxefv51pGoDjYqgceiyKZQctOUvYzjt3kmuOxpHlipob2iJiONbvxUhoQc666A+3P11wzVUUI3IzTVL+MqiOUQuIpJCUFFgNAk0xzTXnjKxFRNdbTJgTdM17j4eEhnpZILVcI38WI05WqME116gYUdY2DL63NthOtIii7x5XrsJW4eoxAto1d9pBLvCZ1Rqgl8gapoZn1bYtth0308JErkXSguvZJjktRtRXZU2zzCV+Roi+/YfffAL3Pr03WGyyuOYOnAyru9erk2wt1zjEFsPRRFYYlhgRMrDmMgoCqtBA29Fxi9wiinEhBy7o09w1pwURA7UsIGTVNGgSDZcg4uOoxFVWtt1M8SsiWwwpGFO1uubCDpPq34/3V9d4L2uJH9HNUdWpMmHrzqPY3nMyOIcsiFxhcfH+57kzQ3Z9GHS1XOOpoXLw/4jGiJxmjxGlnT/5WGGFBuaag19ZQRGf5YKV5k0iWUEntJ5ekAVR1udLi71lDI8g4uNQCcfXjvThr374QkRoeBZR+vFp/PQycMZXrZM8rIfl4nh/MdKzqdqETnpZ01w6TpVCvFLYrkcMoYVDFkQqjffkQHjt9bJAKtGOj/cXce7nNuOXOzy2ZB8TRCMdI6rmfJVaRDd+50l8d9vu1O2ohlxaG4iQNZc8drmvkcUsopKiOgR/55LyiGiX00AOaUHEIVs0mV1z5PsdJosobTH9o397Cv/6y114cf9JaVzZ6Ns0flosh7ggqpW+zfKI7n9hP/7iB88JtdWqFURZ/eN8fc/itkrKck88jy/0Q4tIfGb8uLSoHB8IrcFqzyujkkXpV7uOoVh28K2f7QQgWkQjKoiqZM0FcyCjEP/FjsN4ds+J1O3cQGBQiR/1dllZc6Tg0vb0uWiH7yd3iwqCKIGsEFhLp4Ek0oKIwZQESaUJrcMXIwr/Vy28NE6VRZdFkJSlgCm3iGp3zYV/VdofX4wrQVqg+unfHsMDL+yvyDX36EsHce7nNuO5DIsVRyBsEY3J/eKVw5jzF5uw7bVjbOze3xMDJYxr9Yir9RNE2Z8XWWQ0hr6h8LknWWiPv3oEv9p1tMoRRkExokpjHaRAZc3jczO+D7RFWowoa2qCJbHmcoJFpHLNResjqh5H1hhVNRgs2YnVQeoNLYgYImSFCllztXYzjYPKVOdwJE2LUM5Y4kf2Uw/U0SIiFpnruuzFDY/JLaKKqkakCKJv/Xwn1m1+Sfg97Xk+8pLXz+fp3ccSt4uMhcXB5PH85DeHvL8vH4qM43h/CdPGtQConwVSiSCi9gPj2/IAsrvmVq1/HL9/52NVjjCKSgrY/u8HXg4st0pjRHZGYUf3MC2PKEwhSD5/YBFJnpOi7YTeCCaI+OESS/wwt3e98Z3HXsPv/MNPR6wKuxZEDHKMJ+sDpoclW1T1guiaU/3u/ZVPb9uV5RHR4jNYUrsJqgF/WVQ+fU5WqIT9lFZepeS/5Pz3tEWItjUqjPXxpEP5FLTo9BfDRd51XQyWbAyVHUwb6wmioTpZRJWsSScHvTGNb/UFUbFxMSIg/flv33cSX3/kFTy+40hF+wXnyegCDAQRdReOuRVZS+zIgsj3+AmsOW6BcsGWJaF1OFxzPScG0Ve0cXygvkV546AFEUPUNZdtP3INDJdFlOaai1tAPXdH+gIcISswSnWtyhadu8yIE44giMKJfuDkIA6fGkIWBItQjAupbLsRNlba+xon0NPACRnygpH3D8Zp6rbjueUANIVFNNZ3zfWzMaro5D/5zSH815N7ahxhFJnLNZEFJMVmssaI3Ix5dUGMyMpW9DS9soL3l6onkIDjnoeizd3h4pj5d7bj4v4X9ksehrQrqhxUF69aVmul0IKIQV6AvvP4a5i99r5g0YgDJf/JrrF6IS2hlX6WT5+5VQIFTMkiKtePNcctF1U7C14I8oqvPoolX34o43HF48so+daQ7YYtn9OuhV56+Tne/8J+fPa78e2cbbYgyOOhagn9zO3lMQhFQdQIssLJAW9MNO9JARlTsJSCcfW/bMX/SrgP1SIQLCkCRQ7OV2wROdny6miTtMoKtF3a+ckqpnWCPnOFLy5GJJMV7nj0FXziO0/ikZcOZhaEH9uwDV9/uDtxGxkUPzzaN4osoi1btmDevHno6urCunXrIr+7rotbbrkFXV1dWLRoEZ566qnUfb/73e9iwYIFME0T27ZV19ytUshkgxd7PBZaWtCOJg4VvCT0nBioS7JZWpwjzNURf8tavFW2iAaL1cWITvSXIgsYDb3M3IT8eqpNRqR7Hrd/2WckOY4bPJfUnKNAEInfP7bjCO57rid2P+7Hl4dD5+bWhuO6EYuoEWSF3iFvDHQPyTU3oS0/snlE5BpO8XHJfcIq7UekUhTU2/muubRacwluMw4zqKQgKjp8TnDXLD9eWPfS+0wxR8/TEd1ehRd7TuI3B08lbiOj13fbqiryDwdqFkS2bePmm2/G5s2bsX37dtx9993Yvn27sM3mzZvR3d2N7u5urF+/HjfddFPqvhdccAG+//3v48orr6x1iDWDm9Dfe3IP/r+Nzwu/0wTjMab9JwZxxd88GgTAawF/z5LICvJP9IJntYho0gsvRWZGkotlf/cT3PXYazFjY64ERTC2UgS9WOIEke+Ws10Xed8pr3pfD/YOYvba+/DLHYeD+ye7ONM0aV6GJeqa8y0iSRCRy4NiRPVyzVWi+JBFROMfKNowDGBca37E6dtA+lyQt6Nxk+ciaf+kTqeRbeUYUcwulRY9lUuH8bhhXEIrzRs612/29/rbOInUbmGcjhshdJwcLOHCLz6Ax189otxn1Lnmtm7diq6uLsyZMweFQgGrVq3Cxo0bhW02btyI66+/HoZh4NJLL8Xx48fR09OTuO/8+fMxb968WodXF/BF5E+/9yzueuw1PMnouCr69p5j/Sg7bl1K/QtZ+SpBFCS2xVhEqWwkURDxF4Em+/qf7sA1t/88+P7n3Yfxyx2H8eqhUzh4chAl28XhU0PYc0yyHt1w3CqWUbWCKM0tU7YdP8kUyOfiWz5v3enRkP/f46+FsTb5XP5xYsdCFimi1c7JIuJEAIfFiKaPbwVQz1pz2belxYbuYX/RRnveQiFnjmiJn3LKsyTIFoC8fZLwzLpoA4y+nWJJZxVuJqst5x3P+16Iydnqd5zcd47rVUnp9V28/UWbud6Tr6fsuJHnuX3fSZwYKOHvHviNch+aG0dHiyDau3cvZs2aFXzu7OzE3r17M22TZd+RRNyE477cK+ZOBQD80092BN+pas0dPuU9wF4WG6gUz+05gYMnB0XWnGKMccUXs7J6SFsKNDbuCvT//8qml/Brll/zh996Ah/85ydw878/ja/d/3JgNfYPiaVqeJzKVgjMaqmnadpoYBE5bqKvn8bbls8FK1DEIkphH4blhqLnCGNEMa65OrPmKrmfFKeymSBqK+SQt4wRtYgCBSXmnI7j4mv3vxQodbRZZL4nWUQVuPGognlaZYWsMRq52gYpYv1CjEgdl+0PBBHwyqFTwvdxZa4effkgvsdIJaq+ZC2+cjZUFt9XArnm6t3KPg41CyKVK0B+keO2ybJvGtavX48lS5ZgyZIlOHToUPoOCYh793gnTXqAv2VxI/Jt8yD3kT6P/UUPtBr87u0/x+VffVQYl9o15/2Vb6fsU4+DTN8WLKKUl6xvqIzewXJQjYFr/nxsNnPNpV1PFqRpo9RszHbdoEeQaltyj7QXrNgYEQm1OHBtm89p13VD1pxEVjgxUIJlGpg8puCPt7qFf6hs4/m9oYJQSdk1qjYfWkRljGmxkLPMusWsVHh+7wncyRQ5my3UKlfrvhMDuOPRHYGbO+7ZJysL/jYZLovmQS7Bpettl35e/ju9ZzQWgawQU+Kn33+vXNfFMUYcGCjaShYqAPzb47/FP/2UKcpOtKgstZxQKUCu6zKywiixiDo7O7F7d1i/ac+ePZg5c2ambbLsm4Y1a9Zg27Zt2LZtG6ZNm1blVXiI03z6JeotIC7WRHXlZIXDvd4DPFWDIAI8d5lYDVsliKIvpkdVzaaxyQmt/MVK89BQLxmyiPqGZEFE2q6rHGfVrjkpYC0jdM2FZAWV4kMvenvBClwy0QoVtIAka8auZBGV7LCVgOCac71yOu0FK1Bsql34v/jf2/Ger4cu0+xJ2E6gJDmBILLRlrdQsMyaLaLj/UVc90+PKV3T//3sPvzt/S8H56YhX/V3P8Xv/ONPI9vT77SQxz37pHhmHKEnaVvTSHHNZY1t+bcyeL+Y4CcUY0r8DPjbOK4rWC/9RTtW0aQcOn48OUZE1p6qXX0fE3KjJka0dOlSdHd3Y+fOnSgWi7jnnnuwcuVKYZuVK1firrvuguu6ePzxxzFhwgR0dHRk2nckEduqQNJmAZn55T1kQ2ERnarBNSef0zuv6nfVdtndX3KJH5udJEsSaMl2gyRYua0D9+urWEa1xoji9vdiUhDo20muufZCjpEVxG3KinFz8BgdXxSGyrayerLjun4jNBOFXHpH1yQ8u+e48DmLHNr0XA9e9oPeQHh9A0Ub7QXLd81V91wIOw71YevOowHzlMO2w5I+8iL6mwNRdhfdd7pHMlkhOG4Gq7WiEj9WClkhZQ6e6C/hCxufD4RJMP6ArBBH31a45hzReukvlmNJSmXHEfLrVDEiVY07Qi9LqRgpi6jmDq25XA633347rr76ati2jRtuuAELFizAnXfeCQC48cYbsWLFCmzatAldXV1ob2/Ht7/97cR9AeAHP/gBPvWpT+HQoUN497vfjcWLF+P++++vdbiJiJtwPM4TBsnDB0gPmS/aRyhGNFi7j1Uu3Om6Lh5/9SgunTNZcHHyhbIS9xqNfyiwiNi5U7U90SLql1xzPH4VuDIEMkTi4WOR6przWUW2E7rmVIKEXvRCzoxowsEYnXABUb0wcXlEXgdO9diLZQd5ywzGVq1FRIU5g2OnLLSu6+KT//4Ufv/iMDZL97CvWMbYlhzydbCIkhZpTk7IooiE1QdE1pnsbsoSx6uINZexHxEdn6zfsu3g//54B1460Iv7nu1B3pLICiT4hYRW8VgETlYQBVFIVpAFcqks3leKlf7jQ924rGsKls6eHOyrsoiITZm3jBGLEdWlVfiKFSuwYsUK4bsbb7wx+N8wDNxxxx2Z9wWAa6+9Ftdee209hpcZcROOu9dUFX/L0gsCIKgQUEuMiHCEVRtwHBfP7T2BP/jnx/Gdj16CK+ZOU2pGlVgdMlmBm/FpGqTH5nGDFyZKVgjHoLImqy1PksYIpMoKaXlEAyWqWxa6QKOCiF549Vi424crI4MlO6YShnevCzkzWKSqFUSV1kcs2W7gGiSQUjVQtDFtbAvyObNmFh8dU/V4eFWMbHk9CLbn+8tzJyk+pvIaxIE2yaXkEfHvy46Lgr/9z7oP438/GLLRJrQVcPjUUKDwcZYiIdUicl2hKj53zcnXVHLCYqrkoi87Lr7x41dwYqAkCaJ4i2jWpPYRY83VRRCdLohbE/uUFlG4cajphPsc8U3aerjm9hwL/ey26+Kg39lz99EBYdxxiaKpFpH/e+jDDn9LExQ0yakag3y9gfCJiVmlFYyMQ5p/nr/0FHRWnYpedI/c4H0nu+bSSB+81QU/x5AU3+NjL5YdFCwThmGgYJmJrcWTEGldknKYkLHF5zT87zzXnGEYVbXu5khK9gyC946TyZVI97AoueYqsYjSlAmOaIwo7pjRfQBgTIu4rNJ8klmp/UIjQnUSeX8QIwo9FuNbcxgolVllbnGApIR5YySh7/gWqKggDCpYc0TdftOcydh/YhCu61ZMIqsUWhAxxC26pxSCiC+AJcVLV6tFxMfCc3Nsxw0myv6Tg8J5BSuI5yWkxoi8yVlSWUSp1G8vv4GqMfQXYywiTlYQXBrJx086L/8b/T1kAOYTLCJiB5Udh1k24jZp1hdnzQmuuZK6H5TteI3gKD5UyFXPUpMtorSYHglokYAT0onbCjnYTKOuFknPh+Zm2XYjOVvKYwWuONHzID/Pf/7pq3jqt8fww09eHjmGq5h7cZAtojT6tvy/vD0925JEBuovqS0i/u6S+4675iaNKXgWUZxrznYiDFgSTiXpuagujVxzH79iDuZMG6u89npD15pjiJtwvQqyAtfGyoEZ7H0u2U7gW63WIhIWazYsnoOy/8SAcF4+fD6+NKsmQlbg9OpU15y3uJFm1VcsRyjMdBxVtWBOJsgKXkonnjUXvnBJMSKqQF22WVFWaTtewofj3qf34mfdhwTtX4wR2cKzG1Ow/ON42q0giKpsFR5nEe083Kfsq0RzlQsigb5dsOoaI1Ld83BxdCJWjQqkrITPVDwOYcehU3j1UJ96PDGLtvJ8FCNKISvExWRlazIQRJJFx2+NIIjYD2H8Ocz5mdheEGNE0vhKrOEe/aX301bEs2XI7UFGAloQMcRNuFOMcKC0iOjh+nwbEkJjW3JV07fjNf1QY9nvu+i4m+pEfwnb950UraM0i4i5OhxmvptGVtecg4FiKIyFxnpsbCrmUtlxgwU5K/iQbEfdY4bXLaOkUrX25z2rkh0eJ04Qyc/k0//xDD78ra2MNSeObags5sWQy4a75gAvKKyqdp0FcTGir93/Ev7se89GtudCh0D3cKBErLna84iSrEiaX1SYNgnH+oph8zgpFisfm7dViJ6zeosoth+RRFYgyEKcBEjJjr8ncZUV+LmG/HjOhLa8l0cUk9hbsl3hnQZCUgK9F0nuelJS2n3FaSSgBRFD3IQTXHP+JiJrTvS70gszZWwBRduJzV5Ogqzt8QrS5Jo7cCLqmvvWL3big998vKIYEb+Wop9/kzMNWKYReXF5fhLgvdhl2xWED+/0GfixHTVrznHcIJcmK2j/QlC6R3FN7MXOJ7hYwjI3GVxzcS4aRlaQWXP8M7VbsF03ICvQdVRLDohjzfUOliMMRiCcq6JrzqPfuy7QVsjVpcRPomuO3qEUi2iwZOOiLz0Y1HaUWWeyksRbb8ugx5DBIIrGiFKEGyBbMeKzlNcF1TyKaxXOx1S0PeVlbIsl0LcjxY7ZfaVjEZkoi0Cm8ZOiNBLQgoghTjvjC6uKBkqLXpDvQL7cdi9rvpo4kVwSnzO/AtdcECMKx983VMapwbKwf1ochi86Q2VvEpumAcMwIvfEc8WFB3QcFyXHEWig/VLyJoCAwUb/B9fpuEGWd1bQmFpiKia4rphJTvdO9YKTdVlKcM3xoqYqhG4ocaGTWXNjfYvIdSFYRB5ZoV4xIu9vf9FWWjVlZYzIDZ7ZmBarLiV+eO5V2XbwzO7j7HzkqorW5uOgeU6lpWjsg2Ub//XknkiL8JLtxCsLMcJLhWiMSL1dHDM1TriWbMdnVorf5y0jtrJCcC7Hizm25Ey05XMYSEpoddzAyiUlk9hxZebqi0PRdmEYUbfvcEILIoa4CdercM2pWHP0cOnhU/mWatxzMpuMqjjbTlh+48RACYMlljTphCw27prKSlYAfPeG7VtEhhF5cV1fM+PHLtuuIIi4BcnziFRasuPGu+Z+9+s/x3pWqiQYr78/FTOVX1z5c85SW07Fcpj/VLadIJFR3i7NIuKaKb9f3rMJtxtT8F1zju+ay5FrrnpXmGWpXXMDRVsp3DhVO/zODQRTW95zzZGbtloEuVeO14L9vXf8IminQothWVEDjUOu0kHv2R2P7sD/+u6vI/eMXHMqz0ZcbULbcfEvP98pzN8wRhQfW6Rr48eRx/nN65dExq+63vZCLrbWHB/TUNlGS95Ee8FCf8lmSp64LScq0LGKwXfprrmS7eW4DTdTjkMLIoa4h3NqqByJH7huuD1nzf3w1/uCOnQT2/PB/rWOhVtEvJnc/hODQuBezkJPui6CLFRt14Vl+K45aZI7bmjxAd59KEmCSG55EI5N/I7OHSeIntt7Al/Z9FLk+8A1RxaR7JqQrrcQxIjE7/l9LDvpMaI0F41nEYlaMr/3ca65ljqy5uh0gyW1RRRUWpes2jAukAvIHXJ/oEoUZF7gliwbqm3H84iSXHN9Uk5a2j2i31WHjHu2L+w7gb/+0Xb84pXDALz7RjXtqA1EnB4nkG6Ed8j7v02KsZTK6uK541pzEQs1ci7XY8215CxPEDGyQtQ1FwpdmTihIpFEYkxlJ3BnjxQ0fZshPijpmbZtBSuSA2OZVmBRlG0Xt9z9dPD7ZN81d7KK6gryC8oLd54YKKMlZ2Ko7ODwqSFhQtL4hkoVCCI2WUmrtCzPGoq65qLlQnhlBUAuiRSOIQwys+M5buSFTQMtAPmc73KTxiO7leJcLNwqKNlO7IudxADj43GYsKX9hPwS/zpJmIdkhepZalHWnG8RlaIW0f4Tg5Fuw215y7eI/OKvvmsO8BZUnhIjX31SfonNqNZB470hsbdOWTG/OOSqJGkMO17LTb4vcRW7yWVF+973bA++fN+LAMJ5c+dPduAnvzmEt82bhsOnilj7rvOCawuPH7WIWvOSIGIWkWGEAq5jQiv2HR9MvE7HhS+ITLQVLBTLDko5tXVDz11lgZXs6FweKNmB25j2y1cYt60VWhAxJM3zobLtCSLF5JNpmYRJNbjm5GNxQXRyoIRp41qw59iAnzRJ+4QTLC34yREhKzieRWSYqvhLdKH3KiuE3yktIsE1J7r20lhztuPiM//5DD5+xRzsOHQKS2ZPBpBgEUXia+rtuBVXZgmt8u1Ky1mir12FRcS15rZCDqYRujeHM49IFSO69LaHI/u35k3YTlgZo913zQG+5dviX6MTjW04LmDFKM48WG5ATKKl+/jPP3sVL+6L1qIjUNpEa97EYCk9t0lOeBXHqlYmAmq1vw8XfiTMjvQV8WLPSfzct5pIEInFgZmCGggicV5zMsXEtjyO+ezaMya04de7TwSCXWU1ua5XWaGQMwM2Gyl8EVcyuw+yiz+07sPv+v3STuE4w5SHkYJ2zTEkaWekOfGFpRwIItLwxIdOE6YaBlKENSe55qaOjbaYdphFpPJ5x6GksohiWHPE3hH3dzBYtgP2mxAjCvZT++k9skJ0GvJtjvYVsfGZffjXX+7CH9/zDO5/fj8AoOCTHOT7HrUm1TRcXt6Es+bk+yUnUkbGyvZzpcWJ37/2ggXTJ4BE84jqxJrzDzPgxxDSlBDPInICxaWVCSI+JlUb76Rjc+FN84sK4pKi8NRrx/DqYXXeDxCSfNry2d4jVfV4eTyROKFNbDI/qM/eJ15NX/V8VH27vG1dYdx8fHQbJ/reEgA4Y3wLirYT5LRR0WGuoAUWUd5CG8UaXfpNUn78jyWF61MuvAoAv9p5TJi3JdsZUcYcoAWRgDSLCJC0IEkAyS8KCSJ5oXxo+wG8/W9/nNxRUtqHNN+S7aJ3sIxp43xBZMuCiMZbmUVE2t+QL4hypuEtmtK+nmsuuvAPFu1AOPYryAoA09Qk91VBwZrj56B73+Mn8NICFdC3pdso3++gxI8siMrcNRdq/PLtigsKB78z15zsduTHai9YMP24W1FyzdWz1pztuMHx0o7bmrf8ys7evWjJmxjT4j2PLHGLOHAmaUAZHxItIpmMIIOsE3lBj0NgESkEVlzuEbmw6d3l7lpec1D1rsYx5UKLSOGa88fBk0XPmNAGADjU6+UF0vB5Ho/t+GQFy0S7dFyVW5C+j4sR8ffy5n9/Cg9sPyAcIx9n6g4TtCBiSMo2poWdr3G8fAYQneStMZrcF374AnYe7sP+E4P4zH88g9lr74ucLy5GRP1B4iyi0DWnrl2lQtl2g8lNFpHpW0RK+nY5OrkHSjamjPW0PN4Kgt+vIJicwSLi10/3/oCfwNvvFyot+C9LxCKy5XtH+SDiOYZiLKJI8DfNIgpe7jTXnOUlCatcc3WLEYnWcLHs4LZNL+Jffr5TuX+LZBG15CyMbfFJNsylrIpbZLOIwn1pXtB8lFuGyCCFQ17Q4yD3K+LgsUqOgE3GqOEELuRVAl2OB4bj8LaVBahnoXi/TWSCaOYEr108lQUjJZQLnICskDcjiaZxFR5UMaIyey7C97a430i75nSMiCHZNee7FRxR4wCi1EgCTUS5BfI4nz11crCE7z+tbo1OE4OCmrRPj5/EOs1f9AVB5IRj4otsaokfx0V7i4XeobKQ0OoxA8VtvfhGdAHpHSxjQlsehgGhSjC/p6qFQkXfdl1XuGekOR/086aownecRRQhK8TUmqNn2l6w/Dwi8p9LgjYhI55fj+tGKzLzc7bnPddc0Y/rkUXUUkeyguu6giUzZNt49OWDOGtyu3J/ihHRfGnJmUG8oHeIsQoVVkYSeYC7K2WLKK12H4FcvMlbsXOSYpjgRpSVjKD1ib8Pf2/4vVUN1ZHmMYHmuSxAizZ3zXGLyBNEgUXkb9PKBI7reu96yxgz8r4I55Y8IfIzCujb0n3gFlBJx4gai6S1IJyw4UOjhxpnEbX7vlw58Y6ESlKiKx2LtKL2Qg6WaQSVuKcqXHM8JlGRa852gpfG8SevaRowTUVypwsUFeVoTg2V0Zq30Ja3pBI/4TYqUoftuEFiqnAOdl1kCZEPnbqd0ssSF5AlpLnmxrTkULYd5nMXr40vqirEseZkV117IQfLMAJrtR55RLIDRbaISrarjBUQiDUXuOZyZjA/RYsoOUYig1Pe6f0ILKKMlW57WdWLSqDaPK7oaVgHzreI2L1LS+iMdc05DnKmIcSYvHOErjluEXX4rjnZImpTWUQ5KyIk+PXyWJ4qTytMaFW77GicI82a04KIQeWaI6FDmpLjuiFbS9LsZBdcW8FfKCUJN77Vm4RJbXhpYlNg0jINjGvNYa/felnlmuMJldw1l4Ws0MKSQx0nTGiVJ7IqRgR4i0Zr3kRr3hJIAPyeqlhNqjwi2bd94OSg8Dv58eXnwK+HIx9TvJKe6biWXKY8ojSLSC56ylmMgOeaM4yQJFEP1lw0niWWWyqWncQabK15y48/hGMii0joWaSwiJJcvmVm/dLiSKy5rC0meEHaSpBEVpCnrty+m987w0jOnZIb4xFKttcDS1X1gs43gQmiKWMLyJlGxCKKCiKPECQLIlWRVMB7t+KIPHS9F3ZOEL73juEEbu+RghZEDKoFm6wa0pRstnCmuRhaA9ec2iI6ltD9MLCIfPPcNLwSMXt9i4jICmJwkrnmYhptxZ2Lrsl2fYvIMLzAuoI1pxZEZbQpLCK+t8oiUtWa88rChNsc7B0SfifXEwV85YThSFWKmAx5bhHxEj/yNAjL1UAJzsgSrT2x1hyRFWgucYuo2tpuqjwvHnAvlh2UE9o6tORMQRC15Kwg8VbV/uSrv7cIX3rvBd65Ei2iMB5IMUXKI0pTjAhkkVV6b1RkhTirNsi5CUrhcEFkRJokcsQntIaVCWSrio4/gbHmcqaBKWMLEYuoVRBEfomfvIlCLr71B1d6eV8ieZz09Z+8c563La+dWQ57eI0UtCBiUL1XlIQ4xALthZxkEcW8WCTE5ED0ON8iOpZoEfkBy0AQGRjXmg8WebKIhmLICvyFysKa49UHqKupusSPmkE0VPYSflvyZix1nBYkwWpQxIhkivihXtEiIs2aSijJSZrywhVX4ocsk7GBay7ZIop1zTGSA99EZRFZRiiISEDWYhHJVrzrimw3rzeNqzx+3jKQsyhGZMMwvO+CGJGCrFCwzLAAb5JFxN4NuRlfkmLErYhqXXPqPBzvb1weUUBWYNa8mSaIhGcdFURA1L1H7y+3iAzDwNSxLTh8qhiMP2caIn3biXfNxbHmyooYkVw4NlCqeQqHds01FqqFpi0QRCHjR354cZphW4xFFDLgwgU0EiAPXHNcEIXcElqE4/KIZAGVBM81Z/nn9SZwWOInqnHHdROlGNFgBTEix/EEBX9f5UQ8ihERaKGlgO9JKdYmu0Lj8ojomY5tJddcdMx8vLEJrU64nyxk5RiRYRjBYtQisebSmtopz61wzfH7P1T2rKEhhSDKmZ5QKQeLnKfFt/gtzEXXnLc/5ZcB2Vlzch5R0n48rtJbrWsugawQYc2RIHKiCpxpRLv1qo4JiMKvbIc9tmT3HLmD5WTXtrwVzEeK0fJcnmTXXPi/2E7CiViHslLFleqy7eDXu49r11yjoVoHqIcMaUq2E8aITg2VMViyYxf6Qs6EYaiC6d7no32hRRRXLy2wiEwvlgF4L8i4lhzyliEKHMcNJqVY4ifhouEtMmFLBc+qsiiPSEnfVh+wNW9VESNyYBmG4ApwHGSKEQUllCSLKI6sEFfeZUzBEkr80N9i2cG+4wOhay5mAaXn60IiKzhhzG5MwcLMia0weYwoqL4d5ohVCtXzkWNEcYIob3nP2HZCbRvwNHS5l1ZQaNa3lAFg4zP7sG3XUeW45OrbQDSPSIU8mwe9gWuuQotIsXlcY7yQvq1wzSHNIlK75ooJFhHNFUs6rmUaIavUVlhEbljiRxZEriQECSqSilzAtxAQflw8uP0ArrnjF/jtkX5N324kVC9Iu2QReXRj77vr/ukxTGjLx2qyectA3oz6/2kycLJC2XbB2Z6kybTlw9YBZBGNb8sHGtOA1HJBSVZIdc2FcRqKz1gBa07c1nGilRUIrXlTwZqLug04ldZxmZbt70ZFQQlyjIhYc5SdHnHNSYOOJyuEGqbNLCJ6nt99cje+ct+Lyox9/sx5jpmc0+G4LjomtOKxP18GwLtWOUZEf3luUVYoyQo8RmQ7KMe45go5zyIKkiXZuce25pRkBcs0YZnesb52/8t496KOoOQSB7dAaL6fyiCIuEWUZXsVVK68NNZcaBFx11wKWSEmRuS9y2qLiN4BU/o+Zxmhsut6HomowPGemVz1QBaC/Psoa45IV95nbhEd9hXj3qGypm83EirLhkr3DzF2DV8sTgyUYoPYectzcciuIpoc3OV0rL+II6fCz7JFVLLdILZErLtCzozUdVO55rJ0aOUWke1bRCrWXFyMCPDcC61yjIhTS6U8Ijq0ZYoMI5mswO8LEOYRjWmxUMiZkaKyUdecuvo2aZg5nyzgSOM6cqooJuey3fmhOKOShH7OTwa2XVfQqk1DIYh4bbcKEY0RueiXLCJOz+bImSYsy3fN+YFwwtiWvBQjcoLr4ovoYExSKncB0eJIczUpRsTvVZaY0mVvmBL5ThVSoikRUUbkGBG7T6lkBXas+BiR6R+LfvMFesQiMgXyk2UZSqWkJWcFxX7DcXAhyEgHthPLmgsKBzOLiCu1WhA1EKr5TjEa6vvjuIjkvcRpbF4ugRl5kWj7Pcf6g+8uW/cILv7yQ5FteJmg0CLy/uYtU1h0aOGj8aaNDwgrdoeakf8i+AuOipUVF1hv811zImuOvaBSZQUal2UaQl8dxxETWuXh0zVbhoHxrfmIay5KVohPaPVqqxnKWnORTpsxMQFa5F03HCu3srh7xjDCxS8o8cMsokqhcs1x4UCLuZKskDMCgcldc4Dn+j01FO3DlWOuOUBcuDn4ghe45orpFg4nfmQxhFZfNhuP/fk7hO/UlRViXHOBRRR1zaXFiJISWokgQ5ZRUEiWYm1SDCZvGgh7OHmuOVXFkZZ81DWnUvboOHF5RNEYkSO03ZCZecMNLYgYVC62guWZwl7bZ/87Oe9FsR91OFR1u6TJkUTfpheDBGHZdgNaLTFuCjlTcMPYDnfNZSMr0MTlrjlPEJkJeUTq41GMSKjqwDaVWyXTsU1DtIhk15wM2q8lb2FCWy7osir/TggTWsXjkCDKmaZXfdsVxyyPQSBZsHs6yC0iN1ywaSHgXhila46KjFZhESldc4q27UpBZJowDc9iT3PNBZq8aYCvg9yVxREsqsy6DdpAJAoipG7DQfluqnMLx5ViI4SwZULUNWcYRsSFJp5HdMOG/4fBflJCqL9PIIgUMSKeGG8acRZRSh4Rp2ErYkQyaSNMzneFzsraImogVAu26WsmQ6UwKVBVkkZG3vQYSLTIPbfnBC74wv041DuUmtcDxFlEKtccjxFlq6yw5fke7PKrHpPAI23Y8RdPy4BfoFOsl+YkuOZafdecYBElxIhorDlTJCvICa1xGN+aw/i2fCRGFC16qmZ5DZacgCHG3Rg05kjBSK4Bs1Pw+KFXyh9B7MVxXWExMxlrjtO3gfpZRPz+0/xQkxV4jMgRBZFEVggXLlNwV3ELgoPXNKOY3UDJVmrpAFOEYgRGHEwzKiySyAquKxFoiA1LnWsliyjJNRcXIyrZTmARBey5wCIKBfpt71uIj15+jv+7IbjmchJrjtCSsyLfJ5X4iSR7S3UTW6xQ0eVufi2IGgj+zEiTyZkGWvImfv7KIXz9kW4ASAwWEsgdlLMMlBwHrx3tw6mhMg6cHEzU9jiFEwhzkcq2i/GyRWRJMSImNIYSXHOf+c9f419/uUs4D09o5RYRF26A9xLHxogKyfRtWdsNLCJTTPxz3VCY8HwLGWNbc5jQlo/EiCKVFXJxMSLfIvIXAVtyWyS55vjLT4s8ueZMI2yh4UgxIl5ZoaUOFpG8XrsSWYGsEJXyk7MMMUbEXHMRsgKxvaTSNXGCKIgROa6wOA6UbCWZgFvk3n7Kw0ZgGVGLSFmOiF2+7bh4eX8vhsq2kEdELdwJXowo/tx8PkVcc6ZkEUmuOdMw8AeXnIXPv+d8AJ7Vzt8L04ySFQDvPZUrY6ta03j/O5Hn7roQqsJTvIm3i+fjHSloQcTAHyg9bM8isvCbA6fw9UdeARC1iFRyJccmYIllODtutOwGB2n3QR4RVfB23CDRkCoKtETICki1iKgoJjH2SOunxdBxKUbkvUS/2nUMf/RvTwnniFswW3NmECOKK5fjjcc/FsWIDJEtZTuh+48Xh5QxrjWP8a0Ki0gmK8R0aB0sOWjNm8EiUAoWAu93WaAJixkXRCWib8MnJyBooeE4ohuG/x9hzVXlmpPG6HiCiPJU+opl1W4AQouIclT4vB7XkhPJCnZovYoWUZxrLrRs+HzvHyqrLSKqdej/lDWJVSZPAGohxt/tU0NlvOfrP8P3ntzLCha7EavRixElWESxtOmQ/UiWftD1thwKdPk6eEFSmb5NaMmZyorr/Nx8TLZCaSwzS4nc4rbjSK45HSNqGPhLPWWMV7mALCIO1QSR52s4EQ0ha98zlyF0ROQ40U/Z5JJrzuauuZCsIMSI3DCBUiArKLT3sI6XqJ17uUheiQ96wR9kvUrkqgccbQUvRuS6oZvJcdU9c/g1WpJFZDOrizcQ4zAMryDshLYoWSHaVDC+xI+Xqe6dm+4ZCdFIbI9bhuynQeaaIwuIEkVt31VHMBWCSNaWK0E0RuQpMzR/k3r+FCzP6uUJrYSxLbkgGRYI72nOFBdCFRsPkCwitkj3Fe1k11yFFpFsTXtjVVlE4Tl7B8so2S6OnBoSyAqydZdmEfHH5UhCSbaIciwWw78n8BhRkNCqWGeodJA4DtEaI7x2pA+7/ZJgHGUnTJ6md09bRE0ExwVmT2nHlk9fgXlnjAPgLRwtUuM25QSRajORJkTUYM5WsR0HnZPalGMg7Z40OCIr2I4bsOY4WYFrMbzoqdgeIpycJLho8SYrQKBv+y+C6iWMqzUHhGQFABik1uFutKS8nN1tmaYgrHjF5km+RZSXgr9jW3IwTQPj23I4OViOrbfl7eMJLnn9GyKLyB8fp2EDFbDm2H6u75ozTcNvr+1GWHOEghwjqodF5Lo40DuIWZO9+dWf0PMnZxmwTBOuH1dqYYlsNO9CyrUT7pPBIlKV+AG8+nEqD0JL4D6l/bNbRHGKDgdfrOma+kuha65kiyQPID1GJNaaC7/n9O1chDWnFkQ8RkRFh1XrjOq7OPr2/3nkFXxL0Yeq7IQKa2ARnQ4xoi1btmDevHno6urCunXrIr+7rotbbrkFXV1dWLRoEZ566qnUfY8ePYrly5dj7ty5WL58OY4dO1aPoSaCAsvnnTE+WIRzphEpx6EKIsol38MJKFKDqYROm9TcinA8YhH5rSRsT3h1TR+LhZ0TvXHkTCnPJY6sEB6fXrbABSix5ijAqWIjAX4ekaINBICg6CnArQQFy1CKEXluQEZWcMOKzVQuf/aUMbjy3GlB3ghVmZjQloftuMJ9kC0iqhkmU3cHy97iSwsZt+IABVkhJkYU7OezFk2fMUnxNr6Y8QVItoiqISuoas0dPDmEmRPaYBjJFlHeMoN52jcksubongRddQOLSMojSosRud59pJqNvYNqpigpMAGjMiNZwVAICxXRhT8vegf6h8pCfqB8LWm15myXV+IX83fkygqkqMax5niMiIoOy2kigFpA0LX9atdR/PPPXo0dL6HMcuZorpYdV5gro841Z9s2br75ZmzevBnbt2/H3Xffje3btwvbbN68Gd3d3eju7sb69etx0003pe67bt06LFu2DN3d3Vi2bJlSwNUbpM0C4V9LweeXXXVA1P1EEzTnm9y0xvCFXoUwRuTtELLmvITWhz7zViyeNTE4R1ESOKrKCvylppeNAvy04FO1CNcN6aMq6mq6ReRdN1leXiWKZEFkGoYw8R0n9KWTa258Wx533XAJLvKvndyU9JcvcHJsx/KtO1lTHio5aFX1d/G3i9C3BdYcd3eGLj2BrOB4JZdk1hyBztuSYhF957Fd2Hc86mLxxiF+tl0XB3sHMX18KwqWmRIjCt1aA8WyKIhYoiPAY0Sia67suBELlO9HlTjoOcrEEsKnrzoXbzxrYsWuuZLtRqyLv9nyEj76r78SvuM6CM3N/qLN6NuO0rqLk0M/7z6M5/acCOatTOUO8odM0TKieyUXt/ZK/IRCMReb0Br9zmO8lfH7dz6GHYf61APm2/uKseHHwKjwLbeIKq3wUStqPtvWrVvR1dWFOXPmoFAoYNWqVdi4caOwzcaNG3H99dfDMAxceumlOH78OHp6ehL33bhxI1avXg0AWL16Ne69995ah5oKxw1zPmhyWwr2ikpTyUnfhaw502/ZG7pvypKWzHF8QLSIeB6RDBWNPLCISk7g/xVccySIBsSCkjJrLs4ictx4zZ1K/ACSRRST9yAktMqVFRyRrEACjtxHlFMVau7xrrnAIpJdc2UbLXkzYs0G7iHpONv3ncQn//0p3PHoK8KxwjwiBC845WC5rkeFD8cS/h9xzSnu69G+Ij6/8QV8+FtPRH7zzulGti/ZLqaPa/Es5qF411zejxEBXuyGu6CD4HoQI/I1eStqJQwqBChPzizbbkCwkQvUEt567jRcMXdaQK/O6por227EhbzjUB8efumgNJ6oMsZdc2XbjSTnJllEX/jh83j5QG/AyBQKj5ZD+rbMmivGueZMQ1DQVCV+ALWAePVwH6786o+V41SBLCKLKdujPka0d+9ezJo1K/jc2dmJvXv3Ztomad8DBw6go6MDANDR0YGDB8WJRVi/fj2WLFmCJUuW4NChQzVdC3ejcItoQPKzc186QbZwKEZU8CsrcLObNJ4NN1yCeTPGCfvJLjOeRyRDRSOn8wyW7YDaqrKIBvyXkBYaHiOiYKmqQ2XmGBExyRQWkZwrIpf44ay5Sb4mTYskaYQUL8vFaKQcVMBVdmMNkkVkqi0i2bL611/uwo+e7cG3f7FLXVkBYcHYwCKSlA4zWJgM9n88WYH2PCTV2wvHKn7e77eSn+FbRP0prDn+jLmlLxeKDYqeKmIyKvecwJqzHUzwq4HIxJLwfOGi71GMY4ctoGQ7qaQCQO2aGyjaomtOes9NM77WHAlUpWvOcSMxooL0jCPrhSWV+EkgKwj7+cc5fEo9P1QgwhTdbyJUjeqEVlUyp8zqiNsmy75pWLNmDbZt24Zt27Zh2rRpFe0rg9wqQLhgWKYRabymjBFJE4s0pZxfa47WtCBh1DTx1nOn4YvXLBD2O9FfxK93H8fLB3oBAO35MI9IhopGTos8uSxMU9YGwxfm5GApmPwCa85vdaxyzbmuusRPzrccSRAJrrkYiyigbyey5kKqOsAsoqASuchG8v6XyQreQhVNaLV9soI62B1n+Q2WbNE1J1lEoQWmTmgFxDmURFagsZwcLOPKrz6Kx189Ivwuv0OhIEq3iHKWmBMkuubIIhJdc/KzApIFkeN4tPgJKRYRJ8fIuWtZENfW+6P/+ius/d6zSsJO31A5uOf9pXIkvhJnEbmuGyT75gNBFP7uxYhEck2Qr8PyiDhkiyhnmpnICvLczQIqxkv6F7kFOVlj1MWIOjs7sXv37uDznj17MHPmzEzbJO07Y8YM9PT0AAB6enowffr0WoeaCpc/HHLRGUbEz66aILJrjnJXciblEYWaF48RyQLsxEAJ19zxC/zo2R5BK1Jpy6qGcvwFltt9P/LSAWzbFZI+Tg6UgnGpas2p5qLjAkcVpYnIJUcutEFWf00u0ihXVoi2gQjjDuTSIQEXWkTe9/Siy/RZDpVrrmw7ftVxK/LsQrKCWhCdGioLL22Y0OrFiDy/O1WlELVq+p8/u4LktuHgz/O3R/vx599/ThqruE/PSS+WNH1cq09miS78dM8KskUkuOYoRiTRt6XKCoCaOcdZcyXbwcQ2dcsODhLYtutCVaaHY87UMfjjZXNx5bnThGuS8fBLB3HPr3YLrrMBySsAAM/vPYlHXxY9KnFLcZEt2mFld3H+BaxZtg54+6pdc2RBU+1H01QrvAXZ5VdFJ9Wy7cBxQtdc3jJxasgWkqNV5x5O1Hy2pUuXoru7Gzt37kSxWMQ999yDlStXCtusXLkSd911F1zXxeOPP44JEyago6Mjcd+VK1diw4YNAIANGzbgmmuuqXWoqeBZ8IFrzjKCas8EtSCKZ815MSLve2rFzWNQHMfZi8oFkSozXlXqg6+dlu/+oYX/Cz98Af/Hrw4BeEIvqDXHcm140VMZjuPi4MnBoEMsodV3IQZFYpMsIikAbkrMRMcl94YRsONIAJFAGpcQI1KSFUyxgCsJj9a8GSgNhDCPKF4r564ysggcP75BtF/SPPkzDiwilSBSxlrEMew8LAaj5Wlx4IQ3runjW7yiuAqLqN2/hzIVW82aIy09dClVYhFRjGhMSw6WaQhJsjK4ay6NQDhpTAH/c/m54XuU4knhlmMQI2JkBcIty+YG/xuGAdUM4KWP4jucShaRFHNTWUQAgtyzWIuIBBG51di6I7N740Dn4OEHmUQiK2fDjZrPlsvlcPvtt+Pqq6/G/Pnzcd1112HBggW48847ceeddwIAVqxYgTlz5qCrqwsf//jH8Y1vfCNxXwBYu3YtHnzwQcydOxcPPvgg1q5dW+tQU0HaLMBccyqLSEWrlDST0EdsBgsSILq+AEDuDc81Rp5LkMU1Z7Pz0NgttgD3DYnJhCcHyyEbyjL9XJuwDYTKLVF2XBzsHcLMia3C9/QStOZEsoLrRv3NkTwiw8Bnrz4Pf/6u84LfS2WPAjtGEkSBRSS55sQmZY5gaVp+DEFV966QMyMv3a92HcNb1j2S2Mr9IGtfHvQjYnRtuu+qNhB0XkKSa06V/BlXWgbwFsFxrTm05r2aZCr3IikLSTEiOW4VV/QUAP72gZfxhOQypHnluqGrqr1gxbLmAAiuuTSygiwMk4qTAuJ9JNfcqcFolYffv7hTGI8qJ4m7F4NYmpTLE7DlgsoKZvAboFJcw5gcxWhVDDmaK2GibDSul4ay7ee7Ma+MrCCMtGuuLo3xVqxYgRUrVgjf3XjjjcH/hmHgjjvuyLwvAEyZMgUPP/xwPYaXGZw1x9lznZPa8crBU8F2Kk1FfjFo4uV9/2ugWTIyABCdkFwLt8yw8GFJ8WJGacfiC2eZBuCE38mB65MDpZB95mvH3CJSaZmHTw3BdlzMnNCGZ/ecCL4PXXMUIwoXZ5ULEWCsOcvAvDPGBWWHiDCRM40gFiS75mjcqrbVJcdr9Ff2Fxwv6Cy55sjVpAi+82ctw+u35AgWUVD01BEb/dlOSOcm0FrBn52sLXOoBNHe4wPonNQOwFvop49rwcevmIPbNr8Ixw2fRRwFlwRRwTKlGFHompMrAcQVPQWAH798CNPHteBNc8LeQIJF5JNzxhRyya65QKlIJyvIzyzONReMhwkKaiNyfCCqaLQyIpKqQzEgKosyM5WeuUxWiBQ9jbGISr7bzGshE70mOfbEleIkqj5H2XF8hSm8Bspf9ITvKHTNnU5wJHOV/v7bx96EP7jkrGA7NZtFPbE8soIrUJZpovHtCHwx8ggA3u+fuercyDlV9G0elCXCATVsk335JwZKATuPWgJQjCiOrLDvuGcJzJwoVoagxS+gbzN3lazZRRJapXvusebiLCJyzYkxIu66LJUdgdloGgYMiT0YUsejZIUknDHeswQPnuSCiGJEUfq247hSjCi6gHhxl+wW0Us9vcH/juvi7Cnt+PiVcwQWlHwOjjbummNadCEX1a4D+nbgUlIv+j0nxHbufL5T8L29xYolKwBhzg6Pdcbl20ViLDGkgvD/8HuyiFSxLe7eMgy1QORWnek/a1IUS5LFE9cGQn63+Nyn9A6VhSPTwvnczcrvkF3GnkXkXROxVPMxSsxwQQsiBl6gkr/UM8a34p0LZgTbkcuLI0JWEFxzjqAxJcWIuCCy/FYSu9a9G59ivmuCaoEXyApW2FNILl8CeK3KeekWrzU4T8qM7IL9J7xguOyao4Wf3DvdB09h9tr78Nuj/bGuOZ5HBIQvp+O6frtlE+NacxjbksO0cV5Miujs4ySLiGuufcVyUI+PtolzzcktKNJwxgTvug8xuiwd1kWYFM3p26oYkfzsCjm1G00liOReU4Y0Z6npmirxGgjvIc8jkscU9Klh9c/ylifQVYLowElJEPnjJmuxkDMxppCLrazAx0/uaxqjCrKAUilNcpM4Qlw1CMCziIL5GMPs5W4s23UxsT2PY36b7aCavdQGItIYTxKcvC9QYBElWHlciFQKyiMymAJICsKUsQVhvCMFLYgYSJsFojRuHgNSUVijrrlQE+INqqKsOfER8JcnbZJxDXZMwYq45toLlr8gQkpWM3DujLH49i924bUj/cF3pmEEWnnONGAoeEP7TiRbRC05L9b0yx2H2fnU9G36KxNEKFcpZ3mU8Ic+81Z8YKlnkV5w5gT81e+ej7cSW8qIuuZ6B8tC+4ggZsPWeS4EK/GHj2/Noy1vCRYRISx66vdyckVB4Y3F+ytbs3mpSkYwTsVCKFR4cMNj0mlorrbHlJGislFyjKgtz11zImvOTlCegKhFRPsNsfnUXrACt5ZqaocxopCsEOdeVCWEyhiQyl8F30uCiHsnqCI5oK5PCIiuuaGSg5kT27DjUB/mfW4zNj7j5UGGFpHooisxgo54PTxG5CWjJ1nqcqIsx4KZ45X78DJeXOnma1BgEY02+vbpBNJmgajGwR+MqdBWIq45bhHZYhtqzyISJyihLFhEKYLIIiaZiSljW4KFkEBMJbnEe2vewjc+9EYc6SviB0/v9c/lacfcdaCKWfScGIBlGpg+TmLN+dq3YRhozVlKZhEhbAMB4R6EQkVMCjxjQqsQpP3IW84JfPmqGNGpoXJA+6ZtZH9/SEc2KmII5S0TU8cVBIuIwMkKOT9uwDPY+Xjle9ISYxGpSCqc2uyqmJ7+Oca2qFtojG/LYensSbjgzAnCPOZMyDBmETIIacFSucF6B8tCrTJy+QaCyHezkuYtFxLm46b7CMRr5hGygmJMXOCo8ogIFDMLXJbMIlKTFZggKjs4c2IbnnztGIbKDv7+wW5h3HEWUTQBPnTdkdBPstRVrDkA+Mhls3HfLVco96HrLDlO0K4EEO8lCSIdI2ogeJKX/HLzxUrlmrMirLlwApYcl7mjSLv0tpMnZDHGnaACTe6FZ04IGD5Ki8gVLaK2vIU5U8cCQJCsmzMNGIb4oqg09J7jg5g2tiWyQHBtuq1gCe4L+X7RokBac2h9htddZkmBSVAKosEyxvpCmM4va7dhEU8zKKyaBXnLwJQxLTgouaIAom9TcibRt8UXnawj+UUvWKbQQC48pkoQiedUxTWB0H0poyVn4bs3Xoa3njstcOMBHuU7vE6RrWk7TrDoxTHU9rN7QoJ+iOXbcAtN5TY0jKggKsTMAXmRViltXBDZCRbRmb513yIpN/EWUTi3B0u24KYmBS2S0JqhDQQQutct08CM8S1Yc+UcfPbqeZExBLFRSVFRMe0Az9qkd9T2mwCqCFMTg2r3WhA1DGIekfcdzwcimKaikkJEwzGD/cp22GacSp4E2iXbr2CZQrHStGrMPX685oIzJ/hEA1d4ccYUcp6AcsQ6Um0FK7DqSDskNw2n6araS58cLGF8W04Y94zxLYKrrlXS7k1TvE6bWYf8HnCNuMTuURKCF5gtNKeGfEHEBFzEImLXeWZMSw4VcpaJsS059CqqWrvMNWeZCDrmcmWdboP8oufjLCLFSsgtIu5ONqRjxwkimdpOmMx6P4WsOT8Iz9zJcTk7+5l7LowRkWLjxYgIrYqW10IeEZEVMlpEKkHEvQD8NsoxonP9MlttBZFqHRcjki2ijgnh/KFYppzQqurQysFZirZfFcUwDPzFivmRMmD8euV3NE4Q5cywDiQVPZXJLQAwgQTRCJMV6kLfPl1guxACeAAnLcgWkfigZBM5NM1NL0EzaBUgasn8uG0FS3h5VBoyx4V+Jer3XdSJn3UfjmhH7S0UIxLbR9OEzFthh9ecX4mZFkP+P4dXOkik8P7XjZcJ2nSrFJswDJEKzq1DgLHm/L/UEfbCzgmJ10/jBEJXEOBbRK2+sLQZxVZwzYWWXyXaX94vi6NiKDluqMzkTDNoFc4XyTjXnFxJPTimUhCJv8e75mIEkZB7Irqcg+sMWHP+s2JVruMsIh4nKkuCKG8ZaG8J50Vr3owQNMIYUcj+jLOKo6656DaDca452SLyFRHKgaN3mViQMoQYUdkWlDB6hkFjTEsUbvR7NMYVxm+oQ2twbYrpGbQukeZMUkytJRBEXlX4UAEM95k9ZQxacmasEjNc0IKIwWV+U1MSFPyFsBQxoihrTjR7iSpa9oORnN5NaC9YQtvroRSL6Iq509B967v83I5ojTXPIvJoy1zAtQSCKGTL5H2GHm9lHNeWwJIsnPFtecHn3yr5/w2IC0XfUBmvHDwVYc3JdQazxG5o8eX1604VyxjHXHOmwjUXuEj8+z9lTAFH+uITWAk8mC2Dl/ihihZyQmvgmlOQFbLmEcWRFQIrngRRzGIiz2UVeNsCx3HxyqFTAckh7vpfOxJWfZDbkeQt0SJacOYEHD1VxNG+YlBXMXiWTkjuyRojUltEzDWXECMib0ZrECMKrRela465nSlGRKAkaHJDyjHm0CJSXw/FiLiwV9XfjLeI1ASVnBVWLyHWnDxfAOC9i8/EVfNnYHxrdnd1PaAFEQMPLIeBPO8vfyFUlamjgkmcgDzpMY6BxOMsQLaOndyNINcqI4vIkejbbf6E5Iuh5Se0luwMgkiidsv3Qm76J/c2Otg7hKv+7if4x1WLhf3jmIdJkKtv95e8mlnjWvNCMFZujBcEw/1Fp3NSWyZBJPfj4XBcBHlDlhGyk1SVFVT0bZUrlC+gXrkoF7YtuuZk6z3NIlIVN5XLwwQsL8fFfz25B0++dgxffu8FwvE5Lu+aiu88/ho+evk5mNheCAQJWVQ5ySL6yGWzsXT2ZH+xF5lkLmN/xmn40YTW6Ha84CsXKANS3x1SeEIrJnST8QZydAxuEbmumMpAimSbFG/i/Z3I7cbB+xoNFG1BmVMRMUJBJHcGiL9fbcwi4pY0jxO25k20FVqUxxhO6BgRg+Mw11zwUkfZbV6vELVpTeCuOSDUXKiygiqhNa5raxaYhhEp0jmm4FkFD790EH98zzPheSjzXnLRmIaY+R3X7oGC8QQ5ZiAvaqahLiBJrhMahnycNNYg34cWLspTCVxz/jateTH+xmNEADLHifJWMpspKI9kUsxOTGilaSPHR3KmgZ91H8YjLx2IHI9Ayb1c3yALDAj/JsWI/v4DF+LDl84OPtNznDJGXHzoGsu2gydfO4apYwv40JvOEvYhXDV/Bv5ixXwc7y/h+095LEzZksuZZlCWCRDpx6TFc9dcpaw51WacxRdH3z5jfGuEIXvbtQtxwZnjcebEtsAFO4nFz04OloT0gMljCrhuiVcaiCyi1ohFFFojqhgbjWGw5KCvaEvpB9FrDreX6mDG3K+Lz54cuPJtP0YkX3chZ1bc/aBe0IKIgS8aYazI+8xfCFUeUZTOLbr0aMJ4FXbFNhOEuLyPLLBMIxojKlhKbSqoNSYkMJowM8SIABJa8T5s2bIzDHWVBhLOMmuOcKwvPvkxPLe3Ly00RBvnrDnT1wYFFhWjbwPAOVPHpJ4LiObeyLAdL28oZ4ZtIFQJrbKmP3eGx2L8x4dfEb7nZAVybUXp23Rs728Sfft3FnTgrCntwWe6b1PHFoTtQtec6ycI5yPxUwB4/otXY/2HL8a5/vhp8ZfdxIWcIVDqVe49XuKHBHCcVRyxiBTznJe84UrVQMnGG6aNwazJbfjKtQsFhisAXNY1FT/61BUo5Mzg/vzZu87DjW99AwCPqs7TFwzDwFd/70Is6pwQuOADi0jyjBRtJybm43151LfKqX8Tvy8qQSQX5o2ziL65egn+x2Wzg31sJxoPjyM6jAS0a45BlUdkSewXAMryG1GygmiS06LLE/wABItW2XGFOleVwjSiLLsxLblI4c68ZQRmP714hhG6r7K45ihBlCAvAnLjQNNQ160j4Uz3UhaaqlwdGXKVaGKzjW3NCeNszVuChhx0HPV/v/ntXSjZLh596SC6E2rNyT18ZFDHUMt/pq4r+vjjYkRfuXYhSraLn/xGbEXA3YnkahPp21GyQhAjUrjmZCF61Bf2k8eIgiggKzgO+ou24Fbjhyj4CozhD7Pku33k2ErONCNJxjLoK5GsEENHlq5DpejwPmLc7TlYcjChLY/v/9FbAAC/8WNUqudKgmjmhLZgDvcXbcya3B7ZlrvT5BgRjzslWURH+rw5T+w1IPpc+fYy4mJEQLgWUbsJOYUkad/hhraIGORmUUC4yHLrQWUR8Rfmj5fNxbsXeX2V6CHTixCyZsTjAbVZRKZpRLTQ9oIVVE4gdE0fh07fDRUWZg2p5JliRKZo4cj3QiYrmIY64EqFUeV7TojrSioe27c4yzb2HOsPLCIiK9Dza8tbQvA6qDoe3Psc/mLF/MD9FYcksgKAIKGVYlIOe+G9awyPw2EYBs6e3I5DvUOCu4VbRCQMuJuJ5xEFwWd/QVW55uR7fMk5k3HujLH40985T/g+iL3ZLk4NlQWiAe+ISgoXKVS2nywpI2cZgiBSLfq8xI9cKidyvCwWUYwgAqS6epYoLDjo9ntz3vu/bDtKIc+tkTBGJHpGyrarFJr0+9FTZBGluOZiXGh0v370qcux8ea3CL8JhAhO3/b30RZRk8Bm5VgM6eGLJX6iLxJ/MT56xTkB64QWHErsozgF3z5nGhhC1KVVCUzDQKksLgB88SB8/6bLgklPyYI8gY+OEZdHRL9Z7D7JQobyMQiGIb5MvJ05P79KS04D7fvvT/wWX7nvRXzlfQsBhBYRHbKtYCkXeHnxSQtL5aVCoUQgCI/rBIoKb/wXHl9tEQFA52RPQdh7fABvmOa5ulQWEXfBinlE4oKqWizl65vQlscD//Otke2CTH/HY1xOHyfWFrRMA6Zk7ZF7WMX0y1uiRaQS5mEeGVItIpmcoFrcOVlhSIqlCJXGFV4PAuUR5azQHc1zqji4R6M1qOcnuv2KtoMxCoUztIgUgkiKM/Htb3vfQhzuHcLdW3+LfScGA2F4wZkTguOcd8Y44frkqvD0fdZ+RsMBbRExuG40pyVkvYiLSSRYKpEZCDkpRqTKI6D/2xSCIytMI9oqgrtTCG0FK5YlZBlhXChnGvj6By/CO8+fETkG1w5VmlnUIgpfYu7zpxbb8r2uBPQSHeodQl/RDnzsFCMK7m1KjIiPNfl8okUk01x5PyKqK6h0zSkW2Fl+a4c9xwaC7wSLyF/AuMXB3cmyZSnTt73qGdnuceAyth30D9kRa90yoy5Kj4KuFkQ5U7SIVCw3uq28+nZcYmVciRyOONccEGMRKV1zNN7w3pVtRzlXuUURZc0l7xuNEUUtIn6NpGScPaUdn1o2l7VJEZ/Tr7/wTvzHJ94sjsERq8KHMSLtmmsKcLKCTCmW/bNJLwJfzEiLoSCmShDRNjW55hQstzGFHG562xsCjUhGSKgIXXNFRiB441mTsP76JZH9LBbzUWmiMvtPdOWEU062iLhQe8O0Mfj3j70p5mpD0PlpoTnm91UZ15IXLLfWvCXQduUYUXC8lIU6b4lKyHipPBCV8Keq564rKx3eX5UbhHoM7T4aulO5G44qQ8uVFejwsoYrWxNpzeNk5CwvztVXLEesa485Kh6fxqaqBpG3TLH+nzJGRBYRzyNSjzlLHpHompMtIpaOYajvF40FCFml3nfhvXzjWRODbVtZrbq4WnPcEuEgIUExIn6vDCO6Bq17/yLc/PY34NJzvB5QJFjj6O7e/iFzj6eQBDGiBlpE2jXHwAO/MqOEkwrUrDmeZxR+L+cR8RgMoS4xIiPKmhvTYuHPfuc8fPyKOXjjlx6M7CO/LAYTZkkBeZ5HpbSIImSFKLUYCNuJB/eY3bdPvPUNuKxrauwYCHR+ur/UXG9MiwXTCBcMzzXn4MipIYxpybFac+L40wwGOUY0XrI6vGTE0AXpGqI7LMk1N31cC/KWIVpE7Jm25S1fwIX7KMkKGZlmacibXpJt35AdiZ2ZZlQQ5S1DqKtoGGGLjNa8JbqWEmJELnfNxVDlsygQ3CJKqkDA26DLcJlFJKcsvPjXv6Ns1c1d7HKMSDV2fu4jyhgRPddwzNPGteCzV4dxvaANS4Igskyv43N/sSz1Xmt8jEhbRAxiHpH3HZ+c3HJYfv4ZQg2ovOS6I8h5RNz1FW4Tau3VghMNCLR4TGpXZ0nLgsgy42thCedidGzVSxUVRKErj48xoG8rhFrWl8IKBH1oEbXmvSRFnnzalrdQtB2svP0X+Majr0TyiPhYk5CzxNbisRaRGdK3uSWSJIhM08CZE9uw51hoEdkKi0ggK7A5G8Y1YxbvCmNwOcubU33FMsa0KFxzCmXMtsM239z9GEmYVcwb+kqorJCrj0UkN8Hj8ytMuk22JsT32kBbQRSu5JLmJa5U1qlqrPTd0b4iWvOm4CYL2zXEP7+wcWTyezN1TAGHTxWVtea0a65J4Cpcc8LkY7GUj15+Dm562xvYb2yiVRojsmq3iAwjmlNAJVni4gItQYwopE8HL2VSCXpLJCvIUCW0Buw2FqcJEloVMaLMgohp0YC3+JBGyhNv6bu9xwfQc2IwnqyQclovoTXBNWc7QfM4ysxXVVYoWOpnPWlMQSjzxMkKJIi4leQmuOZkqKyQJOQsE6cGy3DdcC4Fx4pxzfUOlfC2r/0YgCiI5EVOtRjznLA0skKkMV6KRTRUtoW5yscTZx3L4xUs2wQFTLSIsgkiHiOa2CZS6WXilAotGVxzADBlbAuO9A0J9S7pb9q+wwntmmOwFa45bnoH5XRISCmsGm9fRPah9STsz8KOq2hktvrNZ2PFwo7MY7fMaGUFfryPXn6OUBPLG5t4jSYjKyQtyLytg+rliLD/GFmBC8sk1lzWl0I+f3+xzNophzEZrqUOlOzQHVMhWUFOaJ2gsIgsX/BSqRqRNUfHUZ9nXGteKCFTFgSRGbWIEujbAPDV31uEfccH8A8PdVfhmjMCoShbRJ5rTjxe3jKw63B/QJMv5ExgKBw7h8o6UyW0yqQOYinGJZQXcmHxWE7XL9oO8mZYZLUgWETp7mg5iVvtko665rqmj8WZE9swk1XoVu3LiQTynAq3iX8nSLCmWTVTxhZw5FTRJ1/4x1XEwUca2iJi4EFIpUUkuZAsxW88HgJENbqhhDwi7tJadclZeNOcKZnHzq0Z1bk//57zccPl5yh/J0FoGrwfUbKv2VTcA4IqRqSa4wNSjIgvLnEWg2oswjFLTrB4cVIFXxwGijaziMT9Zevx2/9jKf5jzaXBZ7mFs8yac5hrDhCLkgLh/IoTtONaxHbaKrJCWSYr+IdSWUTXLZmF913UKfyeFTnLxHESRJnJCrw2XoJFpIwReX+HynakinUwphiqdcD8YufkcaGhktjfigu4tAKrdHz+SiRZRFzpmd8xHr9Y+w5MZpUrVPsKrRgkQcQJE3FoUdSPVGHq2BYcPjXkJ7SSIhqGHBoFbRExqMqlqGJEobAK97ViHqasNQauOSn/AhDdH5W26jUNI0LfTkNepm+bBgvOJp8rENQZLCIvnye63ZB0L/ihsjJ4ZEE4UCwH9840w1gWH1N/0Y5U/g7HKh5/xrhWnM9aL+ctU1hEx7eJr1DZp2vz42aNEQFeEipvKhghK5gyWSFaa06OEdF4K9V4c1a8ReTRt2VrxRSsEKr4bhrR+ZxU4ueD//wE09ajFtFASfHcuID3rbCi7QQEo6Gy44/XG1+LQFZIX+hzljiHVe8HHbNNMXetFGsqiYnJC8fGIWuMiCyiKWMLEcVFW0RNAu7mmNBWgGUaAltI7roo0LRNcTEgyK4FFWuOJhhfLLM0heMwjTBO8qVrFuB7N705dR8aW04x9riAN20fxNIUL5UsROIsIooRmYrzZ21VbDIXA+AJmRy3iEgQsSTbgZIdGwuTrYaCFCzPWYbA5FLnEYnPV4wReX/jFgxZEJE2/Jnl5+JdC8+AZYhVxNOUJ/650hhR3jQDN2EkRqRwzVmmIeRq0XxsyVmR90KlffNNaF+ZrBC2jJddfeLvgKf0kZUyVLaF8fLF/vo3n433XXQmPvHWMOYrQ8Wak9GiiBGprk0ZI2LzXbaIsgjK0DWX/N5MG9uCou3geH+JsebUCsxIQgsiBp6Tsfz8Gbj/01cEHRcBCAscIAsT8TeCbO4Xy1HWHE0Ann9TaYdEPpYzJ7Xh4rMnp+4jN+9K09oI3uJvRBZcgrLoqeJ4gyUn9uWqJHDKxzpYsgXLlecREQaKYYwojTWnqqqerL06QR5RMD6VRRTjehzXmvcFZdi/CgA+dsU5mD6uNSBBEGQyBBDVnJOo9klIs4jkuZ2zzICS/5HLZmO1X2STC4Dfu9hzE6oULfk6TCM65jjXnMrlWbKdYGEeKjvCeCcyJum41jz+7gOLY2MzdF6Vi5UjICsoSEdmzHwIjx/vmovLeeNoyZkoWOnVs6f4LsKDvUORmKK2iJoEDsuCt0wDXdPFRNAgDkQ+ecFtF3UvAdEFNahuLZAVyDVnRb7LChUzKw15aQKqrDQAWMBcU952CLZXKVFK+rY/pnXvW4jPLD8XgCc04pIsK8lp4MfoL4aaL6eZC665Ujk2RiQPR1YIqEMrQaW9ynlmYqXy6ILJQfXhiHosuxCTyAokn2Itoopdc2ZgOUbyiAz1ecgieueCGUHwnl/ruvctxNOfX55IYeaf5blMFlJk20DAi7RsmouuK87pie0iMy0N8liUJX7861SlYcS5alW/y3OKBOg0qcwSx2VvmIL3XJhObpo61lOsi+WwwgOduVKLuZ7QgoghLuuZkJctImFiivEjeR+C2iKKau1ZupNypJn+KsiVFUS2X/jh7jWX4l5WQDGkbqurassvomGEwnv6+BZ8YOksAJ6WGlu8sQJBxO/lALOIeGUFrqUOFJ3AhRap4hyxaKMauWARtapiRLIVhMj/cTFAKt1C7rlAEDEFSbaIaMhUFy3itgpiAJXNKa4MRcgKpqEgEhhC4F/ucgx483rSGLUQkKetZUbbhwS5b/JzofqJ0pg4W0+wiBKsHxVykgs4K3072J6/n4pHL1pE4r1ecvYk/NXvno9br70gdnzvXHAG/u66xbG/E3jfKd4RVx7DSEMLIga5UrKMnBQjUlVHkIWAHOuQA/R03Lwl+twrJSsIY8loEUVqzXGLSIqD8DJBtNBxi4Nj5sRWXL1gBmZO8DQ4L0ZEFmPo0hks2bFCs1rXnOtyy1VtEQ0Uy35zQoV7iH01ZUwhEgOSKyuoLCK5I63SNRdrEXnHO+kz51QWUans4AdP74FDva0Ci0hdFieJWJIEuY09h9cKJV7pylsmq1yf7byROJJhRC3UmJYhcfeVK0U8tjepRosoSQFTx4jU80H13QQpAd0wDHzkLefUpX331HFR9h4pD9UUHa4XNGuOQeVv58hbplBtWmWqx7kSCCVl0VOvCkDBUmtvWaBy/6RBJiuokncJosVFf9UWUUvOwj99eAn+x7e3Yt+JQYE15wmi8AWIE0SVZHnL11tg7CVVbkd/yY4tPknjvLxrKv6fotad3KF1nExWcKlVeHhs1SIUJ4jIwuIWkWnwih8GtrywH1te2I9SWewAywt0coQWUYWCiF2n7Jp73xvPjCzmoiITuifjyvTIkN8d2cUJhO9THH1bVvwEAhB3qcZUG4lDJI9IcS8D1lxMYjrR2+PmHVmUSbGqWiEUnvWHMeotoqNHj2L58uWYO3culi9fjmPHjim327JlC+bNm4euri6sW7cudf8jR47g7W9/O8aOHYtPfvKTtQyxInAqrAp5Kz4IzZNCxX0ki0iRPJc3Db/hGnNh1BAjqtQ1p2pMJ7/QKkFnGsnnCnzQTLOVg9xxpecriRHFaeZr3zUfX7nWawvBcztcF+gr2okU4jhh7pX4CX+Tx08WiuB6ZYei29iSQFYAmCCSOrzy/w+cHBSUJydmQaHnW3mMKHzO8vNYc+Ub8PtLZgnfCXk6rO10VqVK5ZqT30fudhX3zWARsXGMS+k7JSNSWSHBIoor1cXfgbhzAFEru54oMLJN4JoLWl2MUtbcunXrsGzZMnR3d2PZsmWCkCHYto2bb74Zmzdvxvbt23H33Xdj+/btifu3trbiS1/6Ev72b/+2luFVDE6FVSFnmrEul4CpZcj7iF/ENcbLWyZrMlb5opH2kqgg+9v5MeRJqXJLqILJqn24a84wxAUhrgdTVvo2EBUadM/PmToG8zvGK8/TO1iOac4mHkOGXH1bpsDSeEzpM2FSewHtBQutBfX1EVnh1JDnmnOceEFU8vvK8KrVgOrZRffNAhJgE9sLmdpHRCwi/2NW15zSIpIFkSnOWfncssDkn/mcqtRNKbe7V91LlfXNIRenlUHvxYS2ytyGlcAwDKEEFuA1PwRGsUW0ceNGrF69GgCwevVq3HvvvZFttm7diq6uLsyZMweFQgGrVq3Cxo0bE/cfM2YMLr/8crS2xrNEhgPpZIUEiygmRmQYhnJRFYqe+vXLeKWDrH1jVGPJuuCQ9qjSMqOlb6JjV7lOVGMyDTHviGuXcT2YKlkoIouVwprigh4AegdLyryJLIuFTDTZte7d+PsPLGbHCN2X8rHe/8ZO3P/pK2Ndj+Mk11xZLhHEBZHtCMpTnGuO3D6VCiK6X1PHZlsYc5JruVKShNo1J40pYM2pCRkRi6igds1VgzSvw+QxBYxtyWH21Ggbcb5/Iy0iIHQd0jDKUhyyEahJEB04cAAdHR5lsKOjAwcPHoxss3fvXsyaFZrwnZ2d2Lt3b+b9RxLkj4+DnEMiEg7UQVRvv+h3cozIWyjV2l4WGAqLJQ3JCbri1DAUi6EclJcRuua41u79RvdLlYVeKSJxhJgxcZfJqcGyUgOky4zTDr0F3YxsL1ukcXT6Qs7ErMnqhQoIm9nxGJFK4QG8AqtceUry9ZtmlFyQBnpGRPlN3V7yEARU9czJyeJnS4gthsel34RtTcprkwRRrnomamR87JSqRXtcax5PfX453j5veuL+SXMLGAFBJDXta4YYUaqj9KqrrsL+/fsj3996662ZTuCynAdCpdp+EtavX4/169cDAA4dOlT1cWicSQtrzpKT2thvtDgr5no+ZwJFsTEXn8iXnDM56Cgqu66yQtDAM+5OCwQviUOIc1s5vIstc7+ox0SCKLQk6T4XLK84pZyxXw0igijm/rXlLZRtFwMlG72DZeVikqa15nNijMhSWFCeBcJdrxkvBB5Jo5AzBdZcnPJTskWyQkjfjo69KovI335KZkHELaLwmWdVrCJMOOaay1smhspOQJCYKJEN3nXBGWjPW9hx6JTwvUDfrmKhvWLuVPys+3BkfNWkHXAFTgXL9Nxmw10FmxiQtA4TEaVSAkc9kboKPPTQQ7G/zZgxAz09Pejo6EBPTw+mT49qAp2dndi9e3fwec+ePZg5c2bm/dOwZs0arFmzBgCwZEm0m2hWkKae6JqTXmYlfVtpEUUnFt/3w5eeLWxbKXUbkBl82SayTFbgp1VbCwbA+uvwmnMq8BpyNDy6z3SNtfRgCs4jC6KYF/lT7+jC/pODuOPRHegdLCm3S4un5KVW4XLla/qfP/JKFa/xrMyPR1YQ44kEzzUXtTbj5lu1ZIXMrjlBkQktomrJCjkzTJgmQbSocwI+8dY5OO8MMcl6UedELOqciM/85zPC93x+VRoXAoBvrV4aFOflj7GaY6UqOZY57NYQEN4Tej8/evk5KORMfPCSs4b93HGoSfSuXLkSGzZsAABs2LAB11xzTWSbpUuXoru7Gzt37kSxWMQ999yDlStXZt5/pECB3qT5lZMC1bwnj8q9RUiLEXHkTSOzIOEQ/dfZ9pHziMxA+1QXKZVdC6lkBSFGRIulWOm4lh5MhKyuuQ+/eTbecd4MAKjaIpLnQLggiUpJFu05Du2FXLD42baY28bPzdu6A+G9jbOIKnW9UOwgs2tOqm5tsfmUBSqLKFoPzYgIIQ4518Zj73n/V/ocaH+yFMRnWvGhgvcnTohZpjEigiggK/jDKORMfPTycxrKmqvJL7J27Vpcd911+Na3voWzzjoL3/3udwEA+/btw8c+9jFs2rQJuVwOt99+O66++mrYto0bbrgBCxYsSNwfAGbPno2TJ0+iWCzi3nvvxQMPPIDzzz+/luEmgl7iJO31neefgTPGhwQK3lk0ifabFiMSts2ZmVlGHNWV+BHJCqpMeA7v3rjCopDFIuLHlgVRHMOoEqTV9+Mgwdc7VMZMsy3yu9wiXkaOKQqyFRT+L5YBqtQSoXwTwLOIxDb0TBAF3XS9z0ntAiypw2gWUMHTrBaRmNAqWjNZoEpSDZWj+Bgsx6evmos3nj0Jt9z9NADvXlAfIss08Nmr5+HyDC3o08ZXTWA/yWtCYx0JQUTvQDVW3XChJkE0ZcoUPPzww5HvZ86ciU2bNgWfV6xYgRUrVmTeHwB27dpVy9AqhpvBNXfV+TNw1fkzgs8WEz6qoDVB9SLGLfZ5y8ycAMiRFkhVoSDV7Qosnhh1L9DomDsq6VwBPdThcQzvLwnnuOS/ShChbycsfKrumcKxUrRn3uJBReP2tol+rgSmEeZ22I6rjEUCXo8dgAt5f5sYC7xSEszxfhJE2SyiOLd1tYLIYTlUeclqj8PE9gLOmTJGGEfOMlC0vedw89u7Mo1FPT72fxWLeNBsM2bf9hYrKEo6nCAmYTUW4nBBV1bwYTvprjkZXMORF2kO1YsYJ2vyVSwYfCxxY1CeK6CLiwtr3MKhcpMknSsnCKLwf36Oeggi+cUuJNy/9hQ6b3CNKd06AZlJKB6jGjo9355yO6IdXqMWURh/S7KIRLZfFlDl7cxkBar35leBpoKpWQWRPJW4AhOWoUo/jiy4834folrpyfx5V8MwS3PNffX9Fwa1BocT7fnTzCI6nZDkX48D7+aa5NJRuebi8kjyOTPzi8uRVstKBbkNRJrGJguiVLKC/1vZcYMik0QQoGPVwzVXkUVUqM0i8o4fdbEIQslArPDIAss0BYsojr4dNp4ThbzquiaNyWNShayo4xW65oIGa1TCKRBE2a5ffo5lO1Rgsrrm5G281Ihs1lT6cdXnyApVsWSO82fGx77qiTCPSAuipgO5NSphOHFWWFIipGphiFuAPRdKbfTtrIJIzluypJdeRhD09Y9/7oxxOGNCfNIxbec4Lv7qmgU4r2Mcrpzr+eepm2xdyArS5SYJcrH5YPQ+pcWIvP1UMSJuHcW3gcgCywzL9UTo2+z/viFbGAe5PVXX9S8fWVqx0J85sQ2HeoeEis1JoPtC9596KmVVrOQFWnDNxRQ7VR5HuF/qElbVoNYYUZAT1WBLRE5obQZoQeTDzcCak8H7EiWZ3dEgbLyW6MWIqjH7K1/4gjwiaWGNjxGJAuv//MFFiccPEuZcF+Nb81hzZdgBk7R3mb790cvPqVhzV7WTjt3W8orLem2k4ysryAvNH1xyFu59eq/wm9gCXDyGFfNbFliGEVhEXmFYNX27T7KI4kr8AMD0hF42cfjm9Uvw/N4Tmd2nJADp/pcUdRWTIN+nErOIVBXi448jWkT7Tw4CACaPqY0IUDeyQuPIaQDq44WoN7Qg8pElj0gGN7VV+SQEWTi15qOtkwm8wkIlqKXoqbywxseIIGyXdUwkdDjIbSO/FJ9/T+XMSFmepN2/ca05HOkrVlRr7rb3LcRt71so/CYQEhh92zTEe1TpomWaIWtObk0iWkSeIIrUmquTqjttXAvefl723D4SgKFFFCYvZ4H8TtiOEzI5JWZnEvgm/F58/Io5mcYRe9yYsk2Z9w/mVmMlEb1zRP9vBmhB5CNLHpEMlWas8v/K3yUlcZ41uT3SljkLqslbmdiex5xpY3DujHHCfmkxoqwLHa0/jkoQ1dE1p2rnnQQSRMoYkeKZylCz5tgxDDFnp2LXnMHo25JFxI8lu+b8W1pzUL5aBB1/JYuo2oRWTlY4Z0o7Oie24Y1nTUw9juzK/PsPXIhJ7QVMH19b7cpaLaK0OoYjBVp/hrQgaj7QYllJjEjII1JoyQR50iaZxl9PcXfFjoUvhBkVrta8hUf+5G1sv2SLqFIfNy2g5QSLqHVY6NvJ4xvvEyeqqTUHqCut83nDKxyrxpcGIY/IcWMrXpwaUrvmqqnMUQ/IrMvKBZFsEYWMwbZCDv/gW6SVHCdnGrj2os5M+1Vy3KosoiZxzVFF8qGynbLlyKHBt6R5EFe5OAliOwTvO5UQkBeiloRCn3ILgayoxRUUHEPyx0d/94+f8SW0pAWSgxap9roktIqf01xBVOE6qbJC0kKjqqLBD2UZBrqmj8Xc6WODz5XAMo3gntlOfEKrfO6Q+dmY1zonxRyvf/NsXHLOZHzwTdlKx8j33Gu7Hu/yjj1OHd4F5XFjFIJK9280WYHYsto114SozTUXH+QGogtlawXdR7OiVm3N28/7G5dQG15jtuPRdqoYEX1XjzwieeFN08CpDIwqqB8XI+JQxYhk4oJhGNj4ybfgv57cgyWzJyVfgATLNDBYSk9oJUQqmzdooQvICn6i9LRxLfjPT7w58/7ytOOMwWoUxEr3S0M1KRIcMtmnUaDUEe2aa0JkKfEjg2vPtJtKCHAhZTtuXRZfGbxib7UvH+2XahFl7S/DWHMySo6arFANZHmS5poji6iaDq1AmOwaGyPyP7QXcrj+zbMTx6KCaRjwPZewXRcFMy33SfyuUTGiII+oSotMZRGZCe9V7HGqSGXIdFzFM65sf7o/jRZE5JprHkGkXXM+spT4kcErKxBrSimIJFprXHvsWvC7izrCcVWpcQUJrSkxoqwWUVBZwVbFiLyXoD4WkXi96a65vHI/ID2pl/8m3mYeI0o8fSo8hcW7P2XHlayteNdcML4GxYh4ZYVqkPTuVHJPq6lEX+lxq3nHeLpHI0GhgWaKEWmLyEc1rjnu8w01t+h2NGkLlonBkjMsrrnp41ux+Y+vwOOvHql6oqey5uhFyhoj8ndQWUTkRqqPRSQuNqlkhdb4fJLQ6ksSRGZkG5k1Vws8QeT978hkhYSyRPL4RhpBQmsVRXsBtbBJq0agwnC55kRXbPX7N41rrtQ8FpEWRD7CWnPZJ4lhGEE5l6QYEU3aQs4CUK4LU0yF+R3jMb+j+jIhaW6QtBYJMrr8YP15Z4yL3aYujfGk4WShbwPAYCmqEWa5RmWMKIa4UA0swwhYnHJCq+rZyF+dLq45/l0lypXQ4LGeMSLu8qsqj6jyaxkONKNrTgsiH2GJn8r2s3yWWxLbygwsIt81NwwWUT0QUtBjfq9QEL313GnY8ukrMG9GvCBqqUM3SvnFTqMvkyDqL6oEkfc3raq4YcRbQdW6pgiWGVZWcBwxoTUprkVoFH1bTmitFElKXCXvpZBHVMd7UXsekfe30TGiDr8s1xVzq2uHMRzQgshHUpvlJHjlXEJtKSmPiOIPwxEjqgeSmvsBrNZcBatCXBOzv3n/Qnzr5zvroh3KL3Yqa87PI1IJoiy15uicca0eau06a5rcInKU9O28FVa3lg2QhllElhgLrRSqYVfjzpKp9PWCcNwq7nE1DMDhwPTxrdj6F8syV1UfCWhB5KOaEj8A5RCluOYqqKzQSKRZA5VaREn4wNKz8IGl9WlNLI8nq2tuQGkRZVv45KaA/BnX+nwtI0wCdly1q2lsSw7H/H5BzRMjIkFU3flVjNU05UgFOaG1Xqhb0dMGx4gA1Fxlot5oTtW8AaiGrACE8SHaTTXHooKoOW87jTPuPWmWhDwZYauAbBo5kRWoaKh4LO9v2kKTN02pmkL4W63P1zJNobICX0xpXGNbQx1SXsAb9XhIANaTtZdEAopDLb2gkseiVjyy7+/9bbRF1IxozhWxAagmjwhAUPI/MaFVSuwsWM1qESVbPM2SByGDxkOxt6wJrYkWUco1WpYhxG74wtRSq0VkQqisoFoAxzCShzzUSudwvVArfVuFqsgK3CIaBqEI1OaaazZFrhmgBZGPavKIAN9FkzGhddDn7VdLbx1u8LwoFZrJtcBBLzaxEdP6OY1NICsEcbAKY0R881rJKHKtOS746f9xrVwQkXBqrIIjN8arFefOGJup5JIMUWAMUx7RKE5obUboGJGPBTPHY8dXVqDSKWIahlDiJykpjzTwemqM9USa661ZXQtBYcw8WUTJ46NF/L2LZ0aPlTGgzLvyAqIVUqtrzjTCWnORhFb//zEtUYtoy6evxIs9J2s6dy2QG+PVgkf/19swZWwBJ4I4WPZ9Ka3CcetM32aHqkYZC4gwTabINQO0IPJhGEYkHyULcoFrzvusegdp4hFvv1AHyvJwoN707ZECUXRJEKUJ+rxl4vkvXq1Mps2qteZMU5gvgkVUs2vOYGQFtUU0tiUaI5o1uR2zJrfXdO5aELLmap/f50wdAwDoHfQrjFfBZnVct67Wu+F7PtwqBRzNF+2ai6I5V8RRBIoPJbmt5HnX6MZYaYiPESX/3iiQoM/qmgO8hVzNcKS/6RZRXPC6Zvq2EbrmyrYjsvMUgqhZXKVyh9Z6IOvziOxXZzdhOJ7qlbFKiwa/nqBvSY0gt1ySW6vShMtGwQ2SepNjRM3mWqAFsC1PrqHqxxfW20uxiCyxXUc9WXM5lkfkuFJswlAJoppOVzeQAlBPRcsKFLzq9qu30lQLc7RZY6zNAO2aqxGWYQjxAtX8vOUdc3G8v4QTAyU88tLBpnXNpVHYm9UiMgNB5MeIalgIswbHc8wdK29fF7KCyxJamVAkoct7WjXLwkZjq+f8rnbxDirJ13mueuNxq1LGyBJqdo9II6DvSI0Qy/uorYVJYwr4+w8sDjT1ZiUrkDso7iVrllpZMmi8Y1pyKFhmTeOjXdMWC8s0BYFc/8oK3v+OI+WvKJJGm0QO1VxZQYVqElqB7OzHShFaRNXsq11zcdAWUY3wLCLvf49BFz/xqSRLPYK5w4Gg8OsoyyMissJHLpuN919cW1voUNgmbxelb4f/11o/zzIMlIM2EI6SrMDnUPNYRPV3zU1oy+O9i2fiknMmV7RfaBHV912rpYK2WaVQfT1AC6Ia8cl3dGHKGK9mEy/1owK1x843qWuOYkTxeUTe32Z7kWi8Z05qQ8eEtpqORZeWtoBddNZEZS4PULvFaJoGHNcreCqX+JF7W8nnbiQmjyngfRediTe/YUrdjmmZBv5h1UWV75dRoagU9SErNMfzaiZoQVQjrll8ZvjBSJ5kf7VyAb5y34t4U4Xa3UiB4hJpFlGzvUj11H6zuk/++poLpP1qPnUAsnr2nxwE4LXcln8TCqE2yeOwTAN/94HFjR4GAN7gsN4Wkfe3lurbzfb+NAO0IKojfv/iTlyWoA2+YdpYfOsjS0dwRJUhJCvECCL/nW421lx7IQfTEAP41SK4xkoXsDreElqodh3pAwDMmhRaeUFdvRyPETXX82gGWMFzrHOMKGPCswrDxeQ7HVDTm3v06FEsX74cc+fOxfLly3Hs2DHldlu2bMG8efPQ1dWFdevWpe7/4IMP4uKLL8bChQtx8cUX45FHHqllmCOGW69diLfNm97oYVQNx0lmzVFp13r2eKkH3nvRTNyz5s2JnVezotoYQD3dY3SsXYf7AUBIUqVFLM/ddc31OJoCad2Gq0U1JYcIzZr+0AyoSRCtW7cOy5YtQ3d3N5YtWyYIGYJt27j55puxefNmbN++HXfffTe2b9+euP/UqVPx3//933juueewYcMGfPjDH65lmBoZQe2p69mPaCTQXshVHMyOQ9Z+RDLqKYhIm3/Nt4jOnBhaRCqygtawo6j2OaahHq65ZmOdNgNqEkQbN27E6tWrAQCrV6/GvffeG9lm69at6Orqwpw5c1AoFLBq1Sps3Lgxcf+LLroIM2d6dcAWLFiAwcFBDA0N1TJUjQxwMsaITuc0iHyw0FcqiOo3BrrPOw/34YzxrQIdXFUxQLvmohjePKLqlLFmaYzXjKhpSTlw4AA6OjoAAB0dHTh48GBkm71792LWrFnB587OTuzduzfz/t/73vdw0UUXoaVF3U1w/fr1WLJkCZYsWYJDhw7Vcjmve5Agis8jQuLvpwPeOm8avnTNAnRNH1vRfvUUBrR4vnakH7Mmtyl/K1hmICz1uhbFcC369aisoAVRFKlkhauuugr79++PfH/rrbdmOgG14ObI+tK+8MIL+LM/+zM88MADsdusWbMGa9asAQAsWbIk03E11AgEUYx60qysuXqivZDDh988u+L96nlLOFnh3Qs7pPOQRWQib5ko2XbT0LfrgT/9nXmYNan2wq2G4T2TeluLpmFUPf9fD4pctUgVRA899FDsbzNmzEBPTw86OjrQ09OD6dOjgfrOzk7s3r07+Lxnz57A7Za0/549e3Dttdfirrvuwhve8IaKLkqjOiybPwN3PLoDbz1XTbig8vraFRRFXckK/oo1VHYE6jYQuuRyluHHiU4vQfRHb+uqy3EswxiWUjq1CCLtmotHTU9q5cqV2LBhAwBgw4YNuOaaayLbLF26FN3d3di5cyeKxSLuuecerFy5MnH/48eP493vfjduu+02vOUtb6lliBoV4I1nTcKude/Gws4Jyt/NlDyp1zPqKQu4xizXbVswczxWLZ2FN541KSAsnEZyqG6wzOoFRhIMo3qLphbG3emOmgTR2rVr8eCDD2Lu3Ll48MEHsXbtWgDAvn37sGLFCgBALpfD7bffjquvvhrz58/HddddhwULFiTuf/vtt+OVV17Bl770JSxevBiLFy9Wxo80RhZplSNezxgOiwiI1iVsL+Sw7v2LMKEtj4KlF7Y4mIYxLKWoanPNaYsoDjUltE6ZMgUPP/xw5PuZM2di06ZNwecVK1YEginL/p/73Ofwuc99rpahaQwDTLP56sw1C+opC/g9TioHRb+dzizGamGaw0OTNo3q44G6skI89BTWyAwjpajr6xn1zSNigiih1hBZS0Y9yzqcJrCa0SLSMaJYaEGkkRm1vISnO0gQjW+tvWoWF2qFhHwmElJUqVsjhDlMMaJajltL5e7THbrWnEZmxPVb0vC03C+uXIAr5k6ty7EISRYRueaKZS2IZAxfjKh6i6aWXkanO7Qg0sgMbRElY/Vls+tyHG4RJbvmvO2oz5VGCMswhqUmomkYNbPm9DsUhZbNGplhaPr2iIDf46S22xPbCwDUSeOvd3jEmvovb7XESXWMKB7aItLIjDeeNUlThUcAuYyuua++fxH+rfO1uhV8PZ1g+snX9T9u7a457d6OQgsijcz4w0vPxh9eenajh3HaQ8gjysUvWpPGFPDJd8wdiSGNOljmMFZW0K65ukO75jQ0mgxWxhiRRjyGK55pGtXnJ9F4dApEFNoi0tBoMnBFXgui6nDW5Ha0sfYZ9YJRg0XUrP28mgFaEGloNBm0RVQ7vvTeC4bluLXEiJadNwMn+ktoL9RfQI52aEGkodFk4E3vWhJYcxojj1pcfvPOGIc/XzG/ziM6PaBnuYZGkyFrHpHGyMPUZa6GBXqWa2g0GcTKCnrRayZ4bSAaPYrTD9o1p6HRZNAWUfNi2rgWlGxdUqne0IJIQ6PJkLWygsbI46u/t6jRQzgtoQWRhkaTgVdWkBvjaTQW7QW9ZA4H9CzX0Ggy8GB4UmM8DY3TBXqWa2g0GcQ8Ih0Z1zj9oQWRhkaTQWDN6eY1Gq8D6FmuodFk4K45nbOi8XqAFkQaGk2G4egsqqHRzNCCSEOjyaB7Pmm83qAFkYZGk0H3q9F4vUELIg2NJoNuE6DxeoMWRBoaTQZNlNN4vUFPeQ2NJsNwtLjW0Ghm6BmvodFk0HJI4/UGPeU1NJoMOkak8XpDTYLo6NGjWL58OebOnYvly5fj2LFjyu22bNmCefPmoaurC+vWrUvdf+vWrVi8eDEWL16MCy+8ED/4wQ9qGaaGxqiCZs1pvN5QkyBat24dli1bhu7ubixbtkwQMgTbtnHzzTdj8+bN2L59O+6++25s3749cf8LLrgA27ZtwzPPPIMtW7bgE5/4BMrlci1D1dAYNTC0RaTxOkNNgmjjxo1YvXo1AGD16tW49957I9ts3boVXV1dmDNnDgqFAlatWoWNGzcm7t/e3o5cziu3Pjg4qF9MDQ0NjdMYNQmiAwcOoKOjAwDQ0dGBgwcPRrbZu3cvZs2aFXzu7OzE3r17U/d/4oknsGDBAixcuBB33nlnIJg0NDQ0NE4vpK7uV111Ffbv3x/5/tZbb810Atd1I99lsXDe9KY34YUXXsCLL76I1atX413vehdaW1sj261fvx7r168HABw6dCjTmDQ0NDQ0mgepguihhx6K/W3GjBno6elBR0cHenp6MH369Mg2nZ2d2L17d/B5z549mDlzZub958+fjzFjxuD555/HkiVLIr+vWbMGa9asAQDl7xoaGhoazY2aXHMrV67Ehg0bAAAbNmzANddcE9lm6dKl6O7uxs6dO1EsFnHPPfdg5cqVifvv3LkzICe89tprePnllzF79uxahqqhoaGh0aSoSRCtXbsWDz74IObOnYsHH3wQa9euBQDs27cPK1asAADkcjncfvvtuPrqqzF//nxcd911WLBgQeL+P//5z3HhhRdi8eLFuPbaa/GNb3wDU6dOrWWoGhqjDrodhMbrBYarCuKMUixZsgTbtm1r9DA0NGrGtl1HMXNiG2ZObGv0UDQ0qkbWNVlT0TQ0mhBLZk9u9BA0NEYMusSPhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDoQWRhoaGhkZDcVrVmps6dWrNVboPHTqEadOm1WdAI4zRPHZAj7/RGM3jH81jB07f8e/atQuHDx9O3f+0EkT1wGgunDqaxw7o8Tcao3n8o3nsgB6/ds1paGhoaDQUWhBpaGhoaDQUWhBJoLbjoxGjeeyAHn+jMZrHP5rHDujx6xiRhoaGhkZDoS0iDQ0NDY2GQgsiH1u2bMG8efPQ1dWFdevWNXo4mTB79mwsXLgQixcvxpIlSwAAR48exfLlyzF37lwsX74cx44da/AoQ9xwww2YPn06LrjgguC7pPHedttt6Orqwrx583D//fc3YsgBVGP/q7/6K5x55plYvHgxFi9ejE2bNgW/NdPYAWD37t14+9vfjvnz52PBggX4x3/8RwCj4/7HjX203P/BwUFccskluPDCC7FgwQJ84QtfADA67j0QP/663n9Xwy2Xy+6cOXPcHTt2uENDQ+6iRYvcF154odHDSsXZZ5/tHjp0SPjus5/9rHvbbbe5ruu6t912m/unf/qnjRiaEj/5yU/cJ5980l2wYEHwXdx4X3jhBXfRokXu4OCg++qrr7pz5sxxy+VyQ8btuuqxf+ELX3C/9rWvRbZttrG7ruvu27fPffLJJ13Xdd2TJ0+6c+fOdV944YVRcf/jxj5a7r/jOG5vb6/ruq5bLBbdSy65xH3sscdGxb1PGn8977+2iABs3boVXV1dmDNnDgqFAlatWoWNGzc2elhVYePGjVi9ejUAYPXq1bj33nsbOyCGK6+8EpMnTxa+ixvvxo0bsWrVKrS0tOCcc85BV1cXtm7dOtJDDqAaexyabewA0NHRgTe+8Y0AgHHjxmH+/PnYu3fvqLj/cWOPQzONHQAMw8DYsWMBAKVSCaVSCYZhjIp7D8SPPw7VjF8LIgB79+7FrFmzgs+dnZ2JE71ZYBgG3vnOd+Liiy/G+vXrAQAHDhxAR0cHAO8FPnjwYCOHmIq48Y6WZ3L77bdj0aJFuOGGGwLXSrOPfdeuXXj66afxpje9adTdfz52YPTcf9u2sXjxYkyfPh3Lly8fdfdeNX6gfvdfCyIAroI4mCTxmwW/+MUv8NRTT2Hz5s2444478NOf/rTRQ6obRsMzuemmm7Bjxw4888wz6OjowJ/8yZ8AaO6xnzp1Cu9///vxD//wDxg/fnzsds14DfLYR9P9tywLzzzzDPbs2YOtW7fi+eefj912tIy/nvdfCyJ4Env37t3B5z179mDmzJkNHFE20BinT5+Oa6+9Flu3bsWMGTPQ09MDAOjp6cH06dMbOcRUxI13NDyTGTNmwLIsmKaJj3/844H7oVnHXiqV8P73vx8f+tCH8L73vQ/A6Ln/cWMfTfcfACZOnIi3ve1t2LJly6i59xzy+Ot1/7UgArB06VJ0d3dj586dKBaLuOeee7By5cpGDysRfX196O3tDf5/4IEHcMEFF2DlypXYsGEDAGDDhg245pprGjnMVMSNd+XKlbjnnnswNDSEnTt3oru7G5dcckkjhxoBLSIA8IMf/CBg1DXj2F3XxUc/+lHMnz8fn/nMZ4LvR8P9jxv7aLn/hw4dwvHjxwEAAwMDeOihh3DeeeeNinsPxI+/rve/nuyK0Yz77rvPnTt3rjtnzhz3y1/+cqOHk4odO3a4ixYtchctWuSef/75wZgPHz7svuMd73C7urrcd7zjHe6RI0caPNIQq1atcs844ww3l8u5Z555pvvNb34zcbxf/vKX3Tlz5rjnnnuuu2nTpgaOXD32P/zDP3QvuOACd+HChe7v/u7vuvv27Qu2b6axu67r/uxnP3MBuAsXLnQvvPBC98ILL3Tvu+++UXH/48Y+Wu7/r3/9a3fx4sXuwoUL3QULFrhf/OIXXddNfldHw/jref91ZQUNDQ0NjYZCu+Y0NDQ0NBoKLYg0NDQ0NBoKLYg0NDQ0NBoKLYg0NDQ0NBoKLYg0NDQ0NBoKLYg0NDQ0NBoKLYg0NDQ0NBoKLYg0NDQ0NBqK/x+z3k27qTtHGgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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P4EM33G89hubZTDtUJno3u9FBzFlUFrzP/Ll/4Pp7ja7W/XOrnQuBkfwdpOei94U+T/apAmEcdy6Qi47kfTaxUGH0Ltposnb2T/VBQMblHDUGFcMrMJiQCKO40CA5115SE6nAOFSSYfglymDS/bbDGC12XDdZVIdZYKgqKfn9tbc8ge/cnxUYdEyvKqm5LKA0SNC71O/x6NQpoUwcBv/dNKbSrTZf6PH3cdfhRqn+5bbXg91xEDC0AqMdSQ+hMC7DMGJx3lxAMIyStJ0mcN5kIsHQCiO0GFfuZf4NYyAjF5B6gRvT/cQ9BmLMZXpzIcgH6LlkVFJ9br9TV+IybrUKC80TGCWN3kcvGcHOg/0QGIOlbuwUQyswwihSfPGLBAHfnefh1sf24fs/39Vz/4QNo6RKqkzVsZgLjB5VUrLNrk+dN0QWdmWrkdFrapBuVCbdYraLFXWDrNG7v53r1OjdSS4pwKwCqgSdGb3XLB7pC8OI4WwY84J2KOMw2lFxEJ88Nv+4Kz7+I7zpn2/puX/jnTIMoZKyH8MNolwl1cviMoz02OYlZbNl0S12e6dz6TAxiF5S+gLH52g/Fj9qoTTD0K7702178cTeKfWYIobhdeZWe/SSEezqQy2dYc//NrQCg7vVhlHxy0xxB3NlwxipEsMoZ8MgimxTe8RakB7PutvLBBxGlZTJSypO1ZKmWIlO/fyz15u7RZzbliiWZ76RYRjs/34YvjtNuKgH7v3KP9yM5/35d7Rjsu1zlE4NojGMXgVkHMdzkmJmtjDcAkPs/IoZRqf1MHqdGDRJyzKMIpVU1vDIfutBBg6hvDB6SeUZ8eUOtrubnVOjd/osv3XfTrzgz7+D3X1Qg/SKDMNgn/vhWttxHEbcu5dUUDY1CGMYrTDGgZIqZhvK2F8GGUMrMNosNUjiJVXOCFY2wvlQj1GsdL2yXlLipbHcR57BtTcbxvBNXpOXVJizqMvAve4QzaFKiu5j9+EG2lFcev7MJvJsGP3oX6d2mzKeRgoLzVFTFqcGSf4etWQEALDzUG8C3MVhzBO4SiqKi1/mThnGvi6S8oVRjJ2HZtL/k+/KGr1tAVu3PLoXr/rbH2K6ZZ/YPamkhnDyKgJDExSm+xGH96iSmsvAvUFKx28L3AP6IzDEdcqyfxSrF4tsGPRdoZcUU0kBwK6eBYaLw5gXtKNIpMVoR1FhHEYnBXcAYG8XAuP6u7bj+X/+HRxutDtWSdlSddy3/SDueupArgDryeg9hJPX5DIpIteNbrX0t7uBmku3WpnuJnkwgygweJdafUiw1bGXlMhwYD+e/2Ka49RGoZdUFMP3gNUTSXof2hB2g06z8g4ihlZgcN11VMLo3THDmOpcYOw+1MBMK8Jkoy36U9bobVN70OTKi0jtheIO4+RVXSZVVWOeSqpb4TiXbrW6TabTax6aafW/n9xepqmD+pH9uVOVlNgA5Byv2DDyGEYJo3fge5gYqQIADvfAqKRg7LqJecfQCgyeCymMi20Y8gUst2rsnezcuEXXaIVR1wxDn9u8TRsWslutrsIxGr3Tr7qO9J5LGwY975z7sSGOY7zo/30Xn7klP3Nzp9DtAbxH/Uj53o3Ru+h4kw0jjqXRml6nZgmjt+95GKkmS+VMq/v7lYKu/DPdc7iBmx/e0/U1+42hFRi0o4yEO2U5gdFO7QwfufHnxgdHOWb2d8EwuOGVJvNMju2Bw5a/XzKMPIHRC8Po+tR5g0kllWdnGKYCSvp9dJKZoBXG2H24iZ19iBcw9Yn6w9+bA9MtfPrmbT2x3DKMQTm+hBMD7w/1/9v378SF//dGHJhqSZVUiYp7ge8JN/my77MJ3bhn/8ant+L1n/hxT9ftJ4ZWYLS1nXdZlVQYxfju/bvwkRsfxJP7sn7utUoyJN3YMCQbkAKsbA59m1so3VaeSqqXne8g6Mg7hclLKi/7bq+qAKGSmhMbRvK3G5VU2Xeh8z6pAoN//sY9O/B/rrsHj+2ZMp3aUfvlI72Tv3mH899oPJ7cN42ZVoQ9kw1m9C7wkopjBJ6HauAj8D3M9JCxtptSvxSLowcmzheGVmDwxRkokxokzVYbxhlhw0HfdWPDaIu+RGIhK8qGKfpn0eOWETy9rGPD6OJn9JIK7YuO/K67e+WR3v/248e68qArC53FdLIbbefYcXqBsviGatAc1cTgO/W7nzqAL9z6ZAcXSP50VA8D+S6qJhsG7dKnW2Fpo3cYxfBTrcNIxe9NJSUEXfnnc/SSUQDoSSD3E30RGFdffTVWr16Ns846qx/NlQLpTsvuqkKxE42sPvtRFIsFviuGITxcYsWoVmaCWFVSpWwYC0slpSxgwkvKPg86VXnooPHdfbiJP/ny3fj63fnlgHuBzmI6MSo354BhtKJImTO0CPP5+R9bn8D7rr+34/bLx2HI/233qgqM5C+5ps+0ovJG71QlBSTZG3pRDXVqqwGSgEEAeOxIYhhvectbcMMNN/SjqVLgaTJohxBG+QExIVtQIo2dEPjk6Sb3fciEmFLasYTroc2tlprJMy4utNQgSrZaTdDmekl1OU76ebOpT7Z5yZWBEJolz7njif34+HcfVr7bP9XEAW3u8y7p1S1n2pK5y37EHZUAzjvy0zdvwwl/cL3KvNjCa0+lI/+nMSV2MNMKpYYix+h93R1P4b7tBzWB0T3DEIKxgyYoJ93jeya7vm4/0ReB8fznPx/Lly/vR1OlYFuM8xa/UCy80r6gL8K8rW7SoEsWowqvMnYMmdZa+17oWu396WXNH3aVVBjFuP7O7bj5kb0AirykVLTDqFRqC73NfpTqtF5Le5gd2TDaWaF5YKqF3YcbeOu//DQjCL76s6fx0W/9XPlu43v/G+e+95vKd3yOtKNIGchGKjz5uxSX8FpU27f/9t6v3Ju2n1UxxbF98TWxEKGSakqVVCNnM/d7196B2x7fLxIV1qt+TzYM6lJnm4Dk2EFhGHNW03vLli3YsmULAGDXrt7Sh/PJ09IW+UpgPkdhGJbYBr6wdycwGMPgNL7Ebstm+Cujkup00VcW3CEUGLp++rc/c5v43IlK6l9//Bj+8XuP4Md/9Iv519OGvqwjQzfQn0dHAkNTy9391AG88m9/iJXjdew+3MDntj6Ozc8/WblWmebzGEbDwDDKxEWp7duPpfeQO/Pyhdd2pslLitswykZ6AzLv1EglEAKyG3TjrUf9e3xABMacGb03b96MrVu3YuvWrVi1alVPbfHJqEzUvIlHBukwtqovlLKnXQgM7onF13fbpPytf78VX79re3pOel2LW22+SqqzfnLhM4QaKY1haL8Z7ke8qNr3zxxslIrc1RfxftR2tl5Lv58uvKTofqk+PCUwpKqOhGSHbm9/56EZTDXbGsNQhcyMYBjqe9jJRqTMkbw5/jw7sWEoAkPEYRQ/Sz9dJetVvyd2GVve8TyQ1uOZA/11le4WQ+klxSdnoyQr4EJCqI60t7NXhkEvXzuMlRfRJjD++95ncNvj+7T+qceIKnu5KqnO+qoKjOGTGJG2ONl+I9i8U6J0h13E0PTfZ1UlpTOMLryk2hbPQV1gcLZtwgUf+BZe/bGbVIYRRcpuf9pg9KYKmHEc41M3PYpPfP+R/I6XcQphx8jnaRd4/FsSKtTXRisU7ZWxL5JKaqTSo9GbeXeVBY3rXFUKLcJQCgy+q1Amao7qR6YGkVHYLe0hKAtpDwxDV0mZJmUcx2m8hno92wKYN7E7FxhcfVDu3HYY4dkfuBFfvv2pjq41G+BdziywOUZvfZhkBHD+9fShpw3Ao7sn8fw//05fA+X0/nfCMISXlNh8qB33VXkhBKYJNC8e3Hk4kwrEyDDYnKLDwyjGe75yLz7wtfty+92JWixWBIedyZjmCBmsp5nRu4xKSrjVVntzq6U+dbK0cKeeQUBfBMbrX/96POc5z8EDDzyAdevW4ZOf/GQ/mrWCq2d07wwbeCCUMHprKwHtHGuB31XKA6k+SnZu1ZwykHqqkkIvqVwbhqEvUYz3fuVePGbwruhGJTXdCrHrUAN/+MW7yp3QJdphhM/e8niBAwNXSZlVeByx+Gs+tmgXnzV6J4vkQzsP4/G9U33VL5cRgDZQ2V6bF6CvSQzptZO9Bk/vrwfu8QknbBgG1tfP2jMm/X8uwzDMkWlh9I6sY2SCYBg9uNVe8v++i99JbW0mr7s/+tJdRld+zjAGwUGlL0bvz372s/1opjRULyn5AK+/82lsOmE5zjpmifUc1UtKfQC0QxutBV3p9kl4tcJkQo5UArTCtpEdCHuH9tLadph5E9u0SO481MA/3/Qojl8xhjc/d5HyW7OLeuBe+tLkpVnvBz75w0fxwa/fDwB4/QXHGY/Ry7Jy5KUG0fcAfMyrFmcJwK6SIiFeRq1RFj0F7mnzWh8LXSVF4xHFMXyov9nS8odaHAZ1j2+w6PedB8ulAi/zrpn0/2XdaoVHlMHo3bIwDP4cFLfaLu1Xj+yexCO7JzP3ACQbj8/85HG84NRVuGzDUcpvTcVGCwQaS5xrDKVKirMK7m76Z1+5F//yo23mc9hLJFNIawIjnTyj1aArhqEH7o3UAqVdc38g+gWYKuuVUUllvzPplgllVG/fuu8Z/HTb3kw/Zhu0W89TFZgivcVvuW615mOLVVLmedIqIcw7RT8C92zMKdAYRl7adl5ZTgncC812j7ZBzfnw7sOl+s1bs81Hk+NCjE6N3tk4DBsL4mPie/1RSYn+6BuXnHnYUuyqs2c7K4uhFBg2G4bpM4G7p9o8j4TAqAVdpcLm8R1RlAgeW59odyp2uektWb2kOmQYMgI3+1srzL5MOv7ymz/HP35PGivLlIGdbLTxk0d6y6x5OFWFTIzYya+qklJ/My04Uv9t/r5YJaV+1hmGbZfaDWxxOGWgpwbJMgztWjkLlSIwWJ8SY3b2eJMjxcM7zQLjhD+4Hn/2X/eIz7oXlgkmt/Mojq3vKVcJ0xjKSG9p9LYJnNDAMOo9Gr1539TPyV+TyqlsnNlcYSgFhi0OA7AvrCaXV1scxkg16OrhtNmOM4xiITDyGIZe+CerYqG+5jAMQ1/LMowwjvHE3il8675ntP5FHXtT/c5nbsOvbvlxV5l+CVRvgCJcTTD52BPMDCO7OwXsjgY6bDaM1iyopPT+5+no9QVGT5OjP3svo5Ky3/9BK8OIzAzDwPoe3pWoYJaMVjPHc02Aoj4qMGIrP8d2gRrFcqGn+1Tdas2bRgJ/D32mkmr0gWHYtAimO9HjzOYbQykwumIYmkE6aUdjGOm5Y7WgJ7faMEpVUmkOfZMbJgk2XX2QmUyWl185xtBVU44fAl/g4jjGX3zjAbz1mq34yI0y6ldPGV9GYGzdti+9ZvcTm0p+VnKUtfyWsl5l2eOFSspiUC7yFNOvIVRSYfGzseGd//kzfPt+KaTjOMZ/bH0is2GwzcP//YU78bufvV35ThcYeluB7lZLDMtwDZtKysYwVIGR/H1kV5ZhmAMrixmGydMttrRHP1bSAArd6D3VDBWPJdPz54KIpuJI1UdTS/uTh7ufOmBk3Pp8yktd0+oxNqzfGEqBwXcFzbDcC8bpus2QzG0YRQvkdDNLTblbbRzHqOeopGwvt00l1anRmwSGaffb0ozepAb6z61P4rbH9+HHj+xBFHfuTcVZzc0P78Hffeeh4pM0kHdO3hqc5yWVtyDpw2TLEKyjSCXVTeT3dXc8rRTGuW/7Ibzr83carm3u3ON7pzIpr2mO0FTJekmZ2+b3N5Eyu/1MYEyxud4KYzGeVSbU29omBAAOpsKfzyOTSoffot0mkR7LhEviFmw7PhabDnrOxA6mmkm/akEyIKY1w2b0BsoHbn7kxp/jfdffm/FwtKmkTLfea7qifmMoBYbCMNrlGIbRS8oSuDdaC3LdWHcfbuDc934TP3pot/J9pF2jjEoq4yVlUbF0mq2WjHOmgD++kISRTOXejiJ85MYH8eEb7s8wjDIufXRPzXaE13/ix/iLbzyQO44mHG5QRbQcARnx/pvHSzleLDYq6NDiTMfq77TwcBVkpwjjWDlvumUu/ZmnY89slsQmRBp3ObJeUlmGRZscnt6flxluMy+paiCXj3aYfSa0sPLfTAKD36LtfmXQG/suzjd6E8OI4zgdr2RcKCV7vaIyEA5FJSUC9zqrujfdCjHTijIaBpsWwWjDaEeiRo+zYXSJPBuGVWAwI5fNT1y41ValW+3PntiPJ/epO7mdBxtotqOM/72wS6S0VQgMQ59E/IXYFcpdsKKjL6GSMq3lxIAKvaTiGHsOpwIjjNFsh4mXVxQb1Qxl0AojYWB9en9nQW2kksrPPKzuMjnMkd5m9mZjdbbzCdKGUfxsbNAFsi163LZItKPYOvf1BZuQsWFYDMkAsPcwExgsQSN/fyrMit4yuNWSYOUp/im7LffY4qyhkGFofc07nvoXRqqgmkxZLC3EJjuGopLSGEZZw3ezHaHZzgqMjJ1K3Fu2jVYYYyz1tnQMo0vk2TBsRu9QW8wBg8BocxtG8v//uPZ2/O23VNUK7Qb1mASu6opiCBuGiWG0NEFh2zXTv51GepO/uOll0G0Ye9JcQ60wEsIiimPNt778ZG20IxyzLC38sreztMwkMPK8g/K8pIxGb/obm48tujdbpDfNvU4FhmSiTLXaocAIDQKDGAc1q++EszactD+GHT4PIuMG8BYroEQLLgAlywKNJxdYesbYGmMnpVRSQrjz+7E/uxhyoQ/jOFdgGGN32NB5wobRucBohVFGcCfeXTH+6Et34d6nD+amDGmFEUbSjKqdpIyfLQylwFDiMLRB1NN9iHOUbLXUjtnoPVKVbrWTjRCHGmoQ03STdKFmgdGOUoZRy3OrVQUFX+i4IJMqqRwVTQ7DMKVF5/2ZbISYTI8NoxitVFiEUWxN7FhkfGuGEY5ZmgiMTqOgbQZbwu2P78MXb5PV3MqlBkn/Zr7Pv5btGr0G7vF4HYJVYFgWxHYYZ9Sx1J+2RSVF97F/qolv3/+MkXnRs51sSjWUyjAkW+CLPn/vqDkusGj+Up+4/UOJyrYJACH11e9Mz+7Ltz+FPYcb4hpRFCubu8MZhpFtgwtz6jttAMuqpBrEMLTjoyhxKvjMTx7Hd3++01pCOEpZ/qhgGP3zxusWQykw8hmGxUsq/bod5RRQ4jYMlqpcN3CT0Ux/ISWLSRbdeoWMZCYbhqoH5902Bdbl+fqbdiYNbRfMwb/blbKL5YtqaEWxYGAJw8guAgAwVbDDarUjrByvAwAe76C0JM/JZNs5vubvf4Qfp7UvgKzwylNJ6RSjbC4pW6S3CNzLSQxpgonhWlVSOYw5s1kSuaSSzxmBkV7iC7c9hbdes1UsnJFhweYCjNswiD0DQEWxYcjjTSoxEqq02NZYHQKFYVju16Q+i5E1eh+YauF/fu4OPLlvWvQvimNx3cUjFbFByrNh8LWZ7o3e58lmG9f8aFshs2yFEZqhyYYh7SkzTRkTos8zvoEFgJsf2TPvudyGUmAo0j+zyypmGHpZT0KzHSHwPdQCH1GcFoIJo8yOgrvncch2k4k8UsLobXoRWoadfd7uwqTvz7VhsAWO0jesnqgLVkHZRm22gqlG1kCrZOcNI7EIlK1F/MTeKVzwf78lPpfdtNucBJS+WXZwepR92Ws0NYbREothiId2Hirss8mG1jHD0OJkkn6orFWft1xVFMdSSPH7F0n5WNucYTTa8tkqXlKGucLncZuNESAX6+T44vs1RnrHBnUh+0LaMKRKatmimrhHYhimd8TE+Ospw7j54T1493/do2RCMKEZJgwjy/S4x5Z08dVvnfpFNow//tLd+J+fuyP3mrONoRQYeiARR8uysNJhnGFkUoOEEWqBL3WfUeLJotsqaDHWv9eN3hXfQ8X3OnKrpfP1fuu7SQ7TLefFYUwxdQMxjKOWjKQqqSQPlq4j5y/1YYPAONzku1BpJypbKUxvs6zPeZZhZI8RbrWaUqps6Va9K80wUuq/0zi98/N34sV/9X3j+HDIZ85sGFZmbGcYNqM3zUM975GebJA2Ifz2xWLPNhVcYEw32+IY7iXVCiPsOtRIVV3Z/uoqKS4wOjF6Z3JJ6c/fYKyOmA1j6VhN/E6MochLqi1UUsnx9HyLvOOa7YRhmFSDlANvmkWd2+J9RvMSnc0x5qziXj+RZ8OwG72zL6fJ6F2rSIHRjmLjA5dZL3XKn75sqZ3E9z3UKr6ZYWhpHBQhaNqtdaiSmhZxGNnfnjkkk8LtSv8/anFSbL7RiuD7yQtqYxjklsjBy38225EY2wNdRn2XNbLrNisjw0iHrlujt2nRboZyh0879a3pjvPAdCs3Ut0UB9Q5w8gKDD1rQJZh0PWTvyQU9MA8QGMYTCU12QxRTd8PLjDCKMbrP/FjPLTzMDYeuzTTX8nCSCXVmdHbFrinPzv+TpNKKvGSSq67fExGnefGYSgMI1UNpQKGNly0pjTaIR7ZNYkzjl6stNFM2Ziuwo1ilmq9GVozEbQ0QTUIGEqG0Y0Nw0T/TenNq4xh0EKgCwxSRekCg8chAElkba3iW9xqNYHB78mQTbbTinsyDiN73o4DMyJdAwmM1anAmG6FosSmasOQ/5t20DwyuNGOoKdtL0KZFB8m6M8wN34jo5KiZyC/u3/HQdy3/WBu34DkHtsaw1iUComiOuEmVmkTGNbkeCnDMdWOl4FqZoahx/ZIW460T/BziWHUAh+TjTZiJHmpuGtsK4zxUJo7yjRXudoOUIWNKRW5Dqm2UedkhmGwz1XmJUUbqGWMYeR5SSllE0JVJUXvP33/x1+6Gy/76A9EZUMCPQ/y/OP9pjWB3rfkHtV+6CqpQcBQCoy8OAyb6oZPJN1oKc5tR6hXfJFCgadD5qBJr+8c5I4jFRh+8pKZ3WrVlzs07GiSNmnHl7cQZn/LU0ltPzCNtakX097JBmoVH4vTZH8zrTDx8krtN/Ia8nyu0iIc0FwvdWP+vU8fxP075EL86Zu34c4n94vPdAvvuXxDcr2SKimTWsCWIly39UjvFPn9B66/D+/5yj3KcaY9SKMdCvWnEBjpi83ZlgmmYMxOVVKmoEFupwNMRm91cyKz2ya/8+Hh842+Hx+pYLKRqFB8z7NGevPFdkx4CqZ9StVkCsNgfbQFi1ptGDleciTQYqaSWszyWpFazKSVUBm/3ADy4+mYHz6YBPDq7zmN72FNYERxLI6dboWsRrnaBx4XNigYSoGRyzAsO/F2JLNXCl2vQQfMVVIyu6V6HO0wZixutVQn2PM8VAMzw8gmHeT96F0lJQSGYcF55mADxywdSc8F6oEvdnxU75hc+kzXKGIYzba0YdB5L/+bH+ClH/mBOOb/XHcPLv/YTZn2aYEpE9Ua+F5h0BVg94Iy7fSnmmFG5WZkGC3OMJK/xDAOWGpJiPYMxnZ74F5+3/n8JxWTEBham7odQK8BrlSJNKS/GK9XMNVsI4qT2ATOMGybOD1js1BJBdzozRiGhW2YvaSyXlW8H9VAMgg6b1FdLr65gXvcESC9BkV86x6OtE7wuEhu49IZZxjF4nlPMS+pB3YcxEl/eL1I+SKToQ7OMj04PekAqnpJmzA5bnlk5BIqKV3/nRqq6UXgJR05hJeUls5BuhMSw/BQt9kwIvUljSwvnHQH7kwlJfI6GVz6OMMAgCDw2G4sNSbGWhwGa8Zkw1CDuyLGMMoxBTpMvOQlTqv4nvF52wyhNhsG/75tsFnZ0k7TM6ENQVmB0VEchm0DpHloXbnlZnxu6xPpORaGoQmGTAJM/l4ZJNXESAWHG23EcbIZqrDkVPxd4v+PZASGgWGw4eX32zKwHN1An42azjIMLjD4bj0vDoO3S5suut2mJpjpXbONn77BimP5vGda0obxH1ufRBQDX7tre3r/qcBwKqneoE4qs6eIjnYUiwkijN6Z3UniVisFBgW/RcqCPm2xYehJzoQNw2j0JhtDjP/1uTtw+xP72D1kd1x5HhmmBc2mkjow3cJMK1IERsXPqhcSl0UzwzBFunKhqjCMKJuG29RfkW6CBVuZwHdx1cA3q6T069m+N+ysW2FcmMoBSJ4xLRwklIVKqpBhpAt2mUhvCz2SbDY5j8emhOmYm9w5+fWFSsqgQzeNa8IwkgXO99SMwqVVUmmfKkpqEH5f8n++6BoZRpydJ3zOisC9WJY04Abk/DgMJgBDuQFM7kVdP0x1t3nfdRtGFMciRoW71dYDNTBQ2DCqg+ObNDg96QC59TAsDz+O5QSRDCNrMDUJDCDRvY7VkuGyCoxQfRF9P0cllR57YLqFL2nBOLTL/eJtT2UWBhPMXlLqpCPsSIPjFIbhewgMu0XuoswvYVKf8O94CugwjkXWUoJpMaLFu8JcIU2oV2TVs2pgUUlpc0DsTi3H6R4x2VQO2X402qES3AlAROQWMgwD+7JlQLWppNraoq+cE8dKgB1BV3/mMQzTIjoxUsGT+6YRxTE8eGouKYMtBUAm2wG9U7x1k0cSPyc5JvlbxDBMNoww4ipPueQJL6kwsSnw2CmTWy3ZMHRnAdl3ti7xwEdNJRUxhjHNVFK1ig80krmw61ADn/zhowCA0drg7OsHpycdIM+GQbsr5bv0sy4w9IWrramk+K6Z2zGmLMZwmjD08gdeMgmoj3snm8Ig2tLUGRytMMYHrr8Pf/Slu3DrY/vStpPj+EtKMGktGoJhqPe4/UAiMMiGkbTpKwyD+st3cPylNgkvnv6AM4wwioUnlt4+BzVPgiuMY9z2+D68/KM/UAQz130Hvm82WGrNS5dMfV7Q8erikHFHNSyezXbWhsE3ATr+c+sTQtVgYo2dqqRMrrm8v6ba0/ouXbdhFGWeGK9XMNlMVFK6l5QplQZgt2EoGwIuBBRbXpZhKKfF2UWbf+aR3vQM+eJbZ8LhT798N37r328ztkPvqqcZvU0qbUIxw5Dqbl0dO9OK8L+/cCe+dteOpM/O6N0b8uIwgOxLpEd2miJc6XPge2InwRcORXg0zZHeNKnp5ffTqPGpZoh2GOG89/03zn3vN5Vr666PQDJBH9LKW9I9mQoL5cdhqKsAZSFdsagu1DuBr+qj1d2iQWCE2T7PtEPUgkTwNJkNI46RcTc0CQx6FFwl9b6v3ot7tx/EvczNtc5ensAvjtIF7MkHTVUO21FsYBgGlVRbxmE0tR3nfoOX1Ds/f6dYkEzXtXlJmb6OY+mQYMxTFmXVUcl9qPej25mKXJkX1SuYaiQLnK/bMNicUYzeFpWU4g2l9D1rxAeY0Af/Lt8dm9xqo0iyrdFqlmG0oghPH5jG0/unje1QX4VKSnijadoNywZAN3pzGwaPwyC33Zl2qDy/0drgKIKGUmAo0j8nT5P8TAxDTTduYicV3xeLFl84+AMkY3dDs21IhpEKjNSGcfvj+/GSv/6+ci16gUzqnVY7UuoR8Pus6lVwYPYCstkw6N7rVV94fXBWpYPGkstWU5nKRitxSa4FPlpaHIbOMEyLo4geFlXSpAqALwoKw/A8s8HSZvS2XJMfTqlgVJfOzCWSOAxt0SbjqM4w9mgCUzCMEjYMk7Di/TGdF0Wx8RmZVE+8vTwHBd9LBMYkRXrrNgyDKzhgYBhtyV55fwl8k9Y0qKR4F2NI2wRtflSVlGSrNOajtazROwwTBw+ldLGixUgFhqaSymUY7LlkshgwGwYvF0tzu9GKsGyRjBdxDKNHKF5SJpVO2/wg61rumKzRO4bvS/c5rgqx/s8EiWQN0khGC/8ju9U0323LpKN29F0qLS5FDOOxPZM43GhbvaRoItcCGW8SaEZvpZ+CKeSP+Uw7RL0aoJoGKvLdKwkMkknGRS5Wd3FhHItayvxF5CklPM+cdmXvZFOpYSI9bMzzwhRlryxWhmfUaIeSYWiBoLrAuOdpNRCw18A9m55ftB9no8D5fdiM/3kR777nYVGtgplW4h3me+omwxToBthtGLp7LGGyoaaY0fuuBu7J76knqsCQ59J5YwaBQVHzJnd2ZQx89f4yGgp2TkNhGPY4DECuIaSSarRDLGXxIs6G0SPyvKSAbD4pXSWVZ/Su+L6wE3BWwdkGFxi/9Hc3icVJutWSDcPDnU8eMN6DaSGospKSGYYhVFLZR8abuuLjN+MT339Eelpo16HxqlZ8VSVlaBfghlH5XRHD4DYMAHjmUGI3IYOj2egt++J7yUtOAo23xd0xA0uerss+8n1c/OHv4Lo7nkr7XsQw+GKXtMdfeNPiMdUIM7mkRDoUi8Agf3rhKq2oVi0Mo4BBmcaS163nxNHGJHQBZrKT+Z4nYhgmG2HiJaUUUDLfi81LSs8JReDqG5PRm3edG71pk8fHi4RaxASL4lbL4jQSgcEZRmYIhIC0pRay5QbLCgx1fk1qDGSmpb4/LjVIj7B5I4jfLTaMumbDMFHKwPfEznaa2zCa6o6AJt6DOw/jq3duV9prMBvGX73uXADI5BYyGWv5BM64dkaksslnGPunmiKhYDVIFtTYsPOpBUwlFXjGRSK5p6yR0s4wfOEVxic8ZcS1qQJ5+xQQFsWx4kdPqGsCwxZ3w68LsdgUq2La2sIGmGNCDs60WCxEnJ6bfH5096QSLX7P08mmgXT+MvBLjkMnFfd0pw+dOfGgyzpLIy6rTqrt6eNQNW0ePCnwD820kzgMS3pzjqzRmwSGPCaOpTA9bGEY8h5VZkJ9ps0Pf6c9L9m0JSqp5DvFrbYqn0crRyX1glNXAWCBe4xh2NyJ82wYfBwAGdfEswlPsk2prpIqUy55tjCUAkM1FmYHz2SbALLZKU2ChTLMAupDndb+X850jN97YBcA5t8ubBjAa89bh9+9ZL1SkAYwB+JVS9TuDQyqI+4FxA2ei+qVjCcJjQ3PyhswVqVDd73k98fRaCWVwShQkV+TVFK0uJlVUslf30tUTYpKKuaLgOyn75kj2UXfNRWMzejNvyd2yllUlMYdAMmO2fOSQEU9dxhfrD510zbxPwmuxMNI7v51ldS565bgJWeuwfnHL8vcA4duLNcPofT0gFwU6X55oBi/P/6XszhCYsMghtHOMAyb4KbFTqY3j0RfAOC/fvY09k01Ua8EqFV8HGICgwflGm0YbCw9mOeK5+WopDSGwecljd+nr74A//jG89MxyMZh8CBWm4rRxAK5rYYYhthwtiJMs/ViVAvcy5nys46hFBh5cRj678lnaejlMMVwcIYxYzB6Jy9dhBXjUmBsfWwvJhttmd48/UsL8litYsh1b2cYebtmk9GbbpfaFCkFKqo6gH6rpPdIa281RyUVGhZVU8xAI2UYFKjInwEJWxJeNO5cBy4ZRuKllqik0t+iOHMcnW/b2QJykRJ7U8PCyu8xOUfu8oBkjoRhLHbdge9hol7BwZm2NHazBeTMoxfjBaeuUmxCNI/iOPGsoy7z59JoR1g1MYItb9qEFWwzYlKH6QzbFHciEuaxxf+mh3dj43u/iT2TqrpTF2BmgZHYMACkFSg9q1stB3n4NA0qqQd2HML/+OztuO6Op+F5wES9ouRd6sSt1jPMFXL9DSMpQI2R3mnuM1PA7NqlI4KVyMA9Gq9ISetfpr4JgW9AecVLIJl73ANzpKIKjLLZE2YDQykw9B2z58GaCA2QOmN94E06yIAxjIbBrZZefp71shXGSmI9Au1IeP4afi0dtCjpLzSHyeit5xXilQMBVYXUTDPyAmAMI08llWUYxsC9lGEk0deRkdVQX3g2XwHBMLz0JYdRJRUqC0K+SirLMMw765D9LmpJpM/+lD/+Om64Z4cQ5oHvYfFoFQenW1mBEUVYMlrFuccuBc8kq9eTNhu9Q7G4+2xcilVS2apzvE9cJfXkvmnMtKKMm7O+e+eeaDQtfM/DWDqPD88kDIOrrmxZdUerqqMJvT9RDDyyS7qOe0iSG9pUUiZ7U2QQGG1tfpBKir42RXq3o1jU3xZtpyf4GqPl/WpHsSLg8uLDdJhqjPPUQiQwnnvyCiVhIh+Dnz9zSHHumAsMrcDw2MPT3UKzAXnpjltjGKZITR6HwY3bFC9BD5KuT6VI9ZgMQGUYHDc9tBv378hWZqMdzzOsVGm2TRPDUAUGLei6Vxj9X9MWpkrgGQURIIWvWEwqviXSmzGMMFKEtkLP23Inx2+FG2l9j9RAlOwtq5qg/tsKZgFZdqQvabJOhsoMk/tRbQM0Pr7nYfFIFQdn2pnAvVYYoxJ4ws6kCx8g0dHrzwtIi3elz4XLUZPA0Bm26ZimNgf49fTFTPee4psvEgqeJ+1whxthrpcUB819mg/0TsVxjCfYYud7HsZzGIb0dAP7jhv3s0ZvD4kdkcdr8Dkn05snwoIXV9NjL5IxSFg53cveySb+3zcfkGOgqQrzoLjpN1WBMdMKMd0McdmGNfjMb1yUYXx0Ly/568S5Yy4xOBEhHSCMYtQCPw2eilGv+KlB0fJCiMAYdadvenGU5IPtrA2DFstfPH01Tlk9jgtPXIFf//TWTJoQgDEMTQd51T/9xHhftLPLExgm91d6iWiSyiyXqsGRjpECI/ku8H2zoRPZXfqIJTfWTOolNRN4aLVjYQ+iIlT8+kIlxVZGacPwhBpBz90DqAuCzUuKQMLEtNgA3DtI/QwkGwS+0JPKzveAxaMVHJyRDEN6zSTsrcpUHdUgWQBWjtew+3ATk43QwjAi8fwLGYbmXWUKuOPxNmI82ur8IOSppGiD4HseM3q3sHxRTUsNYn4OdP1myrjIgyyMYzy5TwbKkUBSbBgGN9dMttr0ssKtVrNh0FyiecPnHLGvNnMSaEUR6n6Q8b4iBJ4n7Gb/sfUJZQMTahuOPMy0onRjJI3etLGdaSXPlMZbZ/9OJdUh2lGUca/M06fSZBjRJLUpDiPwWXrzJrdhkJ46+TtWq+CPX3EmVi9OGIaeJoT6BWSNVjZUK8nxthKf+n0S5I41+avn0edxKc12nFmYcgP3yOhNY1gNzEbvdoiRaoBaJUAjXcRICOkMh+fa0u/B85J+RczobXNx9Qu8pGhhtZVotaXKoGvyHEDUU8EwmNGbjPntMFbUe3SfM60QKxYl8+Rwo63UficW02xHYnFVGIbRhqGOpy11CaCqpFqROk/ENTQBxjcPNSYouZchj/SuBp51Eaum0f/tMMJ0S7oiRxEUgQF4STZczjDYczc5BkXc6G1wwfa9xEWbq6S4AOAFlOhael10nXn7bJOiez6WiashzLRCoWqSDCP5rdFOGAatG/q76YzeHYIYBkHXwWdUUmQArOo2jKw3VeCDMYysDUNP0SHqSDSzWTjp30U55To5RKSnZbJxV1gOYfRuk0oq6YsImtL8w3WVVOB7RmM6kI30Hq0FRqP3jCEOg66jCizGMHjGUraj81O32oqBYfAFNPDMzgOy76pKSn/RdIHBhc9MKzS6Q5IN4xBTSZHxlWquVIXzAuntpZPEZKOtLPC0yHTEMDSjt2kBIWHF1bA6I5LjoI4H34xJlZSnCHjPk+/ASDWwMowkE3Ji1+LBqFEci7oPQPKujNc7s2EgztowVIGRbj4iGa/BXx+RGiSMZTEszeMtMDAM6oJeb6RM9mHCdCvERFq0bEoYvSXDmGqGGCNju9YHUwboucJQCoxWGGcYBp/M+q5TqKR0hmG0YUiGQTmjAt8TAoEmBb1I1A8SKPwa1KeyJRapTdtkq1V8mIiAzA2kq6QMO/x2JNRa1FbF94zuukA2GnqkEhj1s5JheEIfrKeTp76YjN70KITvfCQD9/h46IFZeXVC9BiSOAZ+9FDiKXRoppW5Ny5YG+0IB1gt67VLR/Grm47FP715EyZGKjgw3VKEL9ltKky91woTVUizHWFFauuabLaVRY1nne3OhiE9gH7vF0/Bn7zijPT75F74xkqoLg01Uvi1+DnEen1PfV68gNJINcgkbBTnB4nLdiuMlYDGSLNheF6R0Vu9B/pOCIz0Oz5e1McwXWDJZZug2jBUliltHprAYJ9pHD/02rMz1y6yYUy3QpG2XGd4060w+T1dN7hHI/WtSOU1W+iLwLjhhhtw2mmnYf369fjQhz7UjyZzQaVURaSylzCMMcOOGmBxGDrDsMRh6DaM1RN17E8jr+kc2v3Sy0UCg3th0Au2qGTyMD05oul3z8AwpFpD7lZ5X5QdPmcYzEvKFBAIyB08XWOkmm/DqAV+6uvPdnDtSKYFCc0MgxsvyUvKNzAMvn4GvpdbiVAwAPFNjG17prB/qoX9Uy0WsKYeDyQCkDOMiu/hw798DjasXYLFI1WxsJF9qtWORbZj2nm3wkg8S3KV5UZvAMLQyjdBvpcdFw6+MH3ga/fhD75wJwBg9eK69IwzqKQI+vMLtd27YsNQGIY8x/ckqx+pmlP4AwkLoYzNxDCWjVUxrdmIEqN3Vaik4jjGLdv2id9N6UtiSJUUfZtVSXlCsOjsXAp7KXT1RJL67t7w+uGsY5YAyHerpRLIhJlmKAojtTQWn9yvmnSQay7COM5Eh88VehYYYRjit3/7t/H1r38d9957Lz772c/i3nvv7UffrGiFiXGRq1QC32MeGTrDSP7WNcOuKcDPVA/jmKWjoo6ESK2hMQxiI4rAIIZhcKs1QaqkzLUREpWU/CxrR6j3I9xqq1m3Who7QLVhFMVhcLdEXaDFcZJMrZ661c5obKsRRrIv7UgYM1WBgbRPiSdLFJsZRhjFuOT01fjRH1ySCAzNCM5hUklx+4Fe41pVSUVKHQ++2HA3xzERZ5CkO68EMi8XD6JcyVRSfNqFoXQKMDEMk41GZ8bfun9ncv+elxkzU3nPrFNI2pf0a84waqnA0RkGFxh5yfGIcbXCSDCMZWO1TL43D0m9jWaY1CP5zgM78ZWfPY2Xn32U0kd+FmcY9INu9Ka5FMVZtlDxExsHzxgtYyyKGQbBVEOjyTZJAATDJMy0I+FyTLY2nS1zzYTyrkTmqpdzgZ4Fxi233IL169fjpJNOQq1Ww5VXXonrrruuH32zotlOa28zgVHxZa6bTByG8JLKV0kJhuGRwEjUN2uWjIhoXTpHt2GQHlJRSXXIMIpUUvWKasMIhMBQ6bTUX0svqTBK6ks0WpESU5D89Y0vAsB3P8QwskbvJHdRsjjVKr5Q33GjN09zTayA3wvtIClwL4xiWRKTLZpxHGPFohrWLh3NeEnVNKHXDlXX2DjmbpNRZmetqqRUhsF313y3OMaS67WjCJXAV+5bxO0IhhFq5T8lCzHZMMwMwzw/fKaapTEzMQx93uuupCrDIJWU6hjhAQjS/ublOiKX7UQllbD0ZYtqGaHlpW61QBLn8ejuRF119S+cmPTREEsTx3HmGWYC99K5xKP15e/JRmlaERjqfNffC51xJPefzdDAN2YAlMwQQGLzFFHwBq85QHWW4ankozjOZI6YK/TsVvvUU0/h2GOPFZ/XrVuHn/zE7DbaLzSJYfgAwuTBH7t8DLXAx2N7pjLpImgS6QsK6TZFYRStROt0K0Q18LFmYgTfPZjs4mhC0QPUbRjcwE1zrWx6Yrp2nkqKFpNqWofb8+TuiwQFBRzSRG62I7z1mp/iuw/sQuB7eO7JKwDInaxeolUZIy354KiBYRA9pvQOVGCKxiaO5cLFk7ypRu/kr1QjMDUBZxgsPqPie4pOu1aRL38t8IUgE9eAfDl5gj5Tje0kAVwZhsEFRpxEzfvSPXrHATluY7UgZRgqY4pjcoFN2uJPwmjDsBj6A0/qukVWYkPUto4ojvHRGx/El25/EoDmJcXUZBmjN7Nh2GAyei8bq+HBUI1DUuM82mJOTYxURR8BLQ4DPIYk+U63Yfh+GrjHbGIE2mhyb0hZblX2n0NnHPz+dbdaHrOkC4yZtvSCMrlZA3aGEUbzp5LqWWCYrPUmPfuWLVuwZcsWAMCuXbt6uiYxDB549umrL8Dje6fwrft3ZvTatCCYXh7ybAHMKqlq4GPN4jommyEON6RnTDXwlL+0UHFXO2rHT+0rpuA+Dj+9dq7R25fMhhbXWDCM5G9Dc6u966kD+G6a74p7mKnJB/O9pLgNg1ItBL6H7z6wE2/51E/Fb7XAFy81H28edS6M3kYbBoSXFL20ehZRbnvRx4dfjzYE4hpMDdUOY1bLOnudhlbEhl9r8YhBJdWOUrdaWb3wjZ+8RRw3Ug2SehKaSioRYCkb6MJLioM2ENQfIOvoYUIUA39948/FZ5OXlN6vsiqpapDWSElVUhU/cZ/VWQ4ZvYEkueFko41axVc2HdRXQszmCLlM6+1SmpkoNsRUpO+biWGQUDfFYXCQQNSv3QwTmx6JxRWawIjjbJyU/lgXWWwYcWx3vZ9t9KySWrduHZ544gnx+cknn8TatWszx23evBlbt27F1q1bsWrVqp6u2UofhlBJeZ6SPVPXBZoWMIKSx183erfCRCW1OCln+szBGdE2XYsWX1pcxpm6gu9GynhKBSmFLmPDqAZ+GrPA3UJ1L6nkmnoqCBqHgO3Uy6YGGWG2CAD40cN7xLHEMHh/CbSo/MN3H8b1aalSfkVpw/AUNQK/FpAsEtRsRmCw6y2qBdDrWnOGETJXSyFE+A6xFSlpqfkmaNkiKTBWTiS66X1TrVQN4SmLLGGk6gu3UbUkbCSLY5FHEjvdHIdhFhgUwwLIRahMamw9jsMkMHw/6yUl3WrtywhXSe2fbmHJaNWY0sWDpzCMw402xusVMd/NKinubqseB0Ckl6F4DX0fSwyDG9+lDUMew6G/JjzTc6i51fL5uGRMTe8ByHfCtgEYtTGMOC7cfM4WehYYz372s/Hggw/i0UcfRbPZxLXXXovLL7+8H32zQqqk1J0mefrYApNM+lyho49ixLGqz2+lSecoOO+ZgzMyDiM9xvOSMqxEayc4w/C4wCgmc346gcuppPx0R+kZd+KAXCz0KHSe7gFI7tlPDYA6hNE7UtskobaKGfPqKcPg/ZV9Sf7/yaN7RVoUvhjqAXlhJBdGPQ6DOztwZBlGpAbrsQWmHUmVl4x0lteZaYdK4B7X2B23fEz8v3ZJspnYeWgmsYFZ0qxQJt+WFpnNo4wp7YvXA8PQY1fKqqQ4TM/QN3hJUX/zVVKq0XvJWBW+l3U79T05RxrtCJONNhbVA5nyw8AwjEbviP8ei/eDp8snBOk9zGgMY7LRFlU1M3YP7Qt6D4GslxQfe9OGTDAMy/O0Gr3jeN4YRs8qqUqlgo997GO47LLLEIYhrr76amzYsKEffbOilUYr+2LBI9WKGjBFoBeiEiSUPdGpJ/pFobMURi714SQqqXRRONgQiy3fRVYDSWs5w+DtlGMYnsh9YwKPw6hXfEStxJAn3Go1QUkTUt+NmHJJJX+zLrMi8tXCMCbY/dYrgeJYwMfItKjwhU8E7vkeAvJs0VwdgUSwS4GhLoYKw6hX0khq+XvMrtlW1FyqSg/IMgzfIvzXLh0FkMwNiuMxMYx6NchkTk36IY2xtMFQmJfJhmETGMzOoOcTy8OkZX7w/4n5ETxPumLnbYYqfhIU2gojHJhqYWnKMHR4nic2dI1WiMONEItqFZmFVrjPqnNGN3pzoZ+oCCHsYfp1/dR2x1VSzTDChnd/I73nrHrdxGpFkkzNLZtvUE33XMQw7F5SMbMbzm0oXV9ySb385S/Hy1/+8n40VQrNMEK1Ih+UvvBlvECYj3/VT3zGJ0aqaBxuYKoRAuM82ZivvBi6SuqodEfJd5GJsTWZqNyGwedImWhvPWKdhBuhXvHFDqsaeGi0ZRoNABnbzagQGOpuxJStFkhebj1P7kwrxJn/5wasXz2utEkLEt/916u+MRsoP49DTVue/OWeLXogIh3Hjd7K/dbUa7dDVWBEsdzNq/WiDUbvdoSD09lIb8LK8Tp2H25g5Xgd1cATbtdV366SotxaahxGxOYxjYHHfjcxDPU5U7s+8/DTPeXyoOcu4w4QNcZGdS+pdcvGUKv4OGfdEnz2FhhRCVLX1gjY32hi1XjdaDj2PDX1yKRQSSXHypov8pwoziZO1BmGVG+aXWQD31MZBptrJs/BjA0jkGPOmeNMK1JUdSa7rsm7CgD++lfPxV/9989x1JJReR2LSmquBcbcXq1PIP2gLigovYWukqL3K/CkumDJKM/tz9xlfdUAXA0S3fNI1ceeyaZou6odQ3EYNobxwlNXYd0yOQFM0Gmzno69VgnEokLqKQpMSu5bV0mpLr8EmUYb4p75X46v3vk0ppqhKDXL1QaAympI7ULgC48pn5ZJJeWn9ii70VvaMPjC+s7LTsPbX3iy+FzxfbQ1lVTMFhhdCAGaW20rVBiGvnAfu3xU3OPK8Tqe2p/kRaoE5mJUIxaGwT/7muDg48JhylAApCqWdMw7MXrvOKAKjFogn5XMCuCJbK1JX4Gz1y3B/e99Kc47bpm17STS20cYJ6nAx0eqRtWnB6kybrQjTDbbWMQEhtA8Kbcux46+DjVhTA4UCTNN+84YXSVj9JbnmwSGvu6Tatz31IV/ppXUuP/Bu16E77/zRcZ7tiX8fOU5a/GDd11idKABkvWMVFIm4TubGE6BEUaoVTwxmTIMQw/IEz7+clFckrpGUgAM0ckkzYg8lyj5eOrhQm1nGUa+DeN3f/EU/O+Xnp57X2Sk068tPjP6S15SiVttVtcPyN2lrnIQi4BuAzJM4B8/slc5RldJ8YW3XvUVGl4rYBi8u/Se006WJ5ZTF3eZlJAvzM89eQVOXrVIfK4EtJuX7caIzXYR5jlF7U5ruaR0R4pjlyV2jEMzbayeqGM7CQwrwwgSIRaqAqPFvLWESqoHG4YeuKfHHpmwQ2cYlewc1NVm4t3zPaxdOmJtm4p1ka2m6nt2lZTYjIRWo7cS6R3LdzuOyWtK9agLPDmXqO/kFk1zbaaZ9ZLi98phc7RINihcJZU45hy7fAzHrRgz3nMlMMc/GY/V4zCohkZOLrXZwHAKjJRhmFQqQNaIRHQ2YC8zTZrDDZluGci6mNLxY7UKppqhaLui0fYpA8PIGsjydwOhpp7QFx6eGoTsGYlbbfK7zqyk0budaSe5hjputuC9pG9J26Oa0Zsv5r7nKTRcNXobVFJaEBa1IVVSWdsCr5PB818F2kJU8b1UJSV33xEzejc0zytAbjSWjFYx00rqKksjsjq2G49dmpwLYNVEHdvTXTqP9OYYSVWo3PsLgPKZph1fL0wqKRJsr33WMcr35Jad9JcYRrFKKsswsvOfSqDSGPM7nBjJegARiHFFzBXbNM18rpJqSaM3XUizbYvvuFozjtVNSBglWXVJJUXvzud/8zl452WnCSGu2zBEn0os5ly9qzMMPufNDMM+FjpsRu+8ejCzgaGshyFSg5B7JduZVYNsjQT66DOVFPnSH9Zy0eveIPTyU9AVLSpcJVWrSE+L8bp8ebI+2/nyOYy58MuqNnikd5V5NtkYBi3uxKJqQWK/IZWDdBrIGvJtqLNgQABohvJlqwUaw2CqjSKjN3erJZ23TX3E3akJeiRy4KuBe77vKdlN1XQjyV/aDEyMJJuD6WaIpWM17D7cyIztW557AlZO1PGKs4/GrY/tw/YDSWAnj/TmGKkGqASJy7QShxFGSnEm/hcwG73pHn7/stPQDCN89c7tYjxoDDpSSWkMg58jvQGTzxQsa9LJm1BJBTl5pSU2DQPDgKeqpBqhopJ66JlDuOHu7Uo+uBgxuByPkXWrFXMplpkD1q+ewPrVE0n/At2ttsCGoX1Hzy7wVVfhZjtS5rxpvGR6I/UZm47lG9QwihPbK+xsc7YwvAyDLZ56CL3NSyrw5aK9eFSmIQDkwJtsGEBitJ5sysA9m0pqXIn0Nk8uG6IoVliTfrxkFcmLwKOigazAoMWd+kbjJDOQqgyjqH8AU0mxmI9a4OOzv3ERzly7WFlsTN42+v2K/1ngXuAn0bl6QjiAksgl/ytGWE8d76RGQ8RKv6YLjIW1AJJhUPryZhhhWeo/r4+t73u4/Ny1CHwPqyeka3HF8NwA1YZhSj7I74evSXlxGHodE595+NGYnXbUBC46abmoDGmC7hlXFWoW6Trra0K6rOqc4hSIYdCc1eF5LC9bK8RkUzV6X3PzY/jNf7tNUcHoDKMdaWVWU5tgqKmklP7pNgxu9C6hklIZBnPLboUwpQnSr53x3LKMq84wKDUILwU8Fxg6gRGRLjTwMzpJACJISDlH2DCkukDaMJKBb3MbBntoUiUVYLIRCgpY1Wi7UNkw4aUTiqIdPOWyAszBdGToJ+8O30NqIE5+191qq2nUMXlJEePQI7111V4ehEqqJQXGaC3Ac9J0I3wHWOSHrhq9k78e7KlBRJ1lgw0j8HWGkez4IqaSihnDMNVaoDmweKSKPWmwI9VuzyvUtEoTGKbnTB5wPP4DSBY5PTNqcaS3jJRXBAZbjGnMVo7Xce3m5+TaGXTwbMbCwUD8lWy+DKppXFM7zd1V8c1qGKqOVw087J9qIo6RMgz1uO2MDcWxOpan/ckN+NRN28TndiqgbG61QHbO87XD7M1ls2F4qpdUW/WSshm99evbxlUvBcDjMOaSZAydwOCZPemBkscTAJGGgIMvGvSASHVEJSHVWA15HAmYRbUKpjjD4MbpwLwwZugr+/xPb9qEj165UfldCUoLslXwamlK9yDd+VFFsdjCMII06pgoN/l1ywUByj3aiihxZBiGlmRNYRiBfSwAcxyGlzInXvQmkxBOE3RAViVVTXNJ0RXIW6atCSHeD/pt8WhFOAosTRlGXn2DpSyKtxr41nEkhsHtEu2QG+bT/pP6x5N9u/Wxffj77z6k9FPfVHC3bOpv0OECD6iZAHQhZmJBAPAL61dk2kmYsJc6MSQGWt+wq2a3jHolwJ7JxLl7Ub2S6fe23ZPKZxMDE7+lNgwKyjUJAF1NzJ+zaZOjk0euklJySbVCxcvRbPT2Ml5XZRhGGMXYOykd4PPKFPcbQycwpDFPBrHx3D41reb0gemWWOR9T+7+qoFadJ5eQp16U1DSWD1hGG3tRQQgajjz83kbBJ5C/MRViwTLIUSpjhfIqsbo3sgoHPCXkXTwmmqB3AYJI1aGocZl5EE3epM3iLyGWQ1VJDAy9TAMKilRZ9nAiHxNJZUwjEjJOhoD5mDAtBtkx+LzqQzD4EZfkyqRQJ40esU9PTMqj+aPUu+fKz7+I/z5DQ+Ic+h4m9DU66aXVSHRdWX76hw0eXIBwD++MbsBqrB2OMMwCS+6TL3ii8VwvB5k+s0FBg/uNIGM7KFgGNljsgxDdeIoOp6r7/Q4nroSh5G9dsWQJdom2PmciuIYuw/JdD9zaccYOoFBwqDKopK5SqpWkcVcDky3cOH/vRE33vcMgGRScgPjonog3dPErk1dPGlRJ4bRipJkhUrlLs4w2IPN85KqGehoyPSs+mIgzvE8IShokeTpLjgC3zOm5zDVw9D7x8HTeZPKbbpJgXtq9UPV6M0EhuFFMKmk6J7CiKmP0ucci2OyKqksw/CUSG/yJqNr6q66dC8AFEG+dJHZhsHBx8eWSwqQu1A1vXnMBIAcA0COn87E2myu6mo4rpLyPCZcu2AYvif7pFeg01sbr1dw8fqVynfc+y6KeEXL7DXJC4sLjEW1SmbRfnSPFBj8eZoQRnIuce86pY/anC8yelu9pFhdcyonUMQwEi8p9Xs7w5CDNtVIPPjILpW3mek3hk5g0Etdq/hCj6cwDCZIDk63MNOKhNtgkmFTCgReQ5jbMDho8RirB5hshqIMJ0etor60hIzRWzOm6y8x7YjoWH0BT9RwyTVWLx7Bqom6Mb25vJ7KqPSiTxm3ZMtCx4u/0OJIgrbZDhXBoKQGKWAY5DtP/wO08EMYSfl9iQA3UtnoAkNnGFGsuOvyNvR0I4Bq9CYsTxlGnsDgDMP03ORvyU5bZRgyt5TuJUXjxxfFRjtSGYbGaGkBbLajjBdZWdQZM6AxjplAt7VnU8EGnidsNYFvPpe+qlelSoobvQkqw8jfXSdu6mmWYpvAyLFhmJAVGPwek3MbhhgYmw0jKzDsc4ewM2UXRy1JBcYcutYOncDgDIMW+yU6w9AKufMKbzxojQsM7iUFSM8i0k8vqlXQbEeYaUVZ7yXLTloXCHwhqRpsFDzS28gwKr5I5vZ/XnkmPvGmTaqXlKaS8hWB4Sv/A3IScz2sCVzlRHEmh4XAiKzxFsq4WNrWEwCSjUZRSWXyfZkZBpfj5C1Hrz91hRZ+PlahxtA4Y1gmBIZ9IeHHkw1MDyoEkucRGgL3dKFGZ4q5yALLGq1IsaPxnafPFuNWGCsCtQN5IQQVF0jUZaHiMqwcpsR8gNx9hynDMKld6DuFYdQrmX5z+RDHqseZjna6AZMV97LH6Co3zjx1b8vkeMs9MhsGudgrcRgm+4khDsMmMAJFYCQb4KPSlEW23GKzgaGLwyC//yT2gXaEzOjNVFLkTUIP3vOYysnzlKLzYjHShIFQSaXusgemW8aAOoKqklL7znfwpihPlWFkbRj1io+rLz4RLz5zjZiMvmePw+CFkbi7J31HL6lu4NfB+1nxfRGTAqg1wqmPhCIbBpCMewVq4J7HhCBdAwBiFk+TtKnqiNV+qiopke/HwDBk4GPyHWcMSy1utRyKl17aJ4o0/9Brz8aVFxyn9IkzhmQhTfuoGahpnj22Z0ocP9MOEUZS3cTnG99kNNpRRqCWBa/ISO3FGZWUgWFkbHZy900CN/A8NYNwCjqzXpEeh4sMDIODe72ZILLVFrjVEmqBrwR0mrLI2oQipaIBJMPgKinTXZi9pMz3wvv5TFr9c808CIwhZBipSoq9KLpKqqExDG4AlDYMme4DYK6K2hOTDCN5+FQEhoMLENqRAvleUjWWnp0QRaoNRT+/XvFx4spFeMGpsp6Imksqa8PgrILa5rmHeL9MRjj9PnwveZEVhsHTYVv+D3wPb3vBSZn0KHoRIy/tFy92JGoUsFiNpE21j4rRO1AT/Xls5039JvDUIIHvKVlCqbRqnp64btgwkKcUV0uIOAzFSypiqjZVJUUCdxvT28+0QlG3hR8LqF5NzXaYUdHlQWW/zEtKU0lJoZZtg/dlvF4Rm63Al0XBeLI+DqGSYotsUjvD3ucYMq2KCWEUi7kURrGR2fAN4mgtUOp7GxmG1kSVsXM6XNS0L0g+aI7DKN607dIZxhx6SQ0hw5ButQTd6E1J4+glbxm8pHzfw6J6JXOsLgykDaOYYYxUfWWxyQvcqxpenJDlSbIF7unwchgGN3onKilPOU5EytOu2PdQr/iZZIW6XWZcExhjYzxDLxOKGsP4w5edAQB4/qkr8eXbn8InfvBoJj0195KidZXUGbo3kaKO0Y3efrJTlSqp5DeRAVersQEkaRYSgcFVUsVutfyehYtyxQcaarQ7ZxgJM1RLxeouq9TWI7ukwPiDL9yFmx/ZIxwY1OzGMvmg7oxQRDBGqwFaYfJMa6KQExMYUDcZpoWNE+Lff8mpePEZa8R9kVedTXBRe3yRnRgpZhh5+nvuJRXH5mtX2XeLaoESxGfaJFi9pAIZuCcERqHR289oIexGb66SShnGEscwCkGLHV+0FRsGM3rrQVpUoAhIJv6EwYZhM3orDMNiw1g5XlcWjyzDSI4j9Ymui1cC9wJT4F42vUbi/WMRGEyXXmFGb3oRqK90zPJFNSUieLQa4LINazKV1jgza7SjjAAV/eUCg7WxYe0SQaelDUO2n/jOQ9P1S2Ox3u9kHJDZUYdRjL+44X7xmV9Pr+JH41L1PSX4cumYWlqzCFz4ZsbA94UKir5Xckl5cgwAYMWi5Fnc/Iisakj/69589D9Pb57ngKGDC0maZ6pKKm0nh2Hw8V++qIZj00JTFd9TbCD8XMFY0s/E1mqVJFV+XrejGDARv2OXj2K8XsHv/eKpylwyLcZ80zFSDdRU5wZhpDMFbsPQjd5FgXsVg5eUNXCPC4yDDUzUK6KEq/OSyoEobh+oOxECt2HQA5QxFuoDphrLiqtixoaRLBj0QplUUvTy63V7bR4VSXlV+TLSS6Ik1vP9jEHOxDDohQC0KFVPNXrXAl8EV524MsnqqgfA/ckrz8SWN50v2rjtTy/FP75xk7JIeB65I4fpNSNrviKbuzG/JgmBOI5F+4GvLqRAYuzVjd6Kl5Sv5iiisf7yHU+LvlN/AbNKKsl15CdJ71Is1WJliiBdlFOVlKauIpUU7T5bYTa9OS0aZ65dDAC45dG9meuYduwBM/xze5h+nAmcGZsC9/SgySIvKf67r/VDT+HCj6dxITWz52WD2wiJSiq7WC4ZreLu91yGi09ZKWx8Ni+p0Zp8P7hdFLAwDP2drsgNqG70LmIYFT/rKWl7TKqX1AxWTtRZ/R/nJWWFSSXFd7iKl5TmEcN3TL7vYWKkinYUY7oVZlwbCdLoLRmGvqOmz/puVH/4eh1w6gv3phALjm9gGAUqKa42obbpnErg4XWbjsXNf3gJzk0zrVLzPOX76gmZQkJP7khjM16viAh53eht668+rmLHz1RSPJo4jCkFTPLdwZlWxlPHZtQ9ZuloRtjSObQImAL3qCb3WDXZHNQrvnhm5OlUBO7GrI8Bj8OoC4YRWRfj9avHxVw54+jFynVaBjdw31ddt1W32vx+m+pHcyFMspuaNLXneebnoUej83NFVHz6HY0Ld2SxsSObW62JadoExgkrks0TZSyYaTOVlKltXSXFmJ5gGK0swzDdQi3wM9/bGMYJKxcJe2oUJ6pSGlfHMHLA3WpNMKmkaHHg2WoDz8PyNChr31RLJDUzRVcDkmFQxk3TMWRL+eBrz8axy0czD18PkKMJfPa6Jfi1XzgBf/v6ZwkddOB7GY+tmuGeVaN3VmBwo7fneTiaVfEyRUyb3IJ13foippLSjd6mcdGvwT//yZfuxp1P7k8r6SW/eamaLYpioSLbO9lkKqlsm/Tvv731Qnzpt56b8fiiY8nzRcklxYzeFd8Xiyftum/6g0vwyTc/23iPOvSYFj3zK8Vh0Ng0WT0MafROPtcCX1Q6fNZxS3OvR//bDN1FqUHGatlNS4W1p9uCzH4/2bmS/G9nGPQu0Tdkw+COLFZhZ3GrVYVovlvtyavGxf9VLXOtCVYvKRa4R+xPTW9uYBgG13rbvf7WC9fji29/rvhcY5sZZ8PIAU8NYkK+SspjOwLp0bRvsik9hyxPjKspdKEiamykqrHXX3AcfvCuSzJt6HEQXH3x7ldtwPErFsndc+ApBjm6Nx2+b84lRX0UbrUWYaP/xm9NV/3QojOuC4wSKqmMwEjbuuGeHbj8Yzchjpmbryd34pTYb+9UM2v0Nqg9Lj5lJVYvHjG8iCmjMZZ9lUbvSiC9pGiTcMzSUWPFQA59M2C2YSTCvZXaqigVv0x5khzHBeLpRyVpuJ+VskLbdWk89Boh8v5zu49RZsMI2IaGFnSbYV6HKXUI76PuGaRnHSA1Dlcz24SdlWFoY5LHMFSB4Ss2DBP0+xYqKd9nKqnsGmUSViYbRp6tSXHqCHzGMJyXlBWcYfzzWzYpZTQBqZK65kfbxELKI4R5apDlqc1hz2RTBu5ZYhG4UVDfvdIkyyskAyQP3POyNbV5ezKdgsGGYWUYcocsv6e+khose1+6Sir5XxrmdeMyHaZ7SdkEBmeBGV2t9ubFsZq2PExjKIhh7JtsIkyNqD4TqnwcOHQBKbykTG616bC1wyQLMuXLGisQEhyj1QCHGm2pdqyYGUZy7RC+7wk2nE1vTosucMGJy3Hjfc8INaIOfTddtQiMQhsG2w1zZkl90dOy2NY1qpeh94v/r6ikAjnfAK6SKmYYcRwbU4PojCaKkZZozTZ0DCubXA18xa3WhIwNgzk5dMowqn75SG/9Ny4w5jKX1NAKjFrFxyWnr8n8TilD3v1f92R+43EJXGDsm2wy3ayFYbDFQ1eHHZxOqvZxvasNVd9X0kcn/eI7EblI6zYMXUUF6OnNGcPQ7CWm4k3SPZW/0OlvBn00/V1Ur6DRTmoPlLVh6H3XX7wojpWqblGcfLdyPHlGeyclw/C1hTX5X722PnZ0jjB687oKgokmwW6+72VcpIswWksEBl2Vrs8Nn/ScKW1HteIrDEPfnQd+Ynd61blrM67Osk11N803FXwIOlFJcWaZcas1jD2HrlYDDAxD2SmrAkgIjJFiG0aMZF5UtZIGqiqOUoOYhaYuVHngnvH+Miop+azaGsMoKqCUFJNSv8t7TL42jrRhMgUYzhaGViVls2HULd8DUFI2BL7KMER68/T391y+AR++4mxxbiWQC4i+ez2Y1n5eXMAwknPlLpAWiKo2Eah/+iJrSrdMenHAbMMQgWRGg3l2py5TP/AXKfnLBQaQFJ9qpbtyE2xutbx/hF2HGkpAHsVeLBurIfA97JtqljZ6m9qnj9KtVi7APPkgPduxWqVQDcVBHk26ms9UE4RcXgXDEAZldbGlRJOL6hVrbW59J+8xocE3CUVutaMGG0bgyfb1iHlrgJnhd31RVtRVukoqXWSLducAsH+qhf1TLWG4Nl7Py89WC0AUwKoGxQIj6yUlPcrCKMJ0MxSR+WoBpWxbpnxyuSopzjAqMhA3nEMvqaFjGA3GMEywfS/ULyxwb/FINVmMJptiR0OT7c3PPSHTxuKRKqaaYca2cPpRyWKhe7KYwBMC0vtsynBb8aUNY9lYFfumWsYd7/JFNTyxN5mgrXbimhrHTBiR3cQwY00qKZN9QI/unUgFxt6pJOdPnj2J37dybe3zfdsPyd2pnwbupa6hy8aq2DvZyiQfzIszyBi9PWIYWS8panffZFPozsdqgfBzL4O/ef2z8JNH9uKoNJiqpjE83t9mOxJBpM0wq5KinvOFdcRSm9vkCVUNPDRDTejnywsjw+BGdBmHQZ0zt2OK08gzzFs3fjythuVaj6UR8CesXIQHdx62Xi/PhgEA333nC9Fqx3jXF36WqT6oI8MwyCaaZhb4X5+7AzfcswNACZVUkM2rlac5VFR5jP0VJUzsJ4ZOYIhstZaJlpdaOvldLog+LUZTTaxLdZl5ZUoXj1aw42D2mDdedDx+Yf0KUSc4DzwJoAy+yi6siQtw8v1fvW4jjlk2arSRrJ6oY+u2vQijGAdnWpioV3Bwpm30ktIhddXc/c/L5GUyqaSAZIEF7M9CUY/o6jXtRXl412GxaFEwItVPWDZWwz6DSirPqKvbf6jvgo21mUoqTvThDzxzCK/euBYAcNqaCaxfM46yWDxSxaVnShUpzRHODJRMsn4SVd9sGyruGe6vGlD9duCCE5bjlecenTmGu1JPNkPwx1LMMFQjs+8l54h4GUtGXR0mlRXvo15xrxaox4tMrzxK3dLnbelO/oQVY8r3/HjpJWUXGGO1ClBLNpN5Ef1JP9XPZPQmG8aPH5VBlkVOB9zNX/Q9z4bBx5GtI64eRg6aXTIMacBVd/fLxmrYe7jYSwqQMRm6Ssr3vVLCIjlXqgwkC8i+XFxHOVYLcOoac/trFo9g31QL920/iKlmKIyjpjgMHaaIaeoX/yrrVpss7PumWso1dNgqESZtqse2I5YWxZOJA33fw7JFtcRLKpN8MIdh6M8x/WhMbx7HePrADA7NtHFayhY/+ZZni1Qm3YDmmYlhNNqRCKpssSJP0ktKHW/6jnbdv7JpHd70nBOS6xgM/zzwjp+fB90pouL7qY5dZRhFXlIm9qc/J9WtVjV6k8FYieGxXIxybB2vqaT4hjsQ9jCzpxKHSeWrIxOHoWWrPfuYJcbzbOOfmaa5DEPdQEiG4QL3rGiFEXzPvrDbFi/urso/i8VIMzyaQDYKk3qnLCq+L3YlMpcTVx3IXb/JKK2D9K//fW9SJOr845el11GFka3OtKl9buzkv/PAPYAxDJsaUNOvK78ZxpmrpLjOfHnKMGSkN9J7zGEw2meqa25LPvjAjoMAINxYe0Wt4qVeeRYbhic9+rK7d/M9USAY99gz7eT1eie8TRuym6CkbeozGb31FOw6TF5UgSIg1MhtYfROPzdMLqk5NoyV4zXFBReAEv3teyhUSYm+lShRnPHGM3hJlTnP9n1pGwbLDecC93KQ55UD2NUjXL8LyJdpxaJkMWpbAvc4FlsYRifgOZ10tREgvYm4DSNvd0g5mb5xzw5MjFRwSsp0siop0wINcS0OnqWUPgNyASOV1B6LSoovupwxKdfIsako9pMgYRj7mFCXgkU9j0MXkDLJpJp8MEhVFvfvOAQAVibXKSq+r+jhk2sxLymf4jAM6c2ZtxgH6cTHLDFBNG56JgH9f3N/swshd6uNMgzD3F6RSopvhIBsHAYtuqr+397v45aPZd4PniqD5mxocavlsKX359DteTUWh9GOYhGf9KevPFM5z87IdIFhv7Ze7yXw1TGbCwydDSMvshgo3u0GQiXFGAaLwzC5rhLIMF5mYtnAjd6mHb5gGIG0YeQyjMUJw7h/xyE875SVmWJIMhI1Oy4m1Q59Nr3wdNgYS8QIZMf8c297Dp7ePy3uJ0T2ZTXfE/WHHeclEfn7prjRm4SQutjktU8Cg1wQZTxPIjAe2nkYRy0eydRZ7xaVwMuMi4zDSIzeQcXLZRg62xUCo5o1UANyTtcq0hZEKFRJaX0l47TuJSUZRP6O2SowdJWUtgD/zxefgmY7whXnrcv0vV7xM15MyxfVM2yHL6DC2aEdFQrNot8B+WyojIJ8l5PrHm608dINR+GtF5+onZcvYIuO03/jFTRNSRJnC0MnMH75/HW46KTl1t9tHjs02LpL69LRKvZPt1id5ByBwfL7d4tfffaxghXkudVyG0aemoznfrropBWK0RxgOY1MAkNcy7BYGBYiGkMKbCOBoe/ml4xWxcJLgVy25INKfzz1enTcktFq8jKmi74uaG3VzExt67uxqu8jihIXYcrV0w8sG6sJt22C8JJK3WoD38OhVjtbetZgwwDk3CaGx9vk42myWxWqpAxzQInD0NSBtikp1axqW/x/VSWlul2tGK/jw798jtqm2M1nBYapLjZ/xiL+JooLU7zr8/j5rO6M3h71RSmgFEaYbITK8yHYrq1/nyfYFecBZsNwDCMHZx2zBGdZDEtAjg1DWxylAElC+lss35QNZMNoFOSbycPm55+s9OlFp63Cs45bxr6TrEKnvybwDLkvOHUVdh9OcuXrWVNNglCopAyLubJD1HaNIynDoIBFm5Dm52TiMPJsGNrudFFVZgoGsrmkTIsh1y3//ktOQzXw8P7r78u8XJUgceGdaUciBqAf+B+XnIK3aK7ZnGGQl9QexjD0560LQuqfOWZCHlsPssyrUCVlmAOcYfSSGkQXGObUIPa+8bQhh6BmdqgEfuZcRWCQd1wY5W689H5++uoL8LxTVmaP0dR+agGlGIdmWhmbCu9H3jWT4+z90wUtufTOpVvt0NkwimCqGQFkF0faCZGAIe+MfIaRTISpgvQBZeF5Hj71axfgRaevFt9Jg66XYQsm8EVlw9rFmV23sGFYUqOb2vc9NQJVX8iIYVDAYp5NyRQIaPqcXFc9h65NL+D+NO5DD9zLU0mN1gK8/YUnG3d9QLLgxHGMmVaIkZz76BRLxqqiHoTeJ2nD8EWdD8+Ti6xnEJwARP94fIgp7sFs9M5fLHU1ayXdsOjJB2190/tjUjtRn0yCrIx3kGljUvWz6c95Mj56n9phCaM3G4NaxVx7nPp7+tETOHXNuPDQouJYk81QyTun30PR950YvUl97gL3eoA9cI8WUHWRpMk805J1km0ghjFtSdPQD0iG4ZcSGEASBzI+UlFqbIjkd7mBe+qxsg/ZRZsfT3ls9k+VYBi+7RrZ/ngG4eJ7nvDKOjBtVkmZdo70nKlvthGspQwzjOOOa190Clm/ICkdSokyQ63etBCc2rCO5DEMk0qKfVekjtFVUhSDQV/rkd629iQbVdvi/aX7SoRH9hgd9JNpniVeV+q5JobRDKOO3Gpt7xy1d8KKRfj3X79IOZ7UZeP17DyyvcIdGb21caT+usC9HlAkMFaO1+F72ZiKmVZY6IdNgXPkojkb4AwjMKgWTHjfL50lz9dYA7nwmtxqzzt+GV5y5pqs0dvzMnaEpB/ymNFaINRftt27em7xTkrmsZLfVXxPjLtUSan3aBoe4WKq+fnrSFRSQKMVoj5RNx/UJ+g5w6qBj1Y7Qhip42EzLNOCaYzKZsfWDPPG5HTAF1adYZyzbglOP3pCCJI4ozYzD6jpeSupZ3xPeX55Ffz0vpve7Urg5xu9fSmkO3GrLc6VlWVkhHEDwygfh2Hvox6455IP9gE2DyaaOBevX4nvvfNFoi4EHT/TKvaiGE9VI3PDMDymcil/fideUi84dRVeYDHs5SUfBBK11J7DiYooL4WGzK1kZxi0eBkD8rhKarqp/J5n9BZZeolhWF7Eip9ElTfakeLKORvQa40Qw4jjWNn9ChuNrpKqBqixCF/APAYmlZQ+t/W4AZ1hfPwNSeXFh3Ym7sa6l5RtTppUVroKitu1ZFxHzkKZwzCqfr7RmxuGiwWGZ/yfg7Mj9XvZt3GDDcPKyLR2ipggocaM3nOZ3rwnpe1//ud/YsOGDfB9H1u3bu1Xn3qCTT1CD8LzPEW3TC/KTDssNIrRzs6WObQfoHWVdMhAZ15ZXKUF5Mdh2NvQA/eSv3x4RmuBiGUw6Wz1c/OM3qSb54F7/DgSGGRk99kYmdoGpJG2VqCSIqeHmVaYq1rrB/QFnJIPhpGqktIZFGGk6mcSIuZ5SfHv9CHSveZsKXHomcgCSml71gJK6XlazADvr1S5eYUqLt4HPa4l6befUTVxG4bN+G5CxSCIM32xMGY+nKYNlE3QdmLD0PtKyVTnsoBSTwzjrLPOwhe/+EW87W1v61d/eobN6G2bAPSiNFph4YQit0s9FUE/QWqoIPDwvFNW4k3POR7HLB0tOIudr9kl8iK989rI85ICpOEbKFBJ2QzrrDvVig80Q7EE6eoMXSWlMx4Te9BTyNgYRjVIAq7mgmHoSSa5DUO12yDts3r+a89bJ1KXEEzPpozRu1rxgQb7HPh4xdlHi9K7os/pg+rUS0phGJoDhc/a0NOhmEA/GVVSvpcRXqY4DN6ODXxTZfVqEvNZ/X75IqnONDEMuclR81VlMyDk95Eg3O+DIRIYZ5zRfa6d2UKRDSNzvLBhRIUR3KsnRvBvb70Q5xxrd+vtFTwO4+glo3jvq88qOEOFyUDN/5ZtIy8OA5AGWHIPtcGUSE//rO8ydXXYWDWA50mBEbCXJTkme12qpvi8U1ZZj6E2mu1o7hmGBxHprRf3oWel9+eik1bgopPU2uKSYcjv9Fxl+v/JNbIqqr+76rxMn2lB14MLrTp5g0DhDINX3OP2jLx1Ms9LSk81AugqKfl9J261NsZls8mddpRMVDlhMHorrJEpKDqxYXCo8R9DIjA6wZYtW7BlyxYAwK5du2btOkXpzXVUhA2jmGEASQnQ2YQpg2xH5zOBA6g1hztpQ1cLAZpKilWlKxNsZNKhE3SBpBeK8f3EU0pnGLaXFwCOXT6G7/x/L8RxqfrR1sWq7yOK23PDMLR7rgVBEgOUpnEnvODUVfir152rlA+1tmkI7qSF1cRaCPoGwrZZkoF71E6+CskcS6P+blJJ5b16QmAYaoJU/Kz7a9vgJQXke0AC6hjYNpgmTz4AOIWllDG71VJ/tY1TRiWV20UB/l63B8mt9sUvfjF27NiR+f4DH/gAXv3qV5e+0ObNm7F582YAwKZNmzroYmfolGFwG0aZbJWzDX3B7/b8QFNFdWLD8D1P8VTiNZ4JpEsfz1FHATkqqRyVl7LApOdN1Csid5VuF7EJ+hNXStWhTedeCbxELRTFc8wwPOHBNtMKM+zttSw1Rm6bhgWsTLZa3YZhdRbJ2DDsQlrpjyIwGMNgkdlceJRSSRmEGqV95yA2pPejaPNexq2Whklf6HnxNLNKyixosvUwyr2ntFGopDms5gqFAuPGG2+ci370DbY8U7YJQC/KVCPMDUCbK+jqlk6hG4JrfVBJyUVJHkMMI89+kZxjXtRNHjz0rpiEycRIFU8fmEmPky8LPy8PtmMqgY/JRrYG82xAN/7Ss2m0ooxOvCxMC7it9jtHhmFYGK14TiW9pGRCyOwzpP+F6tHvzK3WavTWGYah8qTeDxPKeUkVMyLTJiov5sl0XBGqgWxvaLykBhG2nZLVrTJ9YpPNdkeL6myhXwyDzl+7dARHLR4ppd4g+L4t+aC6CwbUWufGtiw7srz2ld8CEhgsfxIxCwPzscH2/GuBh+k0rsZWBrVf0O1CtEGZ1hhGJ6gwN2wCtWuKeCYQuxGfC3T2USaXVP7x/Gd9c8BZSCm32vSadqO3itOZY4AeBJoHU+XLbF/MTAGQpQVGDRsPLiSV9rQ+lZ0GIvVN4A1PevMvfelLWLduHW6++Wa84hWvwGWXXdavfnUNz/Nw/vHLMuVMbbKAIqCnmmFuFty5gozc7c2GQX9XjNfx4z/6xVLlYwk1LdOqydA5Wkt+L2QYFrWRiWHk1QfhNF/PT1XG9mQ7ohr4ItWLrQxqv8A3AVXGMHSVVCcwpXinZ8dTRtCzs3nN2WwYooCS9rnIhmGLAan4vsI6pUrK3B5v0xiHEag2jL/8lXPxiTdJlbfiQFFkwzCkirf1xfT7p6++ADe+4wVGYWpNw1MgQGzgziytQVJJ5eE1r3kNXvOa1/SrL33DF97+XPzZf92Df/nRNvGd7UFQjqWpZmjMtzTXsKXSKIuy6UTy8K6Xnm5e0FmT0uhdYMNIFwX9JVJdPpO/pjoQdBwvT6sLll5VUqTynkuGMT5SES99WYcLE/IYBt94cq+5VhgaVFIWBq4zDAML5DAbvfmizVinr7rY2iDSm5uM3poN47nrV2AJyzqsGtzt1wDMlS912HKjAcnmaf1qM5M3qepMfSq7b5gvldQRF+lN0Cm23eidfB9GcaZM5Xyg1wXfZjPoBM8+YbmxTVMchikNAoduDxFtsrZkBuHkc62SfXHHDSm985IP5l2P3Fnpf4JJR95PcJXHeL0qFvaZVlTaO0aHaSEi5sIZBi2ctYpvZNM2dawI3NMKKNm6a9pc6AxDSQ1SRiVF88IQY1XVvKT0nX/eb9m+dx+4VwRp9Nau2bXRmzEMZ8PoHZmSk5bnwF+UQTB694th9NPjy5SihFKcj5VQSZlePlOcB73ca1mgIh232KCS4jXAi8CPoMVy+aKaIiTmk2EUqUtsyLVhhFlvIbp33amiKO6AUJit1qC+VN2JmSrRKxfpTT+Zng9XayXta0y2E5UUZxhWG436tyzo0lmbRT7jsIEH5rr05n2AvmMqivQ2nTMf6BfD6HYBMsFU42CsWtKt1veML5+aDTf5S10+jqVuoRdswiAwgNTo2aFKihbUoxaPqEJw1m0Ycn5N1CtCJ99oF9dqsMEU50LtcvdSenZ07/pcr9q8pHRVYpGXFFM3ie90GwYT+NKGkccwUpVUicA9U5JF22+ZtrgatiBVSqcMwyZobQK5CPw5OobRB+jpvO3J5xjDGACBIVJ79OhW20+GYdIzUxxGXuJBAJn64LJN9Zik/eQvt4vQfXDBpO8oy7282R34UUtGlL6NzCXDqPfHhmF0qzV4SUm1TvLbS85cg7e94CTxuzWXVHribzzvxPR66Q8FXlL8dhSG4XGPIV4DxHx/yTmqsOPQjd4ZFY/BVmZDJ0bvblVSHhLPwndedpqxT50yjLn2kjpibRi6Sso2AbgOexCM3nLH2KuXVP/uRTIAvrhSHEaBW61vZktmo7r9OG70VnXiZoGU6YeBYaxZPKJcc9ZtGJpKiq493QpL7yxtbaoLNNkwsllb6d6fddxS/MqmY/GP33skOSdng7LtQ68Q/xcZqXX1YnKsZEGep2arLRPXQE0Z4zC0DUOejaAzt1rzMbbMBUWQTAq4570vZdfRVVLl2uVeUofbs1duQccRKzAyRm/LBOCCpT4ADOOUNRO48MTlOP2oieKDDbAlR+sFpl1V2cC9xOht1j3r//N3ZbxeweFGWxq9uUpKO7fMu2takNcsrivG1rlmGKRKSBhGb22axpPvPHWVVCaQr+QGo8hLyqSy0tWswlDPVVK5Ru8ChsGvry/A3EuqyIahqc7y+tKpytfGTIo+21Bh7tFOJdUH6Dpaq1vtgNkwli+q4XNvew7WLB7p6vzA9zBS9QsX8k7bBLLpzQFk4l10JEZvQ5uGnR9/RuuWJYZvehlsNgyewC4P/AiqFLhqoo6jl8px7mdNbxP4gjQxUhELYBSXM9ybYE7FkfwfxnaVVFE8gPV6NBcsv5uMu7ojBzd6l4r0TuePqWokTzWiXxdQWUknKinbsWUYkQnULf0+s15d5drjKX+GJnBvkKFTbKvA4DaMAVBJ9QrP8/D533wu3nDR8X1r0+S+SoLCVPCew/c8426tiGH88vnr0vYTVRTP1cNfsrIqKZ7249BMQuFXjdexdon0yOpnTW8TdIbBbWbdOin4qdFfV9MB5jTfNoZR+noFO2yTW7cuKHgCvzI2gbxrJm668rM+F3jUddE84dmPiyrkdSrg7QzDfFwRaN2qBmq69NnGkauS0hapYfGS6gfOOqa/6ddN+YPOXbcUf/KKM/Dck/Oz9ybBWdnv6YUcqfpGG8ZbLz4Rl5+7FqtTpsWN3voCUWatXbYom3J65URdEUSzzTD4IjQ+kqjcCN0u4EDWsYAWVlPWViEwejSy5/UFMKcGCQJ1HiV1V4qvKeI2DGNU1bykslUK5eQrYqIyGM6+DnTrhWh1Q87YMMq1x1VSjmH0AVmGYT5u0OIwBhGmBb0S+Pj1551UmLDPxjAA4AOvOQtf/d3nGRmG53lCWABaLinOTrxyKqnli2ri/5eddRQA4JTV41jLVFKzzTA46pVALbXai8DQDL8iOlupPJf8pWt2ez2Tyknvi/67EBjaLlsJ3MtTSZGAMRjm9eSDeju8QmHZwL08c06QI7zyYBu3orgMG7hKai5tGEcsw8gG7lkYBlt8BiHSexBh8nwpi8C3C+urLkzUZkWGVEA1eisxHIb01iZQQSUA+NVnHytqVnMUFdDqN5R8XT1cWlfLkf1q9YSsAkdju2ZxHbWKj7HUu+1/vfhUfOXOp0tfq8hLyhRvoOf8MgmMMiop0yKtx+Hoc5SrpIptGMXCQDfcl4VUw5nbk8eVa0+wocCf08C9I1Zg6It/0Y4IcAzDhjJFbmy4eP3KwpK2ZfIJ1StBUtK0HSkvVcX3O7Zh9JI2pZ9QGEYPfdJjUU5eNY6PXrkRLzx1dab9V5y9Fr/9ovVCFfd7Lz4Fv/fiU0pfqygyWw/CpP55HrOFiWhpKejy7l4wjBJGbx0jisDIH2NRjCrnWXQfh5H81b3B9GbKtiu83ubYS+qIFRi6GsQ2CTwvyRraDKMjxobRb+TFSRThjc85obj9ki/h4pEK9k42NR//zvs1KAKDRy73YsNIGIb63as3HqN8puZrlaT0b7coyqVE9S4yySY9L3Ouz2wYeTYBpbyphorvI47tO2xljAvdas0eZBx5wisPnmXT1W3FPYKrh9En6DaMvPeRG5AcsuiWhpduPyin8hqvVzILlalEZ+H1tOOvfPaxWMXUN3OFfgkM3YZhglQD9fYQTWlilOtY+hL4XsbbTon0zrlm3iKdGL2LhQ1vx4YyaXm63TwJhlFgw+i03WrFqaT6grK5pAA5UZxKygxplJ6dnXmZBHRA4mLr+zOZvnVqetI3Ex+64pzOGugTPM8TarZeWA9PE553DNCbcZ23Y2uFswb9PF31qGQyzumWrZYEkOjwwyjsqO82lFJJdSkwbClQikq2FqHqe2hFEeI4nrX3k2PBCIy8B0zHDkIuqUFEt3rbsigbDDVer2QWvAtOXK54QOVh8UgFB2fas3Yf3aAeJAKjFy1ZxfcLBQGxmV6j2YvsTZXAnp1Y2jAMKqkujd5V30OzQ72/DWU8yDop2sVh95IyH1cW1bSeSxjFXeef6wRHrMAoG7jHj3UMw4xuo1vLoqxAmhipZPrwZ5dvKH2dJWNVHJxpz6sN4zO/fqHiLlyv+jjU6E0YrxivYcV4vtB83imr8PGrzrMW+CmLIpvDr5x/LNYbygHzFDFGt9qca9LQ2BiG75VkGIVutapAM+HUNRN47XnH4FnHLS11TYLtPjMR9x3OA/Lsa0cxZjkVWnK92b/E/EAP3MtbIyosatIhi7JG6W5RthBSopLqvg9LRqt4AtPIsZHOOp67Xg10pNQVvQixa37tgsJaHrWKj5edfXTX1yAEBQv8+tXjRqHEDfNCJeWVjcPIU0kV22/IqaWsDSMv0/NoLcBfvW5jfkMG2DZF3dbDIJB7bTOMCmOi+oEjVmDoDCPvhSRm4RiGGSR7Z0uTUyafEACctGqRksqjU5Ar6cGZVtdt9Bv1HiOvAWBZSZVcP9BtTE6SU8xXzvV9mQUgXyWV/DUavUsY8WuVRGAU9dnzvFL2oG5Al9a7kFchsAxokztX0d5H7ApJkpfsEnkPglevcsiioqkS+g1T+nQTfvMFJ+Mrv3tx19e54rwkP9Wxy8YKjpw79Jrbaa7RLdusMOcEI8PIOTePYZRZ4Ekol2FxST/7/yxsTErvUqeXJoExV7EYRyzDEIbsdHeRp22qBI5h5MEUjNXX9ksGBipeNV3givPX4RXnHD0n1L0sKH/VsOxVTGlcysD3vUycQxBwlZS9wcJcUgXXlkK5uJ9lk1l2CqvRu0Mbxuc2X4SDMzIHGWlSnMDoEYoQKDAq6mzEQcXse0nN3S57kIQF0B+V1FyiTFS+CXwhNhVQyrdhJH9NCzkvyGRDJ2NcCcplDugU5eth5Ldz4UkrlM81wTCcSqonUO58GtBcLykXh5GLWY/DoGEfjjWzrxDqkiFTSXVsw/CzwoFnGs7jCXleUkDxItuJ2q9qcQvuFVYbhrbkdO4llRw/V9HeR+wKqauZ8t1qnZdUHkzpzfvafpfBUEcC6kNmwygRZ2eEiWH4nozSzptbeTaMMp0RnmglxrhXtacNnmdOmUL3VulyU0Zr1lzVxDhiV8ilo1W84aLj8KLTVgHISnKOqovDyEUvuaRKtT/LAmmQQYvZ8KmkuvGSUm0RgV8uyj8vl1SZvtQ7smEUB0F2C9/L8ii9OFS3brVz5SV1xNowfN/D+3/pbFx7y+PJZxfp3TXK5NjpR/vDssvuJ6QHzzx3pCS6zSt21UXHY+lo4tZMj5m71ebtrLlXlQlljd5lltRKMDtutQDSRJn6d5JhNNH5O+C8pPqMMhWyyHvDMQwzysZJ9N7+AhQY1eGyYUg32M76+0ZWMpjUM50G7vm+h0+8aRN2HJjGn153T+Z3G0goN9vFi2qFpTDpN0wGetoodJtNgdYuZ/TuE8qoO6ouW20uZjvS26mkhkdYdutWq+Pyc9fighOXlxJANC8qvodLz1yTSZlfGOmdCoxGuziFSNn6Kt3A97LjllG3dTiwtUpyvGMYfUKZLJ3S6D0cL+1co1v9auftL7zx7ySobBDQr83DR698FgDghw/uTtuzH1tkwygiO7VUKDfKMIwSqUa6BTfyE/SSr90yjHbkBEZfUEbdIVxwnUrKiFk3es+yymuQ0YlBdhDQ7zQxZdorigMqdKsNiGEUL6pvuOh4jNdnZ1k0pX73MyqpLr2k2k4l1ReUSUcsCijNVoWgIUe3vvdlsZCN3rRJmcOiaT2BBFy/1LedqqRMKJqXl21YAwDYsHZxYX9ef8FxeNW5awuP6wael71P3a22ay8pxzD6gzJpLaqBj+osekcMO+YqvfkClBfChjFXL3yvOHnVOP7m9c/CC1N39V5Ryuhd4LhSNC9fsuEo3P++l857lL/veRnvMt2ttts4DGfD6BP0B2JCIjAcu7BhrlRSC5FhkJdUO5rHnOsdwPM8XN7HHXhQwq3W8/JTjpeZN/MtLIDU6K0xDJr73bJsmUtqCOIw3vnOd+IrX/kKarUaTj75ZHzqU5/C0qVL+9S1/qDMYvRLzzoGxy0fnAymgwaZOG2W2p9lBjPIIP36XKV2GDSUqel92poJbDx26Zz0ZzZB7sTqd8nfbll8bY4ZRk/b6ksvvRR333037rzzTpx66qn44Ac/2K9+9Q20GOV5SW08dimuvvjEuerS0MHzPLzvl87C5RuPmZX2F7INozrHyeMGDWW8g1573jp8/u3PVb77+6vOw8evOi89dzjmTRK4Z/GS6pphzG09jJ4Yxkte8hLx/0UXXYTPf/7zPXeo3yiTesChGDzwqt+YbaP6IGOu01MPGsqkNzfh5ax64LAwUxPD0F3KO30FqnM8f/qmuP/nf/5nvOxlL+tXc32DiMMYllm1ALGQVVJzXTFt0CDShPTw7Idlo2FiGMJLKujNrXZgbBgvfvGLsWPHjsz3H/jAB/DqV79a/F+pVHDVVVdZ29myZQu2bNkCANi1a1e3/e0YC9mgOiwI+uzbP0wgddyweEn1G91GOHMMy0bDNzEMYcOgyqCdtTlwXlI33nhj7u/XXHMNvvrVr+Jb3/pWrqTfvHkzNm/eDADYtGlTh93sHjJZ2pDMqgWIuSygNGiQ6akXJsMoU6K1CMPCMBbVK1hUU5dcmYki+dzpOxD4iRCaK6eJnmwYN9xwAz784Q/je9/7HsbGBtPLaLZrOTj0DmfDAMIFyzCSvwths7DljedjSZqxlyAD97qP+K8G/pxtOHoSGL/zO7+DRqOBSy+9FEBi+P6Hf/iHvnSsXyiTS8phfjHbNcMHGXOdbXTQ4HVp7B1GnLRqPPOdjBNLPnezaar63nAwjIceeqhf/Zg1zHamVYfeMds1wwcZc51tdNDQD5XUMKPXXFIAUK34w+clNaigB+JsGIML6SEyzx2ZB4ylOm1dVbFQsNA3dDIOo3uV1OlHTWDVRL2f3bLiiE8NMtt5kBx6R7e++EcCTj9qAu//pbOUuIKFhEX1AIHvYfECFZiesGF0Lziv3fycvvYpD0e+wCiRrdZhfrGQ05t7noc3zGJQ5KBjxXgdN77jBQs2Nc+qiTpWTdRx4spFAAb/HTjiVVLrlo3h+aeuwjnrls53VxwsWOhqiYWOE1cuWrAbuiWjVfz0j1+M845bBmDw34EjnmGM1gJ8+uoL5rsbDjlwakOHhY4+xC/OCY54huEw+HDR+A4LHd6QsGwnMBzmHd0Wj3Fw4HjJmWvmuwtdwxsShnHEq6QcBh9lUlw7OOThgfe/VARBDiOGJRbJCQyHeYdTSTn0Cip1O6yQRcoG+x0YXpHscMTAGb0dFjr6keZ9LuAEhsO8ox8prh0chhnekASvOoHhMO9wGYUdFjqG5R1wAsNh3uFsGA4LHTTzB/0dcALDYd5RcTYMhwWObmt6zzWcwHCYd6wYr+GklYuwfnW2XoCDw0LAsHhJObdah3nHWK2Cb/9/L5zvbjg4zBuGpZCUYxgODg4O8wxvSBiGExgODg4O84x6xVf+DioGu3cODg4OCwAbj12Kv7/qPDz7hOXz3ZVcOBuGg4ODwzzD87yhqLroGIaDg4ODQyk4geHg4ODgUApOYDg4ODg4lIITGA4ODg4OpeAEhoODg4NDKTiB4eDg4OBQCk5gODg4ODiUghMYDg4ODg6l4ASGg4ODg0MpOIHh4ODg4FAKXhzH8VxfdOXKlTjhhBO6Pn/Xrl1YtWpV/zo0x3D9nz8Mc98B1//5xjD3P6/v27Ztw+7duwvbmBeB0Ss2bdqErVu3znc3uobr//xhmPsOuP7PN4a5//3ou1NJOTg4ODiUghMYDg4ODg6lMJQCY/PmzfPdhZ7g+j9/GOa+A67/841h7n8/+j6UNgwHBwcHh7nHUDIMBwcHB4e5x9AJjBtuuAGnnXYa1q9fjw996EPz3Z1CnHDCCTj77LOxceNGbNq0CQCwd+9eXHrppTjllFNw6aWXYt++ffPcS4mrr74aq1evxllnnSW+y+vvBz/4Qaxfvx6nnXYavvGNb8xHlxWY+v9nf/ZnOOaYY7Bx40Zs3LgRX/va18Rvg9T/J554Ai960YtwxhlnYMOGDfjoRz8KYHjG39b/YRn/mZkZXHDBBTj33HOxYcMGvPvd7wYwPONv639fxz8eIrTb7fikk06KH3744bjRaMTnnHNOfM8998x3t3Jx/PHHx7t27VK+e+c73xl/8IMfjOM4jj/4wQ/G73rXu+aja0Z873vfi2+99dZ4w4YN4jtbf++55574nHPOiWdmZuJHHnkkPumkk+J2uz0v/SaY+v/ud787/ou/+IvMsYPW/6effjq+9dZb4ziO44MHD8annHJKfM899wzN+Nv6PyzjH0VRfOjQoTiO47jZbMYXXHBBfPPNNw/N+Nv638/xHyqGccstt2D9+vU46aSTUKvVcOWVV+K6666b7251jOuuuw5vfvObAQBvfvOb8eUvf3l+O8Tw/Oc/H8uXq4Xobf297rrrcOWVV6Jer+PEE0/E+vXrccstt8x1lxWY+m/DoPX/6KOPxnnnnQcAmJiYwBlnnIGnnnpqaMbf1n8bBq3/nudhfHwcANBqtdBqteB53tCMv63/NnTT/6ESGE899RSOPfZY8XndunW5E3IQ4HkeXvKSl+D888/Hli1bAADPPPMMjj46Kfh+9NFHY+fOnfPZxULY+jtMz+NjH/sYzjnnHFx99dVCpTDI/d+2bRtuv/12XHjhhUM5/rz/wPCMfxiG2LhxI1avXo1LL7106Mbf1H+gf+M/VAIjNjh05UnQQcBNN92E2267DV//+tfxd3/3d/j+978/313qG4blebz97W/Hww8/jDvuuANHH300fv/3fx/A4Pb/8OHDuOKKK/CRj3wEixcvth43LP0fpvEPggB33HEHnnzySdxyyy24++67rccOS//7Of5DJTDWrVuHJ554Qnx+8sknsXbt2nnsUTGof6tXr8ZrXvMa3HLLLVizZg22b98OANi+fTtWr149n10shK2/w/I81qxZgyAI4Ps+fuM3fkPQ7kHsf6vVwhVXXIGrrroKr33tawEM1/jb+j8s409YunQpXvjCF+KGG24YqvEn6P3v1/gPlcB49rOfjQcffBCPPvooms0mrr32Wlx++eXz3S0rJicncejQIfH/N7/5TZx11lm4/PLLcc011wAArrnmGrz61a+ez24Wwtbfyy+/HNdeey0ajQYeffRRPPjgg7jgggvms6tG0MsOAF/60peEB9Wg9T+OY7z1rW/FGWecgXe84x3i+2EZf1v/h2X8d+3ahf379wMApqenceONN+L0008fmvG39b+v499PK/1c4Prrr49POeWU+KSTTorf//73z3d3cvHwww/H55xzTnzOOefEZ555pujv7t2740suuSRev359fMkll8R79uyZ555KXHnllfFRRx0VVyqV+Jhjjon/6Z/+Kbe/73//++OTTjopPvXUU+Ovfe1r89jzBKb+v+ENb4jPOuus+Oyzz45f9apXxU8//bQ4fpD6/4Mf/CAGEJ999tnxueeeG5977rnx9ddfPzTjb+v/sIz/z372s3jjxo3x2WefHW/YsCF+z3veE8dx/vs6DP3v5/i7SG8HBwcHh1IYKpWUg4ODg8P8wQkMBwcHB4dScALDwcHBwaEUnMBwcHBwcCgFJzAcHBwcHErBCQwHBwcHh1JwAsPBwcHBoRScwHBwcHBwKIX/H2EECxTbuaU2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35fae86ccc7546ebb5cc8cc89a21897c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fb1de6b5ec14d839f4bf82043c6b2a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86864a29207440d4a6314df30e06075e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b233348c3ec4440398769f101ce99350",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "44d5dd1f6ed04dc4a2d35ac1b4300cb2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "728187666c6643eb975a366adcf040a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3702ca6f3edc4978b829724ec19ba835",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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uVTwTFZbM/oS/TWTOHgpiZg+/yDRVkz3GyfBP5vzRV1wXp0pRP1xZ9+7dY8aPH8/YWtdh1NRUGU1NdcbBwYH5/vvvmffv3xd7TH5/2P379zPnz59n2rdvz2hoaDC6urrMkCFDmNevX8vs//LlS2bu3LlMmzZtGCMjI0YkEjE2NjaMm5sb4+fnV+x73Lp1ixk2bBhjbm7OCIVCxsjIiGnRogUzd+5c5sED2b7CNjY2RfqmlqR+/foMAMbAwIDJzs4ucb/s7Gxm586dTIcOHRgdHR1GJBIxVlZWjKurK/PLL78wcd7ebD/c/P1XrVrF1K1bly3fDz/8wGRlZZWrHy7DMIxEImF27tzJNGrUiBGJRIy5uTkzY8YMJikpqcSy7t+/n2nRogWjoaHBGBoaMl9//TXz9OlTZsWKFQwAxsfHR2b/mJgYZtSoUYyJiQnD5/MZAMyKFSvY7X5+foyLiwujp6fHaGlpMZ06dWJOnz7N+Pj4FNlX0XDxHecxTNkfxjk6OuJhoUYARHn9s+c5Vs24wS6rCPlYuLETXAbULfXYmKg0HNr5BF6nZP8iHzG1KRZt7gxVNeW/cZLfSpV8Jr7Q99/Qkbs4lEXh66UjfV6KhIvvOD3DrYXOH3ktk2yNTDXgce3rMiVbADC10ML8DZ2w+XAv1LEuGIzg+F/PMaXvWSTFZ37haEIIqZ0o4dYyLx7F4sfp19llYzMN7L40CCbm5X8G26KdGXad6Y9u/WzYdY/8PmBM11N4/674AdQJIaS2ooRbiyR8ysTs4ZeRky1t5q9roIa/Lg6Clo6olCNLpqktwtJtXTF1URt2XdjbJIx3/bfE1s6EEFIbUcKtJRiGwYrp1/EhIhUAIFDh4ZdjvSuVbPPxeDwMm+KA5Tu6QSiS/pOKjU7HhB7/4s2zuEqfnxBCagJKuLWE18lgXD9fMK7rok2dYVVP9wtHlF/XvjZYt7cH1NSljaYSYjMxpe9ZhAcnVen7EEKIMqKEWwskxmVi7Zyb7HLHHlZlbiBVXi3bm2HDgR7Q0PpvzNv/km5MVFq1vB8hhCgLSri1wJYld5DwSdpyWE1DBQs3Fh3vtio5tDbB2t2uUFWTzvASHZ4K937UepkQUrtRwq3hXj2Jw5mDQezygg0doald+ee2pWnqaIrlO7tBoCIdLD4kKBHTB11AelpOKUcSQkjNRAm3BmMYBpsX+yF/aJP6TQzQta+t3N6/nbMlFm7sjPwJWp49iMHckZ7Iy5PILQZCCFEUlHBrsNtXImQmGFi0uXpvJRfHdWBdzFrRjl32uxKBjQuKn5mEEEJqMkq4NRTDMNjxY8FsKR26W8K2gT4nsQwc0xBjZjRjl4/89hR///mMk1gIIYQrlHBrqNteEXgZ+AmAdOaf2avbcxrP+Nkt0aVPwYhUa+fcpOn9CCG1CiXcGohhGPy5oWDQ9K59bWBoovGFI6ofn8/Dwo2dYN/MEAAgETOYO/IyQl/TaFSEkNqBEm4N9PBWNALvfAAA8HiQGXaRS2rqKlj1uwsMTdUBAKnJOfhu8AWkJmdzHBmp7SZOnAgej4ewsDCuQ2HZ2tqyk7yTmkH551EjRezeGMC+budsAWNzTQ6jkWVkqoHVf7hi3ihPZGeJER6cjGVTrmHbP33By2/OrAye/AhkRHAdRck0rIEWqyt9GltbW4SHhxe7zdTUFB8/fpRZl5OTg9WrV+PIkSNISEhA69atsWXLFrRu3brI8d7e3ujVqxcuXLiA/v37VzpWZePs7AxfX1+UY4ZUouQo4dYwoa8T4XelIBFMX9qWw2iKZ9/UEN+v74h1c28BAK6efYd9WwIxeX7Ri7LCyogANG25jqJk6WFVdipdXV3MmTNHupARza7XMrEvsu/ixYvxyy+/wM3NDZaWljh06BBcXFzw6tUrmJubs/ulpaXB3d0dY8eOVYhku379eixevBgWFhZch8K6du0a1yGQKkYJt4Y5+vtT9nXDZoawsNHhMJqSuQyoi6DHcTjtIR2UY9syfzi0MUF7F0uOIyOf09PTw8qVK6ULX5iAnmEY/Pnnn/jmm2+wb98+AMDgwYPh7OyMQ4cOYeHChey+ixYtQlZWFrZv317d4ZeJubm5zB8EisDOzo7rEEgVo2e4NUhaSg7OHHzFLk+Y05K7YMpg6qI2cGhjDACQSBjMH+OJj5E05rKy+vTpEzIyMuDk5MSuy39d+Lb0zZs38fvvv+O3336DgYFBhd4r//lmSkoK5s2bB1tbWwiFwoI/DAC8evUKEydOhJWVFVRVVWFqaorRo0fj9evXRc73pWe49+7dw9ChQ2FmZgaRSAQrKytMmzYN0dHRRfYFgISEBCxduhRNmzaFRufO0HV2RovRo7F48WKkp6cjLCwMPB4Pvr6+AKSzbeX/ODs7Fynj57Kzs7FhwwY0b94cGhoa0NHRQZcuXfDPP/8U2Tf/vSZOnIiwsDCMHDkSRkZGUFNTg6OjIy5cuPDlD/ozZ86cwdixY2Fvbw9NTU1oaWmhTZs22LFjBySSogPa5H+u7969w86dO9G8eXOoq6vLlDMhIQFLlixB48aNoa6uDl1dXXTv3h1Xrlwpcr7k5GRs2rQJrq6usLS0hEgkgrGxMQYNGoS7d+8W2V/RUA23Bjl7KAgZabkAAD1DNTh2qcNxRF+mIuRj2fZumPH1BSTGZSExLgvzRl2Gx/UhEAoFXIdH/pOdnY3Dhw8jIiICmohH8yYN0LVjK3z+GzI2Noa6ujoCAgraEDx8KK0R29hIu4RlZmZiypQpcHNzg5ubW6XiysnJgaurKxISEtCrVy/o6Oigbl3ppByenp4YMmQIcnNzMXDgQNSvXx+RkZH4999/cfHiRfj4+BT7XPlz+/fvh7u7O1RVVTFo0CBYWVnh7du32LNnD86fP4+7d+/C2tqa3T80NBQuLi4IDw9HmzZt8K2bGyQMgzcREfjll18wffp06OnpYcWKFThw4ADCw8OxYsUK9vjSGknl5OSgd+/e8PX1RaNGjfDdd98hIyMDJ0+exIgRI/D48WOsW7euyHHh4eFwcnJCvXr1MG7cOCQkJOD48eP46quvcPXqVbi4uJTpM1+8eDH4fD7atWsHCwsLJCcn4/r165g9ezYePHiAQ4cOFXvc7NmzcevWLfTv3x/9+vWDQCBg43J2dkZYWBi6dOmCPn36ID09HRcuXECfPn3w559/wt3dnT1PUFAQli5diq5du6J///7Q19dHREQEzp07h8uXL+P8+fPo06dPmcrCBR5Tjif2jo6O7BeIKBaGYTCoxVF20nf3ha0x3L0px1GVzdP7MVgw/gokYuk/xcnzW2Peuo6cxhQUFITGjRuXvIP/RMV/htvhQKVPU1Kjqbo2dbDf4yi6desms37OnDnYsWMHhg4dCgsLCxw6dAg5OTkICgqChYUFvv/+e3h4eODly5cwMTGpdFzdu3fH2bNnoalZ0DAwMTER9erVg0AgwM2bN9GkSRN224sXL9CuXTvY29vj0aNH7PqJEyfCw8MDoaGhbNJ78+YNmjZtCmtra/j6+so8371+/Tp69uyJQYMG4fTp0+z6Tp064c6dO1i3bh2WLFkCFLpextnaQktLC2pqagBKbzSVH0fhWvf69evxww8/oG/fvjh37hxUVKR1ptjYWDg5OSE8PBx+fn7o2LEje2z+HyErV66USe5eXl7o06cP+vbti0uXLpX+oQMICQkpcqtbIpHgm2++wcGDB3H37l20a1cwslz+51qnTh3cvn2bjSWfs7Mzbt68iaNHj2LkyJHs+qSkJDg7O+P169cICwuDqakpAGkNNzc3F0ZGRjLniYyMhJOTE3R1dREUFISyKPU7Xg3olnIN8eTeRzbZClR4GDCqIccRlV1zJ1NM/r4Vu7x38yP4eStwC+Ba5JtvvsG1a9fw8eNHpKen49mtY5g2YQjCIj6gb9++ePLkicz+P//8MxYtWoT79+9j7969cHBwwLVr12BhYYF79+5h27Zt2L59O4yMjLBq1SqYm5tDRUUFbdq0gZ+fX7nj27Jli0yyBYCDBw8iKSkJq1atkkm2AODg4AB3d3cEBgbi5cuXXzz377//jtzcXGzfvr1IYypXV1cMGjQI58+fR2pqKgAgICAAd+7cQcuWLbFo0aIi58u/lVsZ+/btA4/Hw9atW9lkCwAmJiZYvnw5AGDPnj1FjrOxscGyZctk1vXu3RvW1ta4f/9+md+/uOfKfD4fs2fPBiBN4sVZuHBhkWT75MkT+Pr6ws3NTSbZAtJ2A6tWrUJWVhZOnTrFrtfV1S2SbAHA0tISQ4cOxatXrxARobjXDrqlXEP8e6Dgr7q2XS3Y+WiVxdDJDgj0/4iHt6TPxRZN9MbpgJEwNlOcLk21UeEaEQA0bVwff2xZAi1NdWzZdQQrV66UqeGpqqpi/fr1WL9+vcxxOTk5mDRpEvr164cxY8Zg27ZtbI2rU6dOWLt2Lfr06YPg4GC2NlMaNTU1NG/evMh6f39/ANILeuFnuvnevHkDQFrD+TwhF3ceX19fPHjwoMj22NhYiMVivHnzBm3atGGfIfbu3Rt8ftXXZVJTUxEcHAwLCws0atSoyHZXV1cAQGBgYJFtLVu2ZG/jFmZlZcWWsyzi4+OxadMmXLp0Ce/evUN6errM9qioqGKPK/xcP1/++yYnJxf7e/r0STpS3uc1Vj8/P2zfvh3+/v6IjY1FTo7sDGRRUVEyt/kVCSXcGiA9LQeX/3nLLo+cphy3kgvj83lYuKkTpg+8gIRPmUj8lInFE7yx+/JX4POVqH9uLTF9ohu27DqCmzdvlmn/VatWISoqim0Is2nTJnTv3p290DZs2BC2trb47bffsHp12foPm5iYFNt3Oz4+HgCwe/fuLx6flvblBnr559m0aVOZzpOUlAQA1da1KDk5GQBKbE2dvz4/jsL09PSKPUZFRaXYxk7FSUpKQtu2bREaGgonJyeMHz8eBgYGUFFRQVJSErZv347s7OIHsTEzMyuyLv/z9fb2hre3d4nvW/j3dPr0aQwdOhRqamro2bMn7OzsoKmpCT6fjxs3bsDX17fEGBQBJdwa4Mq/IWxjKV0DVTRpZcxxRBWjb6iORZs6YfE3V8EwwF2fSOzdFAD3RY6lH0zkysRYOhHG5zWc4gQGBmLjxo34448/YGFhgZSUFERHR2PMmDHsPtbW1jAyMsKLFy/KHENJA6Xo6uoCkNZwi6sBl1X+eZKTk6GjU3r3uvykVlItr7Ly4/l8sJF8Hz58kNmvqu3ZswehoaFYsWJFkRqpv7//F7t4Ffe7yo9z+/bt+N///lemGJYvXw6RSISHDx8Wef46bdo0tuW3oqJnuDXA6UK3kweObqhcIzZ9pnWnOhg5vWBmoR0r7+HZwxgOIyLF8X8gne2pXr16X9wvLy8PkyZNgouLCyZPniyz7fOaSFZWVpXE1r69dKKOW7duyfU8+ft7eXmVqdaYf4tXLBaX6fza2tqws7NDVFQU3r59W2S7j48PAJSp9XVFBAcHA0Cxrcsrkugq8nsKDg5GkyZNiiRbiUSC27cVf9pPSrhKLjo8BQG3/+sPyAMGjVGexlIlmfC/FmjS+r/+uWIGiyd4IzMjl+Ooap8XL14gISGhyPrw9x8wc5H0NuvYsWO/eI7169cjODhY5vaujo4OLCws4Onpiby8PADSC3ZqaiocHBwqHfc333zDNroprkGQRCLBjRs3Sj3PzJkzIRQKMXfuXPa5b2E5OTkyyaJNmzbo2LEjHj9+jJ9//rnI/vHx8TJ/VBgaSifyKE8jn0mTJoFhGCxYsEAmUcfFxWHNmjXsPtUhv9X0559dYGBgkWf2ZeHo6IguXbrg33//ZQdK+dyzZ88QGxsrE8Pbt29l+kAzDINVq1aV2ghOEdAtZSV3qdCz2/qNDaBvpM5hNFVDoMLH4k2dMW3QeWSm5yHsbRK2/nAHS7d1K/1gUmVOnDiBDRs2wMXFBXXr1oW2tjZCgh7iorcfsrKy0a9fP8yfP7/E41+8eIGffvoJW7duZfvh5lu4cCFmz56NLl26oF27djhy5Ai0tLTw3XffVTpuQ0NDnDx5EoMHD0b79u3RvXt3ODg4gM/nIyIiAv7+/kWSX3EaNWqEffv2YdKkSXBwcECfPn1gb2+P3NxcRERE4NatWzA2NsarVwWDzRw+fBjOzs744YcfcOrUKTg3agSGYfD2/XtcuX8fr169YhNX9+7dceLECQwZMgT9+vWDuro6bGxsMG7cuBJjmj9/Pi5fvoyzZ8+iRYsW6NevHzIyMnDixAnExsZi4cKF6Ny5c6U/w+KMHz8emzZtwpw5c+Dj44MGDRrg7du3uHDhAoYMGYLjx4+X+5xHjx6Fq6srJk+ejB07dqBdu3bQ09NDZGQknj59iufPn8Pf35/tQjZ37lxMnz4drVq1gpubG4RCIfz8/PDy5UsMHDgQ58+fr+piVylKuEru4t8Ff3kPGqv8tdt85tba+HZpW2z9QdqS8eiuZ3DuXxedeipm68OayMXFBa9fv0ZgYCD8/f2Rnp4OPV0tdG7XAuOG98O4b38s8fGFWCzGpEmT0K5dO8yYMaPI9lmzZiElJQV//PEHHj16hBYtWuCXX34pcwvl0nTv3h1Pnz7F5s2b4eXlhVu3bkEkEqFOnTpwdXUt86AbY8eORYsWLbBlyxb4+PjgypUr0NTURJ06dTB06FCMGDFCZv+6devi0aNH2LhxI86cOYNfT5yAmkgEW3NzfP/99zJ9j6dMmYLw8HD8/fff2LhxI/Ly8tCtW7cvJlyRSARvb29s3boVR48exc6dO6GiooIWLVpg27ZtGDVqVMU+sDKoU6cObt26hcWLF+P27dvw8vJCo0aNsGvXLvTo0aNCCdfS0hIBAQHYuXMnTp06hSNHjkAsFsPMzAxNmjTBrFmz0KxZwSOmadOmQVVVFdu2bYOHhwfU1dXRpUsX7N+/H6dOnVL4hEsDXyixt8/j8XXrYwCkrXxPB4xUuu5AX8IwDFZ86wP/a5EAAGMzDZx5PBp6BpXry1gWpXaKryWzBRXxhbGUldnIkSNx/PhxREdHV+2YyoWvl4415/OqCbgY+IJquErs0vGC2q1DG5MalWwBacvGuWs7ICjwPJISsvDpYwZ+muWLzUd6cx1a9SQzwpk3b95AVVUVxsbK2cKfKAdqNKWkGIaReX47ZELRjvA1gb6hOuaubc8uXz7xFlf+DeYwIlKTbN++HW5ubggMDMRXX30lM3oTIVWNEq6Sehn4CZGhKQCkkwA4Odfcae069rBGn6H12eU1s3yRFJ/JYUSkpti2bRvu3r2LSZMm4c8//+Q6HFLDUcJVUt7/hrCvW7Qzg0i1Zs+uM22JIwxNpS2wEz5l4uf5it/njii+0NBQREVFYe/evSWOxkRIVaGEq4QYhpG5rTpgVAMOo5EPLR0RZq8quLV87shr3Lwcxl1AhBBSTpRwldDb5/EID5aOqypQ4aFt1+oZu1XRdOhuBZcBtuzyim99kJaSU/IBhBCiQCjhKqErhW4nO7Q2gapa7WnoMWOZE3T1VQEAsdHp2PrDHY4jIoSQsqGEq4S8Txck3AGj7DmMRP70DNUwc0XBVF/H/3peMLQlIYQoMEq4SiY8OAnBL6Xj2/L5PLR3rbmtk0vSrZ8tOvawYpdXz7yB3NyyDQBPCCFcoYSrZHwuhLKv7ZsaQl2jZg12URY8Hg/f/egENQ3prfTglwk4vPMJx1ERQsiXUcJVMjcKJdyeQ+w4jIRbJuaaGD+rBbv866r7+PA+lcOICCHkyyjhKpGk+EwE3P7ALnfpXbsH8h88oTFs7fUAAFmZeVg/r3LznxJCSHWihKtEbl4Oh0QinWvC3FqrRkzFVxkqQj5mr2rHLl87+w6+l8K4C4gQQr6AEq4SuX6+4HayS/+6HEaiOJo6mqK3W8Gt9Z9m+yIrM4/DiAghpHiUcJVETrYYft4F08G5DqKEm899YRto64kAANHhqdi/NZDjiGqenj17gsfjwdcvoMR9pk+fDh6Ph927d8sxMkKUByVcJfHILxoZabkAAG1dEaztdDmOSHHoGqhh8vet2eXdPz9ETFQahxHVPI8ePYJAIECbFiXPH3rv3j0A0nmzCSFFUcJVEre8wtnXjl3qgMfjcRiN4ukzrD7qNdIHAGRnibF1KY1AVVVCQkKQkJAABwcHaGlpFLtPRkYGnj9/DlVVVTRt2lTOERKiHCjhKolbngW3k7sPqsdhJIpJIODj26Vt2eULR9/g6f2PHEZUczx8+BAA0K5duxL3CQgIQF5eHlq0aAGhsPb1DSekLCjhKoHoiFSEBBWMLtWivRnHESmmlu3N0KlnQVepdfNugWEYDiOqGcqScO/evQuAbicT8iW1Z9R7JXa70O3keo30oaZOv7aSTF3UBvdvRCI3V4Jn92Nw8dgbDBjdkOuwlFp+wj1+/Dh8PE8WbFA1ZF/S81tCSkdXbiVwy7Mg4ToXmp6OFFXHRhuDJzbGP7tfAAA2L/GD61f1oKFJtzkrgmEYBAZKW317e3uXuj8lXEJKRglXweXkiHHXJ5Jd7tSjdo8uVRajv22GK/+GICk+C58+ZODg9seY/kPb0g8sIwfRr1V2rur2ImdmpY5/8+YNkpOT0aFDB9y5cweIf1iw0VCaXBMTE2FgYAANDQ00adJE5vhdu3Zh06ZN+PDhAxwcHLBt2zZ06dKlUjERoqzoGa6Ce+T3ge0OpKOnCgtbbY4jUnya2iJ8M7clu7xn0yMkxmVyF5ASy7+d3KpVqxL3efToEQCgZcuWEAgE7Prjx49j9uzZ+OGHHxAYGIiOHTuib9++iIiIKOlUhNRolHAVXOHnt45dqTtQWfV2qw+retK+ypnpufhzw8NSjiDFyU+4rVu3LnGf/IT7+e3krVu3YuLEiXB3d0fjxo2xc+dOmJub4/fff6++gAlRYHRLWcEVfn7r0t+Wu0CUjECFj8nzW2HljBsAgGO/P8O4mS1gYatT6XNX9jatMilLws1/xls44ebk5CAgIADz58+X2bdXr17SW9OE1EJUw1VgH96nykw2T92ByqdjDys0aWUMAMjLlWDnqnscR6RcJBIJHj9+DJFIBAcHhxL3K66GGxcXB7FYDFNTU5l9TU1N8fEj9Y8mtRMlXAVWeHSpug31auVk85XB4/EweX5BzezC0dd4/TSOw4iUS1BQENLS0uDg4ACRSFTsPmlpaXj79i20tLTQsGHR7lefPwJhGIYei5BaixKuArtdaHSpbv1suQtEiTV3MkU7ZwsAAMMAvyzz5zgi5REQIJ2o4Eu3k588eQKJRILWrVuDzy+4nBgZGUEgEBSpzcbGxhap9RJSW1DCVVC5uWLc9XnPLhceQYmUz+T5rZFfqbrlGY4HN6O4DUhJjB8/HgzDYM+ePSXu06lTJzAMA19fX5n1IpEIbdq0KdJ319vbGx07dqyWeAlRdJRwFdTTezFIT5V2B9LSEcGqXuUb+9RWdRvqo8fXBeNP71hxl4Z8lIN58+bhwIED2LNnD4KCgjB79mxER0dj+vTpXIdGCCeolbKC8r9WULtt0c6UnntV0vj/tcT186EQ5zF45PcB93wi0d7ViuuwarQRI0YgPj4eP/30Ez58+ICmTZvi0qVLsLGx4To0QjhBNVwF5X+9IOE696fJ5ivLzFILfYY2YJd3rLhHtVw5mDFjBsLCwpCdnY2AgAB07dqV65AI4QwlXAWUmpyNZ/dj2OVWHag7UFUY/W1TCIXSf/JP7n2EnzeNeEQIkR9KuArowc1oiMXS2pdJHU3oGqhxHFHNYFJHC31HUC2XEMINSrgK6G6h28lO3Sw4jKTmGTWtGYQi6T/7FwGxuHk5vJQjCCGkalDCVUCFG0x17kXdgaqSkZkG+o+0Z5epxTIhRF4o4SqYj5FpePcqEYB0OMemjiYcR1TzjJzWFCJV6aw2r57Ewed8KMcREUJqA0q4Cqbw7WRbez2oqlHPrapmaKKBgWMKhiHc9dN9quUSQqodJVwFc/d6ocnmaXSpajPC3YGt5QY9jsPtK0VbLFMSJqRm4uq7TQlXgTAMI9P/tp0LNZiqLvpG6ug3vKDF8h9rH8h8CQUCAXJzc7kIjRBSzXJzcyEQCOT+vpRwFUjwiwTEfcwAAIhUBajfxIDjiGq2Ye4OUPmvX+7jux/x8FY0u01bWxspKSlchUYIqUYpKSnQ1taW+/tSwlUghWu3jVsaQSCgX091MjHXRM/BBWMs/7HuAfvawMAAiYmJiIuLQ05ODt1eJkTJMQyDnJwcxMXFITExEQYG8q/QUIscBVK4wVTXPjTerDyMnNoMXidDIJEwuHs9Ek/vf0RzJzOoqqrC2toaCQkJCAsLg1gs5jpUxZBeaD7h2CDu4lAWcYU+ryD6vLgmEAigra0Na2trqKqqyv39KeEqiNxcMe77FtzSdOxCz2/loY6NNlwG2OLaOWnXoD/WPcSuMwMAAKqqqjA3N4e5uTmXISqWo00KXo+mWn+pmhT6vOguSa1H9ywVxNN7MchMlzbS0dYVwdxai+OIao9R05uxr30vheHVk7gv7E0IIRVDCVdByEzH196MpuOTI5sGejIjev3180MOoyGE1FSUcBVE4QZT3frS81t5Gz2joJZ75VQwO9oXIYRUFUq4CiAtJeez6fjomaG8NXAwZCeKYBhgz6YAjiMihNQ0lHAVwH3fKJqOTwEUruWeP/IakaHUD5cQUnUo4SqAwt2B2natw2EktZtDaxO0bG8GAJBIGOzdTLVcQkjVoYSrAGSn46Pnt1wa9W1BLfffA0H49CGdw2gIITUJJVyOxUTJTsfXrC1Nx8elVh3M0KiFEQAgL1cCj+2PuQ2IEFJjUMLlWOHZgazsdGg6Po7xeDyMmt6UXT72+zMkJWRxGBEhpKaghMuxws9vO/Wg6fgUQXtXK9g20AMAZGXm4eiup9wGRAipESjhcujz6fg69rDiMBqSj8/nYeS0glruwR2PkZ6Ww2FEhJCagBIuh0KCEvHpg3Q6PqGIj/oONB2fonDubwszS+nwmqlJOTix5wXHERFClB0lXA4Vvp3cwMGQpuNTIAIVPkZMLajl7t8SiJxsmjGIEFJxdIXnUOEGUzQdn+LpNcQOBsbqAIC4mAycPfyK44gIIcqMEi5H8vIkuO9bkHDbuVhyGA0pjkhVgKGTCqZX27MxAHl5Eg4jIoQoM0q4HHn+MAbpqdLp+DS0hLCw1eY4IlKc/iPtoa0rAgBEhqbA62QwxxERQpQVJVyO+F8rqN02bWNC0/EpKA0tIb4e34hd/uvnh2BoInFCSAVQwuVI4QZT3frR81tF9vW4RlDTkA5IEvwiAb6XwrgNiBCilCjhciAjPReP735kl9t2teAwGlIaHX019B9pzy7/uZ5quYSQ8qOEy4GA29HIy5U2vjEwVoe+kTrHEZHSDP2mCYRC6dfl6f0YPLgZxXFEhBBlQwmXA4VvJ7fqYMZhJKSsjMw00GuIHbv81waauo8QUj6UcDlQuMFUt/623AVCymW4e1Pw+dLGbf7X3uN5QAzHERFClAklXDmLj83A66dxAAAeD2jhRDVcZVHHRlumgdvun6mWSwgpO0q4cnbPp6B2W8daGxpaQg6jIeU1clrBBPVXz7xD8MsEDqMhhCgTSrhyVng4RxpdSvnUa6SP9q4Fv7e9m6mWSwgpG0q4cvT5dHxdaPxkpTRqekEt98KxN4gKS+EwGkKIsqCEK0fv36UgOjwVACBQ4aFRcyOOIyIV0aSVMVq2lz57l4gZ7P8lkOOICCHKgBKuHBXuDlSvkT5UhPTxK6vCE9Sf3PsCnz6mcxgNIUQZ0BVfjvyvFbqd3JtuJyuz1p3MYd/MEACQmyPBoR1POI6IEKLoKOHKiVgswb0bBQ2mCje8IcqHx+PJPMs9+vszpCRlcxgRIUTRUcKVk1dP4pCcIL0gq2mowLaBHrcBkUrr2MMK1na6AIDM9Fwc+/0ZxxERQhQZJVw5ueMdwb5u3NKYpuOrAfh8nsyz3AO/BCI9NYfDiAghiowSrpzcvlKQcF0H2nIXCKlSLgPqwsxSCwCQkpSNI7895TgiQoiiooQrB2kpOXjsXzAdXztnen5bU6gI+Rgzo+BZ7r4tj5CWQrVcQkhRlHDl4J5PJPLypNPxGZtp0HR8NUyPr+1Qx1obAJCanINDO6nFMiGkKEq4cnD7Sjj7moZzrHlUhHyMndmcXd6/9RG1WCaEFEEJt5oxDCPz/NZ5gC13wZBq4zqwLizr6gAA0lNzcXD7Y24DIoQoHEq41SzsTRI7nKOKCh8OrUw4johUB4EKH+MK1XIPbHuMpIQsDiMihCgaSrjVrHDttr6DAQ3nWIN1628r0y/Xg8ZYJoQUQlf/alb4+a1Lf1vuAiHVTiDgY9ysglruwR1PEBeTwWFEhBBFQgm3GmVl5uHhzWh2uWNPaw6jIfLQta8t6jbUAyD9/f++9gG3ARFCFAYl3GoUcDsaWZl5AABdA1V2gARSc/H5PEz+vjW7fGL3c4S/TeIuIEKIwqCEW40K305u06kOh5EQeXJytkBzJ1MAgFjMYPuPdzmOiBCiCCjhViO/KwXT8XX/qi6HkRB54vF4cF/Yhl32OhWMpw9iOIyIEKIIKOFWk+iIVIQEJQCQ3mZs0c6M44iIPDVqYYSufQvmPN6y2A8Mw3AYESGEa5Rwq0nh28m29npQVVPhMBrChW/mtYJARTor1MNb0bjlGV7KEYSQmowSbjW5cSGUfe1M3YFqJUtbHfQbYc8ub1lyhx1TmxBS+1DCrQYZ6bnwvxbJLjv3s+UuGMKpcTObQ11Tencj+GUCTux5wXFEhBCuUMKtBv5X3yMnWwwAMDBWh/l/M8mQ2kffSB0jphZMUr99uT8S4zI5jIgQwhVKuNXAp9Dt5I49rDiMhCiCYZMdZKbvo25ChNROlHCrmFgsge+lMHa51xA77oIhCkGkKsC3S9uyyyf3vsCLR7EcRkQI4QIl3Cr29H4MEj5JbxmqqgvQsLkRxxERRdDe1RJO3SwAAAwDrJ1zExIJdRMipDahhFvFfM4X3E5u4WQGPp/HYTREkXy7rC2E/80W9eTuR5w7/IrjiAgh8kQJt4oVfn7bd1h9DiMhisbSVgduk5qwy1uW3EFyIs2ZS0htQQm3CoW/TcK7V4kAAL6AB8euFhxHRBTN6G+bwchUAwCQ8CkTmxb6cRwRIUReKOFWIZ+LBbXb+o0NoKZOo0sRWeqaQnz3oxO7fNojSGZUMkJIzUUJtwoVHl2KWieTknTuZY1u/QrGWV4x3QdpKTkcRkQIkQdKuFUkKT4Tj/w+sMuFB64n5HPf/egEHX1VAMDHyDRsXXqH44gIIdWNEm4VuXk5HGKxtJuHuZUW9I3UOY6IKDJ9Q3V8t7zg1vLxP5/jvm/kF44ghCg7SrhVxPt0CPu6G01WQMrAZYAtOnS3ZJeXT72O9DS6tUxITUUJtwqkpeTg9pUIdrm3G3UHIqXj8Xj436r20NQWAgAiQ1OwdvZNjqMihFQXSrhV4MbFUHayAkNTdVja6nAcEVEWRqYaMq2Wzx56RQNiEFJDUcKtAldOFdxO7j6oHoeREGXU82s79Pi64N/N6pk3EPYmkcOICCHVgRJuJaWn5uCWV0E/yr7DGnAYDVFW/1vZDpZ1pXdGMjPy8P0YL2Rn5XEcFSGkKlHCraQbF8MKbiebqLMXTULKQ11TiKXbukIokn4lXz2Jw+bFNAoVITUJJdxKuvzPW/a1K91OJpVQv4kBpi1xZJeP7nqGC0dfcxgRIaQqUcKthKSELJnbyf1H0u1kUjmDxjRE517W7PKyqdcQ6P/hC0cQQpQFJdxKuHIqGHm5EgCAmaUWLGzodjKpHB6Ph/kbOsKmvi4AIDdHglluFxEVlsJxZISQyqKEWwkXjr1hX1PtllQVTW0R1vzlCt3/hn5MjMvCd4Mv0HjLhCg5SrgVFB2egoDb0dIFHg12QaqWuZU2Vu5yhsp/E9a/fZGA+WO9kJcn4TgyQkhFUcKtoIvHCxpL1WuoT2MnkyrX1NEU89Z2YJdveYZjufs1SCQMh1ERQiqKEm4FMAyDMx5B7PLX4xtxGA2pyXoOtsPI6U3Z5XNHXmPVdz5gGEq6hCgbSrgVEOj/EWFvkwAAKip8uAyoy21ApEabNK8V+o0oaCNwcu9LrJ93i5IuIUqGEm4FnD7wkn3t5GwBNXUVDqMhNR2Px8Ps1e1lhn888ttTbFlyh5IuIUqEEm45pafl4PKJYHZ5xDQHDqMhtQWfz8P89R3RrZ8Nu27/1kCsn3cLYjE1pCJEGVDCLacrp4KRmZ4LANAzVEPjFsYcR0RqC4EKH4s3d0HHHlbsuiO/PcV8GneZEKVACbecjv/1gn09YKQ9eDweh9GQ2kZFyMfSbV3RtW9BTffKvyGYNuAcUpKyOYyMEFIaSrjl8DwgBs8exAAAeDzgq3HUOpnIn0hVgKXbusq0jn9wMxrjXU4hOpxGpCJEUVHCLYe//3jOvm7V0Rx6hmocRkNqMz6fhxnL2mLKgtbsurcvEjC03XHcvhL+hSMJIVyhhFtGSfGZuHS8YCjHCbNbcBgNIdLWyyOmNsXCTZ0gEEgfbSQnZGP6wPPY9dN9GiCDEAVDCbeMTnsEITtLOu+tsZkGGrekxlJEMfT82g5bjvaGgYl0tDOGAX5bfR8zvr6AxLhMjqMjhOSjhFsGubliHP7tKbs8fKoDNZYiCsWhtQn+ODcALdqbsutueYbjq5ZHcfVMCIeREULyUcItA68Twfj4Pg0AIFIToO8wmhmIKB59Q3X8vL8nRk4rGAoyPjYTs4dfxqIJV5CUkMVhdIQQSrilYBgG+7YGsssDRtlDVY1GliKKSaDCx+T5rbFub3cYGBdMqHHh2Bt81fIoLv/zlkanIoQjlHBL4X/tPV4/jQMA8AU8jPm2GccREVK6tl0tsOfyIPQcXDAcZNzHDMwf64Vvep3Bm2dxHEZHSO1ECbcUf/0cwL7u3MsaOvrUFYgoB21dVSzc2Bmr/nCBfqEubA98o+DW9jjWzvFFUjw1qiJEXijhfsF930g88I2SLvAg0+eREGXRsbsV9l35Gm7fNAafL23sJ5EwOLrrGXo3PIRdP91HWkoOx1ESUvNRwi0BwzD4bfV9drldNwuYW2lzGBEhFaelI8L0H9rirwsD0bKDGbs+LSUHv62+j94ND2L/1kfIzMjlMEpCajZKuCW45xOJh7eipQs8YOaKdtwGREgVsGmgh40ePbHiN2dY1tVh1yfFZ2Hz4jvoWd8Df6x7QC2aCakGlHCLIZEw2Lb8LrvcwdUSZpZaHEZESNXh8Xjo3Msaey4PwoKfO8HYXIPdlhiXhZ0r76GHnQc2LrxNYzMTUoUo4Rbj0vE3MpMUzPzRieOICKl6AgEfvYbYwePqYMxa6QQjs4LEm5meC49tj9G74SHMHHIRt6+E01CRhFQSjylHpzxHR0c8fPiwOuPhXFZmHvo3PcwOdNFnaH18v74jx1ERUv3yciW4cSkMx35/hoiQ5CLbrevrYoTjfgzuche6mpnAaErApSo8Ih31f671KOF+5o91D7Bz5T0A0mnQ/vEfBk1tEcdRESI/DMPgnk8UTu1/icd3PxbZribKQS/Hx/jqx7Vo280CAgHdKCsRJVxSCCXcQsKDkzC49TF2kgL3ha0x3L1pKUcRUnO9f5eM88dew/OfYGRm5BXZbmaphYGjG2LQ2Eao10ifgwgVHCVcUggl3P8wDIMpfc/i7vVIAIChqQaO3BgCgQr99U5IZkYufC6E4fKf/+BVhFWx+zRra4r+I+3Rc7AdNTLMRwmXFEIJ9z9nDgZh6ZRr7PLOk33RqAVNwUdIYea3dfEizArn7zjh3N1OSEkVFrtfyw5m6DWkPnoOtkMd61rcf50SLimEEi6AqLAUDHH8mx1tx3VQXSzZ0oXjqAhRPHX8dNnXCWaL4f9ADZe81HD7rjry8oq/G9TcyRTdv6qHbn1tUd/BoHZNbUkJlxRS6xNuXp4EE7r/i8f+0sYhmtpCHLs9FOoaxf/lTkhtVjjhplotZl8np/Dgc1MdV2+o4tETdUgkxSdVc2ttdO1jg659bdDOxbLmf88o4ZJCan3C3bnyHv5Y94Bd3nqsN5o5mn7hCEJqr5ISbmFJyXzc9FPDVR9VBDxWg1hSfM1XpCqAY5c6cOpmASdnSzRpbQyhUFAtcXOGEi4ppFZP7Op9OkQm2Q6Z2JiSLSGVpKcrwaB+GRjULwPJKTzc9leHn78Idx+qIiOz4JKTky3GnavvcefqewCAuqYQrTuZw6mbBRy71EHjlsY09zSpUWptDff10ziM6XqS7epg00AXf54fSH0KCfmCstRwS5KXBzx9IYLfXVXcviNCeOSXp7pUUeGjQVNDNHU0QbO2pmjaxgR2TQygokw9B6iGSwqplQk3MjQF41xOITY6HYD0ue3B60Ogo6fKcWSEKLbKJNzPfYgRICBQFQGBQjwMFOJTfOnfP1U1AWzt9WHXWB/1GhnArrE+7BobwLq+rmLejqaESwqpdfdrYqPTMKXvGTbZ8gU8bDnam5ItIXJmbirGgD4ZGNBHmos+fBTg0RNVPAwU4tlzFUR9LFoDzs4S4/XTOLx+GiezXiDgwcRCC+ZWWqhjrQ1zK23UsdGGmaUWDE01oG+kDgNjdaip17pLHlEgtepfX2RoCqb2P4v376QzoPB4wOo/XGDXyIDjyAip3Xg8oI65GHXMpQkYAFLTeAh6LULQayGevxAg6LUQcYnF/2EsFjP4EJGKDxGpeOT3ocT3UdcUQt9IDfpG6tDSEUFTSwhNbRE0tIXQ1BJBU1u6rKkthLqGEOqaKtDQEkJdU7qsoakifa0phKqaoHZ1cSKVVmsS7uuncZg28Bw+fchg1y3Z2gXtnC05jIoQUhJtLQZObbLh1CabXZeSykNYhBChYSoIDVdBSAgQ/l6I2PgvPw/Ol5mei8z0XESHp1Y6Pj6fBzUNaQLOT8QamvnJ+b/ELBgJbSYT+kiDgcdLtqatb6QOQ1MNaGjW8G5RREatSLiXjr/Bj9Ousw2keDxgwYaOcBlQl+PICCHloaPNoLlDDpo75Misz84BYj8J8DFGBTGxAnz4yEf0Bx4+xvCQlMRHcooKktOEEIurrsGVRMIgIy0XGWm5iC9pJ36ngtfu14uWR18V5lbSW99mVlowt9KGuZUWbO31YFNfD9q69KirJqnRCTc9NQdbfriD438+Z9cJBDys+csVbbtacBgZIaQqqYoAKwsxrCzEJe7DMEB6Og+JyXwkJ/ORnsFHRiYPGez/eUhPA9LSgfR0IDMTyMziITOTh6xsPrKyecjK4iMrW4CsHEGJI2uVR0piNlISs4s8k85naKoB2wZ6sLXXk/6/gR5sGugpbiMx8kU1MuEyDINbnuFY8z9fmVtH2roibDrYC3ZN6JktIbUNjwdoaTHQ0vpyYi6rPDGQlcmTJuUsaTLOT9DSZR6YtVeQAg0kQAsfujsiMZGHpGQ+kpIFSEwRlVrjjo/JQHxMBgJuR8usV1Hhw6aBHuwa66N+EwPYNZb+2NjrQSSiRKyoalzCfR4Qg23L7sL/2nuZ9U1aGWPd3u40ty0hpEqoCPITeH53n6JJXHuNH/s6dXlDmW0SCZCQyEfMJwFiYgX/3RIXIDKSh4hIAaJjVEusReflSRASlICQoARc+TeEXS8Q8GBTXw92bBKWJmRbe32IVCkRc61GJNy8PAlueYbj0M4nuOcTKbNNRcjHd8vbov9Ie2pRSAhRGHw+YGQogZGhBA6NcotsF4uBmFgBIiJVEP5eBRHvBQgL5yH8vRBxCSW31n73OhHvXifC+7RsIraqp4v6TQxQj03EhqjbUI9G85Ijpf2k8/IkeOQXjWvnQuF54i3iPmYU2adDd0vM/akD9I3UOYiQEEIqTiDI7yolRvu22TLb0tN5CHuvgtAwId6FCRAcwkNouBCfSmitLRYzCHubhLC3ScDZd+x6Pp8Hq3o67C3p/NvTdRvpU5/laqAUnyjDMPgYmYZXT6Qd3p/c+4iA29FITy36VyEAtGhvhpk/toVtA305R0oIIdVPU5OBQ6PcIjXjjEwewiNU8C5MiJB3PASH8BAWIUJsfPGVDomEQXhwMsKDk3H9fCi7nscDLOvpwqa+rrQFtaW2tBW1pRbMLLVgYqFFXZoqgNOEm5MjRlpyDlISs5CSnIPUpGykJmUjPjYDH96nSTuyv09F6JskpCRmf/FcauoqcB5gizEzmsPMUktOJSCEEMWhoc6gccNcNG4om4gzM3kIfy/tu/z2LRDyjoew96qIjVcHwxR91MYwwPuQZLwPSS7xvdQ1VKBvLO1XnN+3WN9IDZraImjpSH/yBxHR0hFBS1sEDW0RRKoCiFQFEIr4EKkKatX49Zwk3G3L/HH41ydsv9iK0tASwrGzOfoMb4DWHcwhUKZBzQkhRE7U1Rk0ss9FI/tc9O2ZvzYDWdmJiHgvxLswFYSE/Fcjfi/Cx0/FJ+LCMjPykBmeWulBRAQCnjQB/5eIRaoCCIV8CAR88AU88Pk88PjS//P5AI/Pg0BQeJ3sa76Ah2+XtkWrDuaViqs6cJJweXxeuZOtQMCDnpE6bOx00bCFIeybGcHYTINtCBXyKrE6QiWEFFKn0OukmFjO4lAW2oVeK+rnZaINmDQD2jcrWJeTw0PkBzXEfBLhU7wQsfEixMbyERungrhEVSSnqiI3r2paPYvFjDR5V7ICVtgI96ZVdq6qVK7ZgoyMjGBra/vFfT59+gRjY+Myne/1szjk5UqKrOfzeVARCsBXsgprdl4qVFW0S9+xhqFy1y55kiQY175iAwBSM7OhrV77Rn8qX7mZ/2ZGYgCm6PW9hCOku6P4WjXD/qf0niYCVU0YWNQpdb+y+PTpExo3bgxPT88qOV+5Em5Z1JQp/Cqitpadyl271NZyA7W37FTuqqFkdUhCCCFEOVHCJYQQQuSgyhPu1KlTq/qUSqO2lp3KXbvU1nIDtbfsVO6qUeXPcAkhhBBSFN1SJoQQQuSgTAk3MjISs2bNQocOHaChIe37GhYWVupxDx8+xNSpU9GoUSNoaGjA2toaY8aMQWhoaKnHKoqKlv1z69evB4/HQ+fOnas+yGpQ2XIHBQVh2LBhMDIygrq6Oho2bIjt27dXX8BVpDLljoiIwIQJE2BtbQ0NDQ3Y29tj2bJlSE9Pr96gq8DJkyfh5uYGGxsb9ve1ZMkSpKaWPqhBVlYWFixYAHNzc6irq6NDhw64efOmHKKuvIqWW9mvbZX5fRembNc1oPJlr8y1rUwJNzg4GP/88w/09fXRpUuXMp0YAP7++2+8ePEC//vf/3D58mVs2LABjx49gqOjI96/f1/6CRRARcte2Lt377B27VqYmJhUcXTVpzLlfvjwIdq1a4fs7Gzs2bMHly5dwvfffw+xuPJzkFa3ipY7PT0dPXr0wM2bN7FmzRpcvHgRU6ZMwZYtWzBp0qRqjLhqbN68GQKBAOvWrYOnpye+/fZb/P777+jZsyckki/3pZw8eTJ2796N1atX48KFCzA3N0fv3r3x+PFj+QRfCRUtt7Jf2yrz+86njNc1oHJlr/S1jSkDsVjMvt69ezcDgAkNDS31uNjY2CLrwsLCGB6Pxyxfvrwsb825ipa9sF69ejFTp05lunXrxnTq1KmKI6weFS23WCxmmjRpwnz99dfVGF31qWi5vby8GACMl5eXzPpFixYxAoGASU9Pr+pQq1Rx31UPDw8GAHPt2rUSj3v8+DEDgNm3bx+7Ljc3l7G3t2cGDhxYLbFWpYqWW9mvbRUtd2HKeF1jmIqXvSqubWWq4fIrOORTcSNO2djYwNjYGFFRURU6p7xVtOz5jh49ikePHmH9+vVVFJF8VLTcN27cwMuXLzFv3rwqjkg+KlrunJwcAICOjo7Mej09PUgkEjAK3jaxuO9q27ZtAeCL39Vz585BKBRixIgR7DoVFRWMHDkSXl5eyM7+8qQjXKtouZX92lbRcudT1usaUPGyV8W1Te6NpoKCghAbG4vGjRvL+63lLjExEXPnzsXGjRthYGDAdThycfv2bQDS53rt27eHUCiEiYkJ/ve//yEzM5Pj6KpPjx490KBBAyxatAgvX75EWloarl+/ju3bt2P69OnQ1NTkOsRy8/X1BYAvfldfvHiBunXrQkNDQ2a9g4MDcnJyEBwcXK0xVoeylLs4yn5tK2u5a+J1rSxlr4prm1wTbl5eHqZPnw5jY2NMnjxZnm/NiQULFsDe3h4TJ07kOhS5iY6OBgCMGDECvXr1gre3NxYuXIg9e/Zg9OjRHEdXfdTU1HD79m1IJBI4ODhAW1sb3bt3x4ABA/Drr79yHV65RUVF4ccff0SPHj3g6OhY4n4JCQnQ1y8673T+hTghIaHaYqwOZS3355T92laecte061pZy14V1za5zhY0c+ZM3LlzBxcvXiz2S1qT3Lp1CwcPHsSjR4/YGY1qg/xGB2PHjsXq1asBAM7OzhCLxVi8eDFevnyJJk2acBlitcjKysKIESMQGxuLQ4cOwdraGvfv38fq1auhoqKC33//nesQyywtLQ1fffUVVFRUsH///i/uyzBMsf++Ff0WenHKU+7PKfO1rTzlrmnXtfKUvSqubXKr4S5ZsgR//fUX9u3bh169esnrbTkzbdo0TJ48GZaWlkhKSkJSUhLy8vIgFouRlJSk8M+2KsrQ0BAA0LNnT5n1+b9zZWi5WhF79+7FjRs3cOnSJYwdOxZdu3bF/PnzsWXLFvzxxx948uQJ1yGWSVZWFgYNGoR3797By8sLlpaWX9zfwMCg2FpsYmIiu10ZlLfchSnzta285a5J17Xylr0qrm1ySbhr167Fhg0bsH37dowbN04eb8m5oKAg/PHHH9DX12d//Pz8cPfuXejr6ytVjac8HBwcAKDIX7/5NZ7KNkJTVM+ePYO+vj7s7Oxk1js5OQGQ/ntQdLm5uXBzc8P9+/dx6dIlNGvWrNRjHBwcEBoaioyMDJn1L1++hEgkQv369asr3CpTkXLnU+ZrW0XKXVOuaxX9tw5U7tpW7beUd+zYgWXLlmHt2rWYNWtWdb+dwvDx8Smybs6cORCLxdi5c6dSXIgqom/fvlBVVYWnpycGDBjArvfy8gKAcj0XUyZmZmZITExEcHCwzO/23r17AAALCwuuQisTiUSCMWPG4Nq1a7h48SLat29fpuMGDRqEFStW4MSJE5gwYQIA6fPM48ePo1evXlBVVey5YytabkC5r20VLXdNuK5VtOxVcm0ra/+hEydOMCdOnGCmT5/OAGB27drFnDhxgrlx4wbDMNI+aAKBgFm1ahV7zLFjxxgej8f06dOH8ff3l/l58eJFhfsyyVtFyl4cZeuvVtFyr1y5khEIBMySJUsYb29vZv369YyamhozYcIEDkpRfhUpd2hoKKOtrc00aNCAOXDgAHP9+nVm48aNjLa2NtOmTRuZ/r2KKL+sS5cuLfJdff/+PcMwJf++R4wYwejp6TG7d+9mrl69yri5uTGqqqpMQEAAF0Upl4qWW9mvbZX5fX9O2a5rlSl7Za9tZU64AIr96datG8Mw0gsOAGbFihXsMRMmTCj1OGVQkbIXR9n+YVa03BKJhNmyZQtjZ2fHCIVCxtramlm+fDmTk5Mj/0JUQEXL/eLFC2bYsGGMpaUlo6amxjRo0ID5/vvvmYSEBPkXopxsbGxKLHd+OUsqd0ZGBjN37lzG1NSUUVVVZZycnBgfHx+5l6EiKlpuZb+2Veb3/Tllu65VpuyVvbbRbEGEEEKIHNTMFiyEEEKIgqGESwghhMgBJVxCCCFEDijhEkIIIXJACZcQQgiRA0q4hBBCiBxQwiWEEELkgBIuIVWEx+OBx+OBz+cjJCSkxP1cXFzYfQ8cOCCzbeLEiey2FStWlHgODw8Pdj9nZ2eZbTdu3GC35f9oaGjA3NwcXbt2xYIFCxAYGFiZohJCKoASLiFVSEVFBQzDYO/evcVuf/v2LXx9faGi8uVhzFVUVLBv3z6IxeJit+/evbvUc9jY2GDFihVYsWIF5syZgwEDBiA9PR2bN29G69atMWbMGKSlpZWtYISQSqOES0gVMjU1haOjI/bv34+8vLwi2/fs2QOGYWQGPy/OgAEDEBkZCU9PzyLbgoKC4Ofnh4EDB37xHLa2tli5ciVWrlyJdevWYffu3QgICEBgYCCaNWuGo0ePYtiwYeUrICGkwijhElLF3N3d8fHjR1y4cEFmfW5uLjw8PNCxY0d2qq+SjBkzBurq6ti9e3eRbfnrpkyZUqH4WrZsiatXr8LY2Bienp44c+ZMhc5DCCkfSriEVLFRo0ZBU1MTe/bskVl/7tw5xMTEwN3dvdRz6OnpYdiwYbh48SI+fPjArs/OzsbBgwfRrVs32NvbVzhGExMTTJs2DQBw5MiRCp+HEFJ2lHAJqWLa2toYOXIkPD09ERkZya7fvXs3dHR0MHz48DKdx93dHXl5eTINq06fPo34+PgyJe3S5De2un//fqXPRQgpHSVcQqqBu7s7xGIx9u3bBwAIDw+Ht7c3xowZAw0NjTKdo3PnzmjcuDH73BeQJm19fX24ublVOkYLCwsAwKdPnyp9LkJI6SjhElIN2rVrh2bNmmHfvn2QSCTYs2cPJBJJuWumU6ZMwbt373D9+nUEBwfDx8cH48aNg5qaWqVjzE/iPB6v0ucihJSOEi4h1cTd3R3h4eHw9PTE/v370aZNG7Rq1apc5xg/fjxUVVWxZ88etqZbFbeTASA6OhoAYGxsXCXnI4R8GSVcQqrJuHHjoK6ujmnTpiEqKgpTp04t9zmMjIwwePBgnD59Gvv27UOHDh3QtGnTKonPx8cHgLQ2TgipfpRwCakmenp6GDp0KCIjI6GpqYlRo0ZV6Dzu7u7Izs7Gp0+fqqx2Gxsbiz///BOAtAsSIaT6fXmoGkJIpfz0008YMmQIjI2Noa2tXaFzuLi44OzZs5BIJOjdu3elY3ry5AnGjx+PuLg49OvXD4MGDar0OQkhpaOES0g1sra2hrW1daXOwePxKpQUw8LCsHLlSgDSQTfi4uIQEBCAgIAAANKabX4tlxBS/SjhElJDhYeHY9WqVQAANTU16OnpoUGDBpg/fz7GjBmDli1bchsgIbUMj8nvG0AIIYSQakONpgghhBA5oIRLCCGEyAElXEIIIUQOKOESQgghckAJlxBCCJEDSriEEEKIHFDCJYQQQuSAEi4hhBAiB5RwCSGEEDmghEsIIYTIwf8BifjPt2dEPb0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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yySfsyZMnSvcpeR9z69at7NChQ6xLly7MyMiImZubsxEjRrD79+/LlY+MjGTz5s1j3t7ezMbGhonFYubs7Mz8/f3ZhQsXlH7HuXPn2KhRo5i9vT3T19dnNjY2zNPTk82bN49dLfcuoLOzs8K7kRVp3rw5A8CsrKxYfn5+heXy8/PZxo0bWdeuXZmZmRkTi8WsadOmzM/Pj61bt07hndL8/Hy2bNky5urqytVv0aJFLC8vr1rvgTLGmFQqZRs3bmTu7u5MLBYze3t7Nnv2bJaRkVFhXbdu3co8PT2ZkZERs7a2Zm+99Ra7desWW7p0KQPAQkJC5Mq/ePGCjR07ltnZ2TGhUMgAsKVLl3KfX7hwgfn6+jILCwtmYmLCunfvzvbv389CQkIUytaJOn4PVMAYU/nBW6dOnXDt2rXayeT1FGMMM4YE4eIJWTOIsak+tp54C5bW8u+67fn9Djavvs6tf/ljH4ye0bZOY9Vpu8p0DhlX82fJJb0wCSE6pGx+6tSp0qKauMbpGWgtOx30mEueALD8V1+F5AkAb0/3wOAxLbn17xacR1x0Rl2ESAghRA2UQGuRVMrw4/LL3HrvQc5o31n5+14CgQCzv3gNzs1l3dPzXhVhxYehqEYDASGEkDpECbQWnTzwEA9uy164FooEeP/LzpWWF0tE+Oz7Htw7oWGnnuDUQcUZKAghhPCPEmgt2v7DTW65/3A3pU235bXwsMaQcWWacj89j8JC5e9hEUII4Q8l0Fry4HYK976nQABMmeel8r6T53rB1EI2FdHT2Czs3xZVxR6EEELqGiXQWvLX73e45bbedrC2M6qktDwzCwlGTy/tgfvziivIz1N8UZsQQgh/KIHWgoL8Yhz96wG3PvFDz0pKK/fmRHdY2cqafJOf5+Lw7gdV7EEIIaQuUQKtBeeD45CVKZsmyNRcDK8u1ZtpAQAMDPUwcmobbn3L99dprFxCCNEilEBrwdH/RXPLfQa7qDSfojKDRreAkbFs1obY6AycOxZXxR6EEELqCiVQDcvNKcSZw6UDSA8d10rtYxmbivHG6Bbc+pY11yspTQghpC5RAtWwiyfi8SpX1uHH0sYArq0sa3S8EZPdIRTJ7mCvnUtEZERSjWMkhBBSc5RANSz0aCy33Gugc42PZ9fEBH0GuXDrW9dG1PiYhBBCao4SqAZJpQyh/5Y+p+z7ZrNKSqtu5LulnYmO73uItORXGjkuIYQQ9VEC1aA74UlIfZELAJAYiNCqvY1GjtvCwxrunrJjFRVJEbTjnkaOSwiRmTJlCgQCgdIJvfni4uLCTVpNtBPN2qxBZ8s033r6NObGtNWEwaNb4N7NFADAX7/dxuSPvNTu3Us04OaXQG4831FUzMgJ8Fxe48O4uLggLk557+9GjRrh+fPnctsKCgqwfPly7Ny5E2lpaejYsSPWrFmDjh07Kux/4sQJ9O/fH4cPH8bgwYNrHKuu6dOnD0JDacIIXUYJVIPCTpdOW/b6W5ppvi3Re7ALfvn6GnJzCvHk0UuEn09Ep54OGv0OUg258YCxC99RVCwnVmOHMjc3x0cffaSw3cTERGHb559/jnXr1sHf3x+Ojo74888/4evri3v37sHe3p4rl52djenTp2PChAlakTxXrlyJzz//HA4O2nNNnTp1iu8QSBUogWpITlYB7lwt7SHbsZt9JaWrz9BIH75DXXHkvxGO/rfpDiVQUicsLCwQEBBQZTnGGH777Te888472LJlCwBg+PDh6NOnD/788098+umnXNnPPvsMeXl52LBhQ22FXS329vZyCV4buLm58R0CqQI9A9WQa+cSUVQkBQDYNDaCuZWBxr9j8JjSd0KD9z1ERlqexr+DEHUlJycjNzcXnTuXTttXsly2Gfjs2bP45Zdf8NNPP8HKykqt7yp5Pvjy5Ut8/PHHcHFxgb6+vlyiv3fvHqZMmYKmTZtCIpGgUaNGGDduHO7fv69wvMqegV6+fBkjR45E48aNIRaL0bRpU8ycOROJiYlKY0tLS8PixYvRtm1bGBkZwdzcHJ6envj888+Rk5OD2NhYCAQChIaGApDNBVzyp0+fPgp1LC8/Px+rVq1C+/btYWRkBDMzM/Ts2RP/+9//FMqWfNeUKVMQGxuLMWPGwMbGBgYGBujUqRMOHz5c+Q+6nAMHDmDChAlo2bIljI2NYWJiAm9vb/zwww+QSqUK5Ut+ro8ePcLGjRvRvn17GBoaytUzLS0NCxcuROvWrWFoaAhzc3P07dsXx48fVzheZmYmVq9eDT8/Pzg6OkIsFsPW1hbDhg3DpUuXqlUXTaA7UA25FFLafOvdo0mtfEcLD2u08LBC9N00FBVKcWT3fYx/v/rj7BJSHfn5+dixYwfi4+NhbGyM9u3bo1evXhCJRHLlbG1tYWhoiPDwcG7btWvXAADOzrJXul69eoVp06bB398f/v7+NYqroKAAfn5+SEtLQ//+/WFmZgZXV1cAwLFjxzBixAgUFhZi6NChaN68ORISEvDPP//gyJEjCAkJUfpctrytW7di+vTpkEgkGDZsGJo2bYro6Ghs3rwZhw4dwqVLl+Dk5MSVf/z4MXx9fREXFwdvb2+89957kEqlePDgAdatW4dZs2bBwsICS5cuxbZt2xAXF4elS5dy+1fVaaigoAADBgxAaGgo3N3d8f777yM3Nxd///03Ro8ejRs3buCbb75R2C8uLg6dO3dGs2bNMHHiRKSlpWHPnj148803cfLkSfj6+qr0M//8888hFArh4+MDBwcHZGZm4vTp05g7dy6uXr2KP//8U+l+c+fOxblz5zB48GAMGjSI+92Ji4tDnz59EBsbi549e2LgwIHIycnB4cOHMXDgQPz222+YPn06d5yoqCgsXrwYvXr1wuDBg2FpaYn4+HgEBQXh33//xaE1azCwWzeV6qIJlEA15NLpBG655wCnSkrWzBujWiD67mUAwP7AKEqgpNY9f/4cEydOlNvm6uqKrVu3onfv3tw2gUCAGTNm4IcffkBmZiYcHBzw559/wtTUFOPHjwcAfPHFF0hLS8NPP/1U47iePXuGNm3aIDQ0FMbGxtz29PR0jB07FkZGRjh79izatCl9Dezu3bvw8fHBtGnTcP165SN7PXjwADNnzoSLiwtCQ0Plno+ePn0a/fr1w9y5c7F//35u+4QJExAXF4dvvvkGCxculDteSkoKTExMYGBggICAAJw5cwZxcXEqNY+XWLNmDUJDQ/HGG28gKCgIenqyf8KXLl2Kzp07Y+XKlRgyZAi6lUsiZ86cQUBAgFyyHjduHAYOHIjVq1ernECPHDmi0LQslUrxzjvvYPv27ZgzZw58fHwU9rt+/ToiIiK4/+CUmDx5MuLi4rB7926MGTOG256RkYE+ffrgww8/xLBhw9CoUSMAQOvWrZGYmAgbG/k3HBISEtC5c2fMW7euThMoNeFqwMuMfETfSQUgm/uz/WuNau27eg92gZ6+7LRF3UjB4/vptfZdhLzzzjs4deoUnj9/jpycHNy+fRszZ85EbGws3njjDdy8eVOu/LfffovPPvsMV65cwR9//AEPDw+cOnUKDg4OuHz5MtavX48NGzbAxsYGy5Ytg729PfT09ODt7Y0LFy5UO741a9bIJU8A2L59OzIyMrBs2TK55AkAHh4emD59OiIiIhAZGVnpsX/55RcUFhZiw4YNCp2L/Pz8MGzYMBw6dAhZWVkAgPDwcFy8eBFeXl747LPPFI5X0nRaE1u2bIFAIMDatWu55AkAdnZ2WLJkCQBg8+bNCvs5Ozvjiy++kNs2YMAAODk54cqVKyp/v7LnskKhEHPnzgUABAcHK93v008/VUieN2/eRGhoKPz9/eWSJyB77r5s2TLk5eVh37593HZzc3OF5AkAjo6OGDlyJO7FxiK+XM/w2kR3oBpwI+wZSnqi2zUxhuF/A8DXBjMLCXz6OODCCVmT8aFd9/Hhsi619n2kYSt7xwIAbdu2xa+//goTExOsWbMGAQEBcndgEokEK1euxMqVK+X2KygowNSpUzFo0CCMHz8e69ev5+6Iunfvjq+//hoDBw5ETEwMd7dRFQMDA7Rv315he1hYGADZP9DK7u4ePJB1xIuKilJIsMqOExoaiqtXryp8npSUhOLiYjx48ADe3t7cM7gBAwZAKNT8vUlWVhZiYmLg4OAAd3d3hc/9/PwAABERiqOVeXl5KTS5A0DTpk25eqoiNTUVq1evxtGjR/Ho0SPk5OTIff706VOl+5V9Ll6i5HszMzOVnqfk5GQAsvNU1oULF7BhwwaEhYUhKSkJBQUF8jEkJcGpcfVnwFIHJVANuH7hGbfs6VP7J67vm824BLo/MApzlvpo9J1TQqoya9YsrFmzBmfPnlWp/LJly/D06VOuY8jq1avRt29f7h/OVq1awcXFBT/99BOWL1ft/VU7Ozul70KnpspagzZt2lTp/tnZ2ZV+XnKc1atXq3ScjIwMAKi1V2EyMzMBoMLewiXbS+Ioy8LCQuk+enp6Sjv/KJORkYHXXnsNjx8/RufOnTFp0iRYWVlBT08PGRkZ2LBhA/Lz85Xu21hJQiv5+Z44cQInTpyo8HvLnqf9+/dj5MiRMDAwQL9+/eDm5gZjY2MIhUKcOXMGoaGhyC8sVKk+mkAJVAPCL5T2xuvi61jr3+fTxxEmZmJkvyxAUmIOIsKewbt77XRcIkQZOzs7AFC4A1EmIiIC3333HX799Vc4ODjg5cuXSExM5J6LAoCTkxNsbGxw9+5dlWOoaCARc3NzALI7UGV3qKoqOU5mZibMzMyqLF+SpCq6C6upknjKD15R4tmzZ3LlNG3z5s14/Pgxli5dqnDHGBYWVukrScrOVUmcGzZswIcffqhSDEuWLIFYLMa1a9fQunVruc9mzpzJ9WyuK/QMtIby84pw++oLbr1tJ7ta/06xRITeb5QOVE9D+5G6VtL81qxZ5QOGFBUVYerUqfD19cW7774r91n5u5W8PM28ltWli+yRxrlz5+r0OCXlg4ODVbqrK2lSLS4uVun4pqamcHNzw9OnTxEdHa3weUhICACo1LtYHTExMQCgtPe0OolLnfMUExODNm3aKCRPqVSK8+fPVzuGmqIEWkNRN5JRWCC7WMytJLC0MayT7y070tHRPdEoyFftIiREVXfv3kVaWprC9ri4OMyZMweArNdpZVauXImYmBi55lQzMzM4ODjg2LFjKCqSTf0XGhqKrKwseHh41Djud955h+uEoqyDjFQqxZkzZ6o8zpw5c6Cvr4958+Zxz03LKigokPvH39vbG926dcONGzfw7bffKpRPTU2V+0+CtbU1ACA+XvUhIadOnQrGGBYsWCCXeFNSUrBixQquTG0oecWm/M8uIiJC4Zm3Kjp16oSePXvin3/+4QbeKO/27dtISiodoMbFxQXR0dFy7+AyxrBs2bIqO4XVBmrCraE710pPbsu21nX2vR7edmjsaILnCdnIzS5E6NFY9BtOI5cQzdm7dy9WrVoFX19fuLq6wtTUFA8fPsSRI0eQl5eHQYMGYf78+RXuf/fuXXz11VdYu3Yt9x5oiU8//RRz585Fz5494ePjg507d8LExATvv/9+jeO2trbG33//jeHDh6NLly7o27cvPDw8IBQKER8fj7CwMIVkpoy7uzu2bNmCqVOnwsPDAwMHDkTLli1RWFiI+Ph4nDt3Dra2trh3r7QFaMeOHejTpw8WLVqEffv2oU+fPmCMITo6GsePH8e9e/e4RNS3b1/s3bsXI0aMwKBBg2BoaAhnZ2eFV4bKmj9/Pv79918cPHgQnp6eGDRoEHJzc7F3714kJSXh008/RY8ePWr8M1Rm0qRJWL16NT766COEhISgRYsWiI6OxuHDhzFixAjs2bOn2sfctWsX/Pz88O677+KHH36Aj48PLCwskJCQgFu3buHOnTsICwvjHhnMmzcPs2bNQocOHeDv7w99fX1cuHABkZGRGDp0KA4dOqTpaleKEmgNlU2gHevwOaRAIEDfYa7Y+fNtAMDBP+9RAiUa5evri/v37yMiIgJhYWHIycmBhYUFevTogYkTJ2LixIkVPocsLi7G1KlT4ePjg9mzZyt8/sEHH+Dly5f49ddfcf36dXh6emLdunUq98CtSt++fXHr1i18//33CA4Oxrlz5yAWi9GkSRP4+fmpPIjDhAkT4OnpiTVr1iAkJATHjx+HsbExmjRpgpEjR2L06NFy5V1dXXH9+nV89913OHDgAH788UcYGBjAxcUFn3zyCZcIAGDatGmIi4vDX3/9he+++w5FRUXo3bt3pQlULBbjxIkTWLt2LXbt2oWNGzdCT08Pnp6eWL9+PcaOHaveD0wFTZo0wblz5/D555/j/PnzCA4Ohru7O37++We8/vrraiVQR0dHhIeHY+PGjdi3bx927tyJ4uJiNG7cGG3atMEHH3yAdu3aceVnzpwJiUSC9evXIzAwEIaGhujZsye2bt2Kffv21XkCFbBqTAXQqVMnbmQRIjOk7Q48fpABANjwvzfQpoNtnX13wuOXeKf/AQCASCTA2afvwqIWhhDUCbvK/EM+ruazW0RFRSk8Z5HTQGZjaSjGjBmDPXv2IDExUevGxCXVUDY/depUadEqr3EV0B1oDWRl5nPJUyAAmrdRb1xPdTm6msG9vQ3u3UpBcTFD8N8xGD2jbZ3G0GBRcqpXHjx4AIlEAlvbuvsPMNF91ImoBu6Glzbf2jUxhlii+KJybev7ZunoHod2Kg6STQip2IYNG+Dv74+IiAi8+eabcqP7EFIVSqA1cLvM88/WXvz8z7X3IBduEIWIsGd49iSLlzgI0UXr16/HpUuXMHXqVPz22298h0N0DP13qwbuXCt9/7ND17oZOqo8SxtDdOjWGOHnZS9R//u/aEz9pHbeAyOkvnn8+DHfIRAdRnegNXCnTBNuq/aKAxzXFb+hpe+E0qAKhBBSNyiBqinlRS6eP5GN0SjSE8ClhQVvsXTv1xT6YtmpjL6bhodRii+/E0II0SxKoGoq23xr39QUIj3+fpTGpmK5MXiP7lEc5osQQohmUQJVU9kBFDw68t/13XdoaW/coJ33UI3XewkhhKiBEqiayj7/9OKpA1FZPn0cYWQim4c0MS5LrocwIYQQzaMEqqaoG8nccqt2/HUgKiGWiNBzgBO3fuQvxcGvCSGEaA4lUDWkJuUi5XkuANkQek2cTXmOSMavTDPukV33UVys2kS5hBBCqo8SqBru30rhlu0cTCASaceP0bNLY1jayMbCTU/Nw5UztTOxLyGEEEqgaimbQFvU8fi3lRGJhOgzuPQu9PBfNLQfIYTUFkqgarh/K5VbbvuaXSUl657vEBdu+fjfD5GfV8RfMIQQUo9RAlWDtt6BAoC7pw3sm5oAAHJzCnH23zieIyL1Qb9+/SAQCBAaGlphmVmzZkEgEGDTpk11GBkh/KEEWk0F+cV4dC+dW3dtZcljNIoEAoF8ZyLqjUs04Pr16xCJRPD29q6wzOXLlwHI5g0mpCGgBFpND6PSUFQk691qZiGBsamY54gUlR1U4czhx8jKzOcxGqLrHj58iLS0NHh4eMDExERpmdzcXNy5cwcSiQRt29KctKRhoARaTWWbb51bmPMYScWcm1vArbXszriwUIpTBx/xHBHRZdeuXQMA+Pj4VFgmPDwcRUVF8PT0hL6+fl2FRgivKIFW04M7pR2I2vA0B6gqyjbjHt5NzbhEfaok0EuXLgGg5lvSsNB8oNVU9g60lSf/IxBVpM8QV2z67joA4FLIE6S8yIVNIyOeoyK6qCSB7tmzByEhIUrL0PNP0hBRAq0GxphcAnVz164euGXZ2Ruj3WuNcPvqCzApEPx3NMa/78l3WETHMMYQEREBADhx4kSV5SmBkoaEEmg1JD/LQXpKHgBAT1+Ixk2Vd6jQFr5DXHD7qmzataCd9ymBaoiH+Ee+Q1DZ3YI5Ndr/wYMHyMzMRNeuXXHx4kWlZdLT02FlZQUjIyO0adNG7rOff/4Zq1evxrNnz+Dh4YH169ejZ8+eNYqJEG1Bz0CroewACo0dTSAUCniMpmq9BjpDpCeL8c61JMQ/zOQ5IqJrSppvO3ToUGGZ69dljwq8vLwgEom47Xv27MHcuXOxaNEiREREoFu3bnjjjTcQHx9fu0ETUkcogVbD/dulzbfNtWwABWXMrQzQqUcTbj1oxz0eoyG6qCSBduzYscIyJQm0fPPt2rVrMWXKFEyfPh2tW7fGxo0bYW9vj19++aX2AiakDlETbjU8jErjlrVhEm1V9Bvhhsv/DSq/b2skZi/prPV3ztqups2iukSVBFryjLRsAi0oKEB4eDjmz58vV7Z///4VNgUTomvoDrQaHkaWJlBtG4GoIl39msLUQjbYQ1JiDi6HJPAcEdEVUqkUN27cgFgshoeHR4XllN2BpqSkoLi4GI0aNZIr26hRIzx//rx2AiakjlECVZFUyuSG8HNubsFfMNUglojQd2gzbn1/YCSP0RBdEhUVhezsbHh4eEAsVj7iVnZ2NqKjo2FiYoJWrVopfC4QyLd2MMYUthGiqyiBqigxLguvcmUzmxgY6sHC2oDniFQ3YGRzbvn4Pw/xMoOG9iNVCw8PB1B58+3NmzchlUrRsWNHCIWl/5zY2NhAJBIp3G0mJSUp3JUSoqsogaooJrJMD1wtf32lvOZtrEqH9iuQ4t//RfMcEdEFkyZNAmMMmzdvrrBM9+7dwRhTmKVFLBbD29tb4d3REydOoFu3brUSLyF1jRKoih5GlTbfurXW/h645Q0scxf695a7PEZCGoqPP/4Y27Ztw+bNmxEVFYW5c+ciMTERs2bN4js0QjSCeuGqqOwdqLsWD+FXEb+hrvh9VTgKC6WIvJ6M6DupaNHWmu+wSD02evRopKam4quvvsKzZ8/Qtm1bHD16FM7OznyHRohG0B2oisregbq2tOAvEDWZWRqg6+tNufX926N4jIY0FLNnz0ZsbCzy8/MRHh6OXr168R0SIRpDCVQFUinDozLvgDo1185pzKoywL+0GfdAYBQKC4t5jIYQQnQbJVAVPIuX74FraW3Ic0Tq8e5hz83Ikpmej9NBj3mOiBBCdBclUBXElBlAQdd64JYlEgkxwN+NW//r19s8RkMIIbqNEqgKyg7hpwtj4FZm0JiW3FB+V0KfytWNEEKI6iiBqqDsHWirdrrdc9XO3hhd+5Z2Jvrfpjs8RkMIIbqLEqgKyiZQl5a6MQZuZYaNb8kt/7M1CjnZBTxGQwghuokSaBUUxsBtoZs9cMvy6moPR1czAEBuTiGO/PWA54i0E2OM7xAIIbVAU9c2JdAqPIvPwqucQgD/jYFrpTtj4FZEKBRg6NjSu9AdG29RsihHJBKhsLCQ7zAIIbWgsLBQbvJ3dVECrULZTjaNm5rUm5kk+vs3h4GhbCCqh1FpCDv1hOeItIupqSlevnzJdxiEkFrw8uVLmJqa1vg4lECrUPb5Z8mA7PWBiZkYA0aWvtKydW0Ej9FoHysrK6SnpyMlJQUFBQV0h06IjmOMoaCgACkpKUhPT4eVVc3fqKCxcKtQ9g7Uvb3ujYFbmRGT2yBox30wBlw8+YTGxy1DIpHAyckJaWlpiI2NRXExjdpEiNZLSSldjlIcrlQkEsHU1BROTk6QSCQ1/jpKoFUoewfq3MKCv0BqQRNnU3Tv54Tzx+MBAIEbbuCrTX15jkp7SCQS2Nvbw97enu9QCCGqaNOmdLkOWo2oCbcSjDG5QeRd6lkCBYCR75b+wh3adR/Jz3N4jIYQQnQHJdBKlO2BKzEQwcJa93vglufR0Q5tOtgCAIoKpdix8SbPERFCiG6gBFqJ8mPg1pceuOWVvQvd8dMtZKTl8RgNIYToBkqglSjbgcjNXbfHwK1M935OXPN0Xm4R3YUSQogKKIFWouwdqLtn/eqBW5ZQKMDY99px69t/uIGszHweIyKEEO1HCbQSch2IWlrwF0gd6D3ImRveLyerELt+pqnOCCGkMpRAKyDrgVvmFZbmFvwFUwdEIiHGziq9C9267jqyX9Ig84QQUhFKoBV49iQbudmlPXAtbepfD9zy/Ia6orGjbMLwrIwCbN9wg9+ACCFEi1ECrcDDMs8/GznU3x64ZenpCzHxA09ufcua60hLfsVjRIQQor0ogVagbPNts1b1ZwzcqvR90xXOzWVTtr3KLcKm767xHBEhhGgnSqAVkOuB61V/e+CWJxIJ8c7HHbj1XT/fRmJ8Fo8REUKIdqIEWoGyd6CuDegOFAC6vd6Ue22nqFCKn5Zf5jkiQgjRPpRAlSjfA7c+joFbGYFAgHfnd+TWD/55D5ERSTxGRAgh2ocSqBLPE7KRkyXrgSuWNIweuOV5dWkMnz4OAGSTGqycd47mxCSEkDIogSpR9vmnXRPjBtEDV5mZCztBpCer+/WLzxD8dwzPERFCiPagBKqE/PNPC/4C4VnTZuZ4a6I7t776swvIe1XEY0SEEKI9KIEq8ajMEH6tPW15jIR/E+Z4wtxSNnP784RsbFsXwXNEhBCiHSiBKhETmcotu7VuWD1wyzMxE2PyR17c+u/fhuPF02z+AiKEEC1BCbQcWQ/c0jtQp3o+Bq4qBr3dgmvKzn9VhLWLLvIbECGEaAFKoOW8eJrDDaKuLxbC2s6Q54j4J9IT4r3Fr3Hrh3c/wKWQBB4jIoQQ/lECLadsB6KG3AO3vA5d7dFnsAu3vvz9EBTkF/MXECGE8IwSaDllX2FpaAMoVGXWok4wMtEHAMTFZOKP76/zHBEhhPCHEmg5Ze9AW3s17B645VnbGWHqJ6Xj5P628iriYjL4C4gQQnhECbScsgnUrbUVj5FopyFjW6JlO2sAQGGBFF99GEojFBFCGiRKoGUwxuTmAaUmXEUikRBzl3eBUCh7Nnzx5BMc20sjFBFCGh5KoGUkP8tBVuZ/PXD1hbBuRD1wlWnZ1hrDJrTi1r/+KBQZqTTxNiGkYaEEWkbZDkS21AO3UlM+8oJtYyMAQHpKHr5dcJ7niAghpG5RAi2j7AAKzs3NeYxE+xmbijF3eRduPWjHfZw7FsdjRIQQUrcogZZBPXCrx8fXEX5DXbn1pbNDkJNVwGNEhBBSdyiBllG2Cbe5B/XAVcV7i1/jBpt/kZCNdV+E8RwRIYTUDUqg/5GNgVuaQJ1pDFyVWFgbYPaSztz67l9uI/x8Io8REUJI3aAE+p+U57l4mZ4PANDTF3IdZEjVfIe4oIuvI7e+ZOYpmjeUEFLvUQL9j9wYuPbUA7c6BAIBPlzmUzrMX3QmfvnqCs9REUJI7aIE+h+5MXBbWvAXiI6ytTfGjM+8ufUtayMQGZHEY0SEEFK7KIH+h3rg1twbb7eAp08jAIC0mOGLaadQWEgzthBC6idKoP95cCeVW3ZrbcljJLpLKBRg3tddIZaIAAD3b6di06pwnqMihJDaQQkUsh640WUSqGsrSqDqcnA2w5R5Xtz6ryuvIioimb+ACCGkllACBZAYl4WcrEIAgFgigrUdjYFbEyOmtIaHt6wZvLiIYeG7J2jybUJIvUMJFPLNt/ZNTagHbg2JRELMX9UdEgNZU270nTT88vVVnqMihBDNogQK4MHt0gRKIxBphqOLGd6d35Fb37w6HLevveAxIkII0SxKoIDc88+23nY8RlK/vDnRXa5X7qKpJ5GfRwMsEELqB0qgAO7fTuGW3VrTHaimCIUCfLKyGwyM9AAAj+6lY2PAZZ6jIoQQzWjwCTQ/rwhx0RncuksLC95iqY/sm5rKDbCwbV0EIsKe8RgRIYRoRoNPoI+i0lFczAAAZhYSGBrr8xxR/TNkbEt07G4PAGAMWPTuSbzKLeQ5KkIIqZkGn0DL9sBt2syMx0jqL4FA1pRbMlZufEwm1tO0Z4QQHUcJ9E7p8093GsKv1tjZG+O9RZ249R0/3sLFk/E8RkQIITVDCbTMHahHB0qgtWnAyObo4lc67dmid08iIy2Px4gIIUR9lEBv0xB+dUUgEODjr7vCwsoAAJD8LBfL3z8DxhjPkRFCSPU16ASalvwKKc9zAQAikQD2TiY8R1T/WdoY4uNvunLrwfticGjnfR4jIoQQ9TToBPqgzPuftvbGEIka9I+jznTt2xSDRrfg1r+aG4rEuJc8RkQIIdXXoDNG1I3SWUKauVPzbV2atbATmjibAgBysgqxcOpJFBdLeY6KEEJU16ATaGSZabY6dG3MYyQNj6GxPj7/vgeEItnA/dfOJWLr2gieoyKEENU16ARadp5Kd0/qgVvXWnvZYvzsdtz6xoDLNHcoIURnNNgEmpNVgNj/hvATCADXVha8xtNQjXuvPdzb2wAAigql+GzKceS9ogHnCSHar8Em0Hs3U1Dy9oSVrSEkBnr8BtRA6ekL8dmaHpAYyuYOfRiVjnWLL/IcFSGEVK3BJtCyzz+pAxG/HF3M8N6i17j1HT/ewoUTNEoRIUS7NeAEmsQte3Wx5zESAgCDRrdAF1/5UYrSkl/xGBEhhFSuwSbQsp1V2nSkDkR8EwgE+HhlV1hYy0YpSnmeiyUzTtEoRYQQrdUgE+ir3EI8jErn1t2oCVcrWFobYsG33bn1M0disfvX2zxGRAghFWuQCfTB7VRIpbI7GwtrA5oDVIt07u2AEVNac+vfLTiP6DID/hNCiLZokAmUOhBpt3fnd+TOS2GBFPMnBNOrLYQQrdMgE2jZ559ePjQCkbYRS0RYtK4nJAayV1tiItPw/ecXeI6KEELkNcgEGkkdiLSec3MLvLe49NWW3b/cRsjhxzxGRAgh8hpcAs3NKZSbhaV5GyseoyGVGTS6Bbr3c+LWF087iaTEbB4jIoSQUg0ugd4NT0JxsawDkaWNAYxNxTxHRCoim4C7C2waGQEAMtPysejdU1wHMEII4VODS6A3Lj3nllt7UfOttjOzNMDna3pAIJu0BWGnnmDbOpq1hRDCv4aXQMOeccudezvwGAlRladPY4yZVTpry/olYbgT/oLHiAghpIElUMYYbl4uvQNt91ojHqMh1THpA09u1pbiIoYFE48jJ7uA56gIIQ1Zg0qg8TGZSE/JAwDoi4VwdDXjOSKiKj19IRau7Qmj/wa9iI/JxMp553iOihDSkDWoBFr2+aeTmzmEQgGP0ZDqauJsig+W+XDr+wOj8O//onmMiBDSkDWwBFr6/LNDN5qBRRe9/mYz9B3myq0HzA5BYtxLHiMihDRUDSyBlt6BUgci3fVBgA8aO5oAALJfFuDTySdQVNygfpUJIVqgwfyrk/2yQG5Q8pIOKUT3GJuKsXBtTwhFsib4iIvP8PuhATxHRQhpaBpMAr199QVKppa0aWxEM7DouDYdbDHpQ09u/ZeDA3E92rWSPQghRLMaTAKNKPP+Z5sONIBCfTBmZlu07yx7FUnKhPj01yl4mWPIc1SEkIaiwSTQq2efcstd/Bx5jIRoikgkxGff94CpuWw4xmepVlgWOAaM0VB/hJDa1yASaH5eEW6ElXYg6tCVeuDWF3b2xpj3VVdu/diVjti3NZLHiAghDUWDSKA3Lj1HQX4xAMDcSsINTk7qh54DnTGqz3lu/eu5oXIz7hBCSG1oEAn0ypnS5tv2NHxfvfTZ2H/QwjERAFCQL8XH447RUH+EkFrVMBJoaAK33GOgM4+RkNpiKCnEmtlbYCjOBwA8vp+Br+ee5TkqQkh9Vu8TaPbLAty6XDpzR4cujXmMhtQmtyYv8MWk/3HrB/+8hwPbo3iMiBBSn9X7BHo5JAFFRVIAsvc/LW3oNYf67K0eVzCofw63vuKDM4iJTOMxIkJIfVXvE+iFE/Hcchdfen2lIZj/YSacnQoBAHmvivHJuGN4lVvIc1SEkPqmXidQxhjOH4/j1vsMduEvGFJnjAwZvl6SBrFY1vIQE5mG5XPO0PuhhBCNqtcJNPZBBp7GZgEA9PSENAJRA9K8WRE+mZPJrQftuI+/frvDY0SEkPqmXifQ04cec8st2llDXyziMRpS14YNysWQgaXPQ1d+fFZuSEdCCKmJep5AH3HLA/zdeIyE8EEgAOZ/mAH3lrL3QYuLGD4a/S+Sn+dUsSchhFSt3ibQlBe5uFlm/s8e/Zx4jIbwxUACrAxIg7mZbCSqlOe5+GRcMAoLi3mOjBCi6+ptAj1z5DE3fVnTZmYwtzLgNyDCG/tGxVi+OB1CoewXIvx8Ir6ee5Y6FRFCaqTeJtBje2O4Zb9hzXiMhGgDn075mDX1Jbe+d/NdBG64wV9AhBCdVy8TaMqLXFwOKR2+b8AIev5JgIljsjHw9Vxu/fvPLsh1NCOEkOqolwk0eF8MpFJZ85yjqxls7Y15johoA4EAWPRJOtq3lY2XyxiwYGIwoiKSeY6MEKKL6mUCPbL7Prc8eEwLHiMh2kYsBr5dloYmjYsAAHm5RZg9/DCePcniOTJCiK6pdwk05m4qbv43eLxAALz+JjXfEnmWFlKs+ToVxkaykYqSEnMwfdBBpKe84jkyQoguqXcJdN+2SG65TQdbWFhT71uiyNWlCCsD0qCnJ2vqf3w/A7OGHUJOFs0hSghRTb1KoAX5xTi0s7T5dux77XiMhmi7zt75WPp5OgQCWRK9cy0Jc/yP0MDzhBCV1KsEemTPA6Sn5AEAjEz00alnE54jItqun+8rLJibwa1fOfMUs986jNwcSqKEkMrVmwTKGMP29Te49WHjW0EkqjfVI7VoxNBczJ5WOvA8JVFCiCrqTYa5dDoBD+6kAgCEIgHenu7Bc0REl0wamy2XRK+GPsXU/geoYxEhpEL1IoEyxvDTiivceo9+TjA1l/AYEdFFk8Zm4/3ppUn09tUXmNB7HxIev6xkL0JIQ1UvEujFk08QcVE2TZVAAMz43JvniIiumjgmG/M/zOA6FsVGZ2Bcr70Iv5DIc2SEEG2j8wm0uFiKdYvDuPVu/ZzQyMGEx4iIrhv5Zg6+/jIN+vqy90RTX7zC1H77sfPnWzQAPSGEo/MJdN+WSETdkA3FJhAC73/xGs8RkfrAr1ceNn6XCov/pkErKmL45qOzWDDhODLS8niOjhCiDXQ6gSY/z8H6JaV3n2+Od6dxb4nGeLUvwLZfk+HeonRwhX/3RuOtDrtw7lgcj5ERQrSBziZQxhiWzjqNzDTZwODGJvqY9mlHnqMi9U3jRsX47YdkDBuUw21LfpaLWcMOYd6Yf/E0ljoYEdJQ6WwCDdxwA6FHS+8CFq7rCYmBHo8RkfpKIgYWfZKB71akwtKiiNt+/J+HGNJuJ9YsvIiUF7mVHIEQUh/pZAI9fzwOaz6/yK33fsMZPn0ceYyINAS9uuVh1x/J6N+3NFkW5Bdjy5rr6Nc8EMs/OIP7t1J4jJAQUpcErBrdCjt16oRr167VZjxVCj+fiBmDg5D3SnYn0NjRBH8cexNiiYjXuAi/mlww55azmn5e6993664Yazea4V604vvGLdtZY/CYlujR3xkt21lDKBTUejyEEMjeYyxRBz3mdSqBnjr4CJ9OOs4lTwMjPWw9/iZsGlHHoYaurhMoAEilQOgFAwTuNFGaSAHAupERPDs3grunDVq2s4GjixnsnU1hbimBQECJlRCNogSqKD+vCBsDLmPr2ghum1giwq9BQ9C0mXkle5KGgo8EWoIxIPyGGAePGCH0vCEKCqt+MqKnJ4SJuRgmZrI/YokIAgEgEAogFAogEAggFAIQCGTby/2N/5b19AQwMpEdw9hUH8amsmUrW0PY2hvDtrExbO2NYGImpoRN6r86TqBa3eumuFiKk/sfYd2SMDx5WDrEmrGpPjb+PYiSJ9EKAgHQqUMBOnUoQHZ2Js6cN0TYFTGuhIuRla2vdJ+iIikyUvOQkVo375QaGOrBvqkJHJuZw9HVTPbHxQyOruZo2swMxqbiOomDkPpE6xIoYwz3b6Ui9GgsDvwZhfiYTLnPXVtZ4rvAfjRRNtFKJiYMQwbmYsjAXEilwMPHengQI8b9aD08fizA8xdCJKVKkF9Qt8/s814V4fGDDDx+kKH0c0sbAzi4yJKqg4sZHJuVLts7mUIspj4GhJTHWwItKpIi5Xkukp7l4Fl8Fh5GpiH6bioiwp4h+ZniKwF6+kJM/MATY2a2pU4ZRCcIhUALtyK0cCvC4AHynxUUANk5QmTnCJCbK0RRESBlAjAGMCkg/e9vBoCVbP+vRapkmTGgqFiA3FwBcl8JkJMjRO4rAV6+BFJSBEhJFSA1XQ/pGfpVJuz0lDykp+ThzrUkJfUQwM7BGHb2xrC2M4J1I0NY2xnBprERrO2MYGVrKGs+NhHD6L9mZEMjPWoyJvUeLwl05cdnsfOnWyo1UevpC9F/hBumftwB5lZ010nqB7EYsBJLYWUJAMW1+l2MATk5Ajx7IULiMz08faaHhKdCJCQI8PSZCC9SJCgqqvi5rVTK8PxJNp4/yVb5O4VCAYxM9CExEEFPXwh9sQh6esLSZX3Zskgk4J77CoUCCISyfcfMbAffIa6aqD4htYaXBGpkIq40eYpEAjR1M0f3fk7o1LMJxBIRXiTm4EViTsU7kQatSZnljBeKd1EEsDUBbFsAni3ktxdLgbR0fTxPEuN5sgTPk8RIfK6H5y/08CLFAOkvDcBY9e4mpVKG7JcFyFZzoKY+gyl5Eu1XrV64NjY2cHFx0cgXJycnI/25AIWFshkvRCIh9MU6Oa6DnPyiLEj0TPkOQ+N0oV4iYTHMDLOqtU/Wq3yYGta/uWNrv14lbcxMtqy8hKwYBEq3A8qTskAkgY2zk8L25ORk2Nra1iBm7UT10m42NjY4duyY0s+qlUA1ie93SmsL1Uu3UL10R32sE0D10mW6f8tHCCGE8IASKCGEEKIG3hLojBkz+PrqWkX10i1UL91RH+sEUL10GW/PQAkhhBBdRk24hBBCiBrUTqAJCQn44IMP0LVrVxgZGUEgECA2NrbK/a5du4YZM2bA3d0dRkZGcHJywvjx4/H48WOFsi4uLv8Nni3/58CBA+qGXSV16wVAaawCgQA3btyQKyeVSrFy5Uq4uLjAwMAAnp6e2Ldvn+YrU4a69QoICKiwXgYG8gNb1PX5+vvvv+Hv7w9nZ2cYGhqiVatWWLhwIbKyqn6VJS8vDwsWLIC9vT0MDQ3RtWtXnD17VqEcH+dK3Xpp+7VVk/OlzdeWuvXS5msLAIKDg+Hn54fGjRtDIpHA0dERb7/9NiIjI6vcV5uvL41iagoJCWF2dnbsjTfeYP3792cA2OPHj6vc75NPPmHdunVjP/30Eztz5gzbuXMnc3d3Z1ZWViw+Pl6urLOzMxswYAALCwuT+5OWlqZu2FVSt16MyV6ImzJlikK8OTk5cuUWLVrExGIxW716NTt9+jSbMWMGEwgE7MiRI7VQIxl16/XkyROF+pw8eZLp6emxUaNGyZWt6/Pl4+PDRo0axXbs2MHOnDnD1q1bx8zNzZmPjw8rLi6udN9x48Yxc3Nz9vvvv7OTJ0+y4cOHMwMDAxYRESFXjo9zpW69tP3aqsn50uZrS916afO1xRhju3btYvPnz2d79+5lZ86cYdu3b2dt2rRhpqamLDY2ttJ9tfn60iS1E2jZX4xNmzap/A9yUlKSwrbY2FgmEAjYkiVL5LY7Ozuz8ePHqxuiWtStF2Oyi3zx4sWVlnnx4gUTi8Xsyy+/lNvu5+fH2rVrV+14VVWTepW3fft2BoAdPnxYbntdny9lv0uBgYEMADt16lSF+924cYMBYFu2bOG2FRYWspYtW7KhQ4dy2/g6V+rWS9uvLXXrxZh2X1s1qVd52nJtVeTevXsMAPv+++8rLKPt15cmqd2EKxSqt6uykSmcnZ1ha2uLp0+fqhuOxqhbL1UFBwejoKAAEyZMkNs+YcIE3L59W2lzmyZosl6BgYFo1KgRBgwYUHXhWqTsd+m1114DgEp/l4KCgqCvr4/Ro0dz2/T09DBmzBgEBwcjPz8fAH/nSt16afu1pW69VKVr50sZbbm2KmJtbQ0A0NdXPk0foP3XlyZpRSeiqKgoJCUloXXr1gqfHTp0CEZGRpBIJOjSpUuttvlrwi+//AKJRAIjIyP4+fnh3Llzcp/fvXsXEokEzZs3l9vu4eEBACo9X+BTQkICQkJCMH78eOjpKQ6lzPf5Cg0NBQClv0sl7t69C1dXVxgZGclt9/DwQEFBAWJiYrhy2nKuVKmXMtp+bVWnXrp0balzvrT12iouLkZBQQGio6Mxc+ZMNG7cGGPGjKmwvC5eX+riPYEWFRVh1qxZsLW1xbvvviv32dChQ7Fx40YEBwdj586dMDAwwPDhw7Fjxw6eoq3chAkT8PPPP+PkyZP4/fffkZqaCj8/P5w5c4Yrk5aWBgsLC4WpnqysrLjPtdmff/4JqVSKyZMnK3zG9/l6+vQpvvzyS7z++uvo1KlTheXS0tJgaWmpsL38OdCWc6VqvcrT9murOvXSpWtL3fOlrdeWj48PJBIJWrZsiVu3buH06dOws7OrsLyuXV81ool24Jo8U5s5cybT09NjwcHBVZYtKipinTp1Yo6OjmpEWX01fVb48uVL5uTkxLp3785tmzZtGmvcuLFC2QcPHjAAbPv27eqGq7Ka1Mvd3Z116NBBpbJ1eb6ysrKYt7c3s7e3Z0+ePKm07Ouvv866dOmisP348eMMADt79ixjTDvOVXXqVZ42X1s1qRdj2ntt1aRe2nptRUZGskuXLrFdu3axjh07MgcHh0r/7dCl66umeL0DXbhwIX7//Xds2bIF/fv3r7K8SCTCqFGjkJCQgGfPntVBhDVjamqKwYMH4+rVq9w2KysrpKeng5UbvyI9PZ37XFtduXIF9+7dU/o/ZGXq6nzl5eVh2LBhePToEYKDg+Ho6FhpeSsrK6X/uy1/Dvg+V9WtV1nafG3VpF4ltPHaqkm9tPXaAmTN0D4+Phg7dixOnTqF7OxsrFq1qsLyunJ9aQJvCfTrr7/GqlWrsGHDBkycOFHl/Up+2Loy2z1jTC5WDw8P5Ofn4+HDh3LlStr727RpU6fxVUdgYCD09PQwbtw4lfep7fNVWFgIf39/XLlyBUePHkW7du2q3MfDwwOPHz9Gbm6u3PbIyEiIxWLumQyf50qdepXQ5murJvUqT5uurZrWSxuvLWUsLCzQvHlz7jmmMrpwfWmMJm5jq9skuGHDBgaAff3119X6nsLCQubt7c2cnJzUiLL6atqEm5mZyZo2bcp69erFbSvpuh0QECBXtm/fvqxt27Y1CVdl6tQrPz+fWVlZsWHDhqm8T22fr+LiYjZq1CgmkUjYyZMnVd4vIiKCAWDbtm3jthUWFjJ3d3c2ZMgQbhtf50rdejGm3ddWTepVnjZdWzWtlzZeWxV5/vw5MzY2ZjNmzKiwjLZfX5pUowS6d+9etnfvXjZr1iwGgP3888/cS7eMyd5BE4lEbNmyZdw+u3fvZgKBgA0cOFDhpeC7d+9y5Xbt2sVGjx7NAgMD2enTp9nu3btZjx49GAC2e/fumoRdK/VavXo1mzZtGtu5cycLCQlh27ZtY23btmX6+vpcm3+Jzz77jEkkErZmzRoWEhLCZs2axQQCAQsKCtK6epXYt28fA8D27dun9Nh8nK+SeixevFjhd6nk+VNFdRo9ejSzsLBgmzZtYidPnmT+/v5MIpGw8PBwuXJ8nCt166Xt15a69dL2a6smv4eMaee1xRhjb731Flu+fDk7cOAAO336NPv1119Zq1atmLm5Obt//36l9dLm60uTapRAIZtXXuFP7969GWOMPX78mAFgS5cu5faZPHlylfsxxlhYWBjz9fVldnZ2TE9Pj5mZmbG+ffuyY8eO1STkWqtXUFAQ69atG7O2tmZ6enrMysqKDR06lF2+fFnh+EVFRWzFihXMycmJicVi1q5dO7Z3716trFeJYcOGMSsrK5afn6/02HycL2dn5wrrVFKHiuqUm5vL5s2bxxo1asQkEgnr3LkzCwkJUfgOPs6VuvXS9mtL3Xpp+7VVk99DxrTz2mKMsVWrVrGOHTsyc3NzZmhoyFq2bMlmzJgh13Kli9eXJtFsLIQQQogaeH8PlBBCCNFFlEAJIYQQNVACJYQQQtRACZQQQghRAyVQQgghRA2UQAkhhBA1UAIlhBBC1EAJlBANEQgEEAgEEAqFCuN7luXr68uV3bZtm9xnU6ZM4T5bunRphccIDAzkyvXp00fuszNnznCflfwxMjKCvb09evXqhQULFiAiIqImVSWEgBIoIRqlp6cHxhj++OMPpZ9HR0cjNDRU6YTJ5Y+zZcsWFBcXK/1806ZNVR7D2dkZS5cuxdKlS/HRRx9hyJAhyMnJwffff4+OHTti/PjxyM7OVq1ihBAFlEAJ0aBGjRqhU6dO2Lp1K4qKihQ+37x5MxhjGDJkSKXHGTJkCBISEnDs2DGFz6KionDhwgUMHTq00mO4uLggICAAAQEB+Oabb7Bp0yaEh4cjIiIC7dq1w65duzBq1KjqVZAQwqEESoiGTZ8+Hc+fP8fhw4flthcWFiIwMBDdunWDh4dHpccYP348DA0NsWnTJoXPSrZNmzZNrfi8vLxw8uRJ2Nra4tixYzhw4IBaxyGkoaMESoiGjR07FsbGxti8ebPc9qCgILx48QLTp0+v8hgWFhYYNWoUjhw5Ijdhcn5+PrZv347evXujZcuWasdoZ2eHmTNnAgB27typ9nEIacgogRKiYaamphgzZgyOHTuGhIQEbvumTZtgZmaGt99+W6XjTJ8+HUVFRXIdjfbv34/U1FSVknBVSjofXblypcbHIqQhogRKSC2YPn06iouLsWXLFgBAXFwcTpw4gfHjx8PIyEilY/To0QOtW7fmnpsCsiRsaWkJf3//Gsfo4OAAAEhOTq7xsQhpiCiBElILfHx80K5dO2zZsgVSqRSbN2+GVCqt9p3jtGnT8OjRI5w+fRoxMTEICQnBxIkTYWBgUOMYS5KyQCCo8bEIaYgogRJSS6ZPn464uDgcO3YMW7duhbe3Nzp06FCtY0yaNAkSiQSbN2/m7kQ10XwLAImJiQAAW1tbjRyPkIaGEightWTixIkwNDTEzJkz8fTpU8yYMaPax7CxscHw4cOxf/9+bNmyBV27dkXbtm01El9ISAgA2d0yIaT6KIESUkssLCwwcuRIJCQkwNjYGGPHjlXrONOnT0d+fj6Sk5M1dveZlJSE3377DYDslRlCSPVVPpQJIaRGvvrqK4wYMQK2trYwNTVV6xi+vr44ePAgpFIpBgwYUOOYbt68iUmTJiElJQWDBg3CsGHDanxMQhoiSqCE1CInJyc4OTnV6BgCgUCtJBcbG4uAgAAAskEcUlJSEB4ejvDwcACyO8+Su1BCSPVRAiWknoqLi8OyZcsAAAYGBrCwsECLFi0wf/58jB8/Hl5eXvwGSIiOE7CSvuyEEEIIURl1IiKEEELUQAmUEEIIUQMlUEIIIUQNlEAJIYQQNVACJYQQQtRACZQQQghRAyVQQgghRA2UQAkhhBA1UAIlhBBC1EAJlBBCCFHD/wHr34Kx8BhWrwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4ZCYsFSIacZ19etz/QSGCPEfhmRtixY5DZsRSIaIRl3e4Fl39Bry0IBFq91FixyEzYqkQ0Yjq1xuxZk8FInycMT7EQ+w4ZGYsFSIaUY+tO4CDlW14cMZoLg9shVgqRDRiqlq7sfFIHR6ZORqLNMFix6FhwFIhohHzzaFaAMAdmhCRk9BwYakQ0YiobuvBB7tOIzHIHSHenI7FWnGWYiIadoIg4MEP96G9V4c3fzNO7Dg0jHimQkTDbldZMw5Xn8Gf58VhfIin2HFoGLFUiGjYvb9DC29ne9wyLlDsKDTMWCpENKzO9Ojww4lG3DIuEI52CrHj0DBjqRDRsNpSXI9+gxE3JarFjkIjgKVCRMNGEAR8UliJQI9RSA72EDsOjQCWChENm52lzSgob8H908L59LyNYKkQ0bDJ3lYGfzdHLJnIhx1thVlKJT8/HzExMYiMjERWVtYFXxcEAY8++igiIyORmJiI/fv3m2O3RGTBmjr7sP1UExaMD4SDkjfobYXJpWIwGPDII49g48aNKC4uxtq1a1FcXHzeNhs3bkRJSQlKSkqQnZ2Nhx56yNTdEpGF+/pgDQxGAfOTOYzYlphcKgUFBYiMjERERATs7e2xePFi5ObmnrdNbm4u7r77bshkMkyaNAltbW2ora01dddEZKFK6jvw6qYT0IR6IsbfVew4NIJMLpXq6moEB/8y22hQUBCqq6uveBsish6vbymBUiHnlCw2yORSEQThgs/99yiPy9nmnOzsbGg0Gmg0GjQ2Npoaj4hGWHuvDpuP1ePWcYFc1dEGmVwqQUFBqKysHPi4qqoKAQEBV7zNOZmZmSgsLERhYSF8fX1NjUdk9Rrae7EspxCaFzbj73nHBv0lbiTlHapFv97IKVlslMmlkpKSgpKSEpSXl6O/vx/r1q1Denr6edukp6fjgw8+gCAI2L17N9zd3aFW8+laIlM9m3sEE1/6DttPNWJMgDuyt5Xhua+L0a83ipJHZzBi5Y+liFO7ISnIXZQMJC6Tp75XKpVYsWIF5s6dC4PBgKVLlyI+Ph4rV64EADz44IOYN28e8vLyEBkZCScnJ7z33nsmByeydT+VNCJn12ncOi4Qv5sViQgfZzy/oRjv7dCipKED7/xWAxeHkV3d4uuDNdA2d2PV3Ro+7GijZILY58oXodFoUFhYKHYMIovT2tWPm97cDjuFDPmPTz9vosbP9lXhmc8PYU6cCivvmjCib+5L39+LkoYObHtqJkvFCl3OezKfqCeSoL/nHUNjRx/+vWTcBTP/3jYhCM9cH4NNR+vxaWHlEK9gft39emw/1YQ5cX4sFBvGUiGSmF6dARuP1OGWcQFIDPIYdJtl10QgNdwLf887jubOvhHJ9eOJRvTrjUgb4zci+yPLxFIhkphtJxvR2afHTYmDj6AEALlchhdvGYuOXh1Wby8fkVzv7ihHgLsjUsK8RmR/ZJlYKkQS88X+ang62WHyaO+Lbhfl54rZcX74ZG8l+vSGYc20V9uCvdpWPHDtaNgp+LZiy/jTJ5KQ081d2FRchyWpIZf15n335FA0d/Uj7/DwTov0yd5KuDoosUgTfOmNyaqxVIgk5OOCCijlMmRMCbus7aeO9kGEjzM+2HV62DL19BuQf6QONyT4Y5Q9ZyO2dVZZKr06A17OP45r/7EVq34qEzsOkdlsL2nChFBP+Lk5Xtb2crkMd00KxYGKNhypPjMsmXKLqtHZp+cT9ATASkvFXiHHpiN1EISzQy///OVh1Lf3ih2LyCRt3f0orm3H5AifK/q+hROCMMpOgQ92ac2eqbtfj9c2n8T4EA9Mjrj4PR6yDVZZKnK5DBsfn4aNj03DnDg//KewCss/PyR2LCKT7ClvgSDgkjfo/5v7KDvcMi4QuUU1aOvuN2umrw/WoKGjD89cH8tnUwiAlZYKADgoFXB2UCL7bg2enBuNrScaMfdf27DtJGc+JmnaVdoMRzs5koKvfE6te6aEoU9vRM5O895b2XCoFqHeTkgN5zBiOstqS+XXMqaE4ZGZo6EzGrEspxDfH68XOxLRFdtd1owJoZ5XtTRvjL8rrhvjh9Xby9DeqzNLnubOPuwsbcaNCWqepdAAmygVB6UCT82NxRcPTUGMvyse+HAf/vntCegN4szkSnSl2rr7caK+A5PCr/6+xaOzo9Deq0fODq1ZMq3ZUwGDUeANejqPTZTKOR5O9vho2UTMS1Djze9P4dF1B7DqJ/P95mYu9e29oq+JQZbl3P2USVd4P+XXxga6Y06cCqu2l6OzT29Snq4+Pd7bUY7ZsSpE+3G5YPrFyM6LbQHcR9nhjcXjEOQ5Cm9tLUXe4Tqs3l6O/zw4GUGeTiOSoatPD4VcBkc7Bdp7ddh4uBaljV3wdrbH0Zp2fHWwBr+ZGIKkIHd8sOs0HpoxGipXR9S09WB+cgAvNdig3WVn76ckmrhGyaOzo5C+YgdydmrxyMzIq36d93dq0dqtwyOzrv41yDrZXKmc89TcWNw7NRynm7txz3sFWLRyFxaMD8ITadGQy4fvTburT4+b3tyO9h4dxoV4YsepJvToDLBXyNFvMMLRTo4po73x8Z4KfLwHcLZX4HcfHxj4/uq2Hjw8YzSLxcbsLmu56vspv5YY5IGZMb5Y9VMZ7pwYAg8n+yt+jbbufqz8sRRz4vwwPsTTpDxkfWy2VADAx8UBPi4OWJ2Rglc3ncCKraewu6wZtWd6kRjkjrfvHG+2N++vDtbgo92nUdnSjbr2XqSEekHb3IX5yQFYkhqCxCB3tHXr4OqohEIuw77Trejs0yM13AvbS5pgFM4O3/zHphP47lg9XrktEZEqXnawBW3d/The144n5kSb5fX+eF0Mbn17B5749CBWZ1z5Ylr/+2MpOvv0eGpujFnykHWx6VI5JzXcC588MAl/+KQIm47WIznY4+zU4m/vhL+bA2bGqNCnNyIxyB3JwR6X/EfYpzdg3+lWAMDx2g5sOFSD/RVtiFK5IDHIHX+IjcailAvnSPJ0/uW3Rs2vZnq9Lt4fADArVoUpkd54ddMJzH39J9yREoy/3RwPe6VN3RqzOQVmuJ/ya2MD3fGnG+Lw/IZibDnWcEVT1Td29CFnpxa3Jgcixp+/1NCFWCo/k8lkeG1RMnRGI5RyOW56cztO1nXgeG07Nh39ZQhycrAH/nhdNHaVNuP6sf4wCsAbW06iV2dEd78eZY1dcHeyQ1Vrz8D3jA10w7JrwvH09bEmFYC9Uo47J4YibYwf3t5aivd3alHa0ImshYkI93E26c9Plquosg1Kuczk+ym/9tvJoXh/pxb/2nwSs2JVUFzmJd+cnVr06Y34/ewos2Uh68LlhIfQ1t0PnUGAURDQ0auHm6MSm4/V4/UtJWjsOLvokUIug8EowNfVAWHeTpDLZPB3d4S2uRv3XRMON0clBAGYEeM7LPdAPt9Xhb99dRQKhQyfZE7mb45W6t73ClB7phf5j0836+t+fbAGv197AI/MHI2n5sZecvszPTpMf2UrJkV44Z3fasyahaThct6TeaYyhF/fwPRzO/v/d04MxfzkQKwrqECc2g1bjtXDz80RS1JD4D7KbsQzLpwQhJQwL9z+zk7ctXoPPntwMkK9ecZibY7Vdlzx1CyX4+akAGw72Yi3fyjF7ROCEXaRs91+vRH/2HQc7b06/H4Wz1JoaLwYf4VcHJRYNi0CUyN98OzN8Xjw2tGiFMo5Id5O+Oi+idAbjLj/g8JhX4yJRlZrVz/q2nsRO0xnoU/NjYGdXI7/u8hs3h29Olz3rx/x0e4K/CY1BGMDzXcZjqwPS8UKRPm54rU7knGyvhPLPz9s8oNtZDmO1bUDAOLUbsPy+io3RyycEIQ1eyrwwobiCx66FQQBWRuP43RLN/55exKeS48flhxkPXj5y0rMjFHh0dlRePP7Euwua8aK34zDhFBO8id1x2o7AAxfqQDAszePgVIuw6rt5ejq1+OB6aMR5uMMncGIP3xShA2HanHv1DAsnBA0bBnIerBUrMgTadGYEeOLx9YdwKNri7D5ielwsuePWMqO1bbDx8UBvq4Ow7YPRzsFnp8fD4VchpxdWqwtqISLgxJKhQxt3To8NTcGD107etj2T9aF7zhWZnyIJ/55ezIWvbML7/xYhj+kmeeBORLHsdp2xKmHf1SfTCbD39Lj8cC1EdhwsBY1Z3rQ0tWPqaN9Bn2mimgoLBUrlBruhTlxKny4++y8YY52XDdcinQGI0rqO3Hv1LAR26fafRTunx4xYvsj68Mb9VZq6TXhaOnqx5vfl3DGY4kqa+xCv8E4rPdTiMyNpWKlJkd4Y35yAN7aWoq73y1AfXuv2JHoCh3/eeRX7Ahc/iIyF5NKpaWlBWlpaYiKikJaWhpaW1sH3W7p0qVQqVQYO3asKbujKyCTyfD6Hcl4Lj0e+0634s5Ve8y+PjkNr9LGLshk4BQ8JCkmlUpWVhZmz56NkpISzJ49G1lZWYNud8899yA/P9+UXdFVkMlkyJgShnfvSUFZYyfe2Tb0A25kecqbuhDkOcrk6e6JRpJJpZKbm4uMjAwAQEZGBtavXz/odtOnT4eXF5+ZEMukCG/MjffHmt2n0cUHIyWjrLETET4uYscguiImlUp9fT3UajUAQK1Wo6GhwSyhyPwyp0egvVePpz87BIORN+4tnSAIKG/q4qUvkpxLDimeM2cO6urqLvj8iy++OCyBsrOzkZ2dDQBobGwcln3YonEhnvjzvDi8mHcMmjBP3Ds1XOxIdBH17X3o7jdgtC9LhaTlkqWyZcuWIb/m5+eH2tpaqNVq1NbWQqVSmRwoMzMTmZmZAM5Os0zms2xaOLaVNOK1zScxNtAdKWG8JGmpyho7AQDhvPxFEmPS5a/09HTk5OQAAHJycjB//nyzhKLhIZPJ8Pz8sXBxUOKOd3ahuKZd7Eg0hOLasz8brpFDUmNSqSxfvhybN29GVFQUNm/ejOXLlwMAampqMG/evIHtlixZgsmTJ+PEiRMICgrC6tWrTUtNVy3cxxl5j06DnUKOtQUVYsehIRTXtEPlOrxzfhENB5OmafH29sZ33313wecDAgKQl5c38PHatWtN2Q2ZmaezPeYlqLH+QDVmxakwM8b0y5ZkXkdr2hEfwCfpSXr4RL2NevDa0XBxVOLe9/biw11asePQr/TqDDjV2In4AC6GRdLDUrFRMf6u+PGpmZgTp8Jfco/iua+Pco4wC3GyvgMGo8AzFZIklooNs1fK8dad43HnxBC8t0OL7aeaxI5EOPskPQBEqjjyi6SHpWLjHJQK/PXmMQj0GIVnPjuE/+NULqKraO4GAAR7OYmchOjKsVQIDkoFXrktEe5O9ngx7xhyi6rFjmTTKlq64efmwHVwSJJYKgQAmBrpg69/NxXjQjzw+CdFeOLTItS09YgdyyZVtHQjhGcpJFEsFRqgVMjx/r2pyJwWgQ2HanHjv3/CgYrBlzOg4VPZ0s1LXyRZLBU6j/soO/xpXhw2PT4dbqPssOT/duO7Y/Vix7IZfXoDatt7eaZCksVSoUGF+zjjswenIErliswP9+GFDcWobOlGeVMX+vQGseNZrerWHggCWCokWSY9UU/WzdfVAesyJ+GZzw/h/Z1afL6/Cm09OkyP8sW796RAIZeJHdHqaJvPDicO9WapkDTxTIUuytlBiRW/GY9vHp0GhVyOMWo3/HiyEX/+8jDe3V6O1i4uUWxOZY1nS4WLc5FU8UyFLkuMvyt2/2kWlAo5Xso7NrA08Q8nG/H+PSmQ86zFLMqauuDhZAdPZ3uxoxBdFZ6p0GVTKs7+dVl+Qyz+724NnkiLxraTjVi4cidqz3D4sTmUN3Yhgqs9koSxVOiKyWQypI3xw+9nReKV2xJRUt+JBz7chzPdOrGjSd7ZJYR56Yuki6VCV00mk2GRJhj/uiMZR2vaMeufP+CvuUfQ1acXO5okdfXpUdfeiwguIUwSxlIhk6WN8UPuI1OREuaFNXsqsCynEHqDUexYknP65zm/wrxZKiRdLBUyi7GB7lj52wn4+61jsausGeuLasSOJDnnpsUJ9BwlchKiq8dSIbNapAlGfIAb3vjuJNp7eY/lStT8PNghwMNR5CREV4+lQmYlk8nwPzeOQW1bLzI/KOTCX1eguq0H9go5fJy5Lj1JF0uFzG7yaG/85aYx2F3Wgn2nOSHl5app64Xaw5HP/JCksVRoWNw2IQhO9gr8p7BK7CiSUdPWgwB33k8haWOp0LBwdlDipkQ1vjpYg2quy3JZatp6EODBUiFpY6nQsPn9rCgAwLO5R0ROYvl0BiPq23sRyJv0JHEsFRo2wV5O+P3sSGw51sDFvi6h7kwvjAJ4pkKSx1KhYXX35DC4j7LDW1tPiR3Fov0y5T0ffCRpY6nQsHJxUGLp1HBsOdaA4pp2seNYLG3T2VIJ52SSJHEsFRp290wJg4uDEm//wLOVoWibu+FoJ4fKlc+okLSxVGjYuTvZYZEmGN8erUdbNxf1Goy2qQth3s58RoUkj6VCI+LWcYHoNxiRd7hO7CgWSdvcxYkkySqYVCotLS1IS0tDVFQU0tLS0Np64QifyspKzJw5E3FxcYiPj8cbb7xhyi5JosYGumG0rzNyi6rFjmJxDEYBlS09CPXhuvQkfSaVSlZWFmbPno2SkhLMnj0bWVlZF2yjVCrxz3/+E8eOHcPu3bvx1ltvobi42JTdkgTJZDLcmBiAAm0LGjv6xI5jUWraetBvMCKcZypkBUwqldzcXGRkZAAAMjIysH79+gu2UavVGD9+PADA1dUVcXFxqK7mb6u26Iax/hAEYMuxerGjWJRzw4nDOPKLrIBJpVJfXw+1Wg3gbHk0NDRcdHutVosDBw5g4sSJQ26TnZ0NjUYDjUaDxsZGU+KRhYn1d0WotxPyj/C+yq+dG07MeypkDZSX2mDOnDmoq7vwTeDFF1+8oh11dnZi4cKFeP311+Hm5jbkdpmZmcjMzAQAaDSaK9oHWTaZTIbr4/3x7o5ynOnRwX2UndiRLEJ5UzdG2Sng58bhxCR9lyyVLVu2DPk1Pz8/1NbWQq1Wo7a2FiqVatDtdDodFi5ciDvvvBMLFiy4+rQkedeP9cc728qw9XgDbhkXKHYci3C6uQuh3k6QyTicmKTPpMtf6enpyMnJAQDk5ORg/vz5F2wjCALuu+8+xMXF4YknnjBld2QFkoI84O/miE1HeQnsnHIOJyYrYlKpLF++HJs3b0ZUVBQ2b96M5cuXAwBqamowb948AMCOHTvw4Ycf4vvvv0dycjKSk5ORl5dnenKSJLlchunRPthZ2gyDkatCnh1O3M2b9GQ1Lnn562K8vb3x3XffXfD5gICAgeK45ppruKQsnWdqpA8+LaxCcU07EoLcxY4jqurWHugMAsL5jApZCT5RTyNu8mhvAMCO0iaRk4ivvJkjv8i6sFRoxKlcHRGlcsGOUywVzk5M1oalQqKYGumDvdoW9OkNYkcRVXlTF5ztFfDl7MRkJVgqJIopo73RqzPiQEWb2FFEpW3uQqi3M4cTk9VgqZAoJkZ4Qy4Ddtr4JTBtUxcvfZFVYamQKNxH2SEhyAM7SpvFjiIancGIytYehHHkF1kRlgqJZupobxysbENnn17sKKKoau2BwShw5BdZFZYKiWZqpA/0RgEF5bZ5tsKRX2SNWCokmgmhnrBXyrHjlG2WSnkTp7wn68NSIdE42imgCfW02edVtM1dcHVQwtvZXuwoRGbDUiFRTY30wfG6DjR12t5qkOVNXQjz4XBisi4sFRLVlJ+nbNllg6PAtM1dvPRFVoelQqJKCHSH+yg7bD1+8VVDrU2/3ojq1h6Ee3M4MVkXlgqJSqmQY3acCluO1UNnMIodZ8RUtHTDKPAmPVkflgqJ7vp4f7T36m3qEpiWI7/ISrFUSHTTo31hp5DZ1Cgw7c9T3ofzwUeyMiwVEp2jnQJj1G44WNUmdpQRU97UBfdRdvDkcGKyMiwVsgjJwR44XHXGZpYY5sgvslYsFbIIScEe6Oo34FRDp9hRRoS2qZsjv8gqsVTIIiQFewAADla2iZpjJPTqDKg508MzFbJKLBWyCOHeznB1VKLIBu6rVLR0QxA4kSRZJ5YKWQS5XIakIA+bOFMZmEiSI7/ICrFUyGIkBbvjeF0HenXWvW49n1Eha8ZSIYuRFOQBg1HA0ZozYkcZVtrmLng528N9lJ3YUYjMjqVCFiP555v1RZXWXSrlTV0I48gvslIsFbIYKjdHBLg7Wv19FW1TNy99kdViqZBFSQr2sOon63v6Dahr7+X0LGS1WCpkUZKCPXC6uRutXf1iRxkW5+b84pkKWSuTSqWlpQVpaWmIiopCWloaWltbL9imt7cXqampSEpKQnx8PJ599llTdklWLinIAwBQZKWXwM6N/OIzKmStTCqVrKwszJ49GyUlJZg9ezaysrIu2MbBwQHff/89Dh48iKKiIuTn52P37t2m7JasWHKwB+yVcqudsbj85zOVUN6oJytlUqnk5uYiIyMDAJCRkYH169dfsI1MJoOLiwsAQKfTQafTcU1uGtIoewVSwjzxU4l1loq2qQs+LvZwdeRwYrJOJpVKfX091Go1AECtVqOhYfAlYQ0GA5KTk6FSqZCWloaJEyeasluyctOifHGivgP17b1iRzE7bVM3n6Qnq6a81AZz5sxBXV3dBZ9/8cUXL3snCoUCRUVFaGtrw6233oojR45g7Nixg26bnZ2N7OxsAEBjY+Nl74Osx9TRPgCA3WXNmJ8cKHIa8ypv7sK10b5ixyAaNpcslS1btgz5NT8/P9TW1kKtVqO2thYqleqir+Xh4YEZM2YgPz9/yFLJzMxEZmYmAECj0VwqHlmhWLUrHJRyFFW2WVWpdPbp0djRx5v0ZNVMuvyVnp6OnJwcAEBOTg7mz59/wTaNjY1oa2sDAPT09GDLli2IjY01Zbdk5ewUciQEulvdCDAtJ5IkG2BSqSxfvhybN29GVFQUNm/ejOXLlwMAampqMG/ePABAbW0tZs6cicTERKSkpCAtLQ033XST6cnJqiUHe+BoTTv69Uaxo5jNL8+ocOQXWa9LXv66GG9vb3z33XcXfD4gIAB5eXkAgMTERBw4cMCU3ZANSg7xwKrt5ThacwbjQjzFjmMWPFMhW8An6skiTY7whkwG/HjSegZrlDd1Q+XqAGcHk36XI7JoLBWySN4uDkgK8sDW44MPU5ei8qZO3qQnq8dSIYs1K1aFg1Vn0NjRJ3YUkwmCgJP1nYjxdxU7CtGwYqmQxZoVe3aI+g8npH+2Ut3Wg84+PaL9WCpk3VgqZLHiA9ygcnXAVisolRN1HQCAWJ6pkJVjqZDFkslkmBmjwk8nm6AzSHto8Yn6s6USzVIhK8dSIYs2M1aFjj49CrUXLqsgJSfqOhDg7gg3TiRJVo6lQhbtmigf2Clkkr8EdqKugzfpySawVMiiuTgoMTHcG99LeGixzmBEaWMnL32RTWCpkMWbEeOLUw2dqG7rETvKVdE2dUFnEHiTnmwCS4Us3qQIbwBAobZF5CRX5/jPI79i/NxETkI0/FgqZPFi/V3hZK/A/tPSvFl/sr4DCrkMo1V8mp6sH0uFLJ5SIUdysAcKJVoqR2vaEeHjDAelQuwoRMOOpUKSoAn1xLHadrR09Ysd5YoYjQIKtS3QhFnHTMtEl8JSIUmYl6iGUQC+2F8ldpQrcqK+A+29eqSEeYkdhWhEsFRIEmL93TA+xANrCyogCILYcS7b3p8HF7BUyFawVEgylqSGoLSxC3sl9HT9Xm0r/N0cEeQ5SuwoRCOCpUKScVNiAFwdlVhbUCF2lMsiCAL2lrcgJdwLMplM7DhEI4KlQpIxyl6B+ckByDtci16dQew4l1TV2oO69l6k8iY92RCWCknKzBgV+vRG7K+w/Etg5+6naHg/hWwIS4UkJTXcCwq5DLtKm8WOckl7ta1wdVQihgtzkQ1hqZCkuDraISHQHTslUSot0IR6Qi7n/RSyHSwVkpzp0b44UNGKujO9YkcZUktXP041dPLSF9kclgpJzoJxgTAKwOcW/CDkvp+nlEkNZ6mQbWGpkOSE+TgjNcwL6/ZWWOwyw7vLmmGvlCMh0F3sKEQjiqVCkvTAtRGobOnBur2VYkcZ1NbjDZgU4Q1HO04iSbaFpUKSNCtWhdQwL7yxpQRdffqreg1tUxdyi6qxp6wZJ+o68M2hWrNMAaNt6kJZUxdmxfia/FpEUqMUOwDR1ZDJZFg+LxYL3t6J7G1l+ENa9GV9X0evDrlFNdA2dWHV9vKBzyvlMuiNAm5OCsCf58XBw8nuqs8yNhfXAwBmxfpd1fcTSRlLhSRrfIgnbkxU439/LMVNiWpEXeR5EEEQsKe8BW9tPYWfSpoAALdPCMJ908Lx3bEGHKpqQ4y/G1Z8X4KvD9ZgXIgHvnhoyhVPr2IwCvhgtxYTQj0R4u1k0p+PSIpMKpWWlhbccccd0Gq1CAsLw6effgpPz8GnpDAYDNBoNAgMDMSGDRtM2S3RgOfS47GrtBnpK3Zgzhg/pCcFYEaML+wUchypPoMwH2dsLq7DxsN1+PbnM4g/3RCLaH9XzIj2hUwmQ6z/L8v8Xhvtg4/3VOLz/VUoKG/BxJ+XMr5cm47WobKlB3+6Ic6sf04iqZAJJlxEfvrpp+Hl5YXly5cjKysLra2tePnllwfd9rXXXkNhYSHa29svu1Q0Gg0KCwuvNh7ZiJP1HcjZqUXe4Vq0dusQ4+cKTZgn1uypgIeTHdq6dbBTyPDY7CjMTw5EsNfFzyB6+g2YnPUdAtxHYUlqMKZG+iDC1+WSOfr0Blz3r21QymXY9Ph0KBW8ZUnW5XLek036W5+bm4uMjAwAQEZGBtavXz/odlVVVfjmm2+wbNkyU3ZHNKhoP1e8eGsCCv48B2/fOR5nenRYs6cCs2NV0OmNeODaCBQ/fz1+NyvqkoUCnJ248pWFiahq7cZfco9i0Tu7LutBy9Xby3G6uRvP3hzPQiGbZdLlr/r6eqjVagCAWq1GQ0PDoNs9/vjjeOWVV9DR0XHJ18zOzkZ2djYAoLGx0ZR4ZGPsFHLMS1Dj+nh/6IxGOCgV0BmMsLuKN/jr4v2xM9IHx2vbcfe7BfjTF4fw3r2pQ25f396LFd+fwpw4P0yP5qgvsl2X/Nc2Z84cjB079oL/5ebmXtYONmzYAJVKhQkTJlzW9pmZmSgsLERhYSF8ffmPk66cXC6Dg/LsyK2rKZRzXByU0IR54Ym0aGw90Yj8I3WDbmc0CvjjpwdhMAr4nxt5L4Vs2yXPVLZs2TLk1/z8/FBbWwu1Wo3a2lqoVKoLttmxYwe++uor5OXlobe3F+3t7bjrrrvw0UcfmZacaITcPTkMX+yvxqNrD8DT2Q53aILxh7TogZFh72wrw/ZTTfj7rQkI83EWOS2RuEy6Uf/UU0/B29t74EZ9S0sLXnnllSG3/+GHH/Dqq6/yRj1JzpluHV7MK8bp5m7sKW+Bj4s95DIZbkoMwAe7tJgb748VvxnHFR7Jql3Oe7JJ91SWL1+ORYsWYfXq1QgJCcF//vMfAEBNTQ2WLVuGvLw8U16eyGK4O9nhlduSIAgCvthfjV1lzTjV0Il3d5QjIdAdf1+QwEIhgolnKsONZypkyfr0Bmw8XIc5Y/zg4sDniMn6DfuZCpEtc1AqcMu4QLFjEFkUDqYnIiKzYakQEZHZsFSIiMhsWCpERGQ2LBUiIjIblgoREZkNS4WIiMyGpUJERGbDUiEiIrNhqRARkdlY9NxfPj4+CAsLu+rvb2xslPSaLFLOL+XsAPOLTcr5pZwduHh+rVaLpqami36/RZeKqaQ+IaWU80s5O8D8YpNyfilnB0zPz8tfRERkNiwVIiIyG6sulczMTLEjmETK+aWcHWB+sUk5v5SzA6bnt+p7KkRENLKs+kyFiIhGllWWSn5+PmJiYhAZGYmsrCyx41yWsLAwJCQkIDk5GRqNBgDQ0tKCtLQ0REVFIS0tDa2trSKn/MXSpUuhUqkwduzYgc9dLO9LL72EyMhIxMTEYNOmTWJEPs9g+f/2t78hMDAQycnJSE5ORl5e3sDXLCl/ZWUlZs6cibi4OMTHx+ONN94AIJ3jP1R+KRz/3t5epKamIikpCfHx8Xj22WcBSOfYD5XfrMdesDJ6vV6IiIgQSktLhb6+PiExMVE4evSo2LEuKTQ0VGhsbDzvc0899ZTw0ksvCYIgCC+99JLw9NNPixFtUD/++KOwb98+IT4+fuBzQ+U9evSokJiYKPT29gplZWVCRESEoNfrRcl9zmD5n332WeEf//jHBdtaWv6amhph3759giAIQnt7uxAVFSUcPXpUMsd/qPxSOP5Go1Ho6OgQBEEQ+vv7hdTUVGHXrl2SOfZD5Tfnsbe6M5WCggJERkYiIiIC9vb2WLx4MXJzc8WOdVVyc3ORkZEBAMjIyMD69evFDfQr06dPh5eX13mfGypvbm4uFi9eDAcHB4SHhyMyMhIFBQUjHfk8g+UfiqXlV6vVGD9+PADA1dUVcXFxqK6ulszxHyr/UCwpv0wmg4uLCwBAp9NBp9NBJpNJ5tgPlX8oV5Pf6kqluroawcHBAx8HBQVd9C+spZDJZLjuuuswYcIEZGdnAwDq6+uhVqsBnP2H2NDQIGbESxoqr5R+JitWrEBiYiKWLl06cAnDkvNrtVocOHAAEydOlOTx/3V+QBrH32AwIDk5GSqVCmlpaZI79oPlB8x37K2uVIRBBrNdrIktxY4dO7B//35s3LgRb731FrZt2yZ2JLORys/koYceQmlpKYqKiqBWq/HHP/4RgOXm7+zsxMKFC/H666/Dzc1tyO2kkl8qx1+hUKCoqAhVVVUoKCjAkSNHhtzW0rIDg+c357G3ulIJCgpCZWXlwMdVVVUICAgQMdHlOZdRpVLh1ltvRUFBAfz8/FBbWwsAqK2thUqlEjPiJQ2VVyo/Ez8/PygUCsjlctx///0Dp/mWmF+n02HhwoW48847sWDBAgDSOv5D5ZfK8QcADw8PzJgxA/n5+ZI69uf8d35zHXurK5WUlBSUlJSgvLwc/f39WLduHdLT08WOdVFdXV3o6OgY+O9vv/0WY8eORXp6OnJycgAAOTk5mD9/vpgxL2movOnp6Vi3bh36+vpQXl6OkpISpKamihl1UOfeFADgyy+/HBgZZmn5BUHAfffdh7i4ODzxxBMDn5fK8R8qvxSOf2NjI9ra2gAAPT092LJlC2JjYyVz7IfKb9Zjb86RBZbim2++EaKiooSIiAjhhRdeEDvOJZWWlgqJiYlCYmKiMGbMmIHMTU1NwqxZs4TIyEhh1qxZQnNzs8hJf7F48WLB399fUCqVQmBgoLBq1aqL5n3hhReEiIgIITo6WsjLyxMx+VmD5b/rrruEsWPHCgkJCcLNN98s1NTUDGxvSfl/+uknAYCQkJAgJCUlCUlJScI333wjmeM/VH4pHP+DBw8KycnJQkJCghAfHy8899xzgiBc/N+qpWQXhKHzm/PY84l6IiIyG6u7/EVEROJhqRARkdmwVIiIyGxYKkREZDYsFSIiMhuWChERmQ1LhYiIzIalQkREZvP/AwllCibVyj1gAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "819e9dba4b744eebad21a6d2d22bfb53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "215fc7bc56134dd59798cca678055648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eaca809d0f53475992af2381199c1cc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0a355cae61a48eabf85f3b96ee23bf8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2da3b8d72ba47b38219eb8221c4d3b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1bf0d6947a34e5f860102d950adf0b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47ee50fa97b94020b3b3aeec7ca62bec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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9rsPXSDQOVNHt27eZcePGMQ4ODoxEImH09fUZFxcX5ptvvmFevHih9JzC8Zi7d+9mTp48ybRr147R09NjjI2NmcGDBzNPnz5VOD40NJSZM2cO4+7uzpibmzNisZixt7dnvL29mevXryv9jKtXrzJDhw5lrK2tGZFIxJibmzOurq7MnDlzmLvvjQW0t7cvMTayNA0bNmQAMGZmZkxubm6px+Xm5jJbt25l2rdvzxgZGTFisZipX78+4+XlxWzcuLHEmNLc3FxmxYoVjKOjI1u+xYsXMzk5ORUaB8owDCOTyZitW7cyzs7OjFgsZqytrZnp06cz6enppZZ19+7djKurK6Onp8fUqVOH+fTTT5kHDx4wPj4+DADG399f4fjXr18zI0eOZCwtLRk+n88AYHx8fNj3r1+/znTv3p0xMTFhDAwMmI4dOzLHjh1j/P39SxxbLap5HCiPYf7rf14OHh4eCAwM/PCBpNpkpudidJc/8fxJGgBAIOTh261d0eEj5V3R46Iy4TPdH7ERRdN9rdjRHUMmlpymrTYr7IWpNdTQiYgQrVM8P3mU/XtQFb/j1Aaq5b6fFcAmTx4P+H5nj1KTJwDYOhph69F+aNbagt23Yvpl3LhQsQHUhBBS21EC1WKXTkbh1OFn7PbM5Z5w7/ThFQj0DET4wbcHGjWXjxWTyRjMHXMe8dGZaouVEEJqGkqgWiozPRcrZ/iz26061MWAUU3Kfb6+oRjf/eYFM0v5tGkZqbmYPeIs8vKUd1knhBCiiBKoltq5JojtcasjEWDp5pLj7z7EzEIXPtu6QSiU/zcIDU7Crz9QGzchhJQHJVAt9CruLQ7+fJ/dnunjCSMT1WYZatbKApPmtWa3f1sTiIeBymeOIYQQUoQSqBba/v0d5ObIq1otrPXQy7vkiggVMXhCU7RoI59TUyZlsHjiBarKJYSQD6AEqmViIzNwbE/RCg+zVrYr12oZZeHzeZi3pgMkevJ5NZ4/ScO+zfcqdU1CCKnpKIFqmQPb7kMqlQ/dtXMyhmc32yq5rnV9Q4yf5cZu//LdHSTEUK9cQggpDSVQLZKRloO/iz19Tl1UtQPmB41zRgNnUwBAbrYUa+dfr9LrE0JITUIJVIsc9X2M7Cz5Qr2mFhK06fLhMZ8VIRDy8dUKT3bb71gk7t+u2DJLhBBSW1AC1RIFBTIc+uUBuz1mestKt30q49LaEt0+dmC3182/hgrM9kgIIbUGJVAtceVsDF7HZwGQj/vsM7SR2j7rszmtIBTJ/2uE3HwF/1PRavssQgjRVpRAtcTfu4vW6/Ma6AixjkBtn1XP3hD9RzZmt39aeI0W4SaEkPdQAtUCSS+zcOVsNLs9bHJztX/m6OktoacvAgDEhGfg2N6wD5xBiPaaMGECeDye0gW9ueLg4MAuWk00Ey2orQWOH3jCDl2xcTCCraOR2j/TpI4Ew6c2x+4NIQCALd/ewscjGrNJtda7/y3wToNWsMlNLnqtYw7o2QGuKyt9WQcHB8TExCh9z8rKCq9eKXYyy8vLw8qVK3Hw4EGkpqaidevWWL9+PVq3bl3ifD8/P/Tq1QunTp3Cxx9/XOlYtU23bt0QEBBAfQy0GCVQDccwDP4p9vQ3ZGL1rVE5eEJTnDj4BCmvs5GalI1DvzzA5Hnu1fb5Gu1dLKDvwHUURfjiote69YCs6Cq7tLGxMWbPnl1iv4GBQYl9CxcuxMaNG+Ht7Q1bW1vs378f3bt3x5MnT2Btbc0e9/btW0yZMgVjxozRiOS5evVqLFy4EDY2NlyHwrp48SLXIZAPoASq4Z7cT0bUs3QA8sWye3zSoNo+W6IrxLiZrti49BYAYNdPwRj5RQvoG4g/cCapSUxMTLB8+fIPHscwDH799Vd89tln2LVrFwBg0KBB6NatG/bv34/58+ezxy5YsAA5OTnYvHmzusKuEGtra4UErwmcnCo3RSdRP2oD1XBn/xfOvm7Z1gq6etVbhdpzkBOsbPQBABlpuTjy66Nq/XyiPZKSkvDu3Tu0bduW3Vf4ung18JUrV7B9+3b8/PPPMDMzU+mzCtsHMzMz8fXXX8PBwQEikUgh0T958gQTJkxA/fr1oaOjAysrK4waNQpPnz4tcb2y2kBv376NIUOGoG7duhCLxahfvz6mTp2KhIQEpbGlpqZiyZIlaN68OfT09GBsbAxXV1csXLgQWVlZiI6OBo/HQ0BAAACAx+OxX926dStRxvfl5ubixx9/RMuWLaGnpwcjIyN07twZ//vf/0ocW/hZEyZMQHR0NEaMGAFzc3NIJBJ4eHjg1KlTZX+j3/PPP/9gzJgxaNy4MfT19WFgYAB3d3ds2bIFMlnJjoaF39fnz59j69ataNmyJXR1dRXKmZqaikWLFqFp06bQ1dWFsbExevTogX///bfE9TIyMrBu3Tp4eXnB1tYWYrEYFhYWGDhwIG7dulWhslQFegLVYAzD4NyfRQn0kzHO1R6DSCzAyGktsGmZ/D+n77pgjJjWgtpCa5Hc3FwcOHAAsbGx0NfXR8uWLdGlSxcIBIo9wS0sLKCrq4ugoCB2X2CgfHk8e3t7AEB2djYmT54Mb29veHt7VyquvLw8eHl5ITU1Fb169YKRkREcHR0BAOfOncPgwYORn5+PAQMGoGHDhoiLi8Pff/+N06dPw9/fX2m77Pt2796NKVOmQEdHBwMHDkT9+vURHh4OX19fnDx5Erdu3YKdnR17fFRUFLp3746YmBi4u7vjiy++gEwmw7Nnz7Bx40ZMmzYNJiYm8PHxwZ49exATEwMfHx/2/A91GsrLy0Pv3r0REBAAZ2dnfPnll3j37h3+/PNPDB8+HPfu3cMPP/xQ4ryYmBi0bdsWDRo0wNixY5GamoojR47gk08+wYULF9C9e/dyfc8XLlwIPp8PT09P2NjYICMjA5cuXcKsWbNw9+5d7N+/X+l5s2bNwtWrV/Hxxx+jX79+7P+dmJgYdOvWDdHR0ejcuTP69OmDrKwsnDp1Cn369MGvv/6KKVOmsNcJCwvDkiVL0KVLF3z88ccwNTVFbGwsTpw4gbNnz+Lk+vXo06FDucpSFSiBarCHd18jPvoNAEAk5qNNF27aZ3oNdsLBXx4g6eU7ZKTm4H+/PcKEOa04iYVUv1evXmHs2LEK+xwdHbF792507Vq0Di2Px8Pnn3+OLVu2ICMjAzY2Nti/fz8MDQ0xevRoAMDSpUuRmpqKn3/+udJxvXz5Es2aNUNAQAD09fXZ/WlpaRg5ciT09PRw5coVNGvWjH3v8ePH8PT0xOTJkxEcHFzm9Z89e4apU6fCwcEBAQEBCu2jly5dQs+ePTFr1iwcO3aM3T9mzBjExMTghx9+wKJFixSul5ycDAMDA0gkEixfvhyXL19GTExMuarHC61fvx4BAQHo27cvTpw4AaFQfgv38fFB27ZtsXr1avTv3x8d3ksily9fxvLlyxWS9ahRo9CnTx+sW7eu3An09OnTJaqWZTIZPvvsM+zbtw8zZsyAp6dnifOCg4MREhLC/oFTaPz48YiJicHhw4cxYsQIdn96ejq6deuGr776CgMHDoSVlXy1qKZNmyIhIQHm5uYK14mLi0Pbtm0xZ+PGak2gVIWrwc4Uq75t3cFarWM/yyISCzByagt223ddELLf5XMSC6len332GS5evIhXr14hKysLDx8+xNSpUxEdHY2+ffvi/v37CsevWbMGCxYswJ07d/D777/DxcUFFy9ehI2NDW7fvo1NmzZh8+bNMDc3x4oVK2BtbQ2hUAh3d3dcv17xuZfXr1+vkDwBYN++fUhPT8eKFSsUkicAuLi4YMqUKQgJCUFoaCjKsn37duTn52Pz5s0lOhd5eXlh4MCBOHnyJN68kf+RGxQUhBs3bsDNzQ0LFiwocb3CqtPK2LVrF3g8HjZs2MAmTwCwtLTEsmXLAAC+vr4lzrO3t8fSpUsV9vXu3Rt2dna4c+dOuT9fWbssn8/HrFmzAADnz59Xet78+fNLJM/79+8jICAA3t7eCskTkLe7r1ixAjk5Ofjrr7/Y/cbGxiWSJwDY2tpiyJAheBIdjdhX1Tf9KD2BaiiGYXDhn+fs9idjq7/6trjeQxri8I6HSHr1DmnJOTjq+xjjvnLjNCaifsWfWACgefPm2LFjBwwMDLB+/XosX75c4QlMR0cHq1evxurVqxXOy8vLw8SJE9GvXz+MHj0amzZtYp+IOnbsiO+//x59+vRBREQE+7TxIRKJBC1btiyx/+bNmwDkN2hlT3fPnj0DIK8OfD/BKrtOQEAA7t69W+L9xMRESKVSPHv2DO7u7mwbXO/evcHnV/2zyZs3bxAREQEbGxs4O5e8H3h5eQEAQkJCSrzn5uZWosodAOrXr8+WszxSUlKwbt06nDlzBs+fP0dWVpbC+/Hx8UrPK94uXqjwczMyMpT+nJKSkgDIf07FXb9+HZs3b8bNmzeRmJiIvLw8xRgSE2FXt265y1QZlEA11JP7yXgZW1R926o9tz0ExToCDJ/aHNtWyP9a3bkmCMOmNIdEl/4L1UbTpk3D+vXrceXKlXIdv2LFCsTHx7MdQ9atW4cePXqwN84mTZrAwcEBP//8M1auLN/4VUtLS6XzQaekpAAAdu7cWeb5b9++LfP9wuusW7euXNdJT08HALUNhcnIyACAUnsLF+4vjKM4ExMTpecIhUKlnX+USU9PR5s2bRAVFYW2bdti3LhxMDMzg1AoRHp6OjZv3ozc3Fyl59ZVktAKv79+fn7w8/Mr9XOL/5yOHTuGIUOGQCKRoGfPnnBycoK+vj74fD4uX76MgIAA5OZXX+0Y3f00lP/JKPZ1c3dLdm5aLvUd2giHdzxkx4Ue2xuGkdNafPhEUuNYWloCQIknEGVCQkKwdu1a7NixAzY2NsjMzERCQgLbLgoAdnZ2MDc3x+PHj8sdQ2mLKRgbGwOQP4Eqe0Itr8LrZGRkwMjow5OXFCap0p7CKqswnvcnryj08uVLheOqmq+vL6KiouDj41PiifHmzZtlDklS9rMqjHPz5s346quvyhXDsmXLIBaLERgYiKZNFcfET506le3ZXF24vysTpS6eKKq+7avGieMrQqwjwLBJLuy279og5OdLOYyIcKWw+q1Bg7LHJRcUFGDixIno3r07Jk2apPDe+08rOTk5VRJbu3btAABXr16t1usUHn/+/PlyPdUVVqlKpeX7HTI0NISTkxPi4+MRHh5e4n1/f38AKFfvYlVEREQAgNLe06okLlV+ThEREWjWrFmJ5CmTyXDt2rUKx1BZlEA1UEJMJp7cl0/NxuMDbbtpzuwofYc3grGpDgDgVdxbnDr8jOOIiLo8fvwYqampJfbHxMRgxowZAOS9TsuyevVqREREKFSnGhkZwcbGBufOnUNBQQEA+Q34zZs3cHFxKe1S5fbZZ5+xnVCUdZCRyWS4fPnyB68zY8YMiEQizJkzh203LS4vL0/h5u/u7o4OHTrg3r17WLNmTYnjU1JSFP5IqFOnDgAgNrb8U0JOnDgRDMNg3rx5Cok3OTkZq1atYo9Rh8IhNu9/70JCQkq0eZeHh4cHOnfujL///pudeON9Dx8+RGJiokIM4eHhCmNwGYbBihUrPtgpTB2oClcD+Z8qqr5t4GwKfUPNmflHV0+EwZ81Y+fI/W11IAaObgKBgP4Wq2mOHj2KH3/8Ed27d4ejoyMMDQ0RGRmJ06dPIycnB/369cPcuXNLPf/x48f47rvvsGHDBnYcaKH58+dj1qxZ6Ny5Mzw9PXHw4EEYGBjgyy+/rHTcderUwZ9//olBgwahXbt26NGjB1xcXMDn8xEbG4ubN2+WSGbKODs7Y9euXZg4cSJcXFzQp08fNG7cGPn5+YiNjcXVq1dhYWGBJ0+esOccOHAA3bp1w+LFi/HXX3+hW7duYBgG4eHh+Pfff/HkyRM2EfXo0QNHjx7F4MGD0a9fP+jq6sLe3r7EkKHi5s6di7Nnz+L48eNwdXVFv3798O7dOxw9ehSJiYmYP38+OnXqVOnvoTLjxo3DunXrMHv2bPj7+6NRo0YIDw/HqVOnMHjwYBw5cqTC1zx06BC8vLwwadIkbNmyBZ6enjAxMUFcXBwePHiAR48e4ebNm2yTwZw5czBt2jS0atUK3t7eEIlEuH79OkJDQzFgwACcPHmyqotdJkqgGijgbNGsLT0/1bzpvD4Z0wT/2/kIWW/yERuZAb+/I9W6PinhRvfu3fH06VOEhITg5s2byMrKgomJCTp16oSxY8di7NixpbZDSqVSTJw4EZ6enpg+fXqJ92fOnInMzEzs2LEDwcHBcHV1xcaNG8vdA/dDevTogQcPHuCnn37C+fPncfXqVYjFYtSrVw9eXl7lnsRhzJgxcHV1xfr16+Hv749///0X+vr6qFevHoYMGYLhw4crHO/o6Ijg4GCsXbsW//zzD7Zt2waJRAIHBwd88803bCIAgMmTJyMmJgZ//PEH1q5di4KCAnTt2rXMBCoWi+Hn54cNGzbg0KFD2Lp1K4RCIVxdXbFp0yaMHDlStW9YOdSrVw9Xr17FwoULce3aNZw/fx7Ozs745Zdf8NFHH6mUQG1tbREUFIStW7fir7/+wsGDByGVSlG3bl00a9YMM2fORIsWRf0spk6dCh0dHWzatAl79+6Frq4uOnfujN27d+Ovv/6q9gTKYyqwFICHhwc7swhRj+x3+ehg5Yu8XHn1zMGAwbCsV3LSbq7t3hCCQ9sfAgAaN6+Dv4NGlHoz1UZhYWEl2lkU1JLVWGqLESNG4MiRI0hISNC4OXFJBRTPTx4eZR76wd/xcqAnUA1zNyCeTZ6m5hKNTJ6AfKWWv/eEISe7AM8epeDK2Rh07efAdVjVR9OSU0qxG0edsm8cpKRnz55BR0cHFhYWXIdCtAg1XGmYq+eLqm/bdrPlMJKyGZtJ8PGIomrbHavv0rqGROts3rwZ3t7eCAkJwSeffKIwuw8hH0IJVMNcPV9ULejV34G7QMphyEQXiP4bn/rg9mvcCVDP+DdC1GXTpk24desWJk6ciF9//ZXrcIiWoT+3NEhMeDpeRMpnGxEIeWjuUTUdKtTFvK4eeg12wukj8jFpv64OhKcGPzUT8r6oqKgPH0RIKegJVINc+7eo+raRSx3OJo+viOGfNwdfIO88dNs/Dg/uVN9EzoQQwiVKoBrk5sU49nXXfvZlHKk5rO0M0b1/0SoLv/1IvbQJIbUDJVANUVAgw52AogTarnt9DqOpmJFTm7Ov/U9F49nD5DKOJoSQmoESqIZ4FPgaWW/kqwjoG4pg42DIcUTlZ9/IBJ162bHbv60J4jAaQgipHpRANUTx6tvmHsqXadJkI78omi3k3J/hiAlP5y4YQgipBpRANcStSy/Y1920cEKCxs3rwKNzPQAAIwN819FTKCGkZqMEqgHeZeXj3q2i3qvunepxGI3qRhV7Cj2+/wkS/lsQnBBCaiJKoBog+HoCCvLl6wfWsdSFqbkuxxGppkUbK7TwkE+WLZUy2LMxhOOICCFEfSiBaoC7xWbwcWtfl8NIKm/k9KKn0KO+j5H8+h2H0RBCiPpQAtUAgVeLFoft+JFdGUdqPo9O9dCouXyh4LxcKfZtvsdtQIQQoiaUQDn2LisfjwKLVlxv2Vazp+/7EB6Ph5HTisaFHt7+EBlpZS9cTAgh2ogSKMfu33qFgoKi9k9jMwnHEVVex552sG9oDED+B8LBnx9wHBGprJ49e4LH4yEgIKDUY6ZNmwYej4edO3dWY2SEcIcSKMfuXilq/2zpqd1Pn4X4fB5GTCtqC9235R6y3uRxGBGprODgYAgEAri7u5d6zO3btwEAHh9YyJiQmoISKMeKt3926KE90/d9SPePHWBdX74Y+Jv0POzfep/jiIiqIiMjkZqaChcXFxgYKF/g/d27d3j06BF0dHTQvHlzpccQUtNQAuVQTnaBwuolLdtqdw/c4gRCPkZNb8lu71ofTG2hWiowUL5AgKenZ6nHBAUFoaCgAK6urhCJRNUVGiGcogTKoQd3XiE/T97+aWougZmFdo7/LE3PTxugfgMjAEDWm3zs2UDjQrVReRLorVu3AFD1LaldaEFtDgVeKaq+1fbet8oIhHyM+8oV38++CgDYt+U+xsx0RR1LPY4jIxVRmECPHDkCf39/pcdQ+yepjSiBcuju1aIORO1rUPtncV36OuDwjkd4/iQNOdkF2Lk2CAt/6sx1WJXiIt7GdQgfcIt99ThvRqWuxDAMQkLkNQd+fn4fPJ4SKKlNqAqXI3m5UtwvNv+taw1q/yyOz+dh/Gw3dvuPHQ/xKu4tdwGRCnn27BkyMjLQvn17MAyj9Cs1NRUAoKenh2bNmimc/8svv8DR0RESiQTu7u64evUqF8UgRC0ogXLkYeBr5OZIAQBGpjowr1tzqzXbe9nCuaU5ACA/T4YdP9zlOCJSXoXVt61atSr1mODgYACAm5sbBAIBu//IkSOYNWsWFi9ejJCQEHTo0AF9+/ZFbGyseoMmpJpQFS5Hird/Nm9twWEk6sfj8fDZ125YMOECAODPXY8xZoYrGjYz4zgy1VS2WlQtUgKLXtepumrUwgTaunXrUo8pTKDvV99u2LABEyZMwJQpUwAAW7duxblz57B9+3asXr26ymIkhCv0BMqRwGLtnx16avf8t+XRqoM1WnewBiBfL/Snhdc5joiUR3kSaGEbafEEmpeXh6CgIPTq1Uvh2F69euHGjRtqiJSQ6kcJlAMFBTKE3Cxq/3RrVzPbP4vj8Xj4fKE7eDz59tVzMbhxgaryNJlMJsO9e/cgFovh4uJS6nHKnkCTk5MhlUphZaXYu9zKygqvXr0CITUBJVAOPH2QjOysfACAvqEIVjbKZ3epaZyamqG3d0N2e+28a5BKZRxGRMoSFhaGt2/fwsXFBWKxWOkxb9++RXh4OAwMDNCkSZMS7/MK/2L6D8MwJfYRoq0ogXLg3s2X7OvG/y39VVuMn+0GiZ686T38cSr+2feE44hIaYKCggCUXX17//59yGQytG7dGnx+0e3E3NwcAoGgxNNmYmJiiadSQrQVJVAOhNwouql4drflMJLqZ26lh2GTi6oDNy+7iay3NNG8Jho3bhwYhoGvr2+px3Ts2BEMw5RYpUUsFsPd3b3E2FE/Pz906NBBLfESUt0ogXIgpNgTaE2cgehDhkxqhjpW8mkLUxKz8dvqwA+cQbTR119/jT179sDX1xdhYWGYNWsWEhISMG3aNK5DI6RKUAKtZgmxb9iJBIRCPho0MeU4ouqnqyfCxK+LqgX3bLyHyLBUDiMi6jB8+HBs2rQJ3333Hdzc3HDt2jWcOXMG9vb2XIdGSJWgBFrNird/2jU0hkBYO38EH33aAM3+G/9aUCDDqq8CwDAMx1GRqjZ9+nRER0cjNzcXQUFB6NKlC9chEVJlaufdm0PFq29bd7TmMBJu8fk8zFrRDnyBvEfm3YB4nDr0jOOoCCGk/CiBVrPiHYhqcwIFgAbOphg0vim7vWb+NWSm53IYESGElB8l0GqU9SYPTx8ks9vN3Gr2FH7lMW6mK8yt5PMApyVlY/O3tz5wBiGEaAZKoNXo4d3XkMnk7XwWdfWgb6h8cHptomcgwvSlbdjtI78+xL1bL8s4gxBCNAMl0GoUfKMoMTT3sOQwEs3Sqbcd2nSpBwBgGGDxpAvIyS7gOCpCCCkbJdBqVLwDUZuuNhxGoll4PB5mrWwHPX0RACAmPANbfLivyqVewYTUTFX1u00JtJpIpTKFBbSbu9MTaHFWNgaYusid3d63+Z7CE3t1EwgEyM/P5+zzCSHqk5+fr7B2raoogVaTiMepyHojvyHr6gtR17Z2TCBfEX2HNYJ7p/+WPPuvKjf7HTdJzNDQEJmZmZx8NiFEvTIzM2FoaFjp61ACrSbFq28budShFSmU4PF4+PqHDtAzkFflvojMwKZl3FTlmpmZIS0tDcnJycjLy6PqXEK0HMMwyMvLQ3JyMtLS0mBmZlbpawqrIC5SDiHFqiPbdqP2z9JYWuvji8UeWL/4JgDgwNb76NTLDp17V+/0bzo6OrCzs0Nqaiqio6MhlUqr9fMrLKtoeBQSw7iLgxAuJRf7PQgr+XsgEAhgaGgIOzs76OjoVPrjKIFWk3vF2j9d29b8BbQro/eQhrj2byxuX44HACyeeAF/B42ARV39ao1DR0cH1tbWsLbWggkvDjUrej2KnpZJLdWs2O9BNdQaURVuNUh6mYW4KHl7Gl/Ag1PT2jeBfEXweDzM/bEjzCzkK7akJmVj8aQL7BhaQgjRBJRAq0Hx3qS2jkYQiSvf+6umM6kjwYKfOqGwqfiG3wvs3RTCbVCEEFIMJdBqULwDUav2VH1bXq07WGPYlKLFtzcuvYmHga85jIgQQopQAq0G924WtX96dKIORBUxYXYrOLc0BwBICxjMHn4WacnZHEdFCCGUQNUu+10+wkKS2O3CNTBJ+QhFfCze2Bn6hvKhLa9evMX88f9CKpVxHBkhpLajBKpmjwITUVAgv9mbmktgZFL5rtO1jbWdIeav68Ru3/B7gZ9X3uEwIkIIoQSqdsXHfzZrRU+fqurQoz5GfdGC3f51dSD8T0VxGBEhpLajBKpmxZfm8uxmy2Ek2m/cLFeFRcgXTvBDTEQ6dwERQmo1SqBqJJMxCCnWgahFGysOo9F+AgEfizd0hmU9+YQKbzPzMHv4Wc7myyWE1G6UQNXo+ZM0ZKblAgB0JALYOFR+8uLazthMgm+3dYVQJP+v++xhClZMv0xz1RJCqh0lUDUqPv7T0dmUJpCvIk1amGPmck92++Shpzj48wMOIyKE1EaUQNXoXvEFtDvX4zCSmqffsEboM7Qhu71m3jXcvRLPYUSEkNqGEqgaFe+B27ojJdCqNtPHk51kQSZlMGfEWSTEvuE4KkJIbUEJVE1SEt8hJiIDAMDn89C4eR2OI6p5xDoCfLutK0zqSAAAack5mDXsDHKyCziOjBBSG1ACVZP7xZYvs7I1gFiHJpBXBwtrfXy7tSsEQnn7cmhwElbOoE5FhBD1owSqJsVXYHFtS8NX1KlFGyt8saQNu318/xMc2v6Qw4gIIbUBJVA1Kd4D17M7TSCvbgNHN0GvwU7s9pq5VxF4lToVEULUhxKoGuTmFOBxUCK77eJuyWE0tQOPx8Osle3QuIW8rVlawGDOiHN4+YI6FRFC1IMSqBqEhiQhP08+gbyRiQ5M6+hyHFHtINYRwOfnbjAxk3cqSk3KxuzhZ5GbQ52KCCFVjxKoGhQfvtKkJfW+rU6W1vpYuqUL26noUWAiVs0MoE5FhJAqRwlUDYonUM/uNIF8dXP1rItpizzY7WN7w/DHr484jIgQUhNRAq1iDMPgXrEhLG6edTmMpvb6ZKwzeg5qwG6v/vqKQscuQgipLEqgVSw2IgOpSdkAAJGYj/pOxhxHVDsVdipq1LyoU9E3o84hLTmb48gIITUFJdAqFlzsKce+oQn4fJpAnis6EiF8tnWFobEYAPA6PguLJ12ATEbtoYSQyqMEWsWKt3960ATynLOyMcC8NR3Z7StnY7BrfTCHERFCagpKoFXsXrEFtCmBaob2Pepj6KRm7Pbmb28h6HoChxERQmoCSqBVKD01B5FhqQAAHg9o8t9KIYR7E79pjWatLQDIV275ZtQ5tq2aEEJUQQm0ChWfQN6ynj4kukIOoyHFCUV8LNnUBUamOgCApJfvsPAzP2oPJYSojBJoFSre/tmSJpDXOJbW+liwrhO7ff3fWOxcE8RhRIQQbUYJtArdu0UTKGi6tl1tMGJqc3Z728rbuH/7VRlnEEKIcpRAq0henhQP7xabQL41TSCvqSbMdoOLe1F76Pyx/yLrTR7HURFCtA0l0CoSGpyInGz5pOWGxmKYW+lxHBEpjUDIx8KfOkPPQAQAiIvOxA9zrnAcFSFE21ACrSJ3rxQNi2jayoLDSEh51LU1wKwVnuz2P/ue4NzRcA4jIoRoG0qgVaT44s0dPqrPYSSkvLwGNkCPgY7sts90fyTE0vqhhJDyoQRaBQoKZAo9cFu3t+YwGlIRM5d7wspGHwDwNiMPiyb6QSqVcRwVIUQbUAKtAk/uJyHrTT4AQM9AhLr1DTiOiJSXvqEYC3/qxM5ZHHglAbvWh3AcFSFEG1ACrQKBV4vaPxu3qAMejyaQ1ybNPaww8osW7PbW5bfwKOg1hxERQrQBJdAqEFisAxG1f2qnsTNaoqmbfOpFaQGDhRP8kP0un+OoCCGajBJoJclkjMLE5O4daQJ5bVQ4tEWiJ59+MeppOjYuuclxVIQQTUYJtJLCH6UgMy0XACDRFaJ+AyOOIyKqqmdviC8We7DbB39+gOt+sRxGRAjRZJRAK6n48JWGzcyo/VPL9R3WCO2KTcO4ZPIFpKfmcBgRIURTUQKtpOIdiNr3oPlvtR2Px8PXP7SHcbFVW777KoDjqAghmogSaCUwDIOga0UJtE0XGw6jIVXF1FwXc75vz26f/V84Tv/xjMOICCGaiBJoJTx/koaURPmizGIdAewbmXAbEKkyHXvaoc+Qhuz2qpmX8fIFzVJECClCCbQSij99OjU1ZQfjk5rhiyVtUNdWPinGm4w8LJ1ykRbgJoSwKIFWwt0rRR2I2nvR+M+aRs9AhAXrOqKwX9itS3E4+PMDboMihGgMSqAqkskY3PaPY7c9utD4z5qouYcVhn9etAD3hsU3EBGaymFEhBBNQQlURc8epii0fzo1NeM4IqIu475yRQNnUwBAXq4UCyf8i7w8KcdREUK4RglURbcuvWBfO7c0p/bPGkwkFmDh+k4QieW/LmH3krF91R2OoyKEcI0SqIpuXCxKoF362XMYCakOjo1NMWlua3bbd10wgostYUcIqX0ogaogN6cAQcUmUCg+cw2puQaNbwq3dnUByNvAF33mh6w3eRxHRQjhCiVQFdy79Qo52QUAAEMTMaxsaP3P2oDP52Hemg7QNxQBAOKiMrF2/jWOoyKEcIUSqApuXiiqvm3ZxorDSEh1s6xngJk+nuz2n7+Hwv9UFIcREUK4QglUBcXbP7sPcOQwEsIFr4GO6Fqs3fvbqZeQkviOw4gIIVygBFpB6ak5CA1OZLdp/c/ah8fj4asV7WBmqQsASE3KxvIv/MEwNEsRIbUJJdAKuu0fh8L7pJWNPgyMxNwGRDhhZKKDuT92YLcvnYzCsb1hHEZECKlulEAr6Gax6tu2XWn1ldqsTWcbfDK2Cbu9es5VvHiewWFEhJDqRAm0gopPoNC1nwN3gRCNMHmeO+o3MAIAvMvKx6KJF1BQIOM4KkJIdaAEWgEvnmfgxfNMAIBAwEOzVhYcR0S4JtEVYsG6TuAL5DNRhdx4iW3Lb3McFSGkOlACrYAbxYavODqbQiQWcBgN0RRNWppj3Feu7PbOtUEIOBPNXUCEkGpBCbQCit8Uu/al6ftIkZHTWsCjc1GP7IUT/JAQk8lhRIQQdaMEWk452QUKy5d16ePAXTBE4/D5PCz8qRMs6uoBADLTc/H1qHO0agshNRgl0HK6GxDPTt9nYiZBPXtDjiMimsbYTIKlW7pCIJS3hz68m4i182iqP0JqKkqg5XS5WPWtZ3cavkKUa9bKAlPmu7Pbh7c/xNHfH3MYESFEXSiBlgPDMLhyNprd7vFJA+6CIRpv8ISm6FKsjXzVzMsIvBrPYUSEEHWgBFoOkaGpSIh5AwAQivho4UETyJPS8Xg8zP2xAxo2MwMASAsYzBp2FvHR1KmIkJqEEmg5XDj+nH3d1M0cQhF920jZdPVEWLGjO0zNJQCA9JQcfDn4NK0fSkgNQpmgHC4WS6B9hzbiMBKiTSyt9bH8l+7sH1zhj1Iwe8RZ6plLSA1BCfQDEmIyERqSBADg8YAOH9XnOCKiTZq1ssCc79qz2zf8XmDJpAuQyWjlFkK0HSXQD7h4omix5AbOptA3pNVXSMX0GuykMFPRmSPhWDPvGi1/RoiWowT6ARePR7Kve3s35DASos3GzGiJAaOLVm45sPU+dq4J4jAiQkhlUQItQ0riOwRde8lu0+orRFU8Hg9fLmuDzn2Khrds/vYW9mwM4TAqQkhlUAItg9+xSLatysbeEGYWuhxHRLSZQMDHwp86wa19XXbfugXXsXMtPYkSoo0ogZbh7P/C2df9hlPvW1J5Yh0BVm7vjhZtisYSb1p6Ezt+uMthVIQQVVACLcXr+LcIupbAbn/0qROH0ZCaRFdfhO99veDWruhJdOvy29i87CZ1LCJEi1ACLcX5vyJQeC9zaGRC1bekSunqibDqNy+07mjN7vttTRAWT7pA40QJ0RKUQEtRvPq2/8jGHEZCaiqJrhCrfvVC265FixOcOPAUXww8iTcZuRxGRggpD0qgSkQ9TcODO68ByCdP6D7AkeOISE0l1hFgxfbu6DO0aIjUrUtxGNvtL1qQmxANRwlUieP7n7CvnV3NYWSiw2E0pKYTivj4+vv2mDDbjd0X/jgVQzyP4Nq/MdwFRggpEyXQ90ilMpw4WJRAh0124TAaUlvweDyM/rIl5q/ryC7InZGai2kDTmL793dp6j9CNBAl0PfcuhSH1/FZAAAdiQDtvGjuW1J9en7qhPUHe8PMUt5pjWGAbStu48tBp5CS+I7j6AghxVECfc+xvWHs60697WnpMlLtXFpbYsfx/nD1LBoreuVsDAa1PoyAM9HcBUYIUUDZoZiUxHfwO1Y09+3QSc04jIbUZqbmulizp6dCE0JKYjamf3oKK770x7usfA6jI4QAlEAV/L07FAX5MgBAXVsDODU14zgiUpsJhHxMWeCOH3b1gKl50Tjk/+18jMHuh3Hz4gsOoyOEUAL9j1Qqw/98H7PbI6c15zAaQoq06WwD3zMD0Lm3HbvvxfNMTO57HEsmX0B6SjaH0RFSe1EC/c/VczFIiHkDQD42j6buI5rEyFSCZVu7Yv7ajtA3FLH7/9n3BANaHsI/19pCJuNxGCEhtQ8l0P/s33qffe01wBFiHQGH0RBSEo/HQ89BTth1/lN07Ve0LFpqUjaW+I7FmO/n4FEU9RonpLpQAgUQGpKIW5fi2O3RX7bkMBpCymZmoYulm7ti5Y7uqGNV1DZ6P9IRI1bOxbdTL9GQF0KqASVQALvWFy1q3LKtFeraGnAYDSHl075Hfew+/ylGTGsOkVDeK5dh+Phrdyg+djmAvZvvIS+XJqYnRF1qfQKNi8rE+T8j2O2pi9w5jIaQitHVF2HSN61x/Psf0NX1Ibv/TUYe1s67hv4tDuDU4ac0kxEhalDrE+jOtYHszcW+kTEaNzfnOCJCKs7eKhm/zPkN2+dsR/16Rb1y46PfYMF4Pwxr9z/cuBDLYYSE1Dy1OoHGRmbg2J6imYemLvTgMBpCKq+LaygOb3+I2dPTYWRYwO4Pu5eEKf1OYEq/4wgLSeIwQkJqjlqdQLd/fwdSqfzps34DI3h0rsdxRIRUnljEYIR3Fv4+kIjxo95ARyxj37tx4QWGeB7B/HH/IiY8nbsgCakBam0CjQhNxalDz9jtWSvbgcejcXSk5jAwYPDFpEwc3fcaA/tlgc8ragc9/ccz9G9xEIsm+iEmIp27IAnRYrUygTIMgx+/ucq2fTo2MYGrZ12OoyJEPSwtZFj8TToO+iaiS4ei4S0yGYMTB55iQIuDWDzpAiVSQiqoVibQSyeiFOYRnb+2E4fREFI9HB0KsHZVGn7bkoQ2rXPY/VIpg+P7n2BAi4NYMvkCYiMzOIySEO1R6xJoTnYB1i24xm536mWHhs1o0nhSe7R0ycPWdSnYsSkJHq2KeuxKpQz+2fcE/ZsfwMIJfgi7R52NCClLrUugW5ffwovnmQAAoYiPOd+14zgiQrjh1iIP235KVZpITx56iiFtj2By3+O49m8MGIbGkRLyvlqVQENuvsTeTffY7YnftIKRqYS7gAjRAGwi3ZgEd7cchfduXnyBqf1PYrD7Hzi2Lww52QWlXIWQ2ofHVOBPSw8PDwQGBqozHrXJepOHoZ5HEBMhb99xaGyC304NoJ63pEaod92YfZ1lOQYyHVuVr/X4iQiH/mcA/yu6kDGKvx9Gpjr4ZKwzhk1ujgbOpip/BiFqUfx+Xg21JrUigTIMg7mjz+Pcf1P2CQQ87L04CFY2NOctqRmqMoEWSngpwJG/DXD8tC5yckuuTtSqgzX6j2yM3t4NFRb8JoQzlECr3oFt97H666vs9szlbTFwtDOHERFStdSRQAtlvuHhn1P6OHZSFy9fi0u8LxTy0bFXfXz0qRO69HWAuZVelX02IRVCCbRq+Z+KwldDzrBjPtv3sMXKHV4cR0VI1VJnAi0kkwF3g3Xw9wk9XL2pq3QBbx4PaNHGCl36OqBtVxs097CEjkRY5bEQolQ1J9Aa/T/7/u1XmDv6PJs8Laz1sHRzV46jIkQ78fmAp0cuPD1ykZqWgYsBujj7rw5CnxZV3zIM8ODOazy48xoAINYRoGVbK7RoY4UmLczRpGUdODqbQiSiBeuJ9quxT6BB1xPwxcCTyHojXydRz0CEPX6fUlsNqZGq4wm0NHEJAly+qosr18R4FCYp0fHofUIRH/XsDWFjb4R69oawrm8IMwtdmNSRwMxCF/qGIuhIhJDoCqGjK/9XLBFAJOJTpz9SNnoCrbyAM9H4ZtQ5ZL+Td7kXifnYcrQvJU9C1MC2nhRjhr/FmOFARgYfN+/qIChEjOB7QsS/KjlMrCBfhtiIDMRGVHzGI4GAB6GIX+xLnliFIv5/7wnY9wr3S/SEMDaVwNhMByZmEhibSWBiJoGljT6s6xvCytYAYjE9EZOKq1EJlGEY7Fofgo1LbrB/fIh1BNhytC/sG5pwGhshtYGxsQx9PspGn4/kEzOkpPJx/5EYzyKEeBYuQESkEIkpqo+9lkoZSKVS5OZIqypk8HiAZT15MrVxMIJDYxM4OZvBsYkJHBqbQqxDyZUoV2MSaGLCWyz9/BKu/1u0aLCuvhBbjvaFQyMar0YIF+qYyeDVJQdeXYr2vcvm4dVrAV6+EiDhlRBJSXykpjFISwPS0/l4l81HXh4PuXl85OUXfclk6pn3hWGA1/FZeB2fhXu3Xim8x+fzYONohMYudeDsas5+WdsZUnUy0f4Emp8vxaFfHmL7d3fwJiOP3V/PzhCb/tcHpnWo2pYQTaKny6CBQwEaOBQAyC33eQwDSKVAQQEPBQVAgfS/fwt48v3FtgukgPS/7exsHjLf8JGRyUdmJh/pGTykpDBITOLjdZIIaRliMKW028pkDF5EZuBFZAYunnjO7jcy0UGTwoTa0hzOrhZo0NSUqoJrGa1NoLk5BThx8Cl81wYhLipT4b2PPm2AOd+1p6oXQmoQHg8QCgGhsLBzSNV0EsnPBxKTBHj1WoC4BCGiY4R4Hs1DdKwQick6SpNrZnou7gbE425APLtPKOLDqakZmrSsg8bN66BR8zpo0sIc5nX16Gm1htKqBCqTMXgU+Bqnj4Tj5MEnyEhV/OvV0EQHizZ0QpvONhxFSAjRNiIRYFNPCpt6Uri3ylN4LycXiH0hQnikCM8ihHj6jI/w5zrIelfy1lmQL8PTB8l4+iBZYb9JHQkaNZcn1YYuZrBvaAI7J2NY2RiAz6fEqs00ehiLVCpDxONUBF1PQPC1lwi8Fo+kl+9KHCcS8zF0kgvGzGgJEVWhkFqIy2EstQ3DAK8TBXgWKUJ4hAhPngnwLEKA10kV6xylIxGgfgNjeUJtaAw7J2PUtTWAZT0DWNnow6SOhJ5cK6q2DGPJz5ciPTkHKYnZSEvORmqS/CvpZRZiItIR/SwdsZEZyMstvbedvpEYn4xughFTm0NXX1SN0RNCaiseD6hrJUVdKym6dChavebNWx7CI0WIjBIhIkKAZxF8RMXqICdX+W02N0eKiNBURISmKn1frCOAZT19mNfVg7GpBCZ15ENx5ENy5ENxjEx1oGcggkRXCD19ESR6Quj+9y+1x6ofJwl09TdXcWDrfZXOFekI0Kp9XXw61hnunepRFQghRCMYGjBo7ZqH1q5F1cAyGfDqtQARUSJERgkRFcVH7As+4l+K8Sar7D/683KliIvKLNHHo7yEQj5E4v/GyAr/GzcrVBwzKxDyIPrvfb6ABx6PBx5P3vuYxwPA47GvC9/j/bc9dJILPvrUSaXYagpOEqi+QfmfFsU6AljbGaKFhyVc3C1h42AIgUDenb20v9wIqW3qFXudmZaNAl4iZ7EQRXp8oKWT/Ku4N28FeJkoRvwrCRJeiRH/UoTkFAFS0kRITZcgO7dytWoFBTIUFMgqdY2ydPjITm3X1hYVagM1NzeHg4NDuY5NSkqChYVFmcc8fZiMgvyiHzCfL59lRBueKnML3kBHaMh1GGpD5dNOPB4DI91MZOXkwFBXh+tw1OZNdm4tLh8DMLL/2vjKvn2z7zK8Uo8sygAVuO8KxLCwty//8e8pT37QFObm5jh37pzS9yqUQCtCm+bNVQWVT7tR+bQblU+71ZTyqWdqD0IIIaSGowRKCCGEqEBtCfTzzz9X16U1ApVPu1H5tBuVT7vVlPKprQ2UEEIIqcmoCpcQQghRQYUSaFxcHGbOnIn27dtDT08+QXJ0dPQHzwsMDMTnn38OZ2dn6Onpwc7ODqNHj0ZUVJSqcauFquV73+rVq8Hj8dCpU6eqD7ISKlu+sLAwDB06FObm5tDV1UWTJk2wefNm9QVcQZUpX2xsLMaPHw87Ozvo6emhcePGWLp0KbKystQbdAX8+eef8Pb2hr29Pfv9X7RoEd68efPBc3NycjBv3jxYW1tDV1cX7du3x5UrV6oh6vJTtXzacn+pzM+vOE29v1S2fJp+f1GKqQB/f3/G0tKS6du3L9OrVy8GABMVFfXB87755humQ4cOzM8//8xcvnyZOXjwIOPs7MyYmZkxsbGxFQlBrVQtX3GRkZGMvr4+Y2lpyXTs2FE9gaqoMuW7e/cuY2hoyAwYMIA5duwYc+nSJebXX39l1q9fr96gK0DV8r19+5Zp1KgR4+DgwOzZs4e5dOkSs2bNGkYikTDDhg1Tf+Dl5OnpyQwdOpQ5cOAAc/nyZWbjxo2MsbEx4+npyUil0jLPHTVqFGNsbMz89ttvzIULF5hBgwYxEomECQkJqZ7gy0HV8mnL/aUyP79Cmnx/qUz5tOH+okyFEmjxb8LOnTvLfYNKTEwssS86Oprh8XjMsmXLKhKCWqlavuJ69erFfP7550zXrl017j+4quWTSqVMs2bNmE8//VSN0VWequU7f/48A4A5f/68wv4FCxYwAoGAycrKqupQVaLs92jv3r0MAObixYulnnfv3j0GALNr1y52X35+PtO4cWNmwIABaolVFaqWT1vuL6qWrzhNvr+oWj5tub8oU6EqXD5ftSZTZTNO2Nvbw8LCAvHx8UrO4Iaq5St06NAhBAcHY/Xq1VUUUdVStXyXL19GaGgovv766yqOqGqpWr68PPncpUZGRgr7TUxMIJPJwGhIPztlv0dt2rQBgDJ/j06cOAGRSIThw4ez+4RCIUaMGIHz588jN7f8i1qrk6rl05b7i6rlK6Tp9xdVy6ct9xdlOOtEFBYWhsTERDRt2pSrEKpUWloa5syZg7Vr18LMzIzrcKrUtWvXAMjb0dq1aweRSARLS0t89dVXyM7O5ji6yvvoo4/QqFEjLFiwAKGhoXj79i0uXbqEzZs3Y9q0adDX1+c6xFIFBAQAQJm/R48fP4ajoyP09PQU9ru4uCAvLw8RERFqjbEyylM+ZbTl/lLe8mnr/aU85dPm+wsnCbSgoADTpk2DhYUFJk2axEUIVW7evHlo3LgxJkyYwHUoVS4hIQEAMHz4cPTq1Qt+fn6YP38+fH19MWrUKI6jqzyJRIJr165BJpPBxcUFhoaG6NGjB/r3749t27ZxHV6p4uPj8e233+Kjjz6Ch4dHqcelpqbC1NS0xP7CG3FqqmYuylDe8r1PW+4vFSmfNt5fyls+bb6/cLIay4wZM3Djxg2cPn1a6S+2trl69Sr27duH4ODgGrkArkwmn/B/zJgxWLlyJQCgW7dukEqlWLhwIUJDQ9GsWTMuQ6yUnJwcDB8+HImJidi/fz/s7Oxw584drFy5EkKhENu3b+c6xBLevn2LTz75BEKhELt37y7zWIZhlP6/1JSqaWUqUr73acP9pSLl08b7S0XKp833l2p/Al20aBF+++037Nq1C7169aruj1eLqVOnYtKkSbC1tUV6ejrS09NRUFAAqVSK9PR0jWljUlWdOnUAAD179lTYX/jzu3fvXnWHVKV+//13XL58GWfOnMGYMWPQpUsXzJ07F+vXr8eOHTtw/75qa9eqS05ODgYOHIjnz5/j/PnzsLW1LfN4MzMzpU+ZaWlp7PuapKLlK04b7i8VLZ+23V8qWj5tvr9UawL9/vvv8eOPP2Lz5s0YO3ZsdX60WoWFhWHHjh0wNTVlv65fv45bt27B1NRUI59gKsLFxQUASvz1W/gEU9nOV1x7+PAhTE1N4eSkuGBj27ZtAch/vpoiPz8f3t7euHPnDs6cOYMWLVp88BwXFxdERUXh3bt3CvtDQ0MhFovRsGFDdYVbYaqUr5A23F9UKZ823V9U/f8JaOf9pdqqcLds2YKlS5fi+++/x8yZM6vrY6uFv79/iX2zZ8+GVCrF1q1bNeoGpYq+fftCR0cH586dQ//+/dn958+fB4AKtU9porp16yItLQ0REREKP6vbt28DAGxsbLgKTYFMJsPo0aNx8eJFnD59Gu3atSvXeQMHDoSPjw+OHj2K8ePHA5C3Ex45cgS9evWCjo5mrKupavkA7bi/qFo+bbm/qFo+rb6/VHTcy9GjR5mjR48y06ZNYwAwv/zyC3P06FHm8uXLDMPIx18JBAJmxYoV7DmHDx9meDwe06dPH+bmzZsKX48fP66S8ThVRZXyKaOJ47QYRvXyLV++nBEIBMyiRYsYPz8/ZvXq1YxEImHGjx/PQSlKp0r5oqKiGENDQ6ZRo0bsRApr165lDA0NGXd393IPcle3wjItWbKkxO/RixcvGIYp/ec3fPhwxsTEhNm5cydz4cIFxtvbm9HR0WGCgoK4KIpSqpZPW+4vlfn5vU8T7y+VKZ+23F/eV+EECvki5yW+unbtyjCM/GYEgPHx8WHPGT9+/AfP0xSqlE8ZTfwPzjCql08mkzHr169nnJycGJFIxNjZ2THLli1j8vLyqr8QZVC1fI8fP2aGDh3K2NraMhKJhGnUqBHzzTffMKmpqdVfiFLY29uXWr7C8pRWvnfv3jFz5sxhrKysGB0dHaZt27aMv79/tZehLKqWT1vuL5X5+b1PE+8vlSmfttxf3kersRBCCCEq0NzWWUIIIUSDUQIlhBBCVEAJlBBCCFEBJVBCCCFEBZRACSGEEBVQAiWEEEJUQAmUEEIIUQElUEKqCI/HA4/HA5/PR2RkZKnHde/enT12z549Cu9NmDCBfc/Hx6fUa+zdu5c9rlu3bgrvXb58mX2v8EtPTw/W1tbo0qUL5s2bh5CQkMoUlRACSqCEVCmhUAiGYfD7778rfT88PBwBAQEQCsuehlooFGLXrl2QSqVK39+5c+cHr2Fvbw8fHx/4+Phg9uzZ6N+/P7KysvDTTz+hdevWGD16NN6+fVu+ghFCSqAESkgVsrKygoeHB3bv3o2CgoIS7/v6+oJhGIVJs5Xp378/4uLicO7cuRLvhYWF4fr16xgwYECZ13BwcMDy5cuxfPly/PDDD9i5cyeCgoIQEhKCFi1a4NChQxg6dGjFCkgIYVECJaSKTZkyBa9evcKpU6cU9ufn52Pv3r3o0KEDu4RTaUaPHg1dXV3s3LmzxHuF+yZPnqxSfG5ubrhw4QIsLCxw7tw5/PPPPypdh5DajhIoIVVs5MiR0NfXh6+vr8L+EydO4PXr15gyZcoHr2FiYoKhQ4fi9OnTePnyJbs/NzcX+/btQ9euXdG4cWOVY7S0tMTUqVMBAAcPHlT5OoTUZpRACalihoaGGDFiBM6dO4e4uDh2/86dO2FkZIRhw4aV6zpTpkxBQUGBQkejY8eOISUlpVxJ+EMKOx/duXOn0tcipDaiBEqIGkyZMgVSqRS7du0CAMTExMDPzw+jR4+Gnp5eua7RqVMnNG3alG03BeRJ2NTUFN7e3pWOsXCh8KSkpEpfi5DaiBIoIWrg6emJFi1aYNeuXZDJZPD19YVMJqvwk+PkyZPx/PlzXLp0CREREfD398fYsWMhkUgqHWNhUubxeJW+FiG1ESVQQtRkypQpiImJwblz57B79264u7ujVatWFbrGuHHjoKOjA19fX/ZJtCqqbwEgISEBAGBhYVEl1yOktqEESoiajB07Frq6upg6dSri4+Px+eefV/ga5ubmGDRoEI4dO4Zdu3ahffv2aN68eZXE5+/vD0D+tEwIqThKoISoiYmJCYYMGYK4uDjo6+tj5MiRKl1nypQpyM3NRVJSUpU9fSYmJuLXX38FIB8yQwipuLKnMiGEVMp3332HwYMHw8LCAoaGhipdo3v37jh+/DhkMhl69+5d6Zju37+PcePGITk5Gf369cPAgQMrfU1CaiNKoISokZ2dHezs7Cp1DR6Pp1KSi46OxvLlywHIJ3FITk5GUFAQgoKCAMifPAufQgkhFUcJlJAaKiYmBitWrAAASCQSmJiYoFGjRpg7dy5Gjx4NNzc3bgMkRMvxmMK+7IQQQggpN+pERAghhKiAEighhBCiAkqghBBCiAoogRJCCCEqoARKCCGEqIASKCGEEKICSqCEEEKICiiBEkIIISqgBEoIIYSogBIoIYQQooL/Ax8gDiq/z0yjAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# scale\n",
+    "ori_x = ori_x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# Smoothing\n",
+    "for i in range(len(ori_x)):\n",
+    "    ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "# yes_data = yes_data / 3000\n",
+    "# no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e575fa4ffbd54571928997a0f863a410",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+xEQkM/5Hw5lvB55mmmv9orTNV20OMrHRaSW/oJqgfqa3838Yhrl58yYzatQoxt7entHW1mb09PQYR0dH5rvvvmNevXpV5C7y+nvu2rWLOXnyJNO+fXtGV1eXMTIyYr766ivm2bNnSuVDQ0OZWbNmMS4uLoyZmRkjFosZOzs7xsvLi7l69WqRr3H58mVm0KBBjJWVFSMSiRgzMzPGycmJmTVrFnP7o+NiZ2dXqO9lcRo0aMAAYExNTZmcnJxiy+Xk5DBbt25lOnTowBgaGjJisZipW7cu4+Hhwfz000+F+qzm5OQwy5YtY+rVq8e+v++//57Jzs4uUz9ThmEYuVzObN26lWnSpAkjFosZKysrZsqUKUxKSkqx73XXrl2Mk5MTo6ury9SqVYv58ssvmYcPHzI+Pj4MACYwMFCp/Lt375ihQ4cyFhYWDJ/PZwAwPj4+7PNXr15lunXrxhgbGzP6+vpMp06dmGPHjjGBgYGFylY3FfEZ5zHMf+3Yy6hNmza4U7DRA1FrqcnZmND7OEJD3rPrmjqbYdmv3WBiVrivX0Fx0enYvOQ6Qq7n3zs1tdDB1iN94Ny+8LdidZPXmrNaq8wGSIkF9l2r4hs3EcK1iviM0z1TAolEhpmDzyol0q/GNMWW/31WYiIFgDp2Bli7pyemL20HgUDRQjIp/gPG9z6OW8GvKy1uQgipLiiZ1nAMw2DFtCDcCsrvUD1hbmt8s6htmbq98Hg89B/eBOv+8oShiRYAIDsrF5P7n8T1C0U3WiCEEE1BybSG+9+OJ/hnd/7ILANGN8Hgic3Lvb+W7Syx+e/eqGWhuKLN+SDDNK/T1H2GEKLRKJnWYFHPk7Fubn6fMhc3K3yzqK3K+61b3wgbD/SCuZWi+0J2Vi6+6X8Kkc+SVd43IYRUR5RMayipVIb5YwKQ/UHRJ864ljaW/+ZRYSMaWdsZYu3unjA0VlT5piRlY2LfE0hO+FDCloQQon4omdZQezbfx+M78YoFHrDKtzvEWoJPb1RGdesbYeUOD2jpKPYbF52OWUP9IJUWPYoKIYSoK0qmNdC72Az8tiq/u8PX4x3RsHnhwbArQlNnc3y/qTO7fDs4FmvnFD1cGSGEqCtKpjXQhgVX8SFTMS2UiZk2xn1XeDzUitSxhy3GzHJmlw/++gh+h8Mr9TUJIaQqUTKtYe5cjsWZQ/mJbN46NwiElf9nMOybFujc245d/mHyRbx6mVrpr0sIIVWBkmkNwjAMflp8nV1u0dYCbTrXqZLX5vF4+G5VB1jV1QcAZKZLMWe4PyQSun9KCFF/lExrkCv+Mbj/35B/PB4wf71blb6+noEYizZ3gUCoaDH8+G48tiy5XsJWhBBS/VEyrSEYhsHWpfnTULl52sLSWr/K42jc0gzj57Rml3f/dB+XzkZVeRyEEFKRKJnWEBdPROLJPcXYuzw+D1OWtOMsFq+xzdCuqzW7vHDceSS8y+IsHkIIURUl0xqAYRjsWHeXXe7evx7MLHU/sUXl4vN5mLu2E2pZKoYcTEnMxvKpQSjnBEaEVKgxY8aAx+MVOTk5V+zt7dkJuEn1RDM41wB3r8Th0e13ABT3Sr3nuXAckWLEpblrOmHB2PMAgAsnXuLk/mfoP6IJx5GVw4MfgKwYbmNISMh/fN1M+TldW8BpucovYd+qP6JfvSnyOUtLS7x9qzz+skQiwfLly7F//34kJSWhdevW2LhxI1q3bl1o+4CAAHh6euLUqVP4/PPPVY5V3bi7uyM4OJi+UKoxSqY1wM6NIexjV3drmJqXPK1aVXBxq4N+wxrh5IHnAICVMy+hnbsNattU/b1clWTFAHr23MaQKs5/rPdRC+3MqAp7GSMjI8ycObPQen39wr+zBQsW4KeffoKXlxdsbGywd+9edOvWDU+fPoWVVf48txkZGfD29saIESOqRSJdvXo1FixYAGtr65ILV5ELFy5wHQIpASVTDfciLAnBZ6LY5QnV4Kq0IO95LrhzOQ5vXmUgM02CJRMv4I/T/StsjGBSsYyNjbF06dISyzEMg99//x1jx47Fzp07AQADBgyAu7s79u7di3nz5rFl58+fj+zsbGzZsqWywi4TKysrpWRfHTg4OHAdAikB3TPVcPt+ecA+btSiFuwaGHMXTBF09ESYu7YT8nLntfOvcPjPJ9wGRVT2/v17ZGVloV27/IZueY+jo6PZdZcuXcKvv/6Kbdu2wdTUtFyvlXc/MS0tDbNnz4a9vT1EIpFS0n/69CnGjBmDunXrQktLC5aWlhg2bBiePXtWaH+fumd68+ZNDBw4ELVr14ZYLEbdunUxadIkxMXFFRlbUlISFi1ahObNm0NXVxdGRkZwcnLCggULkJmZiaioKPB4PAQHBwNQ9MfO+3F3dy/0Hj+Wk5ODNWvWoGXLltDV1YWhoSE6d+6M//3vf4XK5r3WmDFjEBUVhSFDhsDMzAza2tpo06YNTp069ekD/ZF///0XI0aMQKNGjaCnpwd9fX24uLjg559/hlwuL1Q+77i+fPkSW7duRcuWLaGjo6P0PpOSkrBw4UI0bdoUOjo6MDIyQvfu3XHu3LlC+0tNTcX69evh4eEBGxsbiMVimJubo3///rhx40ah8pWNrkw1WHpqDluFCgDe8wrfq6oOWrS1xFdjm+HozlAAwNo5V9Cxuy1s6hlyHBn5WE5ODvbt24eYmBjo6emhZcuW6NKlCwQC5UkSzM3NoaOjg7t38xu+3bmjGA/azk4xEtaHDx8wYcIEeHl5wcvLS6W4JBIJPDw8kJSUBE9PTxgaGqJevXoAAD8/P3z11VeQSqXo168fGjRogNevX+Off/7B6dOnERgYWOR93I/t2rUL3t7e0NLSQv/+/VG3bl2Eh4fD19cXJ0+exI0bN2Bra8uWj4yMRLdu3RAdHQ0XFxd88803kMvleP78OX766SdMnjwZxsbG8PHxwe7duxEdHQ0fHx92+5IaHEkkEvTq1QvBwcFo0qQJpk6diqysLBw5cgSDBw/G/fv3sWrVqkLbRUdHo127dqhfvz5GjhyJpKQkHDp0CF988QXOnz+Pbt26leqYL1iwAHw+H66urrC2tkZqaiouXryIGTNm4Pbt29i7d2+R282YMQOXL1/G559/jj59+rB/O9HR0XB3d0dUVBQ6d+6M3r17IzMzE6dOnULv3r3x+++/w9vbm91PWFgYFi1ahC5duuDzzz+HiYkJYmJicOLECZw9exYnT55E7969S/VeKgIlUw12Yv8zpTF4nVxrcxxR8cbOcsatoFi8epmK7Kxc+Ey+CF+/L6i6t5p5+/YtRo4cqbSuXr162LVrF7p27cqu4/F4mDhxIn7++WekpqbC2toae/fuhYGBAYYPHw4AWLx4MZKSkrBt2zaV43rz5g2aNWuG4OBg6OnpseuTk5MxdOhQ6Orq4tKlS2jWrBn73JMnT+Dq6ooJEybg3r17n9z/8+fPMWnSJNjb2yM4OFjpfurFixfRs2dPzJgxA8eOHWPXjxgxAtHR0Vi1ahUWLlyotL+EhATo6+tDW1sbS5cuRVBQEKKjo0tVhZ5n48aNCA4OxmeffYYTJ05AKFSczn18fNCuXTusXr0affv2RceOHZW2CwoKwtKlS5US97Bhw9C7d2+sX7++1Mn09OnThaqf5XI5xo4di7/++gvTpk2Dq6troe3u3buHkJAQ9stOntGjRyM6OhoHDx7EkCFD2PUpKSlwd3fHt99+i/79+8PS0hIA0LRpU8TFxcHMTLnB3evXr9GuXTvMmjWrSpMpVfNqKIZhcOj3x+zywHHNqnVi0tIWYu7ajuDzFTHeCHyNI/9dqZLqYezQfrhw4QLevn2LzMxMPHr0CJMmTUJUVBQ+++wzPHjwQKn82rVrMX/+fNy6dQt//vknHB0dceHCBVhbW+PmzZvYvHkztmzZAjMzMyxbtgxWVlYQCoVwcXHB1atXyxzfxo0blRIpAPz1119ISUnBsmXLlBIpADg6OsLb2xshISEIDf3039qvv/4KqVSKLVu2FGqY5OHhgf79++PkyZNIT08HANy9exfXrl2Ds7Mz5s+fX2h/edWrqti5cyd4PB42bdrEJlIAsLCwwJIlSwAAvr6+hbazs7PD4sWLldb16tULtra2uHXrVqlfv6j7uHw+HzNmzAAA+Pv7F7ndvHnzCiXSBw8eIDg4GF5eXkqJFFDcp1+2bBmys7Nx9OhRdr2RkVGhRAoANjY2GDhwIJ4+fYqYmKprZU9XphrqzuU4vAhLAgAIBDx8PqQRxxGVrKmzObzGNsXhPxUntnVzr8DN0xZWdQ04jowAgM88b6BWG3a5efPm+O2336Cvr4+NGzdi6dKlSldmWlpaWL16NVavXq20H4lEgnHjxqFPnz4YPnw4Nm/ezF4pderUCT/++CN69+6NiIgI9iqkJNra2mjZsmWh9devK4arfPDgQZFXfc+fK26DhIWFFUq2Re0nODgYt2/fLvR8fHw8ZDIZnj9/DhcXF/aeXa9evcDnV/w1S3p6OiIiImBtbY0mTQp3J/Pw8AAAhISEFHrO2dm5ULU8ANStW5d9n6WRmJiI9evX48yZM3j58iUyMzOVno+NjS1yu4L30fPkvW5qamqRv6f37xUDzoSFhSmtv3r1KrZs2YLr168jPj4eEomkUAwFq94rEyVTDfW/HflXpR2614WegfgTpauP0TOdce3CK8RGpSMrQ4ql3wTit5P9qvVVdU03efJkbNy4EZcuXSpV+WXLliE2NpZtVLJ+/Xp0796dPYk2btwY9vb22LZtG5YvL13/WAsLiyL/RhITEwEAO3bs+OT2GRkZn3w+bz/r168v1X5SUlIAoNK616SmKmZcKq7Vcd76vDgKMjY2LnIboVBYZMOhoqSkpKBt27aIjIxEu3btMGrUKJiamkIoFCIlJQVbtmxBTk5OkdvWrl34dlPe8Q0ICEBAQECxr1vw93Ts2DEMHDgQ2tra6NmzJxwcHKCnpwc+n4+goCAEBwcXG0NloGSqgdJScnDh+Et2efiUFhxGUzZa2kLMWd0Rs4f5g2GAK+di8O/epxgwqinXoZFiWFhYAEChK5OihISEYN26dfjtt99gbW2NtLQ0xMXFsfdRAcDW1hZmZmZ48qT0rbqL+7JlZGQEQHFlWtSVa2nl7Sc1NRWGhiU3jMtLWMVdnakqL56PB8rI8+bNG6VyFc3X1xeRkZHw8fEpdCV5/fr1T3ZzKup3lRfnli1b8O2335YqhiVLlkAsFuPOnTto2lT5/DBp0iS2hXRVoXumGsjvcDhyshVTm5lZ6qKBYy2OIyqb5m0s8cXI/Kqrtd9dRnzcp68cCHfyqujq16//yXK5ubkYN24cunXrhvHjxys99/EVRHZ2doXE1r59ewDA5cuXq3Q/eeX9/f1LdbWXV+0qk5VuSkIDAwM4ODggNjYW4eHhhZ4PDAwEgFK1Ui6PiIgIACiyFXZ5klh5fk8RERFo1qxZoUQql8tx5cqVMsegKkqmGujYnvz7Cv1HVP97pUUZ910rdu7T9FQJltHYvZx68vQFkpILT+YeHR2NadOmAVC0Xv2U1atXIyIiQqnK1dDQENbW1vDz80Nubi4Axck4PT0djo6OKsc9duxYtgFLUY1r5HI5goKCStzPtGnTIBKJMGvWLPY+a0ESiUQpEbi4uKBjx464f/8+1q5dW6h8YmKi0heGWrUUX3jL0mBm3LhxYBgGc+fOVUrCCQkJWLFiBVumMuR12/n42IWEhBS6R14abdq0QefOnfHPP/+wg3x87NGjR4iPj1eKITw8XKmPL8MwWLZsWYkNyioDVfNqmBdhSXh4K38c3j5fq2cy1dEV4bvVHTFnhOK+WtDpKJz++zn6Dm3McWQ10+HjF7Dm5z3o1s0D9erVg4GBAV68eIHTp08jOzsbffr0wZw5c4rd/smTJ1i5ciU2bdrE9jPNM2/ePMyYMQOdO3eGq6sr9u/fD319fUydOlXluGvVqoUjR45gwIABaN++Pbp37w5HR0fw+XzExMTg+vXrhRJbUZo0aYKdO3di3LhxcHR0RO/evdGoUSNIpVLExMTg8uXLMDc3x9OnT9lt9u3bB3d3d3z//fc4evQo3N3dwTAMwsPDce7cOTx9+pRNSt27d8fhw4fx1VdfoU+fPtDR0YGdnV2hbkgFzZkzB2fPnsXx48fh5OSEPn36ICsrC4cPH0Z8fDzmzZsHN7fKmbN41KhRWL9+PWbOnInAwEA0bNgQ4eHhOHXqFL766iscOnSozPs8cOAAPDw8MH78ePz8889wdXWFsbExXr9+jYcPH+Lx48e4fv06e1th1qxZmDx5Mlq1agUvLy+IRCJcvXoVoaGh6NevH06ePFnRb/uTKJlqmON78z/MTZ3NYWSqWvN7Ljm51lYau/fHmZfQ3qMupzPe1FTd3FzwLCIaIaGRuH79OjIzM2FsbAw3NzeMHDkSI0eOLPa+pUwmw7hx4+Dq6oopU6YUen769OlIS0vDb7/9hnv37sHJyQk//fRTqVvylqR79+54+PAhNmzYAH9/f1y+fBlisRh16tSBh4dHqQeMGDFiBJycnLBx40YEBgbi3Llz0NPTQ506dTBw4EAMHjxYqXy9evVw7949rFu3Dv/++y9++eUXaGtrw97eHt999x2bFABgwoQJiI6Oxt9//41169YhNzcXXbt2/WQyFYvFCAgIwKZNm3DgwAFs3boVQqEQTk5O2Lx5M4YOHVq+A1YKderUweXLl7FgwQJcuXIF/v7+aNKkCbZv344ePXqUK5na2Njg7t272Lp1K44ePYr9+/dDJpOhdu3aaNasGaZPn44WLfLbf0yaNAlaWlrYvHkz9uzZAx0dHXTu3Bm7du3C0aNHqzyZ8phy1p21adOGHdGEVA9yOYOeDfbg7WvF/cWl293RqWfVNAuvLFkZUnh/fgLxcYrGLT0HOGDzoc+qNIawsLBC92WUVLdZYz7ue6fqrDGJBT7nBbrGaKohQ4bg0KFDiIuLq3Zj9JLKUeJnvBToylSDhFx7wyZSsZYAru42HEekOl19EWat7ICF4xRTtQUcewH/IxHoNbABx5EVUAHTm6ms4BfbNpqf8CrT8+fPoaWlBXNzc65DIWqEGiBpkNOH8htGtO1SB0KRZvx623Sug96D8pPnim+DkPT+A4cREU20ZcsWeHl5ISQkBF988YXSqEKElEQzzrYEUqkM/kci2OUBYzSrX+bkhW3Ye6XJCdlYPbt0AwQQUlqbN2/GjRs3MG7cOPz+++9ch0PUDCVTDXHjwmukJCpaJOrqi9CiTcU03qgu9AzEmLmyPbt85lA4Lp54+YktCCmbyMhIxMbG4s8//yx2lCBCikPJVEOcKVDF27mXLTtgvCZxdbdBzwH5AwMsmxqElKSK6dxPCCGqoGSqASQ5MgSeimSX+48oPPC1ppj8fVuYmusAABLeZWHtHNVGtiGEkIpAyVQD3Lj4CumpitkS9A3FaOhoynFElcfQWAszlufPkXhi3zMEn4niLiBCCAElU41w7p8X7GO3XrYaP8NKxx62cP/cnl1eOiUQ6alVNzsEIYR8jJKpmpNKZbhQoCHO54PVc/jAspr2QzsYmWoBAOLjMrF+ftknkyaEkIpCyVTN3QqKRVqy4qpMV1+Exi3Va4aY8jIy1cb0pfnVvUd3huLaeY5HISKE1FiUTNXcuX/y+5Z26lFX46t4C+r6mT0698ofLnHJpIvITJdwGBEhpKaiZKrG5HIGgSfzW/H2GVIzqngLmr7UFYYmiuret68ysHZu1c9jSAghlEzV2KPb75AYrxhWT0tHiKbOZiVsoXlMzHQwdUk7dvnozlBcLPAFgxBCqgIlUzVW8Kq0VYfaEAhq5q+zW197dO2TP0fmD5MuIDE+i8OICCE1Tc08+2qIggM19PKqRrOoVDEej4dvl7VHLUvFYA7JCdn4YdJFlHN2QUIIKTNKpmrq1ctURIQmAQD4Ah5c3Gr2vIuGxlqYu6YTuxx0OgpHd4VyGJHm6tmzJ3g8HoKDg4stM3nyZPB4POzYsaMKIyOEO5RM1VTQ6Sj2cUNHU+joirgLpppwcauDL0fmD6W4ZvZlREekcBeQhrp37x4EAgFcXFyKLXPz5k0AQBuaW5XUEJRM1VTBKt6eAxw4jKR6GT+3NerWNwIAfMjKxcJx55GbK+c4Ks3x4sULJCUlwdHREfr6+kWWycrKwuPHj6GlpYXmzZtXcYSEcIOSqRpKTc7GnUux7HKnnrafKF2zaOsIsWCjGwRCRX/bBzfe4s/19ziOSnPcuXMHAODq6lpsmbt37yI3NxdOTk4QiajGhNQMlEzV0BX/GMhkisY1VnX12UmziUKj5rUwcroTu7xtxU08vvuOw4g0R2mS6Y0bNwBQFS+pWYRcB0DKrmAVb5fP7D5RsuYaMrE5bgXHIvTee8hyGcwfHYDDNwdDV4+ulFSRl0wPHTqEwMDAIsvQ/VJSE1EyVTNSqQxX/KPZ5W5963EYTfUlEPIxf50bJn9xEh8ycxH1PAXr512Bz7ZuFfo6juJfKnR/FeNGkWufSKaptFeGYRASEgIACAgIKLE8JVNSk1A1r5q5e+UNO3epnoEI9ZuYcBxR9VXHzgBTFuePjvS/HU9odCQVPI+IRmpqKjp06ACGYYr8SUpSdNfS1dVFs2bNlLbfvn076tWrB21tbbi4uODyZZrYnWgOSqZqpmAVb7uu1jVqYPvy6OXlgM6986vCl0y8gPdvMzmMSH3duR8GAGjVqlWxZe7dUzT2cnZ2hkAgYNcfOnQIM2bMwPfff4+QkBB07NgRn332GWJiaKYfohmomlfNBJ+JYh97elGXmJLweDzMXNEeYSHvkfAuCymJ2Vg84QJ+O9mvQr6IqFp1WmH+u5cJAKik6tW8ZNq6detiy+Ql04+reDdt2oQxY8bA29sbALB161b4+fnh119/xerVqyslXkKqEl2ZqpGYF6l49SIVACAQ8NCybW2OI1IPhsZamLsuf3SkK+dicGD7Qw4jUk93HpScTPPuqRZMphKJBHfv3oWnp6dSWU9PT1y7dq0SIiWk6lEyVSNXA/KrxBo0M4VYS/CJ0qSg1h2tMGh8/j289fOvIvxxIocRqRe5XI77j55DLBbD0dGx2HJFXZkmJCRAJpPB0tJSqaylpSXevn1bOQETUsUomaqRq+fyk2nXz+25C0RNjZnVCg5NFQ22pBI55o46B0mOjOOo1EPY80hkZGbB0dERYrG4yDIZGRkIDw+Hvr4+GjduXOj5j6vVGYahe/5EY1AyVRMSiQw3g16zyx086nIYjXoSawmwcGNn9oo+/HEiNi+5znFU6uHug6cAPl3F++DBA8jlcrRu3Rp8fv6pxczMDAKBoNBVaHx8fKGrVULUFSVTNfHgxltkZUgBAAbGYljbG3AckXqya2iMiQvyB2jfs/k+rp2nFqUlGTX4czAJt+Hr61tsmU6dOoFhmEKzyYjFYri4uBTqmxoQEICOHTtWSryEVDVKpmqiYBWvi1sdqh5TQf/hjdGuqzW7vHDceaQkfuAwIs03e/Zs7N69G76+vggLC8OMGTMQFxeHyZMncx0aIRWCkqmaKNj4qOtn9twFogF4PB7mrOkIY1NtAEDC2yz8MDmQJhOvRIMHD8bmzZuxcuVKODs748qVKzhz5gzs7Gg4TKIZKJmqgcT4LISGvAcA8HhAqw7UJUZVJmY6+G51fhXjheMv8c/uMA4j0nxTpkxBVFQUcnJycPfuXXTp0oXrkAipMJRM1cD186/Yx7YNjKBnUHRrSlI27T1s0G9YI3Z51cxLiA5P4S4gQojaomSqBgpW8XbuRdViFWnigjbsZOLZH3Ixb9Q5SKXUXYYQUjaUTKs5uZzB1QJXpu272XAYjebR1hHi+01uEIoUH4XHd+Px68rbHEdFCFE3lEyruWcPE5D4LguA4sTfwNGU44g0TwPHWhgz05ld/mPtHdy9GsddQIQQtUPJtJorWMXbsp0lBAL6lVWGQRMc4dxe0bCLkQPzRp5DemoOx1ERQtQFnZmruYLJtMtndL+0svD5PMxd1wn6horGXW9fZ2DljPzBB6jbDCGaqaI+25RMq7HMDAnuXX3DLru41eEwGs1nYaWHmSvbs8unDjzH6b+fQyAQQCqVchgZIaSySKVSpbl3y4uSaTV2OzgWuVI5AMCijh7MLHU5jkjzdf3MHj0H5M8Tu2xqICAXIy0tjcOoCCGVJS0tDQYGqg/PSsm0GitYxduhO7XirSpTl7RFbRt9AEBmuhRbFj1AUlISEhISIJFIqMqXEDXHMAwkEgkSEhKQnJwMU1PVG3YKKyAuUkkKjsfbsTvNElNV9AzEWLipM2YN9YNcxiDo5Cu062oDz0EGiIqKgkxWDfuhJiTkPw6r4JGcMgvsO55GiSKaQSAQwMDAALa2ttDS0lJ5f5RMq6nXkWmIjkgFAAiEPDRvQ1NVVaVmrcwxfEoL7N36EACwcf4NuHQaiOYujUrYkiPN8ic+R0VfOR8osO9hdFVOSFGomreaKljF26h5LXYOTlJ1hk9piabOZgAAmYzB3JHnkJVJDZEIIYVRMq2mClbx0iwx3BAI+ViwoTN09BQVODERqVg/7wrHURFCqiNKptWQVCrDjcD8IQTbFph7k1StOnYGmLqkHbv8vx1PcPHESw4jIoRUR5RMq6GHN98hM11RnWhgJEbd+oYcR1SzeX7loDRgxpKJF/H+TSaHERFCqhtKptXQlQJVvC5udcDj8TiMhvB4PMxY3p7t55uSlI1FEy5QFxlCCIuSaTWkNIRgbxpCsDowNNbCvPWdkPe95mpADP634wm3QRFCqg1KptVMcsIHhN6LBwDweECrjlYcR0TytOpgBa9x+d1E1s29guiIFO4CIoRUG5RMq5lr51+x3QRt6hmyA6+T6mHsrFawa5A/mfj3489DJpNzHBUhhGuUTKuZglW8bp62HEZCiiLWEmD+BjcIhIr63vvX32LXphCOoyKEcI2SaTXCMAyunS84Hi8NIVgdNXSshRFTndjlX5bexLOHCZ/YghCi6SiZViPPHyXi/ZssAIBYW4BGLWpxHBEpztDJzdG4peL3I5XKsXBcACSSajhmLyGkSlAyrUYKVvE6tjKHQEC/nupKIORj3jo3dpjHZw8T8euKWxxHRQjhCp2tq5GCydS9rz13gZBSsXUwwvg5rdll3/X3cP/Gm09sQQjRVJRMq4msTCnuXoljl9t2piEE1cGXo5rAuX1tAIBczmDhuPM0GD4hNRAl02rizqVYSCWKLha1LHRgbqXHcUSkNPh8Huas6QhdPREAxWD4m76/xnFUhJCqRsm0mihYxduOBrZXK5bW+piypC27fPDXR0qtsgkhmo+SaTVRcMq1LjTlmtrx/MoBHbrbsMuLJlxAWkoOhxERQqoSJdNqIC46DZHPUwAAfAEPLdpacBsQKTMej4dZKzvAyEQLABAfl4nVsy5xHBUhpKpQMq0GrhSo4q3f2ARa2kIOoyHlZWKmgxkr2rPLJ/Y/Q8CxFxxGRAipKpRMqwHlKl6aJUadde5lhx5f1meXl04JRMK7LA4jIoRUBUqmHMvNleNm4Gt2uSMNIaj2pi5pB/Pa/819mpiNpd8E0tynhGg4SqYce3jrHdJTJQAAXX0RbP+bkYSoL31DMb5b3ZFdDjwViX/3PuUwIkJIZaNkyrGCVbxOrpbg5c0+TdSai1sd9B/RmF1eNfMS4qLTOIyIEFKZKJlyrGD/Uo9+9T9RkqibCXNbw9reAACQlSHFIu8LkMupupcQTUTJlEMpiR/w+M47dtnFzYrDaEhF09EVYd46N/D5itqGW0Gx2L/tIcdREUIqAyVTDl07/wp57VKs6urDwEiL24BIhWvWyhyDJzVnlzd9fw0vnyZzGBEhpDJQMuXQZf9o9nEnT1sOIyGVaeS0lqjfxAQAIMmRYcHYAEilNPcpIZqEkilH5HIGVwNesctdaQhBjSUSCzB/vRuEIsXH7cndeOxYe5fjqAghFYmSKUeePniPxP8682tpC9CoRS2OIyKVqX4TE4ye4cwu/7bqNh7eflf8BoQQtULJlCNX/PNb8TZtZc42UiGaa9CEZmjW2hwAIMtlMHeEP9JTaTB8QjQBJVOOFLxf2r0/dYmpCQQCPhZscIOuvmLu09eRaVg2JYhGRyJEA1Ay5UBaSg4e3HjLLrt2o/lLawqrugaYtTJ/MPyzh8Pxz+4wDiMihFQESqYcuH7hFWQyxdWIRR09mNTS4TgiUpXcP6+HPoMbsss/zghGRGgShxERQlRFyZQDBYcQ7OBh84mSRFN9s6gt7P4bhzknW4Y5w/2Q/SGX46gIIeVFybSKMQyDK+fy75d261ePw2gIV7R1hFi0pQvEWgIAQPiTJKybe4XjqAgh5UXJtIqFP07Eu9hMAIBIzEeTlmYcR0S4Uq+RCaYsbssuH/rjMfyPRnAYESGkvCiZVrHLBbrENHEyh0BIv4KarM/ghkoTwv8w6SJeR9LsMoSoGzqTV7GCVbzdv6Aq3pqOx+Nh1soOqG2jDwDISJNg1pCzdP+UEDVDybQKZaZLcO/qG3a5fTdqfEQUk4l//1NnCP+rpQgNeY+V3wZT/1NC1Agl0yp0NSAGuVI5AMCsti5qWehyHBGpLpo6myvdPz22JwyH/3zCYUSEkLKgZFqFgk5HsY879azLXSCkWuo7rBF6DsgfDWvVzEs0fi8haoKSaRWRyeS4dDaKXe75pQN3wZBqicfj4dtl7dnp2qQSOWYNPouk9x84jowQUhJKplXkwc13SE7IBgBo6wrRsDnNEkMK09YRwucXd+gbigEAb19nYNaQs5BIaP5TQqozSqZVJOhUJPvY2dWSZokhxapjZ4AFG9zA++9P5M7lOGqQREg1R8m0igQWSKa9BjbgMBKiDly72WDcd63Z5aM7Q7F/20MOIyKEfAol0yoQ8yIVL58mAwD4fB7adKZZYkjJBk90RPcv8hskrZ1zBVcDYj6xBSGEK5RMq0DQ6fyr0vpNTKCtI+QwGqIueDweZv/YAU2cFENOyuUMZg/1Y7+YEUKqD0qmVaBgFW/Brg+ElESsJcDS7e4ws1T0Sc5Ik2By/5NIeJfFcWSEkIIomVaytJQc3LuSP+pRl8/suQuGqKVaFrpY/ns3aOkoZpiJjUrD1AGnkJUp5TgyQkgeSqaV7Ip/NHJzFaMeWdTRY68wCCmLho61sOinLmwr8Md34jF3hD9kMjnHkRFCAEqmla5gFW/nXrYcRkLUXYfudTHth3bsctDpKKyadZm6zBBSDVBLmEoklcpwpcCUaz2+oPulRDX9hjfGu7gMHPpDMW7v3789Qm1rPXhzHBchNR1dmVaiu5fjkJaSAwDQ1RfBoZkpxxERTTDuu9Zw/9yeXd685AYO8TtxFxAhhJJpZfI/GsE+btelDng8GvWIqI7P52Hu2k5w7lCbXbeC/zVO81w4jIqQmo2SaSXJzZXj/L8v2eW+wxtzGA3RNGItAZZt74YmLRV9UBkeH98LRiKI58hxZITUTJRMK8mdy3HsbB/aOkI0d7HgOCKiaXT1Rfjxz+6wa2gEAMjlCTBbMA63L8VyHBkhNQ8l00pyrkAVb9sudSAQ0KEmFc/QWAtrdvVEXeY9ACCHJ8bUAafw+C7Ng0pIVaIzfCWQyeQ4/+8Ldrn/iCYcRkM0nZmlLnxzt8GCSQEAZKZLManvSUSEJnEbGCE1CCXTSnDnchwS4xVVvFraArRoS1W8pHLZIBE7crfBiMkEAKQkZmNcr2M0ji8hVYSSaSUo2IrXxY2qeEnVaIC3+F22HTq6iu7jie8+YKwnJVRCqgKd5SuYooo3vxXvFyOpFS+pOi2YGKz6szs7M1HC2yyM9TyGyGeUUAmpTJRMK9jdK3FI/G9GDy1tAZxca5ewBSEVq3kbyyITatRzSqiEVBZKphXM/2h+w6O2XaypipdwokVbS/zom59Q37/JwljPfxEdnsJtYIRoKDrTVyCZTI6AYwVb8VIVL+FOy3aKhKqlrZi6LT4uE2N6HqOESkgloGRagZSqeHWEaNnOkuOISE1XVEId3eMfvAijbjOEVCRKphXo1IHn7GPXrlTFS6oHJ9fa+NG3O8T/JdT3b7IwuscxPH2QwHFkhGgOOttXkOwPuUpdYrzGNeUwGkKUObnWxqoC91CT3yu6zdBISYRUDEqmFSTwZCQy0iQAAEMTLTR1Nuc4IkKUObnWxto9PaFnIAIApCXnYHyv47h37Q3HkRGi/iiZVpCTB56yj3t+WZ+mWyPVUrNW5li/1xOGxloAgIw0Cbz7HMeNwNccR0aIeqNkWgES3mXhin8Mu/wFjcVLqrGGjrWwYb8njGtpAwCys3Ix5YuTuOwXzXFkhKgvSqYV4NTBZ5DJGACAtb0BrGwNOI6IkE+r18gEmw70gpmlLgAgJ1uGaV6nlSZoIISUHiVTFTEMg6M7Q9nlL0fSVSlRD3XrG2HTgV6wtNYDAORK5Zg91A+n/35ewpaEkI9RMlXR/Rtv2YHEBUIeenk14DgiQkrPytYAmw72Rh07RW2KTMZg/uhz+N+OxxxHRoh6oWSqoiN/PmEft+tqDR09EYfREFJ2FlZ62HSgF+waGAEAGAZYNjUIO9bd5TgyQtQHJVMVpKfmwP9Ift/SoZNbcBgNIeVXy0IXG/f3QuOWtdh1mxdfx8aF18AwHAZGiJqgZKqCUwef40NWLgDA1FwHTZzMOI6IkPIzMtXGuj2ecG6fP9PRzo33sHT3EMjk1NWLkE+hZFpODMPgwPaH7PKA0U2obylRe7r6Ivzo2x0dutuw644Ed8LcX8dAkivgMDJCqjdKpuV0M/C1UsOjfsNohhiiGcRaAvj84o4eX9Zn1/nfbo1pmyciK1PKYWSEVF+UTMtp/7b8q1I3T1voGYg5jIaQiiUQ8jF3bSelrl5XHzeDd5/jSE3O5jAyQqonSqblEBuVhqDTUezyqG+dOYuFkMrC5/MwZUlbTP3yDLvu/vW3GNPjGOLjMjiMjJDqh5JpOezb9gByuaKJo30jY9g6GHEcESGVg8fjYcqXZ7Fg2BF23fNHiRje5SginyVzGBkh1Qsl0zJKTc7GYd/8EY/GznLmLhhCqshIz2Cs8t4LPl/xJTIuJh3Dux7B/Rs04wwhACXTMvv7t8f48F8jDBMzbXToXpfjiAipGl90uoUNKxOhrSUHAKQm5WB8r38RdDqS48gI4R4l0zLI/pCL/dsesMujvnWi7jCkRunomoPtmxJgbCQDAGR/kGG61xkc2fmkhC0J0WyUTMvg2J4wJMZ/AADo6onQe2BDjiMipOo1ayLFHz+/R53aigFL5HIGPpMDsX3lLTA0XBKpoSiZllJOdi7+WHOHXfYa1xRCER0+UjPZ2siwY+t7NG4oYddtW34L3487D0mOjMPICOEGZYNSOuz7BPFxmQAALR0BvvZuznFEhHCrlqkc2zcloJ1Lfr/TE/ufYaznMSTGZ3EYGSFVj5JpKXzIkirNoDFkUnNo6wg5jIiQ6kFPl8HGHxPRv08mu+7+9bcY6nYY4Y8TOYyMkKpFybQUDmx/hIS3im/aOrpCfD2BrkoJySMSAQtnp2DGNyng8RT3TGOjFF1nLp2N4jY4QqoIJdMSJMZn4ffVt9nl4VNbQqxFA34TUhCPBwwdmIkNKxOho6PoOpOZLsXUAaexZ8t9aphENB4l0xL8suwmMtMV/UqNTLTgNbYZxxERUn11ap+DHT+/R22L/Ja+6+ZewYIxATRIPtFolEw/4fmjBBz5M3+0oxkr2lMLXkJK0KB+LnZuf4/mzXLYdacOPseQjofZmZYI0TSUGYohlzNYPj1YaQxeN09bjqMiRD2YmsixbWOCUsOkF2FJGNzxf/A7HM5hZIRUDkqmxTiy8wlCruWPO7pwoxuNdkRIGWiJge+/S8HiuckQixX3UbMypPhuuD9+mHwRmRmSEvZAiPqgZFqE928zsWnhNXb5s68boH4TUw4jIkR99e2dBd+tCbC2yr9nenRnKLzaHqKB8onGoGT6EYZRDI2Wnqr41qxvKMbUJe04jooQ9daogRR7fnuP7u75gzm8epGKke7/4GefG5BIaNQkot4omX7k0B+PEXwmil1esNENWto0QAMhqtLXZ7BycTJ8FiRBT1eRPOVyBr+vvgOvNn/j9qVYjiMkpPwomRYQEZqEdXOvsMvd+trD1d2Gw4gI0Sw8HvBZzw/Y5/serVrmD0P48mkyxvQ4hu/Hn0fS+w8cRkhI+VAy/U96ag5mfn0GOdmKb8wmZtqYs6YTx1ERopmsLGX4ZUMiZk1JgY52fhXv8b1P8Xnzfdiz5T5ysnM5jJCQsqFkCkAmk2P+6HOIfJ4CAODxgdU7e9BIR4RUIoEAGOyViUO749Gtc/691LTkHKybewV9HPfh6K5Q5ObKOYySkNKp8cmUYRhsXHgNwWei2XUzlrWHQ1NqvUtIVbAwl2P10mRsWpWAOrXzW/y+fZWBHyZdxJfOB3B0VyiyP9CVKqm+anwy9V13F3s232eXPQfUx+dDGnEXECE1VEfXHPy9Kx6zp6XAxCg/cUY+T8EPky6ih8NubPnhBuLjMjiMkpCi1ehkun/bA2xecoNdbuJkhtmrO3IYESE1m1gMfD0gE0f3x2PSuFTo6uTfT01OyMYfa+6gZ4O/MOXLkzh14Bky02ngB1I91Mg+HwyjaI6/delNdp2tgxE27u8FgaBGf78gpFrQ1WEwdngGvPpn4uRZPRw6qov4BBEAIDdXjuAz0Qg+Ew0tbQG69rFHl8/s4NqtLurYGnAcOampeEw550Zq06YN7ty5U9HxVDqJRIY1313God8fs+ssbfTxx8l+0NUXcRgZIeVXx96IfRwXlVqx+76av+/0ugsqdN+llSsDLl3VxqGjenjwWLvYcnUdjODqbo0WbS3RuKUZGjrWgrZOjbxmIFWsRv2VJbzLwuyhfrh7JY5dZ9fACFuP9IGOHiVSQqoroQDw6JINjy7ZiI0T4HyQDvzPa+FltHJiffUiFa9epLKzPfH5PNg3NEZdByPUsTOAVV0D1LE1gIm5DoyMtWBoovjR0hFCJOJ/cvxthmGQmyuHVCKHVCJDrlQOSY5M8ZMtg0QiQ0523nIuJDn/LUv+ez5Hhpz/1kuyZcjJyUWuVA4+nwe+gAc+nwcejweBgAeBkA8dPRH0DETQ0xdDz0AEfUMx9AzFMDXTgVltXRpMppqpMb+N8/++wNIpgUhOyO8o3ryNBdbu7kldYAhRI9Z1ZBg9LAOjh2UgMkqIKze0ceuOEA8ea0MiVf4sy+UMXj5LxstnpZv6TSDgQSjiQyjiQyDkAwwglcgglcqRK61eXXQMTbRgZqkLcys9mFnqoo6tAWwbGKFufcWPRR098Pk0OUdV0fhkGhedhvXzr+LcPy+U1g8c1wwTF7jQTDCEqLF69rmoZ5+BkUMAiQR48lSM+4/EePacj/AIIeLeaYNhSv8Zl8kYyGQydvCW6iwtOQdpyTnFzhGrpS2Atb0h6jUyQQNHUzRoZoqGjrVg18gYYjFdQFQ0jU2mCe+ysPunEBzY/lDpg6GtK8SizZ3RvltdDqMjhFQ0sRho1VKCVi3zW/hmZ/MQFSNE3FsB3r4T4s1bHt68AVJSeEjP4CMjU4CMLCFyc/mQyUtufCjgyyEQMBAK/vtfKIdYxEAskkMkYiAWMxCLoPhfDGhpgf2ffSwGRP+VEwoZMAwgl/MglwNyOcAwQG4uD1kfgIwMIDMTyMziISsTyMjkIyVViOQ0EeQlxJuTLcPLp8l4+TQZF068ZNcLhXzYNjBCQ8daaNDM9L9EWwu2DYwgFFIDzPLSuGQaGhKPQ388xsn9zwp9u2zdyQpLfu4KfUMxR9ERQqqStjaDJo2kaNJIWmJZhgFkMkVjJ1kuD7m5PPD4DIRCQCRkIBAA/GqSa+RyICWVj8QkPhKTBEhIFCD2jQCvXvHx6jUPcW/FSM8suh1Ibq6cTbL+R/PXi8R81GtsgoaOtdDQ0RQOzRT/W9sbUnVxKah9MmUYBk8fJCDodBT8j4Qj/ElSoTJmtXWxYIMbnFxrcxAhIUQd8HiAUKj4gRYDoFwdHaoEnw+YmshhaiJHQ4eiR4bKyODhdZwQkdFCvIwSITyCj8goId4laBVZXiqR4/mjRDx/lKi0XkdXiPpN86qJTeHQ1BS2DkaoY29I1cUFqF0yTUn8gCf33uPJ3Xg8uRuPkBtvkfguq8iy5rV1MXGBC7p8Zk/frAghNYq+fsGr8vyZeLI+8BAZLURklAgvXgoR8YKHl9EiJCYXnWQ/ZOWy59uC+HweatfVh62DosGTTT1D1LbWh6WNPizq6MHSWr9GdUuqFu9ULmeQlpKD1MRspCZnIyVR8ZOanIOEt5mIjUrHq8hUxEalKbXGLQpfwINTO0uMnO6E5m0sqIERIYQUoKvDwLGJFI5NlKu+0zN4eBklwstIIV68FCA8gofIGC2kZRR9W0wuZxAXnY646HTcuPi6yDKGJlqwrKMHYzMdGJtqw7iWNvu/US1t6OmLoKsvgo6eCLq6eY+F0NUXQ0dPqFaD6HCeTE///RzzR59D+YaOUBBrCdCslTl6DXRApx621GeUEELKyECfgVNzCZyaKw/RmJSsqB5+GS1CRIQAUdE8vH5T/JVsQXktjsurYFclxY8AIhEfIjEfQmH+OoFQ0U+Xz+eBx897DPD4PPQZ3AiDxjuWO4bS4jyZ6hmIypRIeTzAyFQbtg5GaNbKHPWbmMCqrgH4AsUV6KvItEqKlJDqq06Bx88fJxZbTtV9p7yLL7Yc0Ux8AA51FD89O+Svl0h4ePtejDfvtBD3Tgtv48V4/56PhCQhklLESE7TLlUL6U+piK5KLdtaqhRDaZV7OEEzMzPY29sXWv/+/XuYm5uXeX9x0elITvygtI4v4EEo5Kvl/c6c3HRoCWmcUDoOmnEMBHwZDHXSVdpH+occGOiUfDWj6eg4AACDtA/ZMNQSoaSGXuyzDK/Ykgz7z0e5gseHeT0HFeJUZmZmBj8/vyKfK3cyLY66jtlb0eg4KNBxoGOQh46DAh0HBU07Dupzd5cQQgippiiZEkIIISqq8GQ6ceLEit6lWqLjoEDHgY5BHjoOCnQcFDTtOFT4PVNCCCGkpqFqXkIIIURFpUqmr1+/xvTp09GhQwfo6uqCx+MhKiqqxO3u3LmDiRMnokmTJtDV1YWtrS2GDx+OyMhIVePmRHmPw8dWr14NHo8HNze3ig+yCqh6HMLCwjBo0CCYmZlBR0cHjRs3xpYtWyov4EqgyjGIiYnB6NGjYWtrC11dXTRq1AiLFy9GZmZm5QZdCY4cOQIvLy/Y2dmxv8uFCxciPb3kbjTZ2dmYO3curKysoKOjgw4dOuDSpUtVEHXFKu8x0LTzoyp/CwWp6/mxVMk0IiIC//vf/2BiYoLOnTuXeud///03njx5gm+//RZnz57FmjVrcO/ePbRp0wavXr0qd9BcKe9xKOjly5f48ccfYWFhUcHRVR1VjsOdO3fg6uqKnJwc+Pr64syZM/juu+8gk1X/+SMLKu8xyMzMRI8ePXDp0iWsWLECp0+fxoQJE7Bx40aMGzeuEiOuHBs2bIBAIMCqVavg5+eHb775Br/++it69uwJufzTk2mPHz8eO3bswPLly3Hq1ClYWVmhV69euH//ftUEX0HKeww07fyoyt9CHrU+PzKlIJPJ2Mc7duxgADCRkZElbhcfH19oXVRUFMPj8ZglS5aU5qWrlfIeh4I8PT2ZiRMnMl27dmU6depUwRFWjfIeB5lMxjRr1oz58ssvKzG6qlHeY+Dv788AYPz9/ZXWz58/nxEIBExmZmZFh1qpivqM79mzhwHAXLhwodjt7t+/zwBgdu7cya6TSqVMo0aNmH79+lVKrJWlvMdA086P5T0OBanz+bFUV6b8ck7iV9RISHZ2djA3N0dsbGy59sml8h6HPAcOHMC9e/ewevXqCoqIG+U9DkFBQQgNDcXs2bMrOKKqV95jIJEoxj01NDRUWm9sbAy5XA5GzdoDFvUZb9u2LQB88jN+4sQJiEQiDB48mF0nFAoxZMgQ+Pv7Iyen/OO5VrXyHgNNOz+W9zjkUffzY5U3QAoLC0N8fDyaNm1a1S/NqeTkZMyaNQvr1q2Dqakp1+Fw4sqVKwAU98rat28PkUgECwsLfPvtt/jw4UMJW2uGHj16oGHDhpg/fz5CQ0ORkZGBixcvYsuWLZg8eTL09PS4DlFlwcHBAPDJz/iTJ09Qr1496OrqKq13dHSERCJBREREpcZY2UpzDIqiaefH0h4HTTg/Vmkyzc3NxeTJk2Fubo7x48dX5Utzbu7cuWjUqBHGjBnDdSiciYuLAwAMHjwYnp6eCAgIwLx58+Dr64thw4ZxHF3V0NbWxpUrVyCXy+Ho6AgDAwN0794dffv2xS+//MJ1eCqLjY3FDz/8gB49eqBNmzbFlktKSoKJiUmh9Xkn0qSkpEqLsbKV9hh8TNPOj2U5DppwfqzSWWOmTZuGa9eu4fTp00V+kDTV5cuX8ddff+HevXs1en7VvEYII0aMwPLlywEA7u7ukMlkWLBgAUJDQ9GsWTMuQ6x02dnZGDx4MOLj47F3717Y2tri1q1bWL58OYRCIX799VeuQyy3jIwMfPHFFxAKhdi1a9cnyzIMU+RnQd2quT9WlmPwMU06P5blOGjK+bHKkunChQvxxx9/YM+ePfD09Kyql60WJk2ahPHjx8PGxgYpKSkAFN9CZTIZUlJSoKOjAy0tzZ9FolatWgCAnj17Kq339PTEggULcP/+fY1Ppn/++SeCgoIQEREBBwfFbBZdunSBkZERJk6ciMmTJ8PJyYnjKMsuOzsb/fv3x8uXLxEcHAwbG5tPljc1NUVMTEyh9cnJyezz6qasx6AgTTo/lvU4aMr5sUqqeX/88UesWbMGW7ZswciRI6viJauVsLAw/PbbbzAxMWF/rl69ihs3bsDExEStr0bKwtFRMUHvx98+865GVG3gpQ4ePXoEExMTNpHmadeuHQDF34q6kUql8PLywq1bt3DmzBm0aNGixG0cHR0RGRmJrKwspfWhoaEQi8Vo0KBBZYVbKcpzDPJo0vmxPMdBU86PlX5l+vPPP2Px4sX48ccfMX369Mp+uWopMDCw0LqZM2dCJpNh69atanfiKK/PPvsMWlpa8PPzQ9++fdn1/v7+AFCm+0vqqnbt2khOTkZERITS7/3mzZsAAGtra65CKxe5XI7hw4fjwoULOH36NNq3b1+q7fr37w8fHx8cPnwYo0ePBqC4Gjl06BA8PT3V4kokT3mPAaBZ58fyHgeNOT+Wtg/N4cOHmcOHDzOTJ09mADDbt29nDh8+zAQFBTEMo+gfJRAImGXLlrHbHDx4kOHxeEzv3r2Z69evK/08efKkYjv5VJHyHIeiqGM/qoLKexyWLl3KCAQCZuHChUxAQACzevVqRltbmxk9ejQH70I15TkGkZGRjIGBAdOwYUNm9+7dzMWLF5l169YxBgYGjIuLi1L/VXWQ994XLVpU6DP+6tUrhmGK/1sYPHgwY2xszOzYsYM5f/484+XlxWhpaTF3797l4q2UW3mPgaadH1X5W/iYOp4fS51MoZjHvNBP165dGYZRnCQAMD4+Puw2o0ePLnE7dVOe41AUdfxjKai8x0EulzMbN25kHBwcGJFIxNja2jJLlixhJBJJ1b8JFZX3GDx58oQZNGgQY2Njw2hrazMNGzZkvvvuOyYpKanq34SK7Ozsij0Oee+7uOOQlZXFzJo1i7G0tGS0tLSYdu3aMYGBgVX+HlRV3mOgaedHVf4WPqaO50eaNYYQQghRkea3+CCEEEIqGSVTQgghREWUTAkhhBAVUTIlhBBCVETJlBBCCFERJVNCCCFERZRMCSGEEBVRMiWkEvB4PPB4PPD5fLx48aLYct26dWPL7t69W+m5MWPGsM/5+PgUu489e/aw5dzd3ZWeCwoKYp/L+9HV1YWVlRW6dOmCuXPnIiQkRJW3SggBJVNCKo1QKATDMPjzzz+LfD48PBzBwcEQCj89RLZQKMTOnTshk8mKfH7Hjh0l7sPOzg4+Pj7w8fHBzJkz0bdvX2RmZmLDhg1o3bo1hg8fjoyMjNK9MUJIIZRMCakklpaWaNOmDXbt2oXc3NxCz/v6+oJhGKVB/4vSt29fvH79Gn5+foWeCwsLw9WrV9GvX79P7sPe3h5Lly7F0qVLsWrVKuzYsQN3795FSEgIWrRogQMHDmDQoEFle4OEEBYlU0Iqkbe3N96+fYtTp04prZdKpdizZw86duzITk1XnOHDh0NHRwc7duwo9FzeugkTJpQrPmdnZ5w/fx7m5ubw8/PDv//+W679EFLTUTIlpBINHToUenp68PX1VVp/4sQJvHv3Dt7e3iXuw9jYGIMGDcLp06fx5s0bdn1OTg7++usvdO3aFY0aNSp3jBYWFpg0aRIAYP/+/eXeDyE1GSVTQiqRgYEBhgwZAj8/P7x+/Zpdv2PHDhgaGuLrr78u1X68vb2Rm5ur1Ejp2LFjSExMLFVCLklew6Vbt26pvC9CaiJKpoRUMm9vb8hkMuzcuRMAEB0djYCAAAwfPhy6urql2oebmxuaNm3K3mcFFAnZxMQEXl5eKseYNyn5+/fvVd4XITURJVNCKpmrqytatGiBnTt3Qi6Xw9fXF3K5vMxXlBMmTMDLly9x8eJFREREIDAwECNHjoS2trbKMeYlaB6Pp/K+CKmJKJkSUgW8vb0RHR0NPz8/7Nq1Cy4uLmjVqlWZ9jFq1ChoaWnB19eXvUKtiCpeAIiLiwMAmJubV8j+CKlpKJkSUgVGjhwJHR0dTJo0CbGxsZg4cWKZ92FmZoYBAwbg2LFj2LlzJzp06IDmzZtXSHyBgYEAFFfRhJCyo2RKSBUwNjbGwIED8fr1a+jp6WHo0KHl2o+3tzdycnLw/v37CrsqjY+Px++//w5A0Q2HEFJ2nx42hRBSYVauXImvvvoK5ubmMDAwKNc+unXrhuPHj0Mul6NXr14qx/TgwQOMGjUKCQkJ6NOnD/r376/yPgmpiSiZElJFbG1tYWtrq9I+eDxeuRJeVFQUli5dCkAxYERCQgLu3r2Lu3fvAlBckeZdnRJCyo6SKSE1QHR0NJYtWwYA0NbWhrGxMRo2bIg5c+Zg+PDhcHZ25jZAQtQcj8lrE08IIYSQcqEGSIQQQoiKKJkSQgghKqJkSgghhKiIkikhhBCiIkqmhBBCiIoomRJCCCEqomRKCCGEqIiSKSGEEKIiSqaEEEKIiiiZEkIIISr6P6VN2ggBt/TSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a07d72b6c35e46a8a8132892f6555674",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ac3a1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e2a8705",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "716ee1bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c950ec5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v2+v3+v4_Aug23_bf_model_misspecification_1000_data.ipynb b/bf/v2+v3+v4_Aug23_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..7b70087
--- /dev/null
+++ b/bf/v2+v3+v4_Aug23_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2649 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 13.943223\n",
+    "    maxAbs = 221.50381\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v2: scaling\n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "#         temp = dpl[0].data['agg']\n",
+    "        temp = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "#         temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scale\n",
+    "x = x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(x)):\n",
+    "#     x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "25f9ecff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.943223"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "f38d976e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "221.50381"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bdc8ef9f56424a7b91e7c0eca978b050",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.722452</td>\n",
+       "      <td>0.821169</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  7.722452  0.821169"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4146f3b50a064c518d17a3485976c03d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x29e8013c0>]"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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5PCKRiBj7C8xv1nwm1dxfbKlCUbFarTAajUilUsI1xhn1fMY2m02kFXd2duL48eM4ePAgvF6vECKgtqhMTU2hWCzC6/UKS8VkMsHlciGRSGzK3+lqKFFpMei/NRqNolMx4fRHjiPdrFX19Z4ytzPxeBxjY2MNtdzh4cJkMsFqtQpRsVqtACA202w2K75msVg2rahkMhnhfqrnuRSLRcTjcczMzGBmZgYOhwPt7e0AILK/WBRZ7V6ypcLeeul0GrFYDIVCAR6PR8RsGFznc9Q0DQaDAX6/X7wGRaXW539iYkLEYPg6AOD3+5HP51smA0yJSovB00o1S8VsNsPv9wu/+WYdLdxKLSc2Ap6eNU0TsZDVwFRag8EgTr7FYhEHDx4EsBBLqRQVXdc35alYLuitR1RYif7222/jzp076Onpwa5duwAsdPXWdb1mTCWXy4l5KOzxlU6nMTk5iVKphM7OTiHAVqtVuBDlg57H4xEeBHY3qPZsdV3HxMQE/H6/WB/p6upCoVBY1IJ/s6JEpcWg+8tgMAiLhRiNRjGsazM3B1Q1NEtTKBREEgazllYDLRWeoNmqhd2s2WadXXGBhc1sM352Vioq+Xwes7OzmJ2dxa5du/Dbv/3bIlZhNBrFz1zNUonH4zAYDHC73cjlciIoT1GxWCxwu93i4MbXkhu7+nw+WCwWIUyxWExU9lcyNjaGXC4Hn89X1n0cAPr6+lAqlTA2Nlbvo9pQlKi0GPJ8elbuypjNZjGHYbNaKq0qKhx2tdawmy5TXlcbOKeoMKYCLDx7m80memHx7wDEdbVEZSNdl9yU7XZ7XaJSKBQwOzuLYrGIPXv2IBAIlP273W4HUN1yTiQSMBgMcDgcuHHjBqampkRbl1gsJg5vrKanqBiNRhiNRni9Xnz84x8HANG8MplMlmWLyVy6dAlOpxNdXV2LRCUQCMDpdOLOnTt1PqmNRYlKi8FTLH221USFH9zNdNqUf5E2q9gtha7rGB4extWrV9f8XtFoVDQ6bERUaIUwMwlYEItKUZHdX0D1AshisYj3338f9+7dW9V6GiWTyYj2KtPT08tez/kpmqbB5/Mt+l3hM6l2yEkkEkKM8/m8aLmSTCZFrMVkMomuwnIshZ4Cvr7D4RBzjoxG4yJLJR6PY2JiAseOHRPuSnmtXq8X7e3tGB0d3dSZeaQpovL666/jwIED6O/vx5kzZxb9+//4H/8Dx48fx/Hjx/HYY4/h8uXLzbjttoSbMy0V2f0FLIgKsLliF7KotMIvRiXymtdaFJlkYTAYGsrk4/dqmiaK+GRR4aYLLIgJGxlWO5Dw8xQKhTYkxZWiEo1G8dFHHy15aEqlUojFYojFYiJ9uPJ3hRld1dxfiURC1IuEw2HMzc3BZrOJ4mLWubBZpRxYr0x24L/zNSstFb4HHR0dwtUmr1XTNPT392Nubm7DBH0lNCwqxWIR3/zmN/Haa6/h+vXr+MEPfiAG2pA9e/bgpz/9KT744AP823/7b3H69OlGb7ttkbO6qrm/5F+ezdTZlL9ItXzKmx1ZVNa6EE0eF92o+4t1D3RvURAZpJ6enhatSwAsmVbME73JZMJHH3207jVHFBVaWWwnX0mpVMKNGzdw8eJFMemRYinD35VKUSmVSojFYsKVHI/HMTo6KoL1fr8fHR0dZZlfFGVgQbBlV7UsSNUsFQAiRdloNC5qkX/48GGYTCZcvHgRN2/erFph/8Ybb+B//a//hfv379f5RNeGhkXlwoUL6O/vx969e2GxWPDcc8/h3LlzZdc89thj8Pl8AICPf/zjGB0dbfS225Z8Pi9ExWAwVLVUGODdjJYKW1Zs1nTnWsgb+1rHFSgqpVIJBoOhIfdXoVAQva6ABUvFYrEglUohk8ksijXUSitOpVIwm804evQozGbzumcjZTIZ0RAyn88jFApVvS6fz2NiYgK/+tWvykSlEopGpagkk0mkUinYbDYhROxk4XQ6YTAYoGmaKISUe3UB8241VvDLX2PH6Mp0f8aHKCrya5Genh7s3LkT8Xgct27dwltvvYUrV66Ie+RyOdy5cwdzc3PLTpdcaxoWlbGxMezYsUP8va+vb8kshT/90z/F3/27f7fmv7/00ksYHBzE4OBgzQ/NdkbekGsF6pneuJkC4nLzS6D1ZnbI611rUWFHXRbDrfZZ5XI5YXnwMyFbKkxbZnYSYYfdSlKpFBwOB4xGIzo7OxGLxdY1PpbNZoXbCZjf/Ku5rgqFAq5evQqn0wmXy1X2PTJ0f1X+DNFoFLlcDl6vt+zZJxIJlEolTE9PY2pqCqFQSBSNyoe7ypYuwPzzZuYdUN6pOB6Pw+FwiOp82eohbW1t8Pv96Ovrw6FDh+B2uzE1NYWRkREA5VMqN3qceMOiUu3EWWsO9t/+7d/iT//0T/GHf/iHNV/v9OnTuHjxIi5evIjOzs5Gl7flkF0j1WIqFosFBoMBBoNhUwbq6XtutbgK18vMurWE884BNOT+4vvPQL3BYBAbKFOKq21gVqt1UTdj1s7Q4qHnYb02MKZDUyA4HKuaCy4ajSISiaCjowNWqxWBQKCqqNCdRTcWYRsYn89XJgzxeFy0tGERJWOY8p7HuhQ57kSh5joqxcrtdovGl9UsFbZ+icVi6O/vx/79+wHMu2JLpZIQle7u7tYXlb6+vjIf3ujoqJjtIfPBBx/ga1/7Gs6dOyfeEMXKkQd0MSdehh9IeQPZDFSKSqtZKgzIroeoME2V/61WVLghciaIHIDPZDLCiqmE75H8+ansiL1c6nGz4VqYkWW1WkWiwfDwMMbHx8W1sVgM0WgUNpsNp06dQmdnZ01LxWQyIZfLlf2szLyT61A0TUM0GoXBYMCBAwewc+dOAAvCJOP1emEymcpibxRqHgJlEeOsFbk+CZgXUlnUOjs7EYlEUCgU0N3dDZvNhkgkgnQ6LToGBAKB1heVRx55BENDQxgeHkYul8PLL7+Mp59+uuyae/fu4XOf+xz+7M/+TCisYnXww1jZooXwl95oNG5KS6VV3V8UFRadrvW9ADQsKvLwKLp6KFismai22fI9kjc0vn/8zLFKf70OLrJAWq1WkcYbCoXwyiuv4K233hLXzs3NoVQqIRAIYN++fWWTFGUoUHLnZjaNZMYch5wFAgGRAdbX1wev14vjx4/DarUueu1qTSAZp6q0VHg/t9strEOKSigUwrVr10SdDKvt5+bmYDab0dPTg1QqhWg0itnZWbS3twtxquYWXC8aFhWTyYTvfve7eOqpp3Do0CF84QtfwJEjR/Diiy/ixRdfBAD8h//wHzA7O4t/8k/+CU6cOIHBwcGGF75doaVSLZ4CLMRZjEbjpnIxcVOqZ1DRZkROkFgPSwVYSBtf6QwRIm/E/I+nYRZFVvtZlrJU5IPMevYJ48/C9FwWGM7NzYnUYdau0EJwu93iEFBNVNgNmDNngPmfmb3z2trahKjs3r1buOBYzMjnWe330Ov1lrnmbDab+N0FFj7/8qx7tul3Op0A5uMk8Xgc169fx40bN8R7xc4CXV1dMBqNuH//PsLhMJxOJ6amppBMJje0G/jip7EKTp06hVOnTpV97etf/7r48/e//318//vfb8attj0sfKxlqQALWWGbzf0l599vJsGrh3w+j/HxcRiNxrLElLWAAXZuQHJq8ErgRsyW9qyroO+e6a2M0RGe7JeyVPi665UMws8y44VWq1WkFZvNZhgMBnz00UcIBAKi8SKnMuq6Lup0ZJhqzRnwfX19iMfjiMVisFgs6OzsxNTUFLLZLOx2u5g3E4lEREwpn8+LGIoMh3qxrxqbV1JM+Dz5e2C1WhGLxUS3Y8aLWLiZSqUwNDSEqakpET9xOp3wer0YGhpCsVjE9PS0mGaZSCQ2LCatKupbDNkXW2uT4aawmToVU1Tox28lS4UnVFp/ay2I+XxedEzg/eX3PZfL1d1QEVhIM6eIUFRoNVZzlVRmgFUTFavVKjbttYZrHB8fx/379+FyuRCNRjE3N4e+vj4Eg0Hcvn0bpVIJ0WgUFosFZrMZ6XS6LKVahhu4ruvCuolGo4jFYnA4HOjo6MDc3BwymQzu37+PnTt3CqEBFt6Xar+HFDFaDJV1Qvz8y+9RPB4XtTOcPWS1WjE4OAi/3y9SuuXs2t7eXkxOTmJ6ehrFYhGJRALpdHpD4ypKVFqMykB9NdjmoTLQt5HIzS8zmQzu3bvXMtPs5GFXmqateQtyVsFTVDhci1y7dg03b95c9nXk9GG6vzjAjeNvgeqdFypdW7UslUZiPtWIRCK4cuUKRkdHy8Qqm81C13XEYjFkMhk4HA7cv38fJpMJ3d3d6OvrQzqdxvj4uNiMgfk0aLvdXtWql4tCGf+IRqNIpVJwuVzCvUYxtlgs2LFjB5LJJG7duiWKi6u51ujCknutAfOffblTsezqZDYeLcpoNCoyxg4fPoxgMAin04m7d++KZ97V1YXZ2VkkEgnY7XZh1WxkOYESlRaDm8ty7i8KzmapVWEhHwDhA5+ZmdngVdUH288zUL+elgqD9bKo8M/LWXt0ozEgT1Hh2Nql6pkqrZBaosL7NINsNiusjYmJCVy5ckX8mzxsK5VKidoak8kkOjnbbDb81V/9Fe7fvw+bzYZQKCQKFmsltXCz58k+FoshnU4jEAiI2AoFwmg0oq+vD263G/F4XBR/VjvcVc5QkfuMya1a+FlKJBLCzeZwOBCNRoWVdf36dUxMTMBkMuHAgQNCeH/+85/jBz/4AQwGA3bu3Amv1yvcchvZTaMpMRXF+iEXPy7l/tps/b8YCyKbdeRxNeR2HJqmrbnrjtYoA+mcsV55TSwWExlBtdZNIWSrEBYsUiTZzr0SubGkxWJZF1Ghq6i/vx+vvfYahoeH4fV6sWvXLiQSCSSTSbhcLmiahsOHD8Pv98PpdIq2JUePHsXrr79edvDq6urCzMxMVVExGo1i/G80GhXtWQqFAjo7O8uyyID52Sgmkwn79+/HzZs3hcVazVKx2WwwGo3i2cpWIdu48NnR/cZW+xaLRRRaut1uBAIBTE5OYnJyEiaTCV1dXbhw4QIMBgP27duHz372s5iYmCjrccZgfq1EgrVEWSotRqlUEs0GlwvUA5un/5c8A0bTtE3XRXkpuAkzJrQeokJrk/GPyiytubk5DA0NLfk6TGHlZ4EtcphxxNbu1UQln88jm80KFxrXJB8M6GJq1vvIhotOpxOhUAixWEz0EWTQOp1OI51OI5VK4aGHHsLu3bsBzLuVdF3Hb/3Wb6GtrU1kRkWjUZjN5kVdA4CFTDKDwYBIJIJEIoFIJAKLxSLiKbquo6OjA729veju7hbfyxHBdrtdWDIymqbBarUKK5CWSiaTKetUnMvlhLCYTCYR9Of3+Xw+9PT0YN++fSLOdejQIei6DpvNhscffxw9PT04ePAgBgYG0NbWBovFgqmpKdy9exeXL19e9+afSlRaDG4u1dreE3l+xmaKqdCXzCywRqYaric88XOjWOveZXKmHN1flQka+Xx+SdcmYx3yAcNms4mecLKo5PP5MmFIJpMYHh5GKBQSnx++bzL8DDYryzCRSMDpdGJ8fByXLl1COBzG9evXkUwmhXuKbkF2602n02JUQG9vL44dO4Znn30WPT090HUduVwOPT09NWMqDNbH43GEQiHRMqWzsxPhcBhGoxFtbW3o7u4uyyBjK/0dO3bU7CBis9nEe8TvZVNJ2f2Vy+WE5Ujx4zwdCpbX6xUTO3Vdx+OPP45nnnlGWElOpxMWiwU+nw9WqxWTk5PCvbzeB0slKi0GTzjLWSqbzf3FtFV5aFSruMDk9hrNFpVSqYTh4WFxmqSAyMkWlZZKJpMRPvhafcgYZ5AtFbvdLmayU1To+6frKZ/P44MPPsDk5CSKxaLYmCh0lTSrVqVQKIg2LD/+8Y+RTCbR3d2NUCiEy5cvi42RVfB8Xpx74nQ6xWA6dghmcL1WBw+mIjMNd3JyEvF4HB6PB16vV1g5fEYyVqsVDzzwQNV0YsIZ9mxOCUBkEcqWCosVrVYrvF4vSqUSkslkmagA8yJlt9tFrRHrZWQodA6HA8eOHRPdkdcTJSotBje0WsWPQLngbJZNW+64K4tKK7jAcrmccP2wA3SzLKyZmRmEw2GMjIyIzUJOCgDKs790XUc4HEYikcDs7GzN95c9w+SqeY4UTqfTQriYGUVLYGJiAnNzc2Kux0pFJZFI1GxJvxTc+MbHx3Hv3j2R1eVyufDOO+8gk8mIn4XNMBOJBIaGhkRiw8jICK5evVqWUdXZ2VnTkmDNi9vtRjqdRjgcRjKZRE9PjyiItNvtVfuj1UN7e7uYPklx4M/Bw0AulxN1SO3t7TAYDGLMM7P2iNVqLUssqHWg8Hg84r3nmID1RIlKi8Eg7lKWCl0ntYYtbQStbKlQyLmpVQucr5bJyUkRuA2Hw2WFifKME95Pfma5XK7m8+NJXbZUZJcoOxRrmgaXyyUslfHxcczNzSGdTsNsNouAca16DGaJAfOC9Bd/8Rc4d+7cil1i2WwWuVwOY2NjyGQy2LdvHzRNQ19fn+hGzM+12+1GNBrFlStXEAqFUCqV0N7ejkAggB07dqC7u1tUqbe1tS15X6PRCJfLhUwmI5oz2u123LhxA9lstmospl4cDgdsNhvGx8dx584dEc+iBcpDVS6Xg9lsFmtl0gCD/cRisYgebnwPq8EW+iMjI2UxtPVCiUoLIfvWl4qp8FTNVtqbgcqYCtNbN4vo1YLprRwFazaboet6U4L1tCb8fr94r1iwykyzytYwhUJBpArTxVUNFmnKlgrrSgqFAu7fvy/Sd71eL7LZLGZnZzE1NQWDwQCbzSZeI5VKLWmpsFKcjWWTySQuXbok1j43N7fs+F9u6owx7N27F8D8fPZcLodQKCS6A7OZJFN+OavEbrcjEAhgdnYWhUIBPp9PnOxrYTAY0N7ejlKphKGhITidTlitVszNzSGbzSIYDC75/Uths9nQ3t4uKtvlIlpg/r1MpVLi/WbcZW5uTvQeq0yMkK2PWqJCy2tkZASTk5PCVbpeKFFpIXiKrdX2nvDrm6n/F9fN4HG1zKFoNLrms0pWSrFYFNP26HoBmuNWlNN0WWAn9+uqVlHPwVtMdKhVBMlaFA6ikl8zl8uJOotYLIb29naYTCbcvHkT+XwegUAAbrdbuGbi8fiSogLMWxqxWAylUgkzMzOYmJgQBYv379/HyMgIwuEwhoaG8NFHHy2KSTFOFAqFREPHXC6HVCqFkydPwmazweVyob29XazD5/OJIkUW+uq6jtnZWVitVrS1tdV0fRGm6JpMJgwPD4sNfnJyEpqmYWBgoK73shq0NLhGvmcUeVoRmqaJWhqmildOkwQWDpIU/FqiwlhONptFW1sb5ubm1rUXmBKVFoLpxIVCYckTGDcjOSC40TCluFAoiOpueWhUNpvFRx99tCp//FpCy4BT+7ghNMPCkkcsM6OL6aWci0OLhJaKXLiYz+fF7I9K5KmEsvuLPa14Cp6bm4PBYEBnZyfGx8dRLBbh8XgQCASEqzUSidQUFTkAzdoI3n94eBjvvfeesDR+9atfiUyte/fulQlLPB5HJpPBxMQEHA4Hdu7cCYvFgmg0isceewyf+MQn0N7eLtr8MF2YAf54PC6yxJLJJKxW65JBdGIwGMQALLrWhoeHkc1mcezYsbLZ8yuFIp7NZsumTMqdipmJx2SAqakp4basdm9+LvjzV8PpdIqDg91uRz6fX9ffKyUqLYTcYbay6C2ZTIqTiywqm+Hkzw2Rlgp/YegbBhbSHqvN3t5ImBLKEyYDp80Qa9mVOTc3J6rGAQjRBVBWUU//OIv28vl81VMoC/PoMuGfuXa56A+Yn9XBTCSDwYDu7m5xf8YaKCrcCOXXZH+sdDoNXdcxMzMj3GH5fB42m00Ewv1+P2ZmZnD37l2RMs24SSgUwuHDh/Gbv/mb8Pl8SKfTorUJnz3fh8nJSUQiEZjNZvEc7t27h2QyCY/Hs2RhKOHBa3BwUMxpSSQS6OrqwoMPPlj/m1kDuqqmp6eh6zpSqVSZ+4vZax6PB+l0GhMTE3C73SLVuRL+3tjtdkQikaqHG3o02Bwzk8ng7t27uHLlyrqkFytRaSFkf3pHR0fZ12/cuIEPP/wQQPnMi81gqfBEyvVQVFhHwBRKYH7z3iwFm8BC+w5aKmvh/opGoyKbi5sye0AxSM9r6eJhZXkul6t6Yo3H4yJYTVGhhZjL5eD3+0V9Bv+N7j02U2S2XiaTEVl76XQa169fx4cffigsGavVKtqnMJhMq2X37t0wmUzw+/0olUpIJBLo6elBoVDA7du3cefOHSEckUgERqMR/f39MBgM6OnpQbFYxPDwsBAWuk4Z+3A4HNi3bx9MJhMmJiZw8+ZNGAwGcd/l4MHrM5/5DH7zN38Tbrcbu3btwsMPP1y1Un6l2O12YUEZjUYxx4bvP/9OkQDmRwfLvdkqX49Fl/l8ftEhrFAoIJlMCgvp4MGDcLlcCIVCSCaTq85kWwlKVFoIzis3GAxwOp3iBMupdzz1Vbq/NrrAUC7YzOVy4kTIX2gKidwEcLNAgZO7yQLNExXGANjniz87i9kYT5EtFfazoqVSzbfODcputy8SFVZnu1wuIZrM7tq7dy8GBgbgcDjgcDhEzQezrygWmUxGdMtln61cLic2PQabR0dH4XQ6hSspnU7jxo0bYrb7nTt3MDQ0hHQ6LaxBBrb37NkDk8mEDz/8sKwJppzdGAwGsWvXLuEaczqdCAQCVWtLqsFWOGzG6Ha70dXV1bS28X6/Xzxbi8UiEj6ABfcX3ZKRSAROp1P8jlcTAKfTKdxfBoMBMzMz4tBGwS8Wi3A6nchms3A6nQgGg0ilUshkMk0RyuVQotJCsG05/ar379/HpUuXEAqFhJDI2SXcqDbaWuGHnmuhqDA4zbYbLObaqOSCkZERvPrqq8LiAyCyc2gVNFtUmGHFpAq2I+HsDqZh01JhDIpuEFlwZBhTYT8oYEEQ+Rput1tYIRQXjsLln/nZoaUWDofhdrvR2dmJUCgkelmFQiHxc9jt9rIW7MFgECaTCS6XS7h4OJWRLlAGrS0Wi3BbdXV1oaOjQwTv5QmWnE/i9/tht9vR3t6Orq4u9Pb2iqryeuDBhp2oOzs7mzrunL28aJ2nUilREsBgOt8f/g5Uxu9kWO9Ciy2bzYrqeSZtHDx4EHa7XXxmON59vdq1KFFpIVgvwD5C3Ah27dqFAwcOAIDobAosnMI2WlS46fH/3Bw4V4WnLTbs2yhRuXPnDkZHR3H58mXxNVoO7Dt1584dsQk2ClOqGS9gphWLLFljIh8MmD0HzG8ssmtMJpFIiA24cq48RYVZU+x7BaCsgrutra3M7ZZMJpHNZuH3+9HT0wODwSAaGUajUeTzedjtdng8HlF3w1P3r3/9ayE04+PjsFgsCAaDojaDc+Lb29vF51vTNDzwwAPC7eZwOISF0t3djZ6eHiFANpsNgUBAxIJWKio8PPT09KC3t7f+N3EZNE3D7t27sWPHDjE0jG5CPjOOLQYgYiu1Rj1TtIGFxIvx8XHcvXsXNptN7AOMM7F3mdlsRjgcXhcvgBKVFoJtOehiyGQy6O7uRkdHhyiKSqVSZZvOZrJUuA7+MtCM50bqdrs3dIAXRU8OfsqiEo/H8dZbb4ksqUZh5pLZbIbb7RZ1KxQCppBWTgyUi18r+4IRigqwYKFUjgmWi+3o5pPdRj6fr2yC6N27d2EwGETfK3byZWyGVfjBYBAGgwHhcBgGgwHvvPMOLly4gKtXr4quyIlEQvSqMhqNohbG6/WWrWFgYACBQABWqxUHDhxAOByG1+uFx+OB2WwWTR45jleeIVMPrG5nwki1CZGNwk7DDocDxWIRkUgEVqtVZK+5XC5hwXDqo5wUUonH4xGflUAggIGBAXR0dCAYDIq2Py6XS7wHnIjJxplrjWp930Iw88fpdArfOk8t8ohV2VLZDKLCzVqeU8/TOU+K/CXgCWsjkK0Bwl92AGKMq+yOagS6XTh6lq4sxgxkUeG/cYCXPMSrci0/+clPhAUBLIiKPCiKnXsNBgNisZh4TdlS8fv9MBgMSKfTIiPJ7Xbjxo0bePvtt/HUU09B0zSEw2Houi4C6H6/HxaLRbSOn52dFfUXzK66c+eOGCg1OzsrElD8fn/ZZmo0GvEbv/EbGBoawv3796FpGnbu3AmgfPCb0+nEzMyMGAW8XH0K4TjhSCQisqrWAk3ThGU4NzcHj8cjLBWv14tkMilERy4QrkZXVxdCoRAikQii0Sj8fj+GhoYwOjoKYP5gsHv3bty4cQMjIyOwWq34xCc+gYmJiaa69mqhLJUWggFZp9Mp/iz/Esh9mLgxNbOlyGqptFTkk7PVahU+cQAb6v6SxY/PjG4XYF5UKmMcjcDXcTgciMViou0KxVZ2i1FUaKnIM+zlsdHpdBq3bt2q2s1aTv8FIKq44/E4otEoHA6HECoAYmBUOp2G1+tFf38/rFYrhoaGcPfuXbz33ntob28XQfqdO3eKE7HP50M8HheDtXp7e4UrzOVyYWJiAufPn8fdu3cxNTUl+lzJ7eXJjh07cODAAVgsFuzfv18kHMguLnnS4kpqS9iGJRQKLbmRNwNao9FoVMykp3WWSqVEfYlcy1UNs9ksEhOi0ShGRkYQiUQwPT2NTCaDI0eOoKurSwz9OnDgADo6Ohquu6kXJSotBF0Ubre7zFwm8ilfdo9sdFW93GKEYgdAFPAdPnxYiMpGWiqyUHANqVQKDodDpM0y46oZopLNZmEwGEThHgCRyitbKhQyujwoJnyOcnEkrSkGneUZKJXuLzZTjMVi4sRbSXt7uwjmezweXLlyBUNDQyiVSrh8+bLIQqP40koIBAJIJpNiJO7u3buh6zoSiQT27NkjLIREIoFcLodYLAaj0SiskEp6e3tx5MiRmu4pm80mBHE1opLJZGC329dUVGipcNwxkynYYdnhcIguBnRn16KtrQ0ulwulUgm3bt1CKpVCT08P2traRBV/T08PBgYG1iXjS0a5v1oIbgYOh0Ok4MofPMYjOGqYp9mNnqkii0pl11WminJDYG+tWhXca4ksFBxPy+wm+QTfLEuFLp9wOCwm9mWzWdGCh2ml7Dwgdx5mtTw72vJ5zc7OIpPJ4PHHHxdBfa5dfi1gXlQCgQAmJiYAQEw4lOnq6sLVq1eRTqcxNzeHTCaD2dlZka7+7rvv4hOf+AQ8Ho8IpPO1rly5gomJCWQyGbjdbrhcLszNzWHHjh04fPiwSDG/dOmSSFWuJSrLoWka9uzZg0QisSIXD4WELU3k97nZMLuRLjpmtDGuw9gUrdelcLvdMJlMcDgcyGQy2LlzJ9ra2nDnzh3Ral+u5l9PlKXSQlAcmEVSeSKTmx3KTSVX21KERWmNws0wkUiUBWErT85Ac+tAVkqlqAAL419zuZywJppV+yO3aGdwm+8dLQ45Y4v+djaa5LOS04rff/99UT8CQIgT/8xWIQDEbHPeo6ura9EaA4EANE1DLBZDKBQSabAOhwN2ux3j4+MYHh4GMH/Y4bo6OzthsVjEv+m6Dp/Ph2g0KvptdXZ2ore3F5/97GfR3d0tenCtlra2NvT19a0oLiK70ZbraNwoLpdLzIKxWq1iEBffH37OSqXSstYWm2um02lhlbAtTSQSKYufrTdKVFoIBuGZgliZ4SJvyBQVngZXw927d8WEvUa4e/euKNCUfeabUVT4y8geXIxduVwu0fqkme4vYH6z5yZNq4NTF2mtZLNZcV+5QSRQbjl9+OGHZVX/sqXC76E1xD5ag4ODOHjwYNUKbq/XK9JfaQXZbDb4fD6YzWaUSiXcvXsXkUgEDocD4+PjSKVS8Pv9cLvdwnKIx+Pw+/2iM28ls7OzdbVVaTZMDujs7FxzUbFarbBYLGKiJFOLOVZBzrSrJ3utp6dHDBFj3VdbWxump6dF3dNGDOlTotJCpFIpMWeDvZbYXwko35C58TRiqbB2oBFmZ2cRi8XgcDiwY8eOshNYNVHhSXWjRIWnXM5o54bt8XjK1t6MhpKsI6CosL6D1gUr7nmCpYUku7O4bgbxY7EY9u7di7a2NpGxJbtImcwhC013dzcOHz5cdY0GgwF+vx+5XA4zMzPI5/Nwu93o7e0VzS4ZcymVSojFYqIVDzv8stcXh1BVprWyw/FGiAoAkfVVTwPKRuBkx4mJCZH5RxembFnI7YCWwmg04uDBg2JMAAD09fVB13VMTExgfHxctNNZT5SotBCcGZHNZnH16lVcuHABf/3Xfy1OI9UsldWKCgP8PLGvlnv37iEWi6Gjo2ORz77ydFb5M6w3lZYKC+I4IbG7uxu7d+8WgenVID9LurqAhUwrr9db5mdnAJcFenIarTwfhQ0EWWX+yU9+Ek6nc5E7iVXYK4lXMeAPLBTf7d69GzabTcxA6erqQi6Xg8/nw/Hjx7F371489NBD6OrqQqlUEtXibCwp8+tf/1oUCW4EjF/U29pltXC0cTqdxs2bN0UPN9ndxYNjvXU21aZD0o1pNpsxOjoqXKnrhRKVFiGTySCXy4kMkWg0Kiqer169CqC6pbJa95dc27KS7y8UCpiamsLMzAxSqRRmZmaEMFXLZqmcoS1nO603sqWSy+VElTl/4R999FEcPHhQBLtXKrZ37tzBtWvXxN8ZHwEgmgoyNiKPnJXHAFeKitxwkm043G53mRUkw4aSKwlIu1wuBINBUaFvt9uxY8cOtLe3IxaLiZksmqbh4MGD8Hg88Pl8+NjHPoannnqqrKMx4ypkenoa169fx9GjR9elhqIaAwMDOHz4cN21LavFarWio6MD+Xwe7777LgCIWJo8XnslolKN7u5u+P1+HDt2DOFweF1nqQBKVFoGzqRmLnssFsO+ffuwa9cucephKmqlpbKawLJsKaxEVEZHR3Ht2jXcunULH374IZLJZNmaKrHb7YuCiRuVVlzp/qKoWCwW4Q5jVg1TP1cCpwnGYjFhYTCuQRcM/evyCGFaJLLlBKDsulKpJNxKnFHObscy1dxfy6HrOvbt24euri5R4NjW1iaC+BzAZrPZFrmQdu3aJYStra1N1K/w0PCzn/0MTqcTg4ODK3qWzYSCvtawiafNZhNTN/v7+7Fnzx4EAgHhbm1UVNjp+cCBA8jn85icnESpVMLc3FxDXod6UaLSAoyNjeH8+fMolUpwu91ilnlnZyf27NkjLAJg8aYh+71XAjcljkCtF7rNmOrKGgq2YqmE89llEdmoAki5WSeHHDGbia3pma2zmrnf3ChCoVBZexUGvD/44ANRr8H1ABDNE9kWn/8ut6CXLZW2tjbx/lWmk9L9Va+o8P3r6OjAvn374HK5RPeDQCCAAwcOwGazoa2tDTabrawiH5gPJvf392PXrl3w+/3w+XzQNA2zs7OIRCKYnZ3Fgw8+uO5prxsBg+l+v1/06OOAMLav4WewkSw40tvbC5vNhqmpKQwPD+POnTvrYrUoUWkBOImOmV+sQPb5fNi5cyc0TcPdu3cBQGQKMdjLYr3Lly8L4amHfD6PsbExjI2NrUpU8vm82Ay5IVc7JfGEKIveRlgqnKrJICljKhRCBs5l62+llgp/froD+Z/ZbMbMzAzu3LmDn//857h7964YGwwsDOxiax4WtcZiMczNzZUFyYFyUancnPhs6xUVroGZRUxhBeY3LYfDgd27d8NsNgvBkGHaMjBfn9HZ2QlN0xAKhUR9TDMbOG5m+NliO3zG0QhFpVn9x9hHjbNqPB6PKPZcS5SobHLy+TxmZmbEhuZ0OjE1NSV+iW02G7q6ukQWmNVqFZYKRYWuGm469d5X7lBbL4xFsDcRq8LZ4r4Sior8bxslKgDKRIVNGZnaK8epanUHruceshVYKpWEqHAE7vDwMCYnJwHMWyt8HykqFotF1P4AC67ReDwuWszLEyRl6MqrN1AvjzzmvSgqfr8ffr8fsVisquuLMFWXwXq73Y6pqSmMj4+L5ITtANvuMLXa7XaXud04FbJZrVTYLocut4mJCeHSXUuaIiqvv/46Dhw4gP7+fpw5c2bRv+u6jn/6T/8p+vv7cfz4cbz33nvNuO22gOmH3FzYwtrr9YoPXzAYFOmetA5kN4ncqLBeKExWq3VFFg7rKShmLNLiplcJM53kaY9yZ4D1gidy9n/K5/OiawHTfGmdGAyGstYo9SKLCjdrWhOzs7OwWq347Gc/C2Dh+bMuhS43OU4iz5CnRchaiGo1KsBCgd1qLBX6/CkqmqbhwIEDopq+1gnbbDaLanW/3489e/ZgZGQEY2NjYtbHdoGTNffv34/+/v6y2qDK6aLNwOfzid+tGzdurEsCTMOiUiwW8c1vfhOvvfYarl+/jh/84Ae4fv162TWvvfYahoaGMDQ0hJdeegnf+MY3Gr3tsvzlX/4lfvGLX6z5fdaaaDSKUqkkXA0snurr6xPXBINB6Lou3GLAQroq3V9yfUM9MF/ebrcjHo/XlUIrz+C2Wq3w+/3o6OgQKam12m6zSI40cw58vVRaKtlsFtFoVPzS0+Ukb/YrsVQo9JUzTQCICX7t7e3weDxlwW8Kh8lkEhajXMtjMBgQjUaFaMuJBHLfLyKnIdeDLCp8j+TU287OTnR1dYlGkbXYu3cvTpw4IeIwnPh55MiRutaxVeAhirNg5GxDHsaaGV9iYsTQ0BDa2trWvMATaIKoXLhwAf39/di7dy8sFguee+45nDt3ruyac+fO4R/9o38ETdPw8Y9/HJFIRPhT14qXX34Zr7322preYz2Ix+MwGAzwer04cOAAkskkkslkmagwC2dyclJYL3INBE/9KzlZM1+eJ9t6XGcMuMujULu7u8XUu3A4XHUN3DDXYmRvvciWis/nw+TkJKLRqBBsuY6AnYNXInpyWi2ARWnW4XAYHR0dKJVKIr2U4kXXGy0lJjKwRxi73cquKbq4Kg8S8ojilTyXWqLCArzjx49Xrcgnclflzs5O7N+/H4899ljV1jBbGSZ6BINBdHR0iAOCHKRvpqVCEZmeni4rklxLGhaVsbEx7NixQ/y9r6+vrPdQvdeQl156CYODgxgcHEQoFFr1uiwWy4Y3UmwGyWRSbB5msxmTk5MwGo1lbgMWVU1OTi5yjXBjWukESE6fYxda2T1VC8Zu6Kbj/G151kulC6xUKmFsbAzT09Pi3zaiql62VB588EExJIkVymz4SFEBsCL/NF9frpxmenAymUSxWBS+dpvNJixLOaYh/5mWCBsTxuNxxONxEYit5f5izUu9aeKy2FYTFQCrOl1/8pOf3HZWCrBQW8TfLZJKpUTD1WaKSldXFxwOB5xOp+hwsNY0LCrV/N6VJnc915DTp0/j4sWLuHjxIjo7O1e9LvY4amV0XUc0GhVuDU3TMD09DbPZjI6OjrJru7u7MT09LT6w3JA5BGulp1MWYbnd7ro3IcYKGJxn00smGHAYkgy7qBaLRczOzqJQKODu3bvCHbBeUHg5BO1Tn/oUHnnkkbJZJCaTSVhwJpNpRemZfPYUFX42OVESWBjfa7PZhKVBNxgtFW7gFA1WrA8PD6NQKKCtrU1MpmQdi4xsKdW7bopTIpGA0Whc0iJRLA3Flz27CN3LzPBsFk6nE1/60pfwxS9+cd0SIhoWlb6+vrL+U6Ojo4uCb/Vc02zkgVWtSiqVElP1MpkMnE4notGoGOcqEwwGxbx3jiQFIPzyK/GjsyOu2WyG0+kUQeLloPuL/clkVw7bolee7rnOfD6Pe/fuYXp6GuPj47h9+3ZZ5fVaQ8FjA809e/agu7tbjGRmN9tkMllWZFrvwYWWCk+hdH9xngownwJKS4VWnizStD6LxaIYyMQGnR999BEKhQLsdjsmJibQ0dEh3jsZno7rbTPDYjxg3jKj5apYHUykOXDgQFlzVY4BX2qMcKvQsKg88sgjGBoawvDwMHK5HF5++WU8/fTTZdc8/fTT+O///b9D13X88pe/hNfrRTAYbPTWS9LqohIKhfDWW28hm83C5/PBYrGgq6sLc3NzVRvv8QM6MTEhWuPLrGQCJLN87Ha7cJfUcyqXRYUWksViQaFQEJYKq8Lle1H0xsfH8cEHH4geVusxT5swRiI/IzlDi4OPmKHDTLp63ILya3HTkN8fWqLccGipsMU970+rJRqNYm5uDslkUkxaHBsbE9c5nU5RG1IpKtVSuJeiUlTWuj/WVofjKZgZCUAkLcjjwVuZhkXFZDLhu9/9Lp566ikcOnQIX/jCF3DkyBG8+OKLePHFFwEAp06dwt69e9Hf34/f+Z3fwX/9r/+14YUvB9MqW5Hp6Wn8zd/8DcbGxtDW1objx49jYGBATIur1iPJbrejra0NIyMjsNlswjLhpiT755eD1fDMNotEIpiZmVk20M+Nme4humB0XYfH4xFxFnlDZWyBVcWzs7NiEt56iwp/PooeLQMAwhXIeRf8+kotFZ5GWavCGfFtbW0iQ8xut4sCS7mWRQ528xk6nU54vV6xDpPJtCiDTIYjg+sVQ6ZTA0pUmkG1ztw8aOm63lTX10bRlMmPp06dwqlTp8q+9vWvf138WdM0fO9732vGreqGp71W5Pbt29B1HXv37oXb7YbP54PH48Ho6KhomVGNw4cP45133sH7778vqpe5QXKTr9XYUSaTyYh27/L0SNbB1IJptjzJy2nMbrcbVqsV0WgUmUym7JdL0zS4XC74/X7k83n09PRgZGREtDNZa3cLT46s5eH/+bxoAXg8HhGb4ka/WlGhYDCm0tPTI94ru92OWCwmUsEpzsBC2x3GV6xWK44cOYL33nsPwWAQNptNCLncJ4zwe1ZiqTgcDtFpWIlKY8juT5JIJJDP52GxWJSlsplpVVFJp9OYnp6G2+0WmVOsVGY2XLWxrwBw8OBBOBwO3L59G5cuXcKFCxcWFT7WY61wOp/b7Ra+/Ww2u+zplj2/dF1HKBRCLBYr67rb2dmJVCpVFivJZDIiueDEiRPo6elBJpOB2WxGMpmsOtCp2dy+fRvvvPMO0ul0WSNH2VIxGo0i0Ek3Gfto1QNfk4LL+BbH7TKdmO3Q2V6fFieD7swMk0czOxwOPPLIIzh8+DDMZrOoqQEWu780TYPFYqn7uXLmB5tgbpfq97WiWqJEIpEQyRBbwVLZ0qLC/kitxN27d1EsFuH1eoUrhIIwNTUFo9FYs0W4yWTCs88+iy9/+ct46KGHMDk5iTt37gDAikQlEomI7JR0Og23241CoSBahyz1fay4LhQKooCQ99+5cyeMRiNGR0fFWhg7YFt0TdPgcDjgdruRzWbXRVRu3LiBiYkJRKPRsg2/0lJhkJsW2UpEpZb76+rVqwgEAjh06FCZC8RsNosaJbnlysDAgPheq9UqLEoAIu3ZZDKV3a+SynEDtWBDULpAgbUfubvVqXR/6bouOnnz0NDqbFlRoeI3Uuuy3ui6jpGRETidTrFhyP2UJiYmREuMWtjtdthsNhw/fhy9vb0YGRkR40uB5UWlWCwinU6LzKF0Og2/3w9N02rWFpFQKCQsHFZzT09PA1gIUAcCAWHFUDAoKlarFcePH8fhw4fR1dWFQqGwLgOGGAti8LSyWp7ZX9yw+T1Wq1W4qOq5B4CyQkZOc+zv7xfZcnSJtbW1iRolOb3a6/WWZZJRVLxeL5xOp1ijbBnJMBGgHrHmaVqJSvNgbIzPNpVKCYuYZQOtzpYXlfUM9jbKxMQE4vE4XC6X6OM0Pj6OV199FclkEpOTk3V3dHU4HDh06BCcTieGh4fF15dLK2Y2lpx66vF4RMv9yql9JJ1OI5FICP+73W6HxWIRfcO4ubHg7dq1a2WnZZ7g6Obp6uqCpmlitv1awemZrEqX55MQub0K4xUUFaC+YD3jS3T1MRNO0zRxcGCchdbo9PQ0bty4gXQ6jUAgINKaK+NkPN0yqL6UqDCrL5vNLiuGFDKKitPpbPl0142mctppJBIRRcpbwfUFNClQvxnhL2oricrPf/5z/OQnP8HRo0dFTcTk5CQMBgOmpqaQSCTK2rMshcPhgMlkwr59+/D2229jfHwcu3btWtZSYToxP+CsMeno6MDU1BQikUjVlGZWBDOYbTabYbPZEI1GxQkamH9fAoEAZmZmEAwGxcmsMgHA5/PBbDavebCe4sY0XwqGHFcBFgKscpsT2T++XICV9SbAwkbPGA4/q/K0Rq/XC5PJhI8++gh79uxBMBgUo2fpJqM1S1GRW7rwJFwpKrw+FoshmUwuOZe90lJRVkrjsNsERWVubg52ux2pVGpLBOmBLWyp0M+8Hq2em8GPf/xj/OVf/iXm5ubwwQcf4OrVq5iensbjjz+Ohx9+GB999BEAiPqD5WCxYWdnJ0wmk+i1ttzpNJlMQtf1sviB0WiEw+Eoq/6upFgsIpPJwO12i83N7XaLAWFysWZnZ6eYSCfPaJfhPPNUKrWmcbFEIoFcLodkMlkWS6GY8HnJldC8hmuvJ04lJ0sw5sGCUllUgHkhYOGl0Wgse+aMc9BlKIuK3GpmKUvF5/NB1/VlXYt0B5pMJiUqTYTZd9FoFNlsVhxOKgectSpb3lKptQmOjY3h2rVrmJycxMzMjDi50X+dz+dFlo88UIn/EabcVv4nT17k36v9nxtaJpOBy+XCt771LWSzWbS3t6O7u1uMD/Z6vQiFQnW7v9gCvVQqwePxYHZ2tq527QwacrQtRYW1E+xTVblZcVPmaZon8HQ6LTK8SHd3Nz744ANks1l4PB6k0+lFomKxWOB0OjE3N4fp6ek1yzqqnP8OLBYVbqzAQvGanC5dT0xFvp7ZWWwiSOTalb6+Pnzuc5/DH/3RH4nYCrBQic/YSKX7q3IUcTVLhaISCoWwe/fummvO5XIiYSCXy23YDPmtht1ux9zcHMbGxmCxWEScRbm/Njk+nw/AQsXz1atX8dOf/hS3bt3C7OzsouwXtmtnQJmbmjxBsdJ9AWCR6HDjrhQh+WtMCS2VSrBarejp6UEwGMRv//ZvVxUNi8WCJ554YsXPwOv1Ynh4GG1tbYhGo6IWYynY24qCxJ+XRXP5fL5s+h9hrMXlciESiYjv4cYnu7BcLhd8Pp+YfCdXbRPWrnDkbH9//5q4wBgD4qmcExcr3V+0UORxw1xPvYF6ebY8xwOXSiXcvHkTDz/8sOi3xoOI3+8XqdXy1Ejez+l0iuw5zumoTMio5f5yuVzLJrHkcjlYLBZxXSO9+BQL2O12hEIhpNNp9PX1YW5urmpLnVZly4oKT1VXrlzB6dOnhanvcDgQCAQQDAaxb98+9PX1obe3d0uONKW7wu12iz5iy22AnKPCzCIAZe6vfD5ftQguGo2KlFa6erq7u+Hz+TA9PY1kMln2PQcOHMDExARmZ2drjjjlLPTKDrzNhIIHQFS3T01NCcuJlgpjKjxVsviQ1yyH7P7i/1OplCgwnZ2dFU0k5RHGfO/4XlDMmHrMztDBYBClUqksO02+F2F9Cy3fpcjlcnC5XCKVnQc1RWO4XC7xuXM6nRgbG9tSIwC2rKh4vV5omibScB977DGcPHkSJ06c2OilrRs+nw8Gg0H4autptcL4hzwUiiLD1jeVfcXowmNhHjdQq9WKjo4OhEIhRKPRMlEJBAKYnp4WiQC11s9Jl3wfmw17LjFGEY/HMTk5KXqpMSNMtlQArFhU2KATKLdUeB+6XStnoLS1tWF0dFTElbieUqkEt9uN2dlZEfSVLT5aRpXWHdfR3t6OqakpXLt2rWoLel3XRZV3KBRCe3v7ljlJbzROp1N4LJgdWS35pVXZsqJisVjwla98BS6XC48//vhGL2dDYBFhNBqFw+HA3NzckhtgsVhELpcTVol82pVTYSvTkhlnYXyCGxerx+nGkq1Bk8mEQ4cOIZ/P1xQVu90uOveGQiHs3bu36SmtbBPDNF5aBXfu3BFNMIFy95fcKoX1JctRzVJhzAJYKASVLRVgfrO5c+cOMpmMiGsBEDErWVTYzJOvV218NN+bnTt3wmw24xe/+AV27dq1yPJkerPFYhHuR0Vz4Oc9l8thdnYW3d3dWybzC9jC2V/AfE+y7SoowLz75NFHHxUzMMLhcJkg5PN53L9/H/fu3RMnU9YxAOUuFI5BZSdiGXkiIU/9cnDb7/cjk8ksavNitVqFi6sajG0xlrMWQ9doqdhsNvT29gq3RCwWg8FgELUf3Mw5uY8/d72NOivHOVOYzGYzzGazeIaVlgoTHwCIZAW+Fvt8ZbNZsdblRIXvr6ZpePjhh6HrOq5cubLoOlqjuVwOuVxuS52kNxqLxYKenh5R8LvWHdvXmy0tKor51GoG3guFQlnxIjOrGDSkf14WFfmkznhJpaVCMZJFBVg4kfv9fhgMhhV3N2ChGOsu1kpUbDYbHnzwQVHRzNkqFAx5c+amv1JLpfJ1KBxWqxUGg0FUuFcO1uL8ErvdXub+o0uOEzWBxe6vSlGhy4UprBaLBfv27cONGzcWDdKTK74BqHhKE2HGl8FgwK5du7acW3Fr/TSKRZjNZpFllc/ny2oTCoUCksmk6HtFUXE4HBgdHcW1a9cwOjqK0dFRYakAi2dxyJXgwMJUT15vNBrR0dGB2dnZFfXyYmyHorIWkzxZuLhz507hBrNarcJyoHDIa+LgLloxy4lKtY7B8shYo9GIaDQKg8FQ5uICIKrYu7q6hKuMlkrlbBZd15cUFR4QKJ7ZbBa9vb3I5/OLUu/5mqzzUqLSPOR441LFp62KEpUtjslkgs1mg81mQy6XK+sQnM/nRfdZWiushufYYk3TkEgkylqVVG7urJ1gAJqbp7yJ9vT0wGQyYWRkpK6TPbBgHfF1m91cki4/upKYVcX+WuwuIKc7czOnqNRjqVRr7shn297eDk3TEIvFRHKJfB2tRnZu5rppqTBLj7GspUSFFqYsRswQnJubK7s2l8uJzDt+fhTN4/Dhw3UXMrcaSlS2OEyHtVgssFqtmJqaEv8WiUREEd3s7KyYQU5x6erqEm32ubnIs0cIRYYutsqNEZg/ne3ZswfpdBp3796ta+1Wq7XM+ml2dwQGxw0GAywWi0hE8Hq9MBgM8Hg8VUVFdgPWIyrV0nsZ2/D7/UKg2IFAtlRkIaeYyZYK64noqpQD/7UsFZ6Uc7mcsEAq2xkx8ysSiSgrZQ2olpm3VVCisg3g5uxyuUQKIwAR4G1ra0MqlcLs7CwsFouoKZGLHtmjCFjo6iu/DlNyKSrVgsQejwc9PT0Ih8N1dR+22Wyigp1xh0rffyPIGVd0tQHzLiePx4NAILBoMBljPLQM6gnUV7NUZKsBWOiEXNnnjBZCNpuF0+kU7wEtFYoIizbrcX9RjLLZrCiarGapMJ1biYpiJShR2QbQRdLW1iZmngAQp/RAICBiFmznzgItk8kEn8+Hubk5kQXFzZjQ/WU2m8VJu1bwsbu7Gy6XC/fu3Vs2RsIaG9ZmVKuRaQQ5ZkJXkjzSlcWisuuHlspK3F/VLBW5AzCtD1oi8rOTOyG3t7dj//79QrgpKnTj6bqOeDwuKvWXslRYc6TruvhcyLCLsewiUyjqQYnKNoCNBzmulxlgnOrHtuocUsXNnBsTpxKm02lxMqcwydlg8sjbWqKiaRr27NkDTdNw8+bNZV1aXAer2Js5zZM/B1Om5awoTdPE6b2WqNBSWU1MhcLAeFGlKJPKSYFcM7+XWURM47579y6uX78OAIuCwJXuL75upajwHkzIUJaKYiUoUdkG0O9O/z07FtNdwnRVDtgCICwEus0Y2K0UlXw+X+ZWIdXcX/J69u/fD4PBgJs3b2JsbKymC4mNLVmH0WxLhT+jyWQSPyfFlO42uTBNThyoN/tLHtBFZOtuKUuFAkerjvETWlcUl8r6FrPZvKjrbaWlAsxbJD6fD7lcTiRC8F4qnVixGpSobANY6e5yuUSmF7DQBwoAHnjgARw5cgRWq1W4VeQKbbvdLkRIFhX68uVix6UsFcIhYn6/H5OTk7h582ZVK8Tr9YpOuc0WFQ4ko/XR19eHQCAgRIU/o5z+uxr3l9yYk8guLLlgtDKmQuGQRUV+3hzuJM+lByBGM8vI4safqVoGGO+VTqdFzEWhqBclKtsApsuysj4UConphhQVVmuPjIwIQUin04hEIvjoo4/E3BGKCjd3WioGg6Gs3X89BV3MCBsYGEA2m8W9e/cWXWO320UtRSqVaqqo0Fqg+8vtdsPpdCIWi8FoNJbNhidySjF/7spxCJUsFag3m81lbrRK9xctI66Fgk6xYct02WI6evRo1Qap8vRJWVRoiVSKSiKRUFaKYsUoUdkGWK1WEej2eDyYmZkRrfflFNRoNIpCoSCGB3G8cTabRTQaxczMDDKZTFmgnrNnKk/FS7m/KvF4POju7kYkEqkaY/H7/bBYLFVbvTQC58tTEHnqZ2v79vb2sowroLqlApQ3lUwmkxgeHhY/S62UYmBhPDEHa1W6vwCITC3ep1gsClHgejo6OuDz+UTqeDVRlzPCKCzZbBYOh6NsDj3dnOFwGB0dHat/wIptyZZtKKlYgKLCcb/sASaLCmtWOOp0ZGQE+Xwe/f396O7uxrVr1/C3f/u34nQ8NTWFwcFBMbiLfcHoAltp64muri5MTU1hdnZ2UXNDs9mMtrY2JBIJTE9P49ChQ015LuwEwHRiJih0dHRgdHQUAER/JlKt+BEo37AjkQjC4TAikQgeeOCBqpaKbAXR3VbNUgHmRYUWGosy2bKFPcR2796Nnp6eJcW8Ms1Yfl05WJ/JZIS7cSuOhFCsLUpUtgE8aefzeTFHIxqNlrX1CIfDMBqN8Hg8yOVyuHfvnqhVeeWVVzAzM4O+vj5R4JjJZHDlyhXMzMzg3r17omFlMBiE2WxesR/eYDDA6/UiEomIlimEA9MsFgump6cXNWdcLTz987RPFxSbWMbj8bL4A4CyGhx58JZsqVAwSqUSEomESImWfyY5piLXu1SzVORWLBxD3NPTI9ZO15u8zmrI6+X1chsWFqVy1K2maVuu2aFi7VGisg1gBhGrxXVdx+TkpNhAC4UC4vE4XC6X+HMmk4HH48GPf/xjGI1GPPPMMxgaGsKdO3fg9XoRDAbx6KOP4r333sM777yDZDIpLI1YLIbLly+jvb0dLpcLbrcbdrtd/Of3+9HT07No82xra0M4HEYikShLh+VETqfTiUQigdnZWTGF8N69e0in06tqi8+NmgWN3KCZpptIJMRYZhlaF8z+AspFpVAoiIr1eDxetWaEVg6FjP+vDNTz55dTijOZjMjsoqVSD5WWitVqRTgcFt0Tbt68iVAoJBqPtre3l1lpCkU9KFHZBnCTLBQKItNnampKbKCRSEQU/cViMTFy9tKlS+js7MTTTz8Nr9eLXC6Hjz76CNPT0/B4PCiVSvB6vdi5cyesViu6u7vR1dWFy5cvw+VywWq1IhaLIZFIIBwOl82BdzgcOHbsGA4dOiQ2bU5BjMViZaJCy8FqtSKVSmFqagqdnZ0oFov44IMPhCWwf//+FT0XOVAvp+QycM/EhGqiAqBmTIVV+CaTCbFYDDabbZGAyoLEezLOUikqzH7jfeRxzrKlshyyuxNAWbCeFsnIyAgymQyi0SiOHTtW1+sqFDJKVLYBdNkUCgVRixEKheByuUQAnC4gWijT09NIp9P4+Mc/LjLDgsGg2NiZicVgNzc2v9+P7u5u7Ny5s+pMcyYAXLt2Db/61a/w3nvv4cCBAzh69Cg8Hg8cDseijrmMd7Aeg667yclJ5HI5WK1WIYwr6afEWSrsFMCNn004k8mkEAcZefpjNVEpFAqw2WzweDwYHR1FKpVaVJXO+BNjKLKoVAvU06XGvmyypcLss+V+9moxFWBeVLxeLxwOB+7duyeKYtVgLsVqUKKyTeDGxLqD2dlZuN1uEZiXZ5Z0dHRgZmYGe/fuxcDAgHgNm82Gffv24de//jXS6TSmp6dFOw/ZbQTUzv6yWq3YvXs3du/ejZmZGXzwwQe4fv06rl+/jscffxwejwcTExNVXTVyLCOfz2NmZgYWiwUul0ukPK/EXSO7v+SUaFpFvG8lvJY9zwCUWQtM+e3q6hIxkMqOtGyRI6dj17JUZPdXMpkUCQVcC++/UlGprNbv6enBu+++i2KxiIMHD67J+GbF1kelFG8TWF8CzNeksAU+U1mNRiOy2SwymQxsNhtmZmZw6NChRafmvr4+4dZhXQkrwtlSBagv+6ujowNPPPEE/uE//IcIBoP46U9/KgLHldYKXWDMusrn86KrstvtRiaTWXFrfDmtV7ZUKJD8t0p4Pd1uwIKlwvoffl8wGMSePXuqxlRWYqlw42fyABMhqolaLaoF6oGFhIWHH35YjFP++Mc/vuzrKRTVaEhUwuEwnnzySQwMDODJJ59c1OkUAO7fv49PfepTOHToEI4cOYLvfOc7jdxSsUqYZQQA7e3tSCQSwn2Ty+VgNBrFtEMKzr59+xa9js1mg9VqRTqdFhsos5YcDseKRIU4HA489dRTsNvtuH37tkhTlmFatMFgEI0lKYBsGV9NVBh3qVb/IrdKkS2VUqkEl8uFPXv24MiRI4u+j+4yWhvAwqYuFzUuhdydgK49uriqpRTLDSOricpqqvqZfUbBstlsGBwcxBNPPIHu7u4lX0+hqEVDonLmzBmcPHkSQ0NDOHnyJM6cObPoGpPJhP/0n/4TPvzwQ/zyl7/E9773PdHwTrF+sFNxqVSCz+dDNptFPB7H9evXcefOHTEV0uFwYHx8HB0dHSKWUvk6TqdTzC6noDA7q1qhXz2YTCbs378fo6OjVWen0FKhm45Fl5wVYzKZqhZGZjIZjI6OVj3wcDNlzEYWlQMHDuDBBx8sK3wkdJfJRZ8UFQpDNQuHUIhpJfLn4/dWCrLspkomk2IkgXztcpZKrfdFdq3F43F0d3fjwQcfXPK1FIqlaEhUzp07h+effx4A8Pzzz+OHP/zhomuCwSAeeughAPNdUw8dOoSxsbFGbqtYBWyxHg6H4XA4YDKZkMlkhHBMTEyIVNVQKFQzSEtRAYBYLIa9e/eKPlR2u31VlgrZv38/dF3H7Owskslk2UbJgDndRJzKSEExmUxiQqXMxMQExsfHcfv27UX3kydWyu6v5U79cgyk8nvqsVQoKrL7y2w2i1nzlYJEUUmlUiJ5gK9fr6VSS1Tkav1oNAqz2aymPCoaoiFRmZqaEqmIwWAQ09PTS14/MjKCS5cu4WMf+1gjt1WsAjYuZByCM+cHBgYQCATg8XjgcrlEVfXevXurvg4D48B8JtLk5KSYOMj6DWB1otLW1gaXyyVGHMvuLIoHsFCrwRRZtoCX27UTWijVeobJAlBpqSwFLZtqlkrlGIBqyKLCZyfHvCq/l9ZSOp1GMpks6z7cLEuFacSq15eiUZbN/vr0pz+NycnJRV//gz/4gxXdKJFI4Nlnn8Uf//EfL5lV8tJLL+Gll14CANFNV9E4chYTT/zxeFxskF1dXTCbzfjggw/EIK1qmEwmHDx4ENevXxeZTaVSqWwqJLBy9xfp6enB7du3sXPnTqTTabGBsoCTac+8L8XGZrOhVCohmUyWVfMz4F8tM4oCUJlSvJyoVHN/VVoqS4mKPMeFfbzk51VLVBKJBNLpdFmq9kotlUqx9/l8mJiYwK1bt6DrOtrb25d8HYViOZYVlTfeeKPmv3V1dWFiYgLBYBATExNinnkl+Xwezz77LL74xS/ic5/73JL3O336NE6fPg0AGBwcXG55ijqRN9pEIiFaojAmous6otEoYrHYspZkd3c3HA4HcrmcaEPCzZ8bbSOiwuFdstVBIWQzx2QyKSwVnvQLhQKSyWTZphuLxQAsbPYyjGFUphTX4/5iL7VKS6HW5i3DHl+0TuRRxvxZZSpFRRb8lVoq1V7bZrOJ+iTV5l7RKA25v55++mmcPXsWAHD27Fk888wzi67RdR1f/epXcejQIXzrW99q5HaKBrDb7fD5fKIynlMgz549i/fffx+lUgn379+HyWTCnj17lnwtg8GA7u5uEZcplUqiuI8zWFZShCgTDAahaZpoFSPDE72c6SUXJ9J6IPl8XrxGtVktuVxOWCgrsVTYBTiTySxyf1VryVIJRYWWitlsFt9T7dlRsBOJRFmLFqD+lOKlLMhdu3ahu7u7arafQrFSGhKVF154AefPn8fAwADOnz+PF154AQAwPj6OU6dOAQDefvtt/Nmf/Rn+5m/+BidOnMCJEyfw6quvNr5yxYqQ529EIhF0dXXhN37jN9Dd3Y07d+7g/fffx/DwMPbs2VM146kSOVjPRpTAwoji1cI+YclkclF8RK5VoajIsRZaD0SeSllNVNjUcTWWitVqFdlmcv+tylqQalRmf8mWSjW3Gd+PcDiMUqlU5j6ud81LiYrL5UJvb29TmnQqFA1V1Le3t+PNN99c9PWenh4hHJ/4xCfq7k2kWDuY0UMLwGKx4ODBg9i5cydGRkbw4YcfIp/P49FHH63r9ex2e9k8DllUluuWuxydnZ2iXYjcTp8WgqZpwgKRRQUod3PRtVf5dQBlQ7YotvyvHlFhYgBb3KzEUmHLfbry5OdVS1Q0TcPs7CyA8tnz9VoqcvxIoVhLVJuWbYLNZkMgEEAymRRxBtauDA4OIhKJIJvN1j2USU4t3rlzp9is8vn8otnoKyUQCIjxwplMRsQQGDvheF2m1rJ+pVAolM26l0Wl0lKhtUAhkVu01GupxONxhEKhRZbKchs3e46xtYrNZlsywE8Rm5mZAYCqMZV6LBUlKIr1QInKNsFgMMBut8Pj8QhLhcWKRqMRu3btquoiqgVdUbt27RJWCluoNOL+AuZFxWg0Ym5uriwVmB2D6eay2+1lsQiKCjdrWVRY+Clvwgy0yx2K6xEV1uVYLBYhKvJ9lnMjye1YNE2D1WoV31/t2VF4eBiQi1JXElNZKiNNoWgWyom6TZBPqWazGYFAADabTZzs63HbyLCYku3hgQVroFH3V0dHB0wmE6LR6CJRYQo0N0mu2W63I5PJCGHjz8Tvo2VCZFHh5g7Ub6mw1kfXdWF58HWXe47yz8QhYEvFVIAF96XBYFCWimJTo0Rlm1DZ8wlAWWdcuQliPXCTk2Me8ojcRmA/r3g8XrYBy8kGTCfmz+X3+8UGz5+J1gkD4nJchTEV+Vnw/7L4VIOiQpjNxXvWE1PhGtjTTLa4qsE6I5fLVXbNSiwVJSqK9UCJyjZBdsl4vV6YzWY4nU5ks1lRCb8SUaE1Ip/MmyUqwELTy0pLBVjYSGVLxev1wmQyIZVKVbVUgIVuvMD8xk53GIsfAYiGmjdv3qy5NsZgKEryPJl63F9sD0O3FgAx5Kxy7grhdRwjTOq1VCqHgikUa4USlW0CN0Kejo8cOQKTySQC3iv1ucsV9M22VIB5F1gqlVpUAAnMWyXcIGUxcDqdImOMa5Ozq+S6Fw4Xk+tU+D0AqnY1JhQiuX8XN/V6s7+AeeuCz1HTNPT29tZ0HXJdvb29ZV9Xlopis6FEZRvBFFZg4WSby+VEW/yViIrT6SyrCAfq69BbLxQOZjxx/ZqmoaenB+3t7Ys2UoqjbKnoui5O+dUsFVbpk8qCy2rQXcXv5334/3osFVo09Q7CosjVEpV6YioqUK9YD5SobCPkzZOneDnQvpKTrMFgwO7du+Hz+cpqVBqpppfx+XwwGo2IxWJlMQ52Q965c6doZkrsdjsKhUJNUZFdaYy3MJOL9PX1ifvUgpZKZZZZtZkl1aCVZDaba/ZYq+TEiROi8r3aepayVLg2Zako1gMlKtuISkuFJ/flgsS1sNlscDqdZTGVZri+gPkCP7azr4yrFAoFOJ3ORfdiMJs/Fzf7apYKq+0rg+5dXV1iDHAtmDEmJwHI2WX1uL+KxWJZSvRytLW14eGHH67aln65gs1Gm3wqFCtBico2Qt5UCoVC2ZAq+f/1YjabF/XaapaomEwmeDyemqLCeJCc0cXaGVlUgIX4j/w6lYF6meXSimndsCqeMZV6mklyHZyUWS+7du0SXQwqWc5SUaKiWE+UqGwjKkWF8QNugisVFXnMLdBcUQHm4yrJZLKqqAQCARSLRdy+fbtseiLHI3M9wIKoyAJEa4GjfGWWy6iipcL4CUcL1+v+Yj1NPT3WSFtbGw4fPlzVtbicpVJPO36FolkoUdlGyCfoQqGAeDwuKtSB1VkqLDbUdX1NRGUp99eePXuQTCYxMjIi1qNpmrieGy3dYrKo0LpYrajIMRVW+NdrETCjbSWishTLWSq03OSZOgrFWqFEZRshb3b5fB7xeBxut3vVzQbpipHTeJspKl6vF8VisWz2PHt8AfPBfJ/PJzZp9gGjqPA6m822qIMx4y1yOjGhqNQqgpR7hcnB+nqnXtJCrDdIvxxym5il7tdopwOFoh6UqGwj6P83GAwIh8MoFovweDxlLeBXglz/0awWLTIejwdGoxHhcFh8TR4pDJTHdRjr4N95TS1LhbUblT83/17LUmG7GGZVUVTqtVS4yTdrFvxyMaBsNitcgwrFWqM+ZdsIbnbT09MYGxuD2WyGx+NBLpdblRjY7XaYzWaEw2FhHTTTUmFLEjZSBBZ+BllU5CA53WPA4hn0lZbKUoF6oLaoGI3GsqJHiku9opLNZqu63VYLM9BqkclkmiZgCsVyKFHZRnCzKxQKaG9vx9GjR2EymVYtKpqmwe/3IxaLYWpqCkajsWlxAgAiXTkejwv3TjVRAcpHA/Pf2EZFroAn8vTFyhN8Pa1PLBYLSqUSotEopqeny6y1ehpKNqueh/dbSlTYNUGhWA+UqGwj5KaJTqdT/L2RwVodHR3QNA2JRAKBQKCpLhZaQqlUatGGXUtUWOXPa+TJjpXur9XGVIB5l1qpVMLU1BQAiKLLelyI+Xy+bNJkoywlKkwgUKKiWC+UqGwjuHlyjgoAUVW/WreVzWbDwYMHEQgE0NXV1bS1AvObu9vtRjqdFu61WqIi/7vczl+2VGTLYylLZbmYCrAgKqlUSqT0yh2bl6KZnQe43lqiEo/HAWBFNTEKRSMoUdlG0K8eCAQWxR0aCbDb7Xbs2LFjTYrrvF4v0un0our/5SwVpjpTVKrFVJazVJYSFc6ioTXEnmPLPQOuq9p9V4vcf6ySSCQCk8nUtEwzhWI5lKhsI+x2Ox566CG0tbWJkzpP+Js13dTr9SKTydQUFVoasnuMgfNKS0U+zXOcr9z2ntQrKlwHLZV8Pr+sqMgzXpopKnxtmUKhgGg0Cq/X27R7KRTLoURlmyE3UJRbtTQza6uZuN1u5HK5mu4voDytmNlglZMdK0VFzv5aTaBebv3PlN56OgHLsZdmxlS4FqLrOm7cuIFSqYSOjo6m3EehqAfVt2EbIschNrul4nQ6oeu6aP1Ot5HsypJFhfUjhUIBo6OjwiJgCjCR+3StJlAvp+jytetxf9FlttaWCq27Xbt2KdeXYl1Rlso2hKfsdDqNdDotivk2I5zbEo1GxdcqrQ65NoU/RzqdxuXLl0V2VuX3yO6yahbDcgWFrH2RX7teUamVILBaqokKuww4nc6m3EOhqBclKtsQVlen02mkUqlNvfFQVGKxWFnDxuVEJZFIiDn0/B5ZJGRRqWYxVF5ficPhEAFwJgfUM7NEtlTWUlRYta9SiRXrjRKVbYrdbkcymUQ6nd7U6aYUlcq04mqiouu6EJUPP/ywrDX+SkVlOUvFarWiq6tLWHnMJlvO4pMtmrV0f6XTaVitVtWaRbHuqE/cNsVutyOVSgHY3C4SWgTLiQowv6kyXhSLxZDL5cpatcitVGTLppb7a6mYCnuGsTqeolKvpSK7zxqllqXSzO4GCkW9KFHZpsiB5s1sqdDFVJlWXE1UWHyo6zqy2Sx0XRexBV5DC0UWqNVYKrKomM3mFYkKRwSslaWi67rq96XYMDZndFax5rS3t0PXdbjd7k2bTkzcbjempqaEqMitWPh3oFxUKgd1yaJis9nKLJWVikqhUMDQ0JCYIcPsr3pEhW1TmhlTYS0Onwl/dhVPUWwEylLZpphMJnR3d29q1xfhzBd5/LE8cVIWldnZWZEqzYJE+ZpqYrPS7K+ZmRmkUimxJrk2ZrmYSi6Xg6ZpTXV/AeXW22ZPE1dsbZSoKDY9TqcT+XxeWCrycDCgXDBmZmaQzWbFxsqalUpRkWfI1LJUqrU90XUdMzMzAObdTdlsVri/SqXSshu5bG01s8pdHl6mREWxkShRUWx6nE4nCoWCiI9ws6zsBhCPx4UbiJaMpmnIZDJl1gxQLkgrcX8VCgVks1kxQCyZTApLRZ4IWQvGepptqVgsFvE8lKgoNpKGPtXhcBhPPvkkBgYG8OSTT2Jubq7mtcViEQ8++CA++9nPNnJLxTaEacWJRALFYnGRpcKq+Gg0KvpwsU4DmM+EkoWosn5lJe4vfl9bW5vISrNYLELAloPramZKMTAfP6EVxIw0lU6s2Aga+tSdOXMGJ0+exNDQEE6ePIkzZ87UvPY73/kODh061MjtFNsUDutKp9Nls194IgcW2rMA84FrWgS0VBhAz+fzIv7B71uJpSJ3R7ZarcIKYsfj5aCoNNtSsVqtogZmtUPXFIpm0NCn+ty5c3j++ecBAM8//zx++MMfVr1udHQUP/rRj/C1r32tkdsptikUlUwmU9aMURYVps9ys85kMmLTjsViGBkZKXOLyeOHq4kKLZ5K5Kp+u92ObDYrOiOvxFJptiXBTC8mKShRUWwUDX2qp6amEAwGAQDBYBDT09NVr/vd3/1d/Mf/+B+VOa5YFZWWClAeQwCA/v5+8R+tE5vNBoPBgEgkImIthUKhrC3+Uu4vAIuC9bLbzOFwlBVYLoec6tzMOhVgQVQymYwSFcWGsmydyqc//WlMTk4u+vof/MEf1HWD//t//y8CgQAefvhh/OQnP1n2+pdeegkvvfQSACAUCtV1D8XWxmKxwGq1LikqBoMBXq8XwPyGn0qlYLFYYDKZkEwmYbfbRYpxpajUcn8BWNTPq1JUSqUSkslkXT8HLSV2TW52oB4AotEoSqXSpi5oVWxtlhWVN954o+a/dXV1YWJiAsFgEBMTEwgEAouuefvtt/FXf/VXePXVV5HJZBCLxfClL30Jf/7nf171NU+fPo3Tp08DAAYHB+v9ORRbHI/Hg3A4XCYqtTZzu92OfD4Ph8MBi8WCVCoFl8slihVprQCoOSt+OVExGAyixmd2drauSY6V82uanVJsMpkQDocBzNf2KBQbQUNHpaeffhpnz54FAJw9exbPPPPMomu+/e1vY3R0FCMjI3j55ZfxxBNP1BQUhaIWTqcTuVxOiIrdbhcn/0poQVgsFpjNZiSTSRH3oKDIFsdyloqMHFPxer0ifiPXidRCXm+zA/UA4Pf7xZ+V+0uxUTT0qX7hhRdw/vx5DAwM4Pz583jhhRcAAOPj4zh16lRTFqhQAIDL5SoTFQbmWbsiw/iC3W6H2WxGOp0WG3gulxN9uup1f8lwfLDBYIDNZoPFYhGV9MuJSqFQWFNR6e7uBqCsFMXG0lDvr/b2drz55puLvt7T04NXX3110dc/+clP4pOf/GQjt1RsUxwOB/L5fJmlAswHpj0eT9m1mqaJ7C1mjblcLmiahnQ6XZZSXI/7S4bjg4GFWA+LGeuxVOTstWbPjTebzTh8+PCm7+Wm2NqodCxFS0BRiMViAOY3UBYfVqLruhADChFdVfl8XrSf5+z6WinFQHVRYYzFarXCZDJh7969K7JU2OF4LbIh7Xb7pp3iqdgeKFFRtAQulwtGoxHxeFyk+dpstpqiUiwWRYZVPp+Hz+eDwWCoGlNZSUqxHLintZRKpRa146/GWgzoUig2G0pUFC2By+WCwWAQ3YGB+eB9KpVaZE3oui6mMFJYOAWRreeZUrxURT2wtKXC7C+KSj2WCkVFWROKrYoSFUVLQEtFrlVhmnBlajE3d7vdLlxNDK6zTmW5mSb1xFSsVquoianXUuF9l5u7olC0KkpUFC2BxWIRI5ApGi6XCwCQSCTKrmWGlsPhgN/vF9lgHO5VKpVEO/xam3s9lorFYhFCV29MpVQqiViOQrEVUaKiaBm8Xm+ZpWIymWC326uKCjAfc3G73WV9vuQ2LbUyv4Cl61QoCHSrpVIpMf1xKeSYihIVxVZFiYqiZfB6vUilUmUFjzabTbR8J7QYbDabGKDFwD2tFIpDI5YK70FLZTn3lzwpUsVUFFsVJSqKlsHj8ZRZKgCq1ofIlgrb0ufzeTFMi61aloqpVEspZqqy/D0OhwPZbLZsRnwtZFFRlopiq6JERdEyuFwuFItFpFIp8TV5lC9hMN3r9QpR4eCqSktlKVHRNK0spVhu0ULYZ4zjh5eKq3DdylJRbGWUqChaBmaAsQASWJhPL1sv+XweNpsNgUBACAmHe+m6jkwmI9xhS1kMlYO65NoWYrfbxbx7eU5LJaVSSbjpLBaLGgOh2LKoT7aiZagmKmxJQlGhJWK329Hd3V3m/mKTRYrKclXt9YgK28fQXVYZ3yGcbc81K/eXYquiREXRMtQSlWg0KgbEMcPKbreLOfJyTEXXdWSzWdEEcjWWSmVMJZfLlTWsrEaxWCwbJaxERbFVUaKiaBkcDgdMJhMSiYTY7M1mM9566y288soruHfvnnB1GQwGEbvg11ivwqaSS6UUA4tFpVZMhe4vYGFccCXyLBXWtygUWxElKoqWwWAwwO12I5VKiQ2aVkixWMT7778v3F90dbGLsCwquVyuIUtF/h6LxSKq9YHqrfj5vXR/NXvqo0KxmVCfbEVL0dbWVpZWPDMzA4PBALvdjpmZGdEJmKJCl1ehUBAzWJgttlxMpbL1Si1R4QAwAGWZaTKMqTBIr0RFsVVRn2xFSyEXQE5MTOAnP/kJjEYjdu/ejUKhgLm5uTJLRRYVWir1pBQDi0VFnsFCKBIUleUsFY4RVqKi2KqoT7aipaClkslk8LOf/QyJRAJmsxkdHR0AgFAoVCYgTCMuFotCaOQ2L0u5v1ZiqTCOs1RMRbZUVExFsVVRoqJoKTo7OwHMu72SySQcDgeeeOIJFAoFpNNpzM7OlokKN282kGSnYmD52EY1Uamc2EhRyWazYspkNWipVMZgFIqthvpkK1qK9vZ2GI1GDA8PI5/PY3BwEH19fZidnYXBYEAoFEKxWBT1K4xfyKKSy+XEfPrlLJVSqSRcW3Lbe8L292z7slSdClu0KFFRbGXUJ1vRUng8HpjNZgwNDQGYFxnWndhstkXuL1lU+GdaKvXEVIAFt5fcoZjQnUVRqVWnkkqloOu6CtQrtjzqk61oKQwGAzo6OhCLxaBpGnw+n8i4crlciMfjSCaTIn5STVTYn6se9xewICqVHYqBBfcXLSE2q6wkHo/DYDCISZNKVBRbFfXJVrQcwWAQxWIRO3bsEBu50WiE2+1GNpstC8pXigqD6pqmLVvZLsdjgOqiwtega6tYLFbNAEskEqIgUwXqFVsZ1SpV0XL85m/+JlwuFwYHBwHMu6Xsdrtog69p2rKiAqzc/SWLFdE0TXQqtlgsKJVKSCaTcLvdZdfF43EYjUYhKspSUWxV1Cdb0XK0tbXB6/UimUyKDd9sNsNsNqO7uxuappUJh6ZpZaIif4+cyVUJOyAvFVMB5ue2yA0q5d5kwHzVfzKZVO4vxbZAfbIVLYfRaITNZkMymRTNHNnn64EHHkAgEMD+/fsBQAgJA+msW6H7a7n7AEvHVACUvabRaEQ8Hi/791KphHQ6DbvdLtakREWxVVGfbEVL4nQ6y3qA2Ww2YSkMDg5i3759ABY28MomkwBWJSrVxIBuL94/nU6XBetZQ+NyuUQjSxVTUWxVlKgoWhLGMZj5ZbPZRLsU2RKgyymXywlXmOz+Wgq+BnuF6bpedWIjLRUWRsqzU/j9qVSqTFSWcrspFK2MEhVFS0JXUiQSgaZpwlIplUrCDQWgLDjPP8tt85eCr1MsFqu2aCEWi6Vs1ko+ny+rV6H4uVwu6LouxE2h2IooUVG0JBSVVCoFi8UisrKqWSpyxhfnq/DPy0FRkWtbKqH7C5gXqkpR4fwXioqaT6/YyqhPt6Ilka0Mh8MhakR8Pl+Z60kO1APlAlCZHlwNk8mEQqFQl6ViNBpF3Yy8hkgkItapREWx1VGWiqJl4ebc29srRMblcsHlcolr5DTiUqkkpkcajUZh7Sx3D7lKvpalQkEBsMhSYYox16VERbGVaUhUwuEwnnzySQwMDODJJ5/E3Nxc1esikQg+//nP4+DBgzh06BB+8YtfNHJbhQIAsH//fuzfvx9Wq1Vs1NlstixDS46jFAoFUZsiu8yWgu6spSwVq9UqgvUmkwmZTAZjY2OiESVbyjgcDjFxUqHYqjQkKmfOnMHJkycxNDSEkydP4syZM1Wv+2f/7J/h7/ydv4MbN27g8uXLOHToUCO3VSgAzMdVWLlOS4VZXoSiEgqFEI1GxZTGemtFWKVPS6WW+4sZXVarFYlEApOTkxgbGwMwLyqyVaRERbGVaUhUzp07h+effx4A8Pzzz+OHP/zhomtisRjeeustfPWrXwUw/wvY1tbWyG0VikXIMZZqQ7SA+YC53W5flCG23Ovqui5iJLVEBZgXC16fTqcxPj6OYrGIcDgMr9eLYrEIXdeXzTpTKFqZhkRlamoKwWAQwHyTv+np6UXX3LlzB52dnfjyl7+MBx98EF/72teQTCYbua1CsQh5o5YtEKPRiOPHjwOYH/VrsVhgt9thsVhEe/yloFWRyWRqpgLLbfZtNpvoflwsFhGPxxEKhRAIBFAoFJSoKLY8y4rKpz/9aRw9enTRf+fOnavrBoVCAe+99x6+8Y1v4NKlS3A6nTXdZADw0ksvYXBwEIODgwiFQvX/JIptjaZpYsOv1p5e0zQxnAuYn8tSjxuKApBOp2teT1FxOp0IBoOii3KhUMDY2BjS6TS6u7uFqNQjZgpFq7Lsb9Ubb7xR89+6urowMTGBYDCIiYkJBAKBRdf09fWhr68PH/vYxwAAn//855cUldOnT+P06dMAILrQKhT10NHRgXQ6jfb29rKvM9srm80KN9RKYirA/OGoVmCfXy+VSujt7UU4HMbk5CSAeUsdmLfks9msslQUW56G3F9PP/00zp49CwA4e/YsnnnmmUXXdHd3Y8eOHbh58yYA4M0338Thw4cbua1CUZWdO3fiwIEDi9rO0x2VzWahaZpolbISUeHrVIOWB+MuPp8PwHwhZjQahaZp6OnpKZvjolBsVRoSlRdeeAHnz5/HwMAAzp8/jxdeeAEAMD4+jlOnTonr/uRP/gRf/OIXcfz4cbz//vv4N//m3zS2aoViBciiwv5gy81SIXIcpVZgn/3FKCput1tkmcViMTidTjgcjiVrXRSKrUJDn+729na8+eabi77e09ODV199Vfz9xIkTuHjxYiO3UihWDTPA0ul01aaTy8ExxUv167JarUJUODAskUjA5XLB5/OVxXSUpaLYyqiKesWWh6KSyWQAYMXt5/ft24dAIICOjo6a11itVlFFbzabRb1KoVDA7t27AUBMpVSWimIroz7dii0PR/hms1mYzeay2Sf1YDQasWPHjiWvkS0VVs+Pj4+jUCjA6/UCQNn0SYViq6IsFcWWh63x8/m8aNnS7OmLsqgACy6zXC4nin0ZqFeWimIro0RFsS3gUK98Pi/aqjRTVCwWS1kTSY/Hg2QyKdKYAWWpKLYHSlQU2wKKSi6Xg9PpxI4dO2Cz2Zr2+pWWitfrRSqVQqFQgMPhAABhKSlRUWxllKgotgV2ux25XA6ZTAa6rgvroVlYrVbk83kxq8Xlcom6GGaNUVTU1EfFVkaJimJbQEslnU6jVCo1Pa2XBZB0gVFk6GIrlUprcl+FYrOhREWxLaCoLDUXpREoKkxbNpvNZb3GOD2ynhkuCkUro0RFsS2wWq1CUOpte78SOC8lnU4DmG/RwiLLUqmEYrGIYrGoLBXFlkeJimJbYLVaoeu6iHk0M/MLgAjGp1IpAPMWC+ercBxxsVhUHYoVWx6VMK/YFjCNmLUizbZUKkUlkUgIUclmsyKI38yMM4ViM6JERbEtsFqtMBqNyOfzwi21Fq9PUYnH47BYLLBYLEin02LqozxWWKHYiihRUWwL2P8rl8utWQGi3W4vs1QcDgdMJhNSqZSI5yhLRbHVUaKi2BZYrVYYDAYxFrjZlgow7wKTLRXWwqRSKRHLoZtModiqKFFRbAvYVHItLRWHw4FoNApgXlQ4rIu1MZqmKfeXYsujsr8U2wKOFGZB4npYKn6/HwaDAclkEplMBpqmqewvxZZHWSqKbYHZbBYxFVotzcbhcCCbzSKTySCbzcLv9yOVSiGdTkPTNBgMBhVTUWx5lKWi2BaYTCbYbDb09vaKoH2zcTqdAICJiQkAQFtbmxjeFY/HhbWkUGxllKgotgWcYbLcvPlGYAzl3r17AOY7FVssFuTzeWQyGVitVtVMUrHlUaKi2BYwjsL29Gvh/vL7/QCAkZER8XfOWSmVSvB4PE2/p0Kx2VCiotg2sLodWBtRMZlM8Hq9yGazcDqdcLvdsFqtKJVK0HVdWDIKxVZGiYpi22A2m0UX4bWKbTCu0t/fL/7udrtht9vhdrvX5J4KxWZCiYpi22AymcS8k7WwVICF4sZ9+/YBmBcVs9kMs9ksBEeh2MqolGLFtsFkMgn311pZKo899hh2796Njo4OAAuz63VdV+nEim2BEhXFtsFsNkPXdQBrZ6nYbDbs3btX/N1isaCrqwulUkkN6FJsC5SoKLYNTCsG1q+xo8vlEoO5VDW9YjugYiqKbYM8dXG9REUOzitLRbEdUKKi2DbQUjGbzWVWy1oiC5mqpldsB5T7S7FtoJCsd6fgXbt2iQQBhWKro0RFsW2g1bDeosJMMIViO6DcX4ptgwqYKxRrT0OiEg6H8eSTT2JgYABPPvkk5ubmql73n//zf8aRI0dw9OhR/IN/8A9EVbNCsZ7IMRWFQrE2NCQqZ86cwcmTJzE0NISTJ0/izJkzi64ZGxvDf/kv/wUXL17E1atXUSwW8fLLLzdyW4ViVShRUSjWnoZE5dy5c3j++ecBAM8//zx++MMfVr2uUCggnU6jUCgglUqhp6enkdsqFKuiWCwCwLplfikU25GGRGVqagrBYBAAEAwGMT09veia3t5e/It/8S+wc+dOBINBeL1efOYzn6n5mi+99BIGBwcxODiIUCjUyPIUijIKhQIAJSoKxVqyrKh8+tOfxtGjRxf9d+7cubpuMDc3h3PnzmF4eBjj4+NIJpP48z//85rXnz59GhcvXsTFixfR2dlZ/0+iUCwDA/RqrolCsXYse2R74403av5bV1cXJiYmEAwGMTExgUAgUPX79+zZIwTic5/7HN555x186UtfamDZCsXKOXToEGw2G/bs2bPRS1EotiwNub+efvppnD17FgBw9uxZPPPMM4uu2blzJ375y18ilUpB13W8+eabOHToUCO3VShWhaZp2Lt3rxrpq1CsIQ2JygsvvIDz589jYGAA58+fxwsvvAAAGB8fx6lTpwAAH/vYx/D5z38eDz30EI4dO4ZSqYTTp083vnKFQqFQbDo0nb3ANyGDg4O4ePHiRi9DoVAoFKhvT1YV9QqFQqFoGkpUFAqFQtE0lKgoFAqFomkoUVEoFApF01CiolAoFIqmoURFoVAoFE1DiYpCoVAomoYSFYVCoVA0jU1d/NjR0YHdu3ev+vtDoVBLN6Vs5fW38toBtf6NppXX38prB5Ze/8jICGZmZpb8/k0tKo3S6hX5rbz+Vl47oNa/0bTy+lt57UDj61fuL4VCoVA0DSUqCoVCoWgaW1pUWr0bciuvv5XXDqj1bzStvP5WXjvQ+Pq3dExFoVAoFOvLlrZUFAqFQrG+bElRef3113HgwAH09/fjzJkzG72cuti9ezeOHTuGEydOYHBwEAAQDofx5JNPYmBgAE8++STm5uY2eJULfOUrX0EgEMDRo0fF15Za77e//W309/fjwIED+PGPf7wRSy6j2vr/3b/7d+jt7cWJEydw4sQJvPrqq+LfNtP679+/j0996lM4dOgQjhw5gu985zsAWuf511p/Kzz/TCaDRx99FA888ACOHDmC3/u93wPQOs++1vqb+uz1LUahUND37t2r3759W89ms/rx48f1a9eubfSylmXXrl16KBQq+9q//Jf/Uv/2t7+t67quf/vb39b/1b/6VxuxtKr89Kc/1d999139yJEj4mu11nvt2jX9+PHjeiaT0e/cuaPv3btXLxQKG7JuUm39v/d7v6f/0R/90aJrN9v6x8fH9XfffVfXdV2PxWL6wMCAfu3atZZ5/rXW3wrPv1Qq6fF4XNd1Xc/lcvqjjz6q/+IXv2iZZ19r/c189lvOUrlw4QL6+/uxd+9eWCwWPPfcczh37txGL2tVnDt3Ds8//zwA4Pnnn8cPf/jDjV2QxOOPPw6/31/2tVrrPXfuHJ577jlYrVbs2bMH/f39uHDhwnovuYxq66/FZlt/MBjEQw89BABwu904dOgQxsbGWub511p/LTbT+jVNg8vlAgDk83nk83lomtYyz77W+muxmvVvOVEZGxvDjh07xN/7+vqW/MBuFjRNw2c+8xk8/PDDeOmllwAAU1NTCAaDAOZ/EaenpzdyictSa72t9J5897vfxfHjx/GVr3xFuDA28/pHRkZw6dIlfOxjH2vJ5y+vH2iN518sFnHixAkEAgE8+eSTLffsq60faN6z33KioldJZltKiTcLb7/9Nt577z289tpr+N73voe33npro5fUNFrlPfnGN76B27dv4/3330cwGMQ//+f/HMDmXX8ikcCzzz6LP/7jP4bH46l5Xausv1Wev9FoxPvvv4/R0VFcuHABV69erXntZls7UH39zXz2W05U+vr6cP/+ffH30dFR9PT0bOCK6oNrDAQC+Pt//+/jwoUL6OrqwsTEBABgYmICgUBgI5e4LLXW2yrvSVdXF4xGIwwGA37nd35HmPmbcf35fB7PPvssvvjFL+Jzn/scgNZ6/rXW3yrPHwDa2trwyU9+Eq+//npLPXtSuf5mPfstJyqPPPIIhoaGMDw8jFwuh5dffhlPP/30Ri9rSZLJJOLxuPjzX//1X+Po0aN4+umncfbsWQDA2bNn8cwzz2zkMpel1nqffvppvPzyy8hmsxgeHsbQ0BAeffTRjVxqVbgpAMArr7wiMsM22/p1XcdXv/pVHDp0CN/61rfE11vl+ddafys8/1AohEgkAgBIp9N44403cPDgwZZ59rXW39Rn38zMgs3Cj370I31gYEDfu3ev/vu///sbvZxluX37tn78+HH9+PHj+uHDh8WaZ2Zm9CeeeELv7+/Xn3jiCX12dnaDV7rAc889p3d3d+smk0nv7e3Vv//97y+53t///d/X9+7dq+/fv19/9dVXN3Dl81Rb/5e+9CX96NGj+rFjx/Tf+q3f0sfHx8X1m2n9P/vZz3QA+rFjx/QHHnhAf+CBB/Qf/ehHLfP8a62/FZ7/5cuX9RMnTujHjh3Tjxw5ov/7f//vdV1f+nd1s6xd12uvv5nPXlXUKxQKhaJpbDn3l0KhUCg2DiUqCoVCoWgaSlQUCoVC0TSUqCgUCoWiaShRUSgUCkXTUKKiUCgUiqahREWhUCgUTUOJikKhUCiaxv8DxNK0pzQ0/GcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cca6ff23458442ee9a6d6241c78cdc0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "291263746fa04c1aaf018130f14a54d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a466118b06147b7a4652d59ea99d03f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a94a2163e49a47f4adeebeaddea92a80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cd7977d675e4a6395610e8516ecd0dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48d93fceaf0649d4a2af1c85cd75262c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9537d6f498246da9ddfb753bed40ae5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c534ef9280814d90b8f941f4d83aed4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64ccdfe4c2914ccbb9140b556e58092c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae8115d6aeb24c5eba934be412b96383",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16a09aae3e6c41dd82595e9953124102",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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sdRLXL6NGUD9QRffu3WPGjx/P2NraMpqamoyOjg7j4ODALFy4kHn37l2x++T3xzx48CBz7tw5pnPnzoy2tjZjYGDAfPHFF8zLly8Vyj9//pyZP38+0759e8bU1JQRiUSMjY0N4+7uzvj6+hb7HLdu3WKGDx/OWFhYMBoaGoypqSnj6OjIzJ8/n3nwQPH12tjYFOkbWZLGjRszABhjY2MmJyenxHI5OTnM7t27mS5dujD6+vqMSCRiGjZsyLi6ujLbt28v0qc0JyeHWb16NWNnZ8e+vm+//ZbJzs6uUD9QhmEY2f37zO7Fi5nmtraMSCRiLCwsmK+++opJTk4u8bUePHiQcXR0ZLS1tRkTExPm888/Z548ecJ4eHgwABhvb2+F8h8+fGBGjRrFmJubM3w+nwHAeHh4sNt9fX0ZFxcXxtDQkNHV1WWcnJyYU6dOMd7e3kXKqpqqeI/zGKb89+Y6dOiAhw8fVk8mJ9UqOTEbI7v+hXdvU+UreMAcj44YMqZ5iftkZeRi/9YAnDn8kl1nZqGNA1eGolFzo+oOmVP5rTBrhYRC71mTDtzFQapW4c/iDvR3raiqeI9THWgdkJcnw6IxV9jkyefzsPYXl1KTJwBo6Whg9vedsHxbd4jE8n54cTGZmNDrnyK3gAkhpK6hBFoH7F19D37XChoCLPqxKzq7lj26TD7XwXb4Yb8bNLXkVeaJsVmYMegs4mIyqjxWQghRF5RAa7kXj+Oxf0tBU/fBo5ui9+dFh98qi2On+thwsBebRGPepWPWZ+eQkVa0BS8hhNQFlEBrMalUBo9Z1yGVyqu5LW31MNujk9LHa9XeHN/v6Qm+QN4ZPfhRPJZPukpdXAghdRIl0Frsz1+e4tnDWADy8cXX/OICPr/scU1L82kPS3yztjO7fO3sW4UrXEIIqSsogdZSaSk52Lv2Prv8+fgWsLY3rJJj9x/eBEMnFLRe27nyLu56R1bJsQkhRF1QAq2l/tj5CCmJ8oGcdfVFmLak6KwNlTF9aXs4tDcDAMhkDJZN/BfJidll7EUIIbUHJdBaKCk+C4d2PmKXpyxqCw2RoOQdlCDU4GPlrp4wMBYDkHdvWTe3box/SQghACXQWmn/lgBkpMkHgTc00UT/L5tUy/OYmGtjwfou7PKlv17jwp+vquW5CCFE1VACrWXSUnJw/Ldn7PLMbztAIKi+P3PXXtboN7wxu7xm9g3EvEurtucjhBBVQQm0ljl58Dky0wuuPl0H21X7c8769lPUt9IFAKSnSrBi6jXIZNS1hRBSu1ECrUXy8mQ4sucJuzxqZqsyJxCuCtq6Gli62Qn5T3XXOxJH9z4pfSdCCFFzlEBrkWun3yImQn77VCQWYODIpjX23K061MOI6a3Y5R0r/BAVllpjz08IITWNEmgtcnjPY/Zxv2GNIdas2elex891hG1TQwBAdlYe1sy+QaMUEbUwceJE8Hi8Yif05oqtrS07aTVRTTShdi3xJjgRgXdiAMhHHRrz9Sc1HoOGSIAF67tg3peXwDDA7X8jcOHPVxg0qlmNx1LtHn8PZEZwHUXJtK0BxzWVPoytrS3Cw8OL3VavXj28f/9eYZ1EIsGaNWtw9OhRJCYmol27dti6dSvatSvaD9nLywt9+vTB+fPnMXDgwErHqm6cnZ3h4+NDXzLVGCXQWuKf35+zj1u0MYOxmRYncbRoY4bPxjbH6cMvAAAbFtxCtz7WMDThJp5qkxkB6NhyHUXJMsKq7FAGBgb45ptviqzX1dUtsm7ZsmXYvn073N3dYWVlhcOHD8PFxQUvXryAhYUFWy49PR3Tpk3D2LFjVSJ5btiwAcuWLYOlpSXXobCuXbvGdQikDJRAawGJRIqzRwomvR45o1UppavfpAVt4Xs1AnExmUhOyMbmpb5Y79mL05iI8gwNDbFq1aoyyzEMg19//RWTJk3CgQMHAABDhw6Fs7MzDh8+jCVLlrBlly5diuzsbOzcubO6wq4QCwsLhQSvCuztKz5rEqlZVAdaC/hcCENiXBYAQEtHiI7O3H6L1tbVwNxVBQPOn/7jBe5ef1fKHqQ2iIuLQ2ZmJjp27Miuy39c+DbwzZs38fPPP2Pv3r0wNjZW6rny6wdTU1OxYMEC2NraQkNDQyHRv3jxAhMnTkTDhg0hFotRr149jB49Gi9fvixyvNLqQO/du4dhw4ahfv36EIlEaNiwIWbMmIHo6OhiY0tMTMR3332HVq1aQVtbGwYGBnB0dMSyZcuQkZGBsLAw8Hg8+PjIR+7i8Xjsj7Ozc5HX+LGcnBxs3LgRn4waBe1u3aDv7Izu3bvjr7/+KlI2/7kmTpyIsLAwjBw5EqamptDU1ESHDh1w/vz50k/0R06fPo2xY8eiadOm0NHRga6uLtq3b49du3ZBJpMVKZ9/Xt++fYvdu3fjk08+gZaWlsLrTExMxPLly9GiRQtoaWnBwMAAbm5u+Pfff4scLyUlBZs3b4arqyusrKwgEolgZmaGIUOG4O7duxV6LVWBrkBrgcK3b3t/bl+tAyeUV2dXK/QcYAOfi/IPTo9Z3jjzaDQ7nyhRHzk5OThy5AgiIiKgo6ODTz75BD169IBAoDg8pJmZGbS0tODv78+ue/jwIQDAxsYGAJCVlYWpU6fC3d0d7u7ulYpLIpHA1dUViYmJ6NOnD/T19WFnJ+/3fPnyZXzxxRfIzc3F4MGD0bhxY0RGRuKff/7BhQsX4O3tXWy97McOHjyIadOmQSwWY8iQIWjYsCFev34NT09PnDt3Dnfv3oW1tTVbPjQ0FC4uLggPD0f79u0xa9YsyGQyvHr1Ctu3b8fMmTNhaGgIDw8P/P777wgPD4eHhwe7f1mNhiQSCfr27QsfHx80t7XF18OHIzM7G3/fvIkRI0bg0aNH+OGHH4rsFx4ejo4dO6JRo0YYN24cEhMTcfz4cXz22We4evUqXFxcynXOly1bBj6fj06dOsHS0hIpKSm4fv065s2bhwcPHuDw4cPF7jdv3jzcunULAwcOxIABA9j/nfDwcDg7OyMsLAzdu3dHv379kJGRgfPnz6Nfv3749ddfMW3aNPY4wcHB+O6779CjRw8MHDgQRkZGiIiIwNmzZ3Hp0iWcO3cO/fr1K9drqQr0aabmEuOy4PtvQWOWYZNbchiNoq9WdIT/7Rikp0oQGZoKz03+lZqPlHDj/fv3GDdunMI6Ozs7HDx4ED179mTX8Xg8TJ8+Hbt27UJKSgosLS1x+PBh6OnpYcyYMQCAFStWIDExEXv37q10XDExMWjZsiV8fHygo6PDrk9KSsKoUaOgra2NmzdvomXLgvdEUFAQOnXqhKlTpyIgoPRp+F69eoUZM2bA1tYWPj4+CvWj169fR+/evTFv3jycOnWKXT927FiEh4fjhx9+wPLlyxWOFx8fD11dXWhqamLVqlW4ceMGwsPDy3V7PN/WrVvh4+OD/v374+z330MolH+Ee+zdi44dO2LDhg0YNGgQunbtqrDfjRs3sGrVKoVkPXr0aPTr1w+bN28udwK9cOFCkVvLMpkMkyZNwh9//IHZs2ejU6ei7/GAgAAEBgayX3DyTZgwAeHh4Th27BhGjhzJrk9OToazszPmzp2LIUOGoF69egCAFi1aIDo6GqampgrHiYyMRMeOHTF//vwaTaDcX6qQSvn3nxB2wuwGNnqwsNbjOKICxmZamLKo4Fv+vk3+CH2ZxGFEpKImTZqEa9eu4f3798jIyMDTp08xY8YMhIWFoX///nj8+LFC+R9//BFLly7F/fv3sX//fjg4OODatWuwtLTEvXv3sGPHDuzcuROmpqZYvXo1LCwsIBQK0b59e/j6+lY4vq1btyokTwD4448/kJycjNWrVyskTwBwcHDAtGnTEBgYiOfPn6M0P//8M3Jzc7Fz584ijYtcXV0xZMgQnDt3Dmlp8r7X/v7+uHPnDtq0aYOlS5cWOV7+rdPKOHDgAHg8HrZt28YmTwAwNzfHypUrAQCenp5F9rOxscGKFSsU1vXt2xfW1ta4f/9+kfIlKa5els/nY968eQCAK1euFLvfkiVLiiTPx48fw8fHB+7u7grJE5DXu69evRrZ2dk4efIku97AwKBI8gQAKysrDBs2DC9evEBERM21jqcrUDV38fhr9vHAEdUzaHxlDBjRBP/+E4LgR/HIy5VhzZwbOHDl8xoZIYlUXuErFgBo1aoVfvnlF+jq6mLr1q1YtWqVwhWYWCzGhg0bsGHDBoX9JBIJJk+ejAEDBmDMmDHYsWMHe0Xk5OSE9evXo1+/fggJCWGvNsqiqamJTz4p2l3Lz88PgPwDuriru1ev5BMeBAcHF0mwxR3Hx8cHDx48KLI9NjYWUqkUr169Qvv27dk6uL59+4LPr/prk7S0NISEhMDS0hLNmzcH/rs9ns/V1RUAEBgYWGTfNm3aFLnlDgANGzZkX2d5JCQkYPPmzbh48SLevn2LjIwMhe1RUVHF7le4Xjxf/vOmpKQU+3eKi4sDIP87Febr64udO3fCz88PsbGxkEgkRWIofFu9OlECVWMx79Lgf7ugIUPvoarXao/P52Hems74augFyKQM7t+IwrmjLzFkbHOuQyOVMHPmTGzduhU3b94sV/nVq1cjKiqKbRiyefNmuLm5sR+czZo1g62tLfbu3Ys1a8rXf9Xc3LzYL2IJCQkAgH379pW6f3p6eqnb84+zefPmch0nOTkZAKqtK0xKSgoAlNhaOH99fhyFGRoaFruPUCgstvFPcZKTk/Hpp58iNDQUHTt2xPjx42FsbAyhUIjk5GTs3LkTOTk5xe5bv379Iuvyz6+Xlxe8vLxKfN7Cf6dTp05h2LBh0NTURO/evWFvbw8dHR3w+XzcuHEDPj4+JcZQHSiBqrErf4ewj22bGsLIVDX7Wtq3MMYXE1vg7/3yW2Y/Lr6NHgNsYWhcudtZhDvm5uYAUOQKpDiBgYHYtGkTfvnlF1haWiI1NRXR0dFsvSgAWFtbw9TUFEFBQeWOoaS7GAYGBgDkV6DFXaGWV/5xUlJSoK+vX2b5/CRV0lVYZeXH8/HgFfliYmIUylU1T09PhIaGwsPDo8gVo5+fX6ldkor7W+XHuXPnTsydO7dcMaxcuRIikQgPHz5EixYtFLbNmDGDbdlcU6gOVI1d/Kvg9u3g0TU37q0yxs9xhJmFNgAgOSEbO1aU/7YRUT35t98aNWpUarm8vDxMnjwZLi4umDJlisK2j68UsrOzqyS2zp3lXahu3bpVo8fJL3/lypVyXdXl31KVSqXlOr6enh7s7e0RFRWF169fF9nu7e0NAOVqXayMkBD5F/biWk8rk7iU+TuFhISgZcuWRZKnTCbD7du3KxxDZVECVVPR4akI8o8FIB+6z2VQ9U9bVhlaOhqY/X1BPcgJzyAE+sVwGBEpS1BQEBITE4usDw8Px+zZswHIW52WZsOGDQgJCVG4naqvrw9LS0tcvnwZeXl5AOQfwGlpaXBwcKh03JMmTWIboRTXQEYmk+HGjRtlHmf27NnQ0NDA/Pnz2XrTwiQSicKHf/v27dG1a1c8evQIP/74Y5HyCQkJCl8STExMAKBCjV4mT54MhmGwePFihcQbHx+PtWvXsmWqQ34Xm4/PXWBgYJE67/Lo0KEDunfvjn/++YcdeONjT58+RWxsrEIMr1+/VuiDyzAMVq9eXWajsOpAt3DV1LWzoezjRs2MoGcg5jCa8unayxpd3Kzgdy0SALD6a2+cuDcCGhpFGzcQ7p04cQIbN26Ei4sL7OzsoKenhzdv3uDChQvIzs7GgAEDsGjRohL3DwoKwrp167Bt2za2H2i+JUuWYN68eejevTs6deqEo0ePQldXF19//XWl4zYxMcHff/+NoUOHonPnznBzc4ODgwP4fD4iIiLg5+dXJJkVp3nz5jhw4AAmT54MBwcH9OvXD02bNkVubi4iIiJw69YtmJmZ4cWLF+w+R44cgbOzM7799lucPHkSzs7OYBgGr1+/xr///osXL16wicjNzQ0nTpzAF198gQEDBkBLSws2NjZFugwVtmjRIly6dAlnzpyB49OnGODkhMzsbJzw8UFsbCyWLFmCbt26VfocFmf8+PHYvHkzvvnmG3h7e6NJkyZ4/fo1zp8/jy+++ALHjx+v8DH/97//wdXVFVOmTMGuXbvQqVMnGBoaIjIyEk+ePMGzZ8/g5+fHVhnMnz8fM2fORNu2beHu7g4NDQ34+vri+fPnGDx4MM6dO1fVL7tUlEDV1LUzb9jHfYc15jCSipn9fUcE3IlBTpYUr58l4vCux5i8sHpuOZHKcXFxwcuXLxEYGAg/Pz9kZGTA0NAQ3bp1w7hx4zBu3LgS6yGlUikmT56MTp064auvviqyfc6cOUhNTcUvv/yCgIAAODo6Yvv27eVugVsWNzc3PHnyBFu2bMGVK1dw69YtiEQiNGjQAK6uruUexGHs2LFwdHTE1q1b4e3tjX///Rc6Ojpo0KABhg0bhhEjRiiUt7OzQ0BAADZt2oTTp09jz5490NTUhK2tLRYuXMgmAgCYOnUqwsPD8eeff2LTpk3Iy8tDz549S02gIpEIXl5e2LZtG/63fz92//UXhAIBHNu2xY4dOzBq1CjlTlg5NGjQALdu3cKyZctw+/ZtXLlyBc2bN8dPP/2EXr16KZVArays4O/vj927d+PkyZM4evQopFIp6tevj5YtW2LOnDlo3bo1W37GjBkQi8XYsWMHDh06BC0tLXTv3h0HDx7EyZMnazyB8pgKTAXQoUMHdmQRwp3EuCz0bHgAMpn8T3f8znDOBo9XxgnPIPz2o3y0Gk0tIc49GY0GNmU30qhJwcHBRepZFKjTbCwJhd6zJh24iUfFjRw5EsePH0d0dLTKjYlbosKfxR3o71pRZb7Hy4GuQNWQz8VQNnk2sNFTq+QJAEMntMDVM2/x9kUSsrPy8MP8W9jzD/czclRIFUwVRlTHq1evIBaLYWZmxnUoRI1QIyI1dPX0W/ax2xDVbjxUHKEGH/PWFAw2730+FNfOvC1lD0Kqx86dO+Hu7o7AwEB89tlnCqP7EFIWSqBqJiszF37XCmY2cfus9G4EqqplWzOFkZPWf+ODjHRJKXsQUvV27NiBu3fvYvLkyfj111+5DoeoGUqgauaedyRysuXN141MNWGpYnWHFTFlcTt2MIUPURnYu6b8Y3ISUhVCQ0MRFRWF/fv3lzhaDyEloQSqZm5eKphXsbOrFYeRVJ6egRgzvi1o/HB492MEP4rjMCJCCCk/SqBqhGEY3LxckEDdhqjn7dvC3IbYoU0X+TiZMimDNbNvQCot39ichBDCJUqgaiQkKBExEfKpkzQ0+HBoZ17GHqqPx+Nh7qpO0NCQ/ys+uf+BHTOXEEJUGSVQNeJzKYx93LyNKYQatePP17CRAUbMaMUub/v2DuI/ZHIYESGElK12fALXEbcK375V09a3JRk1szUa2MgnA09PlWDT4pofGJoQQiqCEqiaSEvJQeCdgsHXu7o15DCaqicSCzBvdSd2+cKfr+BzMYy7gAghpAyUQNXEA58oSKXy0YfMG+io7NyfldHOqYHCwBCrZnkjLaXmJsclhJCKoASqJu4UGjyhY8/qmfFeFXy14lMYmsj7hsbGZGDzUl+OIyKEkOJRAlUTdwsl0O59rTmMpHrpG2lijkfBrdyTB57jzlUVHrSdEFJnUQJVAzHv0hD6KhkAwBfw0KpD1Uz5pKp69LdR+JLw/UxvGuaPEKJyKIGqgbvXC64+7ZoaQiSu/RNQz1nVCXqGIgBATEQatn/nx3FEdVvv3r3B4/Hg4+NTYpmZM2eCx+Nh3759NRgZIdyhBKoG7lwtSKDd+tTe27eFGZlq4esVHdnlYz8/xcNbURxGVLcFBARAIBCgffv2JZa5d+8eAPm8wYTUBZRAVZxMxuDu9Uh2ubOLeo9/WxGuQ+wUXu+KadeQlZnLYUR105s3b5CYmAgHBwfo6uoWWyYzMxPPnj2DWCxGq1atii1DSG1DCVTFvXqagMS4LACAWEuIRi2MOY6o5vB4PMxb0xk6ehoAgHdvU7F71T2Oo6p7Hj58CADo1KlTiWX8/f2Rl5cHR0dHaGho1FRohHCKEqiK87tW0ALVoa0Z+Hweh9HUPNP62pi5vOCW4B87H8H/djSHEdU95Umgd+/eBUC3b0ndQtOvqzi/Qrdvu/evG/WfH+s7rDFuXAyD/+0YMAywbJIXTvmPgq6+iOvQ6oT8BHr8+HF4e3sXW4bqP0ldRAlUheVk58H/VsHV1qfda+8ACqXh8XhY+ENXTB90DumpEkSHp2HDgptY79mL69BqPYZhEBgYCADw8vIqszwlUFKXUAJVYY/uvkd2Vh4AwMBYjHqWxTfgqAvMLHQwd3Un/DD/FgDg9B8v4DzQDr2H2td4LA6iPTX+nMoKksyu1P6vXr1CSkoKunTpgjt37hRbJikpCcbGxtDW1kbLli0Vtv3000/YvHkzYmJi4ODggB07dqB79+6ViokQVUF1oCrMr1D3lbZdLDiMRDW4DLKD6+CCsXI9Zl1HXEwGhxHVfvm3b9u2bVtimYCAAABAmzZtIBAU9FE+fvw45s2bh2+//RaBgYHo2rUr+vfvj4gIGlmK1A6UQFVY4fFve/S34TAS1THboyPM6msDAFISc7ByxnUwDMNxVLVXfgJt165diWXyE+jHt2+3bduGiRMnYtq0aWjRogV2794NCwsL/Pzzz9UXMCE1iG7hqqiUpGw8D4hll+kKVE7PQIzFm5ywZLy8Pu7W5XAc/+0ZRs5oXWMxVPa2qDopTwLNryMtnEAlEgn8/f2xaNEihbJ9+vQp8VYwIeqGrkBVVH6LUwCob6VDLU4LadvFAu6TC+raNi2+jbBXSRxGVDvJZDI8evQIIpEIDg4OJZYr7go0Pj4eUqkU9eopjttcr149vH//vnoCJqSGUQJVUYWHrWvbla4+PzZ5QVvYNjUEAORkS7F0ohdyc6XcBlXLBAcHIz09HQ4ODhCJiv8Cl56ejtevX0NXVxfNmjUrsp3HU+y3zDBMkXWEqCtKoCrq4c2CBNrVrW72/yyNSCzAsi3dINSQ/ws/exiLPTRKUZXy9/cHUPrt28ePH0Mmk6Fdu3bg8ws+TkxNTSEQCIpcbcbGxha5KiVEXVECVUFpKTkIfhTPLrfqYM5hNKrLvoUxJi8oaB3quTkAvl7UwrOqjB8/HgzDwNPTs8QyTk5OYBimyCwtIpEI7du3L9J31MvLC127dq2WeAmpaZRAVVDgnRjIZPIKUPMGVP9ZGvfJLdG+W8Et7mUTvRD3nrq2qIIFCxbg999/h6enJ4KDgzFv3jxER0dj5syZXIdGSJWgBKqCHtwsGH2onRPVf5aGz+dh6eZuMDbTAgAkxmVh+aSr7BcQwp0RI0Zgx44dWLduHdq0aYPbt2/j4sWLsLGhLlmkdqAEqoIKNyCqS9OXKcvIVAtLNzshv22K37V32L8lgNugCADgq6++QlhYGHJycuDv748ePXpwHRIhVYYSqIrJSJcgyL+g/2drqv8sl3ZODTByZkFf0F0edxHoF8NhRISQ2o4SqIp55PceUqn89qNZfW3oG2lyHJH6mDDXES3bmQEAZFIGi8ZeQUpSNsdREUJqK0qgKqZw9xXq/1kxAiEf323vzja6ev8uHd9Pp6H+CCHVgxKoinlQaPqyTs5U/1lR5g10sXBDQTeJq2fe4ujeJxxGRAiprSiBqpCszFw8ffCBXW7dkeo/ldGtjzWGjC0YFWfTktt4dJfqQwkhVYsSqAp5fO8D8nJlAABjcy0YmWhxHJH6mrGsA5q0MgEASPMYLBh1GYlxWRxHRQipTSiBqpDC9Z9tOtXnMBL1JxIL8P3untAzkNeHfojKwJLx/0IqlZX7GFR3SkjtVFXvbUqgKqRw/89OLpYcRlI71LfSxdIt3dhlv2vvsHfN/XLtKxAIkJubW12hEUI4lJubqzD5u7IogaqInOw8PL5XUP/pSFegVaKTsxXGfFXQP/TXDQ9x81JYmfvp6ekhNTW1GiMjhHAlNTUVenp6lT4OJVAV8fTBB0hy5NNxGZpowsRcm+OIao9xcx3RrlCXoKUTvBAVVnpyNDY2RlJSEuLj4yGRSOh2LiFqjmEYSCQSxMfHIykpCcbGxpU+prAK4iJVoPD4t590pOmeqpJAwMfybd0x6/PziH+fidTkHHwz8hKO3HCHWLP4t4BYLIa1tTUSExMRFhYGqVSN5xrNKJjZB7HB3MVBqlZ8ob9rMP1dy0MgEEBPTw/W1tYQi8WVPh4lUBWhMP6tK/X/rGqGJppYuasnFoy+DGkeg+cBcVg/7yZW/+JS4gTPYrEYFhYWsLBQ8wEt/tey4PFoupKuNVoW+rvSHRJO0C1cFSCRSPHIr2DiYar/rB4t25phxrIO7PLJg89x7JenHEZECFFnlEBVQJB/LLKz8gAA+oZimFvocBxR7fX5+OZw+6wRu7xx4S08KNR9iBBCyosSqAoo3P+zFc2+Uq14PB7mr+uMpq0LBln4ZsQlRIdTi1tCSMVQAlUBhce/7eLWkMNI6gaxphAee51haCKf6SY5IRtzh19EVib1+ySElB8lUI7l5ckQeKdgnFbHTtQCtyaYW+jAY09PCIXyt0Dwo3ispJlbCCEVQAmUY88D45CZLr/y0dUXob6VLscR1R2tOtTD1993ZJcv/fUaB7YGchgRIUSdUALlWOH6z5ZtzUrsUkGqx6BRTTFwZFN2eft3d8o1UhEhhFAC5Vjh/p9delH/Ty58vfJTtvEWwwALR1/Bi8fxZexFCKnrKIFySCqVwf92Qf1nm85q3mFfTWmI5DO3mDeQdx/KzMjFV5+fQ2x0OseREUJUGSVQDr18Eo/0VAkAQFtHA5Y2lR/cmCjHyFQL6/a5QltXA4B8+rOvPr+AjHQJx5ERQlQVJVAOFR7/trmjKdV/csyuqRFW7uoJvkD+dwh+FIfFYys2hyghpO6gBMqhwg2IqP5TNXTo3gDzVndil30uhmHT4tscRkQIUVWUQDkikzHw9y24Am3bheo/VcWAEU0xYroDu3xkzxMc2fOYw4gIIaqIEihHXj9LQEpiDgBAU0sIa3sDjiMihU1e2A49+tuwyz8uuo3r50I5jIgQomoogXKkcPeVpq1MqP5TxfD5PCzZ5ISWbc0AyO8YLBxzGf63o8vYkxBSV1AC5UjhBkRU/6maxJpCrP7ZBQ2s5a2jJdlSfDX0PPURJYQAoATKCYZhFK5k2js14DAaUhpDE01sPNgLxmZaAID0FAmmDzyDiDcpHEdGCOEaJVAOvAlOQmJcFgBAJBbApokhtwGRUllY62HDgV7Q0ZP3EU2IzcK0AWcQF5PBcWSEEC5RAuVA4frPxg7G4POp/lPVNWpuhLW/ukIkFgAAIkNTMbX/GfaLECGk7qEEyoGHhes/Xan+U120/rSewkALIc8TMbX/GSQnZnMcGSGEC5RAaxjDMHhQ6Aq0Qzeq/1QnnV2tsHRzN/D+e+e8fBKP6QPOIDU5h9vACCE1jhJoDQt/nYz495kAAA0RH3bNjTiOiFSU62A7LNrghPyeR0EBcZgx6Cw7rjEhpG6gBFrD7hcavq9RcyMIBPQnUEd9vrDHN+u6sMtP7n/AjEFnkZZCV6KE1BX06V3D7t8oSKBdezXkMBJSWQO+bII5qzqyy4/uvseUvqeRnEANiwipCyiB1iCGYXDfpyCBduxhyWE0pCoMGdMcX39fkESDAuIwqfdpxH/I5DAqQkhNoARag96+SELCfx+sGmIBGrUw5jgiUhU+H9cc89d1ZutEXz1LwES3f/AhiibkJqQ2owRagwpffTZubkT9P2uRASOaYkmh1rmhr5Ixzvkkwl8ncxoXIaT6UAKtQQ8KJdCuvan+s7bp9VkjrNjRE4L/+olGhadhrPNJPPP/wHFkhJDqQAm0hjAMgweFWuB27EkDKNRGPfrbYPXPLuyIRYlxWZjY6xR8vSI4jowQUtUogdaQkKBEhfFvbZsachsQqTadXKyw+XAf6BmIAABZGXmY9dl5nD/2kuPICCFViRJoDbnvE8k+bkLj39Z6LduaYfuf/WBWXxsAIM2TYekEL/yx6xG3gRFCqgwl0BpC/T/rHpvGhtj5V3/YNDFg1/246Da2Lr8DmYzhMDJCSFWgBFoDZDLF8W87OlP9Z11hZqGD7cf6waG9GbvuwNYALJ/kBYlEymFkhJDKogRaA149TUBKonyIN7GWEDaNDcrYg9QmegZi/Ph7b3RxK/jidP7YK8waco7GzyVEjVECrQGF6z+btTIBj0f1n3WNWFMIjz3OGDiyKbvu7vVIjHM5idhoGnCBEHVECbQGFK7/7NbXmsNICJcEQj7mremEifPbsOtePU3A6B5/I+R5IneBEUKUQgm0mkmlMjwsPP9nd5r/sy7j8XgY89UnWLSxKzvgQkxEOsY6/w3/29Fl7E0IUSWUQKvZi8fxSEuR13NpaQthZafPcUREFfR1b4y1+1yhqSUEAKQlSzC1/xn8+08Ix5ERQsqLEmg1u3u9oP6zuaMp1X8S1qfdLbHtWF8YmWoCACQ5UiwYdRlH9jzmODJCSHlQAq1md64WDOHWc4Atd4EQldTEwQQ7/+rP3plgGGDDglvYssyX+ooSouIogVajrMxchXqtjj1p/k9SlEVDPez4sx9ati3oK3pwWyCWTvgXkhzqK0qIqqIEWo0e3opGrkQGADAy1YSZhQ7HERFVZWCsiR8P9YZToVl6Lh5/jRmDzyItJYfDyAghJaEEWo0K375t19WCw0iIOtDUEmLl7p4YPKYZu+7+jSiMd/kHcTEZHEZGCCkOJdBq5Hf1HfvYZZAdh5EQdSEQ8DHHoyOmLGrLrnv1LAET3P5BzLs0DiMjhHyMEmg1iY1Ox+sgeed4Hh/4pFM9jiMi6oLH42HkjNZYstkJ/P/6ioaHpGCC6z949zaF4+gIIfkogVaTO4WuPhvaGUBLW4PDaIg66v25Pb7f3RNCofxtGhWehvFu/yD0ZRLHkRFCAEqg1aZwAu3ai2ZfIcpx6m2NNb+6QCQWAABiozIw3u0fGvqPEBVACbQayGQM/K4VJNCe/W25C4aovU97WGK9pxs7alFibBam9D2NsFd0JUoIlyiBVoOXj+ORGJcFABBrCtCohTHHERF116ZzfWz8vRe0dORJNP5DJib1OU11ooRwiBJoNfAt1H2leRsz8Pk0fB+pPId25vjB0w1irf9u50ZnYHKf04gOT+U4MkLqJkqg1aBw/afrIFvuAiG1TqsO9bD2V1e2TjQ6Ig2T+pymOUUJ4QAl0CqWmZGLAN9Cw/c5UwMiUrXadrHAqp+dIdSQv30jQ1MxfeBZpCbTiEWE1CRKoFXMv/DwfWZaMK2nzXFEpDb6tLslvt/dk51T9HVQImZ/cQHZWXkcR0ZI3UEJtIrduhLOPu7QjSbPJtWni1tDLNzYlV32vx2NxWOvIC9PxmFUhNQdlECrEMMw8LkYxi73+oyG7yPVq/fn9pi+rD27fP1cKNbMvgGGoanQCKlulECr0JvgJESGyltECjX4aP0pDd9Hqt/wKQ74cqoDu3zywHP8vO4BhxERUjdQAq1Cha8+mzuaQkMk4C4YUqdMWdwOvYc2Ypf3rr2P88dechgRIbUfJdAqVDiB9h5qz10gpM7h83lYsL4r2jkVTJu3Yto1BPrFcBgVIbUbJdAqkpyYjcA7BR9WXdyo+wqpWUINPlbu6glrewMAQK5EhjnuFxAZZ8JxZITUTpRAq8jtK+GQyeQNNywa6sLIRIvjiEhdpKsvwrrfXGFgJAYAJMVnY9b2GUjNoP9HQqoaJdAqcv1sKPu4ez8bDiMhdZ2FtR5W/+ICDZH87f022gILf5qE3Dx6uxNSlegdVQVysvMU+n9S/SfhmkM7cyzaUNBH9E5QC2w4Ooy6txBShSiBVgG/a++QmZ4LANA3EsOmsQHHERECuA5phHFzPmGXj3t3x+HdjzmMiJDahRJoFbh25i372Km3NXg8mn2FqIZxcxwxoPNDdnnzktu4/W94KXsQQsqLEmgl5eXJ4H2+oP6z37DGHEZDiCIej4d1k4/C0V7+PyqTAQtHX6bJuAmpApRAKynwTgyS4rMBAFraQjR3NOU4IkIUiUV52DnHE/WM5EkzPTUXXw+9QLO3EFJJlEAr6d9/QtjHHXo0oMmziUoyM0zF7nn7IBbLB5oPe52MRTTwPCGVQgm0EqRSGf795w27PGhkUw6jIaR0DrbvsHJBwVy1vv9GYOtyXw4jIkS9UQKthIe3ohH/PhMAoKklhGPn+hxHREjpejunYuKYVHb5j52PcerQcw4jIkR9UQKthEt/vWYfd3KxhEBAp5OovukT09DTKYtdXv31DRozlxAl0Ce+knJzpfA6VXD79rOxzTmMhpDy4/MBj+VJsLeT913Olcgwd9hFREekcRwZIeqFEqiS7nlHIjnhv9a3OhpwaG/OcUSElJ+2FoPN6xJgaCAFACTGZWGO+wVkZuRyHBkh6oMSqJIuHHvFPnbq3ZBa3xK106C+FD94JEIgkA/v9+JxPFZMvUbD/RFSTpRAlZCRJlG4ffv5eLp9S9RTO0cJFs9NZpevnAzBz+sfcBcQIWqEEqgS/j31BlmZeQAAI1NNNG1F8y0S9fX5oEwM+zydXd675r7CF0RCSPEogSrhzOEX7OMBI5rS2LdE7X0zKwUd2mazy8smeeHF43gOIyJE9VECraCosFQ88IlilwePpsETiPoTCoH13yfCsoG8EVF2Zh5mf3EeCbGZHEdGiOqiBFpBZ44UXH3atzCCibk2h9EQUnUM9BlsWZsIbW358H4x79LxzYhLkEikHEdGiGqiBFoBUqkMJw8WjNriPqklh9EQUvXsbPOw9rtE8HjylrgBvjFYN9eHWuYSUgxKoBVw81I43r+TN7YQiQXoOcCW24AIqQZOnXPw1dSC4f5OHniOI3uecBgRIaqJEmgFHP/tGfvYdbAdRGIBh9EQUn3GjkhHv14F9Z8/LrpFLXMJ+Qgl0HKKDE3F7Svh7PLoWa05jIaQ6sXjAcsXJsGhhQQAwDDAkvFX4H87uow9Cak7KIGW01/7niG/GqhRcyNYWOtxGxAh1UwsArasS4CVpbzPsyRHhq+HnsfrZwkcR0aIaqAEWg4Z6RKc8Axil8d8TVefpG4wMpRh58Z4GBvJW+KmpUgwfdBZxLyjgecJoQRaDqd+D0Zqcg4AQM9ABKfe1hxHREjNsWwgxfYNCWz3ltjoDEwfeJb6iJI6jxJoGfLyZDi8+zG7PGJaK5r3k9Q5zZrkYuOqRAiF8nqMty+SMLXfGSTFZ5WxJyG1F2WCMlw7/RaRofIm/RpiPj6jgeNJHdWxfQ5WLU8Cny9Poq+eJWBq/zNITswuY09CaidKoKVgGAa//fiQXe4/rAk0tYQcRkQIt3o5Z2HlkiR2oIUXj+MxfcAZtoqDkLqEEmgprp15yw6ozefzMHbOJxxHRAj3+vfOwneLktnloIA4TB94FskJdDuX1C2UQEsgkzHYu+Y+u9zr80YwMtHiMCJCVMegfplYtiCJXX764APGufxDrXNJnUIJtARep97g1X/93fgCHqYtac9xRISols8HZmLpNwW3c9++SMLYnifx9kVSGXsSUjtQAi2GRCLFru/vsst9htrD0ESTw4gIUU1DB2dizbdJbOvc95HpGOdyEk/uv+c4MkKqHyXQYvz5y1OEvU4GAAiFfExd3I7bgAhRYb1ds7B1fQK0NOX9RJMTsjGp9ylcPP6K48gIqV6UQD+SnJCFn9c9YJe/nOYAA2O6+iSkNJ065GDP1ngY6MtHLMrOkmLxuH+xZZkv8vJkHEdHSPWgBPqRXavusU3ydQ1EGDubWt4SUh4OzXPx2654WFvlsusObgvEjEFnacAFUitRAi3E3zcax38tmLJsrkcnaIhoyjJCysumYR4O7I1Dty4FCfPu9UgM7/wX7vtEchgZIVWPEuh/crLz4DHjOrvcuKUxnAfZchcQIWpKV5fBpjWJmDqhYFLumIg0TOp9GhsW3kJ2Vh6H0RFSdSiB/mfvmvsIfZUMABAIeFj1szN4PB63QRGipvh8YOr4NGxamwA9XSm7/sjuxxj26Z948uADh9ERUjUogQK4czUCB7YGsMsT5rVBvQa6HEZESO3Qo2s2/rc/Fl0+LbilG/oqGaO7nYDHrOuI/0AzuhD1VecTaNz7DCyb6MVOlm3TxAAjZrTiNihCahEzUxm2bUjE8gVJ0NKUX40yDPD3/ucY0PIw9m8JgCRHWsZRCFE9dTqBSnKkWDj6ChJi5d+ORWIBNv/RB3w+3bolpCrxeMBnAzNxdH+cwtVoRloutn17B4M/OYq/9j1DTjbVjxL1UWcTKMMwWDn9GvxvR7PrVu7uASNTGu+WkOrSoL4U2zcmYvuGeNg0lLDrI0NTsfrrG+jT9A8c2BqA9FRJKUchRDXUyQTKMAx2fX8X548VjJQyfKoDOrs05DAqQuqOLh1zcNQzDgvnJEO/UCOj+PeZ2Lr8Dtwa/Y41s2/g6cMPYPLrVwhRMTymAv+dHTp0wMOHD8suqMIYhsGe1ffxyw8Fow11cbPC6p9dqNUtqZUa+Bqwj9MtZoERGpRSuuZlZPJw5oIOjv6ljYREjSLbm7YywWfjm6PPUHs0sNHnIEIVVfjzir5kcKJOJVCZjMH27/wUWtw2bmmM3X8PgFCjTl6MkzpA1RNoPokEuOSljSPHdfAuSlRsmeaOpnAdbAfXwY3QzNG0brdXoATKuTqTQLMyc7Fsoheunn7Lrmvc0hg7/+oPkZhGGyK1l7ok0HwMAzx6KsK5i9q45qOJHEnx709DE0106N4AHXtaokN3SzR2MIZAUIe+CFMC5VydSKCvnyVgyfh/2fk9Afltoe1/9qPkSWo9dUughWVk8HD1hhau+4jh/1gTeXklJ0gtbSGaOZrCoZ05WrY1g0M7c9g1N4JQWEuTKiVQztXqBJqbK8WR3Y+xy+OeQj8z54G2WLa1W936tkrqLHVOoIVlZPDg90ATN2+Lce+hCClpRetLP6apJYRdMyM0am7E/rZvbgTrJoYQqfs415RAOSfkOoDqwDAMbl0Ox+alvnj7Ioldz+MBUxa1w4jpNFACIepGR4dBL+cs9HLOAsMAoeFCBDwS40GABp4GaSAxuWi9aXZWHoIfxSH4UZzCeoGABwtrPVjZ6aNhIwP5bzsDWDWSL+sbimvqZRE1VquuQCUSKa6feYsDWwMQFKD4hjEx18KGg71g19SIo+gI4UZtuQItS0IiHy9eaSD4pQaeB/PxMkSEhCTlEqG+kRgN7fRhZWeABjZ6sGioi/pWerCwlv82MtXkvtU+XYFyTu2vQKVSGR7dfQ+vU29w8c9X7KhC+QQCHkZMb4VxcxyppS0htZiJsQxOnXPg1DmHXZeSwkdohBBhEUKEhgnx9i0P4e+EiE3QLPVYqUk5CEqKK/JFPJ9YU4D6VrqwsNZDfSt5Uq1nqQMTc22Y1NP677c2tHXKvs1M1JfaJdCMdAlePIpHgG80/H1j8MgvBmkpRUct4fN56NHfBjO/7QATc20OIiWEcM3AQIY2rSVo01rxMyI7B4h5L0R0jACR0UJERvER+Q6Iei/E+1gxJLml14/mZEsRHpKC8JCUUstp6WjA9L+EamSqBT1DEfQMxP/9iKBnKIauvgj6hmLo6GlArCmEWFMAsZZQ4XGtbQil5lQigcpkDLIycpGZLv9JSc5BwodMJMRmIuFDFmLepSHsdTLCXycjNjqj1GNp6QjR170xRs1sDWMzGpaPEFKUphiws8mDnU0egByFbQwjvx0cFSNPsO9jBXgfw0N0DA+xcQLEJoiQlV2+j86sjFy8e5uLd29Tyy5cCoGAB1F+QtUUQKjBh1C4EkJI5T9d/oKGiA8NDT6EGvnb+fLfGvnrP/oRCthtGqL89QKFMhrCYvbTECgeT8iHhkhQxnPxub/lXQ04SaCem/3x9/4gNmFmZVZuAGlNbSHaO1lg4KimaNfFAgL6tkYIURKPB5iayGBqIoFjCe0N09N5+BAnwIdYAWLjBHj/gY/YOB4SEoDEJD6SkoVIThUhT1o1n0VSqfwiIysjt1Cg5gWP/WOr5Hmqk7DYZPxfAv8ocQs15AmXx/uvqve/5Ctf5in8RuHHkD+e+W0HtOlsUf2vqdqfoRgZqZJKfSPT0dOATWNDOHaqh2afmMK0vjb77eZNoVa3hBCgQaHHqfFJkPJySixLys9EGzCxBVraFr+dYYCMTAGSUoRIThEiNV2IjEyB/CdDgLR0HtIzeEhP5yMji4/MLAFyc/mQ5PKRm8eHJFfA/mYY9b8oyMuTIS9PBmSVXbayvpzqUP1Pggq2wjU1NYWtrW2VPfnLJ/HyE1qK/Mv/kuTkpUEs1KuymOoSOneVo27nT0ecCZFQNWY5ScvKgZ4WdRVRRtWfO0b+w+Q//u93JVv2MgoPeCjtaEyhcpUlEOvC2LLkq8+4uDiYmZmV+3impqa4fPlysdsqlEBVkap3rVFldO4qh86f8ujcKY/OXeVU5flT//sChBBCCAcogRJCCCFKUPsEOn36dK5DUFt07iqHzp/y6Nwpj85d5VTl+VP7OlBCCCGEC2p/BUoIIYRwQSUTaGRkJObMmYMuXbpAW1vexzMsLKzCx9mwYQN4PB66detW9UGqqMqeu+DgYAwfPhympqbQ0tJCs2bNsHPnzuoLWMVU5vxFRERgwoQJsLa2hra2Npo2bYoVK1YgI6P00bNqg7///hvu7u6wsbFh/2+WL1+OtLS0MvfNzs7G4sWLYWFhAS0tLXTp0gU3b96sgahVh7Ln7+HDh5g+fTqaN28ObW1tWFtbY8yYMQgNDa2hyLlXmf+9wpTJFyqZQENCQvDXX3/ByMgI3bt3V+oYb9++xfr162Fubl524VqkMufu4cOH6NSpE3JycuDp6YmLFy9i4cKFkEqlZe9cSyh7/jIyMtCrVy/cvHkTa9euxYULFzB16lRs3boVkydPrsaIVcOWLVsgEAjwww8/4PLly5g1axZ+/vln9O7dGzJZ6X29p0yZgn379mHNmjU4f/48LCws0LdvXzx69KhmglcByp6/P//8E0FBQZg7dy4uXbqEjRs3IiAgAB06dMC7d+9q8BVwpzL/e/mUzheMCpJKpezjffv2MQCY0NDQCh2jT58+zPTp05mePXsyTk5OVRyh6lL23EmlUqZly5bM559/Xo3RqT5lz9+VK1cYAMyVK1cU1i9dupQRCARMRkZGVYeqUmJjY4usO3ToEAOAuXbtWon7PXr0iAHAHDhwgF2Xm5vLNG3alBk8eHC1xKqKlD1/xe0XFhbG8Hg8ZuXKlVUao6pS9twVpmy+UMkrUD6/cmH973//Q0BAADZs2FBFEakPZc/djRs38Pz5cyxYsKCKI1Ivyp4/iUQ+wo++vr7CekNDQ8hkMjC1vK1ecSO7fPrppwCAqKioEvc7e/YsNDQ0MGLECHadUCjEyJEjceXKFeTk1I1hB5U9f8XtZ2NjAzMzs1L3q02UPXf5KpMvVDKBVkZSUhLmz5+PTZs2wdjYmOtw1Mbt27cByOujOnfuDA0NDZibm2Pu3LnIyqqBwSvVXK9evdCkSRMsXboUz58/R3p6Oq5fv46dO3di5syZ0NHR4TrEGufj4wMAaNGiRYllgoKCYGdnB21txSkHHRwcIJFIEBISUq0xqrLynL/iBAcHIzY2tsL71SblPXeVzRe1LoEuXrwYTZs2xcSJE7kORa1ER0cDAEaMGIE+ffrAy8sLS5YsgaenJ0aPHs1xdKpPU1MTt2/fhkwmg4ODA/T09ODm5oZBgwZhz549XIdX46KiovD999+jV69e6NChQ4nlEhMTYWRkVGR9/odZYmJitcWoysp7/j6Wl5eHmTNnwszMDFOmTKnGCFVXRc5dZfOFSswHWlVu3bqFP/74AwEBAbVy7rnqlF/ZPnbsWKxZswYA4OzsDKlUimXLluH58+do2bIllyGqtOzsbIwYMQKxsbE4fPgwrK2tcf/+faxZswZCoRA///wz1yHWmPT0dHz22WcQCoU4ePBgqWUZhin2vVrbb3mXpiLn72OzZ8/GnTt3cOHChWK/mNR2FTl3VZEvalUCnTFjBqZMmQIrKyskJycDkH8jk0qlSE5OhpaWFsRimgGiOCYmJgCA3r17K6zv06cPli1bhkePHlECLcX+/ftx48YNhISEwN7eHgDQo0cPGBgYYPr06Zg5cyYcHR05jrL6ZWdnY8iQIXj79i18fHxgZWVVanljY2NEREQUWZ+UlMRur0sqev4KW758OX777TccOnQIffr0qcYoVVNFz11V5ItadQs3ODgYv/zyC4yMjNgfX19f3L17F0ZGRnXqKqCiHBzk8+d9/E0s/0qgsg27arunT5/CyMiITZ75OnbsCED+v1nb5ebmwt3dHffv38fFixfRunXrMvdxcHBAaGgoMjMzFdY/f/4cIpEIjRs3rq5wVY4y5y/f+vXrsXHjRuzcuRPjxo2rxihVkzLnriryRa26AvX29i6y7ptvvoFUKsXu3bvr1Juxovr37w+xWIzLly9j0KBB7PorV64AQIXqYeqi+vXrIykpCSEhIQr/Z/fu3QMAWFpachVajZDJZBgzZgyuXbuGCxcuoHPnzuXab8iQIfDw8MCJEycwYcIEAPKrgOPHj6NPnz515o6RsucPAHbt2oUVK1Zg/fr1mDNnTjVGqZqUPXdVki/K3eGlhp04cYI5ceIEM3PmTAYA89NPPzEnTpxgbty4wTCMvK+TQCBgVq9eXepx6lo/UIZR/tytWrWKEQgEzPLlyxkvLy9mw4YNjKamJjNhwgQOXgV3lDl/oaGhjJ6eHtOkSRPm999/Z65fv85s2rSJ0dPTY9q3b6/Qv7Q2yj9X3333HePn56fw8+7dO4ZhSv6/GzFiBGNoaMjs27ePuXr1KuPu7s6IxWLG39+fi5fCCWXP37Fjxxgej8f069evyH5BQUFcvZwaVZn/vY9VNF+obAIFO0264k/Pnj0ZhpF/YAFgPDw8Sj1OXUygyp47mUzGbN26lbG3t2c0NDQYa2trZuXKlYxEIqn5F8EhZc9fUFAQM3z4cMbKyorR1NRkmjRpwixcuJBJTEys+RdRw2xsbEo8b/nnqaTzlpmZycyfP5+pV68eIxaLmY4dOzLe3t41/hq4pOz5mzBhQpn/r7VdZf73PlbRfEGzsRBCCCFKoJYhhBBCiBIogRJCCCFKoARKCCGEKIESKCGEEKIESqCEEEKIEiiBEkIIIUqgBEoIIYQogRIoIVWEx+OBx+OBz+fjzZs3JZZzcXFhy/7+++8K2yZOnMhu8/DwKPEYhw4dYss5OzsrbLtx4wa7Lf9HW1sbFhYW6NGjBxYvXozAwMDKvFRCCCiBElKlhEIhGIbB/v37i93++vVr+Pj4QCgsfRhqoVCIAwcOQCqVFrt93759ZR7DxsYGHh4e8PDwwDfffINBgwYhIyMDW7ZsQbt27TBmzBikp6eX74URQoqgBEpIFapXrx46dOiAgwcPIi8vr8h2T09PMAyjMGB/cQYNGoTIyEhcvny5yLbg4GD4+vpi8ODBpR7D1tYWq1atwqpVq/DDDz9g37598Pf3R2BgIFq3bo3//e9/GD58eMVeICGERQmUkCo2bdo0vH//HufPn1dYn5ubi0OHDqFr167s9HElGTNmDLS0tLBv374i2/LXTZ06Van42rRpg6tXr8LMzAyXL1/G6dOnlToOIXUdJVBCqtioUaOgo6MDT09PhfVnz57Fhw8fMG3atDKPYWhoiOHDh+PChQuIiYlh1+fk5OCPP/5Az5490bRpU6VjNDc3x4wZMwAAR48eVfo4hNRllEAJqWJ6enoYOXIkLl++jMjISHb9vn37oK+vjy+//LJcx5k2bRry8vIUGhqdOnUKCQkJ5UrCZclvfHT//v1KH4uQuogSKCHVYNq0aZBKpThw4AAAIDw8HF5eXhgzZgy0tbXLdYxu3bqhRYsWbL0pIE/CRkZGcHd3r3SM+ZN8x8XFVfpYhNRFlEAJqQadOnVC69atceDAAchkMnh6ekImk1X4ynHq1Kl4+/Ytrl+/jpCQEHh7e2PcuHHQ1NSsdIz5SZnH41X6WITURZRACakm06ZNQ3h4OC5fvoyDBw+iffv2aNu2bYWOMX78eIjFYnh6erJXolVx+xYAoqOjAQBmZmZVcjxC6hpKoIRUk3HjxkFLSwszZsxAVFQUpk+fXuFjmJqaYujQoTh16hQOHDiALl26oFWrVlUSn7e3NwD51TIhpOIogRJSTQwNDTFs2DBERkZCR0cHo0aNUuo406ZNQ05ODuLi4qrs6jM2Nha//vorAHmXGUJIxZU+lAkhpFLWrVuHL774AmZmZtDT01PqGC4uLjhz5gxkMhn69u1b6ZgeP36M8ePHIz4+HgMGDMCQIUMqfUxC6iJKoIRUI2tra1hbW1fqGDweT6kkFxYWhlWrVgGQD+IQHx8Pf39/+Pv7A5BfeeZfhRJCKo4SKCG1VHh4OFavXg0A0NTUhKGhIZo0aYJFixZhzJgxaNOmDbcBEqLmeEx+W3ZCCCGElBs1IiKEEEKUQAmUEEIIUQIlUEIIIUQJlEAJIYQQJVACJYQQQpRACZQQQghRAiVQQgghRAmUQAkhhBAlUAIlhBBClEAJlBBCCFHC/wE7GkSfkqSr/gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "913ebea6e88a4df28fca35c6222d9fa3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9faad5ae33a54b22b9a1660f792151f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4771f2bd90084fe5bc0379ae3d77eeed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b552aac2a844830b7c3a45c3ba39902",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bbd628ec7c24c6783e8a05491e8b4fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "428c0525f1674d1b91568a7d20229c01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b461f998b85b417abcd8a556b1c5c7e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# scale\n",
+    "ori_x = ori_x * 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "# yes_data = yes_data / 3000\n",
+    "# no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9330ee82cad04ee79092878c00b99ce1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4600d4dbf9d24863b74ee224d800652e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ac3a1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e2a8705",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "716ee1bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c950ec5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v2_Aug16_bf_model_misspecification_1000_data.ipynb b/bf/v2_Aug16_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..1c64103
--- /dev/null
+++ b/bf/v2_Aug16_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,6047 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "#         res[i] = dpl[0].data['agg']\n",
+    "        res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "#     print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "5212526a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scaling x\n",
+    "\n",
+    "x = x*3000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0f0626770274902bbde1dfa32b81370",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15.505203</td>\n",
+       "      <td>0.820109</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Loss   W.Decay\n",
+       "0  15.505203  0.820109"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "edbc6eeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f88f107792bf49499eb3b8725863b132",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YXz7VvF1if7lV8/47bZ+enta1a9dWfY2qDI5fsVhMmUym3DM+NfaXTzVvl9hfbtW8fz228yU1AIAJggMAMHFPBieRSJR7gi/sL59q3i6xv9yqef96bL8nv4cDALB3T36GAwCwd88FZ3x8XE1NTfI8TwMDA+Wes6qGhga1tLSora1NsVhMknT9+nV1dHSosbFRHR0dunHjRplX/ltvb69qa2u1bdu2hfvutLe/v1+e56mpqUm/+tWvyjF5keX2f//739fWrVvV1tamtrY2jY6OLjxWSfvz+byeeOIJNTc3KxqN6vjx45Kq5/qvtL9arv+HH36onTt3avv27YpGozp69Kik6rn+K+1f1+vv7iEff/yxi0Qi7tKlS+6jjz5yra2t7uLFi+WedUdf/OIX3ezs7KL7XnrpJdff3++cc66/v99973vfK8e0Zf32t791b731lotGowv3rbT34sWLrrW11X344Yfur3/9q4tEIu7jjz8uy+7bltt/9OhR96Mf/WjJ2Urbf/nyZffWW28555x7//33XWNjo7t48WLVXP+V9lfL9f/Xv/7lbt686Zxzbn5+3u3cudP94Q9/qJrrv9L+9bz+99RnOOl0Wp7nKRKJqKamRt3d3UqlUuWe9R9LpVLq6emRJPX09Oj06dPlHfR/fOUrX9HnP//5RfettDeVSqm7u1uf+cxn9KUvfUme5ymdTltPXmS5/SuptP2hUEg7duyQJD3wwANqbm5WsVismuu/0v6VVNr+QCCgz372s5KkW7du6datWwoEAlVz/Vfav5JPs/+eCk6xWFR9ff3C7XA4fMf/oStBIBDQU089pccee0zJZFKSdOXKFYVCIUmf/CO9evVqOSeuaqW91fT3MTg4qNbWVvX29i58SaSS909PT+vtt9/W448/XpXX///ul6rn+pdKJbW1tam2tlYdHR1Vd/2X2y+t3/W/p4LjlvmBvDsVvBKcP39ek5OTGhsb0+uvv67f/e535Z60bqrl7+PQoUO6dOmS3nnnHYVCIX33u9+VVLn7P/jgA+3bt0+vvfaaNm7cuOK5atlfTdf//vvv1zvvvKNCoaB0Oq133313xbPVsn89r/89FZxwOKx8Pr9wu1AoqK6uroyLVnd7X21trZ555hml02lt2bJFMzMzkqSZmRnV1taWc+KqVtpbLX8fW7Zs0f3336/77rtP3/zmNxe+bFCJ+2/duqV9+/Zp//79evbZZyVV1/VfaX+1XP/bPve5z+mrX/2qxsfHq+r63/b/96/X9b+ngtPe3q5cLqepqSnNz89rZGRE8Xi83LNW9Pe//103b95c+O8333xT27ZtUzwe19DQkCRpaGhIe/fuLefMVa20Nx6Pa2RkRB999JGmpqaUy+W0c+fOck5d1u0PFpL0y1/+cuEn2Cptv3NOL7zwgpqbm/Xiiy8u3F8t13+l/dVy/WdnZzU3NydJ+uc//6lz587pkUceqZrrv9L+db3+6/lTDtXg7NmzrrGx0UUiEffKK6+Ue84dXbp0ybW2trrW1lb36KOPLuy9du2a2717t/M8z+3evdv97W9/K/PSf+vu7nYPPfSQCwaDbuvWre6nP/3pHfe+8sorLhKJuIcfftiNjo6Wcfknltv//PPPu23btrmWlhb39NNPu8uXLy+cr6T9v//9750k19LS4rZv3+62b9/uzp49WzXXf6X91XL9//SnP7m2tjbX0tLiotGoe/nll51zd/73Wg371/P685sGAAAm7qkvqQEAyofgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE/8LaDBMDsHNza4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5644027ff004de881e778d5dec222df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 50\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*50\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da1273493a0f4261882cdca607de5ba1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all -50\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*(-50)\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d083fe4d29274291b0db6457e39c5c6d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=10, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=16, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=18, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=20, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=23, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=25, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_10', 'noise_16', 'noise_18', 'noise_20', 'noise_23', 'noise_25']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_10\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_16\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_18\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JaGx3uc1CHRB51C+t1GFxv9/5TMAksnpz0WCkdBqiRxrB9/1Rp/Ly2Sah/XYoT5PIKjMzkqLUp0W4/woBiWJUe1wXU3ttJYzNdhmJ63nSBk133ST335HMFUdxOm/70kP41i+3pj+vXa+t8J6JkRhje4eQtIPKA9oNDGkCEpueH61e1oaNxPV8XPrFB/HBb22Qfo+Llu+rlADwXWjq64W3QpNYJ0jueGYXLvj7+0aVMMljfwKAj//XU/g/929m/z9Wa7J7b7hiQGKWd1aci3kRem6u2nIlQRsbR5IxIFHPSNq7/yRGkk611dbl20IQyAq8sPso/nrdxtTn8RQprY/V5dpSbSTG/bdD4LtWH1+450Xc+P1n22qPq7bcyHd6RiL/TQNxl/Lq3gHcu2mP0qZ+8E2oBruSrKopWgDJE62mqd+y49BxHBlqxlbHGwnk3SQ8ve0QntvBWZsoMFzP43YAL32kcOC1lakbsWhnQX5ux2Fc/92nGcMYqDUlAdeakSS3n7Sg0244r6rR9XxJkIsQGZXv+/ivx7YJHml0/kgyknxR5Xncf6WARMVGMtw1dMaRIAn+ej7wlZ++jH//1ev47oZt+NHTO3O1xyPbZUbi+37ISDxJoOTZNbcyjMa1SYwkqxsiL1kb/L+mGYy00AxmZDudRN4YIc+X1QPi5GsqAiEtK3Fz6sh1aEfX//OX9+G7j2/HvmOBF9pAvSmNk1Y2kta5thB7HBl+8xIzuu+6hhGLts5X9g7gE//3Gdz/fH9wXUEjMFKIY6SHjzdw5Vd+ji37BrTn5QlIFNVX9J2xkXQYfmglEV/y9d97Bn/6nSdztcfdf2Ubicg8zvjLu7Dm24+H/09PXQmtaqg3PT6pRPSWczIS5To1jZ6aFts8vvKdQl73X1dRQaq5i8SxMhSjaon0pUCvrXZ2lSyiPXxPxzKqttphJO3WDidBrFNvMdYh2OpqivAbSa+tOPff+zbtwdPbD+PLgipVRJZSu7p1gam2yGvLqLY6A7aDKswQGrUjEBMJPgd/73s+UEflSYjYaoH0YgYf/T+vaougs5HQLrETZYvzIq+NJEhnwu+xoeicxXelE6ravhTotdXOrrKhLOaDtaa08LRSbaVO2qg1tren2tIlRI1c1+PCvaHEvoxkPRLf16vWqOBkK+1CGrWYq7lPlrSRMZIuU21t27YNl1xyCRYvXoylS5filltuAQAcOHAAq1atwsKFC7Fq1SocPHiw7c62A2IkRPXbhdZrS0jWqI6lPC6qNGDqSkDca/sHcHSoEev+SxPrUEY7hirkdDvC+ihkJK08kWpNFwcG6pHvvVAXTxAnX9P1ZEaSQnCSV1Rxqi3en6x6d5XNDNRk1VbcOEyt2iJmoAtIbFO1ReNa98zF1Cz0O/eWlM8fCXi+r1WDWrDC36PniJmm06z/NL501Vmr3cpISqUSvvjFL+L555/Ho48+iltvvRWbNm3CzTffjMsuuwybN2/GZZddhptvvrmI/uYGPdddORKq6UAvTtw1iaoS1eMmq/pFdNVUva/e8vkH8Xv/8mjsTpwWg4ODekGy89BxvL4/GmOi7qR1OmrGSAq0kRwZauCBF/a0PjAGrXZzt/3sVaz+ys+j5/myQbeu2EgkRpJCtSWqXYpAMwWDiENDeXcDdVdqQ2/I9gtRbaWtaRKHZEbCry962InXG0kbie/r2QB5QuueSav3okKXMFO1kXSdsX3WrFk499xzAQCTJk3C4sWLsWPHDqxbtw7XXnstAODaa6/FD37wg3Yv1Rbo/RVVz5kMXbQrqjg2mq7PDI3qe8ya6lw87phGRfXcjiPCAqr0Lbz44cHoLhwAPvWjjbj+e09Hvo/YSBIESZGMZN1TO3HdNzfgSM4Sta3sT3uP1bD3aJSJup486RuKqkB8HnFxDXJ7ejaaF6LqIuuirC6mA7WmJBh1zYmCNE1hq7jj3La9tkK1seaZi++aMRK6Xk4VZ5Hw/KjrP8BjanRdc6X30rrvdLiqigWAntIYcP/dunUrnnzySVxwwQXYs2cPZs2aBSAQNv39/UVeKjPoBe08FDCSssODpXJFopL7b7hTrZZtKX5E1ZPSJbK4kRKOCekiJIOwYnc5VmviUz/aiCOhSiuOkRw53tQKJ3UC6gRJJ4ztpB7UMaA0aJ2IMM6VVJ70TdVrS2gvHSPJtpC1UpfpChelhSgUbCu4H9FhQNdH8fm3ugWu2tIwkoybJhWckUSfj8jC6fnRgqqzHQw3fF/v4UeCJCn9PZDW2B5lJCwgsUzuv10qSI4dO4arrroKX/rSlzB58uTU5912221YuXIlVq5cib179xbVnQjosRIjOXFClf02mMNwzLL/hrumnrIjGdvVF5lUv0EHccCJi764uLMBFR77+GsH8Y1HtmL9lgMAgEMxjKTuRgtvBX2T/6+LI6EFqsiAxLzG8uj5+t8pZY0qxF3flxYdcfF1XVm1lUZTkCV78o+f3YWz/uru2Cy3gMwqsj4aUbU1pbcMAFL+Kt2zFt9paxuJPPZEECvPayvS1fohMHuY57Ng4KZyvbRxJGfe+GN8QsPM24Hn+1rVFuWv1DKSFG7Z0vEJNpKuTtrYaDRw1VVX4T3veQ/e/e53AwBmzpyJXbuCVB27du3CjBkztOeuWbMGGzZswIYNG3DSSScV0R0taODvCutNiIlJ8+SUUndNPWU7WLBc+XsCNxKma18cUGL/hjT1T2gc0sJPC8aRoaZ2UNeb0VxggM7YPjyqrSwptHVoxfZoZx/JfxYK/nozEKxqpHDmCZ6BkTzwQsDQn9kWn8ammUHVpEJkM1P7KgCAo4LqUNdev6D+a3W7cVkVgAK8tmLmkNim5/PxzsopZLCR1Jou6q6H/9qwPVcf4xCn2rIURiI6f7jCuEvzyHTeafRdxenSwla+7+MDH/gAFi9ejI9//OPs+9WrV+P2228HANx+++248sor271UW6AXdLQmR8ECehtEK6hp5HtKjpQ+PsJIMrj3Bf3jx4k7SVGQqDph2lEfFe5HF4Feb3raBSCVsZ3qWzSKM7arzCrz+a1UWzFMwQtVkWf+rx/jD7+9gS2+lkUeeNFrJPYjAyMpOcHUS0oIKdlssgoSoQ9T+wJGckRiJNFz9kqCJPl69HOysT11dyW4ytwSIRrbaaOjXi/N89c5mxQBz9cburmxHVi/5QDO+8x9PK9bxg2L6FTAsy8Ev1VLXVpq95FHHsG3v/1tPPDAA1ixYgVWrFiBu+66CzfccAPuvfdeLFy4EPfeey9uuOGGIvqbG6oUFydKGkbSdD384pV97P9qGvkqMZKYgcAnQMr+CmNxQKPasqzoDlgfQKib6Pp+Ro3tGtVW+F2hqi3F1pMVat2WDVsP4E03P8A2CLSzVye4qNq67/l+9qx6Sk4kzU0WlUOaYyuhjS7Ju0YywmZka3lUW/1HA/thb9lJn2srQeefW1VJ4zmJkXhR91/uFtv6uq/sDSLMT57ck6uPOujqhBC4sd3HrsPH4Xo+nt52CF+450VtpcMkiENGrWNSHaE4klK7Dbz5zW+OHXT3339/u80XDjboMjKShzfvw/u/+Rju+/hbsGDGRCmNvG0BZcfWFrQiZI26FQfUMY1qq+zYEd9z3e5WN5l1KivdsXpje8hIOmAjyUvH1YVrc/8x7Dh0HPuP1TCxWmLvSp3gni9fkxb13ooTeOBl3inKf5NQKbV20xRTYMQtME++fhBnz5mCsiPvCSXVFhMkgmpLc9n+IwEjmTm52lLNmFQWgVdZTG4jtm1SG2s2RqKKmHttyeMnzbuiVCXzTujN10kN6LI6+4QjBCTSfX34O0/C9XysmDeVHddKgH/1wVewaRe3qzU9HyWHC1Ge/bfLGEm3gOeniaqYBmqtF0VaOMmA3RTcf0u2jbIduP/GGbmyuiaKk+GohpGUbSuiLtMZx3VXq7uetp9Z3H87YWzPr9qivyQwKEBNfudqam3Pk98Xfe4p2bkYSRavLVJtJakgWrn/9h8Zwrv+8Rf4yUYeg+P7Pg4M1KV2uY2kFSOpYUpvGT1lJ71qqwOMRJc1giC+ax5HIm8O07yrV/ceA8AX3iJA87Ah2TyCz2JAIjEt6qeofk4iEr7v47N3vyDlB1SFJ21Q3GFmJONGkNCYVtMpAOlUWxQZP1h34fs8erXW8FByLDjhwh43iGnCpZ1bvqZ/FcdmuzBHECSqjUSETl3UiPXaUgSJoDp49NX98Dy/I0kbkwy3aaDG6DB3UBIgTVmwsOv6chTykePBPfVUnMBGIjKSVCqH9DtiYhBJLs+tjO3EVA8fb+DRV/fD931865ev4dy/uxcv7jnKjiMbSWtj+xBmTKrCsqzUAYna8dWuaktxZNFe1xdVW/L10qi2Xg0ZSZGBe4yRaGKTyEbiQ1ZPA/J7SRpnSSn7mWqrRF5bw8tI2lZtdQuiNpLAc8vz06m26L0MKhHCdddDT7mEkmOh1nRjXyBzW0zLSETVVriTrJRszkgcOxKIp1cFRNuuNz1YpegeQh2oJJhIrfe+C+cXGkdSa7p44Pn+AhiJvJBQH7kXj+wmCvDMAaLzwmuhAba37ETUlFkq16V5x2QjSXLTbEjXj7/eHc/sxC9e2Y+/++2z8fDmwI4nGs71NpJoe/1Ha5gxuYpDgw3ouWz02vrFjWwWiU3Egqcfit8YeT6vBdRQGGga99+DoWahWEEiM2AgGOOVki3ZSLhWwUbd9SQniCTVlr6ssWwf6lpje7dAx0hYAag0jCRs4HijGRl8JcdGybZibSSe8H0e1RYJOse2GEsoOZZQDCeBkWiuF+f+q0vaKA78b/5iK3aEniZFJG386Qv9+ON/fwKvhGqG3DYSJqSDv7zksaxqaEg7/OCvqD55bX+wS+0tBzYSKe16Gq+tDHawcgrVli4FhvR7+N3L/cHze33/ACqlaFVCzkhaqLaO1DBjUg/sFIykk6ot5rWVkP1XZCR118MDL+zJ5P4b5xLeDuh2RUZSV9iH6G1WCjcTkitwQn908lEtiMVUW8McRzJuGAm9HtEQWC3ZOFZLx0hokAzWo6yj4tgoOTYarq/12Ki7XoQ9tII4Do4xl2WuFy7ZNt+dMVVAa0FCLq9xAk+E7wcLndhGrcA4EmqLbFR5x75YtAyI7lB5RmZBMGju/7Wwxn1P2cHRoSBbbsWxYwM4VYiBkb7vs9gBHeinJNVWq8h2tbiabVuoONG9YV+lhJJtSSqUn77QD8/zsXL+CQCC/u49WsOMSVW8uvdY6oDEONWpeEwWiNkGklKkuB5fkO94ZhfuCMtL02+t0AgToebNpqCDbo6rc9LzuMquFAaziYlkk/quZyQym6+k2KB0AuOIkcgv2fN9JlzSqbaCowdrbsS1rlq2UXHsWNtDrSEKknQvWBfZHkwe8tqypIULkCcFUVz1avWEnZiO5dearvaeijC2M124oorK246qvlRTZrQyXlNsQZClIIgjoVQ6WYztQGtbGD3rJNWKrIrTtUE7d9pcWBHvLSBYXCZUS5IK5csPvIx/+tkr7P/HG0GA3tS+SiobiaqOHKw3md2sVaaBJCQtwuLvqlpSRBqWQfOgE6otEXQPomqbBCT1Yf8xzkiS1getXVOJ6nfswF7blZHt3QD1/Xg+32mnUW2JNhJV2ldLNiolO1QZRV9gzXUTK8rpoLORND2BkTi2oNIhRsInVk+Yc0fVudaV3XrcNdnxTU/bZ9XY/sTrB/HIy/sixyWBu3mGjKRdry1anD35HrkaI3mHT8+mtxLGkfg+yhmS4ImHtBKKpHpIFCSaCnjy7/Ku2rEsptoQUXIsTKg4kmoruDZvk9qolmzYVpp6JHQfwYeP/+fTuP67z0TazZr+XrxnnbFdTCOv81IE0tmzyAEjjdBxPT+2sqF0XU1TdA/0k+/zcUZzef9AuowCescGeaw7thWo2Q0j6QwiKh6fv5g07r+MkTSaEWFRLTmBCqQZz0iS/O611xPa4SlEfIkW02JEt1bTMBK1O3XBg0md5LqBWou5J1W19eX7N+MT33um5X2JoMuz1OU59dWqazWpLdQsA2kixW0rzOQcqv9oh59GyMlJ9JKPp3f68OZ9eOv/fki7u27VX7VPdgwjKYeM5KiSXVnsI42LcmgYbu3+Kz/zXYePY8+RoUi7WV+pOD+SVLWusKlSoRMOTdeTGDtjJClUW1954GVc8oUH8XL/0cTjdEKT2Af124cfUdmJjCSraktl4zQGuq4eSbdAfaxivY80mV2pgeN1NyLtqyUb5ZIVxmfod7pZ40h040BmJBZTj+iS3FGEq3o9ncGZoBuotaYXOc6yogKm4frYcYgvJmnAC3fFs6Q0YJM0PL2p7NJIFSmqJOMixZnjRGgbIp1z2p2r2qdWx+46PIQX9xzF9oPRlB1Sf3WMRBmHcYyk7FihIJEZibghooW16oSCRPN8hhouSzJJ7+7QYAP9R4cw1PC0Buys71RiJAkpUnw/vvyx7prv+sdf4Mz/9WP2f+prmsC9p7cfAgBs3ZecVkX3ytmcZNqD6HqzXzC2J22mtHnNFFd3xwpVWyaOpDOILJoeV23FRXrL54eMRGNsD2wkTiIjyWojiZuAg5oMrZ6yIAM80EpdEMRjIsW3tIzEjXzfV44mhqO2nnz9kLbfOqgCMLeNJOL+GwoQYiSePNmSrlVxbDgO98CjhTkNW5K8vFIyEoKtMcy3KmylXiPO2F6ybUyoOqkYSaVkw7L04/T7T+7AO7/8c6m2yd/dsQlrvvV4mAQxOsazqivFPum9tvg1sthInt3Bk2MGcz/4nMZG0lcJxrtaYC6ubyJY6WLwMZi03iQNmyT3X/rNsS2UHctEtncMOtUWMZIURYvo2ICRaFRbzEaiX4xVF9XW14sTJGTQjBo1RSFB6aR9hYuJx0QC9Fqo5QhU80BaMMJmn3z9oP6GNFAFYH6vLbm9SO4lze4zbqEvORZTGwaqrXTG9v/xvWfwp995kv2/1XtWBZPOdVtXk1v6XXlgccb2wEZSivRJHKskfMshI9ENv0ODDTRcPyzbG96HH8SsiIxEyhGWcT0T+5QUF+X68aqtVq6vovBIY0uYEIYJtPJU1L3zJ14/iMF6k41t0Z1eh8wBiYLaGwg2JCXbljIKDwfGjSBJ2tGpVPMnG3fjoFLjmzOSZryx3fVi07Zz9Ut7jITsOa7HhRZLkSLcBzES9XI1iZEogkS3o9J4ovVo1Cd5GAkN/nq7jIQ9B+qLzEi411ay8RoIFlInjAnyfG4jaTUvt+4fYAGNQGsGoz573aLZytiufufYetUWeW2piGMktq2/Hk9UKjtg1Jouak2Xu/1mUPEl9SmpsFWi11aLl1UT7jUp+zKhrxoykhZOObq5/Q/3voS/+eFGbmwHT3yqQ7JqK/qdGGANcK8tNR1QpzFuBIm6Mwf4RBUX1/4jQ1jz7cfxkbVPSsfS2QN1V2Nst5lxWxeoV2uKNpJ0/W3FSMSa4jrVVk8KG4m6a9GqthpRry0dI6EJ/syOQ6ldKuny7Rrb41LF0Pf0/52HjjODqXpPtACXw109pbthxvaMgqG115YiSBISZAbX11xTeX+2ZUmVPwklx9Z+Lxf1CktGJxjbxYzX4u9DDS9gJJoNQVZBIjGSFnFRcTt79dmq747uo6/iZFJt5WEkQJAxQfI2S1RtJTCSJNUWYySBTcx4bXUIuomoUwkdCFMn7D4sG41ZZLvW/ddhumldfIW4g0trfIwbUAN1nvCNVV30+XXEPgXtyOdLqi0/fvGj+9HFkeg8wpqeD9sKFpUXdiV7txDU+h1pns32g4ORuB9Wk0FRbfGAxODvzT9+AR/+jye115rcE+zYy6WQkbg+XB+pje0qE20teOTjdbvvRgsGFbGRxMQ/lmyLJYmMO7/eJNWWFRtHIrqtit0/3nAlG4moEsy6NxA3N0lxJIltKM/qiGIbouc6oVKC78vP4dBgHef+3b14/LUD7Lu+1KotfedOP2mi5KKepErPqtoSN1GWFRTQKgmZwYcL40aQJD1WccBSzIaqCqCFYbDejKp6yjbb0eoGW2CED9tJrdrSfz8oBCdymh9lJCwgMSaOhNqIu+aEkM7r3H/jGMmS2ZMBAE9uO5hwZxzqs0ij2rrmtkfx1Qdf1p5HixtTZXlBihfq/0DdZZ5L6sZiUk+QRqREfvhewCKZsb1F3+JKK8chwkg0i4tUVlm7G1V23r7+uErJRlkjZbReWwlxJORWraq2yHgtRrSXbP34awXqk2NbiXEkSVCf7X5FTU330Vuh+uaCRuJoDQcG6qxeCcBjslolKo0bI9P6yqAVyEeyl2iiaivBc8/1fDihw0bJtgoNtEyD8SNIEgagOGEppfOkHlmQ0NlaY3vZSRQk3318G3PvTDuv1MlAO7xjGkGi8z7rKesZiWRoTFAB9IbnB1XY5Da4kOLfNT0fp5zQhxmTqqntJOpim0a1dXiwEan6SKexFCnCc1EX+DgPMXrfZCPx/OCe0hrbVYbR8viYfsnfucLOPtpeNDbK1+7Y0zGS0G7gOLG5tphqq+Fq+yMa27NkBND1qa/ixFRIbN2e+mzFOA2AC80JGkFCz0HclNG4ahVvFtc1cQ55ni9t5iihJiHpcSUZ213fhx1uFsQ8fMOFcSRI4n8TdwgHB4NFaqLKSMLzBxtuxLWuWrIF1VZ01/Lgi3vxwm69bj6+vyrrkfW0Yr4sWsNSMRJRtZWwi2aCSFChOeFA1UXNu16wC501tZdlVm0FVXCkYSS6PGGq/YnHjUTrw+g8iwBZkFAOpHrTZQtwVsHQ6laiCTKji9RQw2MCXXd59ZqiSzvA31fJsVmCQOl8oVF6LuWSFWbFjhcUQ003VtD4oTDjgZzR45JAz6Wv4mgj19X2qhrnAvVdHRAix6mfANc6iJsN0aGAQI+iFSOJe+d1IRZLTJECACdOrCT2vdVv5KHmSYzENoykU0hawMVBQ4WrVEFCi+ag1v03WbUlIu0OTV1ouAsiqWb4oqErTRrHSGSvrXi9Ps8iyhdu8tbS20g8lp4h9T0qY93zA0ZIRYe05/gaQSIYMgFZxaIyEubXrzzfyaTaciw4Nq+nXrLjF1YRqpdMq2eQxthea7pMP0/H/+ylvSwDc0Q16cvskcZw2bFQtqNTXXzfnJHYqWwkOjRc/m5IcGVWbYXva0KllCoJKamnpDaUd7FPZSRNbmwPrsmPp/FS16gVBzLaSC5edFLYpsecfQLVliBIJsiCJGmcib9R2BGvvcM3DmIevuHCuBEkcY+1ZFuoh+nSAV6nQB2gomeUOsApjgQIGEtiP3KqtlhQVI0zEtX9t9702GCqxuj2dRPk1p++jJ++2C8Jr6oQdMhqHYTf6dp2XZ8njEvpMaLLTPzh/3gCl37xZ7GunZ5w3+zaCiMRAxLVnZlaq4QgqjJJLVNverBZErwWgiGjjSTq/qtx0mh47L3Ts7r26+vxW//nYW0bnidXdSRBUrJbMxISEpUEGwm916SEnSxFup2TkYQnTKiWWBkDEWq3KDhW1waBVFs8My4JkuD51CVBElVt0bvUaRtEqOP5zy5biJMn96DpCqot35c2fCdOqCa2EXdfFYUpe77PnC2yzMGiMG4ESdwLItUBDSZSbamTVDz9iKKjrwrG9lZZcZMGyuHBBvMWUY8jhkHuxeICT12tNT3mfaSzYwAyjad7/Pw9L+L933hMGqh0vutz1RZnJNEYlabnM0N1Xs801/Px1LZDAIBfvrpfe47r+xGVGDVDi45Y7EedUOQ2HfXaKrM+ODYXJI5lhSlDWjESvbpNxSt7j2H+DXdioxBpDegZyVDDFdSIvM1D4RhVhVXTk20kV66Yjevfuigwtqe2kdiwkOz+m7RZog1Akm0nCTQmSYCqcR7qc9UzEr1qi0hZ3ZUZiS55pbjYc9VWNvdfx7ZQcgLDt5jGR2IkEdVWUvvR+SmmF7IZI7FNHEnHEDOeeyrcOwkQarKrA1gUJIo7YbVko+qQaiufZwcArPn2Blz11V+GaUnk3/qUCaMa291wpz45NN5V47L/ChNEXWTFvvVoGAkJSxY1r9hIsqaw1qlmFs6YCAB44Pn+yPFeaLRUN1tqipRmAiMBKPeZ/B15bTVcn9lIaiHDc1IIR3W8xDGSO8O6GVv3y3mb9Kotzkhcz4+kDFHrcovqTgA4e84UfOiSBQB47QupzzpBwgISo32nZ5m0M2cp7ROSXR4ebES+I4iMBIg+F/W56gTJ4cEG7n9+Dw4N1vHk6wdxtCZ76pHgIEYiFaLSMBLusdlK2yD3zbYsVs+Gtd/0pA0YjTv1WjqIr5vuuyaocUWvLaPa6hDEAS1OKloUyQB2cCBkJAmLrJr8TlRttWYk/PPL/UdxyRceZBXSqNLdwYFGywkjCxI+8MkLJDb7b6L7ryBIBPWV6/thDp8waE9jSHX9PIxE+b/nM9XdzzUp6VncjGrb8flzAGSDuk4lJe4QCaTacj2P20iaHmzLgmNZrVVVkeBO/XFivqa503rxF1ecCduKGtsplUavoNpSx5YuCFJczEQW0sprqyGotizL0qq26ky1Fb9R4IyEAjnl35/fdQQr/u4n2LTzCPuu/+gQ7n5uV3hPMltQVZxR1VY0Yv9orYkP3L4Bn7vnRfzevzzK2qB3SIGT5OKuU23JrtfB31aR7epQCwJEbUm1pQYsi5kIyk5y1mXxfTG1XJMLEmIkjm2bwladgvhYRX1xVLUV1nJOWAwjqq0EY/s3338eTlANamHbm/ccw5Z9A8w1eEpYEnXfsVpkZ0LGdkLTE1PT+2xAvWHeVCybMwXzT5wQ/KYMTJ1qiyD2XbKR+EFWUdWDKWojseHY6VNY6+JIyOdfrDnOfvd86S9rR4nRaQrHxaWsibORkIqOPtsW4KQwXqqqhLgFQRQG0ydW8eFLF6K37ETiSGgh6xViduj9kOpNFwckfiWOc21ku+dhx6HjODLU4GnkE3Jt0QI82EhgJC1UW1v3DcD3gc1CSvbvPb4df/zvT2CowYNfabyrzyWi8tUwEsLh4w0MNTzmLq46Y3BGEhWoWmN7S0GiMBI78IITNy7qZqAqCRI7ccMitk/jgjYgYhxJENluVFsdgfgSRA+WXjZggxdC+uekSOUjKiNJCEh804LpmDO1V9sXlp02HMhTQzaxf6Ae2d2oqi3PFxK2CQa8hTMn4Ud/+mZMC4WXOrjVIDfxvnYcPM4WgKrgteV5QdTshWecCIACrGTh3PR8IeFhOkGiY0QkyI/Vmhr1IgkIaL+nWxWLFulyKTXcqJ2FVIJueB8Exw4ZSUvVVjy7EyEuJCSwqmUHtaaHzXuO4r5NewDw98SM7R5fpJmhVXVW8GXVlpgJuKTx2nI9H7//r7/CLfdtRt31YFlI9FJj7r8JrJuXgtYLkkPhot5/pCad4/uB0GA2kpAtfPg7T+KmHz8v3aMInbGdQM/6QKhl8P2A6bE4kmo0joSV4NWk8B+oRzNhi4jkPgsTKNZdrs5S1ZPiOyo7dgvVlqh6ljUpotdWydQj6RzEdywzEkoFojCSiGqLf44yEiFFikJdS5pEetSWmq12al+w+O8/VossEuLOiyZpg9Fa3n8SAKS9U4eTlEbelRfabQcH2X2Inllk//ifv3kW7v3YRZh3Ql/wm/BQmI0kQzBU1N+/DtfzMe+EQPCqKkTOSPR6c7bjJAGrMbbTM4iLI2m63NgOBKnZbTtZ5SDWGVf7pEI0VIsedj97aS9+68s/xx98awOe3X6YbQxoo+P5PhtbzDVbdSRwZWO7rNrS20gODtRxYKCOuuuhHLr+tgpI1OWTI9CGrBQTR0Lza7dQt4be51DTZfdEjOTpbYfwzz97lR3baoMlgoJ3xQSsohpY1UaIn0VVo1StlHLduV5LtZsV2kiari9VSBRRiTCS2NuR+lGybcn+Eqi2wnZMqd3OQRYk/LbJqEyLS43tZuMptWpsl1Ok8MXPsS02mHRt0SLHGEm40z8wUI+tAQIIhjYhJkLMaArw+haJAYmKMbrh8pQg5JnlesEAJtXWwpmThLZ5u03Py2wjUftG6qzTpgcGd/U5M0EinCYWKFMFc+AirTe2i5PSsS2u5vA8affuWK0ZiU5wxu0sBwX1SElgf68fGAT8gO197p4X2E6zT2MjISEfTZHiS8lJW6m2/HBRpX/kMBJXj4RsJElGZ9pxl2PiSMjQLhZAo3kw1HB5QGJVLyDU9nTGdgKpog4MyhUIab6RQV9KXqmJbBcfMzGpW+7fjHf/4y+UvsnXd2wrotoi8BgfPtYqLWwk4piy7WCuP771IObfcCceeXkfU20Z998OQjJC2lEbSa0p11tXvX3E13JAyd0jGtvFYC3acaqMhLrCjH+evEPad6weG0cifhaNyrzmdvAbLfbqOiqnSPEizIsGtlhh0RNcCwEeDMWM3GRHyeq1FSdITgwYz5HjrRmJ+JjUXFvioiFC9Zwp2RZPCaMwEu61FX8fukkbJ3jERZjeEb2zaRPKePuyWXhGYCRckHA2IwaLqtcU76uVagvgCRcbridtQrQ2Eua1FS9IyBAfF9lOjERUbTWYIOFMUbUJElp5M4ogQaLaO8Tsv8H1Wxnb+U1sDWu3P78rWtUyTrXVcL2IaoAEicRISulVW45toVqysX5rEC7Qf7QmpEgxxvaOQTZCCjYSwWglG93kFyEKov6jtTACOPh/4P4bHdDlGEHCvY9kZkIDcf+xmiaCl08snccGLTy0W1UXe0ISIxH72sMYCffaIjBGotwPYyQpB7G6OO89Fiwu86cHjgIRRqI8N/Wzpwhm19erthpKjZWKY0u5xUrKvTphoas46Hz249YDUS3EbSTBM5/WVwkD8Vy2IekRjO1km4izkagxNmVJ/65PDUxq0XrTY8e3spEkqbZI3UOCSxV2ZIPUqrYavPponICIzIsEG8kxTW4sWZBkS5ECBHVnAGDv0SHm6hzXN8sK3kHd9SNlLCb2RAVJpUXWXrF927Ii6WFswdjeqrhX0Rg3giSO8rM4koZc3TDO0AsEuvuJPSU2wERGAnAmEsdI1JTyrNZ1OKADY3s8I1F1u74vV7gDREEiP4e6xEh8SbCIfZVTpMilYFUhRfcRRIFn8NpSjuOqrVCQqMkZPfmv2IeSYMdoSIyktWqr5FjoqfD7lY3twXs8Vmvih0/v1N6HTnDGqShERkJuxrQgTOkto1qyUWt6EUbiehobScR7zVc2TPw+4hgJwAWJyEi0goS8tpIYCetjjLFdUG2pSTYDry0yhEcZyb2b9uAVJX1ObwxzAfReVq7ggKFjJKS+U+NI+ioOJveUmCDpP1oLcmhJGxn5WkGhscCDSv2NMRLHwvK5UwAEG9ykqSMOZduK2l6NamsYIL4gkfLTzlutBKiLbBdjusSUGqLXFsAXBmI+VcVG4ocDQhUk9P/9x2qR3XqvRrUlMhISfLR48IVfYSSuPEEijITFilhMV+55PsRbUO0v9KzajWwnQUKuy2qWX1KZ6SoHUsZeMW18nCBpKAtASWIkso3EDr2Y7nu+Hx/5zpMs8l5qT7P7i3sG4uLGGEk4Bqf2BYLE97mjAb1r3+eLNLOR6KL247y2YhgJEAgS0T6m5to6VmviW7/cKuTaSs9IVHl06HidXZNUlySIh5oeuycdI7n+e0/jCSWzNDnL6CAyJ3oWnsfnDbORaFTaIiMhG+Fp0yfgtf2D8Dwf+0L2LDMX+WYpjqThepHnMElgJN/+wAX4wYfeBMdODlhW7Xo0btj1TGR75yG+H5Hy91bIa8tVBpSy2/ODBYcmm5jUsRoWQ1IZSCklI2E6fVJtDdQjaguJkUQECd/VcfWEvrBQoMLgcRKqe+y0vgqWzJqMxbMmMyNzkMdHx0iCv7QQBHEk6WshRDPgBrvi6ZOC/EOqaotej/hqqA9032rApY4d1ZUdYsWxmQpp8azJso3EsqT/6+wDWhtJzIIgCkdHcbWe1ldh/aDjxAwDdG3OSKIu6uJipsYoxIHyx4mqLbGdz9y5CX+9biNLfpjESFiurZg4koODDUwK5w6pt2jRqwlxJGrS1OD36LjqS2Ak0nFVriJshMk46TmSy2/wmYztcooUywJOPXECtuwbwKHjDbY+iN5d6lCzbcrEG1VtkQ2o4jiY0lvGinlTWzp1SMZ2y2IqUQK94pJhJJ2DODF0AYm1hsJIVGO778MCr2EgMZJwZ6C6zvIdZwsbCdtpEyOpR3Y3OmM7q63h+WwClBkjCY7V2Uh6hcVJnERA4IF215/9Bt60YDps22K5tkRBonptRWwkOVVbQGBXmlBxYFsaY7tg3GffeZyR0P0RAmeC4P9C9yPuvyUn2Dn+1x/+Or7xvvOicSSiF5cu1YhOtaUTYE1PWoTVjcaUkJEAXK3XJ7r/qoJEZyPxg3ZvuWYFZkzuiVxLh6C6oara4r+rtoZEry0KSGRJGwPh9p31rwe1ZAYbOD1Mg7M/zIHVFBkJs5FEBYRug5LktSWCFm7XDxwwykL5YZ37r8TcQxvh/OkTsPPQcewMsy8H96s3ygOkfiKvLaU/QmZmdnwLN3PZ2I6IN6iYIqWpbCo6jfEjSITPuoDEuutJC4LOtdK2uJvoxCrPkaPaFegv7ThjGUn4l5UopRQUDTdiu+gta4ztzEbC3Vxb2kiEtBsiI6FBKS6UZDgPVFuiIJHvQ6xqlyWORHdYKYxlmNxbxiOv7MOvhOSNXPDyE2myiBl7+fF8kRKNsg1XVgHRMzv/tBMwta8ScSwQ56tuY5/W2K6q6hxL3rVP7a2wTcmRBNWWhbjI9uC+pvaVceWKOdJvuhQphFpYb70qMBIp75oyflOptoRSu8/tOIL/+f+exZ+ufRJ112NeeeQKTPchRbYr7r+6WB0g2dguQnSjJlZO64CcRp7YkSwgbMvCSZOq8Hzgxd08Kl98FhH3X0G1pf44Mbw/cW3Q2abu3bQH67cEnlnixkHHSESvLSBevdoJjBtBIuXaEo3tQoQoPXjL0rj/hjaSXg0jUVVatBjQQI11/2WqLZmRANFFR2QkYuxLcG9cGNGCasXEkTRckZHwHTvFsIjMg3JMub4sYCxlAWQ1KDIyEp36h/o/uaeMJ18/hN+97VF+vNeakUi6bY+7dIvPr+7KdejV3XpZYSCOho2JSOv+GxEk4XWPh4vWtL4yWxyOqKot35cyP+uu64WMxNL0MYmR1F0vCEgs8bEjPuMeZbHWJSal90Y7dBLOvu+z/GKvhLnkyCuPotzFyos0B1SmEefOmpaR9IUMwPPAXJ3LpNqS4khoc+fh8dcOYP4Nd+KlPcdgWRbLPLG5nxv8hyQVmMJIbEtQbcmYoHH/1am2PvitDbj6n38Z9l0QJBobCWMkgup6uDBuBIkUNxDn/hs++J6So5mkwQTrEwTJxy4/U1qgaFev7u4rimtwrI1E2NkeGowXJInGdlsNSJSfQ931GAtzPUQEiRrVTTUuxHWImfHDtpsuX8zJa4smlef5+O9fX4+HXtoLFTr1T5zbqPhdUxNHQuepjIQErDjpGk3FKK16vyhxJLbyfxU6lYtORaEuwDThKZtuYGyXbSS6gESdGzSAMDmg/K7Ua+lQa4RxJIJ9Tex+tDZPtA2yaai5tsTs0UdDmxc5U1BMCc27oQZ3glCZRpzdLcn2I/ePC+RGGMXPMkRojO31pof/98QOAMD6LQdgW3yObN4jMhJ9TBMQbDzLJSvMNq3YSKpyuQcAsVmXCZKxXRPozBiJPQYFyd13341FixZhwYIFuPnmmzt9uVjIubbEiN+gkI+oN+8p29rIdsviA3xitYQ/u3whNv3t29gxNChoVxnv/hv8Zbm2vCgjOXRcjZ7XCBIhPQIvlcrVE+J9P/hiP/7h3pdQl1KTc0ZCWYPVRZMqEuriSCKMJMy1Jd7jUNPFQy/txeOvHYQKnbCgBW+HoIcmqFl+xe90+m7X85iAFRfDmhJprO7WxUWXsv8SdGpn3YTV3ZuaEp0zkmDxndpXYWMnIkg8zkhoUYrLtaVjTYnGdld1/01Wbak4bfoELJw5CYAujbzglBF+mNJXRqVkM9UWFyR8M1d2bCkmKk6QJAlIEczW5PnsXumZSMZ2wWtLvKZtWZjaG6QweklIOCkWJIsEJNqUIsWLMJJ5J/Sh7FgsLRIdn5jLS7KRaFRbFu8rMIZUW67r4kMf+hB+/OMfY9OmTfjOd76DTZs2dfKS8ZAYibxYVkuONHB6yo6WStsSIylHfqeJqKZbjwgSRUVDA1kciHFqEIAvimIaeRZHYsvuvzSW3veNx/B/7g+S8/WJNpLw2lPCSaIa1SlFivR9eDuMkTD1ks36SYKYFwpKZ0egXRapHMXMybrsvyyOxIkykqbn620kTU9yr1YXWZmRyP/XqeN0mVZ1jES1LRCLIqZBcSQA91jjaeQRYSS6SpGezxmjfK1k918pING2MFB3cd03H0P/kaHEDLsA8NmrluNr166U7pHGoe/7kWfmWBam9ZUjCVKHmjyOxLEttnnqKTna5JsAcMoJfZh/Yh9mTQkcCy5fPBNLZk2OHDdBYHZkbNdlYhBd8UnlSMcRI9l+UDC2x0TAA9z91/Oji/oVS2bikRsuxfSJVen4pOy/YhuWFXXiUePXWhVjKxIdFSTr16/HggULcPrpp6NSqeCaa67BunXrOnnJWMg2EtHAFSz0NYmROJEdkBeqDJixvSfqVcIFiaV1B1b7QgsB89pyfaYiSBIkasZTUbWlLoqe7zOVAhAstKJLaV1RbYluj47NXUpl999kG4n4HQkQnYFWt9iSkL/rI7+Bc+ZNZd5ogJweniBeW7weta+mnwGAv71jEz7xvafZ/9VnJqbnoMh21qZmcuo2HXpBEsNIQgExsVrixvbjTbajpfZUG0m0vC8x52yMxPcDTywWRxJ+/8AL/Xh2x2FJLThBI1QqJZv1kxvbBUaiPDPHDnb3qmpLDAp2hMjtIC26flGcPrGKB6+/hDGiZXOm4Ed/+ubIccxG4oMlqATCUtvCXBfHz7Fw3gQ583iZB98Hc2FOMrbblsXGs7qRqjg2ZkzqiRyfrNrinx1NQCLNUVZmYKx4be3YsQPz5s1j/587dy527NghHXPbbbdh5cqVWLlyJfbujerRi4L4SEXVlhUOWMlGUtbZSHzZRqLxc6edytmzpwRlNsn9N7K48zYBORKbVEyHB+V8XpIgUVwjg8BCrl4CBBWVD/ziFe75JFbda3rcbZgMieKAZ8Z2RbVFn7iKTt65UdtAMiPRqrbCXfrpJ03EhWecKBkzVcEF8MnLJ6xcAZLeo6oGEC+tpg+ZNoGzTfGe4vqs/y7yFesbjR1q9yOXLQQAnDSpypjY4eMN9IRFpugajJFovNeAcMH2OWMU0UoFdHSoIaiS4u9Xt4Eq2bzoGXk7iXEkutQhU/rKTH0rM5Jgw2YLqhvfj1dtiX0AOINUCdgEgcUH9iCyX9pa1RbAsweX7SAj8qRqib0zyoA9lKDasi3OsEWmXHZkuxshs2pLNbar2oixwkh0fszqbmnNmjXYsGEDNmzYgJNOOqmDfeGfZUZiRRhJbzkaGer5CBlJ1GuLQMkcP3jR6WG23BaMhGX/5TYSYgYqIxFVE6rx0/e5nUUMKqNridHYx4aachxJeG2qXyJODDK2u75sO1Ej211hF8kYCQvY4h45KnS7dnFRr5YCjxdVpSXutLjXFgWWyoyEFil10olQXWPFY51wEWHX0/RZ6/6rmcTESGgxpol/zfmnYOvNv4WesiMZ26tlhx0juv9ylaZ8jabnxdpIklKkAEGtDXXs0DXEeaxLXVIRAnJZ9l8hjkQVRqTaitpIvDDXmez9GFcyWWrT5h5n1CcRE6o8HqchMJJq2ZGcIER2SdkFSDBZlsU2elTqoJZgbBerioqsRzWSE2wr2a4huf/a0VxbjsJI7nu+H5+/54XY9opERwXJ3LlzsW3bNvb/7du3Y/bs2Z28ZCx0cQNAsIMhl1XaWfeUnaA2uOezwe77wUDqZXEk0Qn1nQ/+Gr7+vpWYPrEaqnla2EiYQOF62Wmh8e3Q8YY0ocWFXPVocX0eWKiLbBejsY83XEm1RROUCjup2YspEaC4oVWTNjJ1hGPBCa+fhpHoVVv8WfUIHnWAYGQWVVtkbA+fj2ojaXh+EDMQ3oDKPixLP7Fpw2ALwjHoQ+TQ1JHtjJGEgkRntxBtJNWSzcaA6P4rqkZPFGxInkcbHp1qq7VRuqKMHSBgVuLz1o17bhO0NHEkUUYSUW2JaeQF9isWV6s34xdYQGQk9J716kovnCv0+/wT+/Dq3gF2nI6RiM+UmPspISOpaRgzEIwrS1RtCfOqHOO8EJfjjCAFJOpUW/QMwr4+8so+/OvPt8S2VyQ6KkjOO+88bN68GVu2bEG9XsfatWuxevXqTl6SpXlWIam2hEllseyufOdEC9hT2w7inL/9CX749E7mVplkbP/1M07EpWfNBEAJDOUJQVBVQg1hgeytBAkgfT8aHEhQazV4YUCiZQk7M+E31VApByQG157KBIlgI7H4c9EGJCpOA3obiRtpl6BXbcmMBBCqwGlUWxRvQTtFNbsx9b0URjKrC0zFsbVqH3oeqvtvWmO77t5ISNPY0bkSi+qcnrIjecjRhoDbijycNn0CfvThN+PCM04MI9t9aORIYkAiQfTaYvfmyQkudendS8LircaRkHAD+By0bQtTJwSqLTGYdqjhSWn8ed0VucSDDo6yiKpzjuaM6/mouR5bzBfOnISX+o8y1iWOHxIkDddjz5TsJFy1xY8XhwbPxGuzNghxjCTIF+fj3k178J+PvR75XU6REmXZdO+2sKlqxUSLQkevUiqV8JWvfAVvfetbsXjxYlx99dVYunRpx6730xf6cfEXHsSdz+yK/CalSBGT8gmChNtIgt83bD0IAHj01f3BBIWVqNoSEeiNk1VbNLZERlKyLUwO29alJQH0gkk0IAJyQGIkw2/oQ68LSBSZA6VsUL22SEpxgUjqJdFGksJrS7M2lDWMhNQlOmM7ef6Qd5cqSJquj7JtoxzqlFWBtnL+NJw9e0qkH1NCZkiFrXifdaqt6He6nWUaRiK6eQeMhKu22A6ZbCThorts7hQ2hv0YRpLktUWgsS2qnwPVFj8mTrUFBONKV7NdFarkSltvekydBQTPx/U8NoZ+7fQTwzbk8aNjV3R/1HV1wzBRDEhs8piZhTMm4tBgA/sH6jg0WA8X36CRAUGQUJ+m9qqCRB+QSGNGZyNR1wP2XMJ3+MFvbcD/+L/PSr/996+vx9cf2SIdG++1BdbvFK+9EKTLeNYG3v72t+Ptb397py8DAHgpDBR6attB/NbyWdJv4mSQ8ttYYEF0pNenjMA0cXtKDo42g5fyhlOm4bz50zBjMnfb00FkJHEVEsnVkfSyTc+DbVuY1FPGvmN12JaFb113PoYarrQQOMouw/dpwYyyBh9RQ2WlZMO2LSmNPPnI6xiJ5/mRVA503eA+iJHYEUaSlDE2jY0E4IxEp9oiu9QJE4P+iyWHm54fLExOoGKolmwc4/WUAABffe8bMVnDLqcKcTWtjO1a998ERjKBGdujC4q4OEzpLbP32HR9Nh7F0sKi9xGPI4k0mypwj2IaklVbmro7grs72RtKzEYSfWaOxV1pDw7WIxUSaQx94m1noa/i4As/eUkel7YV8ZQjxhXnKUnqYLKRUJr7hTODvF/PbD+E6765IXwOgWsyXUNSbYXPaPaUXskmRMcR6BHqnEDibSTxXltPvX6Qpc0B9Kotuib1VRSAncbw8J5hAsvm2cIdUw04K4VGZdpFV8NBR8a2nrLNItvfeOo0fPePLkw03gJItJFQV2j9aSiMZKLg1XPRmSfhiqUnx6q56LyGQNfpvgAegCWCMRLXZ7SddskqI3E9cn3WGNvD/+sZSbjLbCR4baW0kdBk1RnbSc9+ImMkLjvXC1lmybbwtrNPxu//+qmR6zma3TvAPbccGylUW6L9LRSkmgWh1nDDImi06EWPEReZaULer6NDDV5SmJisoHKkGhRxxvY0CwpPk8O/C2qcJBvbaT6VS5YQkMg3G+pGxraBOVMDY/XL/ceYICaHF0ezaRJVSGWNAI6zkdhW8E5oboiR7QCwcEbgNvzM9sOsLZ0diFgaqVBnTKqip2QjLmmj2o+GNEZiVFsJxnZVPU0VEtXvxL+Npm8ESR4wF0TNoiW+HlG1ZVlgu3NVtUWCpLfsBNl/M7wT0Yc8UiFR0HEDfCGihWESU23xcyzNQk7w/GCgqio7+k1XvIoM6aQSIx2yvPMLVRO+6rVF15XvQxSeqRiJZtIkMhKNjeTQYOCUQIyKrscZSfBMLz1rJj56+ZmR68VNNArQHKzLbJD6/M1HtuBtX3oIgOy1xW0DOtVWEMNTZoIkOv3IHR0IFnZ676IXn+jFRn0jQy1tePKAnqEqOMV70S2yFYGR0FgTS+2qbsqObWHl/GkoOxYeeWWfFNleFwzhABcQomeVzqYV8doK2zht+gTMmdrLNgy+EJAIADMnV1FxbLy2n5fN1d0jPZIlsyfjtOkTMLWvjJ6yE+v+ayv9UDdoOti2hUOC27/IYtQ5bFkW2/CyZ0BeW+HfmutpNxWdQMdVW8OJCpP+GkESq9riiQZVY/uxGk+cF7fTi8O73jCH0WaVyq7fcgDL5kxhu1aVkUxS3ENV6CaS6BsPQLBjRI3tlZLN7rnR9FF1bGZElQVJsBirXlsWazv4K+fasqTvaAKIKgCClpEIiytjJI2ojcQP43oODtYxta/CnsmgENxHdq8kg2PcO6XdeSCoooxk6/5BVq2vGdltutp7GwoZCSVHjLNbUJVErmriKXOm9JZ5IKvrM2HEbSR61VYa0G5bfCRNT45M1zESlsFBIwA8HSOxgizab5g3Db94eb/k/lt3PWmnTeNJHZcqGCMhI3fYxocvXYArlpyMJ8OCWEH+Nc5ISHCLgkovSIJ2r145D1evDGLj6D0BwKd+tBG/eHm/cLz8bEShEPfebcuS1FdHhc9R1+LoukICiv42BHtPpzGmBAlN0GjmXr0+FVCM7Uo6DXqR1bINH8g0Qf/irYvYZ1UgfOau53Gs1uQ2EmGBdGybefXELXI6AVNrepGIffG3SkneLfLkioFKjO55zrRefh0r2FlHAhLjItudeK8tXVEinbG9pGMkQt0Vdq4f9O/QYANT+8rsWZGBdFJPicWgJNH7uJ/IRnJosC6poMSIfWpf3HHTwhGXa6un7LAknnH9ouEqJtIkRjK1r8zGZVAnI7yPkGHGuf+mgS4DtFq+VydIdC63tJBTShIR1P6FC07ELfdvZt8PNVzUGq7E4GnTd1xTx0Xug6wuJPVhb7mECdUSC9J0Q1WvuOkql2ypxoqYlof3OfKVxEi+8chWpT/hZsGJrklx711Vs4qCRHdspLCVwkgarhfLforGmFRtRdObyMfJhmvZzRWIqrZ8P94/P0u/RPz85X08QllgJI4NzJsWeISoFQJ1/SfUm26EaQEhI2l6kkGZMRKX9MWBi+vX/vtKfPcPL2THkYCNs5GQvlAstUs1WFSvrdTG9gRGIh5Pz+7gYB3T+ipsF01FmCb1lAVGEv/e4ib10tCTa94JfXKKFMVNdKjhSsb2smAbUJGWkQyG9zutjxiCxdycp/aWpch2Yltk54sztqfBFI2NRMzeCyTX/xDHnxiQqM5HFh0+rQ++zwXnUMNFrakyktBGIqh29O7NsmqLnjEZ1WXVlicJq5JtSYJE50ijW5CrZQdDDU87tlX3X1E1FTfm1GscjZn/dKxqI2FxJDYXJMZGkgP08upND77vo/9oUMpTndSqm2zJsUJfdZWR8Mhbz/f12fBSQPcydx8eEtx/5YXhojOnA4ivwSAv6sFfsVSqeAzZSCYL7sqVMGEdFbai8y5fMhMnT+mR2tBn/w3+fmf963jv137FFjYxeE/NtZU6RUoiI+HHXfYPD+KOZ3bi4GAD0zSMZGK1BDeMbE+aTHH2hDcvnI4ffvhNeO8Fp0a8mAC+WREz1gIiI4m2yRlJaAiO6RfLktvLXZDJzXlKX0XyXhPjJ5rEHnJueCh1i8RIFPfdpHVJsm2Q+68X9WqjRV2tczIUJo8UHVloPImMRLcBUeNIeEmHMLCUxqVibKd+i4JkUk85cp+6TWRPOUittO3AYOQ3up7ORhLn4LFk1iTp/0mMxLZ0xnb52vWmF3utojGmBAk38Pp44IV+vPnmn+LAQD3KSNQ04bYF1+dG42pZdv/VJS7MArJ5zBR2OrsOH2fXq0uMxMLyuVMT2xP7L+Y4Er/ndoxAWIhxL5WSxYKf6oJPvQrK/eP6vrToUYW+n2zag5+/vI8tqiUp+29UkKgCXe/+G89IxHot2w4cx8v9x3AotJGIgoSKFhEjybsrWz53alicSFb1AKLth8fiiPnVEm0kgstuEqYJDIFUW9P6ykL2Xy4kKZ1NOzYSEqpqri1x/ohpz1XoVFKiaoseCZHOHkU1U296GGrKqi2djYT6I95nnNcWLz3Nx6VobKd+H6+LtokGsxcRtKqtMC5piyYImo6nOZmGkVx93jzp/0eORxkJnarNtaWotuqu8drKBZYEselh95Eh1F0PhwbrUOuTlRVbQqDa8gSvLdlGEtgSstlIREzqKeOBP38LPv/fzhH6ynMn8ZrtgXHMsS3MFWwVKsTBwXc8rsK0gr9+yEjESPyK47C6zuruTL2O60eD3FR5SgLXcbjX1nu/9iv8889ekSaQykp0no5ary0NIwn+74eqLb6DPFproq9C9xe4k4r3d9GZ2fO56dx/JUbi+szN1LaDvEz6OBJX8dpKHlBiXAerWSIY2z1P3onr3lUeSKqtMDalt+zg+39yIRaE9dZ1kI3twWffF3PJeex+AJmRiOpkcafNin9psuyKThSiQAWiZa/pnmg8Jqm2Dg02WO45go65BozEw9b9UUHCjP6aXFtx771acvDDD78Jn3znkqAfGkHC78fC6SdNwFuXzmQbVOojPZZ60zWCJA9El1My7pIQEKHaEsgHn9lIIguYxyLb8+L0kyZGki2+3B8MwEbTx1DDlRaG+z7+Fjz9ySu0bYl0le14XE+yL4hBg40IIwndf73AYBwbaWtZ3Nhuyd+LoKBAEoJA8Nxv+vELcopuxeCuV23xvlRVRqK8yGO1JoYagXcTTaJjQ030hckOPQ8RRvKt687H//7dc5AFjrJDB/jC8LH/fApf+enLQQoW2w4i4UMBfPdzu3DB39+Hu54NMi2Q/p+ed6v0Fcz4TYZb28KEaolHtnvcK0eO+cl0exFIqrwwNqWnbOMNp0yTFnmKBSGo6iKA0veQ8OXefYDMSCZWebLSVoyEmK34+LgbdPB/0SVZ105ZYfXHVUGiMC/dM60yRhJVbaluyGkYCRCw4He9YQ6AaOJWIHhOp57Yh9OmT0BfpYR//v2VLO+Xqt5ruPm1KFkxpry2XI/vFkkINFwvIkjUOJKSY0kUXNXdku653Xeijp99YZj1i3uO4qy/uhuAOMmcSD90/e8tOziIBmoND1N6ZQEJcBuJ6NJI9VKagrFdB1oQXU9Wbanr375jdXa8qq4RhUfgwcWZkU6QiNH5KiNRd/n7w+uKEeAD9SZ6K05oM/DgelEXyLTlWQk6Yzv1iQLZSnZgawsYSSCAP/G9Z3BkqImntx3C25fNYoyEPIZa7RZZ1crwsEk9pcBJQvCWcwQhQ5HtWeJI+iqOtBsXrweAv39aGIVF/pvvP09iKGJALBm5v/v4dpw0UTZeU1uiamZi1cG+Y4E6p6qwBYDXtQd4TJgo4JnXltLPqrCDB/h4VD3MBgTV1mWLZ+BnSmlo3YI8ubeEAwMN5gYugqnYSlHVViuVJs1Vtdw23c/Prr9E+q5ky8LSGNvbhOhRQy+Oon1FqIzEDg2VpINXF/CkHEZZkGaCp3nxjtD/RScHBrqakqCNmiE7iOi2yRmJL1XGU0EVEj3flyatyswODAQCUWQkhLrLF6mBuou7nt3FfPZ1nk0SI2EBiXIcCYHUHWJOqoFaExOqJThO0PemRk8cZxOKgy5FiuqJNFh3UXLswAswXNSpf8ReiJGktZHQOKTrT+opw7at0NNJtv8EGwOPlTtIC9VgCyi5tsgTTDEeAwELEo8V5xWNxcdfO4i7N+6W2hc3SwQan+Sqzo8NPouMgcaNLtsD9VOtUkrjg+KZJHuOzVOT/McfXID3XTg/4p2mm5eLTp6MfcdqUpkGgmjLAGTVViuX3JJjo6/i4PDxeuS3pFozdI/UflPZAHYSY0qQ0Luqux6LX2h60XrJagQ4TyMvu/8SmmTEbPNppfGgSFpcvvPBX8N9H79IamfZnMBNVRUIlsBIaq6HapnXwK6S+6/GFVLqr62PI1G7uF9kJAq7EXdiH/jmY/iTf38C927aAyAuRYpoi7GkoC+VkYgpy+ndHKsF9VbI7uV6fqRPcfcbB50gUSONgWBBcsKNievJtciDvx6qGWwkBHqXk3pK7N2rWZkpT1NWpxDqy6kn9gnX478Hke3RnT5dU4RkI0lIW0+3Lau2+EZH57WlM7bLqVTkxVS1kURVW1E1HBCUU7AsKyJIdJvAs2dPBqAfC7bSH6koVYr3M6mnpGUkunPFol7qMSYgMQdcDSNpaBiJ7LUV7HpoYtqWPqVJuzaS4FppGEn8IvfrZwTZUMWBe9LkwF23psSRAMGCQNl/q47NFuWK4+BorYmnQ7XMpWfNiOmLxYytkteWch/7mY3Ejgxcsa+vht4tccZzIJpHSQz6UgUPtVOyOSMZagQVIMm9uen56FPazMpIxDviVS2jnXcEY3vT84T4CGIkLnrKNlMBxQmSn/+PS5jdCZBVW+JuU0yR4tjcwyqrIPnxn/0Gq3keXE8UnAizPwf/lwVJtC3dZxV0DyIjkQVJ1EZyvBF1/5UYiSMvpiojoe9JtRUn9Oj+1Dr1ule1JBQkALBo5iS8GCaNBfhiTs9SHLtOgpAlTOopa20k+sqKslpPErDG/Tc7PEHtUBNUW1Fju0zPaRJSHIdqBA3UY/m9tghp3mmaHYR4jOiNotacsC1L8lIh43W5ZLGAOwBaP3g6nxvbkxhJoNoKUqTIfdDFj1CfWmX/BeQ0FKpNhVReJVuuYthXKbEAPXGxJWRlJOJlGevVMhJubBd/J7VMrRHESJCNJO5dz53WJ7mAO4yRlNki4THVVqi6EYR+lrWj7FhYPGuy5NYr2Ug8D56gIpHHnnwhXY4sHXRxJBMVZxDWjiayXXS3Zm22YCS0+WEVHEX3X02/VUaiE86TegLDd6VkY7ESA8LUTKRiFoZLpxiJyoKC71peqhCMKUHC9dc+U201vGj8gjoZWBr5UFWgLmZuqB7LmwxPvFbLY1IIEjrmbUtP5kbEZtRoblucyldKXLVVcWx86XdXYP1fXgYgWrqXwBmJvLioz4HyA4mFrQi6BbeWIEhUYSgxkhgbScmxpWfbqzCSiI0koyARd5NqZLvcd4t5AYq/DzXdIAuz66GnbEfULa1Az/uEvooU8EmZEAAxsj09IwnGerKNhNnIwutKyUNVJ4aSyBDinzGzkQjvIV61FRwjqrbo2epUONy1WDa2s0SGzNjOz9UxKVW9HfeqLlk0AxctnB6xq9rK8xILc6XZLE6olHC0phEkmnMZG9MxEqPayg4mSBRje8RrS4kjYWnkXT/0vtHbSNr22kqxfqXVaT75V6swqaeE//vEdgBBH1W1kGVZTK1ScQRBEhrbZ0zuwT0fvQiTe/XDgDy7xB0poJ9UlkXsThEkoVeYGKVf1zCMQA2XkZEw1ZYl9YlUW1SzvV2vLV1qlrom60DJtmHbIRMUS7YONfHBb20I78fByZN7YVvAjMk9kTZ0IIE5f/oEQefOc7MBPLI9S0BinCCJRLbHCCf1OrSzt6zkBYyaEufZxKqekejcf4NFtomr3jiXfad6Lv23c+di3rQ+tsCrxvayhvUAfGwsmzNVuVf9/fzN6qBQ343flwtR0e0zRiIMlzSLe8mxWPlsEbrNrMpIxFeapCovEmNLkNBuUXH/beW1RbtXKoI0pbeMnrINC0EgmC7fVB7QjmFCxcGA4nLJjkm5ClDAlNgncUcIBLr9GvNS4e7E4uJBXl9x/aUKibJqS6OnDb9Tjay1RhAMKer86xrjuYXArVNVK4qMRH2PorFdnGAiI9Elbcys2vI0gkSTzZjcqm3LkmwoW/cPMBfpnrKNJbMn4+lPXqEt16wDPbvTpvdhz5FAjUiCSl1EmhliB8QqniJk1VZ8/i5VzcLsEZaVKMx06pkJMTYS7v7Ln3dPxcH6Gy/XBi7Svc+Y3IN3njOb31N46PF61EZSkRhJyPbPPhn3fPQi/OYtD6VieeoYU11xk47VoWRbkazdgD7QlQQyV6cJjGR4CMnYVG2pgiTJa4t2T67no+HxwlJP/tUV2Pipt2L2lB7B/beYfialmcjqZSEOGnURti2LPYeyY0mqrTSg5xLN/qs/VteHmivn+SrZFhNu4pywYgRRtWRj+8HjODBQjzASYluOwoT6yiU4duAmq5YgBrIb28X5zI3tvC8fuWwhnvmbK3Dxohn4jYUnRVRbohCl95FWiIiYP30CExj0DNUgNNeLZ87f/5ML8Y33ncf+n4aRUD0S3UKq7o6Zq61is1KhW0hV93T1WFGQuJ6P3oojsWRuI0m+Zk3n/isZ3uVNFo+Yj70d7T1ZmkWdkEZ9rY4hgr70gsxERkK1NaYECUlr3+cDRhdHUhF27mIaeVeIOaCBGsQjZA/00oGlueiLX0SyvnhxgKs77aiNxGFpPNK1LZRvlQRJPL2OqLaaHiaHgXVlx5LS2bs+j6qns9SF7Z3nzMYre4/hL//fs5FJJLpyzp7K1UQTqg7rT70ZDcrKbmzXqbYEz7mJFUzuKeNjq87EH73lDNi2/Lso/9QcTllw6gkT2H2pUeKsBoXnxY7TN5wyDZecNQNLZ0/GG06ZyoIoVUjuvwmp+NWvyoKuPk6QWJZ+/EyKYyQOqbZ4/XFdaeO48cf7ytsJ+hqn2rK057Wa++oGkJrRnZbOocbWChI9I5GFljZTd4cxpgSJuNAcOU55sjyolESNIyFBoiuCVKLiTn7u5L8MNIin9pUTEyVmgcxIopOArlkt2aiW7Uz2ATG9fpLXltjvSGR702UMbPU5cyJxIaJeXXf+tRfOx2+/YQ6eeP0gXMUuQe04toXZU3i6DnG3WtMU98nOSARBEkZ6i9/1VWQNcdm2mVFXnMefu2o5fkfQ62cFRewDXD1YUp475f1Kwp0f+Q18/0/eBMfWj4cII4lR7cQJaMeOV23FLWyi15ZY+U98d1R8TZcRQfReS7pujXlticIj3m1ZtzjroNoiVC8yXZtJsGNUW2pQLhAf2a5+7iTGlCARpTX5YDdcOXspEI0j4Qn+vMgOjYRMO9l/CW88dRque9Np+OLvrEhMS5IFko1EmQSWJUfyinme0oDu3fOhGNujfRTrtouoNz1Mn1DBfR9/C25697IERhKcV9b0b8msyeg/WmMpZVSUbRu2zYPIKGkjXb9IRuJ50foafZVoFDQ994mCkFk+b0qiN1MaiCnC6Vri30aG8qol29IK1WhhK30wbqxqy4pfLONcXyXVltAncYEm70KdemfWlB44thVJx8LbCQWJJo5ETeCoO6/VtFTPS1JtZXXxF6EXopb2r/q5kxiTxnaAC5Km6yVn/w31uZ4fVCpUHzwJGd9v3yfbsS38dZjZs1yyAY3BvVUiPxWyIIkOZj5xnDCGIX37lDom+Cy2Gz2W8jXp3H8rJZvlZKqWHNSaLvwwUy2pMahN1fMMAM46OQj82rjziLaf9M5mTenBq/sG0FsuwbYCu0St6Ub61J7XVjQ2pk+pGlhybMYEJ/YEHkYA31FnxTfedx7zrKOuk6BS3V6zJOrTZSII2uKfWT2aFF5bYsR+XBfihnePkLZHrPwnvjsS2LrKmmfPmYJn/+aKCDtU+6qLI6FrlBWnDfG8VrEfKhPiAYnRY+ed0Bf9UkGcANC7zMertowgyQFxo8gEiadhJMruWty96nYkRXltiShOtcU/RwMSZUaydPbkSIK+JDi2UI9duHcxwn9itcTSyKv9DwzrcsW7SslGPcwJBQBvXXoy5kztxb/96jW8tn9Qu7CdFQZ7be6PJscDuACdNTUQJI7N36nnR9UOcWwwDnKJ3ygjmaAwkpJtsUqN4k5bVws8DS4RMg/QGDwYGvDJ/kTPvel5qd3US07U1R3Q1SPR52xSxyqzkdjRBZmdE/N9ybHRU7IxUHdZmVzqI4GERFMnSRBVMYqg/jO7msbYrtvE6eJntP1X1cphU+J5J06o4LNXLY/NJJHUHkGv2kpgJMZGkh06ad1w/RY123nKglrTiyw6VLdD8/7aQtyuOKvXlpw0T+O1RYykZOMP33IGvnbtytRti3rauDiSM06aENsf8jwRVQfVULVFi/P0iRV88KLTWayPbpGfPrGK6TEqC7oOACwOmUuQP0zQs2uYWhaIcoMSXYpQAzpLDrdNicJDV+88K2ixOzgYCpIeOdV8FvffD128AP/f+adEvpcYScImKi7XVtL1VYEk2sbIPV1kJOKiOKEaMpIcc1E1tutUW7qxl1a1pQpVXRVTywqqkKb12tJBa2wn+5CGPZmkjTmg0x82NWnkxQFjWZZgwIyqQZiNBMV6QNDgXX3ObLz/TfOl62WBeLhOtcW8tnLo5p0Yiiw+h7hU9wCY+21FYSS1Jo/toYFO/49T7c2YFC9ISID+xVsX4a/fsQRvXXqyNl9TXogbEdePChJVZRUY20MbSSg8Kk42+1Qc6DUeGAgYNzES7s3ltVz0CFefNw9vWjA98r2tMhJPv7NVv1KTUT79ySvwibctim1b7HfJ4YKEyuMGv4s2kvyC2GGCJDofuGor3l7Uau7rHF3Uz1k2MLE2kgT3X579l/9mGEkO6ARJw4tGtjvK7pr7mEcNsxTd3U4JUx1oIM+a2oNPvnOptm9pIB4fZSSi+2/2zsdR5LQ62LobMI8egR1UHFmQULus6l2M2kk1aOv62VN2cN2bT4NjW4kG1KxwcxjbyY5CgkT0SmoH9OwpdT+5E/NiRumN7bHXEIYRBaRqY4dUY7tS+2NKbzkSLxMXuFcWMi/EMZK+hE1LK9A9sbiqUnTeJAqSjHEkuo1XlrcSxySSisFpVVvDFJE4xmwkekYiqrxsK7oo0kJTCzPHihBtJO3GkYiggaxOxvYCEqNqA5o44i4vT9vSwLbkYx78i4u1mUoJImuplh0cPt5g74oX4Ar+H6fyi8sHBuiFjxwd3d5+6T0XnIp1T+2EZQVjrLWxnQsSUmeRWqZd0CJBjIQVvxJsQu2O04iNxNNX0YwmbYwuZOriHznHtjGEYANXZYxEvwmgPiwVsu6mBVdtadLIU8XKBNVWq2eapNqij1lei24u+36MIFEEiBzZbgRJZrhhcjnxYTc92WeLUlgQKI4ECDx8Jik7x5Jt47gblMEtkpHEFTfKykjEcRINSOReV3nUKjIjEdsVPtsW5k+X7SQqxAR4FcfGtgODeNuXHmbnA3yCxAmSJEai8/TSZZDNi/NPOwFbb/4tvOnmB1gNF+rTYN2NZIoV74EESF6PLRX0vMhGwhiJ8AjaHadyHImctFGEukZVlJ0xEN0AqK+Xdsxl2+aJFmMYScmxcOdH3oy501p7PalgSRuV+Jvg2pbUfxE0tFotyFHVlvg5nXpM6q8ypidUAqeWJPdfat+4/7YJz/Mxra/M8hoBVGpXqAVgy66JlsVfWq2pjyMhYVQoI4kJoMq66LVKkUJoV5DExZGkGadViZHYUsoQul2aH3GMLMkjR0ffxQyyRU0mxw6Se5KNZFJPSbvIiv9nqq0CDO0Afz4HBuroLTvsvRYZzSwb2z24MSwnKY6E0CodOwumcyymAtUVtgKC6Hex/EEWSBsux9Y6qWgZSYIbr3RcTECieG6Wt6LOA/KOTCq9oKbLCa5tBElmuJ4f2dHuOjSEHzy1E0DwoB0rcE0kqii6iqrlagGE1RO9wm0kdJkoI8m24Ou8WghJbCVr23ELVRrqLKm2VDuOYmxvpdpSMwkDKRhJgYLE9Xn6kxMnVLXFzkSnB67aKmaq0fM+OFiX0q2o+ePaQdTY7qdK/qer/KgykjhjuwXOXHW5tgDghAnxDhetYFlBtL3nR+cCU21pxpGaDj4O4hizrPaN7ermZGJPCTjSIqqf2XNkFjccGFuCJHRTFNNw3L1xN6sZ7dg8z5RjWWiGx4vRwjpVE+XrardCogjdS6d+ZYF4uGrYVHdhWRHntSUzutb9Fe0V1UidB1mQxBrbBf15w5VjYXSMo0ivLd5X2dj+sVVnSpUF+fX4tUlVWpixnRjJsTpmT+VpYcTX27aNRPjseVHV1juWz8Idz+yKnEcOHeIiqtaEj2Nvrudz99+SKBQFQTIxPtlpGji2Bc/1I96NpNrSZVVQKx0mtS2eY2vmSzs2EtqI6FyfSxrbFKn4DSPJAS+MTO+tONrKfCWb20OCB+yzFClAYCOJRLY73OZSZGr/uNxU7aRImawsVklR76najvHaEsdmmu6KjEQVaHS/5B8fq9oKJ1LgoSILEt05ulTkIh66/pLMHi00OUm1NWdqr1RulVAWrke2kYkF2UjoeR2tNSVGklXdmATJ2B5Gtovffel3V+CzVy2PnKdjJCrDVMf3re85F1998GXMmtKDnrIDy4ofAydOaE+QWOGcV/vEvLY0102dIkUpTSEezw326fsqjs13nzsHF54xHU9vO6S/tqaPjmXBhY8c+8dcGFOCxA1VVYGuVV9djBZEvksQje06G4nNUnMXaSOJM8C1YyNRGYlofMvTd8nAHqPmSiP4epQ4Evkawfmk+o1ze2TpMRQdsW3pz5FtJNHZdMqJ2Q22thVUjKSiVnHqQvEdEhMpTLUl3OtkQZDEqSHzQLaRBGxcqkbo2NqI+DSCRO3ainlT8c+/HwTJVsN8cHFj9YQ2BQndQ0SQsIDE+Cj/VuNc9nDU2ymyaDTE89/7a6di/onxDi2sHokt9wGuiSPJhaBuQryr6KRqiU1ocddAg8TXpNOgyHa/gOy/IopiJOLYVz3OLOX+skKmyvz7Vobdez56EW58+2L2f8lGUtLbcajanaoKIZAgaSr2kbgkiJ2ykYjG9vg0N/x7bmwvyP1XeN4SI9EYd4uAR9l/U6wU3NjOOxBxp09Y2GZP7cXJCVUjpxeg2gI0NhKai1r33+BvFhuJqC4Xz83yXlRVWZJGgbn/amyXXRHZfv311+Oss87C8uXL8a53vQuHDh1iv910001YsGABFi1ahHvuuafdfqYC1U2Ii7b+0KUL8K9hihDb0rvLDVeuLVWIxV2/FayESUv9zbuQxrEQK+YYwqKTJ0k7fkm1FaMz/+t3LMHGT701ImgI5P2j5lmKu7dO2EgCY7sgSGKEnjjpqdrmTI0tJW8fxLYJJc3ClRdiACYrtZyiTZ3777wT+nDLNStw8aKTIr+p+KO3nIF1H35z7O/tGNsBITGoaiNJKPiW3mtLXsTF4+lz3sh2x9YXIFOvrfOyLGoT1QptCZJVq1bhueeewzPPPIMzzzwTN910EwBg06ZNWLt2LTZu3Ii7774bf/InfwLXTZ8sMC/I2N5T1t/W9IlVLJwZJAAMGIlGkmuM303PC1OkFNdXLsTkyZdVWMmBT/K59L/cjCSFsT2uaXFSiu8jzvhq21ai+ocn7FMYSUwHiowjIdhWWEnTTRYkqvvvvR97C37njfMK6wNhSodUW6KspsJmacYQC7JVjr1yxRyWKy2pb5WSnVj4a2obRcHEfqmLspimRYWdcl6K51qW/n1keSuO0EfLSnaWIcGoUzl3BSO54oorUCoFE/zXfu3XsH37dgDAunXrcM0116BareK0007DggULsH79+vZ72wJkbO+J2dWKjzSweQSfRcNWhJE4nWEkfBchXze7jSTptw4xEovH4sRnc+XfJzGStLs0YluqG32caqvIyHaCYwcVI4mRxKkbxIXKsS3MO6GvkDxb1B5hSi8XvFJphDaHqchIXM9nG7RWEGu2q+Cqlvz9andRpHuIqLYSUqTkiSOxFZskZyTp+6oykqR7VzejAO9319lIvv71r+M3f/M3AQA7duzAvHl8BzZ37lzs2LGjqEvFohnWXI+zkagpP3QLrbqQs+y/Hgo1kjA3ZFuOcG/Ha0sFt5Hke81JgU2tfOPFSSm5/5bS68xFxL3TOCFZUhbzIuBYFh55eT8+c9fzABKM7Qnjqe0+iIKkT6/aanfBFTPMBjXb07GcpDGcNkK8k2CCRBEYlQRje9o4EvG+ls2ZgkWh5kM8N28cidj2qRonkZWnTsMH3nwals/lwZp2zvUkL1q6klx++eXYvXt35PvPfOYzuPLKK9nnUqmE97znPQAQSdsOxD/E2267DbfddhsAYO/evel7rgHVTVg5fxoeeKGfpbDgfeDHiqlRJI8UTYSqmheqCLAdQ3g5W9OXNEhM2d0mI0lKtcDVZvpzyykZSVr3xLgUKe1Um8sKVR7rAiHV6xU9keNsJGVFFdIOpPor5LWV4j1ZVlBxUSfI4uKmhhN0D6oKVSxspSLOlhltm//+7Q9cILdBBvssfVU2vQDwow+/GXOm9UaOnVAt4a/esUR7/qgRJPfdd1/i77fffjvuuOMO3H///UxYzJ07F9u2bWPHbN++HbNnz9aev2bNGqxZswYAsHJl+loZOlAltz+66AxctPAkfPbuF/Dw5n3sd9XXXufVpDN+c9VWW92TIHqK0XXyIIlssF1gAaotdSHhcTjZGIkuH1gaxKVISVO6tqgMqOJz/Ierz4ldFMU+FaVWY32IsZGocQztQDK2h/Xp07ZZdixtFLxu05YFqkdiHtA9qJsSniJFo9piNpLktpOYZ55cW7r3uUxgHK2gM8B3Em2N8rvvvhuf/exn8cMf/hB9fZxyrV69GmvXrkWtVsOWLVuwefNmnH/++W13thVcL2Aktm3h7DlTolRVeKaWYGwvJdlIbDssbNWZyHba/V2x5GQAcl6qLO3ojHHU37zqlSTVFv03djENF1A1LkDtZ3pBMgoYSdjXCRUH7z53bvz1hOdd9EQW5VJcipR2L+lpGElaJlEOS+aqyLOYEh678XI8csOlmc+L64OaQJOpthL6nTX7r76N9H2NU22lBQ2Hrohs//CHP4xarYZVq1YBCAzu//RP/4SlS5fi6quvxpIlS1AqlXDrrbfCyZHGPCs835cmVFKxGdFFT/xedR3mjKQ9Q6EKujYF2H36XWfjzy5fmDm5H7WjS1OelpbHQVzz1Tb4wqA/l9JlqM9TzRWUtm+xgiSFkCzS/RdILuYFKDaSggWJOL4nx7n/trnhEdPYuBRHkrLJsmNrF6+0dT10OCmhqFkW0LX7qtE5DsQY29N6bSU8oDxruWzzyn7+qFNtJeHll1+O/e3GG2/EjTfe2E7zmeF6PqqagjUE8ZHalmhD4MfF1SPxC65HwnMM8b6KuZPSgvqkU/1Y7P5yqrYUwSu3rf+eQM9edcWuNWU38LQ73Xhje+tZVpjXlpVWkBRv6CfEMhJJFdLeNS5ZNAOffOcSPLP9MB7evI+pjNOg4ugZCU3F4VrYdHBiGAmvR1JMHImKdtPI53lmeW2ueTGmIttdX16Y1N1q1IU1KrVVQcJLmBad/TcUJBrHhCyoK5X4pGuE/c27kEqLUwwjiROuJSZI5OeplqnNskARrn/rIvxeWGt8OBkJPYO46HtCJ1VbXJjZkgecbGxv75qWZeH9bzoNE6slFkeSVuBX4lRbKXf2nQRljVYZCY2tShtxJMmCJPib1/03zzMbbkYypgSJmu5aXaMlry1br/pRd/ZkqG24XqE2EnrRnteeIBmoNQHoVVuMkeS0kYgDWM1yq3NUEEEeMOqie+lZM3DChAqmhq6raeeIuDh+6JIFOPeUqQCG10ZC76yVHUtiJAUvnPS81cC9dheeuGs1XS91ZDsA/PkVZ+LaC+dHvtdlkRhuUHXECCNxgkBInQqNqb9b9DtpjLVrI8klSLrJ2D7aQClSCGo6DdX9Vze44xmJ1xGvLV19gSxYNncKLj1rBm7WZGNt30bCz5ujqN1a2UjINVZlJDMm9+CJv1qF5XOnAuC1PbKChGMq1VbBXltpGYltFe/uascJkgIDEnk7FjyfVx5Ng3csn43z5p8Q+T5tOvZOggRJtGqjhfs+/hZcE7Jc9TcgW0CiCivHvSd5kqbBcLtbj6nsv2r0uVoASWQUYhyJuJuIK8TTcIu1kah1OPKiWnLw9fedp/2tXRuJmvFVbjv4G+v+Gy62cVkGKCNwrZE+dc6/XruS+dGTAEkjJIqykdCkjEvBw6+XXshlRSlGkJQT1JB54djgjiZtjv3RoNoaCtWqurLHcQb9tPaNpDnGEj+m6SQ7RxAk7TCSbvDaGm2IMBJltyu+a8vSq2dU1VYnVAZiX9oVJGmu0W4cyQSNobvVjoeem1rIikBMZaiRnpFctnhmpP0091ZcZHvwNy6xJIGEbifUCvTco6qt4gISCY5th5Ht7dsH+Vhsv195QexftZEkIT0jaa3ayvJiJC+8HM+MR7ZnPzcPxrYgUdRGcv6bdKotNXlaUfiji8/A1v2D+N2VUTpdFHReaVlAt3uKphYCd53Wn1uOMbYTSD2kenGlRVwCPh2Ki2zPykiKFyR035OTGElhNpLAhpdFtRXb1iiwkRB0jCQOvH5Rcr+T7quINPJZQcMhb3qkrBhbNhJfZSTRIkjiZ+7V1NpGop7fLqZPrOJr166U8iUVjVYG8VbYc7QGAJivye/DPN5iBrljB3E6cYLk9y4IBOiFZ0zP1TddedE4FJlrC0jBSGg32IF62U4MI9ElCSziWlSLpyjVVpHq4byIi0nSoVCvrdRXbd+FXE0K22mMPUYivPC0xnZbEiSK11aHVFvDgXZzbV14xomYd0IvPnr5mZHfqMWkhaHs2FJ1RBHnnjINW2/+rVz9AvhES1NCuGhjeytGwlJudEK1ZVuYPaUHC2dMij+moHEal/05V1stNh7DiSzVKtOq5NJ5baW/93bXnXYyCeTBmBIknif7u0eM7ZE4kuBzkrFdGiAjPwcyIW2Z0DhMn1jFw5/Qp6ZI485ZduyWwXt5wW0k8TOcgkmLjyNJvqdOu17+/H9cmqhmLWrXL9ejabOtURCQSNDZ/OLgpGRSaRhJlltvN8B0uN1/x5Qgcf0WjET47Nj6xVAX2U7oPkYS/C06lbnYdtI4/eOLz8AbT51W+LUBwUaS6C0TCJLiI9vTuf92wmsLaO2VVZj7r2jwLYiRjIY51JeJkaTrd9K7ZmnkM+xEdUWqssAIkjbgevLgj9pIZKGgEyTR6mnF++cPF9QqjEWCGRATHsqHLllQ+HUJaWwkRRt4nZSMpNxBr60k2BYKcdUlFJkOv1Xc0XCiNwNLTuu1lZiFm+mBU1+WPfvAuzS/asukSMkBtW7CadNlbyPxmVqWJbjIJe9qCaNhN5UF1NtO6OpbxZF0GjyOJFm1BRTnbUeu2q0YiS4+aThAz6Q4919BkLTZ6HDvkJOQpQ9FMJI8QrTdOBAT2d4GVGP753/nHNxyzQr2f7UeCTOkJbwsOatqd6FdG0kSRtp4mmax/uQ7l6CnbKOvIDsN1WpvyUg0pU+HA6yAUlHGdsmm2GZbtLMfBYIkC5yU/U76mddsT3/vPDtCvudlItvbgGpsn1gtSUFsIuJUWyrE30aD62IW6Nybi257pB4Js0Mk2H9+Z+U8/M7KebG/ZwU5b6RlJMMtSNJmqk3dXoGqreGuIV4U0hrKk9aGHPGIggBLf458fvjXqLayQzW2A/GR6SIjSRoE3aza0hXuKrrtkVJVJNWQ6BQYI2nBcMophFwnUHQaErtAQZLHc2k0oIhnmqtCIrHaLlFtjSlGonP1lBkFhM9WKoZRijm/G0C7mY54D40SG8lwCjJy3midtJH6Nrz7tLSG4dTtKe7y7WA0qLbWfehNGKg3M51TBMvLw96dNlVTw73RG/uCRPHU4p/TDQ6ZkbTfx+EE6WQ7aSMZqYUhjftv0aintJF0MkVKEvIEviVB8tpq19g+ClRb58ybmvmctHEkSciVBr5NG4lhJG1AlxPIjmEUE6tl1JzWeZ5KBRYMGm7oAi6LwkirKrj77/Dt+ikJaKWUfNNZ0rcUiaJjNewCN1H0mkaD11YWFPFM86SRz5KUVIfhTts/ZgSJ77fOCSS+k0//9tmpMu9WpDoP3TkJxrTX1jDaIcjY3kpV2K5+Oy9oqBZnbOef22WeI81g86KInFUsjXweY3tery3DSPKBUkQnPzj+28lKxT8AuHrl3Mh3lZLISPL3byTQSa8tfo2ReSjkYjuc6iNSbbUy8JdGQMgBxe9CpejqguJIukyOpHLIad1GqB7LcA4Pps13TTp/uObH2BEkfmtBkvRM4xIIltvMeTOSIL7VCfVPnvKhRaKv6mBqX5kVuhoONJkgGaWqrYIDMCX7YJtDaKQZbF6MlNeWbVuhHbc9RmLiSDKC0molPfg8uwqRkXSbaovKsXTE/XeEdd7VkoP1f3l5quy/RYGptloykuFnS0D76pBIezGOKnnQtaqtAry28sSRAME4ym9sD/8aG0k2cEYSf0yewVDpYmO7l4Kl5cVoSMJXaeGGWzQojqSVgBgpRlK0aqvQgMQ2U36MFIoQzvzcbG3YdgHG9mGaImMmIJFsJImMJEeSE8lGkr1bIwrfJ+Nw8T2nFrtth9kOfu/8oBjX3BbqtE7WbE9C0bnFigzGZV5+XTZeilDh5vVwLNl2bkFiD/MYHDOMxEthbM8zGMqS11b280cSpO7rxM44T/nQbse1F87HtRfOb3mcZVlwbGv4GUnBBu1CC1sVrHYbLmRhUl9/30pM7atEvs8rjBzbyi3AhjtuZ8wIkjTG9jzPVLKRdNmq6XaQkVCT3aaqGC6UbGvYbSSs7kVHbCTFtDWMGW0KQRYmdelZ+rx+eeqRAMEYajdFilFtZYSXQrWVZzfUzdl/OUvrpNdWtz2V4UFpRBhJ8LeoXb+4iSquHkl3jRe7AJbHhVG289phtcMdRzJmBEnTa737zjOG1fK83QSvkzYSYiRdxtKGCyXHHsE4kmLaO2lSlX1uOyCxYCE3XHAK2DDlSSMPBPM2t9fWMAvuMSNImLE9MY6kmF1Vt6CT7r/j0UaSBXOm9mL2lOGLcQGKt0PMEAVJUbm2umzAFBJHQqtsxiZs28qtmuLGdmMjyQTm6protdUeumwOdNj9N/zbbQ9lmPCDD71p+ONICg4SnSjUNm/XFjYasv/mQTFxJPmEUTs2krNOnoRz5k4xKVKyIk2KlHZVU11GSATVVudsJN3G0oYLwx3jAhTPSMT5Ml4j24uJIwn+Zm3Bsa3cgvfty2bh7ctm5To3D8aMaosWzaQHn3csFJFvZyTQWfffsO0ueyZjGUUzEhFtq7a6NNcW3XYRAYl5Itu7ZX6NGUHi0qJZsNcWwHf03bb77qSx3S6A8hsUC1qsUyS1To2pfWWp7bxot1DTSKEIN9q87N1pw9g+3BhDgqR1ipS8r4RFDOc8f6RAC4rTSWN7ly0MYxn0LtwCJcn0iYHBvd3X3K0MdtmcKbjgtBMwd1pf7jbyZP8FAieZYU6OkBtd0s3WYKqtTjASp/1dyUhgOAISu2XHNB5A+wWKHyoC0ycGkdpDDa+tdoa7Yl9ROPXECfjPP/x1yfEgK9gtZ7z1kciOkBeFLI1f+MIXYFkW9u3bx7676aabsGDBAixatAj33HNPEZdJxKwpPfi73z4bi2dNjj8o5zuhNCndtmh20muLWuyScT4uQO/ZLVCQnBgykn3Ham21U3QesG5CXieI91xwKn73vFM60aXC0bbX1rZt23DvvffilFP4DW/atAlr167Fxo0bsXPnTlx++eV46aWX4DjJta7bwYkTq/j9Xzs18Zi8i16JTYLumgUsjqSTXltGkowa0DtJU/kzLf7y7YtRa3i4bLE+/UdazJvWh8sXz8S5p0wrqGfdA5ZGPuN5/+2N0UJ7oxVtrzAf+9jH8LnPfU5aZNetW4drrrkG1WoVp512GhYsWID169e3e6m2kVcQECPptiXT7yQjMe6/ow6ckRTX5pypvfjatSvbUu0AQG/FwdeuXYl5J+S3NXQrRroI3HCgLUHywx/+EHPmzME555wjfb9jxw7MmzeP/X/u3LnYsWOHto3bbrsNK1euxMqVK7F37952utMSuRmJ052LZme9toK/3WY8HcvohLHdoH3wOJKxO1dabjMuv/xy7N69O/L9Zz7zGfz93/89fvKTn0R+8zUDOY4NrFmzBmvWrAEArFy5smWH20HeF9mtPvDuMMSRGDkyekBCvUhju0H74GrgEe5IB9FSkNx3333a75999lls2bKFsZHt27fj3HPPxfr16zF37lxs27aNHbt9+3bMnj27oC7nR95FrxyOgG5bNNPUaMkLFqncbdJ1DKMTxnaD9sE30WN3ruSWkcuWLUN/fz+2bt2KrVu3Yu7cuXjiiSdw8sknY/Xq1Vi7di1qtRq2bNmCzZs34/zzzy+y37mQVxCQaqv7jO2tXaLzwqRIGX04eUoPAGBiz5jJfDQmYI8D9t6REbd06VJcffXVWLJkCUqlEm699daOemylRf44km53/y2+bZ5Gvvi2DfLhY5efiQUnTcQVS9rzsDIoFuMhC0RhgmTr1q3S/2+88UbceOONRTVfCPK+x1KX2kjIVNUJJlV0NT6D9lEp2biqi1xGxwvGg7F9XO0n8+fa6k73Pbejqq3gr/HaMjBIhmXcf8cWchvbKY6ky0ZCmhoteWFsJAYG6TAe5so4EyTtuf+iy5xhKI18J8YvS689rkaQgUF2dJtKPA/GxTKwfO6Uts4vh15bzS5zq5wUeu90pNQuxv4uy8CgCIyHyPZx4Sf47Q9cgNf3D+Y+n3JVNYvMPTEM+Pr7zsPdz+3GrA7UDmc2kvGw3TIwaANFV64cjRgXjGRKbxnL2mAltKNvdBkjmT21F9e9+bSOtD0edlkGBkUgb6ndbsK4ECTtosQihruLkXQSZBsZy7ssA4MiMB42XUaQpAAFJDbc7mIknUWYImUszw4DgwIwHorAGUGSAszYbgQJw3iYHAYGRYB5i47hqWIESQqQsd2otjjGQ0ZTA4MiwGu2j11JYpaBFKiWgsfUZbb2jsIwEgODdOBzZWT70UmMC/ffdvGRyxei7nr43fPmtT54nIDounH/NTBIxngwthtBkgKTe8r42yvPHulujCqYwlYGBunATSRjd7IY1ZZBLrDCVkaSGBgkYjykkTeCxCAXjI3EwCAdmEPKGJ4rRpAY5ILFvLbG7uQwMCgC3Gtr7MIIEoNcsKyxTdUNDIoC23QZRmJgIKNs26xOi4GBQTxMzXYDgxj83gWn4Jx5U0e6GwYGox7jQbVlBIlBLsyZ2os5U4tPT29gMNZgjwN7otFNGBgYGHQQY1h+MBhBYmBgYNBBjIfIdiNIDAwMDDqI8VC7xwgSAwMDgw7CGgfGdiNIDAwMDDoIo9oyMDAwMGgL4yGdkBEkBgYGBh3EWBYgBCNIDAwMDDoIXnJh7AoUI0gMDAwMOgiTRt7AwMDAoC2MhxQpRpAYGBgYdBC2UW0ZGBgYGLQDy6i2DAwMDAzaARMghpEYGBgYGOSBsZGkwJe//GUsWrQIS5cuxSc+8Qn2/U033YQFCxZg0aJFuOeee9q9jIGBgUFXwrHHfoXEtuqR/PSnP8W6devwzDPPoFqtor+/HwCwadMmrF27Fhs3bsTOnTtx+eWX46WXXoLjOIV02sDAwKBbwONIRrYfnURbjOSrX/0qbrjhBlSrVQDAjBkzAADr1q3DNddcg2q1itNOOw0LFizA+vXr2++tgYGBQZfBqLZa4KWXXsLDDz+MCy64AG95y1vw2GOPAQB27NiBefPmsePmzp2LHTt2tNdTAwMDgy4ED0gcu6KkpWrr8ssvx+7duyPff+Yzn0Gz2cTBgwfx6KOP4rHHHsPVV1+NV199Fb7vR46P86G+7bbbcNtttwEA9u7dm7X/BgYGBqMa3GtrRLvRUbQUJPfdd1/sb1/96lfx7ne/G5Zl4fzzz4dt29i3bx/mzp2Lbdu2seO2b9+O2bNna9tYs2YN1qxZAwBYuXJl1v4bGBgYjGqYeiQt8Nu//dt44IEHAARqrnq9junTp2P16tVYu3YtarUatmzZgs2bN+P8888vpMMGBgYG3YTxkEa+La+t6667Dtdddx3OPvtsVCoV3H777bAsC0uXLsXVV1+NJUuWoFQq4dZbbzUeWwYGBuMS1XKw9lVKYzdsz/J1Bo0RwsqVK7Fhw4aR7oaBgYFBofjJxt246MyT0FPurg112jW5LUZiYGBgYNAaVyw9eaS70FGMXa5lYGBgYDAsMILEwMDAwKAtGEFiYGBgYNAWjCAxMDAwMGgLRpAYGBgYGLQFI0gMDAwMDNqCESQGBgYGBm3BCBIDAwMDg7ZgBImBgYGBQVswgsTAwMDAoC2Mqlxb06dPx/z589tqY+/evTjppJOK6dAwo5v7Dpj+jzS6uf/d3Hdg7PZ/69at2LdvX8vzR5UgKQLdnPixm/sOmP6PNLq5/93cd8D036i2DAwMDAzaghEkBgYGBgZtYcwJEirb243o5r4Dpv8jjW7ufzf3HTD9H3M2EgMDAwOD4cWYYyQGBgYGBsOLMSNI7r77bixatAgLFizAzTffPNLdSYX58+dj2bJlWLFiBVauXAkAOHDgAFatWoWFCxdi1apVOHjw4Aj3kuO6667DjBkzcPbZZ7Pvkvp70003YcGCBVi0aBHuueeekegyg67vf/M3f4M5c+ZgxYoVWLFiBe666y7222jqOwBs27YNl1xyCRYvXoylS5filltuAdAdzz+u793y/IeGhnD++efjnHPOwdKlS/HJT34SQHc8eyC+/4U+f38MoNls+qeffrr/yiuv+LVazV++fLm/cePGke5WS5x66qn+3r17pe+uv/56/6abbvJ93/dvuukm/xOf+MRIdE2Ln/3sZ/7jjz/uL126lH0X19+NGzf6y5cv94eGhvxXX33VP/300/1mszki/fZ9fd8/+clP+p///Ocjx462vvu+7+/cudN//PHHfd/3/SNHjvgLFy70N27c2BXPP67v3fL8Pc/zjx496vu+79frdf/888/3f/nLX3bFs0/qf5HPf0wwkvXr12PBggU4/fTTUalUcM0112DdunUj3a1cWLduHa699loAwLXXXosf/OAHI9shARdddBFOOOEE6bu4/q5btw7XXHMNqtUqTjvtNCxYsADr168f7i4z6Poeh9HWdwCYNWsWzj33XADApEmTsHjxYuzYsaMrnn9c3+MwmvoOAJZlYeLEiQCARqOBRqMBy7K64tkD8f2PQ57+jwlBsmPHDsybN4/9f+7cuYkDdbTAsixcccUVeOMb34jbbrsNALBnzx7MmjULQDAB+/v7R7KLLRHX3255J1/5ylewfPlyXHfddUw1Mdr7vnXrVjz55JO44IILuu75i30Huuf5u66LFStWYMaMGVi1alXXPXtd/4Hinv+YECS+xvEsSeKOFjzyyCN44okn8OMf/xi33norHnrooZHuUmHohnfyx3/8x3jllVfw1FNPYdasWfjzP/9zAKO778eOHcNVV12FL33pS5g8eXLscaPxHtS+d9PzdxwHTz31FLZv347169fjueeeiz22W/pf5PMfE4Jk7ty52LZtG/v/9u3bMXv27BHsUTpQH2fMmIF3vetdWL9+PWbOnIldu3YBAHbt2oUZM2aMZBdbIq6/3fBOZs6cCcdxYNs2PvjBDzL6Plr73mg0cNVVV+E973kP3v3udwPonucf1/duev4AMHXqVFx88cW4++67u+bZi1D7X9TzHxOC5LzzzsPmzZuxZcsW1Ot1rF27FqtXrx7pbiViYGAAR48eZZ9/8pOf4Oyzz8bq1atx++23AwBuv/12XHnllSPZzZaI6+/q1auxdu1a1Go1bNmyBZs3b8b5558/kl2NgBYBAPj+97/PPLpGY99938cHPvABLF68GB//+MfZ993w/OP63i3Pf+/evTh06BAA4Pjx47jvvvtw1llndcWzB+L7X+jzL9I7YCRx5513+gsXLvRPP/10/9Of/vRId6clXnnlFX/58uX+8uXL/SVLlrA+79u3z7/00kv9BQsW+Jdeeqm/f//+Ee4pxzXXXOOffPLJfqlU8ufMmeN/7WtfS+zvpz/9af/000/3zzzzTP+uu+4awZ7r+/7e977XP/vss/1ly5b573znO/2dO3ey40dT333f9x9++GEfgL9s2TL/nHPO8c855xz/zjvv7IrnH9f3bnn+Tz/9tL9ixQp/2bJl/tKlS/1PfepTvu8nz9Vu6H+Rz99EthsYGBgYtIUxodoyMDAwMBg5GEFiYGBgYNAWjCAxMDAwMGgLRpAYGBgYGLQFI0gMDAwMDNqCESQGBgYGBm3BCBIDAwMDg7ZgBImBgYGBQVv4/wHfm3p3db9CdAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_20\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_23\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_25\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7b8a37823f74936afdd318ec062ec56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e980937ad714e4da1f03d916e669e57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a3b1958fe7f441baf87b1b6b2cb2d79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b2f1e9ec93e4af89375501592268604",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bb664cd8a9e431a80dfca4b9d8ac4a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abf13993c891455c881b90db54b7eaec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1cd99c3e6ee44bdaac8870ebccdadad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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A2AxaVJi0ksD0xx7kzkojWmvNAIBXzgrzcFpqzDDq1Kix6BKaVrr8HAxaVVpXHCAkFayst+HssDvteQAw6WNRZdaBYRg4K00J8afFAgkMQaTg718+h48/ebTQ28gYUWBmpymLhZZyDrKaDoSgU6sk8RJnmfTn4abheR7vDwquseHpYMJAs0wZcbOoi3UWAIANzRWos+lx77oGAMDblycTzk/W+SAZzZUmXItZQgOSwJjQWisMJ3vr8iRqLHrJGmqqSLQoxr0caiz6tK44kTUNNpwZdiekXCdj0sehyiy41NocFgxMBSQ36WKBBIYgkuAOhvBPv72Ak/2ueT/oiwW/6IaZ04tMCRcZB5tRKz0wxUD45XFfztf0cxFw4SjuWOUAALx7zZX1NUbdQdTZ9NKfNWoVXvzLLfi7/7YeNoNmTpxivgaUIs5KoxTzGJwKQKdWodaiR5VZhxV1gsgsi1XVC+ebEtxcYt+2TOhotGE6EJq3bf+Ej0O1Wcg6W1EnxH3kHpKWL4oLTEtLC9auXYuuri50d3cDACYnJ7Ft2za0t7dj27ZtmJqaUnobBJEV/3liQPqZLZImjj4uDL1GBY068WNtiLllApx8f4/pQEiKvwBAg90Ah1WPE1dz/yyLNSxbVtRCxQDvDbiyen2Ai8AdDMNhMyQcrzLrYNCq4bAZpMp3ER8bntMcNBl1NgOuTwvuv4EpP5oqjVCpBHHdurpOWkekqdKIQVdA+nIy5slcYNY02gAAZ4bSu8kEC0YUGEHkzs2aLPrZn/Tg0z88lpBwsJAsiAXz6quv4uTJkzh+XJit8Pjjj+OOO+5AX18f7rjjDjz++OMLsQ2CyJjzcR9UvwKt7pUg1bwRjVoFnVrejsouf0iKvwBC7GBTS9UcF1Q2TMXiLo0VRjgrTbiUpTUkVrKLqb6zqbPp51S7++aZMinSYDfAy4bhCYYwMBWQXIIAcOuKWgCAXjMjVM5KI9hwVKpnEVxkOmTCqnor9BoVfnG8Hz94/RKePd6ftMfZhHdGYJZWm6HTqPDBsEc6d3g6gN+dG8PRCxPY+3pmvdnkpiAusoMHD2L37t0AgN27d+PAgQOF2AZBpCTef77Y/NqpSNaqX8SoU8v695gOJAoMAGxqqcTQdDDndFnRgqkya7Gsxpy1u01sVVM3y4IRcViTWDBcZi6yertwzVP90+gdmsaaBpv0u00tVfj2x9bgmx9dLR2TYlKTAUSjPCZ9mVswJp0Gf7m1HUfOjuJ7L5zFX/3yPRw+k9gtmQ1H4GXDkotMrWLQaDfgx0cv46P//AZcfg5HL8x0MhieLkwbmfnf2TxhGAZ33nknGIbBF77wBezZswcjIyNoaBCCbg0NDRgdHU362r1792Lv3r0AgLGx/FtmE0SmxPu/5cy+UpJ0D0uLXgNvUD6BcflDkt9fZNMyoaL87SuTUu1IttcEgEqTDq21Zrx9ZRI8z2cUGAeAEbfwEE0pMDY9xjxswjXnmzIp0mAXBOP/vHAWkSiPB25aKv1OpWLw2Q8tSzi/pVrILrsy7kNLtQlRHhlbMADw0IdbwYWj2LCkErt/3DMneUIS47hr7tzgxIunh3FxzIuuvz4MQBCeW5ZXF6xPmeICc/ToUTQ2NmJ0dBTbtm3DqlWrMn7tnj17sGfPHgCQ4jcEoTQ8z2NwKoDltWZcHPMtiIvMx4ahUTMJbpbsr5HagrEaNHDLKDCTcQFmkVX1Nlj1Grx9ZQo7NzhzuiYgxDJaa8zwcxGMuFnJepgPcVxASheZ1QAuEoXLH0KlWQc2HEEowmdkwTTE9nBm2I27OurQXJVeQJurTFCrGFyZ8GHcawcw02csE7RqFf77ViHF2mrQYHhWwH8iVohZbZ655l9ubcdfbm1Hz+VJvHpuFE/97iI+saEJDJPo8l1IFHeRNTY2AgAcDgd27tyJnp4e1NXVYXh4GAAwPDwMh8Oh9DYIImPGvRzYcBTtsYpsn8IusmiUR8e3XsLn9+U3/z1dyq3NoIWXnb9Db0brsGEEQpE5D0y1isHGlsqc4zBTfg4qRtirmP57aSzzrKgRTxA6tSoh+SAeRyy7bCT2bV5qoZ9BkN8Rl5kmBvXToVWr4Kw04tK4T2pcGS8G2dBoN2JolotLFOPqJFbR5mVV+B93r0Lvd+7C//54J+psBox52IK081dUYHw+Hzwej/Tzyy+/jM7OTmzfvh379u0DAOzbtw87duxQchsEkRWie6w9lpmjtIvs5Zh//Y2++ZszpsPPRlI2U7QYNPDIZMHMfHue+3Db1FKFvlEvpnzZF0pO+jhUmnRQqRgsqxFcTBezEZjpIGqtqWtNRNeZaOn4MmyhDyQG8D/cXpvRflqqzbgSJzC11sxdZPHU2w1zOlXHW3upMOs1UvZclEdOdUX5oqjAjIyM4A/+4A+wfv16bN68Gffeey/uvvtuPPLIIzh8+DDa29tx+PBhPPLII0pugyCyQqxfaI/FGJR2kYk9ssRU01zxceE5jS5FrDIKjJgZlczl071UaLx4MssUY0CwYETro8FuQJVZh5P9mfck658KoLnKmPL3YgGmGKvJtAHlbDJ12S2rMaN3yI2nfncRKgaotWb2utk0VhjmdEgQ/w41GVhF9Tbx773wAqNoDKa1tRWnTp2ac7y6uhqvvPKKkksTRM6IWVDtjoWxYMT1RJdNrvjYcEoLRhAYeVxk4jfhZA+3pbHgdi4tY+LrOhiGQffSShy/mrm77eqEXyrSTIbo5hKzzbKxYADguS/eArUqs4QDAGipnunC/P1dG+Zk3WVKg92ICR+HYCgidWV4f3AaTRVG2FO4A+MRC09H3EGshT2nPeSK4kF+gig2rk76UWHSSi4VpdOUJ2LuDncGUwzT4eMiaSwYLTzBcFZZWakQO/3WJHH51Fh0YBjgujt7gZnyhbC0eiZ4vqmlCi+fGcGoJwjHPN/+fWwY415WakyZDINWDZtBg9HY3kSLzmrI7DG4YUllRueJbO9qwqSPw+5bWlCdYYpyMsQEg+vTQbTEXIcn+13oaq7I6PXifZzLv0m+UKsYgpjFtQk/llabpYwsv4wFirMJxbKaAMATG0Oc63W4cBSWNBZMOMojGMq/ml+0YJL5/zVqFWoseukhng2Tfi7hmhtbhAd6JoPDxEaPS9MIDICEan53cGZomhJUmXX4yp0r8xIXQCg8BSANNhvzsBiYCmQsMNVmHVTMjFttISGBIYhZXJkQahf0GhVUzMykSCUQg+GtsW+mudaqiHs0pXD3WA3CQ9QjQybZuJeFzaBJmVJdbzNk/TDjeR5TPg6VcQKzrsmONQ02fPf5M3jrUvrxx1djc2CWVpnTnhdfzS+mbdsMygiMXGxYUoFGuwF/+9I5sOEI9sdidl1LKjJ6vUatQmOFsSDdlklgCCIOLhzFkCuApdVmMAwDk06jaJBfdDeJWVPuHOMkPmnYWAoXWUx45Aj0j/u4tFXpdTY9rmcZUPayYYSjPKpMMwKjUavw5KdvgFatwh/tPYY3+lIXW4u9tpbMU59SF1fNL7okM3WRFQqTToO/3tGJcyMebP/no/i7w+exuaUK65yZx1Naqs24MkECQxAFZWDKjygPLI09qIw6NQIh5WIwEz7hYScKzHSOcRhpmmVKC0ZGgfGwSesvRBw2Q9YusilfrIp/ltttWY0Zr/3VbbAZNDjw7lDK11+e8MFu1M4b9K6Nq+Z3B0PQaVRS4Hwxs3VNHe5d24BzIx58cqMT//GFm7Iqyl1abcLVidw7XefK4pZuglhgRFdLS6z1ukmnVtSCEWtKltXmacGIRYNpgvwA8s4k65/044PrHmxZkboWpN5mwISPAxeOQjfPgC2RSf9MH7LZmHQa3LG6Dq98MIJwJDqnWzQgdF5e2zT/N/r4an5PMAzbIrde4vnfH+9EZ5Mdn7l5adaJGi3VZrj8IUz7QxllnskFWTAEEceAa2aYFIAFcJElWjDuQG4WRqphYyJyWTBfe1YoO/jS7W0pz6mT0oEzt2LEWFSlKblldFdHHVz+EP7kRz0YcQfhDoZw6PR1sOEIAlwEZ4c9GQW9xYyqEU8Q7kBo0cdf4qky6/DwbcszTquOR8yuuzq5sFZM8cg3QSwA4oNafPCYZO5CPJsJHweNikFzTNByj8EIIpiqaHBGYHK3YAJcBCeuTuGhLa1SEWoyHLaZgsZMm15OziMwd66pxzfvXY1/OHwef/Kjt1Bh0qHn8iSWVpvw9btWIRLlsSGDoLdUC+Nm4Q6GF338RS6k5psTfqxzVizYumTBEEQcfjYMhgEMWuGjobyLTIhniKmyudbCiCKYutml6CLLXSzfG3AhHOWlav1UNMXSauNHHsyHOAtmdgxGRKVi8PkPt+IHn+nGiJtFz+VJPPThZRhxB/GVX5wEgMwsmFg9zQfX3fAEQ4qlKC82xOSHq3lMHM2F8pBvgsgQb6yfl+jjNmrVGPMo12JDGBqlh1WvAcPkLjDeearSLTJkkR2PTau8YZ6CQ3EWSjYCM+njoFYx88ZEbmmrwdFHbseFUS+6miuwqaUKT712EWsabBnVmzRXGXHjsir83cvnwYaj+Mja+oz3WMwYdWrU2fS4usCpyiQwBBGHn0scoau0BTMdCKHCqIVKxcCqz72lvlQHk8KCUasYmHXqvATm3WsuLK81p7QyREw6DWosuqzG9E75hUaXmQSvLXqNZK3c2VGPOzsyFwmGYfBPn9qAG/+P0KqqmGIw+bK02rzgmWTkIiOIOHxc4ghdk17ZIL8nGIbNKKxnNWhztmB8XPogv3j9fGIww9MBqdfYfDgrTXOGZKVD6EO2MA/7OpsBjbH2K+USgwGE3mgLXQtDAkMQcfjZcEKqr0GjRlDBVjGeYEiKj5h0agRyXMvPRWDUqtM2Y8y3o7LLH0o5a2U2zVUm9E9mE4MJpQzwK8HyWCPTcrNgxjyslMiyEJDAEEQcPi6cYAUYdSpFBSY+k8mgzV3M3IEQLPN8G7caNHm1ihHceZmJQHOlEUOuQMa91cRZMAuFGCfSa8vnESj2aVvIljHl8+4SRAb42EhCuxWDRo1wlEcokn+TyNlEojy8bFj6Fq3XqMCGc1sn2Qjj2YgdlXOBC0fhZcNZWTDhKD9nUFYqxr1s0u7MSiGmTxdiRkqhEFOVFzIOQwJDEHEIQ7tmLAGxjYgSVoyY+SVaMHpt7gIjBsnTYTVocm6mKbawyVRgxMLRvtH5J1KKHaXT9TeTm4+tE0a5f2x944KtWWjEYsuFjMOQwBBEHP7ZFoxOFBj5LRgx4C5aMPnEeyZ9HCrnCZJbDdqcs9SmA0KdSkWGbqyORhsYBnh/YP6JlGK7nNokEzKVYkm1CVcevzfjlvelgM2gRbVZJ7VDWghIYAgiDh8XTsgiM8R6aSlhwYhtYeSxYOYPkucz1XIqNrOmIsPCRKtBi+W1FryXwehksV3OQlow5cqSBW56SQJDEDF4np8zdlhJF5ln1sArg0YNNpz9OtEoD9esYV3JsOo1YMPCYLJsEYeiZeoiA4B1TjtODUyD59MH+sVCVhIY5WmpNpMFQxCFgA1HEeWRMHbYqFXSRTbXgsllHXcwhCifuo+XSD79yFz+9L3CkrHeWYExD4tPPv1m2n5uYzELppYERnGWVpswNB3I6YtMLpDAlDivnR/DN371Pl49N1rorSx6xPqApBaMAh9IsbGlVcoiU4PNwVISG0XOa8Hk0Y9MtGCyafX+0XUNuHddA05cncKp/tSxGMlFtoBZZOXK0moTeB5Z1SjlAwlMCcPzPP7HL9/DMz3X8A+Hzxd6O4sesWI/vt2K2PQyoEA1v0ca2ZtfDGa+RpEiogXjzaHQzhUQeoVZs2gVX23R46+3dwAATg+mERgPB5NOnbYLASEPSxc4VbksBObJVy/gmdgc63LiyoQf191B1Fr1eH9wGtP+/OexlzJiuxXLAqUpe5JZMOHovDGL2UzGpkFWzesii3VszslFJvRMy3bQVbVFj0a7Ae+nExgvu6AZZOXM8hqhg8H5kfnTx+WgLATmxdPDOHxmpNDbkB02HMHJflfK3795cQIA8LU7V4DngTcvTSzQzooTaWhXMoHJMbsrHZ5gGHqNSpr6KFpL2Vox0rCuedOUc++o7ArkPgmxo8me1oIZ87AU4F8g7CYtmiqM6B2aP31cDspCYOxGrRSkLCWePT6Ajz95FIdOX0/6+zcvTcBh1WPnBidMOjXevDi+wDssLqSxw3EuMqNYB6OAi8wd14cMgDRjnc0y0D8zbjjTIH8uMRgu4xTl2axrsuPSuA8j7uQTLq+7g9IUTEJ5OptsODPkXpC1ykJgKow6qRK5lBCtl28eOC19ixXheR5vXpzAzcurodOosKbBht4FuqmKFX+SjsRSHYwiQf7EmfB6jWjBZLfWqJuFUauWMt5SMRPkz81FlmuvsB1dTdCqGXz/yNw4YCTKY2DKjyVVmXVpJvKno1EQ/FxicdlSFgJjM2oxneOs88XM6cFptNaa4fJz+PZvehN+d3HMi3Evi5tbqwEAK+utODfiydq/X05IFkx8N2UFYzAuP5cQmBfXytZFdmHMi+UO87zxEdGCyeXLlsufu4tsSbUJf7x5CX5xfGBOHHDEHUQowksTFwnl6Wi0AQDODiv/hbMsBMZu1GI6wJXUwzUYiqBv1It7OuvxxduW4+DJoQQ/txh/uSkmMKsabPAEwxiaTu6mIGYsGHOSGEyAkz8GM+FN7B+mz7FrwIURD9pqLfOep1WrYDNopJTjbBBcZLmnEd+7rhGRKI+eK5MJx8XOviQwC0dnkx2NdsOCJP2UjcCEInzOszYWCxdGPfjcT3pwfsSDd65NIRLlsbbJjs9vaYVFr8HTr12Uzj16YQINdoPUontVvRUAcO46uclS4ZViMDMCo1Yx0KlVirjIpvyJHZBzsWA8wRCGpoNor7NmdH6VWSfVzWQKF47Cx0VQmaMFAwDrm+3Qa1Q4NivRRBSY5ipjztcmsqPOZsDvv3EHtq6pU3ytskg8F9tbTAdCRZtrP+0P4bM/eRsDUwGcu+7BqIeFVa/BxqVVsBm0+NTmZvz46BVM+Tho1AxePTeK+7ubJbfJitgD6IPrHty+Svkbqxjxc2EwzEw2l4heq5K9Dobn+ViDyrkWTDYxmItjQj1Dm2N+CwYQamWyFZhsOyknQ69RY+PSyjkC0z/ph4oBGitIYEqRsrFgAOTkGlgsPH7oLIang/jCra0Ymg7inrUNOPyVW6X6gY/GXBCvnhvFS70jYMNRfHxDk/R6u1FIT1yo7JFiRJgFo5kTyzBqc+sRlg4vG0Yowie1YGa3i+F5Hr8+NTRnD55gCL880Q8AaM9QYKpzEBgxA9Oe50Cwm1qrcWbYneCa6Z/0o7HCCK26LB5FZUfB/lUPHTqElStXoq2tDY8//riia4kCU6yZZNcm/Himpx+fu6UF37hnNd5+dCv++VMbUB+bKw4Aa5vsqLPpcfjMCA6eHMSSKhNuWFKRcJ11zvT1COWOnwsnVPGLCJMm5Y3BTPrmVt+nsmDODnvwpWfexfPvDScc/8nRK/j3Y9ewpMqUcQyj0qSTKv8zxRX73OTjIgMEgeF5JMRhLo37pEFYROlREIGJRCL48z//c7z44os4c+YMnnnmGZw5c0ax9YpdYH77gVAk+sBNSwEkn5uhUjG4q6Mer3wwiqMXxrGjq3HON/G1TjuuTPipoj8FPi6SEOAXMSjgIpvpHxZXB6MVg/yJYibWj1wcS6y+PnphHB2NNvzua7dBk6EFUGXWYcKXXcKL1Ek5jyA/MDcOE45Ece66R4oPEqVHQQSmp6cHbW1taG1thU6nw65du3Dw4EHF1stVYPon/ZJrYszDJk3rm/aH8MsTA/jliQGpFmV4OjCnJXokys+pVQEEV8lErNnftQk/3ro0MSc//Xfnx7CsxoyWmvTf9L5w63IwAKK8UHswm7VNdgDA6QWq4i02fGw4IUVZxKhVyx7knxGYmS8LBrHQctZaYjv7y+Mz/aOCoQjevebCLcuroVJl3r6lyqwDF45KfdcyQbR48onBADNxmKMXxsHzPK5M+MCGo1jdYMvrusTipSAR78HBQTQ3N0t/djqdeOuttxRbT8zfj//mPuoOwmbU4sTVKTRWGLG0yoRRDwuVCnjj/DisBg2+8otT8LJhNFUYwUWiGPOwWN1gQ2utGTcuq0IowuP7R85LldE1Fj1qrXqcHXbDqFVjRZ0Fyx0WaFUqPP/+MLxsGDUWHbRqFWosemxcWolfnxqCOxDCbSsdeL1vDFw4Cq2awdfuXIkv3LocUz4Ob16cwKc2L5n379lUYcTX716F3sHppEFfUWDeG5jGh9pq5HhrSwofG06aBKLX5j5pMhWSwMSnKYutYmZZMGI7+0tjMwJz4uoUuEgUNy+vzmpd0SU36eOSWmvJmM6hk3Iq7umsx/882ItfHO+HMfZek8CULgURmGTmebIisb1792Lv3r0AgLGxsZzXs+o1UKsYHLs0gY+ub8DJay78xTPvQsUw4CLCh9mkU8PPRaBRMQhHhf2tabDh4duW48dHL2Pcy+K/b23HO9dceH9gWvKH37K8Go/cswrhKI+/f/k8ojyPr9+9EqNuFhdGvTh6YRxTvhDuXdeAFXVWXBn3IcrzOD/qxS+O96OzyY7V9VYcOTuKm1qrsfvmpfj3Y1fxf186h67mCvz02FVEojz+aFNzyr9fPH/6B8tS/q7CpEOtVY/L4wvT6K7Y8HMR1FjmuoEMWrXs7lVJYOLWEy2Y2WImWjBXJnyIRnmoVAz6RjwAgLVNFVmtKwrapI9Dc4Zxm1w6Kafi0zcuxUu9I/jWr3tx64paaNVMxhlwRPFREIFxOp3o7++X/jwwMIDGxsY55+3Zswd79uwBAHR3d+e8HsMwiER5vPLBKF77m1cRjvJY31yBNQ02bGqphJ+LoHfIjeYqI4ZcAaxpsOPE1Sl8897VqDTr8LH1jeB5PkEE3x+YhkoliJB4/N8/f2PS9cWHQjq+s2Pm5+6WKnzkH9/AH+09BkBoVinXt7wlVSap9oBIxMeFsUQ/96Fr1Kow6pbZgvFz0KlVCX3P9CmaXY56hBhMMBTFiCeIBrsRk/4QGCb7wLsoaJNZBPqncuyknAyVisHf378ed//jG3ipdwRrm+xSs0+i9CiIwGzatAl9fX24fPkympqasH//fvz85z9Xds2WSrj8IXy4vRb1dj0euGlp2pqYP74x0SWVLGCeKdn4yAEhZvSbv/gD/NMrfdiwpALb188V31xZUmVCz+XJ+U8sQ/xsJOGBL2JQwEU25eNQYUp8aEvNLmcJzJiHhU6tAheJ4vKYDw12I1x+DjaDNuPgvohkwXgzF5jpPNrEJMNhM+DfPrcJpwamcecCFPsRhaMgAqPRaPDEE0/grrvuQiQSwYMPPoiOjg5F13zmoZugVjGyfAtbCKrMOnx7u/zvSXOlEQdPCkkI9M0xESHInySLTKOWvQuEj41IvcFE1CoGWjWT1EW23GHB2WG3FI+Z9HHzdk9OhhiDySZV2RXgcm50mYp1zgqsc1bIek1i8VGwsvaPfOQj+MhHPrJg62X7Ta9Uaa4yIcoDQ67AvFlp5QTP8/Bx4YQ2MSJGnfx1MD4uRUJBbOhYPGMeFnesrsDZYbcUCxK6G2dvVdgMGmhUTFbFllO+EBriaq4IIlPoqVtmiIHd/imKw8TDhqOI8oApSZqyXquS3UXm5yLSrJl4DLPW8rFh+LgIVtQJgfCp2PTKSV9uVgXDMFm3i5nOY9gYUd6QwJQZYsX31QkSmHjEaZbJLBhDzKqIRuXrxh3gksd79LPccaIQOGwGWPUaybU1NavVfzZUmbITGJdffhcZUR6QwJQZ9TYDLHqN7LMgIlEeT756QUqfLTbEWTDJWsWIlka2c1rS4U/hIjPp1AldA9yx4WA2gxYVZq3kIpvy5xaDAYT4XqYxGLGTcq7TLInyhgSmzFCpGNywtBLHr0zJet0D7w7ib186h8/+5G2pM0Ex4YvNgrEkDfLnNqclHalcZCZdogUjCp/VoEGFURCGABdBMBTNubJebBeTCa6APFX8RHlCAlOGbG6pxLkRj9QlN1+iUR7/cOQ8ltWYMe5l8eC/vb0g41jlRBqXnERgRCGQM5PMz0VSWkvxbVy8rGCxmPUaVJi0mPKHJOujKke3VaVZm7RtUTLEKv4KcpEROUACU4Z0t1QBgGxWzKVxLwamAnj4tuV48o9vwOkhN77ws+Oyt7hXEmlccoo6GEBeCybARVK4yDQJLjKxDZFFr0GlSQeXn5PiJ7k+9KvMergCIUQyiClN+fOfBUOULyQwZUhXcwXsRi1+3nNNluud7BeaZ96wpAJb19Th/963DkcvTODJ317A6cHpohhVLQb5U6UOA3O7HOdKOBIFF4kmt2C0asmaAiBZglaDBpUmLVz+kNTdOOcYjEkLnkdGFqx4Tr6dlInyhASmDDFo1dizpRW//WAUvzs3mvf1TvW7YNFr0FojpNLet9GJe9c14J9+ewEf/ef/wmd/8nbGLplCIQbPk6Xjyu0i84fSJxTEWzCi8Fn0GthNOriDIYx5hdYx8a3+syGbYkuXDNMsifKFBKZM+ewtLWh3WPDQT49j+xP/hWt5pC2fGnBhbZM9oSXO/7x3De5d14CHb1uONy9O4OP/chSvnR8Dz/NwB0O4MOrB+REPXnx/GG9dmkA0ymPCy+LKuK8gFo8kMEmypcQgPyuXwMTccamC/P64dbxBYYyzSadGZczyODssZOo12HMbM1wdGxEwkUG7GJdMrfqJ8qQ4B9QTeWPWa/Dsn92Mfzh8HvvevIrDZ0fSdmJOhZcN48yQGw9taU04Xm834Mk/vgEAsHW1A1/+j1PY/eMeVJt1Sf3/TRVGjHtZsOEo7lxTh6cf2Jh1D7d8mA6EoFYxaWMwslkwXOqam9lBfg8bhiU2xlmsRXl/YBpVZl3G7fZnUxmzfDKyYPzC+5Isu44g5oPumjKmwqTDd3Z04vCZEbx7bQpA9gJz9MI4wlEeW9prU56zcWkVXv7yFhx4dxBvX5lCU4UBLTVm8DywqsGKC6NeHHh3EDe2VsFhNeDp1y7iru+/jkfuWYU7Vi9MM8TpQAj2FB2DRUtDrhiMKCBJLRitBlw4ikiUh1rFwBsMwxLrWSZaEaeHprEsjzY/kgWTgdtSzk7KRPlBAkNgw5JKnOx3Zf06NhzBr08NwarXoLulMu25Bq0auzYvwa4kg9M6Gu3SBE6e51Ft1uHnPdfw1WdP4chXbkWNZe6IaLkRBSYZqea05EogTQzGFBfvseg18LJhyXpYXivEuDzBMJorM5vlkgwxOWDcM7/AjLiDcNioDxmRGxSDIbBhSQUGpgLouTyJva9flAZcpeONvjF86PHf4vn3hvHhFTXQytRMlGEYPLSlFT/4zEZ4g2Hsff2SLNedj+lACLZUAhOb0yKfi0wUmCRFnTGBEd1o3rgOz85KI2qteunnXNFpVKgwaTGeQUHskCuAporc1yLKG7JgCHy4vRZq1Qe4/1/fBAD8zaFzaHdYsGdLKyx6DU4NuPDi+9dh0Kqx78HN6Bvx4E//7TiW1ZjxZ7cux51r6mXfU5vDittW1uLXJ4fwyN2rFI/HuIPh1BaMTl4Lxi+lRCdzkcUsmJgIedmw1NafYRhsaK7Ay2dG4MxwGmUqaiz6jARm0BXAjcuq8lqLKF9IYAisrLfi1//Ph/Drk0O4baUDv784jhfeH8ZXfnEKgDCn5MZlVXj3mgsP/PAtjHlZtNSY8Isv3Kxol93tXU04cnYUPVcmcVNrdrPns8UdCEmNQGdjSDEILFdmLJjULjLxHG8wjPo4F9WGJZWCwORhwQBAjUU3r8C4gyF4gmE0kgVD5AgJDAFAiIN0NApTOm9eXo2/uL0dH1wXGmIurTLDbtLi9fNj+OxPeqBRqfAfe25SvIX71tUOmHRqHDw5qLjACDGY5B8HrZqBikFCfUo+iGnIyYL8xtkCExeDAYC7Ourw8pnrWJ/nsK4aix6nB6fTnjPkCgAACQyRMyQwRFJ0GtWciYNbVtTi6Qc2gmEYtNdZFd+DSafBnWvq8ML71/Ht7R1SRb3c8DyfNsjPMAyMMo5NDqRJUxbjMvEusvh05NZaC5774ofy3oPgIksf5BcFpilPa4koXyjIT2TFnR312LaAc9R3dDVhOhDCG+fHFVvDx0UQifIpBQYQsuCCMvVWk9KUtelcZGHwPJ8Qg5GTWqseXjacVjQHXULHAAryE7lCAkMsaj7UVgODVoWjF5UTmHRV/CIGrRoBTr4YjEGrSpq4EF/U6eci4PnkIwTypcYipCqnyxgccgWgVTOoXYA0caI0IYEhFjU6jQpdzRWyz6+JR2xJn15gVLJZMKk6KQNxdTBcRArC5zq5Mh1ibVG6QP/gVAD1dsOCdlQgSgsSGGLRs7mlCr1D04rNmBEtmFR1MIBgWcjViywYikj9zWYTn0V2bVLoD5cquy0fxHqadHEYqoEh8oUEhlj0dLdUIcoj1s5GfgKh1K36RQxatWyFlmw4Cn2S+AuQ2Lm5f1IIsishMA6rkPo84g6mPGfIFaAMMiIvSGCIRU/XkgoAwHsD6dNqc4WL1bfo0nQjELLI5InBBEMR6FNYMDq1CmoVAz8XxrVJP3RqFeoUaNVSY9FBxaQWmHAkiuvuIFkwRF5QmjKx6LEZtFhSZcKZIbci1xcLKHUpHvqAEIOZlGmmTToLhmEY2AwaTPpCcAdCaKo0Qq1ADESjVqHWqsf16eQCc90dRJSnGhgiP0hgiKKgo9GG00PKWjCprAogNsp4AWIwALC6wYYzQ9OI8kCzAu4xkXq7EddTWDBDlKJMyAC5yIiioKPRhqsTfriDIdmvzUXmFxiLQQNPUJ4kg2AaCwYA1jrtODvswcUxL5ZUKfeAr7eltmCoip+QAxIYoigQ29icVcBNxmXgIrPqNfCy8ogbO48Fs7bJDi4ShZ+LoN2hXMeEepshpcAMSgJDrfqJ3CGBIYqCVQ3Cg/b8iEf2a2cSg7HoNQiGoghF8g/0p4vBAMC6pgrp509udOa9Xirq7AZ42DB8SdK/B10BVJq0aTPrCGI+SGCIoqDeZoBVr0HfqFf2a2eSRSZOlUz2MM6W+SyY5iojtq524Ik/3pDzWORMaLAL1kmyOMyQK0A9yIi8oa8nRFHAMAza6izoG1FGYFSMkFmVCqtBKML0BMOoMOVXWS/EYFKvxTAMfrh7U15rZIKY/jzsCkrTMkWGXAG0VOc+lpkgALJgiCKi3WFRxoKJRNO6x4CZfmBydBMQLBhlOkNngygqs92OPM9jcIqKLIn8UUxgvv3tb6OpqQldXV3o6urCCy+8IP3uscceQ1tbG1auXImXXnpJqS0QJUa7w4pxL4spmepRRLhwdN5RAGJHYzkEJhiOSk0tC4nDqkeNRY/eWYkT7kAYPi5CKcpE3ijqIvvyl7+Mr33tawnHzpw5g/3796O3txdDQ0PYunUrzp8/D7W68B84YnHTXid84+4b9WKzjGN82XAWFkyeqcrhSBSRKJ82JXqhYBgGnU029M6qLxqkOTCETCz4XX7w4EHs2rULer0ey5YtQ1tbG3p6ehZ6G0QRIg456xuVN5OMDUfSBviBmSC/J08LJhhLKFgMFgwAdDba0TfqTZgLQzUwhFwoKjBPPPEE1q1bhwcffBBTU0KjwsHBQTQ3N0vnOJ1ODA4OKrkNokRotBtg1qllD/QLLrL0HwWrTBaM2JE5XZB/IelssiES5fHB9RnRFrs4Uw0MkS953eVbt25FZ2fnnP8OHjyIhx9+GBcvXsTJkyfR0NCAr371qwCEAOJsGCZ5r6W9e/eiu7sb3d3dGBsby2erRAnAMAzaHBbZLRguExeZaMHk2UlAsmAWQZAfANY3VwCY6VTtDobwgzcuoc1hQY2ZBo0R+ZFXDObIkSMZnffQQw/hox/9KADBYunv75d+NzAwgMbGxqSv27NnD/bs2QMA6O7uzmerRInQXmfF6+fl/bKRSRaZUauGisk/yL/YLJgGuxGNdgOOX53C5z60DH/9mzMY9bD4zwc20qAxIm8Uu8uHh4eln5977jl0dnYCALZv3479+/eDZVlcvnwZfX192Lx5s1LbIEqMdocFox5WmkIpB1w4Om8MhmEYWPT59yMTW/7Pl7W2kGxsqcI7V6fw+vkx/PLEAL5423J0xSwbgsgHxbLIvv71r+PkyZNgGAYtLS3413/9VwBAR0cH7r//fqxZswYajQZPPvkkZZARGdPmEDLJLox5sXFppSzX5OYpfBSxGrT5WzDhxWXBAMDGJRX4zakhfOc3vWiqMOIvbm8v9JaIEkExgfnZz36W8nePPvooHn30UaWWJkoYsX39wJRfNoFhw1GpziUdFr0m7yC/aMEslhgMANzVWY9//u0FXBzz4VsfWzOvu5AgMoVaxRBFhVj8J9ZqyEEmQX5ACPSXogXTYDdi/56b8MsTA9i1aUmht0OUECQwRFFh1mtQadJicEpGgYlEocvAorAbtWln2GfCYrRgACF54hsfWV3obRAlxuL5GkUQGdJUaZTfgpknyA8IdSFDea67GC0YglAKusuJoqOpwogBGS2YTFrFCOuaMOUP5eUmY0OLq5KfIJSEBIYoOpoqTBicCiQt2s0FLhzJqDeYM9abKx/3nGTBUCCdKAPoLieKjqZKIwKhCKZkqoXJ2IIRBcblz3mtIFkwRBlBAkMUHU2xHln5xkMAoXURF5m/FxkwY8Hk454jC4YoJ+guJ4oOcRJjvhldABCO8uD59OOSRWoteug1qrwEJhiKQq1ioM1gPYIoduguJ4qOersoMGze1+JizSczcZExDIOmCmPeMRiyXohyge50ouiosejBMPJYMNkIDCDEYQam8ovBUPyFKBdIYIiiQ6tWodqsx6hHBoGJZCcwzsr8UqTJgiHKCbrTiaKkzqaXxUUm1qVkEoMBAGelCRM+DgEuMv/JSSALhignSGCIoqTOZpDHRRYRhCIbCwbIPVWZLBiinKA7nShKBIGRwYIJZzefRWy22Z+jmywYikJPFgxRJpDAEEVJnU2PCR+LUCyGkiucJDCZu8iA3Kv5gyGyYIjyge50oiipsxnA88D16fzcZP5YLMWoy8yqcFj10KqZnAP9bJhiMET5QAJDFCVrGmwAgPcGpvO6jivWbqbCpM3ofJWKQWNF7qnKZMEQ5QTd6URRsqbRBqNWjbevTOZ1HVeAAwBUGHUZv6Y+jwQDjiwYoowggSGKEq1ahfXNdpy4OpXXdbK1YAChk0CuCQZkwRDlBN3pRNGyqaUKvUPT8OUxn8UdCEGvUWVlVdTZDLjuDuY0LkCIwdDHjigP6E4nipb1zgpEeeDssDvna7j8oaysF0AI9HPhKKYD2Y8LECwYcpER5QEJDFG0dDbZAQCnB3MP9LsCXFbxFyC/ZptkwRDlBN3pRNFSZ9OjxqLD6aH8LBh7lhaMOC7gepaB/nAkinCUJwuGKBtIYIiihWEYdDTa0ZuHwEwHQqgwZikw1tzm0YhdA8iCIcoFutOJoqazyYa+EY80KTJbcorB2PQAgNEsBSYYEqdZkgVDlAckMERRs6rehnCUx6UxX06vdwU4VJiyi8EYtGpUmLRZu8jIgiHKDbrTiaJmRZ0VAHB+xJP1a4OhCIKhKOxZusgAwU2WbZCfLBii3CCBIYqaZTVmaFRMTgLjjqUZ5yQwdkPWLjKyYIhyg+50oqjRaVRoqTHj/Ig369e6AtlX8YvUWbMfeEYWDFFukMAQRc/KOiv6crBgpDYxWdbBAEKq8piXRSSaeTW/NHuGLBiiTKA7nSh61jntuDLhx6HT17N6ncsfa3SZiwVjNyAS5THhzdyKIQuGKDdIYIiiZ/ctLVjvtOMbv3ovq/5grnxiMFYhVTkbNxnFYIhyI687/dlnn0VHRwdUKhWOHz+e8LvHHnsMbW1tWLlyJV566SXp+IkTJ7B27Vq0tbXhS1/6Uk4NAwkiHoNWjf/W3YwpfwjDWQwgm86hk7JILtX8ZMEQ5UZeAtPZ2Ylf/epX2LJlS8LxM2fOYP/+/ejt7cWhQ4fwxS9+EZGI8OF6+OGHsXfvXvT19aGvrw+HDh3KZwsEAQBoc1gAAH2jmQf7XQEOahUDi16T9Xoz/cgyFxiyYIhyI687ffXq1Vi5cuWc4wcPHsSuXbug1+uxbNkytLW1oaenB8PDw3C73bj55pvBMAw+85nP4MCBA/lsgSAAAO2iwGQR7Hf5hTYxDMNkvV61WQcVk101P0sWDFFmKPJVanBwEM3NzdKfnU4nBgcHMTg4CKfTOed4Kvbu3Yvu7m50d3djbGxMia0SJUK1RY8qsw4XxzK3YKYD2Te6FNGoVaizGdA/Fcj4NWTBEOXGvL6BrVu34vr1udk53/ve97Bjx46kr0kWV2EYJuXxVOzZswd79uwBAHR3d8+3VaLMaXNY0JdFPcx0IJRTgF9kVb0VZ7JotEkxGKLcmFdgjhw5kvVFnU4n+vv7pT8PDAygsbERTqcTAwMDc44ThBwsrzXj5d6RjM93+UOosWRfAyPS0WjH633jCIYiGU3EZMNRqBhAq87eJUcQxYgitvr27duxf/9+sCyLy5cvo6+vD5s3b0ZDQwOsViuOHTsGnufx05/+NKUVRBDZUmczYMLHIRSJZnR+Lo0u4+lotCES5TNuUyNOs8wl5kMQxUheAvPcc8/B6XTizTffxL333ou77roLANDR0YH7778fa9aswd13340nn3wSarXwDe+pp57C5z//ebS1tWH58uW455578v9bEASA2lhtyniS4sfzIx5M+riEYy5/fi6yjkZhomam82homiVRbmSfnxnHzp07sXPnzqS/e/TRR/Hoo4/OOd7d3Y3Tp0/nsyxBJMURGwQ25mHRYDcm/O5PfvQWblvhwN98ch0AYbqkJxjOqQZGpLnKiEqTFq+fH8OnNi+Z93zRgiGIcoG+ThElg8MqDgJLtGAiUR6jHhYn+13SMXcwDABZT7OMh2EY3L+pGS/1Xkf/pH/e88mCIcoNutuJkkF0kY16EgVmys+B54G+UQ8CnJDJNR1rE2PLQ2AA4LO3tAAAfnliIP2JIAuGKD9IYIiSocYiCMzYLIERYy9RHjgzLMRLvDELxmrIT2Aa7EasrLfhnWtT854bDJEFQ5QXdLcTJYNOo0KlSYtRT2J1fXxw//TgNADAywoCk0ubmNl0NVfgVL8L0Xla97NhsmCI8oIEhigpHFbDHBdZvMC8P0tgrAY5BMYOdzCMyxO+tOcFQ1GaBUOUFXS3EyWFw6af4yKbiAnMmgZbnAUjxGDMslgwlQCAk9dcac9jw1GyYIiyggSGKClqrfo5HY4nvYLAfHhFDfpGvQiGIlIMRg4XWZvDArWKweXx9BYMG4pQDIYoK+huJ0oKZ6UJI+4guPBMNf+kj4XNoMENSyoRifI4M+yGlxWyyeRwkalVDBxW/byzaMiCIcoNEhiipHBWGhHlgetxD/tJfwjVFj3WNgmV96cHp+FlQ9CoGOg18nwE6u0GXHen76wcJAuGKDPobidKCmelUME/MDVT+DjpY1Fp0qLBboBRq8a1CT+8wTDMeo1sfcEa7AayYAhiFiQwREnRXGkCAAzEzWmZ8HKoMuvBMAwcNj1GPSw8bFiW+ItIvc2I69PBtCPAyYIhyg2624mSot5ugIpJtGCmAyFUxnqO1VqELDMfG5Yl/iLSYDfAz0WkFjSzCUeiCEd5smCIsoIEhigptGoV6m2GBAvGHQhJLWEcNj3GvCy8clswdqHR5vUUbjKaZkmUI3S3EyWHs9KE/pgFE45E4eMikrVSa9Fj1B2ENxiGRWYLBgCGp5MH+kWBkSupgCCKAbrbiZKj1qbHRKz2RazYt8V6jtVa9XAHwxj3crIUWYrUSwKT3IIRxyVnMvmSIEoFEhii5LAZtFIsxBNMbAkjzowZdAVglVFgxEabE0mGnQFxFgy5yIgygu52ouSwGTVwB4VWMLPb8ost/QF5qvhFDFo1rHoNxr1c0t9LFgwF+YkyggSGKDlsBi24cBTBUGSOBZMgMDLGYACg2qJLOq4ZIAuGKE/obidKDtFacQdDkiUjxmAcClkwgOAmmyALhiAkSGCIksMWs0zcgbBkwcQH+T9xQxOclUapdYxcVFt0mPCRBUMQIvJ+hSOIRUCCBSPFYIRbnWEY/P39XYqsW23R4/iV5JMtRQuGCi2JcoK+ThElh2ituAMhyYKR2x2WjBqzDpN+DpEkky2p0JIoR+huJ0oOe8xacQfDcAdDMOnU0KiVv9WrLXrwPDDlnxuHIQuGKEdIYIiSw5pgwYQki0ZpZmph5goMxWCIcoTudqLkEAXFEwzDHZC3qWU6qi06AMmLLVmq5CfKEBIYouQwaFXQqhm4gyF42JlGl0pTExOYsSQC44tN0DSRwBBlBAkMUXIwDCO0i4kF+RfMgjGndpH5uDAMWtWCxIIIYrFAdztRktiMQj+yKT+HigWyYOxGLdQqJmktjJcNw6yjqgCivCCBIUoSm0EDdyCEydg0y4VApWJQZdYltWD8bFjW7s0EUQyQwBAlic2oxYg7CB8XkYLvC0GNRZ+04aWXjZDAEGUHCQxRkjisBlwa8wEAKk0LKTDJ28X42DAsegrwE+VFXgLz7LPPoqOjAyqVCsePH5eOX7lyBUajEV1dXejq6sKf/dmfSb87ceIE1q5di7a2NnzpS18Cz8+teiaIfKmz6cFFhNqTKvPCCUx1CheZjwvDRDEYoszIS2A6Ozvxq1/9Clu2bJnzu+XLl+PkyZM4efIknn76aen4ww8/jL1796Kvrw99fX04dOhQPlsgiKTU2QzSzwvpIqu26JPWwQgWDAkMUV7kJTCrV6/GypUrMz5/eHgYbrcbN998MxiGwWc+8xkcOHAgny0QRFLqbDOB/YV0kVVbdPBxEQS4SMJxHxuBmVxkRJmhWAzm8uXL2LBhA2699Va88cYbAIDBwUE4nU7pHKfTicHBwZTX2Lt3L7q7u9Hd3Y2xsTGltkqUII54C2YBXWRiu5jZg8d8lEVGlCHz3vFbt27F9evX5xz/3ve+hx07diR9TUNDA65du4bq6mqcOHECH//4x9Hb25s03sIwTMq19+zZgz179gAAuru759sqQUiILjIVI9SnLBRiNf+Ej0NzlQkAwPM8fBzVwRDlx7x3/JEjR7K+qF6vh14vfJPbuHEjli9fjvPnz8PpdGJgYEA6b2BgAI2NjVlfnyDmozZmSVSadFCpUn+JkRuxmn/cM2PBBENRRHmQBUOUHYq4yMbGxhCJCD7oS5cuoa+vD62trWhoaIDVasWxY8fA8zx++tOfprSCCCIfdBoVqs26Bc0gA4CGCsFyGnQFpGNeVpxJQzEYorzIS2Cee+45OJ1OvPnmm7j33ntx1113AQBef/11rFu3DuvXr8cnP/lJPP3006iqqgIAPPXUU/j85z+PtrY2LF++HPfcc0/+fwuCSEKdzbDgAlNr0cOkU+PqhF865osJDFkwRLmR1x2/c+dO7Ny5c87x++67D/fdd1/S13R3d+P06dP5LEsQGfHNe1dDp1nYWmKGYbCkyoRrkzMCI1owVAdDlBt0xxMlyy1tNQVZt7nKhKsTPunP/ljKMtXBEOUGtYohCJlZGrNgxKzJGRcZxWCI8oIEhiBkZmm1CcFQFKOxTLKZID9ZMER5QQJDEDKzpNoMADjV7wIwY8GYSGCIMoMEhiBkpntpJVqqTfjGr97HhJfF0HQQDDNThEkQ5QIJDEHIjFmvwd/dvx4TPg6/vziBaxM+NNqN0GsoBkOUFyQwBKEAa5sqoFUzODPsxpUJP1pqTIXeEkEsOCQwBKEAOo0K7Q4rzgy5cW3SjyVV5kJviSAWHBIYglCINY02HLs0gUkfh5ZqsmCI8oMEhiAUYk2DDWxYmKq5tJosGKL8IIEhCIW4Y7VD+rm1lgSGKD8oMZ8gFGJptRlvP7oV716bwoo6a6G3QxALDlkwBKEgtVY97uyoL/Q2CKIgkMAQBEEQikACQxAEQSgCCQxBEAShCCQwBEEQhCKQwBAEQRCKQAJDEARBKAIJDEEQBKEIJDAEQRCEIpDAEARBEIpAAkMQBEEoAsPzPF/oTWRCTU0NWlpacn792NgYamtr5dvQAlPM+y/mvQO0/0JTzPsv5r0D6fd/5coVjI+Pp3190QhMvnR3d+P48eOF3kbOFPP+i3nvAO2/0BTz/ot570D++ycXGUEQBKEIJDAEQRCEIpSNwOzZs6fQW8iLYt5/Me8doP0XmmLefzHvHch//2UTgyEIgiAWlrKxYAiCIIiFpeQF5tChQ1i5ciXa2trw+OOPF3o7GdHS0oK1a9eiq6sL3d3dAIDJyUls27YN7e3t2LZtG6ampgq8yxkefPBBOBwOdHZ2SsfS7fexxx5DW1sbVq5ciZdeeqkQW04g2f6//e1vo6mpCV1dXejq6sILL7wg/W4x7b+/vx9/+Id/iNWrV6OjowP/+I//CKB43v9U+y+W9z8YDGLz5s1Yv349Ojo68K1vfQtAcbz/qfYu63vPlzDhcJhvbW3lL168yLMsy69bt47v7e0t9LbmZenSpfzY2FjCsb/6q7/iH3vsMZ7nef6xxx7jv/71rxdia0l57bXX+BMnTvAdHR3SsVT77e3t5detW8cHg0H+0qVLfGtrKx8Ohwuyb5Fk+//Wt77F/+3f/u2ccxfb/oeGhvgTJ07wPM/zbrebb29v53t7e4vm/U+1/2J5/6PRKO/xeHie53mO4/jNmzfzb775ZlG8/6n2Lud7X9IWTE9PD9ra2tDa2gqdToddu3bh4MGDhd5WThw8eBC7d+8GAOzevRsHDhwo7Ibi2LJlC6qqqhKOpdrvwYMHsWvXLuj1eixbtgxtbW3o6elZ6C0nkGz/qVhs+29oaMANN9wAALBarVi9ejUGBweL5v1Ptf9ULLb9MwwDi8UCAAiFQgiFQmAYpije/1R7T0Uuey9pgRkcHERzc7P0Z6fTmfbmXSwwDIM777wTGzduxN69ewEAIyMjaGhoACB8KEdHRwu5xXlJtd9i+jd54oknsG7dOjz44IOSi2Mx7//KlSt49913ceONNxbl+x+/f6B43v9IJIKuri44HA5s27atqN7/ZHsH5HvvS1pg+CQJcukUerFw9OhRvPPOO3jxxRfx5JNP4vXXXy/0lmSjWP5NHn74YVy8eBEnT55EQ0MDvvrVrwJYvPv3er2477778P3vfx82my3lecWy/2J6/9VqNU6ePImBgQH09PTg9OnTKc9dbPtPtnc53/uSFhin04n+/n7pzwMDA2hsbCzgjjJD3KPD4cDOnTvR09ODuro6DA8PAwCGh4fhcDgKucV5SbXfYvk3qaurg1qthkqlwkMPPSS5Ahbj/kOhEO677z58+tOfxic+8QkAxfX+p9p/sbz/IhUVFbjttttw6NChonr/gbl7l+u9L2mB2bRpE/r6+nD58mVwHIf9+/dj+/bthd5WWnw+Hzwej/Tzyy+/jM7OTmzfvh379u0DAOzbtw87duwo5DbnJdV+t2/fjv3794NlWVy+fBl9fX3YvHlzIbeaFPHhAADPPfeclGG22PbP8zz+9E//FKtXr8ZXvvIV6XixvP+p9l8s7//Y2BhcLhcAIBAI4MiRI1i1alVRvP+p9i7rey9nVsJi5Pnnn+fb29v51tZW/rvf/W6htzMvFy9e5NetW8evW7eOX7NmjbTn8fFx/vbbb+fb2tr422+/nZ+YmCjwTmfYtWsXX19fz2s0Gr6pqYn/4Q9/mHa/3/3ud/nW1lZ+xYoV/AsvvFDAnQsk2/8DDzzAd3Z28mvXruU/9rGP8UNDQ9L5i2n/b7zxBg+AX7t2Lb9+/Xp+/fr1/PPPP18073+q/RfL+3/q1Cm+q6uLX7t2Ld/R0cF/5zvf4Xk+/ed1sew/1d7lfO+pkp8gCIJQhJJ2kREEQRCFgwSGIAiCUAQSGIIgCEIRSGAIgiAIRSCBIQiCIBSBBIYgCIJQBBIYgiAIQhFIYAiCIAhF+P8B3hj7Ii8mJfkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a47a919d5e7e496697108e6a9eeae745",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "39ada8bc516a41dbb728823a47bad07e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b97cff34003c49c6be65c6e3e0cd9d91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d55853f47ce42e2912dac79f9d419d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c277b8fd93524fd5a16029c456578a92",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f09bc4db12f448548c50d2dbf3881bad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75b62fac00944adda4402a16d67ef3f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "489a63e4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "a1ae5b6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "cf24989e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "28f9d726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fad031841e344ee99aabda6bee98770b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7c3409fee9d436a9b81c9d0f0ea85c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fb5e49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v3+v4+v5_Aug23_bf_model_misspecification_1000_data.ipynb b/bf/v3+v4+v5_Aug23_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..6b7dc6f
--- /dev/null
+++ b/bf/v3+v4+v5_Aug23_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2651 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72\n",
+    "\n",
+    "from hnn_core.utils import smooth_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 0.004647741\n",
+    "    maxAbs = 0.073834606\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "        temp = dpl[0].data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "        temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "for i in range(len(x)):\n",
+    "    x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "f087fe6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.004647741"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "d41bbeb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.073834606"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33b70cfde2d14d19b2d8f647dbd9c0d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8.625475</td>\n",
+       "      <td>0.821717</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  8.625475  0.821717"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75e185232c304954852a79aca574c2a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+sJpu3LjBJk+ezFxdXZlcLmcmJibM3d2dffLJJ+z58+dF7pPfH3XXrl3s5MmTrFOnTszY2JhZWFiwESNGsIcPH2psHxoayhYsWMDat2/PbGxsmFQqZS4uLszb25tduXKlyHNcunSJjRo1ijk4ODCJRMJsbGyYh4cHW7BgAbt1S7PsLi4uWn1Di9OoUSMGgFlbW7OcnJxit8vJyWFbtmxhnTt3Zubm5kwqlbL69vbMq0MH9v2CBVp9anNyctiKFStYgwYNuPJ98cUXLDs7u1z9YBljTKVSsS1btrBmzZoxqVTKHBwc2Jw5c1hKSkqxZd21axfz8PBgxsbGrE6dOuzdd99l//77L1u2bBkDwPz8/DS2f/XqFRs3bhyzs7NjQqGQAWDLli3j3r9y5Qrr3bs3s7S0ZKampqxr167s2LFjzM/PT2tbfVMdf+MCxv5rX18GHTp0wO3blW88QarP2cOP8cmEc9yyW3MrfH9wAIxMtFsyMsZw6NcQ7PguiFvX2N0ae/28YW4pq5Z49UV+K1KDVbiRU50OxW9Hql4xjZyIfqmOv3F6BluDPQlLwpKZF7nlJi3r4Mc/3ikyuQLqhiVjZ7XEks09IBKpW1s+DknChyNPIydb+xkTIYSQ4lGCraFUKoYv3juP1/89WzU1l2Ltnr4QiUv/lfcc6IpP1xZ0t7j1TyzWflp0K0NCCCFFowRbQ/2xOxT3b8cBUI+c9+3evjA1l5ayV4G3hzXEe5+245YP/Xof546EV3mchBBSU1GCrYFSk7Oxcck1bnnwuCZo7K49sktpxsx0R4+BLtzy0lkXqJ8sIYSUESXYGmjrV7eQnKDuZ2dsIsHMRRVraCEQCPDx151R18kUAJCZnoulsy6iHO3iCCGk1qIEW8PEv8zE//16n1ueu9wTcqOKd3c2MZPiy409IPyv0dOtgBgc2Vl0p3RCCCEFKMHWMHs23oEiRz0Kk72jCd4e1rCUPUrXzMMGo95rwS2vW3gZr2IyStiDEEIIJdgaJCUpG4cK1V59FravsgH8J83zgKOrej7OzPRcalVMCCGloARbg/y29V9ujGErGzm6D3ApZY+yk8nFWPBVZ2753NFw3AyIrrLjE0JITUMJtoZQKJT4/ed73PLUj9pAKKza6ec8OtaF15AG3PI3H/2DvDxVlZ6DEEJqCkqwNcTF40+RGPcaAGBkIkZ/70Y6OY/PwvaQG6sbTT0OScL/bbtfyh6EEFI7UYKtIf5ve8G8mP29G5VpxKaKsKlrjPHvt+KWt666icwMhU7ORQghhowSbA0Q+TgFN/wKnoeOmuGu0/N5T2sBWwf13JzJCdnYt/muTs9HCCGGiBJsDXB4R0HttXFLa9g5mOj0fFKZCJPneXDLO74LQkria52ekxBdmjp1KgQCQZETvvPF1dWVm9ScGCaacN3A5eWpcGL/A255wpzW1XLevsPd8H/bQ/H8aSqyMnKxfV0QPl3TtfQda4q7/wOyoviOongCMdB4dqUP4+rqisjIyCLfs7e3x8uXLzXWKRQKrFy5EgcOHEBSUhLatWuH9evXo127dlr7+/r6ol+/fjh16hTeeeedSsdqaHr16oWAgAAaGa0GowRr4G74RXONm+TGYnTycqqW84rEQkxb0AYr5wUAAPZvuYuJcz24YRVrvKwowMSV7yiKl1R18zZbWFjgo48+0lpvaqr9u160aBG+//57eHt7w8nJCfv27UPv3r3x4MEDODg4cNtlZGTAx8cHEydO1Ivkunr1aixatAiOjo58h8K5cOEC3yGQSqIEa+BO//6Ie921b32IRNV3179bf2c0aVUHj+4lIjdXha1f3cTKn72q7fykelhaWmL58uWlbscYwy+//IJp06Zh586dAIDhw4ejV69e2LdvHxYuXMht+/nnnyM7OxubNm3SVdjl4uDgoPEFQB+4ubnxHQKpJHoGa8BysvNw4fhTbvndSc2r9fwCgUBjSrtju8Pw7GFytcZA9Ed8fDyysrLg6enJrct/Xfg28z///IOffvoJP/74I6ytrSt0rvznk2lpafj444/h6uoKiUSi8UXgwYMHmDp1KurXrw+ZTAZ7e3uMHz8eDx8+1DpeSc9gb9y4gZEjR6Ju3bqQSqWoX78+Zs2ahdjY2CJjS0pNxZdbt6LlmDEwNjaGhYUFPDw8sGjRImRmZiIiIgICgQABAeq7PwKBgPvp1auXVhnflJOTgzVr1qB169YwNjaGubk5unfvjv/7v//T2jb/XFOnTkVERATGjh0LGxsbyOVydOjQAadOnSr5g37Dn3/+iYkTJ6JJkyYwMTGBqakp2rdvj82bN0Ol0u4Tn/+5Pn36FFu2bEHr1q1hZGSkUc6kpCQsXrwYzZs3h5GRESwsLNCnTx/8/fffWsdLTU3FunXr4OXlBScnJ0ilUtja2mLo0KG4fv16ucpSHagGa8D++SsSGWnqLjJmFlI0bV3+Kekqq10XB7Tr4oCgqy+gUjH8sPIG1h8YUO1xEN3JycnB/v37ERUVBRMTE7Ru3Ro9evSASCTS2M7W1hZGRkYIDAzk1t2+rb5V7eKiHlXs9evXmDFjBry9veHt7V2puBQKBby8vJCUlIR+/frB3NwcDRqoB0I5e/YsRowYgdzcXAwZMgSNGjVCdHQ0/vjjD5w+fRp+fn5FPhd+065du+Dj4wOZTIahQ4eifv36ePz4MbZv346TJ0/i+vXrcHZ25rZ/9uwZek+ahMgXL9C+eXO8//77UKlUePToEb7//nvMnj0blpaWWLZsGXbv3o3IyEgsW7aM27+0Rk0KhQL9+/dHQEAAmjVrhg8++ABZWVk4cuQIxowZgzt37uCbb77R2i8yMhKenp5o2LAhJk2ahKSkJBw6dAjDhg3D+fPn0bt37zJ95osWLYJQKETHjh3h6OiI1NRUXLx4EfPnz8etW7ewb9++IvebP38+Ll26hHfeeQeDBg3i/u9ERkaiV69eiIiIQPfu3TFgwABkZmbi1KlTGDBgAH755Rf4+PhwxwkLC8OXX36JHj164J133oGVlRWioqJw4sQJ/PXXXzh58iQGDNCf6w8lWANW+PZw7yENqmzc4fKa/klbBF19AQA4dyQcc5Ykwa15xWomRP+8fPkSkyZN0ljXoEED7Nq1Cz179uTWCQQCzJw5E5s3b0ZqaiocHR2xb98+mJmZYcKECQCAJUuWICkpCT/++GOl43rx4gVatGiBgIAAmJgUtJxPTk7GuHHjYGxsjH/++QctWhRMVBESEoKOHTtixowZCAoKKvH4jx49wqxZs+Dq6oqAgACN57MXL15E3759MX/+fBw7doxbP3HiRES+eIFv5szB4mnTgA4FU0UmJCTA1NQUcrkcy5cvh7+/PyIjI8t0+z3f+vXrERAQgIEDB+LEiRMQi9WX8GXLlsHT0xOrV6/G4MGD0aVLF439/P39sXz5co1kPn78eAwYMADr1q0rc4I9ffq01q1rlUqFadOmYe/evZg7dy46duyotV9QUBCCg4O5L0D5pkyZgsjISBw8eBBjx47l1qekpKBXr1748MMPMXToUNjb2wMAmjdvjtjYWNjY2GgcJzo6Gp6enliwYIFeJVi6RWygsjJz8c9fEdzy0PFNeYulaWsbePZUX3wYA35dW3UNbAi/pk2bhgsXLuDly5fIzMzEvXv3MGvWLERERGDgwIG4e1ezD/TatWvx+eef4+bNm9ixYwfc3d1x4cIFODo64saNG9i4cSM2bdoEGxsbrFixAg4ODhCLxWjfvj2uXLlS7vjWr1+vkVwBYO/evUhJScGKFSs0kisAuLu7w8fHB8HBwQgNLXnaxZ9++gm5ubnYtGmTVuMnLy8vDB06FCdPnkR6ejoAIDAwEFevXkWbJk3w+ZQpWsfLvzVbGTt37oRAIMCGDRu45AoAdnZ2WLp0KQBg+/btWvu5uLhgyZIlGuv69+8PZ2dn3Lx5s8znL+q5sFAoxPz58wEA586dK3K/hQsXaiXXu3fvIiAgAN7e3hrJFVA/91+xYgWys7Nx9OhRbr2FhYVWcgUAJycnjBw5Eg8ePEBUlP607qcarIG66huFnGz1tHRWNnK4NLbkNZ4JH7TGzYAYAOqa9ZwlnnBpxG9MpPIK13gAoGXLlvj5559hamqK9evXY/ny5Ro1OJlMhtWrV2P16tUa+ykUCkyfPh2DBg3ChAkTsHHjRq5G1bVrV3z99dcYMGAAwsPDudpKaeRyOVq31u6Wdu3aNQDqC3hRtcNHj9R3fsLCwrQScFHHCQgIwK1bt7Tej4uLg1KpxKNHj9C+fXvuGWD/Tp0gFFZ93SU9PR3h4eFwdHREs2bNtN738lI3MAwODtZ6r02bNlq39AGgfv36XDnLIjExEevWrcOZM2fw9OlTZGZmarwfExNT5H6Fn8vnyz9vampqkb+n+Ph4AOrfU2FXrlzBpk2bcO3aNcTFxUGh0BxJLiYmRuO2PZ8owRqoiyefca+79eP/P1OLtrZo19UBQVdegKnUtdivt73Nd1hER2bPno3169fjn3/+KdP2K1asQExMDNdwZd26dejTpw93YW3atClcXV3x448/YuXKlWU6pp2dXZGPRRITEwEA27ZtK3H/jIyS5zTOP866devKdJyUlBQAgKOdXYnbV1RqaioAFNvaOX99fhyFWVpaFrmPWCwusnFSUVJSUvDWW2/h2bNn8PT0xOTJk2FtbQ2xWIyUlBRs2rQJOTk5Re5bt25drXX5n6+vry98fX2LPW/h39OxY8cwcuRIyOVy9O3bF25ubjAxMYFQKIS/vz8CAgKKjYEPlGANUF6eCv6nCxKsrgb2L69Jc1sj6Ir6WeyJ/Q8x50tPOLqa8xwV0QW7/5LImzWYogQHB+Pbb7/Fzz//DEdHR6SlpSE2NpZ7LgsAzs7OsLGxQUhISAlH0lRcmwMLCwsA6hpsUTXcsso/TmpqKszNS/9/nJ/EYuLiKnzOssTz5uAe+V68eKGxXVXbvn07nj17hmXLlmnVOK9du1Zil6uiflf5cW7atAkffvhhmWJYunQppFIpbt++jebNNXtNzJo1i2uZrS/oGawBCroSi9Qk9bc0YxMJGres/tbDRWnZwR4eHdW391RKhu3rAkvZgxiq/Nt7DRs2LHG7vLw8TJ8+Hb1798Z7772n8d6bNY3s7Owqia1Tp04AgEuXLlXrcfK3P3f9eplqhfm3bJVKZZmOb2ZmBjc3N8TExODx48da7/v5+QFAmVpHV0R4eDgAFNn6uyKJrSK/p/DwcLRo0UIruapUKly+fLncMegaJVgDdOFEQe21Q496VT7va2VM+KCgxnB0VyhePE/nMRpSGSEhIUhKStJaHxkZiblz5wJQt5otyerVqxEeHq5xu9bc3ByOjo44e/Ys8vLyAKgv0Onp6XB3r/xEFdOmTeMayRTVgEelUsHf37/U48ydOxcSiQQLFizgntsWplAoNJJD+/bt0aVLF9x59Ahr9+zR2j4xMVHjS0SdOuovxuVplDN9+nQwxvDZZ59pJOaEhASsWrWK20YX8rsQvfnZBQcHaz1zL4sOHTqge/fu+OOPP7iBSd507949xBW6I+Dq6orHjx9r9EFmjGHFihWlNlrjA90iNjCMMfidLBhcYuBo/bg9nK9Np7pwb2+LkMB4KPMYdq4Pwpcbe5a+I9E7hw8fxpo1a9C7d280aNAAZmZmePLkCU6fPo3s7GwMGjQIn376abH7h4SE4KuvvsKGDRu4frD5Fi5ciPnz56N79+7o2LEjDhw4AFNTU3zwwQeVjrtOnTo4cuQIhg8fjk6dOqFPnz5wd3eHUChEVFQUrl27ppXsitKsWTPs3LkT06dPh7u7OwYMGIAmTZogNzcXUVFRuHTpEmxtbfHgQcFY4Pv370evLl3wxdatOOrnh16DBoExhsePH+Pvv//GgwcPuETVp08fHD58GCNGjMCgQYNgZGQEFxcXrS5RhX366af466+/cPz4cXh4eGDQoEHIysrC4cOHERcXh4ULF6Jbt26V/gyLMnnyZKxbtw4fffQR/Pz80LhxYzx+/BinTp3CiBEjcOjQoXIf87fffoOXlxfee+89bN68GR07doSlpSWio6Px77//4v79+7h27Rr3SGLBggWYPXs22rZtC29vb0gkEly5cgWhoaEYMmQITp48WdXFrhRKsAbm6YNkxESoa4ViiRAentqNB/gkEAgw4YPW+GK6ehzVw9tDMPuLt1DHzpjnyEh59e7dGw8fPkRwcDCuXbuGzMxMWFpaolu3bpg0aRImTZpU7HNQpVKJ6dOno2PHjpgzZ47W+/PmzUNaWhp+/vlnBAUFwcPDA99//32ZWxCXpk+fPvj333/x3Xff4dy5c7h06RKkUinq1asHLy+vMg9yMXHiRHh4eGD9+vXw8/PD33//DRMTE9SrVw8jR47EmDFjNLZv0KABgvbtw7f79uFPf3/88MMPkMvlcHV1xSeffMIlCgCYMWMGIiMj8fvvv+Pbb79FXl4eevbsWWKClUql8PX1xYYNG/Dbb79hy5YtEIvF8PDwwMaNGzFu3LiKfWBlUK9ePVy6dAmLFi3C5cuXce7cOTRr1gxbt27F22+/XaEE6+TkhMDAQGzZsgVHjx7FgQMHoFQqUbduXbRo0QLz5s1Dq1YF80/PmjULMpkMGzduxJ49e2BkZITu3btj165dOHr0qN4lWAErx1QOHTp04EZmIfzYvTEY6xaq+wu6t7fFxt8H8hyRNsYYPhhxBo/vq1sJzlzUAfNXduI5qvIJCwvTes6jwZBm06nToeRtCcaOHYtDhw4hNja28mMSF75GdqDPXl+V+jdeBagGa2Aunyu4qHsNaVDClvwRCAQYO7MlVn2obvhw4Me7eO/TdjA1l/IcWRXyKFtXEt4k0hfh8nj06BFkMhlsbW35DoXUINTIyYBkZihw+1JBR+7OXvV5jKZkXfvVh6OrGQAgMz0Xh7ff5zkiQrRt2rQJ3t7eCA4OxrBhwzRGRyKksijBGpCb/jHIVaib/1vbGsHWwaSUPfgjEgkx2qclt7xzQzAUOWXrjkBIddm4cSOuX7+O6dOn45dffuE7HFLDUII1IJfOFUz51bG3/kwMXZy3hzWEtZ0RACAp7jVOHHhQyh6EVK9nz54hJiYGO3bsKHa0I0IqihKsgWCM4crfhZ+/ltzBXx9IZSKMnFYw1uu2tYFQKss2LBshhBg6SrAGIvpZGqKfpQEARGIB3NsZRmOMd8Y24Ro3RT9Lw/ljT0vZgxBCagZKsAbihn8097phUytIpNozY+gjY1MJhk0smErvlzW3UY6eYYQQYrAowRqIG34FCbZrX/5nzymPdyc3h0yu/kLw8N8EXLvwnOeICCFE9yjBGgDGGDfXKgB49tL/Bk6FWdaRY8CogiEdt62lSQAIITUfJVgD8PRBMhJeZgFQNxxq2MyK54jKb+R0dwhF6mH1bgbE4N9br3iOiBBCdIsSrAEoXHtt0qoORCLD+7XVdTJF78EFI09t/5ZqsYSQms3wrtS1UOHnr936Gdbz18LG+BRMRXbxxFM8fZDMYzSEEKJblGD1nErFcDOgIMG261rJgch51KCpFTr1dgIAMAbsWE+1WEJIzUUJVs89upeI1KQcAIDcWAzXxpb8BlRJY2YVDJ948sBDmpCdEFJjUYLVc4X7v7q3sy12/k1D0bK9HVp1UM+Jqcxj2Lv5Dr8BkTLp27cvBAIBAgICit1m9uzZEAgE2LZtWzVGRoj+ogSr5wo/f+3ytv7OnlMehWuxh365j5TE1zxGQ8oiKCgIIpEI7du3L3abGzduAFDPG00IoQSr1/LyVAi8HMstt+taj8doqo5nT0c0aGoJAMjJVuK3rff4DYiU6MmTJ0hKSoK7uztMTU2L3CYrKwv379+HTCZDy5Yti9yGkNqGEqweCw2OR0aaAgBgYiaBo4sZzxFVjfwJ2fPt3XwHmRkKHiMiJbl9Wz15e8eOHYvdJjAwEHl5efDw8IBEIqmu0AjRa5Rg9dhNjeevdgb//LWwnoNcUddJXRtKT1Xg6M5QniMixSlLgr1+/ToAuj1MSGFivgMgxSvcwKlbf8Pt/1oUkViI0TPcsXm5+rndzvXBGDu7FaQGMolBbZKfYA8dOgQ/P78it6Hnr4RoowSrpxQKJYKvvOCW23Ux3P6vxek/shH2/XAXyQnZiH+RidMHH2L4lBal70iqDWMMwcHBAABfX99St6cES0gBSrB6Kiw4Hq+z8gAAZhZS2DsW3bjEkEllIoyY2hw7vlNfwLetDcTQic30fihId+kPfIdQZiEvOlVq/0ePHiE1NRWdO3fG1atXi9wmOTkZ1tbWMDY2RosWml+Qtm7dinXr1uHFixdwd3fHxo0b0b1790rFRIih0O8rWS0WfLWg9trMw4bHSHRryPimMDZVN4qJDE/FheM0Ibs+yb893LZt22K3CQoKAgC0adMGIlHBLf5Dhw5h/vz5+OKLLxAcHIwuXbpg4MCBiIqK0m3QhOgJSrB6KqjQ7eFOXjWj/2tRTMykGDqhYEL2bd8G0oTseiQ/wbZr167YbfIT7Ju3hzds2ICpU6fCx8cHzZs3x5YtW+Dg4ICffvpJdwETokfoFrEeYowh+FpBgm3tac9jNLo3YmpzHN0VilyFCqFB8bh+MRqd++jvl4oQxVy+Qyhd4u0qOUxZEmz+M9rCCVahUCAwMBCffvqpxrb9+vUr9lYzITUN1WD1UFR4KpLi1aMbSWUiOLtZ8ByRblnZGGHAyEITstNUdnpBpVLhzp07kEqlcHd3L3a7omqwCQkJUCqVsLfX/HJob2+Ply9f6iZgQvQMJVg9FFSo9tqwmRWEwprT/7U4o2YUTMh+wy8a927ThOx8CwsLQ0ZGBtzd3SGVSovcJiMjA48fP4apqSmaNm2q9f6bfbcZYzWqPzchJaEEq4cKd8/x7OnIYyTVx6G+GXq/48ot04Ts/AsMVP8OSro9fPfuXahUKrRr1w5CYcHlxMbGBiKRSKu2GhcXp1WrJaSmogSrhwq3IPboWJfHSKrXmELDJ144ThOy823y5MlgjGH79u3FbtO1a1cwxrRm2ZFKpWjfvr1W31lfX1906dJFJ/ESom8oweqZ5ITXePpQnViEQgGatq7Dc0TV580J2XeuD+I5IlIZH3/8MXbv3o3t27cjLCwM8+fPR2xsLGbPns13aIRUC0qweib4WsEttXouZpDJa1dD78JT2Z3Y/4AmZDdgY8aMwcaNG/HVV1+hTZs2uHz5Ms6cOQMXFxe+QyOkWlCC1TOFbw+361J7bg/n05iQXcmwe+MdfgMilTJnzhxEREQgJycHgYGB6NGjB98hEVJtKMHqmcL9X9t2qRnzv5ZX4Vrs4W33kZxAE7ITQgwPJVg9kpOdh/uFuqe4t7flMRr+ePZ0RMNmVgDUE7If+PFfniMihJDyowSrR0KC4pGrUAEArGzksKpjxHNE/BAIBBotivf9cJcmZCeEGBxKsHqk8PNX9/Z2PEbCv54DXeBQXz2DUEaqAoe3h/AcESGElA8lWD1SOMG+1aN2Pn/NJxILMWpGwfB8u9YHQ5Gj5DEiQggpH0qweuLNAf5btqfRbvp7N4K1rfo2ecKrLPy5L4zniAghpOwoweqJZw9TkJKYDQCQG4lRv6E5zxHxTyoTYeT0ggm8f119G7m51VeLpWnzCKmZqutvmxKsnihce23UwpoGRP/P4HFNYG4lAwC8eJ6B0wcfVct5RSIRcnNzq+VchJDqlZubC5FIpPPzUILVExrPX3vW7uevhRmZSDBianNu+ZfVt6FUqnR+XjMzM6Slpen8PISQ6peWlgYzMzOdn4cSrJ4IuhLLvW7tWftGcCrJu5OawcRMAgCIepKKc0fCdX5Oa2trJCcnIyEhAQqFgm4XE2LgGGNQKBRISEhAcnIyrK2tdX7O2jXQrZ5KjMtCZHgqAPUA/01a1p4B/svCxEyKdyc1w4Gt9wAAP39zCwNGNdbpPLkymQzOzs5ISkpCREQElEoDa8GcmVDwOo4ah1WrhEKffRh99vpEJBLBzMwMzs7OkMlkOj8fJVg9cKfQAP9ODcwhlen+2YChGTG1OY7uDkN2Vh6ehCXj4omnePtdN52eUyaTwcHBAQ4ODjo9j078VtA4DOOp9l2tWhT67OnOR61Gt4j1QNDVgtvDtXGA/7Iwt5Jj6Pim3PJPX9+i27aEEL1GCVYPFG7g1LYrNXAqzsj3WnC1+wd3E3DpbCTPERFCSPEowfIs+3UeQoLiuWX3trVzgP+ysLIxwqAxjbllqsUSQvQZJViehQTGIS9X3e3E2tYIFtZyniPSb6NnuEMiUf+3/ffmK9zwi+Y5IkIIKRolWJ4V7p5TW6enKw9bBxP0827ELf/09S0eoyGEkOJRguVZ4RGcPHs68hiJ4Rg7qyWEInUXnduXYhF4ObaUPQghpPpRguWRSsUQXKiLTssONMB/WdR1MsXbwxpyyz9/Q7VYQoj+oQTLo6cPkpGWnANAPcC/o4vuh+6qKcbNbsUNNHH1/HP8e+sVzxERQogmSrA8Ktz/tZE7DfBfHk4NzNFzkAu3/AvVYgkheoYSLI8K93+l56/lN35Oa+61/+kIhAXHl7A1IYRUL0qwPCr8/NWjI43gVF6ujS3RrZ8zt/zjqhs8RkMIIZoowfIk/mUmnj/5b4B/kQCN3XU/s0NNNGmeB/fa71QE7gfSs1hCiH6gBMuTwreHHV3NIJHSAP8V0bCZlcaz2B+WUy2WEKIfKMHypHD/13ZdDHC2Fj0yaa4H8tuHXToXhTvXX5S8AyGEVANKsDwJvlrw/PWt7tTAqTJcGlvCa0gDbnkL1WIJIXqAEiwPXmflarR4bU4D/FfaxLkeXL/Y6xejcftSDM8REUJqO0qwPLh36xXy8goG+De3lPEckeFzamCOt98tGN1py/IbNNMOIYRXlGB5UPj2sHs7qr1WlYkftIZIXDBG8Q1/qsUSQvhDCZYHhUdw6tjbicdIahYHZzP0LzTTzpbl16kWSwjhDSXYaqZSMdy9XlCDbUUD/Fep8e+3gvi/+WLvXHuJK75RPEdECKmtKMFWs/CQRKSnKgAARsZiODib8hxRzWLvaIqBoxtzy/QslhDCF0qw1SzwSkEfTbcWNMC/Loyf3QoSqfq/9v3bcfA/HcFvQISQWokSbDUrPDl4J3r+qhM2dY0xeFxTbnnDl1ehVKp4jIgQUhtRgq1GjDEEXSlIsO260ghOujLu/ZYwMhEDAJ6GJePEgYc8R0QIqW0owVajmIh0vIrJBACIJUI0bGbFc0Q1l1UdI4x6z51b3rLsOnKy83iMiBBS21CCrUaFbw+7NLKASEQfvy55T2sByzpyAMCrmEwc/OkezxERQmoTusJXo8BCt4c70PjDOmdsKsGEDwomZf/5m1tIS8nhMSJCSG1CCbYaBRWqwXr2pARbHd4Z0xgO9dVdodJTFdj5XRDPERFCagtKsNUkMS4Lzx6lAAAEQqCZhw2/AdUSEqkI0z5uyy3v2XwHr2IyeIyIEFJbUIKtJkGF+r861DeDVEYTrFeXnoNc0aiFNQBAka2k6ewIIdWCEmw1Kfz8tU2nujxGUvsIhQL4LGzHLf+5NwyhwXE8RkQIqQ0owVaTws9fu/Spz2MktVO7rvW4gT0YA9Z8cpmGUCSE6BQl2GqQma5A2J0Ebtm9vR2P0dReMxe156azC7wci/N/PuU5IkJITUYJthrcuf4SKpW6tlTH3gim5lKeI6qd6je0wNAJzbjldQsvQ5Gj5DEiQkhNRgm2GhQeYIKmp+PXxLmtYWap/oITE5mOfVvu8hwRIaSmogRbDQqPP9zlbXr+yidzSxkmz/Pgln/++hYSXmXxGBEhpKaiBKtjihwl/r35iltu7Uk1WL4NHtcU9RtaAACyMnOxaek1niMihNRElGB1LCQoDjnZ6ud85pYy1LEz5jkiIpYIMfuLDtzyH7vDEHztRQl7EEJI+VGC1bHb/8Rwr5u1odGb9IVnT0d07Vtwu37lXH/k5dGcsYSQqkMJVseu+0Vzr3sMcOExEvKm9798CzK5ekStR/cSabYdQkiVogSrQznZeQi+WnDrsV0XmmBdn9g7mmrMtrN52XXEv8jkMSJCSE1CCVaH7t54yT1/tbCSwdbBhOeIyJtGTm9R0OApIxfrPr/Cc0SEkJqCEqwOXb9YcHu41VvUelgfSaQizFvuyS2f/v2Rxm19QgipKEqwOnTDv+BC3XMQPX/VV207O6DXO67c8vL3L+J1Vi5/ARFCagRKsDqSma7A/VsFM7a07UzPX/XZrMUdYGImAQA8f5qGH1fe5DkiQoihowSrI0FXXnDdPurYGcHCWs5zRKQkNvbGmLW4oG/sno13cO/2qxL2IISQklGC1ZHrfs+51x40/6tBGDCyEdp2Uf+uVCqGJT4XoFDQZACEkIqhBKsjN/wKBpig56+GQSAQYMFXnSEzUveNDQ9Jwva1gTxHRQgxVJRgdSAl8TUe3I1XLwgAD0+qwRoKh/pmmP5xO275l9W38fh+Io8REUIMFSVYHbj1TyyYevpX1HU0hYkZzf9qSIZNaooWbW0BAHl5Kiya7ku3igkh5UYJVgcK96N8q0c9HiMhFSESCfHxN50hkar/PB7cScBWalVMCCknSrA6cLNQgu3W35nHSEhFuTSyxHufFtwq3vFdEAILzetLCCGloQRbxeJiM/D0YTIAQCgUwL2dHc8RkYoaPqU52nQuaFW8aKovMtIUPEdFCDEUlGCrWOHWw/UbmkMmF/MYDakMoVCAz9Z2ham5+hl6bGQ61nxyieeoCCGGghJsFbviG8W97tynfglbEkNg52CCecs7csvH9oTB99gTHiMihBgKSrBVSKViGgm2x0Dq/1oTeA1pgN6DXbnlpbMuIjYyjb+ACCEGgRJsFQoNjkNS/GsAgMxIDLfm1jxHRKrKvOUdYetgDABIT8nBpxPPITeXuu4QQopHCbYKXTpbUHtt1cEOQqGAx2hIVTKzkOHLjT0gFKl/p3dvvMKmpdd5jooQos8owVahy39Hcq/7DGvAYyREF9zb2WH6x2255V0bghFwJoK/gAgheo0SbBVJScrGvzcKZl95q7sjj9EQXRk1wx1v9Sz43S6e7ouX0Rk8RkQI0VeUYKvI9QvPoVKpx0es62RK09PVUEKhAAvXdkUdeyMAQGpSDj6ZcJaGUiSEaKEEW0UK3yrs2pe659RklnXk+GJDD+4Z+51rL/HtZ5d5jooQom8owVaBvDwVAv6K4Ja9hjbkLxhSLVp72mP6JwXPYw/+dA9/7g3jMSJCiL6hBFsF7lx7gdSkHACAkYkYjd2pe05tMNrHXaOv84oP/BESFMdjRIQQfUIJtgr4nXrGve7QvR4EAuqeUxsIBAJ8uroLXBtbAgAUOUrMH3WG6wtNCKndKMFWEmMMF08WJNj+3o14jIZUNyMTCZZt7QUTMwkA4MXzDHwy4SwNQkEIoQRbWc8epiAqPBUAIBIL0LazA88Rkerm5GqORd9155Zv+sdg9YJLYIzxGBUhhG+UYCvp4smn3OtmrW0glYl4jIbwpZOXE6Z+1IZbPvTrffy29V/+AiKE8I4SbCX9/UfBzCp0e7h2Gz+nlcakAGs+uYxL5yKL34EQUqNRgq2EmIg0hASqW40KBED3ATR7Tm0mEAjwyeouaOZhA0A9u9In48/iSVgSz5ERQvhACbYS/v4jnHvt1sKam5ib1F4yuRgrfurNzbyTmZ6LOe+eQmJcFs+REUKqGyXYSjh3tOD28DtjGvMYCdEn1rZGWPmzF2RG6ufx0c/SMOfdU8jKzOU5MkJIdaIEW0GxkWm4d0s9uL9AAPQY6MpvQESvNGphjS+/LxhO8f7tOHw64Szy8lQ8R0YIqS6UYCvo72MFtdeGTa1gbinjMRqijzr3qY+5yzy55YAzkVg1z5+67xBSS1CCraBTvz3iXg+k28OkGEPGN8XY2S255SM7QvHL6ts8RkQIqS6UYCsgPCQRYXfiAainL+tDg/uTEkz/uC36DCv4P7Jl+Q3837b7PEZECKkOlGAr4ORvD7nXLdraUuthUiKBQIBPvumMtl3qcutWzvXH6d8flbAXIcTQUYItJ5WK4VShC6P39OY8RkMMhUQqwvIfe6Np6zoAAMaAxdN94X/6WSl7EkIMFSXYcrr1TwxePs8AAEhlInTs5cRzRMRQGJtK8M32PtzsO8o8ho/HncXNgGh+AyOE6AQl2HIqPKl25z5OkEhp7GFSduZWcqzZ/TYc6psCAHKylfhg+GkEXonlOTJCSFWjBFsOqcnZOHekYPQm72kteIyGGKo6dsZYu6cv6tgZAQCyMnIxa/AJBF6mJEtITUIJthxO7H+InGz1PJ829sbcmLOElJdDfTOs3dMXVjZyAMDrzDzMGnICt/6J4TkyQkhVoQRbRowxHN4ewi17T2sOgUDAY0TE0Lk0ssR3+/trJNnZQ0/SM1lCaghKsGUUdPUFNyuKSCTAwNE0uASpPGc3C6w/0B/WturbxdlZeZg99BQCzkTwGxghpNIowZbRwZ/uca89eznCxIz6vpKqUb+hOsnmP5PNeZ2Hed6nNRrUEUIMDyXYMoiNSsffRwsaN02a58FjNKQmcmpgjvW/9Ye9owkAQKlk+HLGBez4LojGLibEQFGCLYMDP96FUqm+yNVvaI7G7nV4jojURI4u5tj0fwPRsKkVt27DF1fx7cLLUKkoyRJiaCjBliIjTYEjO0K55ekft+UxGlLT1bEzxoaD/dHa055bt3fTXcwdcRoZaQoeIyOElBcl2FIc2RHCXdgsrGTo0teZ54hITWdiJsXqnW+jW7+C/2sBZyIwrtthRD5O4S8wQki5UIItweusXOzcEMwtj53diptAmxBdkspEWLK5B0b7uHPrnj5Ixtiuh3H570geIyOElBUl2BIc+vU+El9lAQCMjMUYOqEpzxGR2kQkEsJnYXssXt8NEqn6TzUtJQfvDz2FTf+7jtxcJc8REkJKQgm2GFmZudixLohbHj+nFaQyGneYVD+voQ2x8dBA2NgbA1DP6PTrmtuY1OsPRIan8BscIaRYlGCL8dvWf5EU/xoAYGwiwYipNO4w4U+TlnWw9c930KZTwZyy9269wkjPQzi2J5S68hCihyjBFiHhVRa2rQ3klifNa021V8I7KxsjrN3TFz4L20EkVrcFyMrIxRKfi5j5zglqAEWInqEEW4TNy65zLYfNrWQYNqkZzxERoiYUCjDapyU2Hx4ER1czbv3V88/xbruD+HHlDeRk5/EYISEkHyXYN4QGx+GPXQX9Xhd81ZnmfCV6p0nLOvjp+GC8O6kZ8uecUOQosfWrWxjW9iDO3mwLlYpavBPCJ0qwhSiVKqyaF4D8x1luLaw0+iISok+MjCX44H+e+OGPd9CkZcHoYs+fpOKTrdMxctlC+N9xp+ezhPCEEmwhezbewb83XwEABALgiw3deY6IkNI1aVkHm48MxIfLO8LYVMKtf/jcCR9snI3x3Y/A99gTKJUqHqMkpPYRsHJ8ve3QoQNu376ty3h48/RBMrzf+h2KHHXfwuFTmmHOEk+eoyKkfNKSs/F/20NwfHcwshWaMz7VczHD+PdbYcS0FrCwkvMUYS1QeJ5ountQq1GCBZCTnYeJPY8iNDgeAGBjb4x9fiMgllAFnxgmyWknbD/dD4f8uiI3T6LxnpGxGH2Hu2Hw+Kbo2NsJYjH9P69SlGDJfyjBAvjf7Is4ulPdsEkgALb++Q4ataAZc4jhqnfFAgDwKtkCe259jmMnjZCWLtbazqauMQaOboy+77rBo1NdSrZVgRIs+U+tT7BHdoZg2Ww/bnnqRx6Y8AHN90oMW36CBYD0+ouQnQP8fcEYvx81xtMIWZH7WFjL0L2/C3oOckXH3k6oY2dcXeHWLJRgyX9qdYL9568IzB1xmpvrtU2nuvh2b18IBNS9gRi2NxNsPsaAh48lOHveCOfOy5GcKilqdwBAw2ZWeKuHIzp0rwePjnVRz8WM/jbKghIs+U+tTbB3rr/Ae/2PI/u1ulO+la0R9px/F0bGxV9wCDEUxSXYwvKUwO0gGfwuyXH5qhSJydIit8tnWUeOFu1s0bKdHRq3rAO35tZwbWIJmVz71nOtRgmW/KdWJtgb/tGYO+I0sjJyAajHGt5xdihs6prwHBkhVaMsCbYwxoBH4RJcuS7H1RsSPHgkQ56y9OexQqEA9Ruaw6mhBRzqm6Keszkc6pvCwdkMDvXNYG1nBCNjcbXWfFUqhrxcFRQ5SuTlqSASCSASCyGWCCEWC3U/5SQlWPKfWvfV0/fYEyyc/DfXHUciFWLzkYGUXEmtJhAATRvnomnjXEyfBGRnC3A/TILguzIE3RHhYbgMWa+1LxcqFUNkeCoiw1OLPbZYIoS5pQxmFlKYW8lhbiWDkZEYIrEQIrGAS3yi/xpY5SqUyMtVIVehUr/OU7/Oy1UiV6FOnLkKpfrf3MLLKm7fkgiF6nOamkthZiGFmaUMZhYy7rWNvTHs6pnArp4p7OoZw76eKaztjCASUQMwUj61JsEqlSpsWXYD274tGMRfJhdh8+GBcGlkyV9ghOghuZyhQ1sFOrRVj8nNGBAdK8KDR1I8fCRG+FMhIiLFeJUgA2Ml1wjzclVIin/93+xUxSfi6qJSMShylIViKp1IJIC9kynqN7RA/Ybm//1rAeeGFnBqaA4zi6IbjpHarVYk2GcPk/G/2RcRdOUFt87UXIofjw1CPWdzHiMjxDAIBEB9RyXqO75G394F67OzBXgeI8KLV2K8ihPhxUsBYmOBl69EiE+QIC1TjNzc6h/LWyxSQSxWQSRiUCkFUKr++ynDbe+iKJUMsZHpiI1Mxw0/7fct68jh7Gah/hEOhDOLhzMS4JL4GhbWcmocVkvV6GewGWkK7NoQjJ3rg7hbwoC6deS6ff1gbknfOknNVN5nsLqUowAyMoRIzxAiPV2I9AwBchQCKJUC5OWh4F8VwJgAUgmDWMwgFgNiMYNEDEgkDGIRg1gC7n2pFJCIGSQSBolEvY36Pc3HoIUxBqhUQG6uAJlZAmRkquPh4ssQIDFRhLh4AV69AhISRUhIEiMto+QGYCUxt5Shfn7yzf9pZAmXRhawtjWi5FuD1cgabHLCaxzeHoI9m+4gJTFb471hE5tizlJP3Td0IIQAAGRSQGatQh1r/sdCFggAkQgQiRjkclbmmHIUwIuXYsTEihAdK0ZMjAhR0QLExIrwMk6K3Lzia+lpKTkICYxDSGCc1nsmZhI4u1nC2U1967lufTPYO5rCwckU9k6msLKh2q8hqzEJVqFQ4tr55zhz6BF8jz1BTrZS431bB2Os+Kk3GrvTCE2EkPKRSQFX5zy4OucByNF4T6UC4hNEiI4VITpGjLh1txElsEGUwBYRsrrIURR/mc1Mz0XYnXiE3Ykv8n2pTAR7J1PUdTKFfT0T2NQ1hrWtMaztjGBjp/7X2tYIdeyMIZXRtJr6xmATbEpSNh7ciUdocDwCL8fihn8MXmfmam1nYibBe5+2w6AxjakVICGkygmFgL2dEvZ2SrRvo4DZ2hPce2kXFiExSYjoGDGiY8WIihYhMpIhOkaM2FcyvM4u+RKsyFHi+ZNUPH9SeuMwMwupOtnaG8PCSg4LaxnMrWQwt/zvtaUMFtZyWFjJYPFfa24zSxkNj6lDepdglUoVMtNzkZqUjbSUHKQmZSPx1WvERqXjxfN0xEalI+JRMmIi0ks8jq2DMSbMaY2+w93omx0hhBcCAWBTRwWbOgq0aa3QeI8xICVViOcxYkTHiBD7UoyXL4GXr4C4ODESkiXIel32gW/SUxVIT1WU2GWqKKbmUphaSGFiKoWxqQQmZhKYmEphYiaBsZkUJqYSmJipl03MJDA2lcLYRAKpXASZXASpTASpTMy9lslFkMrFkMpEtT5585Jg9/9wF8f2hCHndR5ycpTIeZ2H7Nd5yHmt7vNWUWYWUnTt64xhE5vCrYU1PbsghOgtgQCwslTBylKB1u5Fb5OZJUBcvAhx8SLEJ4iQlCxEUpIACYkMSUkCJKeIkJImRmq6BCpVxZJZRpoCGWmK0jesAJFI8F+yFUIkEkIoEkAkEkAoEqoHABEJuPXCQq/zBwcRiQTqBmsCAddwTfDf6/x/Ufj1f+/P/qID2nRy0EmZyoOXBJv4KgsP7iZU6hgCAWBmKYNzQws0b2uDFu3sYOegHixCxYDHIUlVESohBqleodcpr7Qb1xDdMSv0uio+eys5YFUfaFq/+G1UKiAjU51sU9IkSM8QISNThPQMMdIzREhNFyA9XYD0dBEyskTIzBIjI0uKrGxJqf2YK0OpZHidmYvXmTo7RZFGzyjmG0s1K1c3HRsbG7i6ulbZyR/eS0BergpKlgGRwLTEbfNHejGUSmlOXjpkYrPSNzQQVB7992aZTOWZkIi02yUYivTXOTAzqlld6Qy3TOy/YR8L/QsgLTsX5vKSb2MzjRcCFJdwWKFtKkMkM4W1Y8Vrr/Hx8bC1teWWbWxscPbs2Qodq1wJVlcMrX9tWdS0MlF59F9NK1NNKw9Q88pU08oDVG2ZavcTaEIIIURHKMESQgghOqAXCXbmzJl8h1DlalqZqDz6r6aVqaaVB6h5Zapp5QGqtkx68QyWEEIIqWn0ogZLCCGE1DQ6S7DPnz/HyJEjYWFhAXNzc4wYMQJRUVFl2jcqKgpTpkyBs7MzjI2N0aRJEyxZsgSZmdXcmeoN0dHRmDdvHjp37gxjY2MIBAJERESUad/s7Gx89tlncHBwgJGRETp37ox//vlHtwGXoqLluX37NmbOnIlmzZrB2NgYzs7OmDBhAp49e6b7oEtQmd9PYatXr4ZAIEC3bt2qPshyqmyZwsLCMGrUKNjY2MDIyAhNmzbFpk2bdBdwKSpTHn28Lhw5cgTe3t5wcXHhPt/FixcjPb3kkeYA/bwmABUvk75eFyrzOyqsQtcFpgOZmZmsUaNGzN3dnR07doz9+eefrGXLlqxhw4YsIyOjxH0zMjJY48aNmaurK9u9eze7ePEiW7t2LZPL5Wz06NG6CLfM/Pz8mJ2dHRs4cCDr168fA8CePXtWpn3Hjx/PLCws2K+//srOnz/Phg8fzuRyOQsODtZpzCWpaHk++eQT1qVLF/bjjz8yf39/duDAAdasWTNmbW3NoqKidB94MSrz+8n35MkTZmJiwuzs7FjXrl11E2g5VKZMt27dYmZmZmzIkCHs2LFj7OLFi+yXX35h69ev123QJahoefT1utCxY0c2atQotn//fubv78++//57ZmFhwTp27MiUSmWJ++rjNYGxipdJX68Llfkd5avodUEnCXbjxo1MKBSyx48fc+uePn3KRCJRqX/c586dYwDYuXPnNNZ//vnnTCQSsczMTF2EXCaFfxnbtm0r88Xhzp07DADbuXMnty43N5c1adKEDRkyRBehlklFyxMXF6e1LiIiggkEArZ06dKqDLFcKlqewvr168dmzpzJevbsqRcJtqJlUiqVrEWLFuzdd9/VYXTlV9Hy6Ot1oai/hT179jAA7MKFC8Xup6/XBMYqXiZ9vS5UtDyFVfS6oJNbxCdOnECnTp3QqFEjbl2DBg3QtWtXHD9+vMR9FQr1mJjm5uYa6y0tLaFSqcB4bJMlFFbs4zpx4gQkEgnGjBnDrROLxRg7dizOnTuHnJycEvbWnYqWp/AoJ/lcXFxga2uLmJiYyoZVYRUtT77ffvsNQUFBWL16dRVFVHkVLZO/vz9CQ0Px8ccfV3FElVPR8ujrdaGov4W33noLAEr8W9DXawJQ8TLp63WhouXJV5nrgk4SbEhICFq2bKm13t3dHaGhoSXu+/bbb6Nx48b4/PPPERoaioyMDFy8eBGbNm3C7NmzYWJioouQdSokJAQNGjSAsbGxxnp3d3coFAqEh4fzFFnVCQsLQ1xcHJo3b853KBWSnJyMBQsW4Ntvv4W1tTXf4VTa5cuXAaif83Xq1AkSiQR2dnb48MMP8fr1a56jKz9Dui4EBAQAQIl/C4Z2TShLmYqir9eFspanstcFnSTYpKQkWFlZaa23trZGcnJyifvK5XJcvnwZKpUK7u7uMDMzQ58+fTB48GD88MMPughX50r6PPLfN2R5eXmYPXs2bG1t8d577/EdToV89tlnaNKkCaZOncp3KFUiNjYWADBmzBj069cPvr6+WLhwIbZv347x48fzHF35Gcp1ISYmBv/73//w9ttvo0OHDsVuZ0jXhLKW6U36el0oT3kqe13Q2Ww6RU0VV5bbONnZ2RgzZgzi4uKwb98+ODs74+bNm1i5ciXEYjF++uknXYSrU4yxCn8ehmDu3Lm4evUqTp8+XeRFQ99dunQJe/fuRVBQUI2Z4lClUk/7OHHiRKxcuRIA0KtXLyiVSixatAihoaFo0aIFnyGWiyFcFzIyMjBs2DCIxWLs2rWrxG0N5ZpQnjK9SR+vC+UpT1VcF3SSYK2srIr8BpacnFzqB71jxw74+/sjPDwcbm5uAIAePXrAwsICM2fOxOzZs+Hh4aGLsHXG2tq6yC5K+bV5Q74luXjxYvz666/Ys2cP+vXrx3c4FTJr1iy89957cHJyQkpKCgD1t2+lUomUlBQYGRlBJjOsGVDq1KkDAOjbt6/G+n79+mHRokW4c+eOQSVYfb8uZGdnY+jQoXj69CkCAgLg5ORU4vaGcE0ob5kK08frQnnLUxXXBZ3cInZ3d0dISIjW+rJ8a7537x6srKy4P6J8np6eANT39A2Nu7s7nj17hqysLI31oaGhkEqlGo3BDMnXX3+NNWvWYNOmTZg0aRLf4VRYWFgYfv75Z1hZWXE/V65cwfXr12FlZaUXtaPycndXz4f55jfv/BpSZRuEVTd9vi7k5ubC29sbN2/exJkzZ9CqVatS99H3a0JFypRPH68LFSlPVVwXdPJXNnToUFy/fh1Pnz7l1kVERODKlSsYOnRoifvWrVsXycnJWg/5b9y4AQBwdHSs+oB1bOjQocjNzcXhw4e5dXl5eTh06BD69etncLUjANi8eTOWLFmCr7/+GvPmzeM7nErx8/PT+vHw8EDLli3h5+eHkSNH8h1iuQ0cOBAymUxrHstz584BQLmepekDfb0uqFQqTJgwARcuXMDx48fRqVOnMu2nz9eEipYJ0M/rQkXLUyXXhTJ36CmHjIwM5ubmxlq2bMn+/PNPdvz4cda6dWvWoEEDlp6ezm0XERHBRCIRW7FiBbfu2bNnzMzMjDVu3JjrUP7tt98yMzMz1r59+zJ3DNaVw4cPs8OHD7PZs2czAGzr1q3s8OHDzN/fnzFWdJkYY2zMmDHM0tKSbdu2jZ0/f555e3szmUzGAgMD+SgGpyLlOXjwIBMIBGzAgAHs2rVrGj8hISF8FYUxVvHfz5v0pR8sYxUv0/Lly5lIJGKLFy9mvr6+bPXq1Uwul7MpU6bwUIoCFSmPvl4X8svw5Zdfav0tPH/+vNjyMKa/14SKlklfrwuV+R29qbzXBZ0kWMYYi4yMZCNGjGBmZmbM1NSUDRs2TKtD+bNnzxgAtmzZMo31ISEhbNSoUczJyYnJ5XLWuHFj9sknn7CkpCRdhVtmAIr86dmzJ2Os+DJlZWWxBQsWMHt7eyaTyZinpyfz8/Or9vjfVJHyTJkypdT9+FLR38+b9CnBVrRMKpWKrV+/nrm5uTGJRMKcnZ3Z0qVLmUKhqP5CFFLR8ujjdcHFxaXY8uTHb2jXhIqWSV+vC5X5Hb2pvNcFmk2HEEII0QHDaulACCGEGAhKsIQQQogOUIIlhBBCdIASLCGEEKIDlGAJIYQQHaAESwghhOgAJVhCCCFEByjBElJFBAIBBAIBhEIhnjx5Uux2vXv35rbdvXu3xntTp07l3lu2bFmxx9izZw+3Xa9evTTe8/f3597L/zE2NoaDgwN69OiBzz77DMHBwZUpKiGkDCjBElKFxGIxGGPYsWNHke8/fvwYAQEBEItLnshKLBZj586dUCqVRb6/bdu2Uo/h4uKCZcuWYdmyZfjoo48wePBgZGZm4rvvvkO7du0wYcIEZGRklK1ghJByowRLSBWyt7dHhw4dsGvXLuTl5Wm9v337djDGMHjw4BKPM3jwYERHR2sN1g+oZ/m4cuUKhgwZUuIxXF1dsXz5cixfvhzffPMNtm3bhsDAQAQHB6NVq1b47bffMGrUqPIVkBBSZpRgCaliPj4+ePnyJU6dOqWxPjc3F3v27EGXLl246eSKM2HCBBgZGWHbtm1a7+WvmzFjRoXia9OmDc6fPw9bW1ucPXsWf/75Z4WOQwgpGSVYQqrYuHHjYGJigu3bt2usP3HiBF69egUfH59Sj2FpaYlRo0bh9OnTePHiBbc+JycHe/fuRc+ePdGkSZMKx2hnZ4dZs2YBAA4cOFDh4xBCikcJlpAqZmZmhrFjx+Ls2bOIjo7m1m/btg3m5uYYPXp0mY7j4+ODvLw8jYZQx44dQ2JiYpmSdGnyG0fdvHmz0scihGijBEuIDvj4+ECpVGLnzp0AgMjISPj6+mLChAkwNjYu0zG6deuG5s2bc89tAXWStrKygre3d6VjzJ+kPD4+vtLHIoRoowRLiA507NgRrVq1ws6dO6FSqbB9+3aoVKpy1zxnzJiBp0+f4uLFiwgPD4efnx8mTZoEuVxe6Rjzk7ZAIKj0sQgh2ijBEqIjPj4+iIyMxNmzZ7Fr1y60b98ebdu2LdcxJk+eDJlMhu3bt3M12aq4PQwAsbGxAABbW9sqOR4hRBMlWEJ0ZNKkSTAyMsKsWbMQExODmTNnlvsYNjY2GD58OI4dO4adO3eic+fOaNmyZZXE5+fnB0Bd2yaEVD1KsIToiKWlJUaOHIno6GiYmJhg3LhxFTqOj48PcnJyEB8fX2W117i4OPzyyy8A1F2CCCFVr+ShYAghlfLVV19hxIgRsLW1hZmZWYWO0bt3bxw/fhwqlQr9+/evdEx3797F5MmTkZCQgEGDBmHo0KGVPiYhRBslWEJ0yNnZGc7OzpU6hkAgqFASjIiIwPLlywGoB7lISEhAYGAgAgMDAahrrvm1WEJI1aMES0gNFRkZiRUrVgAA5HI5LC0t0bhxY3z66aeYMGEC2rRpw2+AhNRwApbfVp8QQgghVYYaORFCCCE6QAmWEEII0QFKsIQQQogOUIIlhBBCdIASLCGEEKIDlGAJIYQQHaAESwghhOgAJVhCCCFEByjBEkIIITpACZYQQgjRgf8HzdvOKhYRLOQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x29d9ec880>]"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ce7d24177f04c84a5e21718b228143e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49352e2b288844889c2bd6cdb10793be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78acab0c5e5c4de0b10bb9592e53f857",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34eb0f6c6b834d49bbfe8835c76a6d3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aca9a4accc814a5e94614c4fb8eafa46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60d4d6f3dd824b1b9821190a599ab355",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "096b5a6a27a4412eb2144b4b6105f308",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0d19baae87e4c77af3288698a1b06a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe564157bf404fee9c8e46208dca2064",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b9719b037804943a90e364d07a12480",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f3555775776412bb18ace241073880e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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DJgx0UcRMpKSksHyIJEQQBDz+2TFsOVWNrf81hevLj6DWbi2S/7gNv7whHP8zN3ZYxhjK72Q+UU9EJvPlEQ2+PVGFp28axUIZYa72NpgU6Y3vTlcPeAPVSGGpEJFJGAwC1uwsQkKQG5bdyIW2xDA7zhcXGzpRWNsuWgaWChGZxPcF1Siu78DDUyI4n5dIZsX6QiYDck5WiZaBpUJERuvs1eGP356BWuWMOWP8xI5jtVSu9pgU4YWvjmpgEOkuMJYKERntg/2lqGjuwp9uizebdT2s1Z0pQShv7EJuaaMo4/OrT0RG6dHpsXZfKaZEeSM1XLxbWemSm+P84WynxJd54swFxlIhIqN8crAMdW09+NVUXpw3Bw62CsxL8MeWU1Xo6NGN+PgsFSK6bvXtPXh923lMifLGDWpOv2Qu7kwJQmevHptFuGDPUiGi6/bZoTK09ejw4q2jeceXGUkK8UCEtxM2iHAKjKVCRNfFYBDweV45blB7Qa3ig47mRCaTYWFyEHJLG1Fa3zGiY7NUiOi67DhbC01TFxaN57LA5mhhUhDkMuCrEV68i6VCRNdMEAS88eMFBHs64GY+l2KW/NzsMTnKB18dGdlnVlgqRHTN9hTW43h5M5bdqOZzKWbszuQgVLZ0Y++F+hEbk98NRHRNBEHAGzsK4e9mj4XJgWLHoSu4Kc4Xnk62+PjgxREbk6VCRNfkYHEjDpc2YemNkbBTcq0Uc2anVOAX44Ox/UwNKpu7RmRMlgoRXZM3dhTCx8UOvxgfLHYUGoJ7UkMgAPj0UNmIjMdSIaIhK6hsxf6iBmROiYC9DY9SpCDY0xHpMSqsP1yGXp1h2MdjqRDRkG04ooGNQoY7koPEjkLX4L6JoWjo6EXexeGfZNLoNeqJyDpo9QZk51dgZqwvPJxsxY5D12BqlA92PzMdwZ6Owz4Wj1SIaEh2nqtDQ0cvj1IkSC6XjUihACwVIhqir45o4O1si6mjfMSOQmaMpUJEV1XV0oUfztZgQWIgH3akK+J3BxFd1Tu7iiEIwIM3hIkdhcwcS4WIrqitW4vPcstw27hABHmMzHl5ki6WChFd0dZT1ejRGXD3BM5GTFfHUiGiK9qUX4FQL0eMC3YXOwpJAEuFiAZV3dKN/UUNWJAYyJUdaUhYKkQ0qK+PV0AQgAXjOBsxDQ1LhYgGtelYJRKD3RHu7SR2FJIIlgoRDehcdRsKqlqxIDFA7CgkISwVIhrQpvwKKOQyzBvLUqGhY6kQUT9dvXpsOKLB1ChveDvbiR2HJISlQkT9fHLoIuraerD0xkixo5DEsFSI6DLdWj3W7CrGDWovTIjwEjsOSQxLhYgu88+jFahv78Gj09RiRyEJYqkQUR+9QUDW7iLEB7ohLZJHKXTtWCpE1Of709UobejE0hsj+QQ9XReWChEBAARBwJpdRQj1csTNY/zEjkMSxVIhIgDAgeIGHNe04JEpEVDIeZRC14elQkQQBAGrd1yAt7Mt16Ano5ikVLZu3Yro6Gio1WqsXLmy3/sFQcATTzwBtVqNhIQEHD161BTDEpGJ7Cmsx/6iBiyfroa9jULsOCRhRpeKXq/H8uXLsWXLFhQUFOCzzz5DQUHBZdts2bIFhYWFKCwsRFZWFpYtW2bssERkQqt3XECguwPu4UJcZCSjSyU3NxdqtRoRERGwtbXFokWLkJ2dfdk22dnZeOCBByCTyTBx4kQ0NzejqqrK2KEtUlevHtsKatDY0YvGjl5k51egob0Hbd1a1LZ2ix2PLNDx8mbkljZiyeRw2Cl5lELGURr7CSoqKhAcHNz3dlBQEA4dOnTVbSoqKuDv72/s8JKk1RsgA6BUyHGuug2bT1TiZEULevUGnKlqQ2NHLxxtFTAIArq1hr6Pk8mAZ2fH4MG0MDjY8oefjNet1ePFr0/DxU6Ju1J4LYWMZ3SpCILQ77X/vL99KNv8LCsrC1lZWQCAuro6o7JdqG2Hl5MtHO0UZvEXWI9Oj/LGTvzqoyOob++Fs50SFc1dUMhlGOXrAnsbOW4c5YP0WBUOFDXARiHH9BgVTmqaIZPJcLy8Ga9tPYus3UV4bk4M7kwOhpx36ZARXt92HvnlzXj73iS42NuIHYcsgNGlEhQUhPLy8r63NRoNAgICrnmbn2VmZiIzMxMAkJKScl2Z2nt0+N3Gk8jOr4StQo5evQE3x/nB1UGJGD9XTB3lgwB3ezjaKqHTG6BUDN9NcFq9ARdq2/GPPcX49ngVevUGONoqcHOcH7QGAZlTI3BLgn+/mWDnJfxr/9w4ygfApXI+WNyI17edx2++OonPD5fjlQXxGB3gOmz5yXKVN3Zi7b5SLEwKwpx46zxrQKZndKmMHz8ehYWFKCkpQWBgINavX49PP/30sm0yMjKwevVqLFq0CIcOHYKbm9uwnvpysFGgsrkLy6dHorNXj26tHp8fLoejrRJf5GkAAHIZMMrXBWer2xDo7oB5Cf7wcLJFfKAbtp6qhp+bPW4e44fOHj3kcqCyuRsu9krEBbjCxd4Gnb062CsV6NUbsONsLdq7dejo1aGx49IRyL6iBhgMAi42dqC8sQtKuQz3TghBjL8rxod5QK1yueb/l0wmw6RIL0yMmIivjlZgRc4Z3Lp6L5ZPV+PXM6P4BDQNmVZvwFNf5EMhl+G/Z48SOw5ZEKNLRalUYvXq1Zg9ezb0ej2WLFmCuLg4rFmzBgCwdOlSzJ07Fzk5OVCr1XB0dMTatWuNDn4lCrkM6zMnXfYA1+9uGQ1HGwXKmzpxqLgR52vacPhiE351YwSOXWzGO7uL+7a1t5GjW2vAX7471+9z2yhkGO3vipMVLQj3dkJLlw717T39thvl6wxHWyX83RzwZPooJIV6mGxJVplMhjuSgzAzVoWXvy3A//1QiLZuLX4/bzSLhYbkrR+LcLi0CasWJcLfzUHsOGRBZMJAFzzMREpKCvLy8kZkLK3egBOaZhTVdeDWhAB8cugiqlu6EevvCpkMUKuc0dSpxf6iehwqbkSsvwsKa9rh62qPX4wPRqTKGXZKOZxslaht60ao18is6S0IAl7ZfAbv7S1B5tQI/M/c2BEZl6SrtL4Ds/++GzNH++LNe5LEjkMSMpTfyUYfqVgKG4UcyaGeSA71BAA8PCViwO1+vr5xJSNVKMClo5bnb4lFr86ArN3FiPFzwe1JvIuHBlbb1o0H1+bCTinH87fwDxAyPU7TYgFkMhlemDcaqeGeeOqL43h923kYDGZ7AEoiqW/vweL3D6OmtQdrf5nK0140LHikYiFslXJ8uCQVv9t4Cqt+KERLlxZ/yIgTOxaJSBAElDZ0orqlG3sK6/DlEQ1aurR494EUJId6iB2PLBRLxYLY2yjwv3cmwNFWgQ/2l2LOGD8uB2uFOnp0+OdRDT4+WIZzNW0ALt28MjHCE7+bO5q3oNOwYqlYGJlMht/OjcGOs7X405az2PRoGu8Is3BavQHv7S3BF4fLIQCobulGl1aP+EA3vDw/DqFeTkgMcoebIx9upOHHUrFAjrZKPDZDjd/+8yS+O13DBZcslN4gYOWWM/jn0Qo0dPQiLdILznZKTInyxvzEQCSFuPMPChpxLBULtTApCOv2l+LpL/IR6D4J8UFuYkciEzpT1YoXvz6N3JJGzBnjh4VJQZg52lfsWES8+8tS2Srl+OCXqXB3tMWDa3NRXNcudiQykcaOXix+PxdFte1YcXs83r4vmYVCZoOlYsH83Ozx0UOpEADc/14u6tr6P/lP0lJc1457/nEQzZ1afPTQBNydyvVPyLywVCxchI8zPvjleNS2deONHYVixyEj6PQGLP/0GGpau/HOA8m8i4vMEkvFCiQEuWNhUhDWHy5HdQsX+pKqD/aX4kxVK1bcHo/p0Sqx4xANiKViJR6dpoZcBjz5+THo9IarfwCZlcrmLvxt23nMiFFhdhzv5iPzxVKxEiFejnhlQTwOFjfio4MXxY5D1+jlbwpgEAS8lBHH24TJrLFUrMjCpEBMifLG69vOo6K5S+w4NES7z9dh6+lqPD4jCsGejmLHIboilooVkclk+ENGHAQAv3jnAFo6tWJHoiF4f18JfF3t8MggM2cTmROWipWJ/OluME1TFz7J5Wkwc6dp6sSu83X4RUowbJX8cSXzx+9SK5Qc6okb1F74cP9FtPfoxI5DV/Dz8td3jQ8WOQnR0LBUrNTy6WrUtffgrjUH0NrN02DmSKc34IvD5Zga5YMgD15LIWlgqViptEhvvLs4Bedr2vDU58eh56JeZmfPhXpUt3bj7lQepZB0sFSs2PRoFV6YNxrbz9TgmQ3HIQgsFnOyvaAGTrYKzIjhvF4kHSwVK7c4LQxPpEfhn0crsONsrdhx6CeCIGDH2VpMifLhBXqSFH63Eh6foUaEjxNe2HQKB4oaxI5DAAqqWlHV0o0ZsZyOhaSFpUKwUcjx1zvHQqmQY/HaXJytbhU7ktXbcEQDG4UM6TEsFZIWlgoBAMaFeGDjo2lwtbfBso+PoqyhU+xIVqtbq8dXRzS4eYw/vJztxI5DdE1YKtTHy9kO79yfhKbOXix4ax+OXGwUO5JV2nW+Dq3dOvwihXd9kfSwVOgyyaGe2PjoDXBzsMHd/ziEr49Xih3J6hwsboCdUo7x4R5iRyG6ZiwV6ifc2wkbH03DuGB3PPHZMby7p1jsSFblUHEjkkM9YKdUiB2F6JqxVGhA7o62WLckFXPj/fDK5jP49ef5qOTMxsOupVOLM9WtmBDuJXYUouuiFDsAmS97GwXeuDsJ4d7n8O6eEuy7UI9IH2fclhSIu3i+f1jsL6qHIACTIlkqJE08UqErUshleGZ2DL5+bDJsFHKcq2nDsxtO4L29Jejq1Ysdz+LsPFcHF3slkkLcxY5CdF1YKjQk0X4u2Pub6Tj423TcHOeHP35bgLgXt2LNriKxo1kMQRCw63wdJqu9oVTwR5Okiae/aMhkMhlslTKsvmccPs8rxw9narFyy1mU1nfghXmj4WTHbydjFNW1o7q1GzeO8hE7CtF1459DdM2UCjnunRCKNfcl4+HJ4fjyiAYPr8vjFPpGyi9vAQAkh/JWYpIulgpdN1ulHM/PG42/3jkWh0oacPPru/HhgVIYOI3+dTmpaYajrQIRPs5iRyG6biwVMtqCcYHYsCwNvm72+H32aTyz4QSn0b8OxzUtGBPoBoVcJnYUouvGUiGTSArxwD+XpeGJ9Ch8dVSDL49oxI4kKVq9AQVVrUgIdBM7CpFRWCpkMjKZDE+mRyE1zBOvfFvA2Y6vwenKVvTqDBgb7C52FCKjsFTIpORyGf73zrFwsFXgvndz0dGjEzuSJBwsvrSOzYQIT5GTEBmHpUImF+LliLfuTUJ9ew8+yy0TO44kHCxugFrlDJWLvdhRiIzCUqFhkRzqiQnhnsjaXYw23mp8RTq9AYdLGjGRRylkAVgqNGyemxOD+vYe/CnnrNhRzFpBVSs6evVI5SSSZAGMKpXGxkbMmjULUVFRmDVrFpqamvptU15ejunTpyM2NhZxcXFYtWqVMUOShIwL8cAvbwjH+sNlKKprFzuO2corvfRzMz6MDz2S9BlVKitXrkR6ejoKCwuRnp6OlStX9ttGqVTir3/9K86cOYODBw/izTffREFBgTHDkoQsmxYJO6Ucb+/kHGGDybvYiEB3B/i7OYgdhchoRpVKdnY2Fi9eDABYvHgxNm3a1G8bf39/JCUlAQBcXFwQGxuLiooKY4YlCfF2tsM9qaHYeKwC5Y1c9/4/CYKAvNImTs1CFsOoUqmpqYG/vz+AS+VRW1t7xe1LS0tx7NgxTJgwYdBtsrKykJKSgpSUFNTV1RkTj8xE5tQIKGQyvM0ZjfvRNHWhtq2Hp77IYlx1WtmZM2eiurq63+uvvvrqNQ3U3t6OhQsX4u9//ztcXV0H3S4zMxOZmZkAgJSUlGsag8yTn5s97kwJwpd5Gjw+Q83TPP/mcGkjgEt3yxFZgquWyvbt2wd9n6+vL6qqquDv74+qqiqoVKoBt9NqtVi4cCHuvfde3H777defliRr6Y2R+PxwObJ2F+PFW+PEjmM28i42wcVOiWg/F7GjEJmEUae/MjIysG7dOgDAunXrMH/+/H7bCIKAhx56CLGxsXjqqaeMGY4kLNjTEbeODcCGPA06e/mU/c+OlDZhXKgHJ5Eki2FUqTz33HPYtm0boqKisG3bNjz33HMAgMrKSsydOxcAsG/fPnz00UfYsWMHEhMTkZiYiJycHOOTk+TcnRqCth4dck72P51qjZo7e3Gupg3jeZGeLIhRS/V5eXnhhx9+6Pd6QEBAX3FMnjyZ06ATgEvPYUR4O+GLw+W4IzlI7DiiO1Ry6XrKxEg+9EiWg0/U04iRyWS4a3wwcksb+TAkLs33ZaeUIyGI092T5WCp0Ii6PSkQCrkMX+SVix1FdIeKG5Ec6gE7pULsKEQmw1KhEaVyscfUKG/knKyy6tOi7T06nKluxfgw3kpMloWlQiNudpwfyhu7cLa6TewoojlR3gxBAMaFuIsdhcikWCo04tJjfSGTAd+frhE7imiOlTcDABK50iNZGJYKjTgfFzskh3jg+wLrvbX4WFkzIryd4O5oK3YUIpNiqZAoborzxenKVmiarG+SSUEQkF/ejESe+iILxFIhUcwa7QfAOk+BaZq6UN/eg3E89UUWiKVCogj3dkKkjxN2nre+majzf7qeMi6ET9KT5WGpkGimRPkgt6QB3Vq92FFG1LGyZtgp5ZxEkiwSS4VEM1ntjW6tAUcv9l+G2pLllzchIcgNNgr++JHl4Xc1iWZipBeUchn2XKgXO8qI6dUZcKqylbcSk8ViqZBonO2USArxwN5C6ymVM1Wt6NUZkBjM6ylkmVgqJKrJUd44VdmCpo5esaOMiH9dpHcXNQfRcGGpkKgmR3lDEIB9RdZxtHKsrAkqFzv4u9mLHYVoWLBUSFQJgW5wc7DBjrO1YkcZEfnlzRgX4g6ZjCs9kmViqZColAo50mNU+OFMLXR6g9hxhlVjRy9KGzp5PYUsGkuFRHdTnB9aurTILW0UO8qwOs7rKWQFWCokuqmjvGGrkGPXOct+uv5YWRPkMiA+kCs9kuViqZDoHG2VGBvshoMlln2kcqy8GdF+rnCyU4odhWjYsFTILEwI98Kpiha09+jEjjIsDIafZibmQ49k4VgqZBZSwz2hNwg4YqFTthTXd6CtW8frKWTxWCpkFpJDPaCQy5Bb0iB2lGFxrOxSWXK6e7J0LBUyC052SsQHuuFQsWVeV8kvb4aLnRKRPs5iRyEaViwVMhsTIjxxXNOMrl7Lmwr/WFkzxga7Qy7nQ49k2VgqZDYmhntBqxf6ThVZis5eHc7VtPEiPVkFlgqZjeQwD8hlsLhbi09qWqA3CLxIT1aBpUJmw9XeBqMDXC3uYv3PMxPzSIWsAUuFzMqEcC8cK2tGj85yrqscudiEEE9HeDnbiR2FaNixVMisTAj3RI/OgPyyZrGjmITeIOBgcQMmRniKHYVoRLBUyKxMiPCCQi7DHgtZDfJ0ZQtau3W4Qe0tdhSiEcFSIbPi5mCD5BAP/HjOMtZX2V906frQpAgvkZMQjQyWCpmd6TEqnK5sRU1rt9hRjLbvQj2iVM5QuXKlR7IOLBUyOzeO8gEA7JX4KbAenR6HSxt56ousCkuFzE6MnwvcHW1wSOK3FueXNaNba8CkSJ76IuvBUiGzI5fLMD7MEwclPg/YvqIGyGXARF5PISvCUiGzNCHcE2WNnahs7hI7ynXbU1iH+CB3uDnYiB2FaMSwVMgs/XwdYvd5aS4x3NKpxfHyZtwYxespZF1YKmSWYvxcEOjugO1nasSOcl32FdXDIABTf7rpgMhasFTILMlkMswa7Ys9hfWSnAp/9/k6uNgpOd8XWR2jSqWxsRGzZs1CVFQUZs2ahaamwacs1+v1GDduHObNm2fMkGRFZsb6okdnwN4L0rq1WBAE7CmsR5raC0oF/24j62LUd/zKlSuRnp6OwsJCpKenY+XKlYNuu2rVKsTGxhozHFmZ1HBPuNgpsb1AWqfAiuo6UNHcxVNfZJWMKpXs7GwsXrwYALB48WJs2rRpwO00Gg02b96Mhx9+2JjhyMrYKuW4MdoHP5ythcEgiB1nyHb9dHPB1CiWClkfo0qlpqYG/v7+AAB/f3/U1g48X9OTTz6JP//5z5DLeSqArs2s0b6ob+/BcU2z2FGGbPOJSsT4uSDY01HsKEQjTnm1DWbOnInq6up+r7/66qtDGuDbb7+FSqVCcnIydu7cedXts7KykJWVBQCoq5Pm7aRkOtNGqaCQy7D9TA3GhXiIHeeqyhs7cbSsGc/eHC12FCJRXLVUtm/fPuj7fH19UVVVBX9/f1RVVUGlUvXbZt++ffj666+Rk5OD7u5utLa24r777sPHH3884OfMzMxEZmYmACAlJWWo/w+yUG6ONkgN88T2glo8MztG7DhX9c2JSgDArQkBIichEodR56MyMjKwbt06AMC6deswf/78ftusWLECGo0GpaWlWL9+PWbMmDFooRANJD1WhXM1baiQwNP13xyvwrgQd576IqtlVKk899xz2LZtG6KiorBt2zY899xzAIDKykrMnTvXJAGJfn66/mCReU8weaG2DWeqWpExlkcpZL2uevrrSry8vPDDDz/0ez0gIAA5OTn9Xp82bRqmTZtmzJBkhaJ9L81afLC4AQuTg8SOM6iv8yshlwG3JPiLHYVINLwdi8yeXC7DhHBPHDTjqfAFQcDXxysxKdILKhcuyEXWi6VCknCD2hvljV0orGkTO8qATla0oLShkxfoyeqxVEgS5ozxh0Iuw8ZjFWJHGdCGIxrYKuWYM4anvsi6sVRIEnxc7DAlyhvZ+ZVm93R9t1aPTccqMHeMH9wcuXYKWTeWCknGbeMCUdHchUMl5rUi5NZT1Wjt1uGu8cFiRyESHUuFJOOm0X5wtlNi4zGN2FEus/5wGUK9HDExnMsGE7FUSDIcbBW4eYwftpyshlZvEDsOAKC4rh0HixtxV0ow5HKZ2HGIRMdSIUmZGatCW48Ox8qaxY4CAHh3bwlslXLcmWK+z88QjSSWCknKpEhvyGXAnkLxJxs9X9OGDXka3JkcxGdTiH7CUiFJcXOwQWKwO3afF7dUKpq78It3DsDN0QaPTleLmoXInLBUSHLSY31xXNOC8sZO0TK8urkAXVo9Ps+ciEB3B9FyEJkblgpJzs8TNmbni/Mg5N7CeuScrMbyaWpE+DiLkoHIXLFUSHKCPR2RGu6JDUc00I/wg5CdvTq8+PUphHo54pGpESM6NpEUsFRIkh6YFIrShk58f7r/qqTDxWAQ8OgnR1FS34FXFoyBvY1ixMYmkgqWCknSnDH+CPd2wls7iyAII3O0svlkFXaeq8Pv543GlCifERmTSGpYKiRJCrkMv5oagZMVLdh7oX7Yx6tr68FrW88ixs8F908KG/bxiKSKpUKSdVtSIHxd7fDGjgvDdrRS3tiJl745jbn/twcN7b149bYxUPDJeaJBsVRIsuyUCiyfrkZuSSN2nK01+eevaunC/Df34eODFzE2yB2fPDIByaGeJh+HyJIYtZwwkdjuTg3BB/tK8dw/T2J9phMijbzFVxAEvL69EBVNXThd2YIerR45T0xBlK+LiRITWTaWCkmajUKOd+5Pxt3/OIh7/nEQ6zMnIdzb6Zo/z+nKFnxxuBwnKlpwrKwZNgoZXO1t8NZ9ySwUomvAUiHJi/J1wScPT8Q9/ziI297ah3fuS8aEiKtPQ28wCMi72ITj5c1Y/eMF9OoMUKuc8fwtsbg7NQQKuYy3DRNdI5YKWYRoPxd8tSwND3+YhwfXHsb/3T0OaZFecLLr/y3e1NGLwtp2PL/pJM7XtAMAAtzs8c1jkxHi5TjS0YksCkuFLEaYtxM+e2Qi7nv3EB75MA8A8GBaGCZGeMHRVoH88macrW5FzslLD0yGeDrif+8ci+nRPnBzsIFSwftWiIzFUiGL4uNih28en4zs/ArsL2rAB/tL8cH+0r73K+UyPJgWBh8XO9w/KRSu9lxTnsiUWCpkcS4tmhWMO5KDMC3aB36u9ujWGZAY5A5HOwVseERCNGxYKmSxZDIZ5icGih2DyKrwTzYiIjIZlgoREZkMS4WIiEyGpUJERCbDUiEiIpNhqRARkcmwVIiIyGRYKkREZDIsFSIiMhmWChERmYxMGK7FvU3A29sbYWFh1/3xdXV18PHxMV2gESbl/FLODjC/2KScX8rZgSvnLy0tRX19/RU/3qxLxVgpKSnIy8sTO8Z1k3J+KWcHmF9sUs4v5eyA8fl5+ouIiEyGpUJERCZj0aWSmZkpdgSjSDm/lLMDzC82KeeXcnbA+PwWfU2FiIhGlkUfqRAR0ciyyFLZunUroqOjoVarsXLlSrHjDElYWBji4+ORmJiIlJQUAEBjYyNmzZqFqKgozJo1C01NTSKn/JclS5ZApVJhzJgxfa9dKe+KFSugVqsRHR2N7777TozIlxko/x/+8AcEBgYiMTERiYmJyMnJ6XufOeUvLy/H9OnTERsbi7i4OKxatQqAdPb/YPmlsP+7u7uRmpqKsWPHIi4uDi+++CIA6ez7wfKbdN8LFkan0wkRERFCUVGR0NPTIyQkJAinT58WO9ZVhYaGCnV1dZe99swzzwgrVqwQBEEQVqxYITz77LNiRBvQrl27hCNHjghxcXF9rw2W9/Tp00JCQoLQ3d0tFBcXCxEREYJOpxMl988Gyv/iiy8Kf/nLX/pta275KysrhSNHjgiCIAitra1CVFSUcPr0acns/8HyS2H/GwwGoa2tTRAEQejt7RVSU1OFAwcOSGbfD5bflPve4o5UcnNzoVarERERAVtbWyxatAjZ2dlix7ou2dnZWLx4MQBg8eLF2LRpk7iB/s3UqVPh6el52WuD5c3OzsaiRYtgZ2eH8PBwqNVq5ObmjnTkywyUfzDmlt/f3x9JSUkAABcXF8TGxqKiokIy+3+w/IMxp/wymQzOzs4AAK1WC61WC5lMJpl9P1j+wVxPfosrlYqKCgQHB/e9HRQUdMVvWHMhk8lw0003ITk5GVlZWQCAmpoa+Pv7A7j0g1hbWytmxKsaLK+UviarV69GQkIClixZ0ncKw5zzl5aW4tixY5gwYYIk9/+/5weksf/1ej0SExOhUqkwa9Ysye37gfIDptv3FlcqwgA3s12pic3Fvn37cPToUWzZsgVvvvkmdu/eLXYkk5HK12TZsmUoKipCfn4+/P398fTTTwMw3/zt7e1YuHAh/v73v8PV1XXQ7aSSXyr7X6FQID8/HxqNBrm5uTh16tSg25pbdmDg/Kbc9xZXKkFBQSgvL+97W6PRICAgQMREQ/NzRpVKhdtuuw25ubnw9fVFVVUVAKCqqgoqlUrMiFc1WF6pfE18fX2hUCggl8vxyCOP9B3mm2N+rVaLhQsX4t5778Xtt98OQFr7f7D8Utn/AODu7o5p06Zh69atktr3P/vP/Kba9xZXKuPHj0dhYSFKSkrQ29uL9evXIyMjQ+xYV9TR0YG2tra+f3///fcYM2YMMjIysG7dOgDAunXrMH/+fDFjXtVgeTMyMrB+/Xr09PSgpKQEhYWFSE1NFTPqgH7+pQAAGzdu7LszzNzyC4KAhx56CLGxsXjqqaf6XpfK/h8svxT2f11dHZqbmwEAXV1d2L59O2JiYiSz7wfLb9J9b8o7C8zF5s2bhaioKCEiIkJ45ZVXxI5zVUVFRUJCQoKQkJAgjB49ui9zfX29MGPGDEGtVgszZswQGhoaRE76L4sWLRL8/PwEpVIpBAYGCu++++4V877yyitCRESEMGrUKCEnJ0fE5JcMlP++++4TxowZI8THxwu33nqrUFlZ2be9OeXfs2ePAECIj48Xxo4dK4wdO1bYvHmzZPb/YPmlsP+PHz8uJCYmCvHx8UJcXJzw0ksvCYJw5Z9Vc8kuCIPnN+W+5xP1RERkMhZ3+ouIiMTDUiEiIpNhqRARkcmwVIiIyGRYKkREZDIsFSIiMhmWChERmQxLhYiITOb/AVLpQ++wj9k/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a5f78c3417e476396448a0b8ef0e95f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c5ea00f2c02440d8b31d83f265d0b3e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54c98025af4149c0a1c777c8f44b9b3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45e2ca1de2bc461d86647de76d029f72",
+       "version_major": 2,
+       "version_minor": 0
+      },
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+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
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+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
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+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
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+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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guwlqiZ6TlXf79m02evRoZmdnx7S1tZmenh5zdHRk33//PXvz5k2+++Q8r+rp6clOnz7N2rZty3R1dZmRkREbMGAAe/78uVz9p0+fstmzZzNnZ2dmZmbGxGIxs7W1Ze7u7uz69ev5vsfVq1fZoEGDmKWlJROJRMzMzIw1a9aMzZ49m9397FlJW1tbhWdHC1K3bl0GgJmamrKMjIL/RjIyMti2bdtYu3btmKGhIROLxaxWrVrM1dWVbdq0SeGZ24yMDLZ8+XJWu3Ztrn0//PADS09PL9FzsowxJpVK2bZt25iDgwMTi8XM0tKSTZ06lSUkJBTYVk9PT9asWTOmq6vLqlWrxr7++mv28OFDtnTpUgaA+fr6ytX/8OEDGzZsGLOwsGAaGhoMAFu6dCn3+vXr11nXrl2ZsbEx09fXZx06dGAnTpxgvr6+CnXLhRo+Jytg7L+x98XQsmVL3LtXtsEURHnu33qHsV+c4K5gjU21selIT1jbGeZbPzU5C1t+vIXLp3NvYTm2MMee81/D0FirXGKuKHJGl1ZoeQc+VWvJXxyElJe8+all4b/z5fU3Tn2yFdSnxAzMG32BS7D6hmLsPNOnwAQLALr6IizY0BEe81pwZU8CP2KG+1mkpyn2KRFCCCkbSrIV1E8zr+Bt2CcAgFBTgB0nesPUXLeIvWSjNgd7NMbMFW25sntXo7BgrA+k0mLf1CCEEFIMlGQroBsXI3D60HNue+bytqhpU/AVbH76DKsvd0Xrc+IVdq2hrgBCCFEmSrIVTFaWBKu/y302z6ltDfQaXK9Uxxrs0Rhfj3Lgtrcvv42r58ML2YMQQkhJUJKtYI7sfIzXz+IBAEKhAAs2KM6DWhKTF7aEU1vZlG6MAQsnXERsdGqZ4ySEEEJJtkJJS82Su6U7cEIjVLMouh+2MJoiDSza3BmmFrKVT+I/pmHJpMsowaBzQgghBaAkW4Ec3fMEsdGyVS509EQY/a2TUo5rXE0bc9fkzjnrfy4M/9ut3OnqCCGkKqIkW0FkpGdj74ZAbnvYlMYQawkL2aNkWnaqif5jcp8ZWzvnKndbmhBCSOlQkq0gvA4+x8d3sr5SbR1NuI9rpPT3mDi3BezqGwMAMtIl+GGCDyQSaeE7EUIIKRAl2QqAMYZDvz7ktt3HNVTqVWwOsZYQCzd0gkgk+7V4dDcaf+98rPT3IYSQqoKSbAUQcC0KLx7JFonWEAowcIKjyt6rjoMJhn3ThNvetOgG3kcmF7IHIYSQglCSrQAO/fqI+7ptV2voG4pV+n5DJjVGrTqyRaHTUrLx0yxaG5IQQkqDkqyai/mQiosnc5fAGjOrmcrfU6wlxKyVudMuXvYKxaVTr1X+voQQUtlQklVzZw4/h0Qie2bVytYAdRqYlsv7Nm1dHT0H1eW2V37rj5TkzHJ5b0LK29ixYyEQCPJdNJ4vdnZ23MLopOKiFbvVGGMMJ/cFc9sDxpbv0mse85xx63IkEmLT8fFdCnavCcCsVe3KNQa19eBHIDWC7ygKpmsDWPcr82Hs7OwQHp7/VJvVq1fH+/fv5coyMzOxYsUKHDx4EHFxcWjRogU2bNiAFi1aKOzv4+OD7t2748yZM/jyyy/LHGtF06VLF/j7+9PEL5UcJVk1Fnz/I0KexAGQDXj64mv7cn1/Q2MteMxzxvr51wEAnpuCMGBcI9jYG5VrHGopNQLQs+M7ioKlhCntUEZGRpg1a5ZCub6+vkLZggULsGnTJri7u8Pa2hr79+9H165d8ezZM1haWnL1kpOT4eHhgZEjR6pFgl29ejUWLFgAKysrvkPhXLp0ie8QiBJQklVjp/Y/4752alsDuvqico/hi6/r4PTB53j2MAbZWVKsm3sV2//pU+5xEP4YGxtj2bJlRdZjjGHnzp0YN24c9u7dCwDo378/unTpgv3792PevHlc3fnz5yM9PR1btmxRVdglYmlpKfchQB3Y25fvh2qiGtQnq6YkEinOH3vJbQ+aqLrHdgqjoSHAtB9bc9u+Z8Jw7QKt1EMUffz4EampqWjdOvf3JefrvLecr1y5gt9++w07duyAqWnpxhjk9FcmJSXhu+++g52dHUQikdyHgWfPnmHs2LGoVasWtLS0UL16dQwfPhzPnz9XOF5hfbK3b9/GwIEDUaNGDYjFYtSqVQuTJ09GVFRUvrHFxcVh0aJFaNy4MXR1dWFkZIRmzZphwYIFSElJQVhYGAQCAfz9ZaP2BQIB969Lly4KbfxcRkYG1qxZg6ZNm0JXVxeGhobo1KkT/ve//ynUzXmvsWPHIiwsDEOHDoWZmRm0tbXRsmVLnDlzpvBv9GdOnjyJkSNHon79+tDT04O+vj6cnZ2xdetWSKWKE9fkfF9fv36Nbdu2oWnTptDR0ZFrZ1xcHBYuXIiGDRtCR0cHRkZGcHNzw4ULFxSOl5iYiPXr18PV1RXW1tYQi8UwNzdHv379cOvWrRK1pbzQlayaCrz+DjHvZTM8aekI0fy/lXL44NDMDN0H2OPCP7JRzqtnX8XJ+9YQiZQ/IQZRPxkZGThw4AAiIiKgp6eHpk2bonPnzhAK5X/+5ubm0NHRQUBAAFd2755sQQtbW1sAQFpaGiZOnAh3d3e4u7uXKa7MzEy4uroiLi4O3bt3h6GhIWrXrg0AOH/+PAYMGICsrCz07dsXdevWRWRkJP755x+cPXsWvr6++fYTf87T0xMeHh7Q0tJCv379UKtWLYSEhGDPnj04ffo0bt26BRsbG65+aGgounbtivDwcDg7O+Obb76BVCrFixcvsGnTJkyZMgXGxsZYunQp/vzzT4SHh2Pp0qXc/kUNdMrMzESPHj3g7+8PBwcHTJs2DampqTh27BiGDBmC+/fv4+eff1bYLzw8HK1bt0adOnUwatQoxMXF4ciRI/jqq69w8eJFdO3atVjf8wULFkBDQwNt2rSBlZUVEhMTcfnyZcycORN3797F/v37891v5syZuHr1Kr788kv07t2b+90JDw9Hly5dEBYWhk6dOqFnz55ISUnBmTNn0LNnT+zcuRMeHh7ccYKDg7Fo0SJ07twZX375JUxMTBAREQEvLy/8+++/OL1hA3q2b1+stpQXSrJqKu9VbBsXawg1+b3pMGFOC1zzjkBqShbCQhJw6NdHGDPTideYSPl4//49Ro0aJVdWu3ZteHp6wsXFhSsTCASYNGkStm7disTERFhZWWH//v0wMDDAiBEjAACLFy9GXFwcduzYUea43r17h0aNGsHf3x96enpceXx8PIYNGwZdXV1cuXIFjRrlTkH65MkTtGnTBhMnTkRgYGB+h+W8ePECkydPhp2dHfz9/eX6ay9fvoxu3bph5syZOHHiBFc+cuRIhIeH4+eff8bChQvljhcTEwN9fX1oa2tj2bJl8PPzQ3h4eLFuxefYsGED/P390atXL3h5eUFTU3YKX7p0KVq3bo3Vq1ejT58+aP9ZovHz88OyZcvkEvrw4cPRs2dPrF+/vthJ9uzZswq3saVSKcaNG4e//voL06dPR5s2bRT2CwwMRFBQEPchKMeYMWMQHh6Ow4cPY+jQoVx5QkICunTpgm+//Rb9+vVD9erVAQANGzZEVFQUzMzM5I4TGRmJ1q1bY/amTWqXZOl2sRqSSKTw+Sc3yfYb2YDHaGRMzXUwcnpTbnv78tu07mwVMG7cOFy6dAnv379HSkoKHj16hMmTJyMsLAy9evXCgwcP5OqvXbsW8+fPx507d/DHH3/A0dERly5dgpWVFW7fvo3Nmzdjy5YtMDMzw/Lly2FpaQlNTU04Ozvj+vXrJY5vw4YNcgkWAP766y8kJCRg+fLlcgkWABwdHeHh4YGgoCA8ffq00GP/9ttvyMrKwpYtWxQGRLm6uqJfv344ffo0Pn36BAAICAjAjRs34OTkhPnz5yscL+c2bVns3bsXAoEAGzdu5BIsAFhYWGDJkiUAgD179ijsZ2tri8WLF8uV9ejRAzY2Nrhz506x3z+/fmINDQ3MnDkTAODt7Z3vfvPmzVNIsA8ePIC/vz/c3d3lEiwgGwewfPlypKen4/jx41y5kZGRQoIFAGtrawwcOBDPwsIQ8dmId77RlawaCrgWxS1pp62ricYtLXiOSObr0Q44978QRIYmITU5C1uW3MKKna58h0VUKO+VDwA0btwYv//+O/T19bFhwwYsW7ZM7kpOS0sLq1evxurVq+X2y8zMxPjx49G7d2+MGDECmzdv5q6sOnTogJ9++gk9e/bEy5cvuauWomhra6Np06YK5Tdv3gQgO4nnd5X44sULALJbj58n4fyO4+/vj7t37yq8Hh0dDYlEghcvXsDZ2ZnrE+zRowc0NJR//fLp0ye8fPkSVlZWcHBwUHjd1VX2txgUFKTwmpOTk8LtfQCoVasW187iiI2Nxfr163Hu3Dm8fv0aKSkpcq+/ffs23/3y9tPnyHnfxMTEfH9OHz9+BCD7OeV1/fp1bNmyBTdv3kR0dDQyM+Wf338bHQ2bGvx1r32OkqwauuQVyn3d2sUKQqF63HAQiYX4ZlErLJooe7Tgnz+fYuiUxmjUXD0+BJDyM2XKFGzYsAFXrlwpVv3ly5fj7du33GCW9evXw83NjTu5NmjQAHZ2dtixYwdWrFhRrGNaWFhAIBAolMfGyub53r17d6H7JycXPid3znHWr19frOMkJCQAgMoeA0pMTASAAkdB55TnxJGXsbFxvvtoamrmO2ApPwkJCWjVqhVCQ0PRunVrjB49GqamptDU1ERCQgK2bNmCjIyMfPetkU/Sy/n++vj4wMfHp8D3zftzOnHiBAYOHAhtbW1069YN9vb20NPTg4aGBvz8/ODv74+MrKxitae8UJJVM4wx+J3JTbK9BtctpHb5a+1ihTZdrHDb7y0YA36efRX7fQfke7IjlZeFheyD1edXMvkJCgrCunXr8Pvvv8PKygpJSUmIiori+mkBwMbGBmZmZnjy5EmxYyjod87ISPYc94MHD/K90i2unOMkJibC0NCwyPo5iaygq7myyonn8wlAcrx7906unrLt2bMHoaGhWLp0qcKV582bNwt9HCu/n1VOnFu2bMG3335brBiWLFkCsViMe/fuoWFD+cl5Jk+ezI3YVifqcYlEOCGPYxEZmgQA0BRpoFlr9bntkWPywpYQasr+aIJuvMOF46+K2INUNjm3+urUqVNovezsbIwfPx5du3bFhAkT5F77/KonPT1dKbG1bSubd/vq1avlepyc+t7e3sW6Osy5fSuRSIp1fAMDA9jb2+Pt27cICQlReN3X1xcAijVqujRevpSNE8lvVHhpkltpfk4vX75Eo0aNFBKsVCrFtWvXShxDeaAkq2Yun869im3Uwhwisfo9JlOrjhG+HpXbJ7R+/jWkp2XzGBFRhSdPniAuLk6hPDw8HNOnTwcgG01bmNWrV+Ply5dyt24NDQ1hZWWF8+fPIztb9nvj7++PT58+wdGx7M+Djxs3jhs4k9+gHqlUCj8/vyKPM336dIhEIsyePZvrx80rMzNTLkE4Ozujffv2uH//PtauXatQPzY2Vu6DRLVq1QAAERHFn55z/PjxYIxh7ty5csk5JiYGK1eu5OqoQs7jRZ9/74KCghT64IujZcuW6NSpE/755x9u8pLPPXr0CNHR0XIxhISEyD2jzBjD8uXLixzIxhe6XaxmfPPeKh6oXreK8xoxrSl8Tr5GUnwG3r1Jxr7N9zF5YUu+wyJKdPToUaxZswZdu3ZF7dq1YWBggFevXuHs2bNIT09H7969MWfOnAL3f/LkCVatWoWNGzdyz8nmmDdvHmbOnIlOnTqhTZs2OHjwIPT19TFt2rQyx12tWjUcO3YM/fv3R9u2beHm5gZHR0doaGggIiICN2/eVEh4+XFwcMDevXsxfvx4ODo6omfPnqhfvz6ysrIQERGBq1evwtzcHM+e5c7MduDAAXTp0gU//PADjh8/ji5duoAxhpCQEFy4cAHPnj3jkpWbmxuOHj2KAQMGoHfv3tDR0YGtra3C41J5zZkzB//++y9OnTqFZs2aoXfv3khNTcXRo0cRHR2NefPmoWPHjmX+HuZn9OjRWL9+PWbNmgVfX1/Uq1cPISEhOHPmDAYMGIAjR46U+JiHDh2Cq6srJkyYgK1bt6JNmzYwNjZGZGQkHj58iMePH+PmzZtc98Ts2bMxZcoUNG/eHO7u7hCJRLh+/TqePn2Kvn374vTp08pudplRklUjsdGpeHxP9qlNIADautbiOaKCGRhpYexMJ2xddhsAsGvtPfQf4wCLmorz2ZKKqWvXrnj+/DmCgoJw8+ZNpKSkwNjYGB07dsSoUaMwatSoAvtFJRIJxo8fjzZt2mDq1KkKr8+YMQNJSUn4/fffERgYiGbNmmHTpk3FHllcFDc3Nzx8+BC//PILvL29cfXqVYjFYtSsWROurq7Fnghj5MiRaNasGTZs2ABfX19cuHABenp6qFmzJgYOHIghQ4bI1a9duzYCAwOxbt06nDx5Etu3b4e2tjbs7Ozw/fffc8kCACZOnIjw8HD8/fffWLduHbKzs+Hi4lJokhWLxfDx8cHGjRtx6NAhbNu2DZqammjWrBk2b96MYcOGle4bVgw1a9bE1atXsWDBAly7dg3e3t5wcHDAr7/+ii+++KJUSdba2hoBAQHYtm0bjh8/joMHD0IikaBGjRpo1KgRZsyYgSZNmnD1J0+eDC0tLWzevBn79u2Djo4OOnXqBE9PTxw/flwtk6yAlWAJiJYtW3IzuBDlO3PoOeaPlY2ys65tCM8LX/MbUBEk2VJM+eoMwl4kAJA9z7t6bzd+g1KS4OBghX4fORVtFZ5qdJehMEOHDsWRI0cQFRWldnMYkxLIm59aFv47X+TfuJLQlawauX4x96TdqYdNITXVg1BTA98saoX5Y2QfDLwOPMewb5qiaSvlXI2otWbFe8yEV7H0gbi4Xrx4AS0tLZibm/MdCqlkaOCTmmCM4cbFN9x2h27qn2QBoEV7S7T/Ive29prvr9L6mKTC2LJlC9zd3REUFISvvvpKbhYlQpSBkqyaePEoNndBAG0h6jqWbnUSPkxa4AxNkexX6cGt9zj7t+JITELU0ebNm3Hr1i2MHz8eO3fu5DscUgnRxzY1cd0n91axYwsLtZnlqTisbA3Rf7QDjv4hG0K/YeENuH1VBzq65b/+LSElERoaWnQlQsqg4pzJK7m8t4pdvrQtpKZ6GjGtKYxNZZOfR0elwHOD4vyphBBS1VCSVQNpqVkIuJb7cHWrTqqZ+1SV9AzEGPedE7e9Z30A3r35xF9AhBCiBijJqoF7V6OQmSGbvcXUQgfmlnpF7KGeegysizoOJgCAjHQJNi0q/uoehBBSGVGSVQN5+2Nbd654V7E5hEINTF3cits++/cL3L/1jseICCGEX5Rk1cANn9z+2M69Kl5/bF7N2tSQe8Z39eyrkErpkR5CSNVESZZn7yOT8SpYNgm7hoYATSrBRA4e85wh+u+RnscB0Th98DnPERFCCD8oyfLstm8k97VtXSNo61T8p6osbQzgPr4Rt71h4XV8Ssx/MWdCCKnMKMny7N7V3AWe27pa8xiJcg2b0gSmFjoAgNjoNGxdeovniAghpPxRkuXZ3au5j+607lJ5kqyuvgjf/JA7Qffh3x7hccAHHiMihJDyR0mWR+8jk/HmVSIAQCgUoEGTajxHpFwuve3g3FG2ogljwPJpfpBIpDxHRQgh5YeSLI/uXcm9VWxT1wgisZDHaJRPIBBgxrI2EIllv2ZPAz/i752PeY6KlEa3bt0gEAjg7+9fYJ0pU6ZAIBBg9+7d5RgZIeqNkiyP7ubpj23TtfLcKs7LytYQw79pym1vXnwT0VHJPEZESiMwMBBCoRDOzs4F1rl9+zYA2brThBAZSrI8uuufm2Qr4lSKxTXYwxHWtQ0BAKnJWVg39zrPEZGSePXqFeLi4uDo6Ah9ff1866SmpuLx48fQ0tJC48aNyzlCQtQXJVmefHyXgvCXsv5YDaEADs3MeI5IdcRaQny7vA23/e/REFy7EM5jRKQk7t2TLf7epk2bAusEBAQgOzsbzZo1g0hEqy8RkoOSLE/u5u2PrWMEsVbl6o/9XPN2lnD7qg63vWKaH1KSM3mMiBRXcZLsrVuyR7ToVjEh8ir+zAcVVN4k28qlJo+RlJ/JC5xxxy8SnxIz8Tb8EzYvvolFm134DosUISfJHjlyBL6+vvnWof5YQvJHSZYn967keT7WpXIOevqciZkOpi5pjbVzrgEADv36CN0H1EWrCrwoQmXHGENQkGxtYB8fnyLrU5IlRB4lWR7EfEjF6+fxAGTzFTd0qrz9sZ9z61cb/ufCcOuybDrJxR6XcCJwGHT1Kk4/nqN4O98hFNuTd23LtP+LFy+QmJiIdu3a4caNG/nWiY+Ph6mpKXR1ddGoUSO513799VesX78e7969g6OjIzZv3oxOnTqVKSZCKhLqk+VB3udjrewMoKVddT7rCAQCzFzRFvqGYgBAZGgSNi+mdWfVVc6t4ubNmxdYJzAwEADg5OQEoTB3bMGRI0cwc+ZM/PDDDwgKCkL79u3Rq1cvREREFHQoQiodSrI8CLiee6u4Zaeq0R+bl1l1Xbl1Zw/ueCi3pi5RHzlJtkWLFgXWyUmyn98q3rhxI8aOHQsPDw80bNgQ27Ztg6WlJX777TfVBUyImqk6l1Bq5P7N99zXrVyqZn/kF1/XwZV/w3Hrv1WIFo6/iJOBw2BqrsNzZEV7kjmd7xCKJ/ZemQ9RnCSb02ebN8lmZmYiICAAc+bMkavbvXv3Am87E1IZ0ZVsOUtJzsTzhzHcdiMncx6j4Y9AIMB3q9vBxEwbABD7IRWLPS6BMVrgXV1IpVLcv38fYrEYjo6OBdbL70o2JiYGEokE1avLr49cvXp1vH//HoRUFZRky9nje9GQSGSJxKy6LvQMxDxHxB+TajqYu7YDt+1/LozmNlYjwcHBSE5OhqOjI8Ti/H9Pk5OTERISAn19fTRo0EDhdYFAILfNGFMoI6QyoyRbzvLeKnZ0tuAxEvXQqrMV+o9pyG2vm3tN7kqf8CcgIABA4beKHzx4AKlUihYtWkBDI/d0YmZmBqFQqHDVGh0drXB1S0hlRkm2nN2/9Y77unUVmYSiKBPntkAdBxMAQGaGBLOG/IukhAyeoyKjR48GYwx79uwpsE6HDh3AGFNYnUcsFsPZ2Vnh2VofHx+0b99eJfESoo4oyZYjqZTh/i26kv2cWEuIHzZ1graubBxexKtE/DD+IqRS6p+tyL777jv8+eef2LNnD4KDgzFz5kxERUVhypQpfIdGSLmhJFuOXj+LR1K87ApNW1cTNW0MeI5IfdjWNcac1blXOL5nQvHH+gAeIyJlNWTIEGzevBmrVq2Ck5MTrl27hnPnzsHW1pbv0AgpN5Rky1HeW8V1G5nSAJDPuPS2w4Cxuf2zW5fexo2L9PxsRTZ16lSEhYUhIyMDAQEB6Ny5M98hEVKuKMmWI7nnYztTf2x+POY5o3FL2W10qZRh9tDzeBUcx3NUhBBSOpRky9H9m7lXsk1b0QjL/GiKNLB4S2dUs5BNSpGclIlvvjqDuI9pPEdGCCElR0m2nMTHpCH0RQIA2aIA9ZtUnUUBSqqahS5W7XKFlo5sHty3YUn4dtA5ZKRn8xwZIYSUDCXZcvLgdu6t4pq2BpV+kfayqutYDT9s6oycbuugG++waMIlSCRSfgMjhJASoCRbTvL2xzq1rcFjJBVHe7damLQgd6q+f4+G4KdZV2jqRUJIhUFJtpzkHVncor0lj5FULO7jGqLfyNzp+o7sfIytP97iMSJCCCk+SrLlICtLgkd3o7nths2r5qIApSEQCDBtSWu49q3Nle1aGwDPjYEqf2+6YiakcirPv21KsuXg2YMYpKfJBu0YGmvBrLouzxFVLBoaAsxd2wFtuuQuC/jLghvYt+W+yt5TKBQiKytLZccnhPAnKysLQmH5jIuhJFsO8vbHOjjRqOLS0BRpYMk2FzTJ8+jTurnXVHZFa2BggKSkJJUcmxDCr6SkJBgYlM+Me5Rky0He52Nbda6ai7Qrg5a2JlbtcuUmqwBkV7S71yl/+kVTU1PEx8cjJiYGmZmZdOuYkAqOMYbMzEzExMQgPj4epqam5fK+muXyLlVc3kUBGreg/tiy0NUX4ec9bljkcRmP7n4AAGxefBMpSZmYubKt0qaq1NLSgo2NDeLi4hAWFgaJRKKU45arlDxLBkYH8xcHIeUlJs/vfLDi77xQKISBgQFsbGygpaVVLiEJWAk+ords2RL37t1TZTyVzrs3n/CF/T4AslueZx4Oh1CTbiCUVVpqFn6c7Cv3Aab/mIZY9ltXaNL3V+ZQng8cw+lKnFQBeT9kq8ndJzobqVje/ljbusaUYJVER1eElbtc5QZDndgXjG8HnkNaKg1YIoSoBzrjq1je52OdO9LzscqkraOJZb92RfcB9lyZ/7kwTOx5CgmxNNcxIYR/lGRVLOhGbpKlmZ6UT1OkgTlr2mPolMZc2f1b7zGqyz948zqRx8gIIYSSrEqlpmTh2YPcjviGTjToSRUEAgEmfN8C05a04rpkXj+Px9AOR3H3ylt+gyOEVGmUZFXo8b0PkEhkne/VqutA31DMc0SV29ejG2LR5s7QFMl+rRNi0zGx5ykc93zKc2SEkKqKkqwK5R305NjCopCaRFlcetthw8EeMDbVBgBkZ0vx4+TLWDv3Kq3gQwgpd5RkVSjvoCeahKL8NGpujh0neqOOgwlX9teWB5j69Rl8SszgMTJCSFVDSVZFpFImPwmFM13JlieLmvrY/HdPdOhmw5Vd847A8E7HEPGKBkQRQsoHJVkVCXuRgMQ42VWTlrYQVnblM08myaWjJ8KP210w/JsmXNnrZ/EY2uF/uHX5DY+REUKqCkqyKhKUZ77iOg4mSpvuj5SMhoYA475rjoUbOkIklv26J8ZlYNKXXji44wHNSUwIUSlKsipCiwKoF9d+dbDxUE+YmusAACQShp9nX8XSKb7IzKiA8xITQioESrIqkndkMU1CoR4cmplhx4kv0aBpNa7suOdTjOt+AjEfUnmMjBBSWVGSVYGE2DS8fh4PQDZfdf0m1YrYg5QXs+q62HioJ7r1r8OV3b/5HoPb/Q9PAqN5jIwQUhlRklWBB7c/cF/XsNaHljatKKhOxFpCzF3bAZMXtoTgv7+AD5HJGNXlOM4decFvcISQSoWSrArk7Y9t0qo6j5GQgggEAgwc3wg/7XGDnoEIAJCRLsHcURewefFNSKU0IIoQUnaUZFUgSG7QU00eIyFFadXJCtuPfwnr2oZc2e51AZg+4CySkzJ5jIwQUhlQklWyrCwJHt3N7dtr7ExXsurOurYhth/vjdYuuaPA/c+FYVjHowgPSeAvMEJIhUdJVsmC78cgPS0bAKBvKIZZDV2eIyLFoWcgxoqdXTFkkiNX9vpZPIZ0OIobFyN4jIwQUpFRklWyvOvH1mtMo4orEqFQAxPnOmPBLx0h1hICAD4lZGByn9PYt+U+TVxBCCkxSrJKljfJtulCk1BURG5f1cHGQz1QzUI2cYVUyrBu7jUsmngJGenZPEdHCKlIKMkqEWNMbtATTUJRcTVoKpu4oqGTGVd2av8zTOh5CvExaTxGRgipSCjJKlFkaBJi3stmDtIUacCuvjG/AZEyqWahi18O9ED3AfZcWdCNdxjW6RhC/5tshBBCCkNJVony3iq2sTeCUEjf3opOrCXEnDXtMWmBM3LWeHjzKhHDOx/DHf9IfoMjhKg9ygJKlPdWcYv2ljxGQpRJIBBg0ARHLN3RhRsQlRSfAY/eXji1/xnP0RFC1BklWSXKeyXbyoUGPVU2HbrZYOPhHjAx0wYAZGdJ8cOEi9i69BaNPCaE5IuSrJIkJWTg5dM4ALJFARo2MytiD1IRNWhihu3He6N2A2OubOfqe5g3+gKNPCaEKKAkqyQPbr1HzsWMWQ1d6OiJ+A2IqIxFTX1sOtxTbsrMc0dCMKHnKcR9pJHHhJBclGSVJDDPrWLHFhY8RkLKg56BGCt3uqLPsPpcWdCNdxjhcgxvXifyGBkhRJ1QklWSvIOe2rla8xgJKS9CTQ18u7yNbMm8/0YeR7xMxAiXY3gaRGvTEkIoySpFVpYEj+7kriFLy9tVHTlL5v24vQtEYtmfU+yHNIxxO4Gbl97wHB0hhG+UZJXg2YPcRQH0DEQwt9TjOSJS3jp2t8G6fd2gZygGAKQmZ2FKv9M4c/g5z5ERQvhESVYJ5BYFcDTlMRLCp8Ytq2Pz3z25lZeys6SYP8YHf24K4jkyQghfKMkqwf2b77mvW3eh/tiqzK6eMbb8rxds6xpxZevnX8e6edcgldKztIRUNZRky+jzRQGat6OZnqo6C0s9bPq7Jxq3zB1lvm/zfSyZdAnZ2VIeIyOElDdKsmUUFf4J0VEpAGSLAuSdpIBUXQZGWljj+QU6dLPhyk7+9QxzR3ojM1PCY2SEkPJESbaMaFEAUhAtbU0s2dYZPQfV5cou/PMK3w48xw2UI4RUbpQRyigwz61i5450q5jIEwo1MHtVO/Qf05Aru3o+HFP6eiHlUyaPkRFCygMl2TK6dyWK+7plJ1oUgCjS0BDgm0UtMWJqE67s7pUoTOh5Conx6TxGRghRNUqyZRAbnYpXwf8tCqABNGpuznNERF0JBAKMnd0cE+e24Moe3f0Aj95eSErI4DEyQogqUZItg3tXc69ia9oYQFtHk8doSEUwZFJjfLu8Dbf9JCAaE3udokRLSCVFSbYM7l55y33t3LFmITUJydV3eAPMXtWW234SEA2P3pRoCamMKMmWwb08Sba9Wy0eIyEVTe8h9TFrZW6ifXwvGpO+9MKnREq0hFQmlGRLKT4mDSFPchdpb9SC+mNJyXw5VD7R5vTRJifRqGNCKgtKsqWUtz+2hrU+dHRpkXZScl8OrY+ZK+QT7fQBZ+g5WkIqCUqypZS3P7ZFe3o+lpRen2H15QZD3b0She+G/YusLJoZipCKjpJsKeVNsh262xRSk5Ci9R3eAJPmO3Pb/ufCsXDcRUgkNNcxIRUZJdlSSIhNw4tHsQBk/bGNnS2K2IOQog2a6Ijh3+ROWPHv/0Kw6lt/MEar9xBSUVGSLYWAa7lTKVa31oeOHvXHEuUYO9sJ/UY24Lb/t/sJNi26yWNEhJCyoCRbCneoP5aoiEAgwLQlreH2VR2u7I9fAvHX1vv8BUUIKTVKsqWQ9/nYDt3o+ViiXBoaAsxZ3R7t3Ky5snVzr8H7+EseoyKElAYl2RKK+5iGZw9iAOT0x1bnOSJSGWmKNLBoc2fu+WvGgAVjfRBwLaqIPQkh6oSSbAnduvyG+7qmrQF09ak/lqiGlrYmVv7eFda1DQEAmRkSTB9wlluUghCi/ijJltCNi7lJtp0r3SomqmVooo2f/3CDcTVtAEBSQgYm9/FCdFQyz5ERQoqDkmwJMMZw42IEt92ppy2P0ZCqwrKWAX7a48at8vTuTTK++eoMUpJp+kVC1B0l2RJ4/SweH96mAJD1mTVoUo3niEhVUb9xNSzZ5gINoQAA8OxBDOaNukCTVRCi5ijJlkDeW8X1HE0h1KRvHyk/rV2s5OY59jsbho0/3OAxIkJIUShLlEDeW8Uuve34C4RUWb0H18PgiY7c9p+b7uPoH094jIgQUhhKssWUmSHBXX9aP5bwb/yc5mj/Re7v38oZfrjlG8ljRISQglCSLab7t94hLVW2/JiBkRiWNgY8R0SqKqFQAws2dETdRqYAAEk2w6zB5xD6PJ7nyAghn6MkW0w3fHL7Y5u2pgkoCL90dEVY8XtXVLPQAQB8SszE1K/PICE2jefICCF5UZItprz9sa79avMYCSEy5pZ6WLHTFVraQgBAxKtEzBryLzIzaR1aQtQFJdliiI9Jw9OgjwBkUym2aF+T54gIkanfuBrm/9KR2757JQorpvnR8niEqAlKssVw9Xw4cs5ZlrUMoG8o5jcgQvLo1MMWE+a04LZP7AuG58YgHiMihOSgJFsMl0+Hcl+79KZZnoj6GTLJEd0H2HPbG3+4gctBjXmMiBACUJItUkZ6Nq5dyO2PdetXp5DahPBDIBBg1sq2aNLSAoBs1Z55v49FcLgVz5ERUrVRki3CHb+3SEvJAgAYGIthU9eI54gIyZ9ILMTSHV1Qw1ofAJCWoYXpWybhYwI9bkYIXyjJFuHy6dfc1607W0EgEPAYDSGFMzLVxqpdrtwSjO/jTDFj6ySkp2XzHBkhVRMl2UJIpQx+Z8O47V5D6vEXDCHFZFvPGIu3dIaGQLZ4wKPXdljscYlGHBPCA0qyhXgSGI3oKNmqOyKxBhq3sOA5IkKKp1VnKywYfpzb/vd/Ifht1V0eIyKkaqIkWwjfPKOKm7SqTqvukApl+BdXMMT1Kre9Y+Ud/Pu/EB4jIqTqoaxRiLxJttegujxGQkjJCQTAwuHH0M7xGVe2aOJFPLz7gceoCKlaKMkWIDI0CS8exwIANDQEaNPFmueICCk5kaYUG6fuha11JgAgI12C6f3P4N2bTzxHRkjVQEm2AJe8ckcV2zc0gY6eiMdoCCk9Q700bFj5FoYGsjmNY6PTMK3/WaQkZ/IcGSGVHyXZAngfy+276jGQbhWTiq2WVRZWL4uDUCgbYfz8YQwWjPGBVEojjglRJUqy+YgMTcKD27J+K4EA6NqHVt0hFZ+zUybmz0rgti+fDsWmRTf5C4iQKoCSbD7O57mKtW9kCkNjLR6jIUR5+vVOxbCBuf2xezcE4siuxzxGREjlRkk2H3kfc+g7vD6PkRCifNMnJaFD29zF3Vd964dLp14XsgchpLQoyX7mVXAcnj2IASAbVezSy47fgAhRMqEQWLk4Hg0byAY+SaXA3FHeCLzxjufICKl8KMl+5tT+3GcKGzqZQc+A1o4llY+uDsOGn2JhXVO2+EVGugTTvj6DV8FxPEdGSOVCSTYPiUSK04eec9uDJjryGA0hqmVqIsXmtbEwMZY92pOUkIHJfbwQHZXMc2SEVB6UZPO4dTmSm6tYS1tIE1CQSs+6pgQbf46FjrZsMYF3b5IxqY8XEuLSeY6MkMqBkmweJ/8K5r7u2MMGmiL69pDKr2ED+WdoQx7HYdKXXviUmMFzZIRUfJRF/hMfkwafE6+47YHj6VYxqTratsrA4rnxEAhkifZJQDS++eoMzQpFSBlRkv3Pyb+CkZUpu2VW3UoPdRuZ8hwRIeWrV7c0uckqgm68w/QBZ2nBd0LKgJIsZIuz/2/PE257sEdjHqMhhD9f90nF7KkJ3PYdv7eYOfgcMjMk/AVFSAVGSRbArctvEPEyEQCgKdJA9wH2PEdECH+GuKdg6sREbvuadwS+G3aeEi0hpUBJFsD+bQ+4rzv2sIG2jiaP0RDCv9HDkjF+ZBK37XsmFDMGnkVaahaPURFS8VT5JPsqOA5X/g3ntsfOcuIvGELUiMfYTxg5JHee42veEfim32mkfKLBUIQUV5VPsn9tvc997dDMDFa2hvwFQ4gaEQiAaR5JmDA694r27pUoTOx1CgmxaYXsSQjJUaWT7Md3KfA6kDvD08S5LXiMhhD1IxAAHmM+YZpHbh/twzsfMNLlON6GJRWyJyEEqOJJdu/GQG4wh0VNPTRtXZ3niAhRT6OGJmPOjATuOdrQFwkY3vkYgoM+8hwZIeqtyibZmA+p+N+u3Md2pvzQEgKBgMeICFFvA79OwaolcRBpyp4nj3mfitFu/+DKv2H8BkaIGquySXbvhkDuIXuz6rro2N2G54gIUX9uLunYuj4W+vqyO0CpyVmY+vUZ7FkfAMYYz9ERon6qZJJ9G5aEQ78+5LYnLXSmq1hCiql500zs2hKD6hayD6mMAZsW3cS80RfoER9CPlMlk+zmJTdzp1C01kOX3nb8BkRIBVPHLhuev36EU5PcRQTOHQnB8E7H8PpZPI+REaJeqlySfXjnPc4dCeG2567pQFexhJSCqYkU29bHoH+fFK7sxaNYDG53BF4HnvEYGSHqo0ol2exsKZZP8+O2HZqZoVmbGvwFREgFJxIB82cnYMHseIhEsrtDaSnZWDj+IhaO96Hl8kiVV6WS7KFfH+LZgxgAgEADWLixI88REVI5fN0nFXt3fISNdW6frNeB5/i6+WFcuxBeyJ6EVG5VJsmGhyRg64+3uG33sY1Q04ZmdyJEWerZZ+PP3z+iV7dUrux9ZDIm9zmNpd9cRmJ8Oo/REcKPKpFks7OlWDDOB2mpstGQxtW0Mf775jxHRUjlo6vDsHRBPH76MRZGBrmr9hz74yn6ND6IU/uf0aM+pEqpEkl2x4o7eHjng2xDAKza5QqRWMhvUIRUYm4u6Ti0NxouHXPnOI77mIYfJlzEaNd/aKYoUmVU+iR76dRr7Fpzj9t2H9sIDZqa8RgRIVVDNVMp1iyLw88/xsK8WjZXHnj9HQa2OYIFY30QFU7zH5PKrVIn2WcPYrBwvA+3Xbu+MTzm0yIAhJQXgQBwdUnHkX3RGDH4E4TC3FvFpw89R2/HA1j9/VV8eJvMY5SEqE6lTbKRoUmY3NcLKZ9kox119UX45UB3CIWVtsmEqC1dHYYZk5NwYHc0OrfPHRiVlSnFgW0P0KPBX1gxw4+ubEmlUykzzpvXiRjf4wRi3sv+mIWaAvxyoDsMTbR5joyQqq22bTbWrYzH75s/wrFh7jO0WZlSHNn5GD0a7MfMwedwxz+SBkiRSqHSJdkXj2Iwxu0fvA37BEB2u2rVLjfUc6zGc2SEkBxOTTKxZ1sMNv4cA0eH3Ed7pFKGiydfY1y3k+jf4jD+t/sxUpIzeYyUkLIRsBJ8XGzZsiXu3btXdEWeXDr1GgvG+SA1WXaLWCAAFm/tjM497fgNjBAe1LxuxH2dXHMGmFCPx2gKxhhwN1ALfx3Sw737Ogqv6+iJ8MVXdfDlsPpo51YLmpqV7tqAKEveKXLV5E5IpUiy6WnZ+GXBdRz+7RFXpiEUYMXvXdGmizWPkRHCn4qSZPMKDdPE0ZN6OHdBB+kZio/ZmZrroOeguug5sB6ata1BCZfIoySrXIwx+J0Nw5rvryIyNHfAhK6+CBsP9oB9I1MeoyOEXxUxyeZIThbgrLcujnvpICJSK986hiZa6NjdBi697NCxhw2MqyleBZMqhpKscjDGcN0nAr//fA9BN97JvebQzAw/7XalQU6kyqvISTYHY8DzEBG8L+rA+5I24hJE+dbT0BCgoZMZWnSsCecONdGigyWqWeiWc7SEd5Rky+ZVcBwu/PMKZw8/R+iLBLnXRCINTJjbAgPGNqSl6whB5UiyeUkkQNBDMXx8dXDthhix8eJC69eub4zGLavDoZkZ94+udis5SrIl8z4yGfdvvkPQrfe4efENXgXHKdQRCICO3W0wc0VbGJnS1SshOSpbks2LMeDla01cv6WNqzdEePpcB4wV/eG6hrU+7Oobw7auMWzqGsG2rjFq1TGCZS196BkUnrRJBUBJVoYxhtTkLMRGpyHuYypiP6Qh9r//I0MTERaSgPCQBMTHFLxqh6amBjr3ssX475ujupV+mWMipLKpzEn2c5+SBXj0RIygh2IEBmni+UttZEtKNihKV1+E6jX1YGGlD/MaujCupg1DYy0YmWrDyEQbhiZaMDLVgp6+GFo6mtDWEUJLRxM6uiKIxBp0B00dqGGS1eTjTVfNvIK/f39UdMXPaAgFaNCkGvoMq49OPWyho5d//wwhpGox0Gdo3yYD7dvIJrhIzwCeh4gR8lKE5yFCPHuhgbAIbWRlF7wwSGpyFkJfJCh0RRWHQABo62hCW1cTYi0hNIQaEAoF//3TgFBTkFummfu/hlAAgQAQCOT/19bRxLbjX5b220HUCC9J1sgk/9GCn9PQEKBadR3Ub1wNjVtZoH5jM2jryEJ+E0rTrxFSmJp5vk78GAepIIW3WPhgay7790U72bZEAkS918Lb//5FvRPjTZQm3n/UQnxi4Qm4KIwBaanZ3HKaZaWjx8upmahAiW4Xm5mZwc7OTmlv/uJRDNIzkyAUyG73ikQaEFaC594ysj9BS9OA7zCUprK1B6habdLTSoFYM4uHiMrmU1oGDHSK94Fcddh/tx2lRd5+5F5lAuRXkwFITs+CvlbRfb96ZpbQNawY3WAfP36Eubk532EozeftMTMzw/nz50t9vBIlWVXge8SyKlS2NlW29gDUpoqgsrUHoDZVBMpuT8W/bCSEEELUFCVZQgghREV4T7KTJk3iOwSlq2xtqmztAahNFUFlaw9AbaoIlN0e3vtkCSGEkMqK9ytZQgghpLJSSZKNjIzEjBkz0K5dO+jq6kIgECAsLKxY+6anp2Pu3LmwtLSEjo4O2rVrhytXrqgizBIpbZvu3buHSZMmwcHBAbq6urCxscGIESMQGhqq+qALUZafUV6rV6+GQCBAx44dlR9kCZW1TcHBwRg0aBDMzMygo6ODBg0aYMuWLaoLuBjK0qaIiAiMGTMGNjY20NXVRf369bF48WKkpPD3vOyxY8fg7u4OW1tb7nu8cOFCfPr0qch91fHcUNr2qOt5ASjbzygvdTo3lLVNZTo3MBXw9fVlFhYWrFevXqx79+4MAAsNDS3WvsOHD2dGRkZs165d7OLFi6x///5MW1ubBQUFqSLUYittm77//nvWvn17tmPHDubn58cOHjzIHBwcmKmpKYuIiFB94AUoy88ox6tXr5ienh6zsLBgHTp0UE2gJVCWNt29e5cZGBiwvn37shMnTrDLly+znTt3sg0bNqg26CKUtk3JycmsXr16zM7Ojv3555/s8uXLbO3atUxbW5sNHjxY9YEXoE2bNmzQoEHswIEDzM/Pj23atIkZGRmxNm3aMIlEUui+6nhuKG171PW8wFjZfkY51O3cUJY2lfXcoJIkmzfo3bt3F/vEcP/+fQaA7d27lyvLyspi9evXZ3379lVFqMVW2jZFR0crlIWFhTGBQMCWLFmizBBLpLTtyat79+5s0qRJzMXFRS3+kErbJolEwho1asS+/vprFUZXOqVtk7e3NwPAvL295crnz5/PhEIhS0lJUXaoxZLf38O+ffsYAHbp0qUC91PXc0Np26Ou5wXGSt+mvNTt3FDaNinj3KCS28UaGqU7rJeXF0QiEYYMGcKVaWpqYujQofD29kZGRoayQiyx0rYpv5lQbG1tYW5ujrdv35Y1rFIrbXtyHDp0CIGBgVi9erWSIiq70rbJz88PT58+xXfffafkiMqutG3KzMwEABgaGsqVGxsbQyqVgvE03jG/v4dWrVoBQKF/D+p6bihte9T1vACUvk051PHcUNo2KePcoFYDn548eYLatWtDV1d+sWVHR0dkZmbi5cuXPEWmXMHBwYiOjkbDhg35DqVU4uPjMXv2bKxbtw6mpqZ8h1Nm165dAyDr82vbti1EIhEsLCzw7bffIi0tjefoSueLL75AvXr1MH/+fDx9+hTJycm4fPkytmzZgilTpkBPT31W5PH39weAQv8eKtK5oTjtyY86nxeK26aKdG4oTpuUcW5QqyQbFxcHExMThfKcH1ZcnOJ6shVNdnY2pkyZAnNzc0yYMIHvcEpl7ty5qF+/PsaOHct3KEoRFRUFABgyZAi6d+8OHx8fzJs3D3v27MHw4cN5jq50tLW1ce3aNUilUjg6OsLAwABubm7o06cPtm/fznd4nLdv3+LHH3/EF198gZYtWxZYr6KcG4rbns+p83mhJG2qKOeG4rZJGecGtVrqgTGW75qMfN3aUoXp06fjxo0bOHv2bL4nDXV39epV/PXXXwgMDKw062dKpVIAwMiRI7FixQoAQJcuXSCRSLBgwQI8ffoUjRo14jPEEktPT8eQIUMQHR2N/fv3w8bGBnfu3MGKFSugqamJ3377je8QkZycjK+++gqamprw9PQstG5FODeUpD2fU9fzQknaVFHODSVpkzLODWp1JWtqaprvJ9L4+Hju9Yps4cKF2LVrF/bu3Yvu3bvzHU6pTJ48GRMmTIC1tTUSEhKQkJCA7OxsSCQSJCQk8NpvXlrVqlUDAHTr1k2uPOdndP/+/fIOqcz++OMP+Pn54dy5cxg5ciQ6d+6MOXPmYMOGDfj999/x4MEDXuNLT09Hv3798Pr1a3h7e8Pa2rrQ+up+bihpe/JS1/NCSdtUEc4NJW2TMs4NapVkHR0dERoaitTUVLnyp0+fQiwWo27dujxFVnY//fQT1qxZgy1btmDUqFF8h1NqwcHB+P3332FiYsL9u379Om7dugUTExO1uEIqKUdHRwBQ+PSdc5VU1kFifHj06BFMTExgb28vV966dWsAsp8jX7KysuDu7o47d+7g3LlzaNKkSZH7qPO5oTTtyaGu54XStEndzw2l/b0DynZuUKuzR79+/ZCVlYWjR49yZdnZ2Thy5Ai6d+8OLS2+15Ysna1bt2Lx4sX46aefMGPGDL7DKRNfX1+Ff82aNUPjxo3h6+uLgQMH8h1iifXq1QtaWloKa0Z6e3sDQIn61tRFjRo1EB8frzAg6Pbt2wAAKysrPsKCVCrFiBEjcOnSJZw6dQpt27Yt1n7qem4obXsA9T0vlLZN6nxuKG2blHJuKPXDP0U4evQoO3r0KJsyZQoDwH799Vd29OhR5ufnxxiTPRMmFArZ8uXL5fYbMmQIMzY2Zrt372YXL15k7u7uTEtLiwUEBKgq1GIrTZsOHz7MBAIB69mzJ7t586bcvydPnvDVFMZY6X9Gn1OXZ+EYK32bli1bxoRCIVu4cCHz8fFhq1evZtra2mzMmDE8tEJeadoUGhrKDAwMWL169bjJKNatW8cMDAyYs7NzsScVULacNixatEjh7+HNmzcFtocx9Tw3lLY96nxeKMvP6HPqcm4oS5vKem5QWZIFkO8/FxcXxpjsJACALV26VG6/1NRUNnv2bFa9enWmpaXFWrduzXx9fVUVZomUpk1jxowpcj++lPZn9Dl1+UNirPRtkkqlbMOGDcze3p6JRCJmY2PDlixZwjIzM8u/EZ8pbZuePHnCBg0axKytrZm2tjarV68e+/7771lcXFz5N+I/tra2BbYnJ/6KdG4obXvU+bxQlp/R59Tl3FCWNpX13ECr8BBCCCEqolZ9soQQQkhlQkmWEEIIURFKsoQQQoiKUJIlhBBCVISSLCGEEKIilGQJIYQQFaEkSwghhKgIJVlClEQgEEAgEEBDQwOvXr0qsF7Xrl25un/++afca2PHjuVeW7p0aYHH2LdvH1evS5cucq/5+flxr+X809XVhaWlJTp37oy5c+ciKCioLE0lhBQTJVlClEhTUxOMMfzxxx/5vh4SEgJ/f39oaha+yqSmpib27t0LiUSS7+u7d+8u8hi2trZYunQpli5dilmzZqFPnz5ISUnBL7/8ghYtWmDEiBFITk4uXsMIIaVCSZYQJapevTpatmwJT09PZGdnK7y+Z88eMMbQp0+fQo/Tp08fREZGKkxMDshWO7l+/Tr69u1b6DHs7OywbNkyLFu2DD///DN2796NgIAABAUFoUmTJjh06BAGDRpUsgYSQkqEkiwhSubh4YH379/jzJkzcuVZWVnYt28f2rdvzy2hVZARI0ZAR0cHu3fvVngtp2zixImlis/JyQkXL16Eubk5zp8/j5MnT5bqOISQolGSJUTJhg0bBj09PezZs0eu3MvLCx8+fICHh0eRxzA2NsagQYNw9uxZvHv3jivPyMjAX3/9BRcXF9SvX7/UMVpYWGDy5MkAgIMHD5b6OISQwlGSJUTJDAwMMHToUJw/fx6RkZFc+e7du2FoaIjBgwcX6zgeHh7Izs6WGxx14sQJxMbGFitRFyVnwNSdO3fKfCxCSP4oyRKiAh4eHpBIJNi7dy8AIDw8HD4+PhgxYgR0dXWLdYyOHTuiYcOGXD8uIEvUJiYmcHd3L3OMOQu3f/z4sczHIoTkj5IsISrQpk0bNGnSBHv37oVUKsWePXsglUpLfAU6ceJEvH79GpcvX8bLly/h6+uLUaNGQVtbu8wx5iRugUBQ5mMRQvJHSZYQFfHw8EB4eDjOnz8PT09PODs7o3nz5iU6xujRo6GlpYU9e/ZwV7TKuFUMAFFRUQAAc3NzpRyPEKKIkiwhKjJq1Cjo6Ohg8uTJePv2LSZNmlTiY5iZmaF///44ceIE9u7di3bt2qFx48ZKic/X1xeA7KqbEKIalGQJURFjY2MMHDgQkZGR0NPTw7Bhw0p1HA8PD2RkZODjx49Ku4qNjo7Gzp07AcgeFyKEqEbhU8YQQspk1apVGDBgAMzNzWFgYFCqY3Tt2hWnTp2CVCpFjx49yhzTgwcPMHr0aMTExKB3797o169fmY9JCMkfJVlCVMjGxgY2NjZlOoZAIChVIgwLC8OyZcsAyCbCiImJQUBAAAICAgDIrmBzrmYJIapBSZaQSio8PBzLly8HAGhra8PY2Bj16tXDnDlzMGLECDg5OfEbICFVgIDljOMnhBBCiFLRwCdCCCFERSjJEkIIISpCSZYQQghREUqyhBBCiIpQkiWEEEJUhJIsIYQQoiKUZAkhhBAVoSRLCCGEqAglWUIIIURFKMkSQgghKvJ/yzWizts9HLsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "for i in range(len(ori_x)):\n",
+    "    ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "yes_data = yes_data / 3000\n",
+    "no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "384d0cd910eb4019842a9e261245f8f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f67325d7ec3a49c982930e9e8b49909c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ac3a1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f49003c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e13bc738",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "4db415fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab91654b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v3+v4_Aug23_bf_model_misspecification_1000_data.ipynb b/bf/v3+v4_Aug23_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..33f7555
--- /dev/null
+++ b/bf/v3+v4_Aug23_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2577 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 0.004647741\n",
+    "    maxAbs = 0.073834606\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "        temp = dpl[0].data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "#         temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(x)):\n",
+    "#     x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f3432c4163c6402db6075fa954489925",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.085054</td>\n",
+       "      <td>0.822207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  7.085054  0.822207"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8+uijAIAf/vCHmJiYwF/91V9h3bp16OzslLxxgiBm89jr5wEAwVAEgPCErUVp9IY4VzC6GJlwB1GehgVRatJCp1Hh/T4HACRwfyVukS/g8AZTZn4BfMuXGpsBR3p5t1QqS8Wi18ATzExUXjo5AgC48xNL4Wcj6LN7494fdPhw78GTqLTo8G+v9+Dgh0MZ/X6pyFJRv23bNmzbti3utTvvvFP8+rHHHsNjjz0mx6EIgkhCOMKJT9rCE6zdG0RrtQV6Dd8C3q4w99ekJyC69uaCYRjU2gy4OOlFmUkbV00PTAf6J5J0HbB7g2lZKgAfVzkSjXUkm2cvYDFoU3ZHnsm7vXY0lRtx7aoaPPrqOZwZcePdXjuWVJqxcXEZTg07AQB7dnTgtn9/F0d6J/HZtbNDErmCKuoJQiG4ohXhK2qt4hOsw8uKVkqZWSumGCuFSS8rBtlTIWRiJZrcaDNooVYxCd2DLj8LPxtBhSW948TOsK8rndv9lU1MZcwVQH2JEW3VFgDAcx8N49u/OY4fPcPXBwqusfY6G9Y0lIjW2XxBokIQCkEIwm9qLgcAdA064Q6ExHhKuUmnuJjKpCeA8jQtiJpoXCVR+3qVionr2xXL8BQ/OiBVfESgo7FU/HquVGcgOv0xw5jKuDuASqseVoMW9SUG/NfRfnAccOyiA32TXpwedqG+xIASoxbrFpWia8g5rzNbSFSKnKcOX8QPfn8iaX49sXAQChs7m/lC49e6+UwnwVIpjWkhohQm3cG0LRUhWJ9s0FaFWZcwUC/MoxF+PhVfvrwZ//eL6/HPf7E2ZV2LRa/N2FIZdwdQGRWrr31iKdY2leK2jy0BADz5zkWcGnJhea0VALC+qRRsmEPXoDOjY0iBuhQXOT/8Qxd8bBieQAg/+cLafG+HyCOOqKg0lhmxusGG37zHp/qvjT45l5t1OD+unJHCfjYMTzCMcnN6loowCyWR+wuY3SJfYNpSmTs+IqBWMdieZgzDYuDdX5EIB5VqbgECgEAoDKc/hMqoK27X5c3YdXkzAGDCE8Cjr54DwwBXt1cDAJbX2gAA58Y8WL+oLK09SYVEpYiZ8rHwRS2UY/PsNyUKj9juu59YVoWPBpyotOjFGo0ykw52j3IC9YLVla6lIohCc2ViS6XSqsfxBJ8jQVSq56iOzxah/5eXDYtdi+dCEL1E8Z2f3LQWi8tNCEU4fPljzQCA+mhMZ8Duk2nHqSH3VxHzQb8DAN/XaSRqohPZ8/gbPbhj37v53kbWTEVvsiVGHa5azj+pXrW8SnwCLjfzrhalzERPt5pe4JqV1fjHL6zFhiRP7HUlBgw7/bOKBoedfpSbdQnb6ksl0/5f4y7+b65MkPGm06hw97XL8Z1PrUB1tIOCXqNGjU2P/hlpx7mERKWIef+iAwCwdWUNXP6QOL6UyI5/+GMXXjw5Ouean7/Vi77J+fuAZkKspbK+qRQ7NzXhy1HXCDDddFEpVfWZiopeo8ZNGxuTxjlqbQYEQ5FZLrDhKb8Y5Jeb6Zkq6f2bjEfraNLNRAP4FOcBB1kqRBp8ODCFlkqzmFoYO5CJyB5fMPGTvDsQwv85cALbHnx9nneUHg4vC5NODZ1GBY1ahT07OrA66voCkPOq+vluC5KpqKRCcBUNTcVb/cNOf9qZX5kiiEq6/b+EOpqqDESlscyEfnJ/EelwdtSN5bVWMStleIpcYHIwlmRColCk5gqE8tZXaS4cPr6TbjKEoVTJ/j4pvHJqFGt+8MK81kTILSq1JdMdhmMZcebQUslwUJfQZboijYJPgcYyIwYdvnlr10KiUqT42TB6Jzxoq7agOnrBj7pIVORgzJ34PMY+TXaPFl4W1ZSPhW0OUREfPnIQf/uPwxfhDoTw9V++l3HMxhvMTqQnPUGoGMwppJlQXyI8nE0/1QtdCmKnRMpJpnPqJ9wBGLVqmNMI6gs0lBkRinDzdn8gUSlSesY9iHBAa41VTJUkS0UawgCm0SRuRFfMDPO3z0/My54yYSqmej4RQvaS3NdJIBTGW+cmYNapMTTlR/dI+oI7NOXD5ntfwi/evpDxcSc8QZSZdGml4qZDhUUPjYrBYMz5cXiD4DikXWCZKZnOqR93BzOyUgDe/QUAfZPz4wIjUSlShCflZTUWWPQamHVqiqlIRJiXkWzmiCvmg58LF5JUpnwsSo3Jbzh6jRoVZt0s945U3jk/CXcghG9e0wYAODPiSvtnH3nlLNyBEH57NPlgv2TYPcE5Jz5milrFoMZmiBNdMW05gxhGJlijDzLOdLO/3AGxRiVdWir5upzzY/NjXZOoFCndIy6oGGBJ9IKpKTFg2Dl/wTglYtFHRSWJYMS6v5K1SM8ndm9wTksF4AsA5U4/f7d3EmoVg52bF0GrZnA6TVFxB0L49bv9KDFq8X6fI+MMpX67T/YAel2JIW4fQmA8nU7I2WA1aKFiptPBUzHuDiZMJ56LhlIjDFrVvLlsSVSKlHNjbiwqN0Gv4XPnV9WX4K1zE2LLcyJzwhH+3KVyf5UYtZhM0s02X3Ac35E4VdC6rsQgu6Vy7KIDK2qtsBm0aKm0pO3+euf8BILhCL7zqeUAgJdP8encp4dduOGRN3FyKHlrETYcwekRF1bW2aT/ATG01VhxcsgpziiZLrDMjaioVQxKTTpMpikqE+4AKtIs9hRQqRgsrbLgLIkKMRc9417RSgGAG9c3wO5l8ccPBgsyM6kYEAQ5qfsraqk0V5gwkWTuRr5w+kNgw1zKmx/v3pHPog1HOLzf58D6RaUAgGW1VtH9lWh4VCxvnB2HQavCjg2NKDFq0TU4BQB49NVzON7nwLd/cxyhcOKHpPNjHgRDEaysl1dUNiwqhcsfwqtnxjDq9Kc9WVIKpSZtWiMJItGkgUpr5ntpqyZRIeaA4zhcmPCgOUZUPt5WiSqrHnf/+jjuf+50HndXvAiikixLxuVnoVYxaCgzFpz7a7p9R2pLxe5lZWtAembEBXcgJFapr6i1ot/uw1//x1Fc+8Brc2aCvdE9jk3N5TBo1Wivs6JryIVRlx9//GAQK2qt+GjAiedPjMz6uWAogsM9fKKE3JbKhsX83/GVf38X1z/4ujgpsizN/mLZUG7SpTU8bcrHIhzhMrZUAL7rxoDDB08Wo4szhUSlCBl1BeANhuMsFY1ahV/cvhkAcHY0/UApMU0w+lTs9CX+4Ln8IVgNGlSY9QXXQn4ial2l6oMl1FvIFVf51bt90KgYXL60EgCwc1MTykxa/PGDIZwddeO5j4YT/tyI04/uUTc+3sb/XHudDaeHnThwbBBsmMNDX1yPxjIjfv5Wb9zPcRyH2/e9i+8d4Gezx34G5KAl5vcFwxH8/vggLHqN6GbOBaVpjiQQrOPKLNKbW6v5rsXn5iFYT6JShJwf8wAAmmd0W11Ra8MVrZUF9xRdLASilkqypzmXPwSLXoNysw4OL5vUNZMPhH/zVPM7BDeO1AmQoXAED7/cjacOX8Tn1zeIae0VFj3++S/WYceGRjRXmPDLJKnCb57l2/J/rHVaVPxsBA+93I1V9Ta01Vhx66WL8U7PJF45Pd0656nDfXi9exyLyk34dEcdNGp5b2EMw+Brn2jBbR9bgutW1QLIrZUC8D3Z0mmdMyb0/crCFbdhcSl++oW1aEgx3lgOSFSKkN4JXlQSPaUla99NpEZwfyWbGe7ys7AatKKLqZBG86br/hKKI2NrbrLhvQt2/OMLZ9BSZcE3trTFvXfV8mr89Oa1+ExHPY5edCR0tb3RPY4Ksw7t0dbsghvL6Q+Jo293XdaMFbVWfGv/+zg76saAw4f/7+BJXL60Aq9++yo88t82SPobkvHd69vxfz67UowTeQO5bcBZZuYD9alioVIslWqrATs2NmbUMyxbqPV9EdI77oFOrUJ9gqeOcrOu4DKTigU2ann42QhC4cisp2Bn1P0lPO1P5rDSOlPSbVki1kUkcfGlixCM/9mXO5POGVlVb0M4wuHMiCtuGiLAF49eurRCLFxcVW/Djz63GuPuAL64eREAwKhT4//95Ubs+Nc/Y+fet6HXqBDhONy/oyPl8Cs5WNfE7znXln+ZSYdgKAJvMDxnpfx4NNU9lTWab8hSKUJ6xj1YVGGCOkElcYVZB5eC2pvPF6FwBBFuekqiJ6ap5ItdIzgz4oLLH4ItGlMBUFAZYBPuYFq+f5tBHkvlzIgbVr1mzmmI7VHrY2ZqcCAUxuCUX2yECvBup1svXYxvXbMMJTFtVxZXmPHLOy5Ba7UZZr0aT95xCZqSTG6Um+U1fBwiVe2PVIQamFRTOSeibWnKclQzIxdkqRQhvROeWfEUgXLBNeNhUVuSu+Ci0hCC9GUmPl7iCYTEm9sdPz8CgG/MZzVYRRfTRAFZhBOeQFppr4Klkm5X3GScGXGhrcYyp8WwqNwEs049a5TtyBQvxoks7USsqLVh/+7Lst9slmjUKvzHVy/JeRxC6Apg97BonGM44/H+KSyuMMvWliZXkKVSZEQiHC5MeNFSlVhUBNO4kJ6iiwEhnlIWfSoVZtPExgOcPhZWg0Z82s90tngumfSkLnwEALNOA4YBnBItle5RN5ZFn+SToVIxaK+zoWuGpTIYrZOpT3M8bz65fGll0vHDciFcc0KsNBEObxB/PjsuJg8UMiQqRcaQ049AKJLcUom6ZihYnxmCqAg3Znc0OBvbssXpD6HMpINeo4r7mUJgwh1My9euUjGw6jWSLJUJdwCTniDaUogKAKyst+HkkCuuEHIoKip1pblpJ19sCJbKf3/qGF49M5ZwzQsnRhCKcPj0mrr53FpWkKgUGT1COnGSOdvCTbGQXDPFgJBOXBr1VwtpxTPrOW7a2Ai9VhX9mcKJW0160u9eazVoJVkqQuPShjREob3OBncgFDckatDBn9NisFTmg+YKM7502WIAwJHeyYRrDn40hMYyI1Y3yFvsmQtIVIqMnjnSiYFY9xeJSiZMx1SigXpRVPgbqEGrwje3tKGp3ARdNCsswBaGpcJxXNT9lV4mms2olZT9JYwtnmt2i4CQKtw1NCW+NujwocykhVFHMT+A7//1wxtWo7XaglPD8YXLgVAYfzo9ije6x/HpNXXzkvUmFQrUFxnnx9wwatWosSZ+SiwxaqFWMZikmEpGCOnEgitCqFURLJU/37NFtAI1ahXUKka0bvKNKxBCMBxJO9XUatBIyv4SRKUkDVFZXmuFigG6Bp341GredTPo8CVNQ17IrKi14ni/I+61x9/owY+jbZeuLwLXF0CWStFxetiFZbXWpBkgKhWDsjQb1BHTTAfq42Mqo64AtGpGtGAE9BpV3t1fdk8Qoy6/WJeUbtNDm0FaTEVwnQkJC3Nh0KqxtMoSF6wfmvKnnfm1kFhRa0XfpC8uAeTFLr732V2fXIq1jSX52lpGkKgUGWdGXFheY5lzjdWglZwyutCYKSre6Ad71OlHtdUwy+3Ai0p+LZX/9fSHuOvJo9OddNOMqdgkxlScgqWSZv1GR2Mp3rtgRzjCIRSO4Py4B0uSxAQXMitq4+t67J4g3u9z4Jtb2vDt61YUhesLIFEpKsbdAYy7gylTOaW6NxYigqiUGLVgmJiYissvjuGNRa9R5z2mcm7UgwsT3ukWLRm5v6TFVFQMYNGl5z2/ankV7F4W7/fZ0TPOt6xvl7m7sBLYuLgMOo0KTx/jp2C+eW4cEY4/f8UEiUoRcSYaxBOeaJJhNWjEp0kiPQLRmIpeq4JZpxHdXyPOQML4lV6bf/fX4JQPE54gxsUOxelnf7kDoazn7vD1Otq0i/CuXFYFtYrBy6dGRTdYqmt4IVJm1uGGtfX47dEBTPlYsXFssQkwiUoRIYxpXZbK/aUn91emCJaKTq2CSacWix9HnX7UJLRUcu/+ujjhxV1PHoUjQfsOdyAElz+EcITDuejwpXTnbNiMGoQjHLzB7ERxysemFaQXKDFqsbm5HE8fG8Txvilo1Qxaq+e+hhcqt166GD42jOdPDKPf7kW1VQ+Dtriy5EhUiogLE16YdeqUTQxtRmnujYWIkP2l06hg0WvgDoTgC4bh9IdQnaC/lV6jzrmo/P74AJ75cAg/eubkrPdipzeeHnHBpFOnnaJrjQbYp7K0ZjMVFQC465OtGHD48LM3e7C0ygKdhm49iehoLEFDqREvnBhB36Rv3vqcyQn9yxYR/Xb+IksVsOMD9eT+yoRYS8Ws18ATCIkTIKsTiLhOo8p5Rb0rGtf5w/HBWf+eQgEhAJwadmU07rYy2v48tltAJkz5WNiMmVUjXNFWic+tq0eJUYvPdBRHamw+YBgGW1fW4PXuMZwdc6OxrPiy5GQRleeeew7Lly9Ha2sr9uzZM+t9juPwjW98A62trejo6MDRo0flOOyCo9/uTesisxo08ATDCKeYEU5MI4qKRgWzXo0RZwADDt4aqEloqeQ+piJUoQdCkTgRAaZbnQC8OGTSDl3oLDyc5fRHpz+UsaUCAA/sXI/j378Wf311W+rFC5hrV9UgEIpgzBVYmKISDodx11134dlnn0VXVxeeeuopdHV1xa159tln0d3dje7ubuzduxdf//rXpR52wcFxHAbsPjSWpTaHBfeGm1xgaROMcX9dvrQSXUNOfPmJdwEgSfZX7mMq/XafON5gZi+3mSKTyEWXDGFKY7YjhbNxfxHps7m5XDy/TWl83gsNyaJy+PBhtLa2oqWlBTqdDjt37sSBAwfi1hw4cABf+tKXwDAMLr30UjgcDgwNDUk99Jzc/av38Zv3+nN6jPnE6QvBFQilbakA0jvRLiRiLZVvbGnD59c3iK8lzP6ah5TiAbsXqxv4greZwfqhKV9cbO3Sloq0f2+FWQetmsHQVPaikk7hI5EdGrUKW9qrASCth8hCQ7KoDAwMoKmpSfy+sbERAwMDGa+Rm1dOj+KDGS0Pipk+uxdAeheZjUQlYwIxMRUA2NRcLr6XaEhTrlOKfcEwxt1BdERFZWaHhHF3MC4r7RPL0q9lUKkYVFsNGMlCVPxsGMFQJK2+X0T23LShEVa9Bstqiy9LTrKoJMp1nxlITmeNwN69e9HZ2YnOzk6MjSVuA50OFoNGUe6fflFUUlsq09P9CuPv/8PxQTz8cne+tzEnwRmiIoySBRJfq7l2fw04+H/vNaKoxFsqk55g3ATApUnm6ySjxqbPKqbizKDvF5E9l7dW4oMfXIvqJD3+ChnJDSUbGxvR19cnft/f34/6+vqM1wjs3r0bu3fvBgB0dnZmvS+LXitmzyiBnnH+JpNOiqG1gETFz4bx9384AYeXxZc/tgSWOWZw5xM2HIFGxYgFfalqgXKZUjzhDuCv/+MYAKC1xgKTTg27Z7aoLK4w4amvXopwhMu4hUdtiWFWR9x0yKSZJCGNYmnLMhPJlsqmTZvQ3d2Nnp4eBINB7N+/H9u3b49bs337dvz85z8Hx3F4++23UVJSgrq63KYVWvXKslS6hpxoKDWm9WGeHhmbf/fX08cGMO4OIhTh8Na5iXxvJynBUCSudkKjVuGTy6tw28eWJFyv16gQYHPj/jrUNYJTwy784LMrsa6xFGUmHSZnWCr26KTHy5ZW4Iq2yoyPUWMzYHjKn3FVfSZt74mFieTHRo1Gg4cffhjXXXcdwuEwbrvtNqxatQqPPvooAODOO+/Etm3bcPDgQbS2tsJkMuGJJ56QvPFUmPVqjCtoUNWJgSmsqk+vXYNcc8jl4JXTo2gsM2LSE8RrZ8awdWVNvreUkGA4Mqsg74mvbE66no+p5MZSee+CHWUmLXZd3gyGYVBq0sIRE1MJhiJwBUIoN6WfRjyTuhIDvMEwHF5WbPefDkKcjiwVIhmy+CK2bduGbdu2xb125513il8zDINHHnlEjkOljcWgxYUJ77weM1d4AiH0THhww7qGtNZPu7/yb6mcHHJhbWMpvMEQ3jw7nu/tJCUYiojxlHTQqdUIRTiEI5yY9isX712wY+PiMtH9UW7WxaUUC/GVTMRgJuuaygAA7/RMiHNO0oHcX0QqFFtRb9FrFBNTOTnkBMchbUtFp1FBr1Hl3VJx+VlcnPSivc6KzUsqcH7cIzY/LDRmur9SIYwUlruqftITxPlxDzYujs0+08WlFAsCk0kV/UzWLyqFVa9JOhM9GVNeYZZKYcbGiPyjWFGxKij768Qg39lVqFlIB4teI04vzBenY7oqb17CPxm/d8Gezy0lJRDOzFLRa3Izp16YUb5xcZn4WvmMoWt2GURFq1bhirZKvHJqTGyemQ7O6GeKYipEMhQrKha9Bj42jFC4MEa+SuHE4BQqzLqE3XKTYdKr4Q3ktzX7yaiotNfbsLqhBDqNSrxpFhpsppaKhm/eKHdc5d3eSejUKnTETPkrNekw5WPFa3lCBlEBgC90NmLU5cetj72TdsB+ysfCrFNDm4EAEwsLxV4ZQuqqJ883Vjk4MejEynpbRimGZl3+LZXecQ9MOjXqSwzQa9RY11iKd3oKU1QSBernQrRUZK6qP9xrx7qm0rh254J4CPEMMaYiIVAPAFevqMHfbWvH0YsOnBtzp/UzfDNJslKI5CheVNx5vrFKJRiK4MyIC6vqM5tPzc8Eya+gOrwsykw6UQyvaKvEhwNTmCjAuEqmgXohpiKn+8sTCOGjgSlsWlIW97pQ0S+IiRBTKUtznO9cXNPOZ+O9dT49sae+X0QqlCsq0UBiscRVwhEOHMchEuHw/IlhMXPrzIgLbJhLO0gvILRvzydOPyumNwN8KxGOA17vLrwssIwD9Tlwfx276EA4wsW1iAGmLRW7l8Vjr5/HAy92w6RTQyODC2pxhQl1JQa8nWYNkZMsFSIFik3hEC2VQP7TalNxYcKDG//lz6i2GdDRUIJfHenD2qZSPHnHJWJgO9bHng5mnQajzvxaBDOfatc0lKDcrMMrp0fxufXppUfPF8FwRHwQSYdcBOoP905CxcQH6YFpN9d/vdeP/e/2YV1TKa5fXSvLMRmGwWUtFXgtTaGf8rFF2eSQmD+UKyoyFQBGIhyC4QjYcARsmEOE48AAUDEMGAZgwAAMoGL4D6iKAdQqBhqVSnwt2e8dcwdwatiFH/7hBEIRDoFQGL860oeVdTZ80O/A/32pG6eHXVhSacbiisx6O5n06rzHVJw+Nq6tjErF4NqVNTjw/iCc/sLqdJtxnUoOYirv9kxiZb1NrDMSENxfB94fhFWvwa+/dpmskxOX11rx22MDaf2bOH0sSuoL59+NKDwUKypW0VKZfWMNRzicH3Oja8iJfrsP/XYfRpx+OLxBTPlYTPlYeAJhBMMRyYOutGpGFBn+/ww0agYOLyu6TqqsevzrrRuwYVEZfv/+ILaurMF9z57E3tfOAwBuvyJxq5C5MOs0eY+pOBP437+4eRH2v9uHA8cG8JeXNednYwkIhiPQZhOol8n9FQxFcKzPji9uXjTrPcH95WPDWF5jlX0U76Ko8PdNelPG7rId0EUsHBQrKjNjKj3jHjx/Yhivnh7DB/0OeGJuuBVmHWpLDCgz6VBXYoTNqIVFr4ZOo4JWrYJOo4JOrRIbDnIc33k5wgEcprswR6KvhaOV1qFwRKy6Fv7PhiMIhTnYjBosKjehsdyETc3lorvu5k38iIBvX7cCb56dwIDDl1VrE5NeXQAxldCsJ9+OxhKsaSjBY2/0YOfmRQWTmhoMRaDPqE5FiKnII9zDU3742QhW1s2OnRm1anF8cU2J/F1rBWvy4sTcohIKR+AOkKgQc6NcUYnepF86NYr/OtqPd3v52ER7nQ07Njaio7EUqxtsWFRugklXeKehyqrHn759FU4PuzIqehQw6zQIhCIIhSOyBHQzJdkNiGEYfOuaNty+7wh+faQPt1yyeN73lohMA/U6mS0VVyB5o0aGYVBu0mHY6UdtBrVK6bKoIioqk3O3NXKILVoK7/NCFA6KvTrMOg3UKgaHukbQXGHCd69fgc+srUdDafHMfNaqVVkJCsCnFAOAJxhGiXH+RcUlVl7PvsSuXlGNTc1l+OkLZ7BtdZ2kHlZykW2dChuW5h4VECxqa5LRAKUmbVRU5LdUbAYtSk3alKIitNiptMovbIRyUKyoqFQMHr11I8w6NS5bWlG0swmyRbDUvMH03RUcx+GfDp3BmCuA+25cI+mcCd1sEwV+GYbBD29Yjc889Aa+818f4F9v2ZAXayqWTAP1gtuOlaljgyDCyTLQhLhKLtxfALC43JRaVFx8fUylhUSFSE5hOLRzxNaVNbi8tXLBCQoAmLLoKPDQy2fx0Mtnsf/dPvz++KCk46fqZtteZ8P3Pt2OQ10j+M5/fYCIxIQIqWTq/tKq+WtKLlEREkqSDTET0orrciQqTeUm9KUQlQlP1FIhUSHmQNGispAxR91f6TYL9LNh/OzNHlzTXo2OxhLc/+wpSTdMpy9148Evf2wJ7t66DL89OoC//8OJjAdGyUUkmkiRSdKAkCkmV5dioaN2MktFSCuuyYH7C+AzwPrtvjl75Y25eFGpIlEh5oBERaEIyQfpWiq/f38QDi+LOz7egm9uacPglB8HPxzK+vjpzt3471e34qsfX4J9b10QU6jnm2D0RppRoD4qQEG5LBUxppL4fAnur1zEVABeVEIRDkNTyefWj7uD0KqZhHEyghCgq0OhmPWZWSrPfjSExRUmXLKkHBwHtFSZ8W+vn8f2tfVZuQ/FmEqKGxDDMPi7be0YcPjwk+dP42OtlVknJ2SLIAz6jNxf0ZhKSKZAfYCFWsXAoE28h+tX1yEYikjuTJyM2FqV2ILVWMbdAVSY9QvSnUykD1kqCkWwVBIVf84kGIrgnZ5JXNlWxXcFUDH46sdb8NGAE2+dn90TaswVgD/FfPZMJgQyDIP7Pt8BvUaFJ9+5mHK93AgurEwsFbWKL2qVLabiD8Fq0CS9Ya+st+G729pzdkMXa1XmiKtMuAOotOY/U48obEhUFMp09ldq99fRi3Z4g2Fc0VYpvvb59Q2otOhw/7On4qwduyeIK3/8ClZ87zm8NsfUwCkfC62agTGmhftclJi0uLq9Bi+cGJbcxSBTRFHJMANNq2Zkc3+5AqGkQfr5oL7UCI2KmVNUxt1BVJgpnkLMDYmKQjFF3V/pVNX/+dwEVAxw2dIK8TWDVo0ffW4NPhyYwpU/fgUPvdSNSITDm+fG4YtaKf/4wumkwXW7J4jSmLb36XD96lpMeII4PM8zV7KxVABehGQL1PvzKypqFYPGMmMKUQlQ5heREhIVhWLSCjGV1JbKySEnllSaZ9WUfGp1LX725U3oaCzFTw+dwY+fP43Xz4zDZtDgH25YhQ/6p8ROBTOxe4MZz/u4clkVVAwSutxyRTAUwbCTD05n2jJGp1HJ7v7KJ01z1KpwHIcJd5DcX0RKSFQUikatgl6jSqtT8alhJ1Yk6DkFAFctr8bjuzqxY0MjHn/jPF7oGsbHWiuxY2MjNCoGr5weTfhzdi+L0gwnE1r0GqyoteHoPM2xH57yY80PnsfOvW8DyNxS0aplFJU8u78AoLXagjMjroRpxaOuAILhCOpLiqcjBZEfSFQUjFmvSTmn3uVn0TfpS9jIUIBhGPztp5ZDr1EjwgFfuqwZJp0GHY0leCeJVeHwBlGexbjbjYvLcOyifV7iKn/8YDCud1c2oiKX+8sdCMGS51EA65pK4WcjODMye7SwMG64pSqzEQzEwoNERcGYdKk7FZ8ZcQEAVtRa51xXbTPg5f/5Cbz93S1i7GXzkgp80D8FXwIX26SHRZk585vkxsVl8ATDOD3syvhnM2VmM8hMuhQDgvtLHvHLd0wF4EUFAI73O2a91zPuAQC0VFnmcUdEMUKiomAsek1K99fJoaiozGGpCFRbDTDqprO5LmkpRyjC4djFeHcVx3FweIMZu7+A6amHRy/m3gU203WVlaUim/uLzXtMZVG5CaUmLY73OfDbo/345dsXREvs/JgHBq0KdTkqviSUA4mKgjHp1CkD9RcmPNBrVKjPoqdUR7RIsWvIGfe6OxBCKMJlHKgHgMYyI6qs+nmJq0gVFZ1anjoVNhyBn43k3VJhGAadi8vx22MDuPvXx/G/n/4I33v6IwC8pdJcYYZKRYWPxNyQqCgYs16T0v3Vb/ehscyYVVFdhUWPSosO3TN88HYPX/hYloWlwjAMNi4qw3vzYKmEZriu8hWoF1q05FtUAODez6/G1pU1+MLGRnxuXT0OfjgENhxBz7gHS8n1RaQBiYqCScdS6bMnb8uRDm3VVpweiY9/2L18i/RsRAXgXWAXJrxiA8NcEQxHoI558s4mpViOQH0m3QdyTY3NgEf+2wb85Atrcf2aOrgCIRz8cAgXJjxYniLuRhAAiYqiMetSx1QESyVbltVYcHbUHVcEKYpKFoF6AFi/qBQAcLzPkfW+0oENR+Ju5JlX1KsQlCFQL4jKXB2d88EVrZXQaVT45v73EeGAbWtq870logggUVEw/Jz65JaKy8/C4WXRWJa9pbKs1gp3IIQLE168cmoU4QgHhzd79xcwnTRwckasRm7YECfORQEyaygJRN1fMlgqQvPNQrBUYjHrNfjy5c0AgDKTFq3VZKkQqcm/E5fIGaliKv12HwBIslRW1fPB+tv3vYtzYx58fn0DllTytQzZiopFr8HiChNODudYVCKROJdXxoF6jTyB+kJyf83kO9ctB8dx+HhbVb63QhQJJCoKxqzTIBCKIBSOJBzXK4hKkwRLZW1jCa5cVoXXzoxhSaUZvzs2AADY3FwuDpbKhhW1Vpwaym2tChvm4lxeWfX+UrioaNQq/K9Pr8z3NogigtxfCsYkTH9M0qZ+wM73eWqQYKkwDIN7P7caN21sxK92X4rvXr8CzRUm/PTmtZLatLfX2dAz4Ul7Hkw2sKEINDHur2xiKnK4vwpZVAgiU8hSUTBmof19IDyrWSQAjLgC0KiYrNqpxNJUbsI/fmEtAOBrn1iKr31iqaTfBwAram3gOKB7xI210UpvuQnNcH+pM6zB0GrkCdQ7fSHo1KqkA7oIopigq1jBCJZKsgywEacf1VZ9QRa0La6ITiK0J2/FLpVgmJ9Lv/vKFgDI2LLSyVSnMuVjYTMmH9BFEMWEJFGZnJzE1q1b0dbWhq1bt8Jun12w1tfXh09+8pNob2/HqlWr8OCDD0o5JJEBZnFOfWJRGXMFUFWgbTcEl9xANO6TC9hQBFo1P864d8+nM/55uepUnD624NKJCSJbJInKnj17sGXLFnR3d2PLli3Ys2fPrDUajQY//elPcfLkSbz99tt45JFH0NXVJeWwRJoI7q9kacWjzgBqrIU5dMlm0MJq0GDAkTtRmen+yhStTG1apnwsxVMIxSBJVA4cOIBdu3YBAHbt2oWnn3561pq6ujps2LABAGC1WtHe3o6BgQEphyXSxKwXBnUlcX+5/Ki2FaaoAEBDqRGDORQVwf2VLVq1CqEIh4jENv0kKoSSkCQqIyMjqKurA8CLx+ho4oFNAr29vTh27BguueSSpGv27t2Lzs5OdHZ2Ymws+Qx0IjUmwf2VoFVLIBSGw8ui2lqY7i+Ar5/pz7n7S5qoAHy9ixScfhIVQjmkzP665pprMDw8POv1e++9N6MDud1u7NixAw888ABstuRt1nfv3o3du3cDADo7OzM6BhGPaKkkiKkIfbVqCthSqS814p0czqtnw5G4ivpMESrw2TAHKb0gyVIhlETKj8KLL76Y9L2amhoMDQ2hrq4OQ0NDqK6uTriOZVns2LEDt9xyC2688cbsd0tkhBBTcScQlREnLyqFbKk0lBrh8ofg9LMJU6KlEopId38B/Jx7ZKnNHMfBSaJCKAhJ7q/t27dj3759AIB9+/bhhhtumLWG4zjcfvvtaG9vx9133y3lcESGWHQaqBiIvbhiGXP5AaCwYyrRDLBcxVWCcrm/JATrfWwYEW76AYAgih1JonLPPffg0KFDaGtrw6FDh3DPPfcAAAYHB7Ft2zYAwJtvvolf/OIXePnll7Fu3TqsW7cOBw8elL5zIiUqFYNSk07sGhyL4P6qKtDsL4Bvww7wWWq5gA1HoNNk7/4SXGdS0oqFUcymmImaBFHMSHo8qqiowEsvvTTr9fr6elE4rrjiiri26MT8UmrSJrZU3EEwDCRX0+eSKgsveOPu3IhKKMJBo8r+uUqnkW6pCPNuDFoSFUIZUEW9wilLYqmMuwMoN+kSNposFAQrKlfDuqRmfwm9wqQ0lfSzZKkQyqJw7yiELJSZtLAnsFTGXQFUWgrX9QXwcQajVp0zUQmGI9BKcn9FLZVQ9pa4LyoqRrJUCIVAoqJwSk06OJJYKpXWwnV9CVRZ9RjLkfuLDUcy7kwci+D+kmKpCO4vEhVCKZCoKBzeUkkkKsGCt1SAqKjkwFIJRzhEOEiKqciV/QUARnJ/EQqBREXhlJp08LMR0XcvMO4ufPcXwAfrcyEqghBIcX8JmWNSsr/8QRIVQlmQqCgcYaRvrLXiDYbgDYaLQ1Ry5P4SREWS+0vNC4EUUSH3F6E0SFQUTll0pK/dMx2sH3fxAlNpKY6YisPLIhBK3Gk5W0LR4VpSsr/00aFaASl1KuT+IhQGiYrCKY1aKg7ftKUiPPlXFnDho4CQVjzunh0XkoJgqWhk6P0lRfD8lP1FKAwSFYVTZuYtldgCSKGYsKoI3F9Cw8sRp1/W3ytkbEmxVISCRT9L7i+CECBRUThCxfykZ/pJXxCVYoipCK1aRqbkFRU26v6SElORw1LxsWHo1KqCLkIliEygK1nhlJl5UYltdSLEVCqKIKZSGxWVYZktlZAMlopew1sXkmIqwTAMWvoYEsqBrmaFo1WrUGbSxouKO4BSk1bSDXW+KDfroFOrZBeVoIwxlZnp2pngC4YpSE8oisK/qxCSqbToResEKJ4aFQBgGAY1JXoMF6D7S6VioFOrJGd/CRM6CUIJkKgsACot+lmWSjGkEwvU2gyyi4oc7i+At1YCEgP11KGYUBIkKguASutMUSmOFi0CtSXGnGV/SXF/AXytil9iSrGRYiqEgqCreQFQadHF1XkUQ4fiWGptegw7/bLO5WFlKH4E+GC9FEvFx1JMhVAWJCoLgEqLHu5ACH42DD8bhisQKuiJjzOpsRngZyOY8s1u4Z8tbEh6mxaAt1SkpBR7g2EYtRRTIZQDXc0LAKHIccwVABP19hRTTKUxOqt+wOETOwRIJRSR3lAS4C0VKcWPfrJUCIVBlsoCQJibMu4OiG6wYnJ/NZaZAAD9dp9svzMYdX9JaX0PAAaJloovSDEVQlmQpbIAqLRM98+KROMSxeT+aijlLRU5RUU295dGWkqxNxiilGJCUdDVvACoj96U+ya9EELdwtN/MVBq0sKsU6Pf7pXtdwruL8nZXxp1wsma6eJnI2K3Y4JQAiQqC4AKsw5lJi26R90waFUw69RiS/xigGEYNJaZZLVUBOtCqIrPFt79lZ2lEo5wCIYj1EySUBT0iLQAYBgGbdVWnB11od/uQ2OZCQwj7Ql9vmksM2YsKod7JuHyJ84YE9KA9RJv6HqNOmtRCYrCRqJCKAcSlQVCa40FZ0bc6Jv0itlUxQQvKum7v9yBEL74b2/jl29fTPi+EFw3SLRU9BpV1r2/xD2Q+4tQEHQ1LxDaqi2Y8rE4NexCU3nxxFMEGsqMcPlDadeqjLkCCEe4pELkZyNQqxjJLef1EtxfQioyWSqEkiBRWSC0VVvFr4vRUqkr4fc8NJWeC2zMxbelGXEmnm8fCIUlx1MAwKBRIyDRUpFjHwRRKNDVvEDobC4TA8LFKCr1pfxclaE0G0sKvc6S9QwLhCKy3Mz53l/SLBVqKEkoCRKVBYJBq8ar37kKu69sweWtlfneTsbURi2VdLsVC6KSbA5LgI3IcjPXa9QIRzix63EmkKVCKBFKKV5AVFsN+Ltt7fneRlZUW/VQMcCQIz3313jU/TXuDoANR2Y1jvTL5f7SCiOFIxnHZ4RYDFkqhJKgRySiKNCqVaiy6tN2f41FLRWOix+lLBBgI7IEyKWMFBayxqj4kVASdDUTRUNtiTHtscJjMZMuE7nMAqGwLDdzKSOFxVoZcn8RCoKuZqJoqC8xYDBd95c7gHIz30gzUbA+EIrAIIelEuP+yhS/WKdC7i9COZCoEEVDbYkBQ1PpDesadwewqt4GYDq9OBY/K4+lYhDdX2SpEARAokIUEc0VZniD4aS1J7GMuwNYFC3y9ARn3/DlTCkGkNVMFQrUE0pE0qdqcnISW7duRVtbG7Zu3Qq73Z50bTgcxvr16/GZz3xGyiGJBczKqOXRNTQ15zo2HIGfjaDGxte2+JKKivSbuWCpZBNTEQP1ZKkQCkLS1bxnzx5s2bIF3d3d2LJlC/bs2ZN07YMPPoj29uJMZyUKgxW1fFeAEwPOOdd5oyJi0qlh0KrgS3DDlytQb9bzWfmeQCjjnw1QQ0lCgUj6VB04cAC7du0CAOzatQtPP/10wnX9/f145plncMcdd0g5HLHAsRq0WFxhQtfQ3KLiE0VFA6NWndBS8cuUUmwx8KLizkJUyFIhlIikq3lkZAR1dXUAgLq6OoyOjiZc961vfQs//vGPoZI4upUgVtXbcGIwlaXC3+BNOjVMOo1oucQSYOUpfrRGLRWXPztLRadWQaUqrjEEBDEXKSvqr7nmGgwPD896/d57703rAH/84x9RXV2NjRs34k9/+lPK9Xv37sXevXsBAGNjY2kdg1g4XLKkAgc/HMbxPgfWNpUmXCO4uwxa3v2VKN4RCMkzcdFq4IedZScq8ggbQRQSKUXlxRdfTPpeTU0NhoaGUFdXh6GhIVRXV89a8+abb+L3v/89Dh48CL/fD6fTiVtvvRW//OUvE/7O3bt3Y/fu3QCAzs7OdP8OYoFw44YG/OT503jizR48sHN9wjW+mJiKSaeZFVPhOE62OhWDVgW1ioE7kF5L/lj4UcIUTyGUhaTHpO3bt2Pfvn0AgH379uGGG26Ytea+++5Df38/ent7sX//flx99dVJBYUgUmE1aLF9XT2ePzGCcCRxvUpsoN6oVYvuMAExQC6DpcIwDCx6DdxkqRAEAImics899+DQoUNoa2vDoUOHcM899wAABgcHsW3bNlk2SBAz2bCoDD42jJ5xT8L3BVEx6tQw6tTwzaghkTvryqLXwJVN9hcboamPhOKQ1KW4oqICL7300qzX6+vrcfDgwVmvX3XVVbjqqqukHJIgsLJOqFdxorXaMut9IYZi1PKWyszBXnK3nLcaNBJiKuT+IpQFPSYRRUdrtQVaNYOuJFlg3tiUYp16VkwlIPNwLKshW/eXPMkCBFFI0BVNFB06jQrLaqxivcorp0fxRve4+L4QQxHdXzNSiuW2VCx6TdZ1KnIkCxBEIUGiQhQl7XU2nIyKyleeeBe3Pv6O+J5vRqB+pqj4ZW7kaDFosxIVslQIJUJXNFGUtFVbMOYKYDRBW3sfG4ZGxUCrVsGkU8PLhuM6G09nf8nn/nL5M08pDrDypDUTRCFBokIUJW01fID+uROzC3O9wTCMOv5mbdCqwXHx804CQnGkXIF6fXaBer9M/ccIopCgK5ooSlqr+OaSfzw+JL4mxFJ8wTBMUVExamd3EZbbUrHoNQiEIghmOKiLH2lMH0FCWdAVTRQlDWVGGLQqHO6dFF8bj44Q9rJhmHR8trwgLrH9v2QP1Buy61TsD4VplgqhOEhUiKJErWJQZtLFvTbm5od3+YLTN2vBDeZLZKnIVqeSXf8vslQIJUJXNFG03LSxEe11NvzzX6wFwE97BAAfG5rl/orNAPPHNJyUA0u0U7Ezg2A933+MLBVCeZCoEEXL31y7HM9+8+O4fGklgOlZ9N7YmMo8WCo1Nj0AYHhqdiZaMtgwhwhHs1QI5UFXNFH0lJt5N9h4AvdXwpgKK2+gvqncBADos3vT/pnpuA5ZKoSyIFEhih6tWoVysy7G/TVtqRgSuL+EG7pcKcUVZh2MWjX67b7Ui6P4xVYx9BEklAVd0YQiqLLoMRC9qXsTpBT72Okgup+NQK1ioFHLc/kzDIPGMiP6yVIhCBIVQhlsWlKGt89PwhsMweVnYY6mFJujQfSZKcVyxzIay4zom0zfUpFzpgtBFBJ0RROK4NqVtfCxYTzxZi/8bATt0fb4YkwlEB+ol1tUmspNGVkqQgYaWSqE0iBRIRTBpS0VsBo0+MnzpwEAnc1lACAWQcY2fPSz8qfyNpYZ4fSHMOVLL62YLBVCqdAVTSgCnUaFHRsaxe8XRTOy1Cpm1kjhXFgqi8rNAJB0GuVMxFoZslQIhUGiQiiG269YAgCoKzGAYRjxdbNeDXcgPqVYbrfTilq+F9np4cSDw2ZClgqhVCSNEyaIQqKp3IS9f7kRSyrNca+b9ZoZlor83YEXlZtg0qlxcsiV1vqAzDNdCKJQIFEhFMW1q2pnvWbSaeAJxLZpkX+OiUrFYHmtVRwclgqxVobatBAKgx6TCMVj0avjOgjnwlIB+GmUp4ZdcQPBkkGWCqFU6IomFI9JN9P9lZvuwCtqrZjysRiN9iCbCz9ZKoRCIVEhFI9Fr4lLKeZFRf6beWOZEQAw6EhdBEmWCqFU6IomFI9Jp46rqPezuXF/1dp4UUmnWzG1aSGUCokKoXjMes2MmEpuLJW6EgMAYCgNUfGzEagYQKtmUq4liGKCRIVQPGa9Gp5gWAygB1j5e38BQKlJC71GhWFnepaKXqOOq6chCCVAokIoHpNOg3CEEwsOA6FITtxfDMOgtsSQlqUSCEWo7T2hSOiqJhSPMO7XEwhFx/jKX6ciUGszYCQt91eY4imEIiFRIRRP7PTHXLdHqSsxYMiZRvZXjqwlgsg3dFUTikeYqeIJhmLm0+fIUikxYmQqgEhk7gJIPxumZpKEIiFRIRSPOcb9FRDnmOTm0q8vNSAYjoijjZNBlgqhVOiqJhSPOer+8gSm3V+5qmRvirbcvzA598CuQA76jxFEIUCiQiieOEsllFtLpbmC75B8YWJuUfHnqP8YQeQbuqoJxSPMq/cEw/DnuD1KQ6kRKga4ODH3sC5+pgt9/AjlIemqnpycxNatW9HW1oatW7fCbrcnXOdwOHDTTTdhxYoVaG9vx1tvvSXlsASREWa9kP0VY6nkyP2l06hQX2pEb1qWCrm/COUhSVT27NmDLVu2oLu7G1u2bMGePXsSrvvmN7+JT33qUzh16hSOHz+O9vZ2KYcliIwQ3F/uQEhs5GjIoZXQXGFOK6ZClgqhRCRd1QcOHMCuXbsAALt27cLTTz89a43T6cRrr72G22+/HQCg0+lQWloq5bAEkRF6jQoqBvAGYutUcmclLKow4ULU/fX/Xj2HO/a9O2uNnw3DSJYKoUAkicrIyAjq6uoAAHV1dRgdHZ215vz586iqqsJXvvIVrF+/HnfccQc8nrn9zQQhJwzDwBxtf5/rQD3Az1VxeFmcHXXh8Td68OLJUXSPxI8Z9gbDYlEmQSiJlJ+sa665BqtXr57134EDB9I6QCgUwtGjR/H1r38dx44dg9lsTuomA4C9e/eis7MTnZ2dGBsbS/8vIYg5MEcHdeU6UA8A166sBcMA33v6hDiw65kPh8T3IxEOPjYMo46meRPKI+VV/eKLLyZ9r6amBkNDQ6irq8PQ0BCqq6tnrWlsbERjYyMuueQSAMBNN900p6js3r0bu3fvBgB0dnam/AMIIh2ETsXzMRu+tsSATYvL8db5CVj0GiytMuNQ1wi+dc0yANNTH8lSIZSIpMe17du3Y9++fQCAffv24YYbbpi1pra2Fk1NTTh9+jQA4KWXXsLKlSulHJYgMkaYqTLdpiW3QfK/vroV29bU4pd3XIINi8vQM+4RW+8LA8NIVAglIsn+vueee3DzzTfj8ccfx6JFi/Cf//mfAIDBwUHccccdOHjwIADgoYcewi233IJgMIiWlhY88cQT0ndOEBlg0qn5QD2b+0A9AFy5rApXLqsCABzpnYQ3GIbDy6LMrIMvKioUqCeUiCRRqaiowEsvvTTr9fr6elFQAGDdunU4cuSIlEMRhCQseg2Gpvzw57j3VyIay/jWLQMOH8rMOniC/BRKE8VUCAVCifLEgsCkm3Z/qVUMtOr5FBV+dn2/na9dIfcXoWRIVIgFgVmvEQP18110OC0q/JwV0f1FokIoEBIVYkFg1qnhCfApxfMtKiVGLcw6tSgqZKkQSoZEhVgQmPQaeINh+PIwxpdhGDSWmTDgEERFiKmQqBDKg0SFWBBYok0lHV4Whjy0nG8oMyZwf1GgnlAeJCrEgkDItJr0BObdUgH4uMrAzEA9pRQTCoREhVgQWPSCqATzMhyrodQIpz8Ep5+Fj6VAPaFcSFSIBYEQv5jwBPPScl6sVbH74A2GoGLmt1aGIOYLuqqJBYFgqbj8oZz2/UpGQzStmBeVMEw6DRiGmfd9EESuIVEhFgTCoC4gPxZCbAGkLxgm1xehWEhUiAWBzagVv85HoL7CrINBq8KAg7dUzCQqhEKhnEZiQVASJyrz/yzFMAwaSvm0YjbMUToxoVjIUiEWBDZDjPsrT6m8TeUmXJjwwseGqPCRUCwkKsSCQKNWicH6fGVdLa2yoGfcA0+ARgkTyoVEhVgwCC6wfNSpALyo+Ngwzo25aZYKoVhIVIgFg9UgWCr5uaEvrTID4NOaF5Wb8rIHgsg1JCrEgkFwOeWj9xcALK22iF9/ckV1XvZAELmGRIVYMAhFj/myVCrMOvHrTc3ledkDQeQaymskFgxCgD5fgXqGYbB5STmMWjV01KKFUCgkKsSCYdpSyd8N/ddfuyxvxyaI+YAel4gFgyAqKuq5RRA5g0SFWDAIAXp/KJznnRCEciFRIRYMQoA+wEbyvBOCUC4kKsSCYXEFXxsS2weMIAh5oUA9sWD40mXNqLYacP3q2nxvhSAUC4kKsWBQqxh8uqMu39sgCEVD7i+CIAhCNkhUCIIgCNkgUSEIgiBkg0SFIAiCkA0SFYIgCEI2SFQIgiAI2SBRIQiCIGSDRIUgCIKQDRIVgiAIQjZIVAiCIAjZYDiO4/K9iWRUVlaiubk5658fGxtDVVWVfBuaZ4p5/8W8d4D2n2+Kef/FvHdg7v339vZifHx8zp8vaFGRSmdnJ44cOZLvbWRNMe+/mPcO0P7zTTHvv5j3DkjfP7m/CIIgCNkgUSEIgiBkQ9Gisnv37nxvQRLFvP9i3jtA+883xbz/Yt47IH3/io6pEARBEPOLoi0VgiAIYn5RpKg899xzWL58OVpbW7Fnz558byctmpubsWbNGqxbtw6dnZ0AgMnJSWzduhVtbW3YunUr7HZ7nnc5zW233Ybq6mqsXr1afG2u/d53331obW3F8uXL8fzzz+djy3Ek2v8PfvADNDQ0YN26dVi3bh0OHjwovldI++/r68MnP/lJtLe3Y9WqVXjwwQcBFM/5T7b/Yjj/fr8fmzdvxtq1a7Fq1Sp8//vfB1A85z7Z/mU995zCCIVCXEtLC3fu3DkuEAhwHR0d3IkTJ/K9rZQsXryYGxsbi3vt29/+NnffffdxHMdx9913H/ed73wnH1tLyKuvvsq999573KpVq8TXku33xIkTXEdHB+f3+7nz589zLS0tXCgUysu+BRLt//vf/z73k5/8ZNbaQtv/4OAg995773Ecx3FOp5Nra2vjTpw4UTTnP9n+i+H8RyIRzuVycRzHccFgkNu8eTP31ltvFc25T7Z/Oc+94iyVw4cPo7W1FS0tLdDpdNi5cycOHDiQ721lxYEDB7Br1y4AwK5du/D000/nd0MxXHnllSgvL497Ldl+Dxw4gJ07d0Kv12PJkiVobW3F4cOH53vLcSTafzIKbf91dXXYsGEDAMBqtaK9vR0DAwNFc/6T7T8ZhbR/hmFgsVgAACzLgmVZMAxTNOc+2f6Tkc3+FScqAwMDaGpqEr9vbGyc84ItFBiGwbXXXouNGzdi7969AICRkRHU1dUB4D+Io6Oj+dxiSpLtt5j+TR5++GF0dHTgtttuE10Yhbz/3t5eHDt2DJdccklRnv/Y/QPFcf7D4TDWrVuH6upqbN26tejOfaL9A/Kde8WJCpcgmW0uJS4U3nzzTRw9ehTPPvssHnnkEbz22mv53pJsFMu/yde//nWcO3cO77//Purq6vA3f/M3AAp3/263Gzt27MADDzwAm82WdF2x7L9Yzr9arcb777+P/v5+HD58GB999FHStYW2dyDx/uU894oTlcbGRvT19Ynf9/f3o76+Po87Sg9hj9XV1fj85z+Pw4cPo6amBkNDQwCAoaEhVFdX53OLKUm232L5N6mpqYFarYZKpcJXv/pV0cwvxP2zLIsdO3bglltuwY033giguM5/sv0Xy/kHgNLSUlx11VV47rnniurcC8zcv1znXnGismnTJnR3d6OnpwfBYBD79+/H9u3b872tOfF4PHC5XOLXL7zwAlavXo3t27dj3759AIB9+/bhhhtuyOc2U5Jsv9u3b8f+/fsRCATQ09OD7u5ubN68OZ9bTYhwUwCA3/3ud2JmWKHtn+M43H777Whvb8fdd98tvl4s5z/Z/ovh/I+NjcHhcAAAfD4fXnzxRaxYsaJozn2y/ct67uXMLCgUnnnmGa6trY1raWnhfvSjH+V7Oyk5d+4c19HRwXV0dHArV64U9zw+Ps5dffXVXGtrK3f11VdzExMTed7pNDt37uRqa2s5jUbDNTQ0cI899tic+/3Rj37EtbS0cMuWLeMOHjyYx53zJNr/rbfeyq1evZpbs2YN99nPfpYbHBwU1xfS/l9//XUOALdmzRpu7dq13Nq1a7lnnnmmaM5/sv0Xw/k/fvw4t27dOm7NmjXcqlWruL//+7/nOG7uz2qh7J3jku9fznNPFfUEQRCEbCjO/UUQBEHkDxIVgiAIQjZIVAiCIAjZIFEhCIIgZINEhSAIgpANEhWCIAhCNkhUCIIgCNkgUSEIgiBk4/8Hg0AB5n7kl2wAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f08c9f4c9460490db32ca8d27d17c1de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2949705e0>]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "321eef97867d4c12ba9f644f185a8a5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c070170998234468b715173bb30d07e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bb417f981474f438e30ffcbc144f0c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37b813a48ce34cfbb803a0fd98d02188",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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P4EM33G89hubZTDtUJno3u9FBzFlUFrzP/Ll/4Pp7ja7W/XOrnQuBkfwdpOei94U+T/apAmEcdy6Qi47kfTaxUGH0Ltposnb2T/VBQMblHDUGFcMrMJiQCKO40CA5115SE6nAOFSSYfglymDS/bbDGC12XDdZVIdZYKgqKfn9tbc8ge/cnxUYdEyvKqm5LKA0SNC71O/x6NQpoUwcBv/dNKbSrTZf6PH3cdfhRqn+5bbXg91xEDC0AqMdSQ+hMC7DMGJx3lxAMIyStJ0mcN5kIsHQCiO0GFfuZf4NYyAjF5B6gRvT/cQ9BmLMZXpzIcgH6LlkVFJ9br9TV+IybrUKC80TGCWN3kcvGcHOg/0QGIOlbuwUQyswwihSfPGLBAHfnefh1sf24fs/39Vz/4QNo6RKqkzVsZgLjB5VUrLNrk+dN0QWdmWrkdFrapBuVCbdYraLFXWDrNG7v53r1OjdSS4pwKwCqgSdGb3XLB7pC8OI4WwY84J2KOMw2lFxEJ88Nv+4Kz7+I7zpn2/puX/jnTIMoZKyH8MNolwl1cviMoz02OYlZbNl0S12e6dz6TAxiF5S+gLH52g/Fj9qoTTD0K7702178cTeKfWYIobhdeZWe/SSEezqQy2dYc//NrQCg7vVhlHxy0xxB3NlwxipEsMoZ8MgimxTe8RakB7PutvLBBxGlZTJSypO1ZKmWIlO/fyz15u7RZzbliiWZ76RYRjs/34YvjtNuKgH7v3KP9yM5/35d7Rjsu1zlE4NojGMXgVkHMdzkmJmtjDcAkPs/IoZRqf1MHqdGDRJyzKMIpVU1vDIfutBBg6hvDB6SeUZ8eUOtrubnVOjd/osv3XfTrzgz7+D3X1Qg/SKDMNgn/vhWttxHEbcu5dUUDY1CGMYrTDGgZIqZhvK2F8GGUMrMNosNUjiJVXOCFY2wvlQj1GsdL2yXlLipbHcR57BtTcbxvBNXpOXVJizqMvAve4QzaFKiu5j9+EG2lFcev7MJvJsGP3oX6d2mzKeRgoLzVFTFqcGSf4etWQEALDzUG8C3MVhzBO4SiqKi1/mThnGvi6S8oVRjJ2HZtL/k+/KGr1tAVu3PLoXr/rbH2K6ZZ/YPamkhnDyKgJDExSm+xGH96iSmsvAvUFKx28L3AP6IzDEdcqyfxSrF4tsGPRdoZcUU0kBwK6eBYaLw5gXtKNIpMVoR1FhHEYnBXcAYG8XAuP6u7bj+X/+HRxutDtWSdlSddy3/SDueupArgDryeg9hJPX5DIpIteNbrX0t7uBmku3WpnuJnkwgygweJdafUiw1bGXlMhwYD+e/2Ka49RGoZdUFMP3gNUTSXof2hB2g06z8g4ihlZgcN11VMLo3THDmOpcYOw+1MBMK8Jkoy36U9bobVN70OTKi0jtheIO4+RVXSZVVWOeSqpb4TiXbrW6TabTax6aafW/n9xepqmD+pH9uVOVlNgA5Byv2DDyGEYJo3fge5gYqQIADvfAqKRg7LqJecfQCgyeCymMi20Y8gUst2rsnezcuEXXaIVR1wxDn9u8TRsWslutrsIxGr3Tr7qO9J5LGwY975z7sSGOY7zo/30Xn7klP3Nzp9DtAbxH/Uj53o3Ru+h4kw0jjqXRml6nZgmjt+95GKkmS+VMq/v7lYKu/DPdc7iBmx/e0/U1+42hFRi0o4yEO2U5gdFO7QwfufHnxgdHOWb2d8EwuOGVJvNMju2Bw5a/XzKMPIHRC8Po+tR5g0kllWdnGKYCSvp9dJKZoBXG2H24iZ19iBcw9Yn6w9+bA9MtfPrmbT2x3DKMQTm+hBMD7w/1/9v378SF//dGHJhqSZVUiYp7ge8JN/my77MJ3bhn/8ant+L1n/hxT9ftJ4ZWYLS1nXdZlVQYxfju/bvwkRsfxJP7sn7utUoyJN3YMCQbkAKsbA59m1so3VaeSqqXne8g6Mg7hclLKi/7bq+qAKGSmhMbRvK3G5VU2Xeh8z6pAoN//sY9O/B/rrsHj+2ZMp3aUfvlI72Tv3mH899oPJ7cN42ZVoQ9kw1m9C7wkopjBJ6HauAj8D3M9JCxtptSvxSLowcmzheGVmDwxRkokxokzVYbxhlhw0HfdWPDaIu+RGIhK8qGKfpn0eOWETy9rGPD6OJn9JIK7YuO/K67e+WR3v/248e68qArC53FdLIbbefYcXqBsviGatAc1cTgO/W7nzqAL9z6ZAcXSP50VA8D+S6qJhsG7dKnW2Fpo3cYxfBTrcNIxe9NJSUEXfnnc/SSUQDoSSD3E30RGFdffTVWr16Ns846qx/NlQLpTsvuqkKxE42sPvtRFIsFviuGITxcYsWoVmaCWFVSpWwYC0slpSxgwkvKPg86VXnooPHdfbiJP/ny3fj63fnlgHuBzmI6MSo354BhtKJImTO0CPP5+R9bn8D7rr+34/bLx2HI/233qgqM5C+5ps+0ovJG71QlBSTZG3pRDXVqqwGSgEEAeOxIYhhvectbcMMNN/SjqVLgaTJohxBG+QExIVtQIo2dEPjk6Sb3fciEmFLasYTroc2tlprJMy4utNQgSrZaTdDmekl1OU76ebOpT7Z5yZWBEJolz7njif34+HcfVr7bP9XEAW3u8y7p1S1n2pK5y37EHZUAzjvy0zdvwwl/cL3KvNjCa0+lI/+nMSV2MNMKpYYix+h93R1P4b7tBzWB0T3DEIKxgyYoJ93jeya7vm4/0ReB8fznPx/Lly/vR1OlYFuM8xa/UCy80r6gL8K8rW7SoEsWowqvMnYMmdZa+17oWu396WXNH3aVVBjFuP7O7bj5kb0AirykVLTDqFRqC73NfpTqtF5Le5gd2TDaWaF5YKqF3YcbeOu//DQjCL76s6fx0W/9XPlu43v/G+e+95vKd3yOtKNIGchGKjz5uxSX8FpU27f/9t6v3Ju2n1UxxbF98TWxEKGSakqVVCNnM/d7196B2x7fLxIV1qt+TzYM6lJnm4Dk2EFhGHNW03vLli3YsmULAGDXrt7Sh/PJ09IW+UpgPkdhGJbYBr6wdycwGMPgNL7Ebstm+Cujkup00VcW3CEUGLp++rc/c5v43IlK6l9//Bj+8XuP4Md/9Iv519OGvqwjQzfQn0dHAkNTy9391AG88m9/iJXjdew+3MDntj6Ozc8/WblWmebzGEbDwDDKxEWp7duPpfeQO/Pyhdd2pslLitswykZ6AzLv1EglEAKyG3TjrUf9e3xABMacGb03b96MrVu3YuvWrVi1alVPbfHJqEzUvIlHBukwtqovlLKnXQgM7onF13fbpPytf78VX79re3pOel2LW22+SqqzfnLhM4QaKY1haL8Z7ke8qNr3zxxslIrc1RfxftR2tl5Lv58uvKTofqk+PCUwpKqOhGSHbm9/56EZTDXbGsNQhcyMYBjqe9jJRqTMkbw5/jw7sWEoAkPEYRQ/Sz9dJetVvyd2GVve8TyQ1uOZA/11le4WQ+klxSdnoyQr4EJCqI60t7NXhkEvXzuMlRfRJjD++95ncNvj+7T+qceIKnu5KqnO+qoKjOGTGJG2ONl+I9i8U6J0h13E0PTfZ1UlpTOMLryk2hbPQV1gcLZtwgUf+BZe/bGbVIYRRcpuf9pg9KYKmHEc41M3PYpPfP+R/I6XcQphx8jnaRd4/FsSKtTXRisU7ZWxL5JKaqTSo9GbeXeVBY3rXFUKLcJQCgy+q1Amao7qR6YGkVHYLe0hKAtpDwxDV0mZJmUcx2m8hno92wKYN7E7FxhcfVDu3HYY4dkfuBFfvv2pjq41G+BdziywOUZvfZhkBHD+9fShpw3Ao7sn8fw//05fA+X0/nfCMISXlNh8qB33VXkhBKYJNC8e3Hk4kwrEyDDYnKLDwyjGe75yLz7wtfty+92JWixWBIedyZjmCBmsp5nRu4xKSrjVVntzq6U+dbK0cKeeQUBfBMbrX/96POc5z8EDDzyAdevW4ZOf/GQ/mrWCq2d07wwbeCCUMHprKwHtHGuB31XKA6k+SnZu1ZwykHqqkkIvqVwbhqEvUYz3fuVePGbwruhGJTXdCrHrUAN/+MW7yp3QJdphhM/e8niBAwNXSZlVeByx+Gs+tmgXnzV6J4vkQzsP4/G9U33VL5cRgDZQ2V6bF6CvSQzptZO9Bk/vrwfu8QknbBgG1tfP2jMm/X8uwzDMkWlh9I6sY2SCYBg9uNVe8v++i99JbW0mr7s/+tJdRld+zjAGwUGlL0bvz372s/1opjRULyn5AK+/82lsOmE5zjpmifUc1UtKfQC0QxutBV3p9kl4tcJkQo5UArTCtpEdCHuH9tLadph5E9u0SO481MA/3/Qojl8xhjc/d5HyW7OLeuBe+tLkpVnvBz75w0fxwa/fDwB4/QXHGY/Ry7Jy5KUG0fcAfMyrFmcJwK6SIiFeRq1RFj0F7mnzWh8LXSVF4xHFMXyov9nS8odaHAZ1j2+w6PedB8ulAi/zrpn0/2XdaoVHlMHo3bIwDP4cFLfaLu1Xj+yexCO7JzP3ACQbj8/85HG84NRVuGzDUcpvTcVGCwQaS5xrDKVKirMK7m76Z1+5F//yo23mc9hLJFNIawIjnTyj1aArhqEH7o3UAqVdc38g+gWYKuuVUUllvzPplgllVG/fuu8Z/HTb3kw/Zhu0W89TFZgivcVvuW615mOLVVLmedIqIcw7RT8C92zMKdAYRl7adl5ZTgncC812j7ZBzfnw7sOl+s1bs81Hk+NCjE6N3tk4DBsL4mPie/1RSYn+6BuXnHnYUuyqs2c7K4uhFBg2G4bpM4G7p9o8j4TAqAVdpcLm8R1RlAgeW59odyp2uektWb2kOmQYMgI3+1srzL5MOv7ymz/HP35PGivLlIGdbLTxk0d6y6x5OFWFTIzYya+qklJ/My04Uv9t/r5YJaV+1hmGbZfaDWxxOGWgpwbJMgztWjkLlSIwWJ8SY3b2eJMjxcM7zQLjhD+4Hn/2X/eIz7oXlgkmt/Mojq3vKVcJ0xjKSG9p9LYJnNDAMOo9Gr1539TPyV+TyqlsnNlcYSgFhi0OA7AvrCaXV1scxkg16OrhtNmOM4xiITDyGIZe+CerYqG+5jAMQ1/LMowwjvHE3il8675ntP5FHXtT/c5nbsOvbvlxV5l+CVRvgCJcTTD52BPMDCO7OwXsjgY6bDaM1iyopPT+5+no9QVGT5OjP3svo5Ky3/9BK8OIzAzDwPoe3pWoYJaMVjPHc02Aoj4qMGIrP8d2gRrFcqGn+1Tdas2bRgJ/D32mkmr0gWHYtAimO9HjzOYbQykwumIYmkE6aUdjGOm5Y7WgJ7faMEpVUmkOfZMbJgk2XX2QmUyWl185xtBVU44fAl/g4jjGX3zjAbz1mq34yI0y6ldPGV9GYGzdti+9ZvcTm0p+VnKUtfyWsl5l2eOFSspiUC7yFNOvIVRSYfGzseGd//kzfPt+KaTjOMZ/bH0is2GwzcP//YU78bufvV35ThcYeluB7lZLDMtwDZtKysYwVIGR/H1kV5ZhmAMrixmGydMttrRHP1bSAArd6D3VDBWPJdPz54KIpuJI1UdTS/uTh7ufOmBk3Pp8yktd0+oxNqzfGEqBwXcFzbDcC8bpus2QzG0YRQvkdDNLTblbbRzHqOeopGwvt00l1anRmwSGaffb0ozepAb6z61P4rbH9+HHj+xBFHfuTcVZzc0P78Hffeeh4pM0kHdO3hqc5yWVtyDpw2TLEKyjSCXVTeT3dXc8rRTGuW/7Ibzr83carm3u3ON7pzIpr2mO0FTJekmZ2+b3N5Eyu/1MYEyxud4KYzGeVSbU29omBAAOpsKfzyOTSoffot0mkR7LhEviFmw7PhabDnrOxA6mmkm/akEyIKY1w2b0BsoHbn7kxp/jfdffm/FwtKmkTLfea7qifmMoBYbCMNrlGIbRS8oSuDdaC3LdWHcfbuDc934TP3pot/J9pF2jjEoq4yVlUbF0mq2WjHOmgD++kISRTOXejiJ85MYH8eEb7s8wjDIufXRPzXaE13/ix/iLbzyQO44mHG5QRbQcARnx/pvHSzleLDYq6NDiTMfq77TwcBVkpwjjWDlvumUu/ZmnY89slsQmRBp3ObJeUlmGRZscnt6flxluMy+paiCXj3aYfSa0sPLfTAKD36LtfmXQG/suzjd6E8OI4zgdr2RcKCV7vaIyEA5FJSUC9zqrujfdCjHTijIaBpsWwWjDaEeiRo+zYXSJPBuGVWAwI5fNT1y41ValW+3PntiPJ/epO7mdBxtotqOM/72wS6S0VQgMQ59E/IXYFcpdsKKjL6GSMq3lxIAKvaTiGHsOpwIjjNFsh4mXVxQb1Qxl0AojYWB9en9nQW2kksrPPKzuMjnMkd5m9mZjdbbzCdKGUfxsbNAFsi163LZItKPYOvf1BZuQsWFYDMkAsPcwExgsQSN/fyrMit4yuNWSYOUp/im7LffY4qyhkGFofc07nvoXRqqgmkxZLC3EJjuGopLSGEZZw3ezHaHZzgqMjJ1K3Fu2jVYYYyz1tnQMo0vk2TBsRu9QW8wBg8BocxtG8v//uPZ2/O23VNUK7Qb1mASu6opiCBuGiWG0NEFh2zXTv51GepO/uOll0G0Ye9JcQ60wEsIiimPNt778ZG20IxyzLC38sreztMwkMPK8g/K8pIxGb/obm48tujdbpDfNvU4FhmSiTLXaocAIDQKDGAc1q++EszactD+GHT4PIuMG8BYroEQLLgAlywKNJxdYesbYGmMnpVRSQrjz+7E/uxhyoQ/jOFdgGGN32NB5wobRucBohVFGcCfeXTH+6Et34d6nD+amDGmFEUbSjKqdpIyfLQylwFDiMLRB1NN9iHOUbLXUjtnoPVKVbrWTjRCHGmoQ03STdKFmgdGOUoZRy3OrVQUFX+i4IJMqqRwVTQ7DMKVF5/2ZbISYTI8NoxitVFiEUWxN7FhkfGuGEY5ZmgiMTqOgbQZbwu2P78MXb5PV3MqlBkn/Zr7Pv5btGr0G7vF4HYJVYFgWxHYYZ9Sx1J+2RSVF97F/qolv3/+MkXnRs51sSjWUyjAkW+CLPn/vqDkusGj+Up+4/UOJyrYJACH11e9Mz+7Ltz+FPYcb4hpRFCubu8MZhpFtgwtz6jttAMuqpBrEMLTjoyhxKvjMTx7Hd3++01pCOEpZ/qhgGP3zxusWQykw8hmGxUsq/bod5RRQ4jYMlqpcN3CT0Ux/ISWLSRbdeoWMZCYbhqoH5902Bdbl+fqbdiYNbRfMwb/blbKL5YtqaEWxYGAJw8guAgAwVbDDarUjrByvAwAe76C0JM/JZNs5vubvf4Qfp7UvgKzwylNJ6RSjbC4pW6S3CNzLSQxpgonhWlVSOYw5s1kSuaSSzxmBkV7iC7c9hbdes1UsnJFhweYCjNswiD0DQEWxYcjjTSoxEqq02NZYHQKFYVju16Q+i5E1eh+YauF/fu4OPLlvWvQvimNx3cUjFbFByrNh8LWZ7o3e58lmG9f8aFshs2yFEZqhyYYh7SkzTRkTos8zvoEFgJsf2TPvudyGUmAo0j+zyypmGHpZT0KzHSHwPdQCH1GcFoIJo8yOgrvncch2k4k8UsLobXoRWoadfd7uwqTvz7VhsAWO0jesnqgLVkHZRm22gqlG1kCrZOcNI7EIlK1F/MTeKVzwf78lPpfdtNucBJS+WXZwepR92Ws0NYbREothiId2Hirss8mG1jHD0OJkkn6orFWft1xVFMdSSPH7F0n5WNucYTTa8tkqXlKGucLncZuNESAX6+T44vs1RnrHBnUh+0LaMKRKatmimrhHYhimd8TE+Ospw7j54T1493/do2RCMKEZJgwjy/S4x5Z08dVvnfpFNow//tLd+J+fuyP3mrONoRQYeiARR8uysNJhnGFkUoOEEWqBL3WfUeLJotsqaDHWv9eN3hXfQ8X3OnKrpfP1fuu7SQ7TLefFYUwxdQMxjKOWjKQqqSQPlq4j5y/1YYPAONzku1BpJypbKUxvs6zPeZZhZI8RbrWaUqps6Va9K80wUuq/0zi98/N34sV/9X3j+HDIZ85sGFZmbGcYNqM3zUM975GebJA2Ifz2xWLPNhVcYEw32+IY7iXVCiPsOtRIVV3Z/uoqKS4wOjF6Z3JJ6c/fYKyOmA1j6VhN/E6MochLqi1UUsnx9HyLvOOa7YRhmFSDlANvmkWd2+J9RvMSnc0x5qziXj+RZ8OwG72zL6fJ6F2rSIHRjmLjA5dZL3XKn75sqZ3E9z3UKr6ZYWhpHBQhaNqtdaiSmhZxGNnfnjkkk8LtSv8/anFSbL7RiuD7yQtqYxjklsjBy38225EY2wNdRn2XNbLrNisjw0iHrlujt2nRboZyh0879a3pjvPAdCs3Ut0UB9Q5w8gKDD1rQJZh0PWTvyQU9MA8QGMYTCU12QxRTd8PLjDCKMbrP/FjPLTzMDYeuzTTX8nCSCXVmdHbFrinPzv+TpNKKvGSSq67fExGnefGYSgMI1UNpQKGNly0pjTaIR7ZNYkzjl6stNFM2Ziuwo1ilmq9GVozEbQ0QTUIGEqG0Y0Nw0T/TenNq4xh0EKgCwxSRekCg8chAElkba3iW9xqNYHB78mQTbbTinsyDiN73o4DMyJdAwmM1anAmG6FosSmasOQ/5t20DwyuNGOoKdtL0KZFB8m6M8wN34jo5KiZyC/u3/HQdy3/WBu34DkHtsaw1iUComiOuEmVmkTGNbkeCnDMdWOl4FqZoahx/ZIW460T/BziWHUAh+TjTZiJHmpuGtsK4zxUJo7yjRXudoOUIWNKRW5Dqm2UedkhmGwz1XmJUUbqGWMYeR5SSllE0JVJUXvP33/x1+6Gy/76A9EZUMCPQ/y/OP9pjWB3rfkHtV+6CqpQcBQCoy8OAyb6oZPJN1oKc5tR6hXfJFCgadD5qBJr+8c5I4jFRh+8pKZ3WrVlzs07GiSNmnHl7cQZn/LU0ltPzCNtakX097JBmoVH4vTZH8zrTDx8krtN/Ia8nyu0iIc0FwvdWP+vU8fxP075EL86Zu34c4n94vPdAvvuXxDcr2SKimTWsCWIly39UjvFPn9B66/D+/5yj3KcaY9SKMdCvWnEBjpi83ZlgmmYMxOVVKmoEFupwNMRm91cyKz2ya/8+Hh842+Hx+pYLKRqFB8z7NGevPFdkx4CqZ9StVkCsNgfbQFi1ptGDleciTQYqaSWszyWpFazKSVUBm/3ADy4+mYHz6YBPDq7zmN72FNYERxLI6dboWsRrnaBx4XNigYSoGRyzAsO/F2JLNXCl2vQQfMVVIyu6V6HO0wZixutVQn2PM8VAMzw8gmHeT96F0lJQSGYcF55mADxywdSc8F6oEvdnxU75hc+kzXKGIYzba0YdB5L/+bH+ClH/mBOOb/XHcPLv/YTZn2aYEpE9Ua+F5h0BVg94Iy7fSnmmFG5WZkGC3OMJK/xDAOWGpJiPYMxnZ74F5+3/n8JxWTEBham7odQK8BrlSJNKS/GK9XMNVsI4qT2ATOMGybOD1js1BJBdzozRiGhW2YvaSyXlW8H9VAMgg6b1FdLr65gXvcESC9BkV86x6OtE7wuEhu49IZZxjF4nlPMS+pB3YcxEl/eL1I+SKToQ7OMj04PekAqnpJmzA5bnlk5BIqKV3/nRqq6UXgJR05hJeUls5BuhMSw/BQt9kwIvUljSwvnHQH7kwlJfI6GVz6OMMAgCDw2G4sNSbGWhwGa8Zkw1CDuyLGMMoxBTpMvOQlTqv4nvF52wyhNhsG/75tsFnZ0k7TM6ENQVmB0VEchm0DpHloXbnlZnxu6xPpORaGoQmGTAJM/l4ZJNXESAWHG23EcbIZqrDkVPxd4v+PZASGgWGw4eX32zKwHN1An42azjIMLjD4bj0vDoO3S5suut2mJpjpXbONn77BimP5vGda0obxH1ufRBQDX7tre3r/qcBwKqneoE4qs6eIjnYUiwkijN6Z3UniVisFBgW/RcqCPm2xYehJzoQNw2j0JhtDjP/1uTtw+xP72D1kd1x5HhmmBc2mkjow3cJMK1IERsXPqhcSl0UzwzBFunKhqjCMKJuG29RfkW6CBVuZwHdx1cA3q6T069m+N+ysW2FcmMoBSJ4xLRwklIVKqpBhpAt2mUhvCz2SbDY5j8emhOmYm9w5+fWFSsqgQzeNa8IwkgXO99SMwqVVUmmfKkpqEH5f8n++6BoZRpydJ3zOisC9WJY04Abk/DgMJgBDuQFM7kVdP0x1t3nfdRtGFMciRoW71dYDNTBQ2DCqg+ObNDg96QC59TAsDz+O5QSRDCNrMDUJDCDRvY7VkuGyCoxQfRF9P0cllR57YLqFL2nBOLTL/eJtT2UWBhPMXlLqpCPsSIPjFIbhewgMu0XuoswvYVKf8O94CugwjkXWUoJpMaLFu8JcIU2oV2TVs2pgUUlpc0DsTi3H6R4x2VQO2X402qES3AlAROQWMgwD+7JlQLWppNraoq+cE8dKgB1BV3/mMQzTIjoxUsGT+6YRxTE8eGouKYMtBUAm2wG9U7x1k0cSPyc5JvlbxDBMNoww4ipPueQJL6kwsSnw2CmTWy3ZMHRnAdl3ti7xwEdNJRUxhjHNVFK1ig80krmw61ADn/zhowCA0drg7OsHpycdIM+GQbsr5bv0sy4w9IWrramk+K6Z2zGmLMZwmjD08gdeMgmoj3snm8Ig2tLUGRytMMYHrr8Pf/Slu3DrY/vStpPj+EtKMGktGoJhqPe4/UAiMMiGkbTpKwyD+st3cPylNgkvnv6AM4wwioUnlt4+BzVPgiuMY9z2+D68/KM/UAQz130Hvm82WGrNS5dMfV7Q8erikHFHNSyezXbWhsE3ATr+c+sTQtVgYo2dqqRMrrm8v6ba0/ouXbdhFGWeGK9XMNlMVFK6l5QplQZgt2EoGwIuBBRbXpZhKKfF2UWbf+aR3vQM+eJbZ8LhT798N37r328ztkPvqqcZvU0qbUIxw5Dqbl0dO9OK8L+/cCe+dteOpM/O6N0b8uIwgOxLpEd2miJc6XPge2InwRcORXg0zZHeNKnp5ffTqPGpZoh2GOG89/03zn3vN5Vr666PQDJBH9LKW9I9mQoL5cdhqKsAZSFdsagu1DuBr+qj1d2iQWCE2T7PtEPUgkTwNJkNI46RcTc0CQx6FFwl9b6v3ot7tx/EvczNtc5ensAvjtIF7MkHTVUO21FsYBgGlVRbxmE0tR3nfoOX1Ds/f6dYkEzXtXlJmb6OY+mQYMxTFmXVUcl9qPej25mKXJkX1SuYaiQLnK/bMNicUYzeFpWU4g2l9D1rxAeY0Af/Lt8dm9xqo0iyrdFqlmG0oghPH5jG0/unje1QX4VKSnijadoNywZAN3pzGwaPwyC33Zl2qDy/0drgKIKGUmAo0j8nT5P8TAxDTTduYicV3xeLFl84+AMkY3dDs21IhpEKjNSGcfvj+/GSv/6+ci16gUzqnVY7UuoR8Pus6lVwYPYCstkw6N7rVV94fXBWpYPGkstWU5nKRitxSa4FPlpaHIbOMEyLo4geFlXSpAqALwoKw/A8s8HSZvS2XJMfTqlgVJfOzCWSOAxt0SbjqM4w9mgCUzCMEjYMk7Di/TGdF0Wx8RmZVE+8vTwHBd9LBMYkRXrrNgyDKzhgYBhtyV55fwl8k9Y0qKR4F2NI2wRtflSVlGSrNOajtazROwwTBw+ldLGixUgFhqaSymUY7LlkshgwGwYvF0tzu9GKsGyRjBdxDKNHKF5SJpVO2/wg61rumKzRO4bvS/c5rgqx/s8EiWQN0khGC/8ju9U0323LpKN29F0qLS5FDOOxPZM43GhbvaRoItcCGW8SaEZvpZ+CKeSP+Uw7RL0aoJoGKvLdKwkMkknGRS5Wd3FhHItayvxF5CklPM+cdmXvZFOpYSI9bMzzwhRlryxWhmfUaIeSYWiBoLrAuOdpNRCw18A9m55ftB9no8D5fdiM/3kR777nYVGtgplW4h3me+omwxToBthtGLp7LGGyoaaY0fuuBu7J76knqsCQ59J5YwaBQVHzJnd2ZQx89f4yGgp2TkNhGPY4DECuIaSSarRDLGXxIs6G0SPyvKSAbD4pXSWVZ/Su+L6wE3BWwdkGFxi/9Hc3icVJutWSDcPDnU8eMN6DaSGospKSGYYhVFLZR8abuuLjN+MT339Eelpo16HxqlZ8VSVlaBfghlH5XRHD4DYMAHjmUGI3IYOj2egt++J7yUtOAo23xd0xA0uerss+8n1c/OHv4Lo7nkr7XsQw+GKXtMdfeNPiMdUIM7mkRDoUi8Agf3rhKq2oVi0Mo4BBmcaS163nxNHGJHQBZrKT+Z4nYhgmG2HiJaUUUDLfi81LSs8JReDqG5PRm3edG71pk8fHi4RaxASL4lbL4jQSgcEZRmYIhIC0pRay5QbLCgx1fk1qDGSmpb4/LjVIj7B5I4jfLTaMumbDMFHKwPfEznaa2zCa6o6AJt6DOw/jq3duV9prMBvGX73uXADI5BYyGWv5BM64dkaksslnGPunmiKhYDVIFtTYsPOpBUwlFXjGRSK5p6yR0s4wfOEVxic8ZcS1qQJ5+xQQFsWx4kdPqGsCwxZ3w68LsdgUq2La2sIGmGNCDs60WCxEnJ6bfH5096QSLX7P08mmgXT+MvBLjkMnFfd0pw+dOfGgyzpLIy6rTqrt6eNQNW0ePCnwD820kzgMS3pzjqzRmwSGPCaOpTA9bGEY8h5VZkJ9ps0Pf6c9L9m0JSqp5DvFrbYqn0crRyX1glNXAWCBe4xh2NyJ82wYfBwAGdfEswlPsk2prpIqUy55tjCUAkM1FmYHz2SbALLZKU2ChTLMAupDndb+X850jN97YBcA5t8ubBjAa89bh9+9ZL1SkAYwB+JVS9TuDQyqI+4FxA2ei+qVjCcJjQ3PyhswVqVDd73k98fRaCWVwShQkV+TVFK0uJlVUslf30tUTYpKKuaLgOyn75kj2UXfNRWMzejNvyd2yllUlMYdAMmO2fOSQEU9dxhfrD510zbxPwmuxMNI7v51ldS565bgJWeuwfnHL8vcA4duLNcPofT0gFwU6X55oBi/P/6XszhCYsMghtHOMAyb4KbFTqY3j0RfAOC/fvY09k01Ua8EqFV8HGICgwflGm0YbCw9mOeK5+WopDSGwecljd+nr74A//jG89MxyMZh8CBWm4rRxAK5rYYYhthwtiJMs/ViVAvcy5nys46hFBh5cRj678lnaejlMMVwcIYxYzB6Jy9dhBXjUmBsfWwvJhttmd48/UsL8litYsh1b2cYebtmk9GbbpfaFCkFKqo6gH6rpPdIa281RyUVGhZVU8xAI2UYFKjInwEJWxJeNO5cBy4ZRuKllqik0t+iOHMcnW/b2QJykRJ7U8PCyu8xOUfu8oBkjoRhLHbdge9hol7BwZm2NHazBeTMoxfjBaeuUmxCNI/iOPGsoy7z59JoR1g1MYItb9qEFWwzYlKH6QzbFHciEuaxxf+mh3dj43u/iT2TqrpTF2BmgZHYMACkFSg9q1stB3n4NA0qqQd2HML/+OztuO6Op+F5wES9ouRd6sSt1jPMFXL9DSMpQI2R3mnuM1PA7NqlI4KVyMA9Gq9ISetfpr4JgW9AecVLIJl73ANzpKIKjLLZE2YDQykw9B2z58GaCA2QOmN94E06yIAxjIbBrZZefp71shXGSmI9Au1IeP4afi0dtCjpLzSHyeit5xXilQMBVYXUTDPyAmAMI08llWUYxsC9lGEk0deRkdVQX3g2XwHBMLz0JYdRJRUqC0K+SirLMMw765D9LmpJpM/+lD/+Om64Z4cQ5oHvYfFoFQenW1mBEUVYMlrFuccuBc8kq9eTNhu9Q7G4+2xcilVS2apzvE9cJfXkvmnMtKKMm7O+e+eeaDQtfM/DWDqPD88kDIOrrmxZdUerqqMJvT9RDDyyS7qOe0iSG9pUUiZ7U2QQGG1tfpBKir42RXq3o1jU3xZtpyf4GqPl/WpHsSLg8uLDdJhqjPPUQiQwnnvyCiVhIh+Dnz9zSHHumAsMrcDw2MPT3UKzAXnpjltjGKZITR6HwY3bFC9BD5KuT6VI9ZgMQGUYHDc9tBv378hWZqMdzzOsVGm2TRPDUAUGLei6Vxj9X9MWpkrgGQURIIWvWEwqviXSmzGMMFKEtkLP23Inx2+FG2l9j9RAlOwtq5qg/tsKZgFZdqQvabJOhsoMk/tRbQM0Pr7nYfFIFQdn2pnAvVYYoxJ4ws6kCx8g0dHrzwtIi3elz4XLUZPA0Bm26ZimNgf49fTFTPee4psvEgqeJ+1whxthrpcUB819mg/0TsVxjCfYYud7HsZzGIb0dAP7jhv3s0ZvD4kdkcdr8Dkn05snwoIXV9NjL5IxSFg53cveySb+3zcfkGOgqQrzoLjpN1WBMdMKMd0McdmGNfjMb1yUYXx0Ly/568S5Yy4xOBEhHSCMYtQCPw2eilGv+KlB0fJCiMAYdadvenGU5IPtrA2DFstfPH01Tlk9jgtPXIFf//TWTJoQgDEMTQd51T/9xHhftLPLExgm91d6iWiSyiyXqsGRjpECI/ku8H2zoRPZXfqIJTfWTOolNRN4aLVjYQ+iIlT8+kIlxVZGacPwhBpBz90DqAuCzUuKQMLEtNgA3DtI/QwkGwS+0JPKzveAxaMVHJyRDEN6zSTsrcpUHdUgWQBWjtew+3ATk43QwjAi8fwLGYbmXWUKuOPxNmI82ur8IOSppGiD4HseM3q3sHxRTUsNYn4OdP1myrjIgyyMYzy5TwbKkUBSbBgGN9dMttr0ssKtVrNh0FyiecPnHLGvNnMSaEUR6n6Q8b4iBJ4n7Gb/sfUJZQMTahuOPMy0onRjJI3etLGdaSXPlMZbZ/9OJdUh2lGUca/M06fSZBjRJLUpDiPwWXrzJrdhkJ46+TtWq+CPX3EmVi9OGIaeJoT6BWSNVjZUK8nxthKf+n0S5I41+avn0edxKc12nFmYcgP3yOhNY1gNzEbvdoiRaoBaJUAjXcRICOkMh+fa0u/B85J+RczobXNx9Qu8pGhhtZVotaXKoGvyHEDUU8EwmNGbjPntMFbUe3SfM60QKxYl8+Rwo63UficW02xHYnFVGIbRhqGOpy11CaCqpFqROk/ENTQBxjcPNSYouZchj/SuBp51Eaum0f/tMMJ0S7oiRxEUgQF4STZczjDYczc5BkXc6G1wwfa9xEWbq6S4AOAFlOhael10nXn7bJOiez6WiashzLRCoWqSDCP5rdFOGAatG/q76YzeHYIYBkHXwWdUUmQArOo2jKw3VeCDMYysDUNP0SHqSDSzWTjp30U55To5RKSnZbJxV1gOYfRuk0oq6YsImtL8w3WVVOB7RmM6kI30Hq0FRqP3jCEOg66jCizGMHjGUraj81O32oqBYfAFNPDMzgOy76pKSn/RdIHBhc9MKzS6Q5IN4xBTSZHxlWquVIXzAuntpZPEZKOtLPC0yHTEMDSjt2kBIWHF1bA6I5LjoI4H34xJlZSnCHjPk+/ASDWwMowkE3Ji1+LBqFEci7oPQPKujNc7s2EgztowVIGRbj4iGa/BXx+RGiSMZTEszeMtMDAM6oJeb6RM9mHCdCvERFq0bEoYvSXDmGqGGCNju9YHUwboucJQCoxWGGcYBp/M+q5TqKR0hmG0YUiGQTmjAt8TAoEmBb1I1A8SKPwa1KeyJRapTdtkq1V8mIiAzA2kq6QMO/x2JNRa1FbF94zuukA2GnqkEhj1s5JheEIfrKeTp76YjN70KITvfCQD9/h46IFZeXVC9BiSOAZ+9FDiKXRoppW5Ny5YG+0IB1gt67VLR/Grm47FP715EyZGKjgw3VKEL9ltKky91woTVUizHWFFauuabLaVRY1nne3OhiE9gH7vF0/Bn7zijPT75F74xkqoLg01Uvi1+DnEen1PfV68gNJINcgkbBTnB4nLdiuMlYDGSLNheF6R0Vu9B/pOCIz0Oz5e1McwXWDJZZug2jBUliltHprAYJ9pHD/02rMz1y6yYUy3QpG2XGd4060w+T1dN7hHI/WtSOU1W+iLwLjhhhtw2mmnYf369fjQhz7UjyZzQaVURaSylzCMMcOOGmBxGDrDsMRh6DaM1RN17E8jr+kc2v3Sy0UCg3th0Au2qGTyMD05oul3z8AwpFpD7lZ5X5QdPmcYzEvKFBAIyB08XWOkmm/DqAV+6uvPdnDtSKYFCc0MgxsvyUvKNzAMvn4GvpdbiVAwAPFNjG17prB/qoX9Uy0WsKYeDyQCkDOMiu/hw798DjasXYLFI1WxsJF9qtWORbZj2nm3wkg8S3KV5UZvAMLQyjdBvpcdFw6+MH3ga/fhD75wJwBg9eK69IwzqKQI+vMLtd27YsNQGIY8x/ckqx+pmlP4AwkLoYzNxDCWjVUxrdmIEqN3Vaik4jjGLdv2id9N6UtiSJUUfZtVSXlCsOjsXAp7KXT1RJL67t7w+uGsY5YAyHerpRLIhJlmKAojtTQWn9yvmnSQay7COM5Eh88VehYYYRjit3/7t/H1r38d9957Lz772c/i3nvv7UffrGiFiXGRq1QC32MeGTrDSP7WNcOuKcDPVA/jmKWjoo6ESK2hMQxiI4rAIIZhcKs1QaqkzLUREpWU/CxrR6j3I9xqq1m3Who7QLVhFMVhcLdEXaDFcZJMrZ661c5obKsRRrIv7UgYM1WBgbRPiSdLFJsZRhjFuOT01fjRH1ySCAzNCM5hUklx+4Fe41pVSUVKHQ++2HA3xzERZ5CkO68EMi8XD6JcyVRSfNqFoXQKMDEMk41GZ8bfun9ncv+elxkzU3nPrFNI2pf0a84waqnA0RkGFxh5yfGIcbXCSDCMZWO1TL43D0m9jWaY1CP5zgM78ZWfPY2Xn32U0kd+FmcY9INu9Ka5FMVZtlDxExsHzxgtYyyKGQbBVEOjyTZJAATDJMy0I+FyTLY2nS1zzYTyrkTmqpdzgZ4Fxi233IL169fjpJNOQq1Ww5VXXonrrruuH32zotlOa28zgVHxZa6bTByG8JLKV0kJhuGRwEjUN2uWjIhoXTpHt2GQHlJRSXXIMIpUUvWKasMIhMBQ6bTUX0svqTBK6ks0WpESU5D89Y0vAsB3P8QwskbvJHdRsjjVKr5Q33GjN09zTayA3wvtIClwL4xiWRKTLZpxHGPFohrWLh3NeEnVNKHXDlXX2DjmbpNRZmetqqRUhsF313y3OMaS67WjCJXAV+5bxO0IhhFq5T8lCzHZMMwMwzw/fKaapTEzMQx93uuupCrDIJWU6hjhAQjS/ublOiKX7UQllbD0ZYtqGaHlpW61QBLn8ejuRF119S+cmPTREEsTx3HmGWYC99K5xKP15e/JRmlaERjqfNffC51xJPefzdDAN2YAlMwQQGLzFFHwBq85QHWW4ankozjOZI6YK/TsVvvUU0/h2GOPFZ/XrVuHn/zE7DbaLzSJYfgAwuTBH7t8DLXAx2N7pjLpImgS6QsK6TZFYRStROt0K0Q18LFmYgTfPZjs4mhC0QPUbRjcwE1zrWx6Yrp2nkqKFpNqWofb8+TuiwQFBRzSRG62I7z1mp/iuw/sQuB7eO7JKwDInaxeolUZIy354KiBYRA9pvQOVGCKxiaO5cLFk7ypRu/kr1QjMDUBZxgsPqPie4pOu1aRL38t8IUgE9eAfDl5gj5Tje0kAVwZhsEFRpxEzfvSPXrHATluY7UgZRgqY4pjcoFN2uJPwmjDsBj6A0/qukVWYkPUto4ojvHRGx/El25/EoDmJcXUZBmjN7Nh2GAyei8bq+HBUI1DUuM82mJOTYxURR8BLQ4DPIYk+U63Yfh+GrjHbGIE2mhyb0hZblX2n0NnHPz+dbdaHrOkC4yZtvSCMrlZA3aGEUbzp5LqWWCYrPUmPfuWLVuwZcsWAMCuXbt6uiYxDB549umrL8Dje6fwrft3ZvTatCCYXh7ybAHMKqlq4GPN4jommyEON6RnTDXwlL+0UHFXO2rHT+0rpuA+Dj+9dq7R25fMhhbXWDCM5G9Dc6u966kD+G6a74p7mKnJB/O9pLgNg1ItBL6H7z6wE2/51E/Fb7XAFy81H28edS6M3kYbBoSXFL20ehZRbnvRx4dfjzYE4hpMDdUOY1bLOnudhlbEhl9r8YhBJdWOUrdaWb3wjZ+8RRw3Ug2SehKaSioRYCkb6MJLioM2ENQfIOvoYUIUA39948/FZ5OXlN6vsiqpapDWSElVUhU/cZ/VWQ4ZvYEkueFko41axVc2HdRXQszmCLlM6+1SmpkoNsRUpO+biWGQUDfFYXCQQNSv3QwTmx6JxRWawIjjbJyU/lgXWWwYcWx3vZ9t9KySWrduHZ544gnx+cknn8TatWszx23evBlbt27F1q1bsWrVqp6u2UofhlBJeZ6SPVPXBZoWMIKSx183erfCRCW1OCln+szBGdE2XYsWX1pcxpm6gu9GynhKBSmFLmPDqAZ+GrPA3UJ1L6nkmnoqCBqHgO3Uy6YGGWG2CAD40cN7xLHEMHh/CbSo/MN3H8b1aalSfkVpw/AUNQK/FpAsEtRsRmCw6y2qBdDrWnOGETJXSyFE+A6xFSlpqfkmaNkiKTBWTiS66X1TrVQN4SmLLGGk6gu3UbUkbCSLY5FHEjvdHIdhFhgUwwLIRahMamw9jsMkMHw/6yUl3WrtywhXSe2fbmHJaNWY0sWDpzCMw402xusVMd/NKinubqseB0Ckl6F4DX0fSwyDG9+lDUMew6G/JjzTc6i51fL5uGRMTe8ByHfCtgEYtTGMOC7cfM4WehYYz372s/Hggw/i0UcfRbPZxLXXXovLL7+8H32zQqqk1J0mefrYApNM+lyho49ixLGqz2+lSecoOO+ZgzMyDiM9xvOSMqxEayc4w/C4wCgmc346gcuppPx0R+kZd+KAXCz0KHSe7gFI7tlPDYA6hNE7UtskobaKGfPqKcPg/ZV9Sf7/yaN7RVoUvhjqAXlhJBdGPQ6DOztwZBlGpAbrsQWmHUmVl4x0lteZaYdK4B7X2B23fEz8v3ZJspnYeWgmsYFZ0qxQJt+WFpnNo4wp7YvXA8PQY1fKqqQ4TM/QN3hJUX/zVVKq0XvJWBW+l3U79T05RxrtCJONNhbVA5nyw8AwjEbviP8ei/eDp8snBOk9zGgMY7LRFlU1M3YP7Qt6D4GslxQfe9OGTDAMy/O0Gr3jeN4YRs8qqUqlgo997GO47LLLEIYhrr76amzYsKEffbOilUYr+2LBI9WKGjBFoBeiEiSUPdGpJ/pFobMURi714SQqqXRRONgQiy3fRVYDSWs5w+DtlGMYnsh9YwKPw6hXfEStxJAn3Go1QUkTUt+NmHJJJX+zLrMi8tXCMCbY/dYrgeJYwMfItKjwhU8E7vkeAvJs0VwdgUSwS4GhLoYKw6hX0khq+XvMrtlW1FyqSg/IMgzfIvzXLh0FkMwNiuMxMYx6NchkTk36IY2xtMFQmJfJhmETGMzOoOcTy8OkZX7w/4n5ETxPumLnbYYqfhIU2gojHJhqYWnKMHR4nic2dI1WiMONEItqFZmFVrjPqnNGN3pzoZ+oCCHsYfp1/dR2x1VSzTDChnd/I73nrHrdxGpFkkzNLZtvUE33XMQw7F5SMbMbzm0oXV9ySb385S/Hy1/+8n40VQrNMEK1Ih+UvvBlvECYj3/VT3zGJ0aqaBxuYKoRAuM82ZivvBi6SuqodEfJd5GJsTWZqNyGwedImWhvPWKdhBuhXvHFDqsaeGi0ZRoNABnbzagQGOpuxJStFkhebj1P7kwrxJn/5wasXz2utEkLEt/916u+MRsoP49DTVue/OWeLXogIh3Hjd7K/dbUa7dDVWBEsdzNq/WiDUbvdoSD09lIb8LK8Tp2H25g5Xgd1cATbtdV366SotxaahxGxOYxjYHHfjcxDPU5U7s+8/DTPeXyoOcu4w4QNcZGdS+pdcvGUKv4OGfdEnz2FhhRCVLX1gjY32hi1XjdaDj2PDX1yKRQSSXHypov8pwoziZO1BmGVG+aXWQD31MZBptrJs/BjA0jkGPOmeNMK1JUdSa7rsm7CgD++lfPxV/9989x1JJReR2LSmquBcbcXq1PIP2gLigovYWukqL3K/CkumDJKM/tz9xlfdUAXA0S3fNI1ceeyaZou6odQ3EYNobxwlNXYd0yOQFM0Gmzno69VgnEokLqKQpMSu5bV0mpLr8EmUYb4p75X46v3vk0ppqhKDXL1QaAympI7ULgC48pn5ZJJeWn9ii70VvaMPjC+s7LTsPbX3iy+FzxfbQ1lVTMFhhdCAGaW20rVBiGvnAfu3xU3OPK8Tqe2p/kRaoE5mJUIxaGwT/7muDg48JhylAApCqWdMw7MXrvOKAKjFogn5XMCuCJbK1JX4Gz1y3B/e99Kc47bpm17STS20cYJ6nAx0eqRtWnB6kybrQjTDbbWMQEhtA8Kbcux46+DjVhTA4UCTNN+84YXSVj9JbnmwSGvu6Tatz31IV/ppXUuP/Bu16E77/zRcZ7tiX8fOU5a/GDd11idKABkvWMVFIm4TubGE6BEUaoVTwxmTIMQw/IEz7+clFckrpGUgAM0ckkzYg8lyj5eOrhQm1nGUa+DeN3f/EU/O+Xnp57X2Sk068tPjP6S15SiVttVtcPyN2lrnIQi4BuAzJM4B8/slc5RldJ8YW3XvUVGl4rYBi8u/Se006WJ5ZTF3eZlJAvzM89eQVOXrVIfK4EtJuX7caIzXYR5jlF7U5ruaR0R4pjlyV2jEMzbayeqGM7CQwrwwgSIRaqAqPFvLWESqoHG4YeuKfHHpmwQ2cYlewc1NVm4t3zPaxdOmJtm4p1ka2m6nt2lZTYjIRWo7cS6R3LdzuOyWtK9agLPDmXqO/kFk1zbaaZ9ZLi98phc7RINihcJZU45hy7fAzHrRgz3nMlMMc/GY/V4zCohkZOLrXZwHAKjJRhmFQqQNaIRHQ2YC8zTZrDDZluGci6mNLxY7UKppqhaLui0fYpA8PIGsjydwOhpp7QFx6eGoTsGYlbbfK7zqyk0budaSe5hjputuC9pG9J26Oa0Zsv5r7nKTRcNXobVFJaEBa1IVVSWdsCr5PB818F2kJU8b1UJSV33xEzejc0zytAbjSWjFYx00rqKksjsjq2G49dmpwLYNVEHdvTXTqP9OYYSVWo3PsLgPKZph1fL0wqKRJsr33WMcr35Jad9JcYRrFKKsswsvOfSqDSGPM7nBjJegARiHFFzBXbNM18rpJqSaM3XUizbYvvuFozjtVNSBglWXVJJUXvzud/8zl452WnCSGu2zBEn0os5ly9qzMMPufNDMM+FjpsRu+8ejCzgaGshyFSg5B7JduZVYNsjQT66DOVFPnSH9Zy0eveIPTyU9AVLSpcJVWrSE+L8bp8ebI+2/nyOYy58MuqNnikd5V5NtkYBi3uxKJqQWK/IZWDdBrIGvJtqLNgQABohvJlqwUaw2CqjSKjN3erJZ23TX3E3akJeiRy4KuBe77vKdlN1XQjyV/aDEyMJJuD6WaIpWM17D7cyIztW557AlZO1PGKs4/GrY/tw/YDSWAnj/TmGKkGqASJy7QShxFGSnEm/hcwG73pHn7/stPQDCN89c7tYjxoDDpSSWkMg58jvQGTzxQsa9LJm1BJBTl5pSU2DQPDgKeqpBqhopJ66JlDuOHu7Uo+uBgxuByPkXWrFXMplpkD1q+ewPrVE0n/At2ttsCGoX1Hzy7wVVfhZjtS5rxpvGR6I/UZm47lG9QwihPbK+xsc7YwvAyDLZ56CL3NSyrw5aK9eFSmIQDkwJtsGEBitJ5sysA9m0pqXIn0Nk8uG6IoVliTfrxkFcmLwKOigazAoMWd+kbjJDOQqgyjqH8AU0mxmI9a4OOzv3ERzly7WFlsTN42+v2K/1ngXuAn0bl6QjiAksgl/ytGWE8d76RGQ8RKv6YLjIW1AJJhUPryZhhhWeo/r4+t73u4/Ny1CHwPqyeka3HF8NwA1YZhSj7I74evSXlxGHodE595+NGYnXbUBC46abmoDGmC7hlXFWoW6Trra0K6rOqc4hSIYdCc1eF5LC9bK8RkUzV6X3PzY/jNf7tNUcHoDKMdaWVWU5tgqKmklP7pNgxu9C6hklIZBnPLboUwpQnSr53x3LKMq84wKDUILwU8Fxg6gRGRLjTwMzpJACJISDlH2DCkukDaMJKBb3MbBntoUiUVYLIRCgpY1Wi7UNkw4aUTiqIdPOWyAszBdGToJ+8O30NqIE5+191qq2nUMXlJEePQI7111V4ehEqqJQXGaC3Ac9J0I3wHWOSHrhq9k78e7KlBRJ1lgw0j8HWGkez4IqaSihnDMNVaoDmweKSKPWmwI9VuzyvUtEoTGKbnTB5wPP4DSBY5PTNqcaS3jJRXBAZbjGnMVo7Xce3m5+TaGXTwbMbCwUD8lWy+DKppXFM7zd1V8c1qGKqOVw087J9qIo6RMgz1uO2MDcWxOpan/ckN+NRN28TndiqgbG61QHbO87XD7M1ls2F4qpdUW/WSshm99evbxlUvBcDjMOaSZAydwOCZPemBkscTAJGGgIMvGvSASHVEJSHVWA15HAmYRbUKpjjD4MbpwLwwZugr+/xPb9qEj165UfldCUoLslXwamlK9yDd+VFFsdjCMII06pgoN/l1ywUByj3aiihxZBiGlmRNYRiBfSwAcxyGlzInXvQmkxBOE3RAViVVTXNJ0RXIW6atCSHeD/pt8WhFOAosTRlGXn2DpSyKtxr41nEkhsHtEu2QG+bT/pP6x5N9u/Wxffj77z6k9FPfVHC3bOpv0OECD6iZAHQhZmJBAPAL61dk2kmYsJc6MSQGWt+wq2a3jHolwJ7JxLl7Ub2S6fe23ZPKZxMDE7+lNgwKyjUJAF1NzJ+zaZOjk0euklJySbVCxcvRbPT2Ml5XZRhGGMXYOykd4PPKFPcbQycwpDFPBrHx3D41reb0gemWWOR9T+7+qoFadJ5eQp16U1DSWD1hGG3tRQQgajjz83kbBJ5C/MRViwTLIUSpjhfIqsbo3sgoHPCXkXTwmmqB3AYJI1aGocZl5EE3epM3iLyGWQ1VJDAy9TAMKilRZ9nAiHxNJZUwjEjJOhoD5mDAtBtkx+LzqQzD4EZfkyqRQJ40esU9PTMqj+aPUu+fKz7+I/z5DQ+Ic+h4m9DU66aXVSHRdWX76hw0eXIBwD++MbsBqrB2OMMwCS+6TL3ii8VwvB5k+s0FBg/uNIGM7KFgGNljsgxDdeIoOp6r7/Q4nroSh5G9dsWQJdom2PmciuIYuw/JdD9zaccYOoFBwqDKopK5SqpWkcVcDky3cOH/vRE33vcMgGRScgPjonog3dPErk1dPGlRJ4bRipJkhUrlLs4w2IPN85KqGehoyPSs+mIgzvE8IShokeTpLjgC3zOm5zDVw9D7x8HTeZPKbbpJgXtq9UPV6M0EhuFFMKmk6J7CiKmP0ucci2OyKqksw/CUSG/yJqNr6q66dC8AFEG+dJHZhsHBx8eWSwqQu1A1vXnMBIAcA0COn87E2myu6mo4rpLyPCZcu2AYvif7pFeg01sbr1dw8fqVynfc+y6KeEXL7DXJC4sLjEW1SmbRfnSPFBj8eZoQRnIuce86pY/anC8yelu9pFhdcyonUMQwEi8p9Xs7w5CDNtVIPPjILpW3mek3hk5g0Etdq/hCj6cwDCZIDk63MNOKhNtgkmFTCgReQ5jbMDho8RirB5hshqIMJ0etor60hIzRWzOm6y8x7YjoWH0BT9RwyTVWLx7Bqom6Mb25vJ7KqPSiTxm3ZMtCx4u/0OJIgrbZDhXBoKQGKWAY5DtP/wO08EMYSfl9iQA3UtnoAkNnGFGsuOvyNvR0I4Bq9CYsTxlGnsDgDMP03ORvyU5bZRgyt5TuJUXjxxfFRjtSGYbGaGkBbLajjBdZWdQZM6AxjplAt7VnU8EGnidsNYFvPpe+qlelSoobvQkqw8jfXSdu6mmWYpvAyLFhmJAVGPwek3MbhhgYmw0jKzDsc4ewM2UXRy1JBcYcutYOncDgDIMW+yU6w9AKufMKbzxojQsM7iUFSM8i0k8vqlXQbEeYaUVZ7yXLTloXCHwhqRpsFDzS28gwKr5I5vZ/XnkmPvGmTaqXlKaS8hWB4Sv/A3IScz2sCVzlRHEmh4XAiKzxFsq4WNrWEwCSjUZRSWXyfZkZBpfj5C1Hrz91hRZ+PlahxtA4Y1gmBIZ9IeHHkw1MDyoEkucRGgL3dKFGZ4q5yALLGq1IsaPxnafPFuNWGCsCtQN5IQQVF0jUZaHiMqwcpsR8gNx9hynDMKld6DuFYdQrmX5z+RDHqseZjna6AZMV97LH6Co3zjx1b8vkeMs9MhsGudgrcRgm+4khDsMmMAJFYCQb4KPSlEW23GKzgaGLwyC//yT2gXaEzOjNVFLkTUIP3vOYysnzlKLzYjHShIFQSaXusgemW8aAOoKqklL7znfwpihPlWFkbRj1io+rLz4RLz5zjZiMvmePw+CFkbi7J31HL6lu4NfB+1nxfRGTAqg1wqmPhCIbBpCMewVq4J7HhCBdAwBiFk+TtKnqiNV+qiopke/HwDBk4GPyHWcMSy1utRyKl17aJ4o0/9Brz8aVFxyn9IkzhmQhTfuoGahpnj22Z0ocP9MOEUZS3cTnG99kNNpRRqCWBa/ISO3FGZWUgWFkbHZy900CN/A8NYNwCjqzXpEeh4sMDIODe72ZILLVFrjVEmqBrwR0mrLI2oQipaIBJMPgKinTXZi9pMz3wvv5TFr9c808CIwhZBipSoq9KLpKqqExDG4AlDYMme4DYK6K2hOTDCN5+FQEhoMLENqRAvleUjWWnp0QRaoNRT+/XvFx4spFeMGpsp6Imksqa8PgrILa5rmHeL9MRjj9PnwveZEVhsHTYVv+D3wPb3vBSZn0KHoRIy/tFy92JGoUsFiNpE21j4rRO1AT/Xls5039JvDUIIHvKVlCqbRqnp64btgwkKcUV0uIOAzFSypiqjZVJUUCdxvT28+0QlG3hR8LqF5NzXaYUdHlQWW/zEtKU0lJoZZtg/dlvF4Rm63Al0XBeLI+DqGSYotsUjvD3ucYMq2KCWEUi7kURrGR2fAN4mgtUOp7GxmG1kSVsXM6XNS0L0g+aI7DKN607dIZxhx6SQ0hw5ButQTd6E1J4+glbxm8pHzfw6J6JXOsLgykDaOYYYxUfWWxyQvcqxpenJDlSbIF7unwchgGN3onKilPOU5EytOu2PdQr/iZZIW6XWZcExhjYzxDLxOKGsP4w5edAQB4/qkr8eXbn8InfvBoJj0195KidZXUGbo3kaKO0Y3efrJTlSqp5DeRAVersQEkaRYSgcFVUsVutfyehYtyxQcaarQ7ZxgJM1RLxeouq9TWI7ukwPiDL9yFmx/ZIxwY1OzGMvmg7oxQRDBGqwFaYfJMa6KQExMYUDcZpoWNE+Lff8mpePEZa8R9kVedTXBRe3yRnRgpZhh5+nvuJRXH5mtX2XeLaoESxGfaJFi9pAIZuCcERqHR289oIexGb66SShnGEscwCkGLHV+0FRsGM3rrQVpUoAhIJv6EwYZhM3orDMNiw1g5XlcWjyzDSI4j9Ymui1cC9wJT4F42vUbi/WMRGEyXXmFGb3oRqK90zPJFNSUieLQa4LINazKV1jgza7SjjAAV/eUCg7WxYe0SQaelDUO2n/jOQ9P1S2Ox3u9kHJDZUYdRjL+44X7xmV9Pr+JH41L1PSX4cumYWlqzCFz4ZsbA94UKir5Xckl5cgwAYMWi5Fnc/Iisakj/69589D9Pb57ngKGDC0maZ6pKKm0nh2Hw8V++qIZj00JTFd9TbCD8XMFY0s/E1mqVJFV+XrejGDARv2OXj2K8XsHv/eKpylwyLcZ80zFSDdRU5wZhpDMFbsPQjd5FgXsVg5eUNXCPC4yDDUzUK6KEq/OSyoEobh+oOxECt2HQA5QxFuoDphrLiqtixoaRLBj0QplUUvTy63V7bR4VSXlV+TLSS6Ik1vP9jEHOxDDohQC0KFVPNXrXAl8EV524MsnqqgfA/ckrz8SWN50v2rjtTy/FP75xk7JIeB65I4fpNSNrviKbuzG/JgmBOI5F+4GvLqRAYuzVjd6Kl5Sv5iiisf7yHU+LvlN/AbNKKsl15CdJ71Is1WJliiBdlFOVlKauIpUU7T5bYTa9OS0aZ65dDAC45dG9meuYduwBM/xze5h+nAmcGZsC9/SgySIvKf67r/VDT+HCj6dxITWz52WD2wiJSiq7WC4ZreLu91yGi09ZKWx8Ni+p0Zp8P7hdFLAwDP2drsgNqG70LmIYFT/rKWl7TKqX1AxWTtRZ/R/nJWWFSSXFd7iKl5TmEcN3TL7vYWKkinYUY7oVZlwbCdLoLRmGvqOmz/puVH/4eh1w6gv3phALjm9gGAUqKa42obbpnErg4XWbjsXNf3gJzk0zrVLzPOX76gmZQkJP7khjM16viAh53eht668+rmLHz1RSPJo4jCkFTPLdwZlWxlPHZtQ9ZuloRtjSObQImAL3qCb3WDXZHNQrvnhm5OlUBO7GrI8Bj8OoC4YRWRfj9avHxVw54+jFynVaBjdw31ddt1W32vx+m+pHcyFMspuaNLXneebnoUej83NFVHz6HY0Ld2SxsSObW62JadoExgkrks0TZSyYaTOVlKltXSXFmJ5gGK0swzDdQi3wM9/bGMYJKxcJe2oUJ6pSGlfHMHLA3WpNMKmkaHHg2WoDz8PyNChr31RLJDUzRVcDkmFQxk3TMWRL+eBrz8axy0czD18PkKMJfPa6Jfi1XzgBf/v6ZwkddOB7GY+tmuGeVaN3VmBwo7fneTiaVfEyRUyb3IJ13foippLSjd6mcdGvwT//yZfuxp1P7k8r6SW/eamaLYpioSLbO9lkKqlsm/Tvv731Qnzpt56b8fiiY8nzRcklxYzeFd8Xiyftum/6g0vwyTc/23iPOvSYFj3zK8Vh0Ng0WT0MafROPtcCX1Q6fNZxS3OvR//bDN1FqUHGatlNS4W1p9uCzH4/2bmS/G9nGPQu0Tdkw+COLFZhZ3GrVYVovlvtyavGxf9VLXOtCVYvKRa4R+xPTW9uYBgG13rbvf7WC9fji29/rvhcY5sZZ8PIAU8NYkK+SspjOwLp0bRvsik9hyxPjKspdKEiamykqrHXX3AcfvCuSzJt6HEQXH3x7ldtwPErFsndc+ApBjm6Nx2+b84lRX0UbrUWYaP/xm9NV/3QojOuC4wSKqmMwEjbuuGeHbj8Yzchjpmbryd34pTYb+9UM2v0Nqg9Lj5lJVYvHjG8iCmjMZZ9lUbvSiC9pGiTcMzSUWPFQA59M2C2YSTCvZXaqigVv0x5khzHBeLpRyVpuJ+VskLbdWk89Boh8v5zu49RZsMI2IaGFnSbYV6HKXUI76PuGaRnHSA1Dlcz24SdlWFoY5LHMFSB4Ss2DBP0+xYqKd9nKqnsGmUSViYbRp6tSXHqCHzGMJyXlBWcYfzzWzYpZTQBqZK65kfbxELKI4R5apDlqc1hz2RTBu5ZYhG4UVDfvdIkyyskAyQP3POyNbV5ezKdgsGGYWUYcocsv6e+khose1+6Sir5XxrmdeMyHaZ7SdkEBmeBGV2t9ubFsZq2PExjKIhh7JtsIkyNqD4TqnwcOHQBKbykTG616bC1wyQLMuXLGisQEhyj1QCHGm2pdqyYGUZy7RC+7wk2nE1vTosucMGJy3Hjfc8INaIOfTddtQiMQhsG2w1zZkl90dOy2NY1qpeh94v/r6ikAjnfAK6SKmYYcRwbU4PojCaKkZZozTZ0DCubXA18xa3WhIwNgzk5dMowqn75SG/9Ny4w5jKX1NAKjFrFxyWnr8n8TilD3v1f92R+43EJXGDsm2wy3ayFYbDFQ1eHHZxOqvZxvasNVd9X0kcn/eI7EblI6zYMXUUF6OnNGcPQ7CWm4k3SPZW/0OlvBn00/V1Ur6DRTmoPlLVh6H3XX7wojpWqblGcfLdyPHlGeyclw/C1hTX5X722PnZ0jjB687oKgokmwW6+72VcpIswWksEBl2Vrs8Nn/ScKW1HteIrDEPfnQd+Ynd61blrM67Osk11N803FXwIOlFJcWaZcas1jD2HrlYDDAxD2SmrAkgIjJFiG0aMZF5UtZIGqiqOUoOYhaYuVHngnvH+Miop+azaGsMoKqCUFJNSv8t7TL42jrRhMgUYzhaGViVls2HULd8DUFI2BL7KMER68/T391y+AR++4mxxbiWQC4i+ez2Y1n5eXMAwknPlLpAWiKo2Eah/+iJrSrdMenHAbMMQgWRGg3l2py5TP/AXKfnLBQaQFJ9qpbtyE2xutbx/hF2HGkpAHsVeLBurIfA97JtqljZ6m9qnj9KtVi7APPkgPduxWqVQDcVBHk26ms9UE4RcXgXDEAZldbGlRJOL6hVrbW59J+8xocE3CUVutaMGG0bgyfb1iHlrgJnhd31RVtRVukoqXWSLducAsH+qhf1TLWG4Nl7Py89WC0AUwKoGxQIj6yUlPcrCKMJ0MxSR+WoBpWxbpnxyuSopzjAqMhA3nEMvqaFjGA3GMEywfS/ULyxwb/FINVmMJptiR0OT7c3PPSHTxuKRKqaaYca2cPpRyWKhe7KYwBMC0vtsynBb8aUNY9lYFfumWsYd7/JFNTyxN5mgrXbimhrHTBiR3cQwY00qKZN9QI/unUgFxt6pJOdPnj2J37dybe3zfdsPyd2pnwbupa6hy8aq2DvZyiQfzIszyBi9PWIYWS8panffZFPozsdqgfBzL4O/ef2z8JNH9uKoNJiqpjE83t9mOxJBpM0wq5KinvOFdcRSm9vkCVUNPDRDTejnywsjw+BGdBmHQZ0zt2OK08gzzFs3fjythuVaj6UR8CesXIQHdx62Xi/PhgEA333nC9Fqx3jXF36WqT6oI8MwyCaaZhb4X5+7AzfcswNACZVUkM2rlac5VFR5jP0VJUzsJ4ZOYIhstZaJlpdaOvldLog+LUZTTaxLdZl5ZUoXj1aw42D2mDdedDx+Yf0KUSc4DzwJoAy+yi6siQtw8v1fvW4jjlk2arSRrJ6oY+u2vQijGAdnWpioV3Bwpm30ktIhddXc/c/L5GUyqaSAZIEF7M9CUY/o6jXtRXl412GxaFEwItVPWDZWwz6DSirPqKvbf6jvgo21mUoqTvThDzxzCK/euBYAcNqaCaxfM46yWDxSxaVnShUpzRHODJRMsn4SVd9sGyruGe6vGlD9duCCE5bjlecenTmGu1JPNkPwx1LMMFQjs+8l54h4GUtGXR0mlRXvo15xrxaox4tMrzxK3dLnbelO/oQVY8r3/HjpJWUXGGO1ClBLNpN5Ef1JP9XPZPQmG8aPH5VBlkVOB9zNX/Q9z4bBx5GtI64eRg6aXTIMacBVd/fLxmrYe7jYSwqQMRm6Ssr3vVLCIjlXqgwkC8i+XFxHOVYLcOoac/trFo9g31QL920/iKlmKIyjpjgMHaaIaeoX/yrrVpss7PumWso1dNgqESZtqse2I5YWxZOJA33fw7JFtcRLKpN8MIdh6M8x/WhMbx7HePrADA7NtHFayhY/+ZZni1Qm3YDmmYlhNNqRCKpssSJP0ktKHW/6jnbdv7JpHd70nBOS6xgM/zzwjp+fB90pouL7qY5dZRhFXlIm9qc/J9WtVjV6k8FYieGxXIxybB2vqaT4hjsQ9jCzpxKHSeWrIxOHoWWrPfuYJcbzbOOfmaa5DEPdQEiG4QL3rGiFEXzPvrDbFi/urso/i8VIMzyaQDYKk3qnLCq+L3YlMpcTVx3IXb/JKK2D9K//fW9SJOr845el11GFka3OtKl9buzkv/PAPYAxDJsaUNOvK78ZxpmrpLjOfHnKMGSkN9J7zGEw2meqa25LPvjAjoMAINxYe0Wt4qVeeRYbhic9+rK7d/M9USAY99gz7eT1eie8TRuym6CkbeozGb31FOw6TF5UgSIg1MhtYfROPzdMLqk5NoyV4zXFBReAEv3teyhUSYm+lShRnPHGM3hJlTnP9n1pGwbLDecC93KQ55UD2NUjXL8LyJdpxaJkMWpbAvc4FlsYRifgOZ10tREgvYm4DSNvd0g5mb5xzw5MjFRwSsp0siop0wINcS0OnqWUPgNyASOV1B6LSoovupwxKdfIsako9pMgYRj7mFCXgkU9j0MXkDLJpJp8MEhVFvfvOAQAVibXKSq+r+jhk2sxLymf4jAM6c2ZtxgH6cTHLDFBNG56JgH9f3N/swshd6uNMgzD3F6RSopvhIBsHAYtuqr+397v45aPZd4PniqD5mxocavlsKX359DteTUWh9GOYhGf9KevPFM5z87IdIFhv7Ze7yXw1TGbCwydDSMvshgo3u0GQiXFGAaLwzC5rhLIMF5mYtnAjd6mHb5gGIG0YeQyjMUJw7h/xyE875SVmWJIMhI1Oy4m1Q59Nr3wdNgYS8QIZMf8c297Dp7ePy3uJ0T2ZTXfE/WHHeclEfn7prjRm4SQutjktU8Cg1wQZTxPIjAe2nkYRy0eydRZ7xaVwMuMi4zDSIzeQcXLZRg62xUCo5o1UANyTtcq0hZEKFRJaX0l47TuJSUZRP6O2SowdJWUtgD/zxefgmY7whXnrcv0vV7xM15MyxfVM2yHL6DC2aEdFQrNot8B+WyojIJ8l5PrHm608dINR+GtF5+onZcvYIuO03/jFTRNSRJnC0MnMH75/HW46KTl1t9tHjs02LpL69LRKvZPt1id5ByBwfL7d4tfffaxghXkudVyG0aemoznfrropBWK0RxgOY1MAkNcy7BYGBYiGkMKbCOBoe/ml4xWxcJLgVy25INKfzz1enTcktFq8jKmi74uaG3VzExt67uxqu8jihIXYcrV0w8sG6sJt22C8JJK3WoD38OhVjtbetZgwwDk3CaGx9vk42myWxWqpAxzQInD0NSBtikp1axqW/x/VSWlul2tGK/jw798jtqm2M1nBYapLjZ/xiL+JooLU7zr8/j5rO6M3h71RSmgFEaYbITK8yHYrq1/nyfYFecBZsNwDCMHZx2zBGdZDEtAjg1DWxylAElC+lss35QNZMNoFOSbycPm55+s9OlFp63Cs45bxr6TrEKnvybwDLkvOHUVdh9OcuXrWVNNglCopAyLubJD1HaNIynDoIBFm5Dm52TiMPJsGNrudFFVZgoGsrmkTIsh1y3//ktOQzXw8P7r78u8XJUgceGdaUciBqAf+B+XnIK3aK7ZnGGQl9QexjD0560LQuqfOWZCHlsPssyrUCVlmAOcYfSSGkQXGObUIPa+8bQhh6BmdqgEfuZcRWCQd1wY5W689H5++uoL8LxTVmaP0dR+agGlGIdmWhmbCu9H3jWT4+z90wUtufTOpVvt0NkwimCqGQFkF0faCZGAIe+MfIaRTISpgvQBZeF5Hj71axfgRaevFt9Jg66XYQsm8EVlw9rFmV23sGFYUqOb2vc9NQJVX8iIYVDAYp5NyRQIaPqcXFc9h65NL+D+NO5DD9zLU0mN1gK8/YUnG3d9QLLgxHGMmVaIkZz76BRLxqqiHoTeJ2nD8EWdD8+Ti6xnEJwARP94fIgp7sFs9M5fLHU1ayXdsOjJB2190/tjUjtRn0yCrIx3kGljUvWz6c95Mj56n9phCaM3G4NaxVx7nPp7+tETOHXNuPDQouJYk81QyTun30PR950YvUl97gL3eoA9cI8WUHWRpMk805J1km0ghjFtSdPQD0iG4ZcSGEASBzI+UlFqbIjkd7mBe+qxsg/ZRZsfT3ls9k+VYBi+7RrZ/ngG4eJ7nvDKOjBtVkmZdo70nKlvthGspQwzjOOOa190Clm/ICkdSokyQ63etBCc2rCO5DEMk0qKfVekjtFVUhSDQV/rkd629iQbVdvi/aX7SoRH9hgd9JNpniVeV+q5JobRDKOO3Gpt7xy1d8KKRfj3X79IOZ7UZeP17DyyvcIdGb21caT+usC9HlAkMFaO1+F72ZiKmVZY6IdNgXPkojkb4AwjMKgWTHjfL50lz9dYA7nwmtxqzzt+GV5y5pqs0dvzMnaEpB/ymNFaINRftt27em7xTkrmsZLfVXxPjLtUSan3aBoe4WKq+fnrSFRSQKMVoj5RNx/UJ+g5w6qBj1Y7Qhip42EzLNOCaYzKZsfWDPPG5HTAF1adYZyzbglOP3pCCJI4ozYzD6jpeSupZ3xPeX55Ffz0vpve7Urg5xu9fSmkO3GrLc6VlWVkhHEDwygfh2Hvox6455IP9gE2DyaaOBevX4nvvfNFoi4EHT/TKvaiGE9VI3PDMDymcil/fideUi84dRVeYDHs5SUfBBK11J7DiYooL4WGzK1kZxi0eBkD8rhKarqp/J5n9BZZeolhWF7Eip9ElTfakeLKORvQa40Qw4jjWNn9ChuNrpKqBqixCF/APAYmlZQ+t/W4AZ1hfPwNSeXFh3Ym7sa6l5RtTppUVroKitu1ZFxHzkKZwzCqfr7RmxuGiwWGZ/yfg7Mj9XvZt3GDDcPKyLR2ipggocaM3nOZ3rwnpe1//ud/YsOGDfB9H1u3bu1Xn3qCTT1CD8LzPEW3TC/KTDssNIrRzs6WObQfoHWVdMhAZ15ZXKUF5Mdh2NvQA/eSv3x4RmuBiGUw6Wz1c/OM3qSb54F7/DgSGGRk99kYmdoGpJG2VqCSIqeHmVaYq1rrB/QFnJIPhpGqktIZFGGk6mcSIuZ5SfHv9CHSveZsKXHomcgCSml71gJK6XlazADvr1S5eYUqLt4HPa4l6befUTVxG4bN+G5CxSCIM32xMGY+nKYNlE3QdmLD0PtKyVTnsoBSTwzjrLPOwhe/+EW87W1v61d/eobN6G2bAPSiNFph4YQit0s9FUE/QWqoIPDwvFNW4k3POR7HLB0tOIudr9kl8iK989rI85ICpOEbKFBJ2QzrrDvVig80Q7EE6eoMXSWlMx4Te9BTyNgYRjVIAq7mgmHoSSa5DUO12yDts3r+a89bJ1KXEEzPpozRu1rxgQb7HPh4xdlHi9K7os/pg+rUS0phGJoDhc/a0NOhmEA/GVVSvpcRXqY4DN6ODXxTZfVqEvNZ/X75IqnONDEMuclR81VlMyDk95Eg3O+DIRIYZ5zRfa6d2UKRDSNzvLBhRIUR3KsnRvBvb70Q5xxrd+vtFTwO4+glo3jvq88qOEOFyUDN/5ZtIy8OA5AGWHIPtcGUSE//rO8ydXXYWDWA50mBEbCXJTkme12qpvi8U1ZZj6E2mu1o7hmGBxHprRf3oWel9+eik1bgopPU2uKSYcjv9Fxl+v/JNbIqqr+76rxMn2lB14MLrTp5g0DhDINX3OP2jLx1Ms9LSk81AugqKfl9J261NsZls8mddpRMVDlhMHorrJEpKDqxYXCo8R9DIjA6wZYtW7BlyxYAwK5du2btOkXpzXVUhA2jmGEASQnQ2YQpg2xH5zOBA6g1hztpQ1cLAZpKilWlKxNsZNKhE3SBpBeK8f3EU0pnGLaXFwCOXT6G7/x/L8RxqfrR1sWq7yOK23PDMLR7rgVBEgOUpnEnvODUVfir152rlA+1tmkI7qSF1cRaCPoGwrZZkoF71E6+CskcS6P+blJJ5b16QmAYaoJU/Kz7a9vgJQXke0AC6hjYNpgmTz4AOIWllDG71VJ/tY1TRiWV20UB/l63B8mt9sUvfjF27NiR+f4DH/gAXv3qV5e+0ObNm7F582YAwKZNmzroYmfolGFwG0aZbJWzDX3B7/b8QFNFdWLD8D1P8VTiNZ4JpEsfz1FHATkqqRyVl7LApOdN1Csid5VuF7EJ+hNXStWhTedeCbxELRTFc8wwPOHBNtMKM+zttSw1Rm6bhgWsTLZa3YZhdRbJ2DDsQlrpjyIwGMNgkdlceJRSSRmEGqV95yA2pPejaPNexq2Whklf6HnxNLNKyixosvUwyr2ntFGopDms5gqFAuPGG2+ci370DbY8U7YJQC/KVCPMDUCbK+jqlk6hG4JrfVBJyUVJHkMMI89+kZxjXtRNHjz0rpiEycRIFU8fmEmPky8LPy8PtmMqgY/JRrYG82xAN/7Ss2m0ooxOvCxMC7it9jtHhmFYGK14TiW9pGRCyOwzpP+F6tHvzK3WavTWGYah8qTeDxPKeUkVMyLTJiov5sl0XBGqgWxvaLykBhG2nZLVrTJ9YpPNdkeL6myhXwyDzl+7dARHLR4ppd4g+L4t+aC6CwbUWufGtiw7srz2ld8CEhgsfxIxCwPzscH2/GuBh+k0rsZWBrVf0O1CtEGZ1hhGJ6gwN2wCtWuKeCYQuxGfC3T2USaXVP7x/Gd9c8BZSCm32vSadqO3itOZY4AeBJoHU+XLbF/MTAGQpQVGDRsPLiSV9rQ+lZ0GIvVN4A1PevMvfelLWLduHW6++Wa84hWvwGWXXdavfnUNz/Nw/vHLMuVMbbKAIqCnmmFuFty5gozc7c2GQX9XjNfx4z/6xVLlYwk1LdOqydA5Wkt+L2QYFrWRiWHk1QfhNF/PT1XG9mQ7ohr4ItWLrQxqv8A3AVXGMHSVVCcwpXinZ8dTRtCzs3nN2WwYooCS9rnIhmGLAan4vsI6pUrK3B5v0xiHEag2jL/8lXPxiTdJlbfiQFFkwzCkirf1xfT7p6++ADe+4wVGYWpNw1MgQGzgziytQVJJ5eE1r3kNXvOa1/SrL33DF97+XPzZf92Df/nRNvGd7UFQjqWpZmjMtzTXsKXSKIuy6UTy8K6Xnm5e0FmT0uhdYMNIFwX9JVJdPpO/pjoQdBwvT6sLll5VUqTynkuGMT5SES99WYcLE/IYBt94cq+5VhgaVFIWBq4zDAML5DAbvfmizVinr7rY2iDSm5uM3poN47nrV2AJyzqsGtzt1wDMlS912HKjAcnmaf1qM5M3qepMfSq7b5gvldQRF+lN0Cm23eidfB9GcaZM5Xyg1wXfZjPoBM8+YbmxTVMchikNAoduDxFtsrZkBuHkc62SfXHHDSm985IP5l2P3Fnpf4JJR95PcJXHeL0qFvaZVlTaO0aHaSEi5sIZBi2ctYpvZNM2dawI3NMKKNm6a9pc6AxDSQ1SRiVF88IQY1XVvKT0nX/eb9m+dx+4VwRp9Nau2bXRmzEMZ8PoHZmSk5bnwF+UQTB694th9NPjy5SihFKcj5VQSZlePlOcB73ca1mgIh232KCS4jXAi8CPoMVy+aKaIiTmk2EUqUtsyLVhhFlvIbp33amiKO6AUJit1qC+VN2JmSrRKxfpTT+Zng9XayXta0y2E5UUZxhWG436tyzo0lmbRT7jsIEH5rr05n2AvmMqivQ2nTMf6BfD6HYBMsFU42CsWtKt1veML5+aDTf5S10+jqVuoRdswiAwgNTo2aFKihbUoxaPqEJw1m0Ycn5N1CtCJ99oF9dqsMEU50LtcvdSenZ07/pcr9q8pHRVYpGXFFM3ie90GwYT+NKGkccwUpVUicA9U5JF22+ZtrgatiBVSqcMwyZobQK5CPw5OobRB+jpvO3J5xjDGACBIVJ79OhW20+GYdIzUxxGXuJBAJn64LJN9Zik/eQvt4vQfXDBpO8oy7282R34UUtGlL6NzCXDqPfHhmF0qzV4SUm1TvLbS85cg7e94CTxuzWXVHribzzvxPR66Q8FXlL8dhSG4XGPIV4DxHx/yTmqsOPQjd4ZFY/BVmZDJ0bvblVSHhLPwndedpqxT50yjLn2kjpibRi6Sso2AbgOexCM3nLH2KuXVP/uRTIAvrhSHEaBW61vZktmo7r9OG70VnXiZoGU6YeBYaxZPKJcc9ZtGJpKiq493QpL7yxtbaoLNNkwsllb6d6fddxS/MqmY/GP33skOSdng7LtQ68Q/xcZqXX1YnKsZEGep2arLRPXQE0Z4zC0DUOejaAzt1rzMbbMBUWQTAq4570vZdfRVVLl2uVeUofbs1duQccRKzAyRm/LBOCCpT4ADOOUNRO48MTlOP2oieKDDbAlR+sFpl1V2cC9xOht1j3r//N3ZbxeweFGWxq9uUpKO7fMu2takNcsrivG1rlmGKRKSBhGb22axpPvPHWVVCaQr+QGo8hLyqSy0tWswlDPVVK5Ru8ChsGvry/A3EuqyIahqc7y+tKpytfGTIo+21Bh7tFOJdUH6Dpaq1vtgNkwli+q4XNvew7WLB7p6vzA9zBS9QsX8k7bBLLpzQFk4l10JEZvQ5uGnR9/RuuWJYZvehlsNgyewC4P/AiqFLhqoo6jl8px7mdNbxP4gjQxUhELYBSXM9ybYE7FkfwfxnaVVFE8gPV6NBcsv5uMu7ojBzd6l4r0TuePqWokTzWiXxdQWUknKinbsWUYkQnULf0+s15d5drjKX+GJnBvkKFTbKvA4DaMAVBJ9QrP8/D533wu3nDR8X1r0+S+SoLCVPCew/c8426tiGH88vnr0vYTVRTP1cNfsrIqKZ7249BMQuFXjdexdon0yOpnTW8TdIbBbWbdOin4qdFfV9MB5jTfNoZR+noFO2yTW7cuKHgCvzI2gbxrJm668rM+F3jUddE84dmPiyrkdSrg7QzDfFwRaN2qBmq69NnGkauS0hapYfGS6gfOOqa/6ddN+YPOXbcUf/KKM/Dck/Oz9ybBWdnv6YUcqfpGG8ZbLz4Rl5+7FqtTpsWN3voCUWatXbYom3J65URdEUSzzTD4IjQ+kqjcCN0u4EDWsYAWVlPWViEwejSy5/UFMKcGCQJ1HiV1V4qvKeI2DGNU1bykslUK5eQrYqIyGM6+DnTrhWh1Q87YMMq1x1VSjmH0AVmGYT5u0OIwBhGmBb0S+Pj1551UmLDPxjAA4AOvOQtf/d3nGRmG53lCWABaLinOTrxyKqnli2ri/5eddRQA4JTV41jLVFKzzTA46pVALbXai8DQDL8iOlupPJf8pWt2ez2Tyknvi/67EBjaLlsJ3MtTSZGAMRjm9eSDeju8QmHZwL08c06QI7zyYBu3orgMG7hKai5tGEcsw8gG7lkYBlt8BiHSexBh8nwpi8C3C+urLkzUZkWGVEA1eisxHIb01iZQQSUA+NVnHytqVnMUFdDqN5R8XT1cWlfLkf1q9YSsAkdju2ZxHbWKj7HUu+1/vfhUfOXOp0tfq8hLyhRvoOf8MgmMMiop0yKtx+Hoc5SrpIptGMXCQDfcl4VUw5nbk8eVa0+wocCf08C9I1Zg6It/0Y4IcAzDhjJFbmy4eP3KwpK2ZfIJ1StBUtK0HSkvVcX3O7Zh9JI2pZ9QGEYPfdJjUU5eNY6PXrkRLzx1dab9V5y9Fr/9ovVCFfd7Lz4Fv/fiU0pfqygyWw/CpP55HrOFiWhpKejy7l4wjBJGbx0jisDIH2NRjCrnWXQfh5H81b3B9GbKtiu83ubYS+qIFRi6GsQ2CTwvyRraDKMjxobRb+TFSRThjc85obj9ki/h4pEK9k42NR//zvs1KAKDRy73YsNIGIb63as3HqN8puZrlaT0b7coyqVE9S4yySY9L3Ouz2wYeTYBpbyphorvI47tO2xljAvdas0eZBx5wisPnmXT1W3FPYKrh9En6DaMvPeRG5AcsuiWhpduPyin8hqvVzILlalEZ+H1tOOvfPaxWMXUN3OFfgkM3YZhglQD9fYQTWlilOtY+hL4XsbbTon0zrlm3iKdGL2LhQ1vx4YyaXm63TwJhlFgw+i03WrFqaT6grK5pAA5UZxKygxplJ6dnXmZBHRA4mLr+zOZvnVqetI3Ex+64pzOGugTPM8TarZeWA9PE553DNCbcZ23Y2uFswb9PF31qGQyzumWrZYEkOjwwyjsqO82lFJJdSkwbClQikq2FqHqe2hFEeI4nrX3k2PBCIy8B0zHDkIuqUFEt3rbsigbDDVer2QWvAtOXK54QOVh8UgFB2fas3Yf3aAeJAKjFy1ZxfcLBQGxmV6j2YvsTZXAnp1Y2jAMKqkujd5V30OzQ72/DWU8yDop2sVh95IyH1cW1bSeSxjFXeef6wRHrMAoG7jHj3UMw4xuo1vLoqxAmhipZPrwZ5dvKH2dJWNVHJxpz6sN4zO/fqHiLlyv+jjU6E0YrxivYcV4vtB83imr8PGrzrMW+CmLIpvDr5x/LNYbygHzFDFGt9qca9LQ2BiG75VkGIVutapAM+HUNRN47XnH4FnHLS11TYLtPjMR9x3OA/Lsa0cxZjkVWnK92b/E/EAP3MtbIyosatIhi7JG6W5RthBSopLqvg9LRqt4AtPIsZHOOp67Xg10pNQVvQixa37tgsJaHrWKj5edfXTX1yAEBQv8+tXjRqHEDfNCJeWVjcPIU0kV22/IqaWsDSMv0/NoLcBfvW5jfkMG2DZF3dbDIJB7bTOMCmOi+oEjVmDoDCPvhSRm4RiGGSR7Z0uTUyafEACctGqRksqjU5Ar6cGZVtdt9Bv1HiOvAWBZSZVcP9BtTE6SU8xXzvV9mQUgXyWV/DUavUsY8WuVRGAU9dnzvFL2oG5Al9a7kFchsAxokztX0d5H7ApJkpfsEnkPglevcsiioqkS+g1T+nQTfvMFJ+Mrv3tx19e54rwkP9Wxy8YKjpw79Jrbaa7RLdusMOcEI8PIOTePYZRZ4Ekol2FxST/7/yxsTErvUqeXJoExV7EYRyzDEIbsdHeRp22qBI5h5MEUjNXX9ksGBipeNV3givPX4RXnHD0n1L0sKH/VsOxVTGlcysD3vUycQxBwlZS9wcJcUgXXlkK5uJ9lk1l2CqvRu0Mbxuc2X4SDMzIHGWlSnMDoEYoQKDAq6mzEQcXse0nN3S57kIQF0B+V1FyiTFS+CXwhNhVQyrdhJH9NCzkvyGRDJ2NcCcplDugU5eth5Ldz4UkrlM81wTCcSqonUO58GtBcLykXh5GLWY/DoGEfjjWzrxDqkiFTSXVsw/CzwoFnGs7jCXleUkDxItuJ2q9qcQvuFVYbhrbkdO4llRw/V9HeR+wKqauZ8t1qnZdUHkzpzfvafpfBUEcC6kNmwygRZ2eEiWH4nozSzptbeTaMMp0RnmglxrhXtacNnmdOmUL3VulyU0Zr1lzVxDhiV8ilo1W84aLj8KLTVgHISnKOqovDyEUvuaRKtT/LAmmQQYvZ8KmkuvGSUm0RgV8uyj8vl1SZvtQ7smEUB0F2C9/L8ii9OFS3brVz5SV1xNowfN/D+3/pbFx7y+PJZxfp3TXK5NjpR/vDssvuJ6QHzzx3pCS6zSt21UXHY+lo4tZMj5m71ebtrLlXlQlljd5lltRKMDtutQDSRJn6d5JhNNH5O+C8pPqMMhWyyHvDMQwzysZJ9N7+AhQY1eGyYUg32M76+0ZWMpjUM50G7vm+h0+8aRN2HJjGn153T+Z3G0goN9vFi2qFpTDpN0wGetoodJtNgdYuZ/TuE8qoO6ouW20uZjvS26mkhkdYdutWq+Pyc9fighOXlxJANC8qvodLz1yTSZlfGOmdCoxGuziFSNn6Kt3A97LjllG3dTiwtUpyvGMYfUKZLJ3S6D0cL+1co1v9auftL7zx7ySobBDQr83DR698FgDghw/uTtuzH1tkwygiO7VUKDfKMIwSqUa6BTfyE/SSr90yjHbkBEZfUEbdIVxwnUrKiFk3es+yymuQ0YlBdhDQ7zQxZdorigMqdKsNiGEUL6pvuOh4jNdnZ1k0pX73MyqpLr2k2k4l1ReUSUcsCijNVoWgIUe3vvdlsZCN3rRJmcOiaT2BBFy/1LedqqRMKJqXl21YAwDYsHZxYX9ef8FxeNW5awuP6wael71P3a22ay8pxzD6gzJpLaqBj+osekcMO+YqvfkClBfChjFXL3yvOHnVOP7m9c/CC1N39V5Ryuhd4LhSNC9fsuEo3P++l857lL/veRnvMt2ttts4DGfD6BP0B2JCIjAcu7BhrlRSC5FhkJdUO5rHnOsdwPM8XN7HHXhQwq3W8/JTjpeZN/MtLIDU6K0xDJr73bJsmUtqCOIw3vnOd+IrX/kKarUaTj75ZHzqU5/C0qVL+9S1/qDMYvRLzzoGxy0fnAymgwaZOG2W2p9lBjPIIP36XKV2GDSUqel92poJbDx26Zz0ZzZB7sTqd8nfbll8bY4ZRk/b6ksvvRR333037rzzTpx66qn44Ac/2K9+9Q20GOV5SW08dimuvvjEuerS0MHzPLzvl87C5RuPmZX2F7INozrHyeMGDWW8g1573jp8/u3PVb77+6vOw8evOi89dzjmTRK4Z/GS6pphzG09jJ4Yxkte8hLx/0UXXYTPf/7zPXeo3yiTesChGDzwqt+YbaP6IGOu01MPGsqkNzfh5ax64LAwUxPD0F3KO30FqnM8f/qmuP/nf/5nvOxlL+tXc32DiMMYllm1ALGQVVJzXTFt0CDShPTw7Idlo2FiGMJLKujNrXZgbBgvfvGLsWPHjsz3H/jAB/DqV79a/F+pVHDVVVdZ29myZQu2bNkCANi1a1e3/e0YC9mgOiwI+uzbP0wgddyweEn1G91GOHMMy0bDNzEMYcOgyqCdtTlwXlI33nhj7u/XXHMNvvrVr+Jb3/pWrqTfvHkzNm/eDADYtGlTh93sHjJZ2pDMqgWIuSygNGiQ6akXJsMoU6K1CMPCMBbVK1hUU5dcmYki+dzpOxD4iRCaK6eJnmwYN9xwAz784Q/je9/7HsbGBtPLaLZrOTj0DmfDAMIFyzCSvwths7DljedjSZqxlyAD97qP+K8G/pxtOHoSGL/zO7+DRqOBSy+9FEBi+P6Hf/iHvnSsXyiTS8phfjHbNcMHGXOdbXTQ4HVp7B1GnLRqPPOdjBNLPnezaar63nAwjIceeqhf/Zg1zHamVYfeMds1wwcZc51tdNDQD5XUMKPXXFIAUK34w+clNaigB+JsGIML6SEyzx2ZB4ylOm1dVbFQsNA3dDIOo3uV1OlHTWDVRL2f3bLiiE8NMtt5kBx6R7e++EcCTj9qAu//pbOUuIKFhEX1AIHvYfECFZiesGF0Lziv3fycvvYpD0e+wCiRrdZhfrGQ05t7noc3zGJQ5KBjxXgdN77jBQs2Nc+qiTpWTdRx4spFAAb/HTjiVVLrlo3h+aeuwjnrls53VxwsWOhqiYWOE1cuWrAbuiWjVfz0j1+M845bBmDw34EjnmGM1gJ8+uoL5rsbDjlwakOHhY4+xC/OCY54huEw+HDR+A4LHd6QsGwnMBzmHd0Wj3Fw4HjJmWvmuwtdwxsShnHEq6QcBh9lUlw7OOThgfe/VARBDiOGJRbJCQyHeYdTSTn0Cip1O6yQRcoG+x0YXpHscMTAGb0dFjr6keZ9LuAEhsO8ox8prh0chhnekASvOoHhMO9wGYUdFjqG5R1wAsNh3uFsGA4LHTTzB/0dcALDYd5RcTYMhwWObmt6zzWcwHCYd6wYr+GklYuwfnW2XoCDw0LAsHhJObdah3nHWK2Cb/9/L5zvbjg4zBuGpZCUYxgODg4O8wxvSBiGExgODg4O84x6xVf+DioGu3cODg4OCwAbj12Kv7/qPDz7hOXz3ZVcOBuGg4ODwzzD87yhqLroGIaDg4ODQyk4geHg4ODgUApOYDg4ODg4lIITGA4ODg4OpeAEhoODg4NDKTiB4eDg4OBQCk5gODg4ODiUghMYDg4ODg6l4ASGg4ODg0MpOIHh4ODg4FAKXhzH8VxfdOXKlTjhhBO6Pn/Xrl1YtWpV/zo0x3D9nz8Mc98B1//5xjD3P6/v27Ztw+7duwvbmBeB0Ss2bdqErVu3znc3uobr//xhmPsOuP7PN4a5//3ou1NJOTg4ODiUghMYDg4ODg6lMJQCY/PmzfPdhZ7g+j9/GOa+A67/841h7n8/+j6UNgwHBwcHh7nHUDIMBwcHB4e5x9AJjBtuuAGnnXYa1q9fjw996EPz3Z1CnHDCCTj77LOxceNGbNq0CQCwd+9eXHrppTjllFNw6aWXYt++ffPcS4mrr74aq1evxllnnSW+y+vvBz/4Qaxfvx6nnXYavvGNb8xHlxWY+v9nf/ZnOOaYY7Bx40Zs3LgRX/va18Rvg9T/J554Ai960YtwxhlnYMOGDfjoRz8KYHjG39b/YRn/mZkZXHDBBTj33HOxYcMGvPvd7wYwPONv639fxz8eIrTb7fikk06KH3744bjRaMTnnHNOfM8998x3t3Jx/PHHx7t27VK+e+c73xl/8IMfjOM4jj/4wQ/G73rXu+aja0Z873vfi2+99dZ4w4YN4jtbf++55574nHPOiWdmZuJHHnkkPumkk+J2uz0v/SaY+v/ud787/ou/+IvMsYPW/6effjq+9dZb4ziO44MHD8annHJKfM899wzN+Nv6PyzjH0VRfOjQoTiO47jZbMYXXHBBfPPNNw/N+Nv638/xHyqGccstt2D9+vU46aSTUKvVcOWVV+K6666b7251jOuuuw5vfvObAQBvfvOb8eUvf3l+O8Tw/Oc/H8uXq4Xobf297rrrcOWVV6Jer+PEE0/E+vXrccstt8x1lxWY+m/DoPX/6KOPxnnnnQcAmJiYwBlnnIGnnnpqaMbf1n8bBq3/nudhfHwcANBqtdBqteB53tCMv63/NnTT/6ESGE899RSOPfZY8XndunW5E3IQ4HkeXvKSl+D888/Hli1bAADPPPMMjj46Kfh+9NFHY+fOnfPZxULY+jtMz+NjH/sYzjnnHFx99dVCpTDI/d+2bRtuv/12XHjhhUM5/rz/wPCMfxiG2LhxI1avXo1LL7106Mbf1H+gf+M/VAIjNjh05UnQQcBNN92E2267DV//+tfxd3/3d/j+978/313qG4blebz97W/Hww8/jDvuuANHH300fv/3fx/A4Pb/8OHDuOKKK/CRj3wEixcvth43LP0fpvEPggB33HEHnnzySdxyyy24++67rccOS//7Of5DJTDWrVuHJ554Qnx+8sknsXbt2nnsUTGof6tXr8ZrXvMa3HLLLVizZg22b98OANi+fTtWr149n10shK2/w/I81qxZgyAI4Ps+fuM3fkPQ7kHsf6vVwhVXXIGrrroKr33tawEM1/jb+j8s409YunQpXvjCF+KGG24YqvEn6P3v1/gPlcB49rOfjQcffBCPPvooms0mrr32Wlx++eXz3S0rJicncejQIfH/N7/5TZx11lm4/PLLcc011wAArrnmGrz61a+ez24Wwtbfyy+/HNdeey0ajQYeffRRPPjgg7jgggvms6tG0MsOAF/60peEB9Wg9T+OY7z1rW/FGWecgXe84x3i+2EZf1v/h2X8d+3ahf379wMApqenceONN+L0008fmvG39b+v499PK/1c4Prrr49POeWU+KSTTorf//73z3d3cvHwww/H55xzTnzOOefEZ555pujv7t2740suuSRev359fMkll8R79uyZ555KXHnllfFRRx0VVyqV+Jhjjon/6Z/+Kbe/73//++OTTjopPvXUU+Ovfe1r89jzBKb+v+ENb4jPOuus+Oyzz45f9apXxU8//bQ4fpD6/4Mf/CAGEJ999tnxueeeG5977rnx9ddfPzTjb+v/sIz/z372s3jjxo3x2WefHW/YsCF+z3veE8dx/vs6DP3v5/i7SG8HBwcHh1IYKpWUg4ODg8P8wQkMBwcHB4dScALDwcHBwaEUnMBwcHBwcCgFJzAcHBwcHErBCQwHBwcHh1JwAsPBwcHBoRScwHBwcHBwKIX/H2EECxTbuaU2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b2a74020ca84581a69f44e65d6e0941",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e687e4f82a742329bf8d28241d61efe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64702ba561cc4ee39ea8ec7760459c17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d0ac6e5084242ea89071fc49c0963db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d479735d3f5e4ff5bed90c8b87602fb7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f7dd698442f148b096ada70971f9c8ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a01c989ca17548d6a17308089b0120e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/v/tU5mUZdDxIr6D+V51nJpPUPNjcCNzwrw/g777xeO4xVdT4bomF+JljMwCAU5eOle3mwFFWgy4j5PVF6ON378R77/hhUoCskoaRfOaR3rwfmWA2dt5j+1OBIQSN+ZqKhlGiqx2x+TH/PigNg+du02Mwku97e4mqenDNJgbKYezcuRP33nsvLrvsskE2Wxpld2B5+I1P3IOr/vIbAOQEk5He6gy+5btP4Q/+5QF85K6dyvd5niNziV5zCkVx1hQ1H3Y3VdCpELlbRrg8eyxxD167tDmA3vWGQSYf1M1FX//xAXz54Wd7jsMwaXKmzYYuMEwmKdPu3dR3/XwbugWR0tz0VdpLipxB4mxfojgeqEmqaxmPP/q3B/C/vvZYT232ioHVw5icnMTrXvc6/NVf/RWWLl2a+f2mm27CTTfdBADYv3//oC6rQJiC+lioP/+DZ8TfNAEoDkP3fKAI5gNTaqyBIAKHbZLSzEpVzqNo9UEndJwriF1ZmUhvetFz5s2zqYaxYlFjAL3rDSYTjwmxEBj2Y/SNQBgltRtiSOKWCnCV6VM3jHFoqo2Hnz6Gn9y0Ku1Hdlz1WhZczmS1WvMNdBSTVGEXC1ODKCazku8s3RO/N67ZmUxSPWsYoia5+v23HjuIc9bObZzTQDSMTqeD173udbj22mvx2te+1njMddddhx07dmDHjh1YvXr1IC6bwaAJ2oyGoQkMSjKoL0oi5fN8MUlVPi/O7LrngzpcBd1e3Gpzjn36aCIwhjkMZYr2AJzDyB4Xp7tfPfakm5ohq9bS4HP9lu8+hTf+w/Zc7ZQWUs9Qb17XoGwbv1AxSRV3tFMQ+MZNx1XjMHYenMIN//p9dMNI0ahMJqle949dS/9PtMM5n499C4w4jvHmN78Zmzdvxu/+7u8Ook89Y5BxGN0wEu3VAx++l/WSoprZ+oOUms78iMOoutibvKTmo4KRd1+dHtxq8zinval//TDTM5QV3nmmyA//++M4548/j4OpVkxtdcJIqedddjfMU2rMtEMheCjLsd4P8hwUmy12HWHmKTAt80W9ioZhEy49cRjpcV948Fl8cvsu3LvriGIKNKUR6tkkZfGSmm535/y97Ftg3HXXXfj4xz+Or3zlK9i6dSu2bt2K22+/fRB9qwye96jfNA9HWMI83/PQqPkZN1lptlGvJV0NR9OtNoyyu9T5aJIy7eIIVdJVl/EmI4ExzNiaQaQ3v2V7Upt9X8rJcM/CbkVTDz+uE6rZAfj5JtK75vvK9flxRffJ+1n0nsexLI5le769eEnpfQt8T7rVRpZI7x7eobz+z3SiOU9n0zeH8aIXvWje5ODhzyiMYtSCfBtsJ4zw7LEZbFgxkfnt8FRbPCDfSxIKmiYJkH25+Es0TNB4VPaSiuMMqTcfTVIznQgTFkqhiOjkKIpmj6JYcBjDFJx06TCKcccPnkE7jHD1hachjmP8x7/5Fn75ktNxzaVn5GoYZBLS524njBVTT9n75BwIn282Yrqjaxicw9B5lfTzB3uO4puPHsCvv+QsAKomWPR4y2gjqoZRLfkgoeZ7Ct9XRsPohhE+dveTeNWFp2H1ErMzhU376YYR2swKMldYYJHe1XYKv/3Je/Gi937V6NFweLojXlDPS14w/YF7VpMUmThmV8PYfXgaTx605ybKs2Xngduy57NJ6kROXEwVLylRYCeHZC1LOM8m+M771z9xD377k/cCAB7dN4n7dx3B2//1AQDcnTrbVxIYOr/QjSItR1O5Psm5LueM7mll8pKizZzpON0k9bnv78Wff+FH4rgquaQU4VLGS6piahCC73mKpmrkMLS+/vkXf4w/vfUh/PM9u63XUQQe+5vmfgzgs/fvxT1PHi7V736xsARGRVskeURx0xER3IenpYbheR5qgZ/VMDybSSr5nG3zxYve+1W85P1fs/7eq1ttGMXspU2/qygxWt0QR6dntw5GXiBlteSD6qcOfutDFRgWbe8LDybz+MXnJM4kebmkaCEjkwmPGarqrpocl3x2ItVRIrQsdFJg+JnfxP3p3lKRWuCKb8SKHkenRJBfXmChDfozqAWeYhI3ekmxc1rdEH/79cQldlEzsF6naxgfQAqMKAb+5LMP4t/utQudQWJgbrXzAfwZVlms+bHLxuuYboc4Mt0WaqKHZAeht5nO+czkyUuXMJfoNYYiimJEmvZUtY3/9bXH8Ln791Y6pyryBEYVDaPIOaAXUnQ2YItPuPPhJIngqsWJfU4Q1wYBSAsZLdzSQSM2xhQU9olp00IbjYDQN7fV0kxSCtdB96eRvGGUpiyJYvi+p8Q3FWkYYQnzVU+R3gaTFH1lyyXF255qybmbN6e4cOTHzbTp+SXanF/CBXoQWFAahupxkf/gea59/lCWjdcBAIemOmIy+p6X2iizaqh+XWD+pAYRpokeztPtyVW9g/Ydb2H/8dn1Ec/VMMLy/S5KVMgXh2EmgNN33oR9Kb+ibxBMi35bExjKgq+Yb8r1iXMgiknKImTbGodiElK21Pri+wocRlUNozSHkbmwx/orBbPP1nFbPEquwLBofcIkFSfXcwKjB6gcRv6Df3DvMfE3f2DNNA3Ikel2hsPQdx9kh7VpGMPPVpvu+Kp6ScWxYg5I2qp47Sie9d14XjJIev5lXJuF7dwyTkpwWY/3NAh3XJuG0dYEHv2a99hbQmAk/3ej2Lo45UF6WUWKSUrRHNjftFDWySRl2OTp96l/Kl5SBduhrhKzUXxMWQ4je5gqMNvdCI2aj0bNZ+dkBSdQpGHw8ZHfT7dlRcy5JL4XlsBQ7Kb5xz709FHxt4lESziM5Dvf81LS2xKHoT1wen7Dd6tNPivHYaSL/V9/+RF8NjUrVXfNjWddYJYxSVWJwyilYfR4T8pOulehY3mebQuRfXCqhZ98z1fww2eOQUdb8yLrlcPgQXZ8wQwN7xTva83gki7mq6Yh6akxqsRhqAvu4J6v/gzjWI1haXVDNGu+qPeht83dbvPeEy4c+fmSw4gBp2H0BpW8yl+sH3l20nheJASGNEl5SCa4vqsgLylTxk9g+AKj18SBtGv51D27cPsDCaFadaHUic/ZQJ6XFDczzXRCvP8LP7QKmCKepoqp0wZ+XqfHgE6bYKPFR9auTr4/MNnGniMn8Pj+rCddlsOI+ozDYBxGbDfjZUhvdh1pioLyqWcTthHqJpQxSQ0iDiNm7cdxosE1NQ3D5F5sakvpm8XVeYaR3gmHUarbfWNBCQxl8hU8+B/vkwKDPzyaX0dPdER7nufBN2gYtnTq3PNkmOg1UCiKU0+pMEa7K22lla49JxpGnklKLjLfe+owPvTVx/A9i+thJQ2jR/Wfn9brvDB5SXXDKKthaPdhuq+O7iUVqWaksmZM7lbLI7XVRV0en4n0NmzWpElKTYmh82rJ9fP7VyZmI1TcanuLw4hjVcNrdyM0a4GmYcjjS5ukLFzQiTYFvaQcxhxJjAUmMMrtFOI4xqPPHhdeJbZJS/ZRz0s1DIvpSZ9j880kVQV0i1GcjGGVUqcccyEr801SchEtyitVJDD4aYMwSfUqMEwblCmWT0jnMAh55Ldtc1New0i16RyTlCnSm0hvJdLbokno33MBVMRh8HfQJgR7ySWVV6kwipP7rAeeymFwLbM06W3WkHQOY44UjIUrMPIewt6jM5hqh9i8bikAsydCDLlQSA7DvHPTX8hY+31YsNmR86Crzbq/flnMRc6lMl5SPOWF6XmUid3pxbaf10avlRg5qUw4PtPJ/G4zkXLIdBPJ/5kssqXnC13bbpJSBIYgvU1eUurxoi6GZjI0nWODbYfO0YvbtEnDiDWBWQsSk5Qp0WK7G1vbUvpv4WBmGIcRxyiVWXgQWFACQ1XV7Q/hkWePA4AUGAa1lR4EYPeSsrkv8gCe2UKZid0Lmae7QJaJ9P724wex8e23KW60Rdf72o/2Ydeh6VJ94uCCL1fDiLIaRt7CCeR4SQ1AO+CCqZeklDyZH+/z8Zlupl196GXKjmzfi8yqRVAC/1hbqpCVx+u5pExmZJu3lKnQVVE/ywT59VNxjxBD3n+S/ylCkOaga6ZaRm8ahnkceaR3DMdh9ATbrkbHM2mq6o2nLAJgVvuITALSXFK5JinzyzabPvumdCY6eLf4xPv3R/Zj49tvE+OgnmPeCedN6o+mBaS2P3FIHl9w72/9x3tx87d25h5jAg+GzOUwWOCeNHFkj1PmjI3D4G61vWoYfQod3rXIIjCkcNfmY/q9SbMJ41hJcEcoe5vc/Mo5BquGQWQ7sot/rL03umZhSu9epDmr1fmSv2c6odg0AoPTMLhJKowSXqERSE+p3/rkvfiLLyYpTvj7mzcfFIFn4DCi9LrOS6oH8Bc7b6dAO9PFY0mgu9HrIubWUc+YfNAWIRzl7OgGhbxMrQR+fT4eX3zwWQDAzXfvzJzDb6Vj8dDQMZGmNphqy8Ur63aovpSTrW5PpDgX7rleUkzDyEt1XmaxGIRbbV4RoTJQPQC5wMiapPRHRf2fbhnKhkbmdyXvee88MIWNb78ND+49qpDmPHDNlFQQ4NxJ9jo6uZ2Nv4iVc/W/TTBtBq/7+D248i+/IZ5Db15S6v+6SSqKY9R8DxONGhY1ZUKNv/7KowA0R5s8k5RFA57udNPrUqR3qW73jQUlMFT3R/tLSUFLi9K8USa1z6Rh2ArYZzWP7MTuB0mqAfVlL6NhKIshW/w3rko0q3/8zlP4yg+ftZ7DkcdJLGokL8RUi+12DTswAgmWXhZf/gzKxmHYbPt6e6Xcant8pryNXlLG2DQhk4ah3wdde9owXjpBLb+39+WONHfVZ+7bq5ik7KS3PJcWSpMpTDe5SYGh3Z+ildj7Sf0i0J/f+PF+rf3qXlImk5TUhJJ78H0PN7zyeXjHq56fOV+NwyjWlHl/AWCmrXIYcBpGdSg7aksVPEAKjIl0oTP5OlNADEDJB7OpQewcRvI5KNL70/ftwQv/7MuK0DDlqtGhmqSyL8XREx285eYdpfza826FNIzptj0/Dv9vckZ6eFRFt7RJyuAlZVoYS+wu45IbkTzY7NdlYfMA5BqGaQcOyHucZgKdEMfm/uTNXdqs1ANPLObdKFIEVhHpzRT5TD91DUPX5Ks4IeTt5E2BgL2apBKtit1/FCPwgC2nLcMFG5Znzi/vVmvuP2nXdHtOw+gBej0MwsNPH8Nzb7gdX/1hkqSt1Q1R8z0068ntqwVZkk9dwzAlH7QRhtyYNQiz1K5DJ3B4uqNkf61qkjKZNP6/l50tXAAJZXbZOhYbNIw8Tx06rhd5yneAXMO49ft78cDubPQ+T9tturcyQXm2eVUFZc2lNtgWyWOp8PU9O4dB33OBztsy2dDz5i3Nl0YQqHEY9O5Edo1IT69uGv+MV5RFgOh/m5DnKWiMayn5bEwmVy4Eu1EkiP3AsJqTlsmfmwk2DeNEh8YxSttxGkZlqKYD+feOnQkZe+fDifllppNEYZqCh/hLR014SJMP2jQJfUfH/h+ElkEv6DFmfsjbXZuurdhp0w4vSTkcZbdjeQHzFhASvIpJyqKNAcBkS9pfq4JnKuUC463/eC9e9cFvyuPYTrasW61t8VEXv8pdTq6tmKR60DC4wGETjkxS4/Ugx602+bQKjIomKdISGjVfXEsnvYu8pEyVHHXNPE/DILfcoilE84UKHBnjPkqkDyFMt7t452cfxEGWvBQgN3yuYQCpvDAu5jQHxupBQWoQi8DQzLpzpWEsqPTmNpKXBpUkfasbYqweiP9N5G7MNIzErdY3BDelE8SieSTH9HdP1F9ANT9UNUmpwiOC5wHjKYeTtJ9k6bUtiHn3QWM9xRYkvR2Fw2ixPDgVcOdDz+L/fOdJ8f9MXolW4SUV5eaVUueJua1BRHr3a5KybYZoTow3ArbZUc8VpHc7a5LiAtV0jgncJMV5B5tJyiQs6TyTwNa9DE2aR+B76IRxCQ4juV4zFW4HJuVCrwumZs238gnf330E56xdgg9/4wl81ODdF8dqP8M4RsNP3i+ThkFjOF4PcvnBIpMUneriMHqAjeSlP0nSt1INgzJmmnZDUSwNS4nAMO2as9fl31M7ZdENI/zPLz+iCAZATq7jM1287ZZ7sfHtt5V0q7VoGFGMuu+jWSOBUcIklTOpaaz5gpQJZmT/TrY66TFFd6DiLR/bga/+aL/4/4Rhx0ygnWUYxkJtN5qkyvA3fZqT9Gv35lbLBZvskNAwGoGVw8gzScUWk1SuwBDEtTyOx2Ek9nxzW3qEueldCdmzU/5ncSYyjiN/LOneSBvak9ZmB6QA6jKBYZrnx2c6uPqDd+E3/8/3cN+uw8brqCapWJDegCy0xkFjyJ+bCar1Q35Pc58EyhzJi4UlMPi4m0obSg0jQpNpGKai8on3QaphwEPN962lWPPs9VUExmfu24v/8aUf46/ufET5vsUExqfv25t+J19+m2lHXaRU0jvwPRFQ1CpBwOXdBy3Ok7woTM6Y0HH9RoPneRsJ0jtm6U0K4jDKBO712mddw6uKyLJwCA2jhEnKWAUuNgtB3sRH7noCj+6TcQsdqqkRRkryQr5jt3EHOulNx/HUO3YuQ55jKvFqAgVw1gMfUQzsOSwFhqzlnmoh9cA4p0jofPmH+/CDvdnMv4BukoIgvQFpmlL6xUxSeZsxm2s7Cf9QcCFOw6gMW5oHmngkIGY6ocJhqK53yd/JBEi+832kyQfVB2vy2tD7UWV9OaTZRQktwWGYTVK2a9g0jE6YvHANITBC4zlKW7m7oLR/J2T/sm6HEpL07k9g5Go9hsXLJBDK+ODTeYlXUP8Cg6eFKH2+RfgfExpGzUp6Sy3AbHqyfU9t/cnnHsLP/bXkh2bSudfqhIqm0BWah2aSYs0LDUMTbjVm3rJHeksuyhQpboKuYew/LoNV9ey4Ng2D34utKFiibUH0O4xiBLmkd4Sa7xlz1Kn9N2/miL+je3BeUj1AXSCzAy1MUpqGYTJL8AlApLe+MxQTXNc82N9VFpjj6UK6uKlSS9IkZRYYdq3A3I+ENPSNGoZd+Nj7TQstFxj5GkbvXlKERuAri5oOHpRFL51J6EWGZ68jFgLD71nI2eZmWSgChy0iUsPwZXpzy7m2AL080pt+404WJPBbYaTcFy/KZPPqypqk0rH1fRbJnfY71v5nArEmVsgCDUMh6M2WhzCK4HvJnKrqAMDvUQ1cjEUJZ9PunwosmTJIcFhJ74xb7dxIjAVFettqEkuTVPI/FTchDkMtoCTPJxaDAvf099xOestUIlU8gSg+gbyXCJL0lhxBq1OsFdicALoRmaQSDqOMT3iZBGlHucDQSW/2/+QANIwm89DJI7PDkKcGyR5Xxa22bllQykAxSfXAYfCh4s9L9ZIyczVCYBg0iTg29ydvbGmetjqRmrqcucza0ne0tbxe1HyiYeiaBbT/SYBU0DAEPxFkqkByQVrzfWORtD/53IPYevry/ItA5XPilPSmDanNrbYe+OlGtJyGoZDeZJLq1W2vRywsDcPyUgqTFNcwUukOqIPOdz5Cw/CSc/WdoZ77hrdh0l6KQGSwrmFwDkP/Lu8a6sshj++EMeo+N0kVk955go+uc2xGFp0yRcISpoRbrbXJQjTrPmw75z1HTkgbL9tBGyO9S6Q/EbtgQ/BmWfDT+k0NYhIYE42alfQWsRIWDcNU0InOMfWVNOFW16xhxLFe01ueq9dXERxG4EP3irKXapXZboueR1fRMNSaH/x6vp9mc9CE58fvfhJfY44WV21Za7xOjJhtNlMOg0xSFtK7HiRrUN7GqUjDoN8dh9EDbCYYmlTktZB4SQVCre2YOIxYvjSe5yFgUa369UyaR51KUFZYX2jnXQvUx2LiMMrETvBr677mNW6SKqGt5Jukkr50wliqyhbiFRiMhtFISczkuiqh/5Pv+YqwNSc1PXLiMEpoGDR/TI4PZcGv00tqEH7dFrtfGssx5p5pC1CzmVuMmhcjmHXQxqadERi8Chy/BtcwVC1ImPt8LyNEdEHPSX1TxT4TaKybAQmM7LpAGkYtyO72+YbjVReehg9c8wJmDpOIY5Wk70ac9LaYpALPKKQ4TLmkOqF0FRdrm+MwqsMWqKZrGDPdEM26Lyadya02ERjJ36JEa4bDiDPnUxumAjEAcP0/3Y//8aUfG/t/3JIyo0jDsKfzMC+GRLiNpQF3ZbLSlo1GFfeQGZOshlGVQOabqGY9UFw6RV/0Fz7KJ73LRPnS1/XaYEjvfr2kTC7V4w3GYWhdFOk7TOQ2E6jK92Jxyt4vPeNWN1QWbBHFHRcXUNIJ5zojnIsivRPSO32/CjgMMr/6flY4cg0j8L0k1koxT8dpfEXSyZ86exXG6oHYaHFwkxTxGSZBQV91QsZhlNDea0yg8oBVoWG4invVYUuFIYghpmGMMQ1DnySAzmF4xtQg9K8pZYgQRtpk+Kd7duN/fll1myWInbfWnpn0zk4aHbY8Sd3US0rEYXS48DE2BYCZNrQFpmtYzPKSD/ZKeteZfyL3aCnKF6TvxjjKuMxKk5TZi6YM1Gy1/WkYJoExwbykbG7eVtK7JIfRDSP84b89gCPTUsMw5WjL7OTZ39PCpVr9jS+cJs6CfyYaRrlI726YCBffS8yJpk1UksbDy2wKxfutua42TAKDmaTiWBVqHLQudJhJKt+tVprU6LgTBouAC9zrATYCk09KgLykJIdhKrJC4f2ALNFqi+jOchuyZnElk9SMeSE1kt6lNAz5t5J8MFXBdQ5jstXFwSmz2yCQjOkzR2ew6Y8+j3/8zlPK97xt/TtAFeYUh1E1NQjZrYHUJk0Cw6AhckgyNvsbXyyPnOgYXZtpXjX6EBiDTA1iEhhjNR82DoOnwAjShVH8FmefFSAXYj5vdh6cVp57wmFk+xXrGgYjryfbalqYKI4FR2ith6HNKc4PFMZhpOQycQWRoV+kYdR8z5hXTfIEyfe00eLIaBiMx+QgU3W7S/3Kd6TopDFT3P2WCG8+H12J1h5gSxIXag+cvKRMGoZ03ZMOe1Si1VZxr6O9wFEsdxdVSFLaeeu787bJJMW0gqqV4rpRlGoYahzGu259CNd97B5r/6IYeCqtkvev39stvtc5BMBkkpJ/9xqHwbmdZs1nO8B8kxrdn9Ekxb773998Am/9x+9ljqGFjHvyVIW+U6+KKEfgNAJpXtV30QCzfac7aV8RGPkmKT5vdAGvcxji3EjT3NK/pzuhQgzTb4FWAjlDeqcnffa+vXjP53+IsAJHGKZz3fOSOA9TihXSQlYvaWLf8RaePTaDo9OdjAZLAsCkYehutVEUG4loaqMdRqina1DeGjHVCjHRCBRynDSMiWYg7t+R3j2gKJcUvRdEenvaRNVzQPFJb1Id6d+O4fvAEInK2zfl9SENQ38xTaQ3z6Nk2/XGsdyVH2GZbukF0d1qD0y2c+tNc1NAxzC+AKtDoZukDF5SlU1STMNo1gJGAtoXNUDGEJjjMNT/D0xmNSzuVtuzSSrOjlEV2OIwkn55LGtBNr+SWHjT5841DFO1vaS/EO3Z+q1zGIS7HjsgCgUlbSUHTbIND4/e9j25oPP+ypKsyfdf/uE+fPrePYgiVIj0TrRp3yPNR/7GBVQQeFi/YhxPH5nBZX/2ZVz1V9/IkPBk9qkF2cU5huqS37WYpES/uhGagW80dXNMtrpY3Kwp6w9pGFSHBnCkd08Io1iUQ7S6ykYx2mEkdtdcc+DPLWYqhk8qoc02zGah2I36WUKdm5H2HcsuTMctZDB5MXENYyYn0R8hjGI855RFGK8HePgZmdIg0TCyJqmily/x/kpjV5QCMKr2klxbP1f+XbUexqGpNr76o31iTIFUw4hiHJ3u4OiJbEI5jjwNQzcnmsw9nMPoPQ7Dfs1S5+dwGA3FRTwb+8MX/1rgK26eUWznNvS+ck1k05rFaKcZanXTy0fu2olH901mrk/eVUvGaoqXVOLSmsTx3PCvD4jodd0URfeQaPDpXCh4HN3UwcNPBRIfG+5uXPN9bFgxIYTxM8dmMt5ldJtGQcA2mORWbCKiudZSr2V5Ex3T7S4WNWupQNU0jIY0jbnAvR4QpTvqdmjTMGIxIcbqyWDXfE+aUTQ1WhBKSIRGTKqmZm6K2Pd0WROHwdN/P3tsRlS+A/Iz3LbDJLusyQ8byDdJ1XwPz1u3BA+xHDjdMBaLTM33xIJatBhGsfRUMpmhku+lcFbOZVocVdwrKzB+/m+/hcf3T+HUpWPiO/Krv/BPv5jpo46WRcPYeWAK//K9Pcp3RoGRntcI/EomRg6TFlYFsUFgrElNKA3FvBplxiBki3/N9xCytSUhve1xGLopEwA+9qZL8cntTwmhUJQyhX6jDc+y8bqMk0lNN77nYfsTh4x14fWA3DAun0uKHDyo7oTJVE0cxobl48q5OvFPizIJK89TtQoejBjGZg2DNPN2GGHxWC1118/TMEIsatYwOdPNaBgTPF7LaRjVQQFz+uLKHzwtHqRh8EhLfk4MlcMQtTMsCzsFP9GLZnKr5dlC92k5aSZ58SHejzhGqxth5URDOV4RGJYdShQnfd+8bikefvqYDMaKpBdXs+Zn8vvYEEbSfGFzZeWV7kw40ZFmjLIb7cf3TyVtsrEsk/eHIDQM7fjX/M1d+Nz9e5XvTCY5sQkI8l0g85DHQZQBP4U0wjVLm+lvcpev8wfJd/KZ1YIs6W30kiJXXG4KS3NgUR6ydpoapBHkLyM07yit/ZKxurK4BqnAyN4zmaTUsYvj8k4lnSghl/10h27kMFJBun6FJjDEuCWDITSMVFjV2X3HsXxGZJLK0zCSOIxE28sTGFOtLhY3g9QklXxH7/6iIWgYC1Jg6OH2fNdLtn8q+lNjdmn+nnHykOphAGZBRNd48uCUsrgA6iLHBcazx2QSNEAtjqQkmktt0qsWN5Xj+aKTV8fB94HN65bi2ExXpHYO0xcESHbqehU0GxIbcBqkp5R8lefdv/sIbvz8w9Y4jMmcqnxF4P7nzVpgXCx4m1tOWwpAjQ/gODytppHnx3LI5IN+7wWUFKFaXejocTQAsHZJonEdO9FVMi/rwyrNIGTPL8Nh0CaKmx5TF8806LPVSa7VMHgNqW0ln2SSWjpWU5IPep7ZBi/6YNCuyqc3lyYpshCI9jUvqfVMw1jSrGV4HMFhpJ1tcoEBU7Zas8D40kPP4smD04L0DuMYh6baeP2Hv419x9V1YarVxUSjlsaRaBqG4zD6A6m3zVogJtbXfrRPJMVTNQxZ3IReBJWgZoF7nsxDZco7BQDv+fzDeMn7vya8iITbH1tgONGtZ73ktR1M6RZOWaxqGEp0umWhJw+Us9csBgA8cSDZqRPpDSTj0OqUExhhJH329chx0thue+AZ/N3XH88svGLRYIJRF9Af/sbjmVognOjnY9QwaBicGHzf6y7AG174HACMozEcr8NkkqKFIElO15vE6FfDMEVLr0lNdO3U9RJIOQzNsE+3TR5DqoYR5xZQ4s+Z+k38F2kYpkA2Dt0ktXS8rphykqC57LMQtdjD7L0HJeMwkszMvlhw+RTgHEXN97CoWcOKibr4TvfaEiapdDGos/vmJinKJWUjvX/1YztwohOiEfhJFuwwxj9+50l867GD+OhdO5VjBenNNBHaOC1qOg2jLyQ76sRdtNUN8dj+Sfznj3wXX3woKc0axrEwT1CUc42F5isCAyw1CLxCDeNTOxI3U1rUpMps1jB45S9AJReVGsjp5NA1DFtSMnU8kl0RvdB0nxQ0BCSaFr2EReYWTpAqHEYUCU6ISkfqph3dLKH3+5F9k3j37Q8rBZIA4MfPyDoMXFjz5IOEwJMJIn22CLUs6UrqBm+XlhaMBrBo2yBLnJZFvxwGj/jlHAaB5tuj+yeVMQbkOHfShTFg9x1F5v7ou2veb/Kwa6XmRdM4mq5P2uXSsbqaxyn1krKdpyYyTD7JrbboWXSjCPXUrZYq4Yn2NQ4DAM5euwRAYvahOWwjvRsZk5QUsnFcHH1dD6Q1hF4XXXBOtbpY1AwSwZL2fdqgYbgCSj0gipJFg9RlbsJIfo/FbpM0jBojnSL20JJI7wS+B5EXpijlAZ1VJDB03qHT5e3K72nSZgSGgbzTEccxfE/uPjiXE7BJb7Pxm9qTAkM1m42nAsNU1Y3fk8iXpXmdkV12Rjv/EeZtw1E3CAyutvueNAvOCJOb1obF9q4voNR3Or4X3lu6Zpo5pyL+iHtq0Vxby5wAaEPz+g9/B1//sSp05UIWJVlZPVXDMPaH7b4J9O40aolJSnAYBRoGPaZJoWFIL6koXVht5hv+yRGUzVabatOBwSSlk94A8He/cjF+5+XnAACOTneU47IaBu+zdGemzZ/pnjjI8YRzK7qmMJWS3oEn4zVMXlIu0rsHhOkC2awHaUZa1bYaRpIAlaQ3j5CViz2PCk2SD1IqdL6zz/ZBqu3SREBQS5hq5xnqdwDSwyfXJGXZZdHuTc+cSwWUgETDEF5EBbu1kC0uagW/WNQHt5VNlRqGNEvwMSAt4IQm5HmNDQ4TWRh4UghRbiDetr4o28haXTviizVQLFhNoDYagQ99Q7/nyAmc+Ye3K8GQ1vNZRDfXMPJ4Z76brwWq+SeOCzQMNhZTTNg3aj46YWK2KRIYdP3JVhfNmq/wT0mAm7kqHXXLNC/rBi+pjW+/De+944fKcZ0wcSH3PdVsxPvVZQJjxaIGTlueCGJK16+XQSXtRie9hSaXdjwo0Lwo8r4bybWGP5t2N0q8qRpaHEZaAI4f6ziMkvjSQ8/iS6nJSTdJ6YjiWARxqXEY6oIZpO6xXMOQkduyPXPhHhI62R2QVCWDzCLPo8VNJS1XaxpGaBEw+v36vsc0jOT7bhSJeIpmLZCqd4GphHvU6IF7NJ42DYOOJg1jyVjNmIdI1wpt9v5kAVC/C3yZXsL3PCWdPWAySVkEhs6/RLTYZzcBZUG3kaRzUNvfmXJL/7TDLjB48CBhgtmwA9OKmyJmC1kt8JV4Fh6hbDqHaxikISTFt5JrJ9Uri0jvpI3jrS6WjNVEEB39ZvOSMsVhEIQXovb9//raY8r/3ShGPfCklxQbet4+H5MlYwmPQfE9ei4prp3Le5TzUUSGs3v676/egp86e5XStz1HTghBoFcFBeQGc1GzJvoPJJuy8UagmKEch1ESv/qxHfjVj+0AQLsVEhhRZqKFkeQwmkochlSPgfShsQngQS4+KteQ7Y/QMHLcahc3axlhY8quC0gNY/lEXdlF6KSzCVEMZfemmKQCZpIqqWFEEXerZbEXnMPo2ExScpcJJHZsfj0SFLyyG2AmoQGzfZi7HnLNSkYMaxqGZWecERjpabaEkmUgtBQDWU98mmmTQwg1oQWoC1JeVHGoLIx6ahBLFlsD6U3xM3UW9EnkbR64SWqxWPzSvsUxvBy32lgjqgkkOG31VwhkhvNSfqtIwwASLy6AaxiqwKBrKxoG5KKvpxIBgDf8xEb8zHnrlL7tOXxC5IiKtGsA8l0xRXpP1APl2LnSMBZU4F4YQ1SSa3UMAiM2xGFwDoOZpE7EMmLW8+XD50LCtMDqk0UxSbEJoAubtkJiy+8lSR9gyVhdTOJ26i7I1VkdURSjVpM2a64yk1rdrPs4nCbcK9o5k3+53seQcRjWc9Pbm2IaBk9XIjQMbdFsW7Qek32Y59sJ/Owiqj8vG1mrCwzuVgv0Z5KqG+qqkP1ZF5bG85mQC3wPH3r9RVixqK5kAdBB1+N1pMVvcZxJbQPI58U3SPTs6iwP2UwnUvpkvr7cLCxONQzuym7zkgojs7AAshyhTYgnyQc9BH5Wm5KCNFLGRGoYqcCgOIz0Nsmcy1OExLGqyQFZAltPKbJl/TL46TtM7xWXveS8sKhZS0nv5PsTnRBjjUAREo7D6AHk092sJyapjMAIZaQ3vfyB74sXRgiMtNgKzUEP8mErtcINk5QHNyVtyt+mOyHqadCTfq5S+Y2d1GZEIy/dSvZo6seNtz+Mf7lHNWnIQEZVeHVTuy4AoY3x322IYtUUtvHtt+EHe46iG8Zil2wDOQNMtbrwvOQlMLkP6yap6hpGKuS97CKkyx6rSSpU+yDdamnT0ItJKm2jljVJdSzCkoNzIITA9/CzF6zDfzhrVa6GoaTAyHAYZpOUHuUMqAW+uHZWpGFw7XJxuvjxa3PHDA69rgaHyNXGYilMIEKbTDpRynPyfhGXQFhSoGGYhBt32TVpGIC6gbn+qnPx3tedL5IPhkJgyLGk8Z5oBgiYGW+mE2K8HsBj4d1zpGAsMIERxcJLaqYTZRZlPgHp4fFcLsIkRXlntHoYgBYNHie7rcvPXY3rrzoXAJ8s2cCi6TQIh9vaCWogXnYhbdZ8sfNJjo+VHe+t33864x0TxerCyT1fZOBeYK1hoSOM4gxB+rnv71Xcam2gpo+3uljUSL0+FIFBJqmyHIZBYHiyyExgEBhlTVJ6DAk9D1MN+LLgbeiCi8a0laNh6H0A1AUpz4VTjTdQyVJbASXh8cM6S2npKXCPUBSHQcOVmKTqCqcmebbseUem2/jiQ88Y28xoGJZn0knzplFywyjm7w3EuXz3vzgVGEcsXlLE/+mvC3er5ceLPrNnt3ndkmQtSJMPCs2YnTJlMUlNtxOBwcfMcRg9gHbUzVqQahhZ8lJKcikwBIeh/SY4DEZ663lt1i8fx0f/n0txycaVAKQGUtcmNJA86IlGoNhwCR2rSUq6AXMNoxNGYmdHqrb+4ku3Wrp/pH1kXlKVI73VYxanhXsKTVLMSyrZZar3SeYY3Syj39P1V52Lh/70KqNXEPdV9w0mKVOgnwlFHEbVCHV+jon0Jo2m1Q2tcQXcS4rA+1+Gw6CoZ0VgxJbUIEbSO1lAeWp8vU86fE++V9Ptbjr/5TWSHb/ZJHVgso23/uO9xnaJpKbe2YR4NzW/EtEeRjKViRqHwUlvTcMQAiD5XQYNqmsBQecxZZ/l/zJw2E/PyZqxSGAsaiTJB+k5TrVDkZCQkOPzMFCMtMDQybowkoFqCemtHq9oGMwOmeEwNG8Y35NEoWI6irNqqv7gVbfaULww+uKltpvdeTdqviDjqF2x4w0Twk1fXMM4VrWjlJfhXiFcYBTmkoqzaSQWj9XQjWLhRGAD3VLiVx7AL6lh6Iv3eD1IUiUUcBj8mfH+c9i8wmxeUvVBeEkZSG8yYx6YbOO5N9yOe548ZD2f+97zxcUm/AB1F26O9DYJjORT5TBkUCr3jMoL3OPPhNy5PaFhxCJ2quoOua4t2rmkd5pLijgRPW2Pnoq8WQvQqPkGt1rSMNJrK9fhAoM2LXaB0WAcKiDnHD9HIb3Z+0LBfHzIHIdRAlOaC2eiYRCHEWVSHoQKuUQLPecwkuNqhsXe9B1xJvx3nj4BUNXWZIdVS7Pa2k1SJtt+1iQVicAh0jB0gjiK1IWTB96puaRKZquNskK6HvilOIyMHZuZjwBpjskIDJaKPrle0m+rwGBCPkN6R/Yx169p6nsZ0jsJDjW7dAPmqn369b79eFZg0PmKwFC8pOzjT68BxSTocRgmLyleXIgw2eqKqOlGSQ2DIqyBdJPDcllFEXlJVa9JrQfu2eZuknzQE67yURRnniMP3CMsHasJgUFN0yEmTZP/TetORsNQ6rn4aZvqusFBXpWLRPLBVFNLzbr8HXAcRgnwdOEAhHpLaQv0F5N7SdALVlc4DLN2oAS/sYkRx3FWw+gShwGlTSARcOOpSUq3PNgKErUVgaGapLgtNsnzlF3ofE8uLGEkFwBJegeV6mFkd8dRmn7BL9jlJp+TrW7qV24WjCa32sUsjTMtFKZrEfdEv2dJ75ICw2KSKiMw/s/2p3DF+7+W+V7hMHTBpV1v6XgdOkjY8XQQqoZh7VJmJ62apMwahqn88FS7K8bAZF4xITEFQVw/SD2W6NpxTKS0vf8m6IF7trkr3WrT+cs4jIjdo764N2tBJghVpjdXCXdAzSJgJ72zQpbaMmWMJg1D95Li75Det9nGSAsMvWodRTbnxWEIDYMt9M8cbeG+XUdYanJ1QiXJB5Pj737soHiQtIMH5O5BD9xTo7YlWWVbNBqBLD0KyN1fLfDxH1+wXmRgjWJJvpGpTV8ASYDyOAyKKK8zDqOd5k+q4lYr7ikdZ91dMwuuTteEiYAwY4n07oSREqBG42zakXpsXPkzI2TGvKxJSmgY2U2Djr1HTmDv0RlrPipTHIauYYwZdux0zUVNm0kqT8OQ2kLN9zWTlKWAEp2jcBhdcS53pU3MTOZr85QWtDDzVDUiG4HWQN7mg//OhZEJsh6GJ/I96aZFk4YR+F7mfdLf9fPXL8PzTl2StsHikmykt0HI0jwW+dzYfUy1uqn5z0eQckFxHGO6HaYmKcZhOIFRjMmW7v5IcRipSSrUX1pGbDMO48BkC2/4+++IXYJufuIaxvu/8CP83qfuA2A2SdGDN+Xrb3UjNGoyNz8H7eSokpzoM9sxX3TGCrz1ik3iN2GSipKMoxmTVDoeyguqcSy002mHUakCSvpiN9MJ07TZntVNlc6Nohh7Dp/A2qVNeLpJyuZWG0ZKKUppkspeI45ZPRKTl1Qc40fPHMdbbv4uWt2wvEmKaQf8f+N9skVIv7bnpRptgaZjSrFO7Y7XzRpGLukdmxds6pc5cA/4268/hm89elB8l5ikSDOXz9r3POV/Dp+bpELVzTuKUx7QzyYfLEpoKE2+qYZhNUlFaT0MCgTMaoqmcqqJwFDblO96cv7aZWP4+zduE20QutoaoPcZsGsY/JJJanO1lDS9pxONmhbpbbz9gWMgAuOOO+7Aueeei02bNuE973nPIJoshemWScOQUdy6XzvfIUu32mQIjre6ODKdBLDpJikPqjr5yLOTaXuc9CZvh1QdNeS6aYdSYGRMUulMadbVpHqhZg/lLxZ384yirGmDFikRuBdJDYPHYQDJIlWmgJK+uNECH/i+sdax6EsU47H9kzje6uLCDctFIBWB7P76YtnpxopJip6DKXBPSeLmexm7fhjF+PbjB3Hnw/vw9JEZq4aRcavVOYwcDUPsWg0aRpDySfpGRtdodKHJ2+2N9JZt1AJPCzjLukoDwAN7juI9n/8hvvnoAfFdqyvNoDzxnm/Q5gieZpKixZuuLcym2kqUt/kAuBeibJsQKou3jG4njUY3SSWCTL2e72U3DvSu/8K2DTht2Rh+4eINmRgnwE568zHiqYkAm0kqFHOfNpkimK+hRnqPDOkdhiF+8zd/E5///Ofx0EMP4ZOf/CQeeuihQfStEJNWDiO5rWlNAwkjmTyPu9USnkmLGukahqdNaBJIVI+Yn0OLQV0zawHSFdb37fb0JDkdm/AZkj4rMEjDyLrVajEkMe+fp9xLu5uNW9FhMltRuojE+8Y+nWIA9+06AgB4wRnLDV5SZg2jFUZKKUoaZ5NJKgYnKL0MGRtFcs7M5GkYVrda9RmbwAle/XuqDa9rl/rCZBIY1JySP8orKTDSkyl2hy+OidYYZUxK/3avWrqWILwLNQ3DtlngXlI8iI7+F4JU60BRMKAeW8THm8ya5ORBXlJxnDwHMqeJOIzUWUa5T9/PmqTSYzasmMC3bngZTl85IYSfKjDSNSbHJNXQBEbLUGKAzLd0XBjF0tVW4zDmSF70LzC2b9+OTZs24cwzz0Sj0cA111yDz3zmM4PoWyH0RHdCYIhU21mBYuIwCE8fnVG+o4eXqIRZdZKuB8gFLC81CJVlNJmkOmkRnJpWN1qPDeGTpMF2vCZbtIii9WVbehwKVQ1LNAzkgnNABO5qmWdGiOIY9+06giXNGs5ctVgEUhFaHUvgXjpmdK+kGZlstnEsx8v3shl+wzgWKTROtCsIjEh1HshzDuB2cb0NoWEYHAc4TClC6JqLrKR3jklKcBjJHOOPid6Joh09gZ4DX9A9z26O5FxVh0xiTDuQPJsmMAqCAWvCrVa9R0BmTKZxpjgMItn1GKkkl5SmYRg0QdOco2hrxSSlmb31PgNZDYOsAwqH0ZYCg2KMplhCQh7pPTIcxp49e3D66aeL/zds2IA9e7K7k5tuugnbtm3Dtm3bsH///szvvUDXMGgHQw9Dd7ulHY3Nje/ptISpSOsRyTQCfLdA7YdxtmyjzmHwtaWtcBjqtYXq7Km8hy7g+MSoC6I9Eu1zZLPVSpMSL6AEJAt2UTW5KM66YNKOJxF29kkbx8AP9hzFeWn+HN9T1W/SMEykd6PmZfzWTesTBYJRf5Y01VRpYRSLin4nOhU4jFitCpfnHGATGGGUjpHvZUx/ej9MCRypvXGLSSqPw1Ai/AMvo2F0NNdlHYsagdTIDXmUfM9+/cQURIQtcWrJb3GcmFJ9P2uDLxQYmgavC4woktUhg5T0JucQk1ttlsPIvk9GgZF+pdcdB0waBtt0at5mNOd0LykySZHzAG3QJjLZajNdmxX0LTBMkakme9p1112HHTt2YMeOHVi9enW/lwVg0iAg0pubfqcdMp8ch6dl5TupYcidu2fQRKj9OI7Fbk1kRtVy4SschhAYppiAWJZsVDgMVcDxoaXdNk1sm0mKa0x64KLCYVjWQR4kldEwWAZTG/FJfdl7dAanr0zqJmdMUizSW0/v3gh80Qe6hjEdNrs/quLGbf5RHItNRqJhxPilbafjU7/2E0o7puSDphQrJthSVXAXZ30MdQ7ByGH0oWHoKWH4/CczY57AqAW+GEdTplbfoGGctmwM/+3nni8irMVuP2BxGEzD0BfXQg5D0zD4M9l1eBpn/uHt+NjdO5NjfW6SUgNz6f6zXlK+gcPI9oO63TUJjAzpnfzfqPmZdaVjMElNp0GudFwYx0q6kJHkMDZs2IBdu3aJ/3fv3o3TTjut32ZLYZKZQwD5UpLLmslkFWmTYz8rlZrlMHiwjjyH8iZxk1QmcM+wGyUbss0kVQuoMpgqMGqWhaHBzEn82vxc35OLaxyrZTYBKGmqOXwv6xVCAmfFRB073/OzeN6pS8QzKNIwOlGEg5MtrFkyJtpXTFLMQYGTzp2UaNU1DKtJinEYALBiQpqlwigWNR1IcGxYMY5Ln7tSuW9dYCSCN7spMMGuYUgtRd8sUKzJ9Vedi+UTdaOGQVPCRnrr/JGSkTZSI/x97Teel8yEmu8JQVUzmKRMpPflz1uDN73oucIkxU2hwu6fOilwzylCkcDQXd/5gv3Y/sQp5WN3P5n2WWrZlB5FxGVE6vsg2jdM5TyTlIn0tiUfbLJ7ExxGx6xhLOKkdyQ3wSPrJXXJJZfgkUcewRNPPIF2u41bbrkFV1999SD6VgjykuLBO4HnCTOLifSmBGyEg5MtAMmD0zmMKIrFhOCTRXAYLA5DeDsIgUFaCNLPWHpJ+QaTlHD/U+MT9N2PySQlNYzsrlZ5QVNynPePhKsepFRjCeZkkFRyDXpZm/VAPIN6Ael94HgLUQysXZoUgtKj3bndnifha4dJ+mw9YMy0o45jOReoKysWySC4xCSV9PcYFQPSdtaNWnZnKfgH4TwQi5TwOuhUXYvgOZMyHEaYJG/8zSs24ZRFDWMSwiIvKX3oMzUvxA5fTw2SzL08E1At8ATZ3jCYpDwD6S2cE1Lza4d5+wm32ijh3kyZhYvWP2HyTf/XPaMA4JjIfeUr7wAR70pcltZ/0/zKM0kpjipWDUM1AwPSbEWbBIX0bneFoA58pKS3rKkzkhpGrVbDBz/4QVx11VXYvHkzfvEXfxFbtmwZRN8KQeaQKCbVWuaSApJ04oQlzRrCOLsAH0gFxtlrFmP/8eRv4fEUZeMsAAAxcMO/PoC9R09kduEyklouLoAUJE2LSardTbUPTZh008WKwOcFLaLUdtb2ns1WK9yKNZOUro3xfEHcG4vXDmjWfMZh+Lmk97Op9rZaaBh6HIa8PneHJkcBqWHkm6Rk1s+shhHFMY6n/SUug+7t1t96EW5+06VoBL7RJMVNe/c+dQQv+O9fwm3ffzrTB/LCM5qkSMPQvaS6kkMYbwS5HIYS6c3GIE/DSGItaNerpwZJfsvzSqr5vtQwKEOCtkvWzZHSGSR9N9mum/pNTgqBQUMp4tNqmsmXjzcJCtoc1H1PcUrxUhNYFMOuYRgEhmlNpq9UDiNfw9BT1ANMYLDbznhJMdJ7oqlnq832bTYwkAJKr3zlK/HKV75yEE1VwhTTIMKIzE3MJJUuDvf88cvxX/7pfhyYbGfSAPzypWfgI3ftxPNOXYIfPnMcgOqyR5OEP/wvPfysWFROWzau/N7RNAyeeA2AUi4SAD597x7sOjSNp4+eSFVndfLpAo4vErQ7Jg8LW2oQvqMTAi3tX8PC93D3ZP5y6pluybGg5ntKYJIOMvetIQ3D0+MwZEEoru2QVqaThKYXmpsYPIPACKMYJzrJYnLsRHK/tGM+b/2ydDyCTC6oOOXG6JrffjwJZrvnycP42QvUKmqk5BlNUhYNI0nzkrQ9VgssbrXJs+Q5u/I4DP4/1ywprxJvtxsVCIxAckF1TesEUtI7R8MIlY2Kr2QeIM1LX4xtpX5l+6oGz8f7qFYHPklvnvxNG7AkWzLPLafev1HDyNE6jByGxa2WJ+rUkw/SO5EElsZYnGp2vkZ6L2rUFAk2Ml5SwwT3kqKc8kochljMfOHHHEax8uD/2889H4+8+2eMvv6UtgBQJxDfgepxGJLwSr6nxV8UQhJaRIyZToi3/d/78Bdf+jG+9dhBkY9JDdyLlYWY972haRh8x5T8r/Y/jGOWaoQWfbNJyvek6szTnHBOpVkLhKDRs6DqeOZoor2tWUICwxO29Q999VE8fXQGyycS8xHXMMgZoFFTd7amS3GTFD2vlYu4hgFmkpLmCg6evZdAXBC1SQvS4mY2h5KI9I51gSGj7k35uOi+xupmgSE9AOU1PUXDMC9O1Kcu2/XqJqkwio0mKUpZH/ie2OmSS6qelsK2Qzft5DOpQfws6a17QOoQZlIDZ8QrOdKx1H6nGwkth3Mrev9NC7BpzgmTFDeJReocJIhNmuYwwEHt8Gp71D8ivWnjwJufI3kx2gKDzElAupilaj/twmgxS9z2ZKQnnxzkQ66qicxLKv3O6jaoxWG0Q3XHQvNIVs6T9TAOaXZwsu9yKqLLhFZyPfV43jagZb1l5yY7etWWDdhNUoHviTHhHAavTjZW9xUSXScq+S5UmqQSgUGpQfYdb+H9X/gRAGBZmnSP+Ax6oeuB5DBEPIpRw4CIJaGfbaS3MFdofW7UsiYp4VbraQJjLKugW+Mw4ljU6NB/64SSQxirBzjBOIwj02185YfPiqwCtqzAuRpGrEb4q/xGrFyfgwR4zfcyXlIcCYeRjWNIfoNSpElNDRILhwJ94dQTi+rmTmkFkPdIODKtv1e+ogmQ1yHXvGzmI+WeSpPe+V5SnMMwZSMAoAToJW35CMNY8Bp6HXRvjvLVjrTA2HeMCYx08iUV91QvKa5h6AXfCXyCCA0jzAbm6dB3eWQe0rNp8lKrZI7RBUYjTTKmFGbRBByfJLpbLaDyGORWS+dFyguimqR0u3mQCgDfk4tE4h4pd8N8t2tKDcKF8DPHZrBios4KxyRjwPu+PF3caYctot+ZSYrG1ZQaJI5ZapD0961nLBe/T7e7QmBKDiPbZ5PA8DyZxPFYKjBMJD+1/9TBaaWuBTdJ6QKjpWgYPo6d6ODT9+5BHMd440e+izd9dAeOz3SUoFQdeQJD3+GrbrXJJsDkVksC3Pe4l5R5523TcPhGDVB3+1EM4SWlv1+6A4duMpMFlEhAMyGbMUl5qkmKcUl9cxi+bFf23Sww6H8Th0Gg9YK4CorDqAWJKXNK8ZyS57kCSgWIohj7J1tiQMNQ1gfWd82+LyMlTUE6gGqaII+JMI4Fq2XXMOTfiX3aHOkta4kT4RbjoEHD0E0WuoDjAiPwk+u0mJDg+aTCWAYe0ouru9XaYlYo3UPN98XY0PiJKHG+UzJ4SfFd6/7j0qWW94ebX5anCxSZx1pM+NZLkt4y43Dy+0vOWY073vZT+IWLNyi2bVr0jRpGxksqEVB0f9SOfhz1AQA++NVH8VusWhxpvyaBwXf44/UAe46cwNv+7324+7GDuD9Np3KiE6Lm52gYGXu5PE7lrspzGKRhdKNYeEmZNAxTHAY3hepcAV2eOIxkt2y8LQHdmy2jYbBHcUwTGBMs71InjBLBTV5SFoLabJIyaRh0fdkBa4lWw3ujX9emYdC6MNUOxbMYyUjvYeHgVBthFGPdsmQR6jJ+gnaxIs+R74tISZuG0WA7J5nv3sxhcCg7ft+Dnt6clAVe18JLfaoPTbWUtshLSnWrjewCI534XEjsPTIjFmEypVD/qW4GXQsAS6OS1TBoR8wz71JmWroXfu+Z3br2knMXV0oNwjWbtemz3JNG3Bs1DGGSQgYx5MLBx+l5py5F4KvZR8uYpG7+1k7c9egBIXhlHYfkM8/9dbLVVe6NXHOJS+IgTzAASm10LlemWl1lHHTkJbrjpHM98JW0OJGmNXIsH080vi7LGGzSxH3PTnonpUXVokLSCYONS8GCp983uYvTBoEv2OQyTdh4yiIlhsb31WSEgKH/Rk3KpHXQxlB+p2d74H3W7yWrYSSfk8J9NhBthXGMVifEWLq+8e4UCdxBYSQFxnS7ix8+cwwAcGq6yHCPCz0OI3nZUw0jtJikDA+RqoHx73ToGkYmcE9zq+UmqYNp0OCGFYmnFb3MfD0JY3Xi8UlCZhK+033VB7+Jm77xeDomckLTNXV1mSavqVgMCQtOMHISXjVJZe3YusBQo4ORuW4YJuTrU4emcdejB7DtXXeKPjZqquA2ZquNpFttXlwCIEnvPJPUOz77IK79++9kUqwQ8irrtbqhEuDHA/fInZTQCSXpzFN/8EVrqhWiHvgo62+fJb3lcxep/X1P8FJ5HEYnlBoGF2KSp8iS3vRb4EvXXbq+qmFAMUmRBqV7n2U1GNp0kMCQv+kaxmnLx8U1u1GUrAeeFodhcQvWr6mDvtKTjNK9Ksem46S/NxyRpmFMNNTkg0mQpTT38bbnAgNxq51rvPmjO3B36tpIbq3dKC0s0giU+AQi2ShS0pRoDFBfTu4lRd+W0jACmeFSTw3CczjRzu7QVBs138OpS8ew+/AJ1AMPMdRSq7qGof8deF7G5r433aHzXFi0oxLZainVRmrfNXEYNd9HEEhBQLtRYZJSSqf6YvdPMO0K9XHjmk2z7uP0FeN48uAUPrVjRm1bTz5oeB5K4J72AukChtxq9YWoWfeFZsrb5XEYBFOSQNqxtjqRIJqBbAqMMI7hp7Mr8ZJKF0w2pvy5TqYaRi8gzRBInveGFRNYuaiBODXh2QL3iMPoRlLD4E4VXFOxxYGYOAwaFvre8+Tz+smzVuF//+dL0A0j/JdXnIsr/vxrALKbD8pHRus0j9vgXm4TaR4sWlA7oRTcYcS86izmI/2ame8YmU4QJinjxlTNoqxfhzaYvJ43wAKDu/L9UzaPmSvNDkZSwyBhAQDrlqcmqTDC8ZkOlo7XxYQA+KROYzViM4dh9JKKeOoPiylA4RS8DEegm6Qa6S4xjBIvqRWLGtJ1zpA2pBvmcRjJzkxPCULmFsWtljiDdFdMpg8KdDSapHQNI5ZJEoGsLVZX409bPo5FlhrUdEu0OP/ai8/E9Vedi+ecsghPHpzGUla/3ER683H4n7/8Alx72RlJevPIvADwMVwyVhMCMmOSCmQFQoLuVkswaRi0cLTDyKxhaNXeAOIwknHipDbnd6bb3cKiQje94WL84rYNSZtsAVVTc/h41QXr8J0/fBmatSCt027WMJYRhxHGwkuKCzHhsWYwSclFLfH64zt5zj8k+dik1kG/1QIfz121SLSX2Xx4npLx2Jbfi9yqVU9DFodh4zByhAMHfaVwGBYiPfnOVzZaRRrGIk1gtLqhWIv0e5oLjKTAeP66peJv4jCOnegiiiEWGuFNw234cUUvqUg1ST3wzlfgtRetV87jcqTmy91+hvTWvKTilPQ+ZVFDJBgTQX1sMeE8BKCqxV66iOkaBplbolg1G4SRtLuPMbW4EfgG0jtZBN78ojNx9YVJbjCdA+KqtSlw7/qrzsUt18nEfvwlpL9JUP3CttOxZKyOM1ZO4KlD00r98nrgS9LbUNN72XgdE40g2bHG1H9tAWD/r17cZG1rWlHKYfBdKpmkeJQ1kF8Zb6YTohtJEj5Mn4Wsrx4r7VA/xpnA4IkxySSVh1dsORXPXbU4aZ8LK8Wt1hOu5L4n+Q0TN0Iuyd0oFguXomGwuWUlvT0ySXEOIx2r1KuNa2+2fGS8YBNdm94jQJqkeOoUADhFCAy1b0VxGCaTZx5PYMpgbFrE64GXT3rHusCQHAYgrSaAzmE4gWEF2fwBaeM7lL5cS8fTACMtMpgW4jDKFnwHzBwGeXAQlozVFVISUG2HnMPQq3q1LSaplYsa4h7kb7J9PbsuX3SD1CaqL1zHZ7r4wJ2PpPdNfUsWM9Iw+KRt1gOrhvEbl5+FF5+zWtwLTw/Nd0prl45lTFJj9UCJVTC5B1M0Ptmun3PKBKa1WhVNQ6Q3vxTleeJeUrpCyNezFSyYz0R6t7qRMh6kqa1c1MB/3CoTa5oEBr3w9BvtNvUUGJz4psJayTjI+bX78Anx91S7m5tRVt5P1kTCC1/xFB6e54mFPM+tthtGglvhfBl/tzJutUwT5CapZKEn7TsWmxqZvdV8j7pA830PHjiHkfRrkZbS/sLTl4vjCaSZhzFzM++R9PYNGwB+HR1/+urz8IYXbrQeI50mwkS41ALluFYnEn3ja88cyYvR5DDoZVjcrImBpGAd0jAamq07cXmNMyYeAp+QSi6pnOOArImI+iaiWTUNo8nqYRyaamPLaUuF2abmy9rDBD0yPWOS8ryMe+euQ9P4yzt/rBxPLy5pGHyBaNZ8M+nNzgWQ0dB4G2uWNLPBW57qLqneR/JJ+b5oZ33Gyon0HuRiSdlqfU+2oajjPgBPjTfIaBjs2itzBEYzdavlGhe51QLA+3/hQlzy3JX4m68+Joo+cdDzJnmQCFiV9AZUDYC71baZmWvXoWnx9xSrp50HU5bkhCzNErHcYcLsJZWS3lEsNFI1y0HWJOWnz4HPO04u1wMP3VCapGRxqqRNfdNBSHgsafL1PSgcBr0CixoB9iN5jn/5S1vxss1rRD9Ev9O5zQuKlYn0Ni3K9J2e7gUwC4xXXahm8l7FtF1AbjCnWfEk3laLcRi2d2s2MZIaRieMcMGGZfjef71SDB5lD106TiYpm4Zh85JiC7FSQClrsuBQdrpMwyCbN80jbpLyvKTtQ6lJitKSNGpqnikgm95cNUl5RpMUj+8I2P2HKYfB8/FTn3QN4y0/dSZ++dIzxLkAcRgs+WC6yJ+2bEzJWMrNgLrbsbwP0jBUTmVJKvC5OaZR8/GSc1bj9ZedkbkvQGoYiKUXTyZoivVjxYTkR2xeUor3FvOWqwc+rr3sOVi1pJmrYRB4nQO+E+fH8dQgR09IQcU1jI6FZ9ARpO0oEf8xMs4OQDImtIHI5zAi4aVm1zCIY9LeO18V5DwOgzhF7iVlcy75ibNOwVVbTlX6ThuvpC1Vw2jUfLzy/HVih66+p9LryJbGw9SP/DiMcgJDB2lxBO6WzWuf8KwOfNz1fsw2RlLDaHcjjNcDNGq+GLxDaf4YoWHUVA2D/JgpslRHzaBh8IWCoC8w2TiMSHxPNmJAdaslArrViTDeqCnEcIb0jnJIby/531Y5DoDCwcRxotLq5odmLcCxE2pMyM9fvIFdM/nU3Wrp+7PWJHZz0r4m6gGOt7pZ4llRoZO/ifQmgUHPjXsq1QMPlz53FX5y0yrZlm5i8NRstfpz427Ei5tcYJjjMLgAjQ1zJsk5lRzz7LEZ/O9vPoFFjVqmVgb9H0WxSG0P6KS3FAaUOgWQBb1sfTWBdui2MrD6XOLzUseSdJyiGGgEQdrXSDkfSMaaNgv1IDHpcU1w37EZ7Dsua83IEq0UbCs3FjYO4zUvWI/TV0zg1jRDsO/LmhaAHE8SGPocz5DemqlMd2opKzDyTFJ5udU4iLdL2km+SyK61UwKQEJ6GzWMObJJjaTA6ISRsPvT5CMNg8hSWth5dlM9eR4HD9yTXlLZB6G/tPznwJe5lTxPXfw5t0EpzKkvdC/tbiz6SQijGHXGN+i22MD3MN22CwyeSyqMYrS6YYaHadZ8Y8I7fh2AZatN/z+QpoM/Z+0SAPKlG28kAsP3sxyP+Dv980Q7RCPwlQUdgMj5BNiii9nfvlwAbF5SNA5jdR/jDdUdmKMhTFIyCy/3liOM1QMcO9HBTCfEmz76XTy4N4kLWqXVESeyOdI1DGG6SuqkUD+uueR0nLZ8DP/5I9/NpI6hY5aM1YQnnI7AJjAMpifPU733dPCoctoJP2el9FziPIV0e1bfu0NTbTx+YAp/8C8PiN8lh5F1Wc5zXychEcfSS4o4K26mBlSHDCDrcFGoYZQkvfNMUmUX8dNXjguBIVKDtELNJJV8Jlmd6bmYLQ+ziZEUGEnRenViHp42m6R4LihyozPGYfgGDcOQk9/mDULncRu673lZt9rUJEVpNnzfEzuJdpjszLhVQ48b0U1S3DPLhEC7f7OGkfWS4pDJ4hLzBL1cr7loAx56+hh++2VnJ/dPnj4NmZJZFajqSwskXlKcgJcaBovPMOx+9Yh3DzI3kf47v3azHiieSFmTVJAkKUzJ+GbNV7zNeJ9a3Qhf+eE+PLj3GF58zmp848f7M9HfpGHQs9Z3pLTBaDKN+PJz16AeeJnYGDrm7htepnAgHHq9agK9H3wRKtIwuIvvGadM4CP/zyW4ZONK8R3nuGh89SSRetbZmq8G7lEUPX1nc18XXlR+wmOIOAzItgDpJaXfD98kBh6R3vIdL5dLyv6dXt/G1oYJZ6ycwF1IQgW4SYp7CkoNw8xhjEwBpWGA23wFhzGtahh6SU/uRmf2ksruhJOXXD0uS3rLv/kE8Ty5qwek10wjjebmZBtpGJ1upJixgGzyQb7zIduvKacRgUw7IneTQcNopIuiDdwkxYX1svE63vfzF4rdp+4aGviemu9GGR8SGF2lP7Qo8mylppdBN0npL64tVUYj8JXrmSruATJfVLMegGpRcJBJiopu/cSZpwDIek6RwOhGMeq+5HlMOcbU9rNJBumYxc2a4Bd0iLrR2jx9fP8UAt9TPAy5ObNIwwCAK85dI3bwAHcokaawhvZefvg/bVOcDALfVzYgsipkvoahayC+psHrGoZ+P0pKcd9D4GXTvpuuJ84pWI917qrMOYQNKybE31LDMHMYfP1SI73LXatfjKTA6DAVnhbQQ1MdTLAob/13MgN1tMhpgslLKoqzaYOzpLeXOY++52VI+YvpM4ER+J5YwNppYjSdw7ClABC5pHJqTD+bZvRNvELsHEYeuEnK5jQAAC/bvBa/cflZwvND95IykffT7VDZ8cuCTnJ3bXoZTDZpgMbLfnyz7uPyc9eI7/VKcXR98rpLNIysSapZC9DqRDg01YbnSVOULrzJJNUNY7W2NGkYFpOQSauq4iX1gjOW430/fwHe9vJE+3ts/yQ2rBi3mqQozoGPXV5RJUC+Wzy9Od+gAcDmdUuFMKX+8Q1IFEFJ1V1URoBHlyuBexFpGOqGkcA3Br6nmqh5v8W9ZQSGfUXmG0N+ftld/7WXnYGXnLMa65ePo92N8K3HDihZafX+uDiMilBMUunnkem2Eh2cCdyjF7VKLinD4pMxSRkeJMCINeZWS4SfrpVIl8pIJCYk6HEjqhpa7E63L61DQWYwk4bRtGRA5fcCpAFgYWQ1G5y1ejH+4Kefp3hL6YV29L+n2qGyk+WL1IqJOv7m2otw1urF2T6x+6ZKhQCsAo2abdYCbFqzGDe/6VJcc8npmV10RsOo+YpbLaFZT0xSh6fbWDZet6Ydpx1sJ4pQC3zolRiFhmEwEwJQHCLKeEnxYle/mAZDAomGsfGURcqxvifNmeTSrfr2J3//h7NOgQncA4+T3oD6fJQklaziHzkpkNcS/Z53LX5N30s4oN/71P245bu7AMhkfbrA1b3DyGvQFpWdF/ipI4ln0QRGhQV8+UQDN7/pUqxfMY7vPHEIr//wd7D36IxSoCvQ5rveJ8dh5KCjmKSSz8PTbeHDD2RtqbRgcLc0DiWXFDMbBDkTD7CbpGgXw91qdc8tIJlY1GYSxammOQijWAkq0uMwbJvARuCL8qZ0bBRZOIyCnSQnKW0mPQ4Rje15iLyYfW/QMFpdJTqXL4rLJxp45flqEjpCoAkfTj6adnZ0D9T+S85ZjZekAYkcNBZUta1R843ecmNpKVcKvrTFD5Bm2Q0TkxQNNS0wpEmNZ4R48v/yiQam2ol7bSkNI5DPKrnv5POpQ9O44lz1fn0fSiobGkfOnGz/o5cpGzEObh5aOlZHPZDp1/mz1iObuZs2uRvT+NoC9+h519h77XlJjMy/fG+3OG7comHwOU+kd7sbKe6+HNlkisZuAUDKn2U1jKrQhUwVDcMlH8xBO4zFjowGeaYTqfmHNIEhK+IVm6R4Lin9OeSZpIwcBjNJCYHBFztflpRtdyPFXEV9CLTj+fVsO5mf3HQKfub8dXj55rXimsRhLNV8v/kL/YFrtgqvJ3mPsi9dTYCZIPI9+YAf2zUwIFkwbZHXebmTdEEt00zHxjExBRuaQM+IOLGa7yOO1TK5QKphdBINY+VEw1rPnHaw5I4caBoGxXvoKS2on0vH6yLde5lIb6FhxNLkSdi4KqthEOeiVuKT84/XMNHBd/uvu2gDtp6+HH9660PJd4qGwVPIyPraYRRnvKSsJik/e009aJXXC8nPlCzTzFs1jEomqWyNk54EhnbOIkPZ6ORvwzoyRxrGSJqkOmEkdnR8kPlCqAcQ0SLS7kZGtdeYrTbORnoXeUkRyC4bM9ODqR51zfeEoDt16ZjgWgiZ1CDarsI2MVcuauIXt50uk6/5iVdIEYdxztol2MxydfF71N1qbVAD97Lf8zZPdFQTGRcSeTtqXUuj9jphZHx5qgoM0jB4rAAHRYQfnEwSSNrGhFJvdNKAR6FhpA/5RCch98e1PFXUz+WGOZ0HnVTn/dYFhud5IrKczHpV1h2+0x1vBDhv/TLm7VSsYegehbxN27WkZ1ZyHnc9Tsozp15SgV1gBD6EuZi8pPLqiVAfrfDUiobUv6rQ+6DEZ80TDWMkBUaXLb58oecSWTf/0CDTLl6HOqHSBdIU6V0Qh0GQHEbyf4sVyVHs+r6Hs9cuwQdf/wK89+cvgO+p6m2UG7jnWQnrlYtULYKSwJk4DP6/MWCJcTphZHZL5uAuzdxpQHWrTT6nWl2MswXF8ySnk7dAqlqXqgXlZRotFBhkkjqRLERUsyHrVpuM2TPHZlINwyIwIunFk9jvpfYKSJNUVsOgyPeaEmVeBOmwkdUwVmtpKHwva5LyPQ9LmjVcuGFZ4bX4bl//jj8fnuiyrhD/FEwo28hyCfSpbkK81Ix1kBUh4xqGzilxjYNrGKSg2ErMEgrkRSaew6Zx5kGfQnYNIzvujsPIQYeZpPig8YXHlBoESHb6pt1g3RSHYYr0Lu0lpZqk2l25sw8MD//nLkhyzHCXWyAb6a3+bSas3/yi5+Knz1Nt/6Q2mzQMbj+37aQCX6r/Nnu9fk+B74max3rb9HerG2VJ+DQ9R56Xjr5IcQ7DdA/CrbaihkHpK/RbbrLjEg3DYpIKpUmSKj9Su4A0SWU5jKQ98vzjHFgeRDldsXuXv+lcBDfpJKR3Uo/lgT+5qvA6gBqHwdsE1Hmqaxj0WGWQaza9OeH0lRN48uC0FCiMNPc8iCJk1J9xi4ahV7nzvSQwNxRCK1+jKGuSqvkeWgXH26D3YbGFw5DJB8v1b5AYOYGhR8byRXpMcc9UJ7O60GZfPGschvYgcklvjZzW3WpNJind3s6riFEfdG6EH2vaMf/Xn3t+5jvqy0wnq2HwyGerScBjqdsLOAxBTDJTA5A12RHGaobdYCub0lq9H7Vv3Mxh9JISGka+C7HwktIEhv6c+BiuXFS38i3kVhumnn3UbyEwKPmihcOYaNaEAC2qhwFkkw/y8eeBYMlvvGRvInR1N/I8cG8ngmd438YyHAb1kVUBtHAYt1z3Qmx/4pDivOEzQcU1jCDwhGZRxGH4nofJVkdEWNuy7cpzbKOQeiAK91wfQFg6LYh6DfUcVcPIcqz8Wc2RvBg9k5RM02yYmIotXDVZ6W6YOviEqmlmA468bLV6KVXdrdZEepsChnjUaBjrHAY7N8ckpcP3Eo+SVjdfw8gjHaULZv7srLMx50fqvIO4fsNsPsg1SSnmLV5RzcxhkKZXlsM4ngYOktlCtxHzdlbkkd4UuJdmGNDnVpFJaqIeCK22nIahLuJ8nBZrAkNP2+InEqM0xMaKCQxhQirBYaiZZ0mDUO9x3bJxvHqrrEFT0wSGUtDJ84Rw0p+zYpJKBdSuQyfw4X9/QrkXeR/lNQwP2aJJurt2GeSR3nxYTDyJi8OwQJSaNJh3TAJDD/jRz5HHl9UwdA7D3C4R0rT222rxGgUGk1OhlspEv4+yZTsD30M3SgoD6fZdPm62uI7AYx41BRxGjVVVyzPZEXSzWhmBoQtO+q8bmk1SlCur0CSlXTOKEq02Y5JifV5ZhvSOEg2BmqdFVgiMupn0TkxSnrFvJtD8oDnEx0Ifz0D7jS/mZSDeExaDINxfLRpG0qfkUyG9LRqGDu5RpXc14BxGURxGhrPQxibDYeQIDGYVCITAKLeR48h6ZvH+ZK0ASuaEylfrDSNnktKTqKkmKTuHYZLQHEq2WrZL0+dJXnpzfUH0PNWt1miSMpBrfMeWcBjq7/xv/mK86sLTcLkhtiC5pidqUGc0DEsZVeV8P1tN0IZm3RcvqGe511yTVHrDpTkMFrhn4zBI2BVpGPrOkKfg5uCa3arFTSvp3UkdBeKYBClpGEl/SJCNNdTrCg6jWctkL8hDUYoUDsWsl9r14worj0nDoOvzZ6BvCLhHG/1v4zB08CBN/chEYJQxSWXnuWnjpvbZ3ifFJJUeaAvkzIPep/XLZRoXE+mtaO+OwzCjKwQGCQLzwtPQfjcRcxy9ahg2QprspLKEZGwO3NMnKjsHQJqOXfUi8rw0Y6evmqT+4hcutO6gfU8ms8twGAVeUvS9qQiPCa+/9AzhZWMTGGU0jDxtQBl3tmMMLalfePLHPFAab0I3MrvVcgFx/vplePzAlLG9bhgJb6BawEu0Jr9Pt7uJpphJDZKapBpBqfEQ/dIW8byIY1X78MTcKgtTuVkaej4+GQ0jPUgWG2OaSQFPE1i01+Q3u1ttJpdURsPIvodKnwtMUiqHoTrglAX1adl4Hff+1ytzC6cBruJeKQiTlBaYB5hNUjwmgFDkJcXV+oyGUdokBZG/CUhejvESwssUuGdKWxCmRCxfbPPU+cD3REbaPA6jFOldIDBOXzmB09Ooe5v5jX+febmFSSpvsWN9YxxG15AwEoCoXVGW9CYkZV+zO/VVixIX1Xe+6vnwfc/a124oE9zV2EJFGsZ0O8REPbByJOP1QIxPGQ2Db3aS/5PvTTZ1fk1KDVJJw9BiPvj1+YKb1TCSz45wopBBg0Uu2zqHofTHl15SmWvqGwz9vetLw/DQDWU6fKBHk1Tap3rgZ01mhjgxoWl5LtLbCr1mNn+wSp0DylbrZ48zefnYpLn+ICaasoJXFOsmKdUTg5uXoticZdIUYUrvH9Vizkxmz0OIOOMllWt+8KS9PBOHUcIkxd1qq0Sx2oQjXxcyBGWJBZJrWj7jMEzaAFBBw9B+DyOZgpvj/A3LcPcNL8W6ZYnZwEZ6d6JICozAZwt68vtMJ8yQ/gDLJcVMUo0crzEC3TqZpGj+LmpkX3Vd6Pqeh6hvDSO7QcvUptBMUnUmBAqDQpk2meEwPA/LxutYOlZTMsBm2vDtKXX4MUqfC94t2hgKDqOkM4p6zeSzYVif1PVL5WfnSLlIrjmH1xoI9HTQfJFWTVKq+Sdvkdah8ATab6sWN/GJN1+Gq9PavHk76MBnBV4YeZ3HYfjshaf30BZEVIX09j1P+PzrCzQfN9sGL+AcRoXdDD/S5u1lKmJk+j7TJ7Y4yWAwc2qQdkkOg49nI/DT52Buk4QFYJ9T3TAWrrV1FkPQZRqGUWCkQn1cIb2LFyHp6pr+n/Z7opk9V38GnEsoA12bAdS5SdC1GzqmxTYg9KiLNiOBL816lFqe/zbeCLD9j16OnznvVNPp4vo5FQGM/cg1SXkeC0IkDaN3k5Qe6wVYOAyvuG+DxsgJjK5mkuILnKnOAQ8iIxSpvXz8TfP3RWevEnW4VQ2DaSZQzUtKHvsCDgNAmucmP6jI94pNLPwcshln4zBUP3nb+WU5DA5lLG0mKYuGUSQM5WZAPoduGBntub/9srPx2hesxy9uOz23TW4eW7W4gS5LwZ0Hm8DohBEjRH22oEsvKT1oD2BeUvWglImOQNlpf+b8ZMGk6xVpGLU0RqRKHMZPpFlsqUQvv55Cels0DOGmHUiTYmFiSyZ0qXY9LycMJPM773kFvodjM53c62RNxfZjPUgBTetSvyapvP7oYzyXAmPkTVJ8gVMqtwX2QS167/JMUrINL/N7hvRm5qVuFAlTWJ5brV7v2HQMd6srk5BObyM30jtPw+hJYJg1u3IaRv51SFDwegq2wL1TFjfxP35pa2F/+TVPWdzEgam2SMGdh7zkgx0RTc1LtCa/z3TCTAwGYDNJFT/rxc0a7vtvVwrBQcPMffoJ+rNJNAx7bRUdv3DxBrzknNVYu1QmKOQR2QR9t03vTodFmW85bSne8qLn4tLnrsy9ZuDL4Mdfe8mZCMMYq9I66Ka4KWMbnodjJ/IFRlG6cw7TGtCLwBAahmEumTgMuuwcyovRExi6SYo/13FT4J5JwyiZzltvXz0me6yiYfjEcxg0DNamTXvYffgE3vGZBzPtAkzt98oLDD6pcr2kcjiMTtcswMrC5iWV0TAqmKREm6RhWDiMsuAv/ymLG4ieNntJ6bAJtzBipHegenMBOSapmjRJVSG9gSQlOoGWUJNQUjUMP+Xdqm0GuLBIvks+FdLbxmF0pTNAsxbgjw0ZCnRw0vuGn9kMAPjIXUnwnamutgm+7+GYpSY6weTubgP/bRCkt2ljYPLGJG3QmaRyoJukFH9+U+CeEBiyjWochvlY37j4q6S376klJE0eW1ntIfncsfMQvvnoAeMxXGuqErhHyHAYJVKD+J7qN98LbHEYNhfIogWS18jmGsagXqBTFjURWpIP6rCNW0dxq/UzGkZiksru2y4/dzV+7cVnYuMpiyppGDqItzKbpNTNTuJtU/kSCjivRMhwGOm/vZg4fS+b0p/GpayG4ZfQMKqkN+e/+EJg9M5hGElv5Vmp5vi51DBGTmDoJikOY2oQg021aILyn20PQ3goKAJDbYO71XINw2bG4u1SkJ3pGGmSKs9h8Aln4wz0vunXbJVMDVKmD/yebBpGUWQzj/ql5jphlFvspgpWLW6kRaPM6UY49FKvhG4oay5wbyDSME60u8bd/5qlY7jhlZsVx4Yykd46qDZ6EenNs9X2gypeUq2cd9mGZeN1LNHruaTtk2AuQuBDCIyXPm8NTluWrflRzSQl/w4tPGG5fuWYpEyR3o7DKIZukuJQSn3W1IlbFIfBUSYgRvdGSb5jCy9Ut1quYShaSYZcS/6nIDu97/z8wPcKy6vK+7Av0GXMEL7XG4ehtGExSfXKYQS+hyjOahhVvLjyQNlCbelGOKj0rr7J5W61gcJhyOSDJoHBISK9e9AwyJV6sZHDkH+T91GPj1ZAL3QEZDVa+qlsXA/HDa/crLwbvP0qGgblCvubay8yLu768857/PxYCiwe6+FZ5ZHegYnDKNG3QWPkNAzdJMVh4jBMgUTFGoZn/JuDvuaZOulBrlzUwFjdV9xqORmb1xf6d4a9FFkOQy6S5Ulv+XcvO9VaIL2kiswz1jYsJildMAiBUeQl5WXzCtlKtPYCXqWxzC7ORHx3w1jRivUo5+l2NnuwDtr8lPGS0vHS561Bzffw+svOyPwmFygZ5T0bGobNvMOdAcpi5aKGkjIDkPOliMPgvOOfveZ8rF8+bh37Sm617O922I+GkXyaNQy2Zoi0O07DKESvJqklrBZAkcDgvxaZpHgpX0qV/PMXbxCeO7Tr6ab1EJI27QKD/ucmKZuLXyIwyrvVEsoKGaVfnlc6W621DyVJ72ZJDiPwgThWXx6bl1QvCNjiXualrPse2tp3SWoQSXrrBY5OtMtrGM0ScRg6Tl85gUf/7JXG37immvzv9R0FZhIYtmN64TBMEBpGmC8wAt9DFCbz4/WXnWEUovxYjtya3mxukKnR5MhQBNpImgI0Tc41ch2ofKmeMXIaRifHJMUXQj1v06nMTlkmI6bpbw6RPoTtai5+zgoAwBte+Bxxbfo5imE2SVm0B0XD0O6VE4tlF3++WPdCnvq+1zfpbXOrtaUGKcNh6M4HnTAbld0ruPmojFJmWvg6ilutrwihTipMypukBrsy0NjVGYnav4aRfObnsEo+26yeeD8oq2GYYkSKjiWU5TCoD2U3cso1hPm8QMPQ7mOu0oIAI6hh0CQz7T5Nnjc00Kcskq6GVQL3bKDnxzN1/scXrMfPXrBOyUorvaRkpb98L6mswND7K1XR8tpCHmdQBoHniTxePbvVWgSxLbNoYeCe54mYGk4m97vo/d0bLsZEI8Ajz04a+2uDaVy7PHCPBZ1FUSz4Bb2et45+SO88CG2AxQf1z2GU1zD61VgJtDgXcRiJhm9OTpk91ryRM0ERGMIk1ds7lnxmf0tqvkBkPebXnUsNY+QEBknwosVEr4ehFFAqJL3l37aFQgbYma9L50YGDkMNIjSbmxTSWz/Gl+0PwkuqDPKi08tCtWvL73tODcI5kfSz3zgMALhqSxIl/fh+mYG2DG9jyrSaST7oSQ2DkkEuKtAwNqwYx9qlzYGZ2gjUHBHinudVivQ2tylrbluPEdxQfxsQQlNoGPleUlVMOPocyqOPaMw8T1pA+qmHYZtrNT/ZtDkNowLKEmUy11T2uCqkdxGHEeXsanxPzVarR2ia+iLdau1eUtwkVfZl8zx57V52dFWcBqxt2ExS1sC9oucExMIXnZuPBvMCKWa8Ert7U1oVbpIi0tvzknlDLq+mKGyOX77kDMGLDRL0DE5Ng+8Cr3+PG9+zzw9eItb3gHaaQbgXjZeDBEaRkxSZvsoI/yomKc4pkut5P2611po0ngcgu444DSMHwiRVVsNgo7l6SRP7j7cG4iUlEq/FeQIjMUlFaQEdMi2VM0nJ3ZLNJ7yal1RyTj3we1p4lD73uKrYckllNIwSBZREexG1l3x2w2zRq17Bx72MVmYScJ+7fy8+d//epL1ALgjdKMZUKw2qM8RIcPi+h6ZffQEqAs2DtSm/N4g4jNNXTuB0Q6bY+9/xCi2Bni/meL8CvkoCTqDc/K2afJDabQuB0YdJKkfDaLHfxSZwDvPVjpzAECapgsWEJhGfpKsXJwKj0Kee/Ww70uRWqyOJE4gVL5mkfbt5rIyGIXYWfvmXhdpo9ribG4RJylqi1aZhFNxb4HnCbYPGrWspoNQL1BKm1ReZwFdrm3AOK4xjTKUmqYkCDmO2QLdHGkbiRNBfm2944XOE0wfHMi3Yrh54Yo4PisMogolDtKFSapD0k9e9NyWULEIR/0Pf17S0SE7DyEGnJFFmqpexekkTeBrCdqxj6VgtyTHDBUYFLykd5FbLaxcnbbJjdIFhJL3NWkjgZSu12UDXtAmYr/zeSzLpojlMXhpVYUvqqGsYzZIcRpL7iNpLPqN4cH7p/NmUMZvIdDUJOTlW8zHV5vE0ck6GIdMwhiQwJtN8SiQwEq/a/saurPZar/lijs+VhqHH7OQem+EWiyVG4HmYCc01Z8qAhqFQYDgOozw6afrqokm2fLyOay87Ay86e5X47oy0Ctxkyyww/v0PXopWJyzFYZgivXX4voc4lmRcjS30hKyGkXyWNUmVdUmka9oWvjNXL8aZqxcbf+PXBNQgwCrgpHCeWe4FZ6zAlc9fi01r7P0B5Pjq/RuUwFCCHcuURw3kGLe7EcbqgSowmEkqjCXpbUrbMRfYd3wGgHQ5H4SXVFnUA1+k5xgUh1EEU9YH67GaRpmneZlSdPRSQCkyzGWlT2LDoXlJzWFwxOgJjCguZYf3fQ/vfs35ynd/8DPPw4qJOl55/jrjOcvG68B4HYemZPiVbW5xktXaB8+cplyx5VsIbe4lpR8j3GorvN15mTDLgL/TRW7J1jaURd1+3NqlY/jwf9pWqk8ycE9+P6hFj99nmUWNNIhmKjD0FBZC6w0SzZM0DFPajrnAvlSjFALDn7vdaiPwZZGnAXlJFcGUV84GUWPc9wo976RJSh5TNmUPRxTle43VNA1jGNlqR09gdKOMGeZbb39pYfF4IHkxf/cV5xYep3IY5nbpmDiX9PbSQkg6hyGPsfl756UGKVJdjX3x+xUYTCMYMOndK3hkcl70fK9QXJFLmaTYGLdkHie9X2O1ADOdUGoYPUQFDwL7j6UCIzVJrVkyhuP1/LTfg0LdkBupV5TWstPr5LyyArRXqAVeYboZz/A+9pJNgRxoqnIYc0hh9Ccwrr/+enzuc59Do9HAWWedhY985CNYvnz5gLpmRjeKM8LhNC23TL/gQqLQrTbPJJW61eo7By/HfELH5CUfNEWMF4GO7TmtB+9zryYpv3hcq8C3aCyzYZIqo2EEBUKZIqoXNQNMtUJhGh0W6U0J+NYsTQoQ/dlrzkeMEqvpAMAX+TKbvUFAeDaWSFJI2mLd9zGD/GzF0iQlv+tFU9O5Th26SW0Y2Wr7sn5deeWV+MEPfoDvf//7OOecc3DjjTcOql9WvPPqLbjnj6+c1Wt4bFRsuxdeGc8GMkkJDcPAYdiSD7Zy0pv3knRMqNg9vpx5nENZDJpn4HEoqkAbzAuUFytiQlGEOo39omYNU+1umnjQH5hGVBU3veFi/PSWU5ViTXMlvLgAtpUFroI1S5r4nZefk3uM3OQVCwz9fSkzX33Pwz/9+k/g+quKrRgmyI2l+XdpklI5jDmUF/1pGK94xSvE3y984Qvxz//8z313qAxm+wVTYwTyTVJl3Golh6FGnwOmKO7kf0olTu2Yrt2LSWoQ/EPPbrXBgAWGxTlhUNOD97eMWy290DZtRAiMRg1TrS6mWt2h8RcA8Iotp+IVaVT7XKNhcYDoFdv/6OWFx9D4l6nMJ9O0yzQ/Nog4DN/DJRtX4pKNKwvbN0GYpEpqGCOdrfYf/uEf8Eu/9EvW32+66SbcdNNNAID9+/cP6rKzAj45bLZr8itfNt4w/g4kD5YX0DHV4rWR3hw6jxJoE+UTb74MS8fzHyUdO4hMs71yGArpPQDPjldsWSv+5mM0qHoYeQkSTSBt1Ga/rjGT1N4jHUy3w6GZo4YNVcOYmwXvp887Fd/ffRRrUxNcHoRJSkslboKMh+jvPkjDsGnIWbdapH3r67KVUDhbX/7yl+OZZ57JfP/ud78br371q8XftVoN1157rbWd6667Dtdddx0AYNu2Yg+YYYIvPrbd4ivPW4d3v6aD1120wdpOPfDRCSOR8ljfIZheFNOk64SR8Rg6nbsO2xCIHVP/i/2gU4P0irf81JmsPfn9oExSvL9VIr3p2N+78hz89VcezRSeIpPUZMtcbe9kAE/QOajnVYTfeMlZeP2lZyg1z23QC0HlbUIG5d66IDSMO++8M/f3m2++Gbfeeiu+/OUvz2kAyWyC34ZNYPi+h2svy0a0cjRqPjoGDSOPrDJNOspPw/vne9WINV4spxcEAzAhDFpgcNhqbfQDNdK7DOmtBhy+9aWb8BNnnYKf/9u7leMmGjVMtRIvqaI8UgsVdZGJYe6CCDzPKyUsgGzcUm49DBQLlTKgfWFe8kH+KTWMeSQw8nDHHXfgve99L77+9a9jYiKbP2ZUoXAYfdQgqKdV6ihbqV67oUjDoNQSp69UvcACv3rOH1nYZngcxmws6qb2ZiP5YKlI7/T4Zs0XVexMhXQWN4OUwwixVEuZcbKAOIxhEf5F0K0AZdxq+9WUdEuErU9ZL6m+LlsJfYn3t771rTh+/DiuvPJKbN26Fb/+678+qH4NFWU4jDKoB74olAOwnYH2ycGFyLWXnYFH3/0zWLdMFRi8cFBZ9OtWOwjtwFaidTAYfNtq8sHiNnmkN42XiaOYaNRwohPi+EynMLX5QgVpFnPlUlsVXhr1LjLc5gqMwZiGSMOwvaPkFcgzTw/iulXQl4bx6KOPDqof8wp5eY6qgM7Vs3LmEdC8lGwj8I1uvZ5XXf2tkhbBhLyEib20MVu1HYABkt5cYJQoj0rPasOKcZyWCnkTR0GeUQcm29h6+slukpqfAgNIhJpu/jGBfup33l38nBX4h7uewJbTlhl/18sZ8Docc4WTc7YWgE+OfgQGaSczXcrKqbromRbN5RNMYFiI1l5y/ng5QqoM1NQg/WsYgzdJzTKHUULDIJPUr73kLPzhKzcDMNd2ptxRR090ClObL1TUhUlqDhMhVYTvl4vDGJRJ6mcvWIdtG1+GtUvHjL9zAZb0ia4/dxJj/j6tIULVMPrjMABgpq1m5cwjvcsIjMCvbpLqW8NgLsG9TtBZJb1nIXCP97ca6e2JbKUThqylPPbiZHWrpc1UP+/XbCPwPGPRMx2SH+z/mjZhASTz2vQOjQyHcTKg19xLgFS7Kc2H2K3kuNU2a0Fhignfq77wB/1qGH2eD6iCZtCbIjVwbzgCQ+yaufnOcB4XEnqdiJMFpvID8w2B7zGrQI6GkX7ONpdQ8z2Y0uuMTGqQkwGD4DBOdHQNI/ndthMmu7eNcE8IuR7NQj3ejykFRz8Y9CTnzQ0s+SBrp0wyuVpJzx9uhqKU+ycb6N2YzxzGb16xCa/eehqAokhvpMfM7r0kHIach94QNIyTUx+ugEFwGCfaamWxot364mYNx2e61kUq6EFg6LEgVdGvSUvHbJqkBtV0XhlZE3iBpDzwgkknr8BI34FB2HFmCb/2krPwxIEpAEUaxmDfDRt0DSPp19yWaJ2/T2ueoD8Og7ykVA2jqJ4FEaVWk5Rf3V5a5ONdhKJ6w1Uxm6T3oLykqiZcrJfWMJzAGAUNAyhHLNNPg5p31r5oHEZybW9OvaScwChAf3EYyZO0maRsLwvtQG0CoxeTVL8aRlG94aoYtGdHmej8qqg6VlvWL8PFz1lRWGOam6SWTTgOYz6jDLHMc8PNJpaO1TOBnr43QnEYJwP64jCI9G5rJVoL+ADiMGzX7sUkpWfMrYoyOXWqYtl4Hb/9srMH0pYSbNmHo4LSZsXF7Ipz1+CKc9cUHneypgPhIDfl+a5hCO0hp59zZZK6/qpzMaWVl/Y8z5VonU+o97H4NDKkN+WxJ/utRcNIFxRbnp2xuo+xiiUg9ap/VTFokxQA3P+OVxQfVBJcY+lHK+QYlHD8+JsvVYr2mFxtTzbQM5rPHAaQ7wIvjukzsWdZrFzUwMpFai6sueYwnMAowCA5DD1q1LYgkYbR6obG33/rpWfj8HTb+JsNel3xqhi0SWrQqFrsqFSbA1rLfurs1cr/tEi+8vzh1KKYDxgVk5TkHe3HyIjrub8XD3PLYTiBUYBeavMSROCexmEUeRwRh6HXhCacvnICp1ckSykBYr3HF5Tk5lzaS6uA92pQAmM2M6k+8M5XYPwk1jRI0x0Vk1SZSO9hxCA6DmOeYRBxGNNtXcPIFxib1y0BgIz62Q+6wkuqPw5jviaLq1rsqAxm0+uF5ww7GVEfOZOU/RhvFsy1ZdFLmqB+4ARGAfqKw6jpHIaq3tp2Bv/pJzZi05ol+MlNp/R8bR19cxgUYzBfNYxZIb0H0oyDAY2Rcast5jA87dg5hdMw5hcGk61WTT6oF5jX4fteqSp6VbB+eZI9dcOK8YIjzaBhmKvqaFUxGwJjvtvXRxmjwmFUicMYhsDw5zgOwwmMAgwkDoOSD2qZL+dygr3+0jNw+soJvLhHQdRvTfDZxmyQ3vN9MRtl1EeEw9CLnplAPw3HJDVCFfdOBvRTcS/jVqsVPpnLl8X3PbzknNXFB1ow6FxSgwbvV3MEOIyTHaIexshwGDlutQWZG2YTc81hzO+nNQ8wCA7DlBqkl4yzw8Sgc0kNGrMRuDdf73UhoO6PCoehfprglThmtuDNMYfhBEYBBpKtVvOSApKHPEoL0jB3UaXAujWo1CDD8Ks/WVA279aw4Xtyk2dHei/DiMPoI3N1L3AmqQL0W9MbSExSnqcutqMmMIRbbQ99/u4fvVyJdJ4NzAaH4TB7IJPUfC6gBJRLDVJUrmA24XvAHAZ6O4FRhEFU3IvibDueN7/LU+oQsSM97GZWL2kOujsZOIExWmiMiJdUzfexclEjdw7PVbZa47XhNIx5hX4mtOd5qAceOmGcaSfwvaFEhvYKEXQ4T9dihcOY50SqA09vPr+fVeB7+Pr1l+eW0qXUIMO4lSTSew6vN3eXGk30a8e2vRi+V70u9zAhTVLzc8rwBGz9pHNxmBuMilstkETl55qkKEbpJOAw3Js1y7AFKHneaLwsBOGPPk/77LGZPGiT1Ms3rx1oew7svRglNduCuUpvbsKqJU2sWjy4FEJFcCapWYatslhgqJ41nyHSm8/TLs8Wh/Gjd/30vNWqRhmjUnGvFIYY6f2JN186MK/AMnACY5bRsLgPLmrUsHiECunInP/zc/GcLQ6jqHqeQ2+QbrXzcz5VAU29YQiMuU5iOTor1oiC3Ad1gXHzmy7FKQPMRjvbkAWUhtwRCziHMd+jhx2YW+0C0DD8ef5uDBJOYMwybBzGpjWLh9GdnjHfI71djN1ooVnz0az5mRrVowgR6T1P341BwgmMWcZCsdXKinvzcxs1X3NcOZjRrAW49bdehA0rqhUCm48YpklqruEExizDxmGMGkjgzV/Se9g9cKiKs9cuGXYXBoJ+glpHDfNzu7iAMCoBSkWY77mkXN4nh6HhJDJJjfYqNgIggTHeGG1vm35ySc0F5mm3HE4GpGnSnIbh0DdoDm09fflQ+9Ev5j/pPT/75bDwQWk15+mrMVA4DsOCjadMYOUA3F4f3TcJALj4OSv6bmuYGEaVQAeHUUAcJyLjZDBJOYFhwdeuv2Ig7ew73gIAbNs42gJjvpukHByGBcrcP1+170HCmaRmGc9ftxQAsG7Z+JB70h+CeU56OzgMC84k5TAw/N9fe6Go6T3KEKlBnEnKwUFBRCapk+DdcAJjlrFkrD7n+V5mA+QWvBCyizo4DBTOJOXgoMJpGA4OZpCG4QSGg0MKmXxw4b8UDg5VkMqLk8K12wkMh1KY73EYDg7DQpzapE4G7dsJDIdSEMkHT4KXwsGhCqRb7XD7MRdwpLdDKYyCW+3/e/lZuPS5K4fdDYeTDCeTScoJDIdSGK8HuPrC0/DCM+fvgvz7P/28YXfB4aTEyWOScgLDoRR838P//OUXDLsbDg7zDi7S28HBwcGhFKKTKJeUExgODg4OfYA4jJNAXjiB4eDg4NAPKJfUycBhOIHh4ODg0AdOpvTmTmA4ODg49AFpknICoxT+/M//HJ7n4cCBA4NozsHBwWFkICK9T4Ltd9+3uGvXLnzpS1/CGWecMYj+ODg4OIwUoij5dBpGCfzO7/wO3ve+950UUY4ODg4OOqSGsfDXwL4C9z772c9i/fr1uPDCCwuPvemmm3DTTTcBAPbv39/PZR0cHBzmDU4mDqNQYLz85S/HM888k/n+3e9+N/7sz/4MX/ziF0td6LrrrsN1110HANi2bVvFbjo4ODjMTziBwXDnnXcav3/ggQfwxBNPCO1i9+7duOiii7B9+3aceuqpg+2lg4ODwzzFyVRAqWeT1Pnnn499+/aJ/zdu3IgdO3Zg1apVA+mYg4ODwyiAAvdOAgXDxWE4ODg49AMRuHcSCIyBZavduXPnoJpycHBwGBlEJ1E9DKdhODg4OPQBYZIaai/mBk5gODg4OPSD1CTlNAwHBwcHh1xELr25g4ODg0MZkFutdxIYpZzAcHBwcOgDsSC9h9uPuYATGA4ODg59wMVhODg4ODiUgozDWPgSwwkMBwcHhz7gTFIODg4ODqVA6c0d6e3g4ODgkAvnVuvg4ODgUArCrdYJDAcHBweHPJy5ajEAYLwxsNR88xYL/w4dHBwcZhF/+UsXYseTh7F++fiwuzLrcBqGg4ODQx9YMlbHFeeuGXY35gROYDg4ODg4lIITGA4ODg4OpeAEhoODg4NDKTiB4eDg4OBQCk5gODg4ODiUghMYDg4ODg6l4ASGg4ODg0MpOIHh4ODg4FAKTmA4ODg4OJSCExgODg4ODqXgxVQuag6xatUqbNy4sefz9+/fj9WrVw+uQ3MM1//hYZT7Drj+Dxuj3P+8vu/cuRMHDhwobGMoAqNfbNu2DTt27Bh2N3qG6//wMMp9B1z/h41R7v8g+u5MUg4ODg4OpeAEhoODg4NDKYykwLjuuuuG3YW+4Po/PIxy3wHX/2FjlPs/iL6PJIfh4ODg4DD3GEkNw8HBwcFh7jFyAuOOO+7Aueeei02bNuE973nPsLtTiI0bN+L888/H1q1bsW3bNgDAoUOHcOWVV+Lss8/GlVdeicOHDw+5lxJvetObsGbNGpx33nniu7z+3njjjdi0aRPOPfdcfOELXxhGlxWY+v/Od74T69evx9atW7F161bcfvvt4rf51P9du3bhiiuuwObNm7FlyxZ84AMfADA642/r/6iM/8zMDC699FJceOGF2LJlC97xjncAGJ3xt/V/oOMfjxC63W585plnxo899ljcarXiCy64IH7wwQeH3a1cPOc5z4n379+vfHf99dfHN954YxzHcXzjjTfGv//7vz+Mrhnx9a9/Pb7nnnviLVu2iO9s/X3wwQfjCy64IJ6ZmYkff/zx+Mwzz4y73e5Q+k0w9f8d73hH/P73vz9z7Hzr/969e+N77rknjuM4PnbsWHz22WfHDz744MiMv63/ozL+URTFx48fj+M4jtvtdnzppZfGd99998iMv63/gxz/kdIwtm/fjk2bNuHMM89Eo9HANddcg8985jPD7lZlfOYzn8Eb3/hGAMAb3/hGfPrTnx5uhxhe/OIXY+XKlcp3tv5+5jOfwTXXXINms4nnPve52LRpE7Zv3z7XXVZg6r8N863/69atw0UXXQQAWLJkCTZv3ow9e/aMzPjb+m/DfOu/53lYvHgxAKDT6aDT6cDzvJEZf1v/beil/yMlMPbs2YPTTz9d/L9hw4bcCTkf4HkeXvGKV+Diiy/GTTfdBAB49tlnsW7dOgDJS7Zv375hdrEQtv6O0vP44Ac/iAsuuABvetObhElhPvd/586duPfee3HZZZeN5Pjz/gOjM/5hGGLr1q1Ys2YNrrzyypEbf1P/gcGN/0gJjNjg0JUnQecD7rrrLnzve9/D5z//eXzoQx/CN77xjWF3aWAYlefxG7/xG3jsscdw3333Yd26dfi93/s9APO3/5OTk3jd616Hv/qrv8LSpUutx41K/0dp/IMgwH333Yfdu3dj+/bt+MEPfmA9dlT6P8jxHymBsWHDBuzatUv8v3v3bpx22mlD7FExqH9r1qzBa17zGmzfvh1r167F008/DQB4+umnsWbNmmF2sRC2/o7K81i7di2CIIDv+/jVX/1VoXbPx/53Oh287nWvw7XXXovXvva1AEZr/G39H5XxJyxfvhyXX3457rjjjpEaf4Le/0GN/0gJjEsuuQSPPPIInnjiCbTbbdxyyy24+uqrh90tK6ampnD8+HHx9xe/+EWcd955uPrqq3HzzTcDAG6++Wa8+tWvHmY3C2Hr79VXX41bbrkFrVYLTzzxBB555BFceumlw+yqEfSyA8C//du/CQ+q+db/OI7x5je/GZs3b8bv/u7viu9HZfxt/R+V8d+/fz+OHDkCADhx4gTuvPNOPO95zxuZ8bf1f6DjP0iWfi5w2223xWeffXZ85plnxu9617uG3Z1cPPbYY/EFF1wQX3DBBfHzn/980d8DBw7EL33pS+NNmzbFL33pS+ODBw8OuacS11xzTXzqqafGtVotXr9+ffz3f//3uf1917veFZ955pnxOeecE99+++1D7HkCU/9/5Vd+JT7vvPPi888/P37Vq14V7927Vxw/n/r/7//+7zGA+Pzzz48vvPDC+MILL4xvu+22kRl/W/9HZfzvv//+eOvWrfH5558fb9myJf6TP/mTOI7z39dR6P8gx99Fejs4ODg4lMJImaQcHBwcHIYHJzAcHBwcHErBCQwHBwcHh1JwAsPBwcHBoRScwHBwcHBwKAUnMBwcHBwcSsEJDAcHBweHUnACw8HBwcGhFP5/+LBShHKz80gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae95ccf51438451cb81fe1069a630b79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24768af49bd74ad2be10a3449ac3c6d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f315cb85650b4af8af191ddb658bd158",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c079a638e8b144e7936080cee65549b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36358c47a83e41648f76011ce9e3b631",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b28122fc3994451196bbf713776a7bf6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d468ca5b705742ab81aaf4d60ccc4511",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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vzN9/KY8tq2o0DlTRgwcPmIkTJzIODg6MWCxm9PT0GBcXF+abb75h3r9/L68U9yj/h8kfj+np6clcuHCBadeuHaOrq8sYGRkxw4YNY968eaPwGgEBAcz8+fMZNzc3xtzcnBGJRIy9vT3j4eHB3L17V2Vct2/fZkaMGMFYW1szWlpajLm5OdO8eXNm/vz5zKNHjxTq2tvbK42NLEy9evUYAIypqSmTnZ1daL3s7Gxm+/btTPv27RlDQ0NGJBIxderUYdzd3ZktW7YojSnNzs5mVq5cyTg6OrLvb/HixUxWVlapxoEyDMPIZDJm+/btjLOzMyMSiRhra2tm5syZTFJSUqHv1dPTk2nevDmjq6vLmJmZMZ999hnz/PlzZvny5QwAxsfHR6H+p0+fmDFjxjCWlpYMn89nADDLly9nn7979y7TvXt3xtjYmNHX12c6duzInDlzhvHx8VGqW91UxP9xHsP82/+8BFq1agU/P7/KyeSkeAU7QJT811YlGIbB8q98cHpfflOQmZUOfj7QC3ZOxkXud+3sO2xf+QCZ6fJmMB4PWLajG0ZOa1LZYRcqrxcmKYX4Ap8NZjQnLqneKuL/ON0DJeXGMAx+/vaOQvJs1tYK+68OLTJ5AvLOIr2GOuGPC4Ng62j47/GAlbNu4tLxt5UZNiGElAslUFJuJ/e+wsFtz9jtlh2tseFgb4h1Sn6L3bqOAbYc64sGTfPHry3+4hr+uVa6mVEIIaSqUAIl5fLC7xPWzLvFbjduYYE1e3qAzy9+9pr/MjYTY+2+nrCvJx/jJs2RYd7IKwh7m1hh8RJCSEWhBErKLDkxC/NHeyFHIu9ta2qhg58P9oJAWPY/K0NjbazZ1xPmteRDFtLTcjBvtBcyM2iICyGkeqEESsps/YI7+BCRCgAQCHnYfKwPtMXlHxllaa2H1b93h5ZI/ucZ9DIeq+fUjIHZhBD1QQmUlMltr3CcO/Sa3f5mbQfY2BsWsUfp1HMxw+zlbdntc4dew+uk8gLMhBDCFUqgpNTSUiRYMdOH3W7Wxgq9PlNe1qi8+o2oh56fFVhAec5NxH3KqPDXIYSQsqAESkrt9zWP2PlrtbT5WLZdeUmlisDj8TB7WRtYWMvvhyYnZGPlTB+UYugyIYRUGkqgpFTCg5JwaHv+kJVZS9vAyFRcxB7lo2cgwoK1HdjtGxdC8fdfJZ/yjBBCKgslUFIqP397B9Icea9bazsD9B9Vv9Jfs2XH2hg4pgG7vfZ/t5GeKiliD0IIqXyUQEmJ3b/xHjcvhbHbizd3KnZ9xYryxYKWMDGXX+nGfkjHjlUPquR1CSGkMJRASYkwDINty/KXh2rdpTacm1tU2evrG4owY1H+/KqHtj/D2xdxVfb6hBDyX5RASYn4Xg7D84fydRl5PGD+j+2rPIbugxzh2l6+1BkjA9YvvEMdigghnKEESoolkzH4ZXl+k2m3AQ6wsNar8jh4PB5mLm3NThN4/0YkfC+HVXkcRPNMnjwZPB5P5YLeXHFwcGAXrSbVEy2oTYp17WwI3jyXN5fyBTzMWNyas1gcG5ig/6j6uHhMvlLL+oV30LG3HbS0BFUbyLNlQEY1nuhe1w5ovqrch3FwcEB4eLjK56ysrPDx40eFMokkB6s27sGRv24gISEBLVu2xKZNm9CyZUul/a9evYrevXvj4sWLGDBgQLljVTfdunWDr68vtaKoMUqgpEgMw2DPz4/Z7V6f1YWphQ6HEQGT5rrixoVQZKTlICI4GWf2B1b92qEZEYCeQ9W+Zmmkh1XYoYyMjDBv3jylcn19faWy71ftwJbfj8LDwwO2trY4dOgQunfvjtevX8Pa2pqtl5aWhmnTpmH8+PHVInmuXbsW33//PWxsbLgOhXX9+nWuQyDFoARKinTv+nu8ehILAODzefhiofKVRFUzNhNjzIym2LvxCQBg56qHGDzeuVTLp5GSMzY2xooVK4qtxzAM/jj4F6aMHYR9R04BAIYOHYpu3brh0KFD+Pbbb9m63333HbKysrBt27bKCrtUrK2tFRJ8deDkVPGze5GKRfdASZH2bHjCPu7Y2w4mZtxefeb5bKIzeyUc9ykDx39/wXFEJDYuERkZWWjTwoUta9OmDQAoNAPfunULv/32G3bu3AlTU9MyvVbe/cGUlBT873//g4ODA7S0tBQS/evXrzF58mTUqVMH2trasLKywtixY/HmzRul4xV1D/TBgwcYPnw4atWqBZFIhDp16mD69OmIjo5WGVtCQgKWLFmCJk2aQFdXF0ZGRmjevDm+//57pKenIywsDDweD76+8gUSeDwe+9OtWzel9/hf2dnZWLduHZo1awZdXV0YGhqic+fO+PPPP5Xq5r3W5MmTERYWhtGjR8Pc3BxisRitWrXCxYsXiz7R/3H27FmMHz8eDRo0gJ6eHvT19eHm5oZffvkFMplMqX7eeX337h22b9+OZs2aQUdHR+F9JiQkYNGiRWjUqBF0dHRgZGSEHj164O+//1Y6XnJyMjZs2AB3d3fY2tpCJBLBwsICgwcPxv3795XqVzb6yk4K9cLvEx74RLLb077l/uozj1hHiLEzm2LHyocAgD/W+mH4Fy7QNxRxHJnmyc7OxuHDhxEREQE9PT00a9YMXbp0gUCgeN/ZwtwEOjraePwskC3z8/MDANjb2wMAMjMzMXXqVHh4eMDDw6NccUkkEri7uyMhIQG9e/eGoaEhHB0dAQBeXl4YNmwYcnJyMGjQINSrVw+RkZH466+/cOnSJfj4+Ki8L/tfnp6emDZtGrS1tTF48GDUqVMHQUFB2LNnDy5cuID79+/Dzs6OrR8aGoru3bsjPDwcbm5u+OqrryCTyfD27Vts2bIFM2bMgLGxMZYvX479+/cjPDwcy5cvZ/cvrtOQRCJBnz594OvrC2dnZ8yaNQsZGRk4deoURo0ahadPn2LNmjVK+4WHh6NNmzaoW7cuJkyYgISEBJw4cQJDhgzBtWvX0L179xKd8++//x58Ph9t27aFjY0NkpOTcePGDcydOxePHj3CoUOHVO43d+5c3L59GwMGDED//v3Zv53w8HB069YNYWFh6Ny5M/r27Yv09HRcvHgRffv2xR9//IFp06axxwkMDMSSJUvQpUsXDBgwACYmJoiIiMD58+dx5coVXLhwAX379i3Re6kIlEBJoQ5sfco+dm1XC9Z1DLgLRoX+I+vj1N4AfIxMQ0pSNg7+8hQzl7bhOiyN8/HjR0yYMEGhzNHREZ6enujaNX8eZB6Phy8nDsUvu04gOXskbGxscOjQIRgYGGDcuHEAgKVLlyIhIQE7d+4sd1wfPnxA48aN4evrCz29/F7hiYmJGDNmDHR1dXHr1i00btyYfe7Vq1do27Ytpk6diidPnqg6LOvt27eYPn06HBwc4Ovrq3B/9MaNG+jVqxfmzp2LM2fOsOXjx49HeHg41qxZg0WLFikcLy4uDvr6+hCLxVixYgVu3ryJ8PDwEjWP59m0aRN8fX3Rr18/nD9/HkKh/CN8+fLlaNOmDdauXYuBAweiQ4cOCvvdvHkTK1asUEjWY8eORd++fbFhw4YSJ9BLly4pNS3LZDJMmTIFBw8exOzZs9G2bVul/Z48eQJ/f3/2C06eSZMmITw8HMeOHcPo0aPZ8qSkJHTr1g1ff/01Bg8eDCsrKwBAo0aNEB0dDXNzc4XjREZGok2bNpg/f36VJlBqwiUqfYxMw9UCc85++b0bh9GopiUSYMKc5uy252Z/JMVnchiR5pkyZQquX7+Ojx8/Ij09HS9evMD06dMRFhaGfv364dmzZwr11y+bg+++noiHDx9i7969cHFxwfXr12FjY4MHDx5g69at2LZtG8zNzbFy5UpYW1tDKBTCzc0Nd+/eLXV8mzZtUkieAHDw4EEkJSVh5cqVCskTAFxcXDBt2jT4+/sjICCgyGP/9ttvyMnJwbZt25Q6F7m7u2Pw4MG4cOECUlPla+I+fvwY//zzD1xdXfHdd98pHS+v6bQ89u3bBx6Ph82bN7PJEwAsLS3xww8/AAD27NmjtJ+9vT2WLl2qUNanTx/Y2dnh4cOHJX59Vfdl+Xw+5s6dCwDw9vZWud+3336rlDyfPXsGX19feHh4KCRPQH7ffeXKlcjKysLp06fZciMjI6XkCQC2trYYPnw4Xr9+jYiIqusdT1egRKXjv7+AVCq/p2HraIj6LmYcR6RajyGOOP7HS7x/l4yMtBzs3eiPb9Z2KH5HUiIFr1gAoEmTJvj999+hr6+PTZs2YcWKFQpXYNraIqz9YTbWbt2vsJ9EIsHnn3+O/v37Y9y4cdi6dSt7RdSxY0f89NNP6Nu3L4KDg9mrjeKIxWI0a9ZMqfzevXsA5B/Qqq7u3r6VD4EKDAxUSrCqjuPr64tHjx4pPR8TE4Pc3Fy8ffsWbm5u7D24Pn36gM+v+GuT1NRUBAcHw8bGBs7OzkrPu7u7AwD8/f2VnnN1dVVqcgeAOnXqsO+zJOLj47FhwwZcvnwZ7969Q3p6usLzUVFRKvfLuxdeUN7rJicnq/w9xcbKOy8GBgYqlN+9exfbtm3DvXv3EBMTA4lEcV7sqKgohWb1ykQJlCjJypTi5N5X7Pakua7cBVMMgYCPyfNcsfpreYeMwzueYeLXzTmZ6KEmmTFjBjZt2oRbt26VqP7KlSsRFRXFdgzZsGEDevTowX5wNmzYEA4ODti5cydWrSrZ+FVLS0uVczHHx8cDAHbv3l3k/mlpaUU+n3ecDRs2lOg4SUlJAFBpQ2GSk5MBoNDewnnleXEUZGxsrHIfoVCosvOPKklJSWjdujVCQ0PRpk0bTJw4EaamphAKhUhKSsK2bduQnZ2tct9atWopleWd36tXr+Lq1auFvm7B39OZM2cwfPhwiMVi9OrVC05OTtDT0wOfz8fNmzfh6+tbaAyVgRIoUXLp+FskxWcBAPQMtNC5b9V8myurTn3sUK+xKYIDEiDJzoXnFn98+3MnrsPSaJaWlgCgdAWiir+/P37++Wf8/vvvsLGxQUpKCqKjo9n7ogBgZ2cHc3NzvHr1qogjKSpsIQMjIyMA8itQVVeoJZV3nOTkZBgaGhZbPy9JFXYVVl558fx38oo8Hz58UKhX0fbs2YPQ0FAsX75c6Yrx3r17RQ5JUvW7yotz27Zt+Prrr0sUww8//ACRSAQ/Pz80atRI4bnp06ezPZurCt0DJUpO/JE/JOSziY0gEFTvPxM+n4eJX+ffCz3++wskxNK90MqU1/xWt27dIutJpVJ8/vnn6N69O7744guF5/57pZCVlVUhsbVr1w4AcPv27So9Tl59b2/vEl3V5TWp5ubmluj4BgYGcHJyQlRUFIKCgpSe9/HxAYAS9S4ui+DgYABQ2Xu6LImrLL+n4OBgNG7cWCl5ymQy3Llzp9QxlFf1/mQkVe6F3yd24gQeHxg2uVExe1QP7dxtUdfZBACQnZWLg9uechuQBnj16hUSEhKUysPDwzF79mwA8l6nRVm7di2Cg4MVmlMNDQ1hY2MDLy8vSKVSAPIP4NTUVLi4uBR2qBKbMmUK2wlFVQcZmUyGmzdvFnuc2bNnQ0tLC/Pnz2fvmxYkkUgUPvzd3NzQoUMHPH36FOvXr1eqHx8fr/AlwcxM3q+gNJ1ePv/8czAMg4ULFyok3ri4OKxevZqtUxnyhtj899z5+/tj7dq1pT5eq1at0LlzZ/z111/Yt2+fyjovXrxATEyMQgxBQUEKY3AZhsHKlSuL7RRWGagJlyj4c9dL9nHLDtYwNNbmMJqS4/F4GDezWf690J3PMOWbFjAyKV+vx5rs5MmTWLduHbp37w5HR0cYGBggJCQEly5dQlZWFvr3748FCxYUuv+rV6/w448/YvPmzew40Dzffvst5s6di86dO6Nt27Y4cuQI9PX1MWvWrHLHbWZmhlOnTmHo0KFo164devToARcXF/D5fERERODevXtKyUwVZ2dn7Nu3D59//jlcXFzQt29fNGjQADk5OYiIiMDt27dhYWGB169fs/scPnwY3bp1w+LFi3H69Gl069YNDMMgKCgIf//9N16/fs0moh49euDkyZMYNmwY+vfvDx0dHdjb2ysNGSpowYIFuHLlCs6dO4fmzZujf//+yMjIwMmTJxETE4Nvv/0WnTpVzu2LiRMnYsOGDZg3bx58fHxQv359BAUF4eLFixg2bBhOnDhR6mMePXoU7u7u+OKLL/DLL7+gbdu2MDY2RmRkJJ4/f46XL1/i3r177C2D+fPnY8aMGWjRogU8PDygpaWFu3fvIiAgAIMGDcKFCxcq+m0XiRIoYSUnZuHyifymoYLNouqgUx872DkZISIkGZnpUhzZ+ZzGhZZD9+7d8ebNG/j7++PevXtIT0+HsbExOnXqhAkTJmDChAmF3ofMzc3F559/jrZt22LmzJlKz8+ZMwcpKSn4/fff8eTJEzRv3hxbtmwpcQ/c4vTo0QPPnz/Hxo0b4e3tjdu3b0MkEqF27dpwd3cv8SQO48ePR/PmzbFp0yb4+Pjg77//hp6eHmrXro3hw4dj1KhRCvUdHR3x5MkT/Pzzzzh79ix27NgBsVgMBwcHfPPNN2wiAICpU6ciPDwcx48fx88//wypVIquXbsWmUBFIhGuXr2KzZs34+jRo9i+fTuEQiGaN2+OrVu3YsyYMWU7YSVQu3Zt3L59G99//z3u3LkDb29vODs749dff0XPnj3LlEBtbW3x+PFjbN++HadPn8aRI0eQm5uLWrVqoXHjxpgzZw6aNm3K1p8+fTq0tbWxdetWHDhwADo6OujcuTM8PT1x+vTpKk+gPKYUSwG0atWKnVmEcKDgh1UlrOBwZOczrJkvb5Iys9TBsTvDC/2ArK6un3uHdQvk90L0DUW4/m5ymWYnCgwMVLrPoqCGrMZSKvEFPhvMWhVerxoaPXo0Tpw4gejo6Go3Jy6pHMX+Hy8BugIlrL/254+3GjalsdolT0C+VunBX54hOiIVaSkSHP/jBaYurIRJIKo6OZFK9fbtW2hra8PCwoLrUIgaoU5EBAAQ+DQWr5/9u+Ynn4f+I+tzHFHZCIR8jJ6Rv7TZvk3+yMzI4TAiUp1t27YNHh4e8Pf3x5AhQxRm9yGkOJRACQDg7KH8jhDN2lqp9aTsPYfUhWVt+UQKyQlZOLW36nvnEfWwdetW3L9/H59//jn++OMPrsMhaoYSKIFEkotLx/KXeBo5rfxDCbikJRIovIe9Gx9DIinZWDtSs4SGhiIqKgp79+4tdLYeQgpDCZTg1uUwJMbJu/Tr6muhZQf170TRd3g9GJvJh7DEfsjAxaPKa0ASQkh5UAIlOHswv/m291Cnaj/zUEloi4XwmJI/UfiudX7IzS3ZnJ+EEFIS6v9JScol7lMGbl0JY7eHTFRe5UFdDRrbAHoGWgCA9+9ScO3MO44jIoRoEkqgNdzFY2+QmysfU2rraAhbh+InzVYXegYiDBmf/4Xg97WPUIphz4QQUiRKoDUYwzA4eyB/7OfQSeox721pDJ3UCNpi+aTdb1/E487f1XjyA0KIWqEEWoMF+Mci6JV8snCBgIeeQ4peWUMdGZuJ0a/AmNZd62gmLUJIxaAEWoOdKzD207V9Lejqa3EYTeUZMdUFAqF8VqUndz/g8d3oYvYghJDiUQKtoaRSGbxO5k8cP2Kqeo/9LIqltR56febEbu9e/5jDaAghmoISaA31wCcS8THyRafFukK4tqvFcUSVa+Q0F3Yu/tte4Qh8GsttQIQQtUcJtIa6fCJ/geBOve00YuxnUerUNULnvvlrUu7d8ITDaAghmkCzPzWJStlZUlw7mz8msuBQD002Znr+JPNep4MQHpTEXTCEELVHCbQGunUlHGkpEgCAobE2GjYz4ziiqlHPxQytu9QGADAyYO8mugotqV69eoHH48HX17fQOjO+WQueeWvsPnimCiMjhDuUQGuggs237oMd1XLdz7IaMyN/dftzB1/jY2Qah9GojydPnkAgEMDNrfC1VR88eQkAaOXauNA6hGgSSqA1TFqKBDcvhbHbA8c04C4YDjRtbQUXN/miyVKpDPu3+nMcUfUXEhKChIQEuLi4QF9fX2WdjIwMvAwMgba2CE0aOamsQ4imoQRaw1w/9w6SbPnSXua1dGFfz5jbgDhQ8Cr0z92vkBiXyWE01Z+fn3zyibZt2xZa5/Hjx5BKc9HcpT60tGhRalIzUAKtYS7/md9828ejZl4ptOlqg7rOJgCA7EwpDu94znFE1VtJEuj9+/cBAK1cNW86SEIKQ18Va5CE2Ezcu/ae3e43on4RtTUXj8fDmBlN8NO82wCAwzuf4fNvWkDPQMRxZNVTXgI9ceIEfHx8VNZ58OABAEqgpGahBFqDeJ8OZldesXEwgJWN6vtZNUHnvvaobf8U0eGpSEuW4MSul/j8m5Yl2tdFtKOSo6s4rySzy7U/wzDw95ffJ7569Wqx9akDEalJqAm3Bik4dd+A0TWr89B/CQR8jPoyf1zo/i3+yM6SchhR9fT27VskJyejffv2YBhG5U9CgnxBAl1dMRo3dFTY/9dff4WjoyPEYjHc3Nxw+/ZtLt4GIZWCEmgNEfsxHY/v5E+irokrr5RWzyF1YW6lCwCIj8nE2YOvi9mj5slrvm3RokWhdZ48kY+ndW3SAAKBgC0/ceIE5s6di8WLF8Pf3x8dOnRAv379EBFBS8oRzUBNuDXE9XPvkLeWtJ2TEUzMdbgNqBoQaQsw4ovG+G2NPEns3fAYHp83hlBY9PfK8jaLqpO8BNqyZeHN23kJ9L/3Pzdv3ozJkydj2rRpAIDt27fDy8sLv/32G9auXVtJERNSdegKtIb4+68Q9nHfEfU4jKR66TeqPgxNtAEAUeGp8PozqJg9apaSJNC8e6QF739KJBI8fvwYvXv3Vqjbu3dv/PPPP5UQKSFVjxJoDZAQm4lHvlHsdveBjkXUrll0dLUwdGL+ldOu9X6QyRgOI6o+ZDIZnj59CpFIBBeXwpe7U3UFGhcXh9zcXFhZWSnUtbKywsePHysnYEKqGCXQGuDauRA2KdSpa8je9yNyQyY0hI6e/G5GSGAibl4M5Tii6iEwMBBpaWlwcXGBSKR6iE9aWhqCgoKgr6+PhvXslZ7/7zSRDMPUqKkjiWajBFoDXC3QfNvHg5pv/8vASBuDxjRkt/9Y58dhNNXH48fyhceLar599uwZZDIZWrZsCT4//+PE3NwcAoFA6WozJiZG6aqUEHVFCVTDJcVn4oFPJLvtPoiab1UZNqURtETy/w4v/WJoSAuAiRMngmEY7Nmzp9A6HTt2BMMwSqu0iEQiuLm5KY0dvXr1Kjp06FAp8RJS1SiBargbF0IVJk+wsNbjOKLqycxSV+HqPC1ZwmE0muF///sf9u/fjz179iAwMBBz585FdHQ0ZsyYwXVohFQIGsai4f6m5tsSGznVBZf/DIIsl0F2Vi4y0nOgq6fFdVhqa9SoUYiPj8ePP/6IDx8+oEmTJrh8+TLs7ZXvlRKijugKVIMlJ2bh3vX8uW+p+bZo1nYGCj2UYz+kcxiNZpg5cybCwsKQnZ2Nx48fo0uXLlyHREiFoQSqwW5eDIM0RwYAsK6jX6Pnvi2p0dObIK+TaGqyBJnpOdwGRAiptiiBarC//wpmH/ceVjOXListh/rG6NIvv4kxhq5CCSGFoASqodJSJLh7NX/OUfdBNPdtSU2Y3Zx9nJosQQZdhRJCVKAEqqFuXgpFjkTefGtlo4fa9gYcR6Q+7OsbQ6yb378uJpquQgkhyiiBaqi/T+f3vu35GV19lpaegRby5stJS5EgI42GtRBCFFEC1UDpqRLc9g5nt3sOofufpSUU8qFvlD99HV2FEqI5GKZi5rumBKqBbl0JhyQ7FwBgXksXto6GHEekfng8AYzMClyFpuYgna5CCdEIOTk5CmvXlhUlUA1UsPdtj8HUfFsWWkIxsrLSYWCszZbFRKVX2DdXQgh3UlJSYGBQ/n4hlEA1TEZ6Dm575Tff9hpKCbQsxCIDxCckQCDKBJALBgzS03KQlkJXoYSoI4ZhIJFIEBcXh8TERJiampb7mDSVn4a54x2OzAz5ROimFjqwr2fMbUBqSijQgoGOBdLTUxGfGA+pVAqAh4RkPsxr6YFW5FIhPS7/cUwgd3EQUgiBQAADAwPY2dlBW1u7+B2KQQlUwxSc+9Z9ME3dVx5CgRb0dUxhYijG5J5nkJ0lv6+88XAf9BtZn+PoqqGjjfMfj6WmbqL5qAlXg2RlSuF7OYzd7j2Uet9WBHMrXQyd1Ijd3rbsHnJycjmMiBBSHVAC1SD/XI1ARpp81hxjMzEcGhhzG5AGGTnNBfqG8mEt79+l4Mx+aqIkpKajBKpBCjbfduvvAB7dqKswBkbaGD29Cbu9Y9VDZGbQFH+E1GSUQDWEJDsXPhdD2e0+HtR8W9GGTHCGmZUOACD+UwYO73jOcUSEEC5RAtUQ/1yLYIdYGBprw6lx+btoE0ViHaHCRPO71/shMS6Tw4gIIVyiBKohCjbfdu5rT823laSPRz12Zqf01Bz8+uNDjiMihHCFEqgGkEhy4XMhv/m234h6HEaj2YRafEz7zo3dPv7HS7x7nchhRIQQrlAC1QAPfCKRkpQNANA3FKFBUzOOI9Js7d1t4dquFgBAlstg06K7HEdECOECJVANcLVA823H3nbUfFvJeDwepn/vxs5GdPNSGO7feM9tUISQKkcJVM3l5OTi2rn8BNqfZsipEvVczNCrwEQV6xfeQW6ujMOICCFVjRKomvO7FY3kBHnzra6+Fpybm3McUc0xZX4LiHXks2G+fRGP84dfcxwRIaQqUQJVcwWXLmvfow74fGq+rSrmtXQxYmr+/K9bltyj1VoIqUEogaqx3FwZrp19x24PGEXNt1VtxFSX/MkVYjLx+5pHHEdECKkqlEDVmN/taCTEygfy6+gK4eJmyXFENY+Orha+/DZ/WMvBbU8REpjAYUSEkKpCCVSNFZw8oU03G2q+5Uj3QY5o2kr+5SU3l8GaebfAMLScFyGajhKompI33+Yn0IFjGnAYTc3G4/Ewe3lb9gvMfZ9IhS83hBDNRAlUTT25+wFxHzMAANo6AjRtbcVxRDVbXWcTDB7XkN1ev+A2MtJptRZCNBklUDXlfTq/922bLjYQCOhXybVJ81xhbCoGAHyKSsfudX4cR0QIqUz0qauGcsFTaCIcPL5hEbVJVdE3FOGLBS3Y7X2b/WmeXEI0GCVQNfSY54T4T9R8Wx319qjHTmYhzZFh5SwfyGTUoYgQTUQJVA158/KvclpT8221wufzMP/H9uAL5B2K/G5H48yBQI6jIoRUBvrkVTO54OEq35Xd/myCM3fBEJXqOptgxBf5MxRt+O4O4mMyOIyIEFIZKIGqmcc8J8Tz5As6a+sI0KQVTZ5QHY2f3Ry1bPUBAKlJEqxfcIfjiAghFY0SqJrx4rVkH1Pv2+pLrCPE3FXt2O1Lx9/izt/hHEZECKlo9OmrRnLBwzV+c3Z7CDXfVmutOteG+yBHdnvVrJs0NpQQDUIJVI348eqxzbdiHSE136qBGUtawcBIBACICk/FliX/cBwRIaSiUAJVIwq9b7tS8606MDHTwVdLWrPbR399gYe+kRxGRAipKPQJrCakUtl/et/S5AnqoudnddGuuy27vXTadaSn0bqhhKg7SqBqwu92NBJ4BgAACyYZTVrR5AnqgsfjYe7qdtA3/LcpNywVW5bc4zgqQkh5UQJVE96ngtjHvWX+tHSZmjG30sXMH/Kbco/99gIPblJTLiHqjBKoGpBKZbh29h273Yfx5zAaUlY9h9RFO3fFpty0FGrKJURdUQJVA363opAQmwkAsGSS0IIJ5TgiUhY8Hg/zVrdje+VGh6dizfxbHEdFCCkrSqBqwOtU/tJlvWVPwQdNTq6uzCx1MWdFW3b73KHXuPJnUBF7EEKqK0qg1Zy8+TZ/6bI+zBMOoyEVoftAR/T8rC67vWKmD6IjUjmMiBBSFpRAq7l7198jMS4LAGDFJMKVCeM2IFIhZi9rw86Vm5YiwaIpV5GbK+M4KkJIaVACreYuHXvLPu4ne0LNtxpCz0CERZs7Kyx7tncDtS4Qok4ogVZjmRk5uHYuv/ftQJkfh9GQita4hQXGz2rGbu9Y9QD+9z5wGBEhpDQogVZjPhdCkfnv5ONGptpwBo0b1DRjv2qKxi0tAAC5UgbfjPVCYlwmx1ERQkqCEmg1dul4fvNtz8+cQFMnaB6BkI/FmzuzQ1s+RaXju8lXIZNRUz0h1R0l0GoqKT4Td7wj2O3BY2nuW01lZaOP7zZ0Yrfv/h2BXeuouZ6Q6o4SaDX1918hkErlvTKtbPRQ296A44hIZWrb3RajZzRht3eueoj7PtRkT0h1Rgm0mrpYoPdt/1H1OYyEVJXJc13RrI18kQCZjMHC8d74FJXGcVSEkMJQAq2GoiNS8fhOtHyDB/QbQQm0JhAI+Vi8pTNMzMUAgITYTMwZfhlZmVKOIyOEqEIJtBq6fCL/6tOxgQlMzHU4jIZUJTNLXSzZ2oUdH/rqcQxWfOUDhqFORYRUN5RAq6GCvW9p4eyap3nbWpi5NH/pswtH32D/FlqBh5DqhhJoNfPmeRzevogHAPAFPHQb4MhxRIQLg8c1VLj3vXnxPdz2DucwIkLIf1ECrWbOHAxkHzdxs4SuvhaH0RCu8Hg8zF7WBi5u8kkWZDIGC8Z5IyQwgePICCF5KIFWIxJJLi4efcNuj/rShcNoCNe0RAIs39ENFta6AOSTzs8YdAGxH9M5jowQAlACrVZuXwlnV17R0RPCrVNtjiMiXDMx18Gq37pDrCMEIO+hPXPIRaSnSTiOjBBCCbQaOVug+bb7QEcIBPTrIUA9FzMs/SW/Z26Afyy+GevNTrRBCOEGfUJXE/ExGbh1Jb+TyIip1HxL8rXtZou5q9qx27e9wrFq9k0a3kIIhyiBVhMXj71lryis6+jD1sGQ44hIddN/ZH2Mm9mU3T69LwC/LLvPYUSE1GyUQKsBhmFw9kB+8+1nkxpxGA2pzibNc0WvoXXZ7V3rH2P3z485jIiQmosSaDUQ4B+Lty//HfvJ56HPMCeOIyLVFY/Hw/wf26NtNxu2bOvSezjy63MOoyKkZqIEWg0U7DzUvK0V9AxEHEZDqjstkQA/bO8K1/a12LI1827hzIEADqMipOahBMoxSXauwtR9o6Y3KaI2IXLaYiFW/dYdjVtYsGXLpvsojCMmhFQuSqAcu3omBMkJ2QAAPQMttGhvzXFERF3o6Gnhpz094NTIBIB8tqLvp1zFX/vpSpSQqkAJlGMndr1kH/cdXg98Po/DaIi60TcUYZ1nLzg0MAYAMAzww5c3FP6uCCGVgxIoh4JexrPrfvJ4NPaTlI2xmRgbD/VGvcambNmq2Tdx8Jen3AVFSA1ACZRDJ3bnXyU0crWAmaUuh9EQdWZkKsbPB3vBuZk5W7Z+wR389tMjmmyBkEpCCZQj6WkSnD/8mt2eMKc5h9EQTWBgpI11+3uyK7gAwI6VD7Bq9k3k5tK0f4RUNEqgHLl0/C3SU3MAAIbG2nDrRJ2HSPnpGYiwdm9PtOyQ//f05+5XmD/KC1mZUg4jI0TzUALlAMMwOPFHfvPt4HENweNR5yFSMXT0tPDjbne4D8pfjP36+Xf4ou9ZJMVnchgZIZqFEigHnj/8hNfP4gDIZx4aNoWm7iMVS0skwHcbO2HEF43Zsqf3PmJM51N49zqRw8gI0RyUQDlw/I8X7OOWHWvBwEibw2iIpuLzefjy+1aYsbgVWxYRnIyxnU/itnd4EXsSQkqCEmgVS4rPhNfJYHZ70lxX7oIhNYLHlMZYtr0rtMUCAEBqsgQzh1zEwV+eUg9dQsqBEmgVO7HrFSTZuQAAC2tdODe3KGYPQsqvc197bD3eF+ZW8qFSMhmD9QvuYOm069S5iJAyogRahbKzpAqrZoz9qmkRtQmpWPVczLDzzAA0cs0fK3r24GuM7XwKESHJHEZGiHqiBFqFLhx9g/hPGQAAHV0h+g6vz3FEpKYxtdDBxsN90Gto/pJ5b57HYWS7E7hxIZTDyAhRP5RAq4hMxmD/Zn92e8hEZwi16PSTqifSFmDh+g6Yt7od+zeYmizBHI9L2Pj9XUgkuRxHSIh6oE/wKuJ7OQyhb5MAAAIhD6O/pGXLCHd4PB4GjG6ArSf6wrK2Hlvuudkf47ucQthbGupCSHEogVYRzwJXn1362dOi2aRaaNjUHL+dHYDWXW3YsldPYjG87QmcORBAvXQJKQIl0Crw/OFHdtUV8IBpC924DYiQAgxNxPhxlzu+WtyKbdLNTJdi6bQbWDDOGylJ2RxHSEj1RAm0ChS8+nRtVwsW1npF1Cak6slnxGqM7Sf7wdbRkC33OhWMYa2Ow+92FIfREVI9UQKtZBEhybh29h27PWNRqyJqE8Ktei5m+PXsAAwYld9D/ENEKib3PIP1C2/TmFFCCqAEWsn2/PwYMpn8PpJjQ2M4NTItZg9CuKWjq4V5P7bHsh1doW8ov1fPMMDBbc8wvPVxPH/4keMICakeKIFWovfvknHuUP6anzMWt+YwGkJKp3Mfe+y6OAitOtdmy0LfJmFcl9PYsuQfdkYtQmoqSqCVaPf6x5BK5QsZ16lrqLBGIyHqwMJaD2v29sD8H9tBrCMEIB/TvGfDE4xoewKvnsRwHCEh3KEEWkkiQ1MUrj5nL2/DYTSElB2Px0P/UQ2w+/JgNG9nxZYHByRgTMeT2LToH7o3SmokSqCVZOeqB/+5+qxdzB6EVG+1bPXx84HemLWsDbuyS24ug32bnmCo2zE8DKzHcYSEVC1KoJXgzfM4XDj6ht3+ekU7DqMhpOLw+Tx8NsEZf1wchGZt8q9GI4KTMWX9XCz3HI2UdB0OIySk6lACrQTblt1D3gQu9RqbwrV9LW4DIqSC2dgbYsOh3pj/Yzvo6AnZ8lO+HTF4yRJcP/euiL0J0QyUQCuY3+0o+F4OZ7cXrOvAYTSEVB4+X35vdJ/3Z+jQsw5bHptkhK9HXMb80VcQ+zGdwwgJqVyUQCtQ3iLFeVp2qEXjPonGM7fSxYpfu2HzrL0wM0xhy//+KwSDmx3F8T9eIDdXxmGEhFQOSqAV6OzBQAT4xwIAeHzgm7V09UlqBh6Phz6tn+L8mp8wrPM9tjwlKRur5/hibOdTCPCnIS9Es1ACrSBpKRJs/eE+uz1wdANY1tbnMCJCqp6xfgZWf3EUO34KhI11Dlv+0i8Go9qfxE/zfJGaTJPTE81ACbSC/PrjQ8R/ygAAiHWFmPYdrbhCaq42LVJwdF8Mpk5KgZaWvPlWJmNw9NcXGNj0CC4df0tLpRG1Rwm0AgT6x+LQL8/Y7RmLWkFHV4vDiAjhnrYImDoxFUf2xKCtWyZbHvcxA99O/BtT+51D6BtauJuoL0qg5ZSbK8OKWT7shPF29YzQv8BKFoTUdHa2udi6PgE//pAAM9P8GYvu34jEZy2PYeP3d6lZl6glSqDldPTXF3jpJ+8cweMBK3Z2A4/H4zgqQqoXHg/o2S0Tf+6PwWiPNPB58i+c0hwZPDf7o3/jw/hz90t29i5C1AEl0HIIe5uIrUvzexwOHu+MOnWNOIyIkOpNT4/BvJnJ2P97LJo2zr/qTIjNxMpZNzGizQncu/6ewwgJKTlKoGWUmyvD4i+usZNom1roYPr31HGIkJJoUC8Hu36Jw+qlCahlmd9b9+3LeEztdw4zP7uIoJfxHEZISPEogZbR7vWP8ezBJ/kGD/hxlzu0RAJugyJEjfB4QK/umTi+PwYzPk+GWDt/fVHfy2EY6nYMi6Zcxft3yRxGSUjhKIGWweM70di56iG7PXSiM+o3MeMwIkLUl1gbmDwuDacOxWBg33Tw/r0/yjDA+SNvMLDJEayacxMf3qdyHCkhiiiBllJiXCYWTvBme93WtjfA9O9bcRwVIerP3EyGpQuTcOD3WHRokz/sRSqV4cQfL9HX+RB++PI6woOSuAuSkAIogZaCVCrDgvHe+BQlnyBbqMXHhkO9IBDSaSSkojSol4PNaxPw+9ZYNG+SxZZLc2T4a38gBjY9ggXjvfHC7xOHURJCCbRUNnx7B/dvRLLb323oCEtrmq6PkMrg2lSC37fGY9v6OIVEKpMxuPJnEEZ3OImxnU/i4rE3kEhyizgSIZWDEmgJndj1Eod3PGe3B49viG4DHDmMiBDNx+MBbVtl449t8fh9S6zCjEYA8OzBJ3w36Sp61TuAzYv/QUhgAkeRkpqIx5RiQspWrVrBz8+vMuOplq6eCcH/xnix9z0bt7DAluN9wedX7YQJtR3yx5hGh1HPRFK91L6b//eZbjkeMm3bSnmd12+1cPy0Hq7d1IFUqnwN0KSVJYZMcEbvYfVgbqVbKTEQAlACLdaDm5GYMegCJNnyJiKLWrrY6zUEOnpVP9ctJVBSnVVVAs0Tn8DHuUt6OH1OB/GJyv8feTygebta6DmkLtwH14V9PeNKjYfUPJRAi3D/xnvMGnqJnSxB30gET+/PYGwm5iQeSqCkOqvqBJpHKgXuPxLjopcO7twXq7wqBYA6TkZo280GbbvZok03W7o6JeUm5DqA6uq2VzjmjryM7Cz5lae2jgA7/+rPWfIkhKgmFAKd2mehU/ssJKfwcNVHF9duaOP5KzFkTP5tlvchyXgfkoxTewMAAHXqGqJJKys0aWWJpq2sUL+JGQyNtbl6G0QNUQJV4eTeV1g9+yZyc+UX52IdIX49NwC17Qw5jowQUhQjQwbDh6Rj+JB0JCbxcfe+GDdvi/DwiRgSieJMYe/fpeD9uxRc+TOILbOsrQenRqZwamSCOnWNUNveEDb2BrBxMIS+oaiq3w6p5iiBFiCVyrB16T14bvZny3T0hPjt3EDY2FPyJESdmBjLMLBvBgb2zYBEAgS8FsHvqTYePhIi4K3qpt6Y6HTERKernNDe0Fgbte0NYF3HAJY2erCqrQ8rGz1Y2ej/+6MHPQNKsjUJJdB/xX5Ix8IJ3nh0K5otM7fSxfbT/WBupcdhZISQ8hKJANdmErg2k2DqREAiAYLfaSHgjQgBgUIEvhHgfbR2ofdPASAlKRspSdl4/Syu0Dr6hiJY1tZDLVt9WNbWg2VtffZxLRt9WNrowcRcp8p78JPKQQkUwJU/g7D665tITshfXqm+iyk2HenDSW9bQkjlEomAxs45aOycAwyRl0lzgegPAoSFayEsQoioaD4io4CPnwSIidNGjrT4xSLSUiRIS5Hg3evEQutoifj5yfXfpGplI0+0jg2M4dDABCJtWphCHdToBPr+XTLWL7gDn4uhCuWfTXTGV0ta07dEQmoQoQCws82FnW0uunRUfE4mAxKT+PjwUYCYWAFi4wSIiePj4yceYmJ4iI0TIj5RhJwirmDz5EhkiApLRVSY6snx+XwebBwNUbehCeo6y38c/31sZEKdGKuTGplAE+MysXfjExzZ+Zwd3wnI73cu3dYVbbracBgdIaS64fMBM1MZzExlAHJU1mEYICWFj5g4vjzBsolWgJgYPj7FyhNtekbRH7syGcP2GPa9HKbwnGVtPdRzMUV9FzPUb2KG+i6mcGpkCh1dainjQo1KoNERqTj663Oc2PUSGWmK/wnadrPBos2dqRMAIaRMeDzAyEgGIyMZ6jtJC62XmclDbDwfsbHy5CpPtny8j+QhPEKIT3HaYBjVrV95nZz+uZrfyYnHk49xbeBihnouZmjQxAz1XExhX98YQlroolJpfALNzpLitlc4zh58jVtXwtihKXksa+th8ebOcHGz5ChCQkhNoqPDsE3FqmRlA+8jhQiL0EL4eyHehfIRFsbH+2jV92EZBogITkZEcDKunXvHlmuJ+KjrbIr6Lqb/Xq3Kr1qt6+iDx6PbUxVBIxPox8g03LoSBt/LYXjgE4nMDOVvg8ZmYsxY1ArdBznSvU5CSLUh1gbqO0mVrmKluUBUtBAhoUKEhGohKJiHkHdCRH8Sq7xizZHI8OZ5HN48V+w1rGegpdAEXL+JGRwbmMC8li4l1lJS6wTKMAziPmUgJDARrx7HIOBJDF49icH7dymF7mNXzwhT5rmiQy87SpyEELUhFAD2daSwryOFe5f85d2ysoHwCC2EhAoRHCLA2yAeQiNEiE9U3eEoPTUHT+9/xNP7HxXKtcUC2NgbwsbRELaOhrB1MIR1HQOYW+vCopYeLKx16V7rf1SrBCqTMcjKlCIrQ4qsTCnSUyRIiMtEUlwWEuIykRibicT4TMREpSPiXTLev0tBZrrqG/oFGZpoo2t/e3hMaUwTIhBCNIpYG2hYPwcN6yt+Fqam8RASqoV3YUK8fctDcAgfoe91kJ6hOglmZ+Xi3ZtEvHtT+BAcAyMRLGrpwdRSB4bG2jA0kf8YGGnDyEQbhsZiGJpoQ99IBLGOMP9HV/6vto5Qo+7LcpJAD2x7itP7ApCdJU+U2Znyf3Mksgo5Pp/Pg309I3Qd4ICu/R1g60BJkxBSsxjoM3BtKoFrUwlbxjCpiIvn412YFkJCtfA2mIeQEAE+fBIhrZDEWlBqsgSpyZIik2xxhFp8aIsFEGrxIRDwIRDyIBDyIRTyIRDkP+azj+X/8ng88HjyTlP49zGfz2PLwQP7eMbi1mjR3rrMMZb4vVT6K6iQFJ9VYQvfCgQ8GBhrw9bBEI1aWKCuswlq2xlAqCX/lpORloO3L+Mr5LW4VrvAY015T0RzFPz7TEnMgJQXw1kspHBaABrWkf/075Jfnp7Bx8cYbXyMFeFjjDY+fNJCbBwfCYlCJCSJkJSmjdzc8k/wIM2RQZpTMRdLhRk5tUmlHj9PqZYzMzc3h4ODQ4W9+JsXcSU+kTweDwIhr1Iu/7OlqdAWGlT4cTUBnZvC0blRTcCXAYiFoQ6tbKJKamY2DDTu3DAAI/v33+JTCluD4aFg7dSsHBiItfLrMABQur4qAm19mNpU3NWnubk5vLy8VD5XqgSqqWraOqelQeemcHRuCkfnpnB0bgqnbudGc+7mEkIIIVWIEighhBBSBpRAAXz55Zdch1Bt0bkpHJ2bwtG5KRydm8Kp27mhe6CEEEJIGdAVKCGEEFIGGptAIyMjMWfOHLRv3x66uvI5HsPCwordb8WKFf8OxlX+EYs1Yy2+sp4bAIiIiMCkSZNgZ2cHXV1dNGjQAEuXLkV6enrlBl1FynNuQkNDMXz4cBgbG0NPTw/du3dXqx6FRTl16hQ8PDxgb28PHR0dNGzYEIsWLUJqquo1LQvKysrCwoULYW1tDR0dHbRv3x63bt2qgqirRnnOzeLFi9G7d2+YmZmBx+Nh//79lR9wFSrrufHz88OXX34JZ2dn6Orqws7ODuPGjUNoaGiR+1U5RkP5+PgwlpaWTL9+/ZjevXszAJjQ0NBi93v//j1z7949hZ9r164xQqGQGTFiROUHXgXKem7S0tKY+vXrMw4ODsz+/fuZGzduMOvXr2fEYjEzcuTIyg+8CpT13MTFxTG1a9dmGjZsyBw/fpw5f/48061bN0ZfX58JCAio/MArWdu2bZkRI0Ywhw8fZm7evMls2bKFMTIyYtq2bcvk5uYWue/YsWMZIyMjZteuXcy1a9eYoUOHMmKxmPH396+a4CtZec6Nvr4+06lTJ2bixIkMAMbT07Nqgq4iZT0333zzDdOhQwdm586dzM2bN5kjR44wzs7OjKmpKRMREVGF76BoGptAC/5ydu/eXeIPQlUOHjzIAGAuXrxYQdFxq6znxtvbmwHAeHt7K5R/9913jEAgYNLT0ys61CpX1nOzevVqRiAQMEFBQWxZWloaY2lpqRFfvGJiYpTKDhw4wABgrl+/Xuh+T58+ZQAw+/btY8tycnKYBg0aMIMGDaqUWKtaWc8Nw+T/vQUFBWlkAi3ruVG1X1hYGMPj8ZgffvihQmMsD41twuXzK+6tHThwAFZWVujTp0+FHZNLZT03Eol8Tk1DQ8W5hY2NjSGTycBoQH+0sp6b+/fvo379+qhXrx5bpqenh86dO+PixYuQSgtfYFkdWFhYKJW1bt0aABAVFVXofufPn4eWlhZGjRrFlgmFQowePRre3t7Izs6u+GCrWFnPDVCxn1PVUVnPjar97O3tYWFhUew5rUqa/durAJGRkfDx8cG4ceMgFFarxWuqXM+ePVG/fn189913CAgIQFpaGm7cuIFt27ZhxowZ0NPT4zpEzggEAohEIqVybW1tZGZmIiQkhIOoKpevry8AoFGjRoXWefXqFRwdHaGrq6tQ7uLiAolEguDg4EqNkSslOTc1VVnPTWBgIGJiYqrVOaUEWoxDhw5BJpNh0qRJXIfCObFYjDt37kAmk8HFxQUGBgbo0aMHBg4ciB07dnAdHqcaNmyIoKAgxMfnT/Ivk8nw8OFDAEBCQsUsnlBdREVFYdmyZejZsydatWpVaL2EhASYmJgolZuamrLPa5qSnpuaqKznRiqVYsaMGbCwsMAXX3xRiRGWDiXQYhw8eBAtWrRAs2bNuA6Fc1lZWRg1ahRiYmJw6NAh+Pr6YsOGDThx4gRmzZrFdXicmjFjBmQyGSZOnIiQkBB8+PABX3/9NdtrUJOa6tLS0jBkyBAIhUJ4enoWWZdhGPB4ypOBa0JzvyqlOTc1TXnOzezZs/HPP//g8OHDKr+QcaVmt0kW4+HDh3j9+jW2bt3KdSjVwt69e3Hz5k0EBwfDyckJANClSxcYGRnhyy+/xIwZM9C8eXOOo+RG3bp1ceTIEcyaNYu9D9qyZUvMnz8fGzduhLV15a9NWBWysrIwePBgvHv3Dr6+vrC1tS2yvqmpKSIiIpTKExMT2ec1RWnPTU1SnnOzaNEi7Nq1CwcOHEDv3r0rMcrS05yvxZXgwIEDEAqFGDt2LNehVAsvXryAiYkJmzzztGnTBoD8HkVN5uHhgaioKAQEBCA4OBiPHz9GWloa6tSpAzs7O67DK7ecnBx4eHjg4cOHuHz5Mpo2bVrsPi4uLggNDUVGRoZCeUBAAEQikUKnK3VWlnNTU5Tn3Pz0009Yt24dtm3bhgkTJlRilGVDCbQQEokEx48fR//+/VX2CKuJatWqhcTERKWOHw8ePAAA2NjYcBFWtSIQCNCoUSM4OTkhOjoaJ06cwFdffcV1WOUmk8kwbtw4XL9+HefOnUO7du1KtN/gwYORk5ODkydPsmVSqRQnTpxA7969oa2t/utilvXc1ATlOTe//PILli5dip9++glz5sypxCjLTqObcE+dOgUAePz4MQDgypUrsLCwgIWFBbp27Yrw8HA4OTlh2bJlWLZsmcK+Fy9eREJCgsZ2HirLuZk8eTI2b96M/v37Y8mSJbCzs4Ofnx9Wr14NNzc3dOzYkbP3U5HKcm5ycnLw7bffomvXrjA0NMSrV6+wdu1auLi44JtvvuHsvVSUWbNm4eTJk1iyZAn09PRw//599jlbW1vY2tqqPC+urq4YNWoU5s2bh5ycHDg6OuK3335DaGgojhw5wtXbqVBlPTeAvEdqbGwsPn78CEA+A4++vj4AYPjw4VX7RipBWc/N8ePHMW/ePPTt2xfu7u4K+xkaGqJx48ZV/l5U4ngcaqWCfD1zpZ+uXbsyDMMwoaGhDABm+fLlSvsOHjyYMTU1ZbKzs6s26CpS1nPz6tUrZsSIEYytrS0jFouZ+vXrM9988w2TkJBQ9W+ikpTl3OTk5DADBgxgLC0tGZFIxNStW5dZsmSJRkwuwTAMY29vX+h5yTsPhf3NZGRkMPPnz2esrKwYbW1tpk2bNoyPj0+Vv4fKUp5z07Vr10L31QRlPTeTJk0q9v9hdUCrsRBCCCFlQPdACSGEkDKgBEoIIYSUASVQQgghpAwogRJCCCFlQAmUEEIIKQNKoIQQQkgZUAIlhBBCyoASKCEVhMfjgcfjgc/nF7n+Z/fu3dm6+/fvV3hu8uTJ7HPLly8v9BgHDhxg63Xr1k3huZs3b7LP5f3o6urC2toaXbp0wcKFC+Hv71+et0oIASVQQiqUUCgEwzDYu3evyueDgoLg6+tb7OLsQqEQ+/btQ25ursrnd+/eXewx7O3tsXz5cixfvhzz5s3DwIEDkZ6ejo0bN6Jly5YYN24c0tLSSvbGCCFKKIESUoGsrKzQqlUreHp6QiqVKj2/Z88eMAyDgQMHFnmcgQMHIjIyEl5eXkrPBQYG4u7duxg0aFCRx3BwcMCKFSuwYsUKrFmzBrt378bjx4/h7++Ppk2b4ujRoxgxYkTp3iAhhEUJlJAKNm3aNHz8+BEXL15UKM/JycGBAwfQoUMHuLi4FHmMcePGQUdHB7t371Z6Lq9s6tSpZYrP1dUV165dg4WFBby8vHD27NkyHYeQmo4SKCEVbMyYMdDT08OePXsUys+fP49Pnz5h2rRpxR7D2NgYI0aMwKVLl/Dhwwe2PDs7GwcPHkTXrl3RoEGDMsdoaWmJ6dOnA4DGrIpCSFWjBEpIBTMwMMDo0aPh5eWFyMhItnz37t0wNDTEyJEjS3ScadOmQSqVKnQ0OnPmDOLj40uUhIuT1/no4cOH5T4WITURJVBCKsG0adOQm5uLffv2AQDCw8Nx9epVjBs3Drq6uiU6RqdOndCoUSP2vikgT8ImJibw8PAod4x5C6DHxsaW+1iE1ESUQAmpBG3btkXTpk2xb98+yGQy7NmzBzKZrNRXjlOnTsW7d+9w48YNBAcHw8fHBxMmTIBYLC53jHlJmcfjlftYhNRElEAJqSTTpk1DeHg4vLy84OnpCTc3N7Ro0aJUx5g4cSK0tbWxZ88e9kq0IppvASA6OhoAYGFhUSHHI6SmoQRKSCWZMGECdHR0MH36dERFReHLL78s9THMzc0xdOhQnDlzBvv27UP79u3RpEmTConPx8cHgPxqmRBSepRACakkxsbGGD58OCIjI6Gnp4cxY8aU6TjTpk1DdnY2YmNjK+zqMyYmBn/88QcA+ZAZQkjpFT2VCSGkXH788UcMGzYMFhYWMDAwKNMxunfvjnPnzkEmk6FPnz7ljunZs2eYOHEi4uLi0L9/fwwePLjcxySkJqIESkglsrOzg52dXbmOwePxypTkwsLCsGLFCgDySRzi4uLw+PFjPH78GID8yjPvKpQQUnqUQAnRUOHh4Vi5ciUAQCwWw9jYGPXr18eCBQswbtw4uLq6chsgIWqOx+T1ZSeEEEJIiVEnIkIIIaQMKIESQgghZUAJlBBCCCkDSqCEEEJIGVACJYQQQsqAEighhBBSBpRACSGEkDKgBEoIIYSUASVQQgghpAwogRJCCCFl8H+/jERKXrCo+AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "yes_data = yes_data / 3000\n",
+    "no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "07198fb625524928bec3e1077951f15a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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m+ruLnJrI9Hh0I67rFs6ePXuGfC8oKAgajQZSqRQajQaBgYGDlpHJZKiqqhr4urq6GiEhIQPrX/bQQw9h+fLlIwpPQ+vo7Uf2SQ3+ebQa+eXNkEiAeZP88PN54Vg8NQhSb9crljcYBGz57gLSd57B0tf349W74/lcdxpzcr8/unmFRzeiMOqUWlJSEjIzM5GWlobMzEwkJycPWmbmzJlQq9UoLy9HaGgotm7dik8//RQABsoKALZt24bY2Fhj4hCA5s4+/Dn7NHac0KBbp0eEvzt+tzQKd04LRYiP65Dr2dlJsGb2BCRGB+HhT47hd/88DnmgB6KlXmZMTzR6BOHSfTcT/NxwB49uRGFU4aSlpeGee+7B+++/j/Hjx+Mf//gHAKC2tha/+MUvkJ2dDQcHB2zcuBFLly6FXq/HunXrEBMTAwB46qmnUFRUBIlEgvDwcLzzzjvG/x/ZsLqLPfjZ+9+isrkLq6bLcNcMGaaP9xnRNZlALxe8df90LH/jINZ/fBSf/2o+vFwcRzE1kXnsOd2A4to2vHyXgkc3IpEIV7vIYuFUKhUKCgrEjmFRShs68MCH+Wjp7MP7D8zEnAg/oz4vv7wZ972bh8XRgdh0/wwOJCCrpjcIuP2NA+jW6bH3iZtZOCY23N/J3OtjwOHSRqx86xC6+/T49KE5RpcNAMya6Iunb5uCXcX1ePfAeROkJBLPtsIanKlrx2+XRLFsRMQ9b+U+K6jC2g/yEejlgu2P3IT4MB+TffaD8yfitthg/CXnLL6raDbZ5xKZU49Oj7/tPguFzBu3x0nFjmPTjLqGQ6PvSFkTztS1IdzfHZP8PWBnB1Q1d6O6pQtHK1uw9bsqzJf748010+HtatprLRKJBH+9S4HiNw7iN1uLkP3rBSbfBtFoyzxcgdqLPXjlnnjY2fHUsJhYOBbqVM1F/CXnDA6oG4dcxk4C3D9nPJ5dEQPHUTpN4OniiA2rlbhr0xH8fvspvLFayes5ZDUudunw5teluHlyAOZN8hc7js1j4Vig9w6cxws7TsPHzRG/vz0aK+JDcKG5C2UNHRAAhI1zg2ycK0J8XOHkMPpnRaeNH4ffJETi1dxzWBQVgJXTZaO+TSJTeOubUrT39iPttiliRyGwcCzOkbIm/Dn7NJZMDcIr98QPDEkO8nLBzHBf0XI9vEiOA+pG/DGrGHMn+Q26cZTI0tS0duPDwxW4c1oo7yezEBw0YEEa2nvwqy2FCPd3x9/uVVrU/S/2dhK8cnc8enR6vP1NmdhxiK5JEAT8179PQgLgySVRYseh77FwLES/3oDHthSio1eHt9fMgIez5R18jvdzw90qGbbmV6HuYo/YcYiG9NGRSuw7p8Uzt0cj9BozbJB5sXAsxN9yzyHvfDNevCMOUcGeYscZ0sO3yGEQBLz9TanYUYiu6lx9O/6cfRqLogLwszkTxI5DP8DCsQDfVTTjrW/KsHpmGFbNsOwL8mG+blg1XYYt3/EohyxPb78ej20phIezA/56VzxHVFoYFo4FyNh/Hr7uTnh2RYzYUYblkUVyGAwCNu3jtRyyLK/sOoszde34610KBHg6ix2HfoSFI7Kq5i7sPV2P1TPD4OpkL3acYRnv54aV00Pxaf4F1LfxKIcsQ0FFM949UI7754xHQnTQ9Vcgs2PhiOyTby8AANZY2bnmRxdFQm8QOGKNLILBIOD5L0oQ7OWC/1oWLXYcGgILR0Q9Oj3+97sLWDI12OpG0oz3c8PKaaHYkn8BDTzKIZFtK6zByZqL+M/bouDmZHkjPOkSFo6Ivjhei5YuHdbOs66jm8sevVWOfoOAt3kth0TU1dePv+46g3iZN5LjQ8WOQ9fAwhGJIAjIPFKByEAPzDXB4wTEMMHPHXdOC8Wn3/Ioh8Szad951Lf14o8rpnJyTgvHwhHJsQutOFXThrXzwq166Oajiy4d5Wzax2fmkPmd13YgY38ZliukmDFBvKmfaHhYOCL56EgFPJ0dsHKadZ8CCPd3xx3KUHz8bSWqmrvEjkM2pEenx8OfHIOroz2euZ0DBawBC0cEDe09yD6pwV0qGdwtcAqbkXpyyWTYSYCXdp4WOwrZkOe/KMaZunb87V4lJ5O1EiwcEWzNr4JOL4yZaTdCfFyx/mY5sk/WIe98k9hxyAZsL6zBlvwqPHzLJCyKChQ7Dg0TC8fMdHoDPvm2EgsnByAiwEPsOCaTujACId4ueP6LEugNgthxaIzq7O1H+s4z+N0/j2NWuC+eSJwsdiQaAaMKp7m5GYmJiYiMjERiYiJaWlquuty6desQGBiI2NjYG1p/LNldXI/6tl48YKVDoYfi6mSPp5dF47SmDZ8VVIkdh8YYQRDwxfFaJLy6D5v2lSFZGYp3fjYDDqP0pFsaHUb9aaWnpyMhIQFqtRoJCQlIT0+/6nIPPPAAcnJybnj9sSTzcAXG+7rh5slj7zTAcoUUM8PH4ZVdZ9HWoxM7Dlm5Hp0eB9WNSN95BsveOIhfbSmEn4cT/rV+Ll65Ox7j3J3EjkgjZFThZGVlISUlBQCQkpKC7du3X3W5hQsXwtd38JDF4a4/VvyjoAr5Fc1YO3cC7Mfg/QISiQR/XB6D5q4+vPkVH19AI1fd0oU3vy7FT9/Ng+L53bj//W/x/sHz8HR2wJ/vjMPnj87n8GcrZtQQqfr6ekilUgCAVCpFQ0PDqK2fkZGBjIwMAIBWq73BxOI5Ud2KZ7afwrxJfnhgXrjYcUZNnMwby2Kl+KygCr9dGgVHnvKgYWho78GbX5Xi0/wL0OkFREu9kDJ3AubJ/TEr3HdMjOakYRTO4sWLUVdXN+j1F198cVQCDSU1NRWpqakAAJVKZdZtG6uxoxf/sfkoAjycsfGn08f8eeckZQh2nNTgSFkTFk4OEDsOWbCuvn68sbcUfz9cjn69gLtVYXj0VrnVzS1Iw3PdwtmzZ8+Q7wUFBUGj0UAqlUKj0SAwcGTXJYxd3xr06w149NNjaOrsw7/Wz4OvDZx3vnlyADycHbDjhIaFQ0Pq7dfjl5uP4mBpI5LjQ/CbxZMR7u8udiwaRUb9VTspKQmZmZkAgMzMTCQnJ5t1fUun0xvwx8+LkXe+GS+tjENsqLfYkczCxdEeiVODkFNch75+g9hxyAL16w34zdYiHFA34q+rFHh99TSWjQ0wqnDS0tKQm5uLyMhI5ObmIi0tDQBQW1uLZcuWDSx33333Ye7cuTh79ixkMhnef//9a65vDQRBQG1rN2pbu9HQ1oOmjl706PQD75c2tGPV24fx6bcX8MuFEVg53bIfHW1qyxVSXOzW4VBZo9hRyMIIgoD/2nYSO0/V4Q/Lp+JuVZjYkchMJIIgWN1deiqVCgUFBaJtv7ShA89sO4lvy5uveF0iAUJ9XBHu5478ima4O9njxTvjsCxOKlJS8fT266F6YQ+WTA3Gq/fEix2HLIQgCPhz9mm8e6Acj90qxxNLosSORCYw3N/JHPoxAjq9Af+zV42395XB1dEe//mTKfB1d0S/QYDeIKC5sw/ntZ0439iBpTHB+MPyaAR6uogdWxTODvZYGhOMXcV16O2PhbODdTw+m0bXW9+U4d0D5UiZOwGPc5YAm8PCGYHXcs/hrW/KcOe0UDxzezT8PZzFjmTRbldI8c+j1ThwrhGLp/IZ87Zu85EKvLzrLO5QhuDZFTFW/VgOujEsnGH6rqIZm/aV4V5VGP5yl0LsOFZhvtwf3q6O2HmqjoVjw/LON+GNvWocLmtCwpRAvHx3PB+UZqNYOMPQ3qPD4/9bBNk4N/xhxVSx41gNR3s7LIj0xwG1FoIg8G+0NqS9R4cjZU1472A58subEeDpjN/fHo2fzZ3Am4FtGAtnGP70ZQlqW7vx2S/nwoN3PI/IwskB+PKEBmfq2hEt9RI7Do0Snd6AwgutOFjaiEOljSiqaoXeICDIyxnPrZiK1bPGw8WR1/FsHX97Xseu4jp8VlCNRxZNgiqccziN1MLISzd+HlBrWThj0Kmai3j/YDl2F9ehs08POwkQJ/PB+psnYe4kP6jCx3HACA1g4VyDtr0XT//7JGJCvPDrBI6ouRHB3i6YHOSB/ecakbpwkthxyAQMBgH7zmnx7oHzOFzWBDcneyQrQ3Hz5ADMjfCDt5uj2BHJQrFwhiAIAtL+dQKdvf14/V4lnBx43vlGLYwMwEd5leju08PViX/btVbdfXpkFdXgvYPlKG3oQLCXC9Jum4L7Zo2HtytLhq6PhTOErd9VYe+ZBjy7YioigzzFjmPVFk4OwHsHy5FX3sTHAVsZQRBwtLIF/zxajS9PaNDR24+pUi+8dm88bo8L4V/EaERYOFdR0diJP31Zgvlyf6TMDRc7jtWbNdEXzg52OHCukYVjRQovtOC3/ziOMm0n3JzscXucFHfNkGHWRF+OOKQbwsL5EYNBwJP/OA4HOwlevlvB+wVMwMXRHrMm+mK/2vqeY2SrPv32Ap77vBhB3s545e543BYbzGfSkNF4PPwjOcV1OFrZgt8vnwqpN5/JYSo3Tw5AaUMHalu7xY5C19Cj0yPtXyfwX9tOYs4kP3zx6HzcNUPGsiGTYOH8QL/egFd3n0VkoAdW2djszqPt8nNx9p3jUY6l0hsE3P/et9j6XRUeXSTHhw/MhI/b2H9+E5kPC+cHthXWoEzbiSeXRMGep9JMKjLQA6E+rthTUi92FBrCruI6FFS24M93xuG3S/kzQKbHwvleb78er+9RQyHzxtIYzvtlahKJBEtjgnGgtBEdvf1ix6EfEQQB7+w/jwl+brh3Jp9PQ6ODhfO9rflVqGntxm+XRHEEzihZGhOEvn4DvjnbIHYU+pGCyhYcr2rFL+ZP5JENjRoWDi4N/3x511nMnuiLBZH+YscZs1ThvvBzd8KuYp5WszQZ+89jnJsj7prBoxsaPTZfOKdqLmLtB/nwdXfC66uVPLoZRfZ2EiRODcLXZxrQ26+//gpkFmXaDuw5XY+fzQ3nTBA0qmxurOP/fncBh0qbIBvnigBPZ2zYq4aXiyM+fWg2h0GbwdKYYGz9rgqHS5uwaApvArUE7x0oh5O9HdbOnSB2FBrjbK5wmjr7UFTViuyTGvQbBEi9XbDloTmQjXMTO5pNmCf3g4ezA3YV17FwLMDl+dHuUIbyCbY06myucB6+RY6Hb5FDbxBQ19YDP3cnPqfDjJwd7LFoSiByS+rx4p0CL1CL7KszDejq0yN5WojYUcgGGHUNp7m5GYmJiYiMjERiYiJaWlquuty6desQGBiI2NjYK15/7rnnEBoaCqVSCaVSiezsbGPijIi9nQShPq4sGxEsjQlCU2cfCiqaxY5i8748UYsAT2fMnugndhSyAUYVTnp6OhISEqBWq5GQkID09PSrLvfAAw8gJyfnqu89/vjjKCoqQlFREZYtW2ZMHLISt0QFwsnBjqPVRNbR24+vzjRgWWwwjzTJLIwqnKysLKSkpAAAUlJSsH379qsut3DhQvj68mmZdImHswMWyP2xq7gOgiCIHcdm7T1dj95+A5bH83QamYdRhVNfXw+pVAoAkEqlaGgY+Q19GzduhEKhwLp164Y8JQcAGRkZUKlUUKlU0Go5H5e1WxoTjJrWbhTXtokdxWZ9cVyDYC8XzBg/TuwoZCOuWziLFy9GbGzsoH+ysrKM3vj69etRVlaGoqIiSKVSPPnkk0Mum5qaioKCAhQUFCAgIMDobZO45n9/g+3RyqH/kkGj52K3DvvPaXG7QspHcJDZXHeU2p49e4Z8LygoCBqNBlKpFBqNBoGBIxvmGhT0/3OWPfTQQ1i+fPmI1ifrJfV2gb+HM05UXxQ7ik3KLalHn96A5Qqp2FHIhhh1Si0pKQmZmZkAgMzMTCQnJ49ofY1GM/Df27ZtGzSKjcYuiUQChcwbJ6pbxY5ik7KKahDq4wplmI/YUciGGFU4aWlpyM3NRWRkJHJzc5GWlgYAqK2tvWLE2X333Ye5c+fi7NmzkMlkeP/99wEATz31FOLi4qBQKPD111/jtddeMyYOWRmFzBul2g50cvZos6ps6sQBdSPuVsk4lROZlVE3fvr5+WHv3r2DXg8JCbninpotW7Zcdf3Nmzcbs3mycgqZNwQBKK5tw6yJHMVoLp9+ewH2dhKsnjle7ChkY2x+8k4ST1yoDwDwtJoZ9ej0+KygConRQQj2dhE7DtkYFg6JJsDTGSHeLhw4YEY7T2nQ0qXD/XM4USeZHwuHRBUn88bJGhaOuXycdwET/d0xbxKnsiHzY+GQqBQyH5Q3duJit07sKGPeaU0bjla2YM3s8bz3hkTBwiFRKWTeAC49CI9G18d5lXB2sMNdM2RiRyEbxcIhUcWFXiqc4xw4MKrae3TYVliDFfEh8HFzEjsO2SgWDonKx80J433dcJIDB0ZVVlEtuvr0HCxAomLhkOguzTjAwhlNOafqIA/0QPz3pzCJxMDCIdEpZN6oae1GU0ev2FHGpM7efuSXN2NRVABnFiBRsXBIdAM3gHLgwKg4XNaEPr0Bi6JGNrkukamxcEh0saFekEjA6zij5JuzDXBzsocqnNMHkbhYOCQ6TxdHRPi7c4qbUSAIAvartZg3yQ9ODvxxJ3HxO5AsQrzMhwMHRkFFUxeqmruxcDIfWkjiY+GQRYiTeaOhvRd1F3vEjjKm7D936XHsCyNZOCQ+Fg5ZhNjvbwAtruVRjikdUGsx3tcN4f7uYkchYuGQZYiWXho4cKqmTewoY0ZfvwFHypqwcLK/2FGIALBwyEJ4ODtgor87TvEIx2SOVrags0/P02lkMVg4ZDFiQ7xRzHtxTGa/WgsHOwnm8lEEZCFYOGQxYkO9UHuxB82dfWJHGRP2n9Ni+oRx8HRxFDsKEQAWDlmQ2BAOHDAVbXsvimvbcDOHQ5MFYeGQxYgJufxsHA4cMNbBUg6HJstjVOE0NzcjMTERkZGRSExMREtLy6BlqqqqsGjRIkRHRyMmJgYbNmwY0fpkO7zdHBHm68qBAyaw/1wj/NydEBPiJXYUogFGFU56ejoSEhKgVquRkJCA9PT0Qcs4ODjg1VdfxenTp5GXl4c333wTJSUlw16fbAsHDhjPYBBwQK3F/Eh/PkqaLIpRhZOVlYWUlBQAQEpKCrZv3z5oGalUiunTpwMAPD09ER0djZqammGvT7YlTuaNiqYuNPJRBTesRNOGxo4+nk4ji2NU4dTX10MqlQK4VCwNDQ3XXL6iogKFhYWYPXv2iNfPyMiASqWCSqWCVqs1JjZZsHmTLt2keKi0UeQk1mu/+tLPxwLe8EkWxuF6CyxevBh1dXWDXn/xxRdHtKGOjg6sWrUKr7/+Ory8Rn5eOTU1FampqQAAlUo14vXJOsSFesPb1REH1Y1IVoaKHccq7T+nRbTUC4GeLmJHIbrCdQtnz549Q74XFBQEjUYDqVQKjUaDwMCrP+BJp9Nh1apVWLNmDVauXDni9cl22NtJcJPcDwfUjRAEgU+oHKHO3n4crWzBuvkTxY5CNIhRp9SSkpKQmZkJAMjMzERycvKgZQRBwIMPPojo6Gg88cQTI16fbM+CyADUtfWgTNshdhSrc0CthU4v4GZevyELZFThpKWlITc3F5GRkcjNzUVaWhoAoLa2FsuWLQMAHDp0CJs3b8ZXX30FpVIJpVKJ7Ozsa65Ptm2+/NK1hwNqXscZqayiWvh7OGHWRD7dkyzPdU+pXYufnx/27t076PWQkJCBUpk/fz4EQRjR+mTbwnzdMNHfHQfUjfj5TTw1NFwXu3XYe7oBa+aMh4M97+kmy8PvSrJI8+X+yDvfhL5+g9hRrEbOKQ369AbcwcEWZKFYOGSRFkT6o6tPj2MXOPvEcG0vrEWEvzsUMm+xoxBdFQuHLNKcSX6wt5PgIK/jDIvmYjfyypuQrAzlyD6yWCwcskheLo5QhvnggJo3+Q7H50W1EAQgWRkidhSiIbFwyGItiPTHiZqLaO3i83GuZ1thDaaN90G4v7vYUYiGxMIhi7Ug0h+CABwqbRI7ikU7U9eGM3XtHCxAFo+FQxYrXuYDT2eHgWe70NVtL6yFvZ0EyxVSsaMQXRMLhyyWg70d5k7yw/5zjUPey2XrDAYBnxfVYGGkP/w8nMWOQ3RNLByyaAsmB6CmtRsVTV1iR7FIRdWtqL3YgyQOFiArwMIhi7bg+2luDnK02lXtLq6Hg50Et04JEjsK0XWxcMiiTfBzg2ycK/bzfpyr2l1ShzkRfvB2dRQ7CtF1sXDIokkkEiyIDEBeWRN0ek5z80OlDR04r+3Ekhge3ZB1YOGQxVsQ6Y/23n4cr2oVO4pFyS2pBwAsjmbhkHVg4ZDFmzfJD3YSPq7gx3JL6hAX6o0QH1exoxANCwuHLJ6PmxPiZD44WMrCuayhvQeFVa1InMqjG7IeLByyCjdN8sPxqlZ09PaLHcUi7D3dAEEAr9+QVWHhkFW4Se6PfoOA/HJOcwMAu4vrEObriqggT7GjEA0bC4eswowJ4+DkYMd51QC0dPbhUGkTlkwN5qMIyKqwcMgquDjaY06EH3aX1Nn8NDf/LqxBn96Au2bIxI5CNCIsHLIayfEhqGrutumngAqCgK35FxAf5oNoqZfYcYhGhIVDVmNpbDBcHO2wvbBW7CiiOXahBeqGDvx0VpjYUYhGzKjCaW5uRmJiIiIjI5GYmIiWlsF/86yqqsKiRYsQHR2NmJgYbNiwYeC95557DqGhoVAqlVAqlcjOzjYmDo1xHs4OSJwajC9P1NrsrANb8qvg7mSP5QpO1knWx6jCSU9PR0JCAtRqNRISEpCenj5oGQcHB7z66qs4ffo08vLy8Oabb6KkpGTg/ccffxxFRUUoKirCsmXLjIlDNuAOZQhaunTYf872JvO82K3DlydqkaQMhbuzg9hxiEbMqMLJyspCSkoKACAlJQXbt28ftIxUKsX06dMBAJ6enoiOjkZNTY0xmyUbtnByAMa5OWJboe19D31eVIMenQH38XQaWSmjCqe+vh5S6aWnDEqlUjQ0NFxz+YqKChQWFmL27NkDr23cuBEKhQLr1q276im5yzIyMqBSqaBSqaDV2t7fbukSR3s7LFeEILekHu09OrHjmE1fvwF/P1yBqVIvxIV6ix2H6IZct3AWL16M2NjYQf9kZWWNaEMdHR1YtWoVXn/9dXh5XRpds379epSVlaGoqAhSqRRPPvnkkOunpqaioKAABQUFCAgIGNG2aWy5Y1oIevsN2HmqTuwoZvPWN6Uo03biySWTee8NWa3rngjes2fPkO8FBQVBo9FAKpVCo9EgMDDwqsvpdDqsWrUKa9aswcqVK69Y/7KHHnoIy5cvH0l2slHTx4/DlGBPvLzrLBKmBI75RyufrWvHm1+XIlkZggTODE1WzKhTaklJScjMzAQAZGZmIjk5edAygiDgwQcfRHR0NJ544okr3tNoNAP/vW3bNsTGxhoTh2yERCLBa/cqcbFbh9/+4zgMhrF7I6jeIOCpf52Ap4sjnl0RI3YcIqMYVThpaWnIzc1FZGQkcnNzkZaWBgCora0dGHF26NAhbN68GV999dWg4c9PPfUU4uLioFAo8PXXX+O1114z8n+HbEW01Au/vz0aX5/V4sPDFWLHGTUfHCzH8apWPJcUA193J7HjEBlFIljhPCEqlQoFBQVixyCRCYKA1M1H8c3ZBmx7+CbEjrGL6RWNnfjJhv2YLw/Au2tn8NoNWazh/k7mTANktSQSCf66SgE/d2f8akshOsfYowue/6IYjvZ2ePHOWJYNjQksHLJq49yd8PpqJSqbOvHYlkL0j5EZCPLLm/H1WS0eXSRHkJeL2HGITIKFQ1ZvToQf/js5FnvPNOCZbaesfjZpQRDw8q4zCPR0xtq54WLHITIZzo9BY8L9cyagoa0Hb3xViiAvZzyxJErsSDfsm3NafFfRgj/dEQtXJ3ux4xCZDAuHxozHEyejvq0Xb3xVil69AU8tnQJ7O+u69iEIAl7LPYcwX1fcq+IUNjS2sHBozJBIJHjxzlg4Okjwzr7zUNd3YMNqJTxdHMWONmxfn23AieqL+MuqODg58Iw3jS38jqYxxcHeDi/cEYc/Jcdg3zktVr51GBeausSONSyCIGDDHjVk41yxcjqf5kljDwuHxqSfzQ3H5nWz0NDei6Q3D+JwWaPYka7rm3NaHK++iEcWyeFozx9NGnv4XU1j1jy5P7IeuQn+Hs5Y+34+Ps6rFDvSkC4f3YT6uGIVj25ojGLh0JgW7u+Ofz88Dwsi/fH77afw4o4Sixw2vV/diKKqVjy8aBKv3dCYxe9sGvO8XBzxXspM3D9nPN49UI5/HK0WO9IVLh3dnEOItwvunsGRaTR2sXDIJtjbSfB8UizmRvjhj1mncK6+XexIA/arG3HsQivWL5Lz6IbGNH53k82wt5Ngw31KeDg74uFPjqGrT/y51/QGAX/ecRphvq64R8VrNzS2sXDIpgR6umDDaiXKtB14+t8nRX+Wzv9+V4Wz9e14+rZoODtwVgEa21g4ZHNukvvjt0uikFVUi5d2nhZtEEF7jw5/yz2LWeG+uC02WJQMRObEmQbIJj18yyQ0tPXg3QPl8HV3xvpbJpk9w5tfl6Gxow8fPBDNxw+QTWDhkE2SSCR4dkUMWrp0+EvOGfi6O+LemePNtv2q5i58cLAcK6eHQiHzMdt2icTEwiGbZWcnwSt3x6O1W4en/30S3q6O+Ems1CzbTs85A3s7CZ5aOsUs2yOyBLyGQzbNycEOm+6fjmnjx+HRTwux86Rm1Ld5quYidpzQIHVhBIK9+XA1sh0sHLJ5bk4O+PvPZyI+zAePbinEjhOjWzof51XC1dEeDy6YOKrbIbI0LBwiAJ4ujshcNwvTx/vgsa2F2H9OOyrbaevRIauoFsnKEHhZ0WMTiEzBqMJpbm5GYmIiIiMjkZiYiJaWlkHL9PT0YNasWYiPj0dMTAyeffbZEa1PZC4ezg74+89nIdTHFW/sVY/KNrYdq0G3To81syeMyucTWTKjCic9PR0JCQlQq9VISEhAenr6oGWcnZ3x1Vdf4fjx4ygqKkJOTg7y8vKGvT6RObk7O2Dt3AkoqGzBqZqLJv1sQRDwybeVUMi8ESfzNulnE1kDowonKysLKSkpAICUlBRs37590DISiQQeHh4AAJ1OB51ON3DPwXDWJzK3u2eEwdXRHpuPmPZxBgWVLThX34E1s803/JrIkhhVOPX19ZBKLw0jlUqlaGhouOpyer0eSqUSgYGBSExMxOzZs0e0PgBkZGRApVJBpVJBqx2d8+tEAODt5og7poVie1ENWrv6TPa5H+dVwtPFASviQ0z2mUTW5LqFs3jxYsTGxg76Jysra9gbsbe3R1FREaqrq5Gfn49Tp06NOGhqaioKCgpQUFCAgICAEa9PNBJr505Ab78BnxVUmeTzGtp7sPNkHVZOC4WbE29/I9t03e/8PXv2DPleUFAQNBoNpFIpNBoNAgMDr/lZPj4+uOWWW5CTk4PY2NgRr09kLtFSL8ya6IvNeZV4cH4E7O2Mm3rm/YPl6DcY8MBNHApNtsuoU2pJSUnIzMwEAGRmZiI5OXnQMlqtFq2trQCA7u5u7NmzB1OmTBn2+kRieWBeOKqau40eIn2xW4dP8i7gtjgpJvq7mygdkfUxqnDS0tKQm5uLyMhI5ObmIi0tDQBQW1uLZcuWAQA0Gg0WLVoEhUKBmTNnIjExEcuXL7/m+kSWIHFqEHzcHLG9qMaoz/k4rxIdvf1Yf7P5JwglsiRGnUz28/PD3r17B70eEhKC7OxsAIBCoUBhYeGI1ieyBI72drgtVorthTXo6uu/oWsv3X16fHCwHDdPDkBsKIdCk23jTANE15CsDEG3To89p4ceQXktnxVUoamzDw+L8PgDIkvDwiG6hlnhvgj2csHnRbUjXre5sw9vf1OGGRPGYdZE31FIR2RdWDhE12BnJ8FyhRT7zjXgYpdu2Ov16w341ZZjaO7swx+XT+UD1ojAwiG6riRlCHR6ATtPDX8W6Zd3ncWh0ia8cEcs4sN8Ri8ckRVh4RBdR1yoN8L93PD58eGdVvvieC3e2X8e988Zj3tmho1yOiLrwcIhug6JRIIkZSiOnG9CZVPnNZc9rWnDU/88gRkTxuGPy2PMlJDIOrBwiIbh3plh8HB2wPqPj6G7T3/VZVq7+vDLzUfh6eKAt9dMh5MDf7yIfog/EUTDEOrjijdWT8PpujY89a8TEAThivf1BgG/3loEzcVuvH3/DAR68dHRRD/GwiEapkVTAvG7pVED12h+6G+5Z7HvnBbPJ8VixoRxIiUksmyctpZoBNbfPAnFtW34S84ZhPi4wsXBDt9VNOPdA+W4b9Z4/JTPuiEaEguHaAQkEglevkuB89pOPLbl0pRN9nYSLI4OxHNJU0VOR2TZWDhEI+Tm5IC//3wm9p5uQFSwJ6ZKveDqZC92LCKLx8IhugFBXi48fUY0Qhw0QEREZsHCISIis2DhEBGRWbBwiIjILFg4RERkFiwcIiIyCxYOERGZBQuHiIjMgoVDRERmwcIhIiKzkAg/frCHFfD390d4ePgNr6/VahEQEGC6QGbG/OKx5uwA84vNmvNfK3tFRQUaGxuv+xlWWTjGUqlUKCgoEDvGDWN+8VhzdoD5xWbN+U2RnafUiIjILFg4RERkFjZZOKmpqWJHMArzi8easwPMLzZrzm+K7DZ5DYeIiMzPJo9wiIjI/GyucHJychAVFQW5XI709HSx41xXeHg44uLioFQqoVKpAADNzc1ITExEZGQkEhMT0dLSInLK/7du3ToEBgYiNjZ24LVr5X3ppZcgl8sRFRWFXbt2iRH5ClfL/9xzzyE0NBRKpRJKpRLZ2dkD71lS/qqqKixatAjR0dGIiYnBhg0bAFjP/h8qv7Xs/56eHsyaNQvx8fGIiYnBs88+C8B69v9Q+U26/wUb0t/fL0RERAhlZWVCb2+voFAohOLiYrFjXdOECRMErVZ7xWu/+93vhJdeekkQBEF46aWXhKeeekqMaFe1b98+4ejRo0JMTMzAa0PlLS4uFhQKhdDT0yOcP39eiIiIEPr7+0XJfdnV8j/77LPCyy+/PGhZS8tfW1srHD16VBAEQWhraxMiIyOF4uJiq9n/Q+W3lv1vMBiE9vZ2QRAEoa+vT5g1a5Zw5MgRq9n/Q+U35f63qSOc/Px8yOVyREREwMnJCatXr0ZWVpbYsUYsKysLKSkpAICUlBRs375d3EA/sHDhQvj6+l7x2lB5s7KysHr1ajg7O2PixImQy+XIz883d+QrXC3/UCwtv1QqxfTp0wEAnp6eiI6ORk1NjdXs/6HyD8XS8kskEnh4eAAAdDoddDodJBKJ1ez/ofIP5Uby21Th1NTUICwsbOBrmUx2zW9oSyCRSLBkyRLMmDEDGRkZAID6+npIpVIAl35IGxoaxIx4XUPltaY/j40bN0KhUGDdunUDp0QsOX9FRQUKCwsxe/Zsq9z/P8wPWM/+1+v1UCqVCAwMRGJiotXt/6vlB0y3/22qcISrDMi7VoNbgkOHDuHYsWPYuXMn3nzzTezfv1/sSCZjLX8e69evR1lZGYqKiiCVSvHkk08CsNz8HR0dWLVqFV5//XV4eXkNuZy15Lem/W9vb4+ioiJUV1cjPz8fp06dGnJZa8lvyv1vU4Ujk8lQVVU18HV1dTVCQkJETHR9l/MFBgbizjvvRH5+PoKCgqDRaAAAGo0GgYGBYka8rqHyWsufR1BQEOzt7WFnZ4eHHnpo4LSBJebX6XRYtWoV1qxZg5UrVwKwrv0/VH5r2f+X+fj44JZbbkFOTo5V7f/LfpzfVPvfpgpn5syZUKvVKC8vR19fH7Zu3YqkpCSxYw2ps7MT7e3tA/+9e/duxMbGIikpCZmZmQCAzMxMJCcnixnzuobKm5SUhK1bt6K3txfl5eVQq9WYNWuWmFGv6vIvCwDYtm3bwAg2S8svCAIefPBBREdH44knnhh43Vr2/1D5rWX/a7VatLa2AgC6u7uxZ88eTJkyxWr2/1D5Tbr/TTnKwRrs2LFDiIyMFCIiIoQXXnhB7DjXVFZWJigUCkGhUAhTp04dyNvY2CjceuutglwuF2699VahqalJ5KT/b/Xq1UJwcLDg4OAghIaGCu+99941877wwgtCRESEMHnyZCE7O1vE5JdcLf/9998vxMbGCnFxccKKFSuE2trageUtKf+BAwcEAEJcXJwQHx8vxMfHCzt27LCa/T9UfmvZ/8ePHxeUSqUQFxcnxMTECM8//7wgCNf+ebWG/Kbc/5xpgIiIzMKmTqkREZF4WDhERGQWLBwiIjILFg4REZkFC4eIiMyChUNERGbBwiEiIrNg4RARkVn8Hwd9vImxj5MoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dbac2709c278425d93c2305890d57f16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "0ac3a1b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "609bcc6b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v3_Aug16_bf_model_misspecification_1000_data.ipynb b/bf/v3_Aug16_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..725b082
--- /dev/null
+++ b/bf/v3_Aug16_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,6105 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "        temp = dpl[0].data['agg']\n",
+    "        avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "\n",
+    "#     print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "f856469e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "9787d8dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "65930889",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79af7b9ab51343cea644444010274e79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.75001</td>\n",
+       "      <td>0.817412</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Loss   W.Decay\n",
+       "0  9.75001  0.817412"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "706ec34c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "e5aad1a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kxqxTwekPIpwgvDjpZlFhUMOi5f8Ndk+JejgNDQ3o7+8XXw8MDKCuri7pfYLBIO6++27ce++9uOuuuzI1hyCWPL5gfA+nQq/GpKcwn34Fhp1+sKEIn0xvtODypDfhzTZT3IEQwhFODEcloqlCh8vzhrH1jLnF3Ek2MGuV4DgsKFaYzaSHRYVeBZNWCYYBHIt4RPkiY8HZsmULenp60NvbC5ZlsX//fuzYsWPOPjt27MBPf/pTcByHd999F2azGVarFRzH4fOf/zxaW1vx1a9+NVNTCIIA37wznuCUG1Swe9iCLZsFIA47W1GhR1OFHmw4guEsJsGFcJVZm4zg6HFpaiak5mPD6Ld7sarGmDX7BK8lXlgtEuEw5WFRYVBBLmNg0ijhKNBuEhkLjkKhwN69e3HLLbegtbUV99xzD9ra2vDMM8/gmWeeAQBs374dzc3NsNls+OIXv4gf/vCHAIAjR47gZz/7GV5//XVs3LgRGzduxIEDBzI1iSCyAsdxCWeiFAr+YHhOp4HZVOhVCEW4gu4+LAw7a6rUi6MBLiUx4jldUhKcch1GnQH4WP57cH7MDY4DWrLs4QCIWzgw7ePDbRV6vvigTLd4kUG+SFyWkSTbt2/H9u3b52x78MEHxZ8ZhsHTTz+94LhrrrmmoJ+0CEKgZ9SFO54+glCEw5uPfhxWszbfJsXFH4yfwynX80nlSU8A5iRCSPng0qQXKoUMVpMGQqa3d8KDq22VWTmfIDimJARHWGsz4Q6gsVyHnjG+jUxLNj0cXWIPRwiRVkQ7SZh1qoLtl0edBggiCc6NuuFlw2BDERy75Mi3OXEJhiMIRbhFBWeqgPM49mg+QiZjUGvSQK2Q4dIiI54zQSghTsbDKYteP8GDODfqhlLOzGm4KTWC4MTLywjhRkEMk1m3ky9IcAgiCWYnbE8N8VMzC9E7nz2aIBZC2KWQCwdmt5mRyfibee9E5iG1350Ywv3PduE3xwbmbBduzkJJcSLKojf/qagHceyyHWtqTVDKs3crFTyv6TheS/8ULzhClwKLVlmwHo4kITWCKHWEeSkNZVr897EBHPhwGMEwH15TZPFmkyri8LUYzTsBvmgAKGwPxx2Yu+p/eYU+4XjnZHmh6zLevjCJ86Mu3LWpQdyeSg5H8HDsHhZeNoQPLtvxwDUrMrYtEeZFigb67V4o5YxYmm3RqQo2h1M4vykEUcA4/SFxhPCoM4C+SS8GHb6YUx3zSSA6XloTY7wxwBcNAIUvOLNX/S+v1OPSlDejNjccx4ldlEecfgTDEfG9aV8QchkDfRyRnk151Auye1l09U4hGOZw9crs5JYE1Ao5dCp5XBHpn/KizqIVZ/lYdEq4/CGEZv0bCwUSHIJIArc/BINKgaborJKbWvkhWoW2iFLwcGKNJwD4UJtOJRe7CxcinkAIetVcD4cNRTAcZ/jZfD64bMeNP/gjxmbtPzzth8MbRFudCREOcwapTfuCMGuVCxasx0JY52L3sHivdwpKOSPOyskm5gTdBgZmTRsFFi+jzickOASRBC5/EAaNAl+8dgX+n89egYevXwkABTfOV8zhKOI/rZfrVWJ34ULE7Z87DG15NCHfl2RY7eWTw7gw7sGv3p/J1Zwa4r2bbdFpm4P2mXU9jqjgJINcxkRzJEFcnvKisUwXV9ylpKFMK/4b5jNg5+0QMC9SZJBPSHAIIgmEMI9Ro8TtG+rEYVsF5+GwiT0cgA+rFUvRAMCH1ICZ9TmL8faFSQDAr472i4Ud3UNOMMyMZzromBEcZwqCA/B5nCkvi5FpP2rN2WtpM5tPrreie9i5YJqnlw1hws3OGWtgUPP/Fk+g8NZakeAQRBLMvwkK0zPHCkxw/KFoDifOwk9A8HAKU3A4jltwrYXS6GQ8nCkPi9PDTjRX6tE36cXZ6A363JgLjWW6mfb9swRnOlXB0fHdGoYdvpwJzm0b6iCXMXjxg7lNj4WBcM2VM2OphWvnJsEhiOLE6Q/BOKu5o1ohh0WnLDgPJxANqakThtTUBSs4/mAEEQ5zqtSE0ui+JLoNCDfg/3W9DQDENVMXxtxYWaWHRilHhV41x8NJR3Am3SxGXQFYcyQ4lQY1rm2pROfxoTnFEz984wJqTRrc0FotbhMFx0+CQxBFiTuaw5lNlUFdcDkcNlqZpI5TpQbwK9InC7SfmvBUPv9aN1Xok/JwTg1NQy5jcFu7FRV6Fd6/ZEckwqF3woOVVbx3U1+mxUA0hxOOcBie9qc0Hrpcr8S5MRfCEQ61Oew4cecV9Rh0+PCXPl5Uu3qn0NU3hd3XNc95wBCunYclwSGIosTlD8E4byJktanwhpkJZdGqBIJTrleBDUXgZQuvL5woOPNm06xIsjT6o8Fp2KoM0Cjl2NRUhmOX7Rh0+BAIRbAyGk6zmjUYnuYfFAbtPrChSErdnst0Kghabc3iWIL5bFtbA71Kjr1vnEc4wmHvG+dRoVfhs1cum7MfeTgEUeTEmgjJeziFJTgzHk7iKjWgMNfiCInu2WXRAD8WIJnS6I+GnGir52dqbVpWht4JD45dtgOYyXPUmjRiWfSFcTcAiN5PMiyb1cYmVzkcgJ/H8w+fXIs/9UzgU08fwVvnxvGFa5sXFIgIguOiHA5BFB+hMO8NCNU/AjVm/sY1YM9eJ+NUEXI4iTycCrGBZ+EJjssfO6S2Irr+af60zdmMOf0YdwWwrs4MYKaD86unxwBA9HBqzBq4/CF42VBagrNjw8y8r1zlcAQ+e2Uj/s9b1+DsqAt3bKzD7uuaF+yjUcoglzFUpUYQxYgnwN/E53s4O7csg0YhxxeePzpnIWE+SSaHM+PhFJZ3Bsx4OIZ54cumqHfSG6M0WshFvRctGGhv4AVHEJjXT4+iTKcUhbbGyIvEqDOAC+NulOtVYsuaZDBqlLjzinoAM9cyVzAMg4c+vhLH/3kbnvzMRrG7wPx9DGoFhdQIohhxRht3LnjqrtTjR/dtRv+UF7t/ejQfpi0gmRyO2MCzALsNuOMIjtWkgUohmzMXJxLh8Nh/n8TqfzyE3xwbQOfxIVQb1bhiWRkAoLFMC6WcgYcNo73BInYSEMJgI9N+XBjzYGWVHqnyg09vwOlv3ZpUd4JsoFMpEp7boFZQSI0gihHhJmjSLOx1e01LJe7Z0oiL49lrn58KbDgCGQMoYjz5ChRyA894giOTMWiu1OP08Mxq+7OjLuz/Sz+Ucgbferkbb54bw47oehUAUMhlWBZdELmh0SIeJ1Skjbn8uDjhwYrK1AVHJmNy0mEgXQxqBYXUCKIYEeaNzF6HMxuzVglXIIRwBs0lpSIQikClkCV8+tWr5FDJZYUtODHE/aoV5TjaZwcbXdz60SA/JuIH92wAxwF1Fi12zqvYao7mZjZEw2zAzNyYC+MeTLgDYn+8UsKgURTkwk8aT0AQCYhEODz1ag9qTGpcscwScx9TVIjc/lDep2iyoUjCCjWAj/GXF2h7G08gBBmDmAPk/mplBZ5/5xJODjjQsbwcp4ac0KvkuHltLW5dZ435eSurDDiMUbQ3WMRtRo0SepUcf4nmfLI5PC1fGNQKOApwJg55OASRgO5hJ04MTON/37gKOlXs5zNTAXXnDYTCCfM3AoXa3sbhDcKoid25+aoVFWAY4J1or7QPB6exts4EWYLw4d9+bDn+789egSrj3IWdNSYNuqILKJvKS9DDURemh0OCQxAJEDoJrK0zxd1HyO04/YUgOJGEFWoCFQYVJt3ZrVL7U884vvSLY3jz3HjSx0x5WbGabD5lehXaGyw4+NEIQuEIuoecaKszx9xXoNasmVPGLLCsQieGQJeVqIdDgkMQRcaUhxeR8gTjhwUPx1kQHk4kKQ+nypjdLgn9U1488J9/wcsnh/HjNy8kfdyUm01YanzXFfXoHnbiha7L8AXDac+iubWtVvw5lT5qxYJBoxDL+QsJEhyCSIA9GnYq08e/KQk5nELwcJLJ4QBAtVGDcXcga/3UnnqtBwzD4FMb69DVO5X0tbF7EwvO7RvqoJAx+KfOU1DKGVy3Kr1pm5+Ik/MpFfRRDyeTKanZgASHIBIw5WWhlDMLynRnY9JGQ2q+/IcwUvFwgmEu7tjiTHnjzBhub6/DvVubEIpweCvJsNqkJ7HglOtVeOCaFQCAxnJd3MrBxTDrlPjqtlV4/M51aR1f6Ah9/wqtgSdVqRFEAqbcLMp0qoRlxmJIrSA8nHBSORwhiT7uDqS0yj4Z3IEQJj0sbNUGbFpWBq1SjmOXHLitfWEuZTYcx8G+iOAAwFe3rUL/lBef7mjIyM5HbmzJ6PhCRngImvYF0xblbEAeDkEkYGqREA8AGFQKMEzh5HCSEZxqQXCykMe5HO0GsKxcB3l0ls3lqcUXxjr9IYQi3KLXW6OU40f3bcYNa2oksbcUKY92kyi0SkQSHIJIQDJP3DIZA6NaAWcB9K5ikxQcwcPJxjyfy1MzgiP8fSmJ4WnCzTHX/clKkULtCE6CQxAJmPKwSYWcTFplwXg4yeRwsunh9M8THN7DWXyWzZRYoEGCkykkOARRhEx52YQl0QImjbJAcjjJVakZ1ApolDKMObMQUpvywqRRiF0XllXoEQhFFp0dJNwc463DIZKHBIcgioxQOIJpXzBJD0eRkyq1S5OehDeRQCgMlXzxX2uGYfi1OFlY/Hl5yjtnMeXy6M99MUYLzEYsQU9C4InEmDQKKOUMCQ6RPQrhCbuUmPYFwXHJPXGbNMqctLa558fvYNO/HhYbV86HDUWgVib3a11lyM7izyGHD/UWrfhaaB1zeZE8ziTlcCSDYRiU6QqvfREJTgkQDEfw4M/eR/s3X8HTb5zPtzklgz26RsWSRENOnUoOXzD7K7tHoyGwX3Rdjvl+slVqAH9jt2dhHc6kh0WlYaZ3WZ1FA4WMwaVFKtUm3QFolXLoCrjtfzFRiA1aSXBKgHcuTOLQqRHYqg34/itn8Wr3aL5NKgn8UQGJ1bl4PiqFTGybny0iEQ7CcqB40xzZJIsGAD50ZZf4hhSOcLDP64emkMvQUKZF3yIezrg7gEpj4jVPRPIUYoNWEpwS4LXTo9AoZfj1g3+FtjoTvvzL42KlEJE+guCokxWccHYFxxUIQehEE2u4VjjCIRThkioaAKI3JC8raXsbu5flw5CGud2Zl1XoFw2pTbgDqJp3HJE+5XrpHygyZckJTufxQXzzpVP5NkMyOI7Dq6fHcI2tChadCj+6dzMCoTB+8ufefJtW9Pij45o1SXgMSnn2PRzXrBxdrE7AwvmT9nD0KrChiKShwHhraZrKdeib9CQUt3FXYE4ojsiMCgqp5Z8PB6bxy7/059sMyRia9mPQ4RObGDaW6/DJ9Vb8+v0BvHluHF/7rxP4+buX8Iv3LuPgh8MIZfkpvJQIhPgbsaZAQmqzq+C87EKREOxNOoejk750dtIdu7S5qUIHlz+UsHfbhJtdMLeGSJ8yvQrTviCCBfQ7v+R6qZm0SviCYQTDESiTKB8tdM6NugAAa2pn5rXsvm4lDnw0gl3PdkHGAP99bEB8b0OjBf/1P7cmHXZZyogeThKCo5bzITWO47KWgxA8nAq9KmZILVUPRyiGsHuCaCiTxsZJD1/UMD+kJoxxvjTljVlmHgxHMDWv2IDIDOFa2j0sqqNjtfPNkhMcY3RYlssfKonyy3MjvOCsqjGI29bWmfDL3Vvxs3cv4avbVoHj+JvQm+fG8fVfn8QP37iAr2xblS+TiwYhh6NJosxYuMmz4eQWXqaD0Dqn1qyJWc4ciApOKjkcgF/cKhVxQ2rRtTiXJj3Y2GiJexx5ONIx074oUDCCU/yP+CkizC5xlcialXOjblQb1bDMWyx3xbIy/F/3bERDmQ6N5TrUmDS4p6MRn1hXi2f/3CtOOyTi408xpAYAwbB019UfDM/J1QjfWatZE9PDCaSRwwEAh4SCI4TUyuaVkguNPM+PuWMeJwgoeTjSMbsjeCweeeEDfObH70j6/78YS05wBA+nEGaXSEHPmAuraoxJ739Taw1cgRAujMf+xSdmEEJqyeREhNX9UuZxPvX0Eaz7xh/E10KvtlqzBh42vKA3WUHkcDwBlOmUUMwLV2uUcqys0qN7yBnzOOGmSB6OdAgVf/EW9750Ygjv9U7he384mzOblpzgFNLskkwJhSPoGXWjZVY4bTE2LrMAAD64bM+SVaVDakUD/D5SCs6ZaLhUQAipWc38Kv751WWp5nBMWiVkDCQtnZ1K0F17rdWE7uE4ghO9KVJZtHRUJWjQOvv+1xPH68wGS09wijiktvf1Hvzs3Uvi66OX7PAFw7gyhbnuKyr0MGuVON7vyIKFpUVKHo5CWg9ndjWhL1qR5vIHoVXKYY4+NM0PqwkCpEkyhyOXMTBrlZLmcCbcLCr0sUVjbZ0Jw9P+mB7VmJMfk0AejnRolHIYNYqYgjMw5QPAf28H7b6c2bTkBKdYQ2onBxz4/ivn8E8vfiRue7V7FCq5DNetqkr6c2QyBhsaLfjgsiMLVpYWgSA/PTOZqrOZogFp1rRcnrVwdyIabnL6QjBqFOK46/lrcTwB/tzCdzwZyvQq2D3SPXwNOXyos8ROULda+UrKI+cn8JVfHscfz46J7/VP+VBpUENLbW0kpcoYu19ev53/fm1trsCI05+z5RJLTnCKNaT2/VfOiT8POnzgOA6HT4/iY7YK6NWpFRu2Wo24OO6hNTmL4A+GkwqnATM5nIBEHs7sMIfQ1t8VCMKkVYq9xgSBEXAH+O90Kt8HKccqhMIRDE/70VCmi/l+e70FKoUM//DbD/HbDwbxt8/9BW+eGwfA3wAby7UxjyPSJ16D1oGoV7O1uRzhCIcRp/SD+GIhieAcOnQIq1evhs1mw549exa8z3EcHnnkEdhsNrS3t+PYsWNJHys1RnV0HHABTGdMlv4pL946N45b22oBAF29kzg/5salSS9uak19zO7KKgPYcET80hGx8QeTb4Spljik1jM6k78RbhhOXwimWR6Oh537HXZHBciQguAYNQq4JPpdGJ72Ixzh4gqHWafE3Zvq4fSHcNWKciwr1+E7B88gEuF4wYkjVET6xBtB0T/lhV4lx7o6MwDkLKyWseCEw2E8/PDDOHjwILq7u/HCCy+gu7t7zj4HDx5ET08Penp6sG/fPjz00ENJHys1MhkDg0pRENMZk+WFrstgGOAfb2uFSaPA2+cncfg036DzxtbqlD9vZRVfZECVaokJhFLwcCQWnP6pmRuAcMNw+YMwapSiBzM/hyM09ExVcGK1yUkHIUwTz8MBgC9e2wyLTolHbmzBl29qQfewE2+cHcOQw08eThaoMqrF/NhsBuw+NJbr0FDGX/NBR5EITldXF2w2G5qbm6FSqbBz5050dnbO2aezsxP3338/GIbB1q1b4XA4MDw8nNSx2cCkVUr2VJdt3rs4if/3Txexfb0VDWU63NRag0MfjeB3J4axvt4sViylwsoqftU3CU5i/MFIUos+AYhdK6Rq4OkNhtFQpgXDzHg4U14WFt2M4CzM4YQglzFJ2wwARrUybufpVBES0Yk8leYqA47/88242laJT7ZbUaZT4qnXenjPiDwcyakx8SX0878rI04frGYN6qJzi3IV7ci408Dg4CAaGxvF1w0NDXjvvfcW3WdwcDCpYwX27duHffv2AQDGx8czstmoUSSMW3Mch2lfEP1TPkx6Apj2BWH3sJj2hRAIhREIRcBG/wRCYYSi6yGE5DIDgGH4v4Xt/GsGMgaQCa+Zua/n/A0+RHHo1AgayrT4t0+tBwB89qpl+M0Hgzg97MS/fmpdWv9+i06FSoMKF8YSzydZ6vjz6OH42DCMGiXKdWGMuwLgOA5jzgCqjWro1fFyOCHoVfKUWusYNArJKjb77V7IGMAap2hgPmqFHHde0YBnj/CNZpeVk+BIjdXM/1+MTPthq55ZPmH3BLG6xgSNUo5KgzpnIbWMBSdW99f5X/h4+yRzrMDu3buxe/duAEBHR0c6poqYNMo5v2RDDh/evTiJ4/0OnOh34OK4B644YQalnIFKLoNKMfNHIZt5ohT+TZz4GuDA8X9z/PsRDohE/+Y4DhyiryP8fpHoNpNGifuuWoavbFslzofvaCrD1bYKLCvX476rlqV9DZqrDOThLII/GE66xFjqhZ98wYJMrDJy+kMIhCKoMWnih9QCoZTCaQAffvOwYYQjHOSyzHrADdh9sJq1KfUo/Py1K0TBaarUZ3R+YiG10ZY2w9O+OYLDr5fi7ykvPvyxuKXsUpOx4DQ0NKC/f6b78sDAAOrq6pLah2XZRY/NBiatAhfGPXj2z714+eQQjkVLhPUqOdobLLh7cwMayrRoLNeh0qCGRadEmU4Fk0axYAV1rmEYBj///FUZN4hcWWXAoY+GJbKqNAmEIknfwGf3UpMCfzAMrVIOg1qBCXdgzjoVvSp20YAnEIIhhZJoYKaE2h0Iiet70uXihEfsmZYs9RYtjv3TNnQPOeeMpSakQQi5D0/P5HH8wTB8wbDY2ihRzk1qMhacLVu2oKenB729vaivr8f+/fvxi1/8Ys4+O3bswN69e7Fz50689957MJvNsFqtqKqqWvTYbGDSKtE74cG3Xu5Gq9WER29ZjRvWVGNVjTHjp7xcIEU34pVVeti9wYQrw5c6/mAEFfrkPBypq9R8wTDMWiXMWiUujnvE0uhqowZyGQOtUh7Tw0m1RF4qwQlHOJwdceJ/XNmU8rHlehWuaalM+9xEfGrMvOcyMktw7F6h313uf+8zFhyFQoG9e/filltuQTgcxgMPPIC2tjY888wzAIAHH3wQ27dvx4EDB2Cz2aDT6fDcc88lPDbbPHD1CqyqMWLb2hqxYmupsbJ6plKtXJ98p4KlRCAa1koGqT0cXzAMjUoulrWOufgbRrWJv4Ho1XKxDFognZCacU7njfQ9jL5JD/zBCFqtyff1I7KPWiFHpUE1x8MROj0UpeAAwPbt27F9+/Y52x588EHxZ4Zh8PTTTyd9bLZZV2/GunpzTs9ZaNiE0ugxN7ak0BpnKZHOwk/JcjgsH1KrMqrBhiJil+VqoyA4igUejicQEmP2ySJ2LciwUu10tEea0E2AKBxqzRqMTM8UBQidJfIR2VhynQYInjqLFmqFjAoHEuAPJV8WLXmVWjSHI7TrPzXkhE4lFwVCr1ooOG5/+iG1eEUyyXJm2AWFjEmpkSyRG2pN2rkejleYWZRZzi4dSHCWKHIZgxWV+rjzSYhoSC3ZKrVsCE40pAYAHw06UW1Ui/k7vros8yq12QMJM+HEgAO2agNNki1A6iwasR0WMNMdPB8hNRKcJczKKgMuTtBanHjwHk5yN1CFjF9DJUUOJxLhootOZwRnwh1AtXEmXKZTy+esw+E4Dh42nEZZNP+Um0lIzRMI4b2LU7jGRon/QmRllQEuf0gsPBGKBjKtSkwHEpwlTHOVHv1TXnHuCzFDMBxBOMIl3UuNYfj1WVJ4OEIDUK1SPmc+jG1WuGp+Dscf5O1NO6SWweLPI+cnwIYjuGFN6m2WiOyzppYv5BDybHYPC7N24ZC8XECCs4RprtIjwgGXJ72L77zE8AeTH74moJLLJOkWLcy10Splc55CO5rKxJ8Nqrk90ISfU12Ho1PJIWMWtslJhUOnRmBQK9BBxScFyZpavpBDGOg35Q3mbSkECc4SprlSKI2msNp8BOFIpS+ZSiGTJKQmCo5KDtmsdWGbZwmOXq2Al53xTEXBUaeWQ2EYBgZ1+h2jBx0+vHR8CHdtqk960iiRW8w6JerMGpyJejgj0z4SHCL3NEebeF6coMKB+QhTNlPycBTShNTinXt2rzGDWg4PGxITwdPR7ufCRNtUMGYwE+e5P/Ntaf7nX69M63giN6yuNeL0sAteNoQT/dNzHl5yiSTrcIjixKhRosqoRi95OAsQbsDGFG7gKoUMQQk8HL8YUuMF518/tQ6BYHhOhwmdWgGOA7xsGHq1Av1Ti48GiIdZq0xrXEcwHMFvPxjEtrU11JamwNnaXIEnDp7B//fuZbDhCK7NU2cH8nCWOKtrjPhwcDrfZhQcQojJlEJORKqiAf+skBoAfG5rE75wbfOcfeY38JyZRZP6jd+iU8LhTV1w3jw7jkkPi7s3NaR8LJFbPt3RCJVChu/+4QxUClneFnuT4Cxx/mplBc6MuDARYypgsoQjHLqHnPjFe5fxg1fOYt9bF/DHs2MYmfbH7AheDAiCk6qHI0lIbZ6HEwshVyPkbvqnvKjQq1KuUgOigpOGh/PSiSGU61X469VVKR9L5JZyvQo7tzRCxjC496plKYWKpYRCakucq22V+N4fzuKdC5O4fUNqnbrPjrjw3JFevHp6FBNuvrafYfgxDAIWnRKra4xYU2vE6loTVtcasbxCh3K9SpImpNlCKBNOpepLsqKBJPJHQsdooXCgf4qf4JgOFp0KjujajGRhQxG8cWYMn1hfm9I4AiJ/fOuOdfjm7W1zClFyDQnOEmd9vRlGjQJHzk+kJDi//WAAj/7qJNQKGa5fU40bW6uxaVkZlpXrMO0L4syIC2eGnTg76sKZERd+/f4APLOqqrRKORrKtGgo06K+TIsaowaN5Tpct6qqILpXz3g4qYXUpCyLTig486Z+Xp7yYkOjJa3zWbR8SI3juKQfAt65OAlXIIRb2mrTOieRH/IpNgAJzpJHLmPQ0VSG9y/Zkz7maN8Uvv7rk+hYXoYf3rt5gUBYdCpsba7A1uYKcVskwmHQ4cPZERcuT3kx6PBhwO7FgN2HD/odYg5BKWfwqY312H1dM1pq8td5WLiRpyQ4Cpkko8vn53BiMTuHEwpHMOTw4fYN1rTOZ9EpEYqk1qngD6dGoFPJcTV1FyBSgASHwMbGMvzx3Dhc/uCiOQuXP4gv//I4rGYtfvy5jqTbY8hkDBrLdXHDPv5gGOfH3Pivo/34r6P9+NX7A/j46ip8/poVuLYl9zkCpz8IlUKWUm8wtUKGCQnLopPN4fzu5BBCEQ5N5elNzLREe2rZPWxSghOJcDjcPYqPr67KWy6AKE4o+EpgQ6MZHAd8OLB4tdre189j0OHDv39mg6S9mDRKOdbVm/GtO9bh7cduxJdvakH3kBOf+0kXvvnSKUQiuS0+cPlDKVWoAYBaKZekTZAvONPaJh6Ch3Np0ou/+9VJbFlehu3taXo40f/H6SQLBz7od2DcFcDNaymcRqQGeTgENkZj/8cHHPhYghBJ/5QXz73dh7uuaMDmpuyVVZbrVfjyTavwvz5uwxMHT+O5I31QyBj8421rs3bO+bj8oZQq1ABAo5AjEJQuh5Ooj5suWjTwXu8kwhEOj96yJuXGnQKCh5OoNNrHhvGTP1/E+TE3hhx+aJVyXE+904gUIcEhYNGpsLxCh+OXHXH38bFhPPjz96GWy/C1m1flxC6VQoZ/vm0tAqEInj3Si89sacxZXocPL6bq4cgk8XD8wTDUClnCBK9BrYBSzoi5tzUZTNq06HhhdfhiV6qFwhE8/ItjeP3MGHQqObxsGP925/q8dBsmihsKqREAeC/nxIAj5nscx+Hvf3MS3cNOPPXZjajL4apyhmHwdzevhk6lwFOv9eTsvC5/6rNlNAo5/BJ4OIHoLJxEyGUMtiwvhz8YQWO5Nq2WNgKC4NjjeDjf/v1pvH5mDI/fuQ5/fPTjeOa+zfjslY1pn49YupDgEACADY0WjDoDGJ41ilbgJ3/uxYvHh/C1batww5qanNtWrlfhbzY34JXu0Yy6GqdCOh6ORikTK8wyIRCKiCOrEyGMA2itzWyss+CpTEfX4oQjHP798Dm8f8mOF7ou4z/f7sMXrlmBe69qQrVRg1vX1Rb0GiqicCHBIQDMyuPMC6tdHHfj3w6cxifW1eLh6225NyzKbe1WsKEIXu0ezcn50snhqBVyhCIcQhku/gyEIlAn0aVayKGsrctMcNQKOXQquejhfDQ4jade68HdP3obf/+bD3Hdqio89ok1GZ2DIADK4RBRWq0mKOUMjg848In1M9VOBz4cRoQDvnF7W16fajctK4PVrMHLJ4fxqSvqs34+XnBS93AAXjAyGW4VCIWTKsdeWWXAjz+3GVdK0BeryqjGeHQi5LHLfF7o05sbsKJKj89tbcrLsC6i9CDBIQDwZclrraYFHs4fTo1iY6MFtWZN7ANzhEzGYPt6K372ziVM+4JZTViHIxzcgTSq1KJlzP5gOK2eZgJsKJL0pFGpVvrXmbUYdPDh1GOXHag1afC9T2+Q5LMJQoAeWwiRDY0WfDg4jXB0zcuA3YsPB6dx67rCWG9xW7sVbDiCw1kOq3nYaJeBFEVDEIlM29sEQpGcDzOrL9Ni0B4VnEt2bGqy5PT8xNKABIcQ2dhogZcNo2eMH0X7yin+xl4o/bI2NlpQb9HilVMjWT2PN8An/lP1UmZ7OJkQCCbv4UhFnUWLUZcfo04/Bh0+XNGYnwFdRGlDgkOICM0fT/Q7APD9slbXGLGiMr2WKVLDMAyutlWgq28qq50HvFEPR7dIafJ8hBxOpqXRgXAkpZY6UtBg0YLjgDfOjAHIvBCBIGJBgkOIrKjQo9qoxi/eu4yRaT/+0jeFW9pyXwadiK3NFXB4gzg76sraObxpjJcGIIpEpos/A9GFn7mkPjq47dXTvOCsqc1f41SidCHBIURkMgb/8MlWnBiYxq5nuxDhgDsLbJrjVdEO1O9enMzaOYTWMql6OGqJPBw2DzkcYTHv62dGUWlQo8Kgzun5iaUBCQ4xhx0b6vDJ9VacHXXhGltlwYTTBOotWtRbtDiWoA1PpggeTuohtWgOJ1MPJ5T7kJo1WoUY4ci7IbIHlUUTc2AYBt/5m3ZoVXLct7Up3+bEZG2dCaeHnVn7fF80h7NYe5n5iFVqmeZwklz4KSUapRw7NtThpRNDaKkx5PTcxNKBBIdYgEGtwPcLeA1Ga60Rr50ehT8Yzso8lhkPJ70qtYxzOKHc53AA4MnPbMTtG+qwuYkq1IjsQCE1ouhYYzUhwgE9o+6sfH7GIbVMy6LzkMMB+BzetrU1BTHimyhNSHCIoqPVypfsnh7JTlhNnLiZbkgtg4WfHMdFOw3QJE2i9CDBIYqOZeU6aJXyrOVxxCq1FMN1Ung4bLTxZz5CagSRbehbTRQdchmD1bVGnBnOzlocLxuGSi5LuWGlRpF5WbTgHZHgEKUIfauJoqTVasTpESc4TvqOAz42lHI4DQAUchnkMiajogGhwo0EhyhF6FtNFCWtVhMc3iBGnH7JP9vLhlMuGBDQKGQZeji8WFEOhyhFSHCIomRNdMplNsJq3iRGPMdDo5RnlsMRQmo5XodDELmAvtVEUbLGyq+G785C4YAvAw9HrZBlVKUmHJvMiGmCKDboW00UJSaNElazBhfGpF+L42VD0CnTWxOdqYcTIA+HKGHoW00ULSurDLgwLr3g+Nj0Q2pqpTyzHE6QcjhE6UKCQxQttmoDLox7JK9Uy6RogA+p0TocgogFfauJomVllR7uQAijzoCkn+vNwMPRKGWZhdSi3lE+WtsQRLahbzVRtKys4rsaSx1W8wXT93B0KoXYqSAdZhZ+UkiNKD0yEpypqSls27YNLS0t2LZtG+x2e8z9Dh06hNWrV8Nms2HPnj3i9kcffRRr1qxBe3s77rzzTjgcjkzMIZYYtmpecM5LXDjAV6mlVzSgVcrF5p/pwIaFHA49CxKlR0bf6j179uDGG29ET08PbrzxxjliIhAOh/Hwww/j4MGD6O7uxgsvvIDu7m4AwLZt2/DRRx/h5MmTWLVqFZ544olMzCGWGFVGNTRKGS5PeSX7zEiEgy8YhjbNsQdalRz+DARH7DRAVWpECZLRt7qzsxO7du0CAOzatQsvvvjign26urpgs9nQ3NwMlUqFnTt3orOzEwBw8803Q6HgnyS3bt2KgYGBTMwhlhgMw6ChTIdBu0+yzxSS9unO2dGp5PBKEFKjdThEKZLRt3p0dBRWqxUAYLVaMTY2tmCfwcFBNDY2iq8bGhowODi4YL9nn30Wn/jEJzIxh1iC1Fu0GHBI5+Fk2sss05Ca2NomC4PlCCLfLBqovummmzAyMrJg++OPP57UCWKVrDIMs+CzFAoF7r333rifs2/fPuzbtw8AMD4+ntS5idKnvkyLkwMOyT4vEM2hpFslplXJwYYiCEc4yGXM4gfMg6Vu0UQJs6jgvPrqq3Hfq6mpwfDwMKxWK4aHh1FdXb1gn4aGBvT394uvBwYGUFdXJ75+/vnn8fLLL+O1115bIESz2b17N3bv3g0A6OjoWMxsYonQUKaF3RuEJxCCXp35xPRMPRyhus0XDMOQhj2BUAQMAyjSECuCKHQyeozasWMHnn/+eQC8cNxxxx0L9tmyZQt6enrQ29sLlmWxf/9+7NixAwBfvfad73wHL730EnQ6XSamEEuUeosWADDokCaPI+Rw0vZwoqEwLxtK63gvG4ZOKU/48EUQxUpGgvPYY4/h8OHDaGlpweHDh/HYY48BAIaGhrB9+3YAgEKhwN69e3HLLbegtbUV99xzD9ra2gAAX/rSl+ByubBt2zZs3LgRDz74YIb/HGKp0VDGP6hIVTiQcQ4nWk7tSzOP48ugUzVBFDoZxSAqKirw2muvLdheV1eHAwcOiK+3b98uCtBszp8/n8npCQINZbyHM2CXpnBgprVM+lVqANJe/JlJHzeCKHQoM0kUNZUGNRQyBsPT0gxiE5L2mRQNAEi7Us3Hpr8GiCAKHRIcoqiRyxjUmDQYkUhwZiZuZpbDSTekxg9/y7z4gSAKERIcouipNWsKxsMRQ2ppCo6fDUNLXQaIEoW+2UTRU2vWYNQplYcjjeCk223AGwyl3ceNIAodEhyi6LGaeA9Hirk4bIbdmjViSC29smjK4RClDAkOUfTUmjXwBcNw+tK7yc9GyOGk7+FkWBZNVWpECUOCQxQ9VjNfGj3szHwtDpth88xMQ2qZdKomiEKHBIcoemrNGgCQpHBAHICWZuJerZCBYTKoUstgvDVBFDokOETRI7S3GZBgLk6m4wEYhoFWKU9LcCIRDoFQJO3RCARR6JDgEEVPjUkNi06J7mFnxp8lRbfmdGfiCN0JyMMhShUSHKLoYRgGbXUmfDSYueAEQhGo5LKMmmdq0vRwhO4EVDRAlCokOERJsK7OjLMjLgSjvdDShQ1F0q5QE9Cp5Gl1i/ZHPRwqGiBKFRIcoiRYW2cCG46gZ9Sd0eew4XDGw8+0KkVavdTIwyFKHRIcoiRYV28GAJwams7ocwLBzD0cg1oOTyB1D4dyOESpQ4JDlAQrKvTQqeQ4NZRZHocNRzL2cPQqBTyBdDwcXqSoSo0oVUhwiJJAJmPQajUViIejgDsND8cvejjUS40oTUhwiJJhXZ0J3UNORCLp91RjwxIIjkYBTxpFA2IOhzwcokQhwSFKhrY6MzxsGJcyWAAaCIXTbtwpoFcr0svhsJTDIUobEhyiZGi1mgAAZ0fSz+Ow0XU4mWBQKxAMc2Ij0GQRigYoh0OUKiQ4RMnQWB5tcWNPv4knG4qk3UdNQB/1UFItHCAPhyh1SHCIksGsVcKgVmQkOAEJPBy9mk/6u/2phdXcgRAYhnI4ROlCgkOUDAzDoN6ixaAjMw9Hiio1AClXqrn8IRjUCshk6bfVIYhChgSHKCkayrQZezhSFA0ASLlSzR0IwaimkmiidCHBIUoKXnC8ODfqSuv4gAQejj5tDycIg4YEhyhdSHCIkqK+TAuXP4Sb//0tfDSY+iJQviw6s18LY1Q0Ui2NdgdCMGqUGZ2bIAoZEhyipKgxacSf05mPw4YkaG2jTk9whBwOQZQqJDhESXG1rRI3r60BAFwYS61zdDAcQSAUybi1jEElhNRSK4t2+0MUUiNKGhIcoqSoNKix7/4OrKk14nyKgmP3sgCAcn1mYS29WliHk6KHEwjBRIJDlDAkOERJsrLagJ5UBccTBACU6VUZnVshl0GtkKURUgtSSI0oaUhwiJKkpdqAfrtX7MCcDKKHo8tMcIDUO0YHwxH4gxEqGiBKGhIcoiRZUakHxwGXU2jkaffwgpOphwOk3sBT2Jc8HKKUIcEhSpIqgxoAMOEOJH3MVNTDKZPAwzFpFXCm0NrGFd2XigaIUoYEhyhJyg28aAh5mWRwePl9LbrMw1oWrQrTvuTPLQgOFQ0QpQwJDlGSlEfDYlOeFDwcDwu9Si7JeACzVglH1GNKBpefFyeDmnI4ROlCgkOUJEJYbNKz8KbfP+WN2YXA7mFhkSCcBgBmnTIlD0coMKCQGlHKkOAQJYlSLoNJo8BUDMHZc/AM/vf+DxZsn/KyomeUKRatEg5vEByX3LhrQXCMJDhECUOCQ5QsFQZ1TA9n0OHDyLR/wXa7NyhJhRrA54FCEQ5eNrmybKHAgLpFE6UMCQ5RspTrVWKp82zGXQF42DC888YH2D0syiUoGAD4HA4AOJIMqwnD2mgdDlHKkOAQJUu5XrUgpMZxHMZdfCHBhGvue3avhDkcLf85yRYOuANByGUMNBmOtyaIQoa+3UTJUqFXLQipTfuCYMMRAMD4rDU6HMfBLWEvM6G0etqbnIcjdIpmGJr2SZQuJDhEySKE1GYn7sdcMyIze1Golw2D46SrEhMFJ4WQGhUMEKUOCQ5RspTrVQhFODh9M7ma8VmCM/tnsSxZonUwqeZwnDQLh1gCkOAQJUu9RQtgbj+1MddMddpsD0fq1jIWMYeTpIcTCJKHQ5Q8GQnO1NQUtm3bhpaWFmzbtg12uz3mfocOHcLq1aths9mwZ8+eBe9///vfB8MwmJiYyMQcgphDq9UEAOgenlnkOebkRUajlM0RHHEdjERehkYpg0ohg8OXbNEAjZcmSp+MBGfPnj248cYb0dPTgxtvvDGmmITDYTz88MM4ePAguru78cILL6C7u1t8v7+/H4cPH8ayZcsyMYUgFrCsXAe9So7Twy5x25grAK1SjmXlurkhNYk9HIZhYNEqUy4aIIhSJiPB6ezsxK5duwAAu3btwosvvrhgn66uLthsNjQ3N0OlUmHnzp3o7OwU3//KV76C7373u1SdQ0iOTMZgjdWE7iGnuG3cFUC1SY1Kg3peDkfoZSbdTb/SoJ5TpJAIKhoglgIZCc7o6CisVisAwGq1YmxsbME+g4ODaGxsFF83NDRgcHAQAPDSSy+hvr4eGzZsyMQMgohLq9WI08NOsVJtzOVHlUGNWrNmTrcBMYcjoeDMP0ciXIEQ9VEjSp5Fv+E33XQTRkZGFmx//PHHkzpBrF5SDMPA6/Xi8ccfxyuvvJLU5+zbtw/79u0DAIyPjyd1DEG0VBvhCoQw4WZRZeQ9jjW1RtRbtBhx+hEKR6CQy7LSy6zGpMHJAcei+wVCYbChCLW1IUqeRb/hr776atz3ampqMDw8DKvViuHhYVRXVy/Yp6GhAf39/eLrgYEB1NXV4cKFC+jt7RW9m4GBAWzatAldXV2ora1d8Dm7d+/G7t27AQAdHR2L/8sIAkCFQRhTwAvOuCuA61qqUG/RIsIBI04/Gsp0Yg5HL6WHY9Jgws2CDUWgUsQPJlBbG2KpkFFIbceOHXj++ecBAM8//zzuuOOOBfts2bIFPT096O3tBcuy2L9/P3bs2IH169djbGwMfX196OvrQ0NDA44dOxZTbAgiXYTuz5OeAPzBMFz+EKqMatRFS6aHHHzIyx0IQaOUQSmXbqVArZmfOjq7FDsW2QjnEUQhktFv12OPPYbDhw+jpaUFhw8fxmOPPQYAGBoawvbt2wEACoUCe/fuxS233ILW1lbcc889aGtry9xygkiCmUFsrFgSPVdwfACiORSJh59VmzQAgFFnYsGhWTjEUiGjb3hFRQVee+21Bdvr6upw4MAB8fX27dtFAYpHX19fJqYQREzmCE7U06g2qsVFoYNRwclGlVhtVHBGphNXqrn8NAuHWBpQpwGipBEnf7pZsQy62qiBViVHuV41IzgB6dfBiIKziIcjjJc20nhposQhwSFKGqVcBrNWCbuXFdfEVBn53Eq9RYtB+4yHI7XgWHRKqBUyCqkRRBQSHKLkEcYUjLn8kMsYVETDbJUGFezReTXZWAfDMAzqy7Ton9XLLRaeABUNEEsDEhyi5CnXqzDl5osGKg0qyGR8VwuLTiU213QHgllZB9NUrsOlycSC4w7wY6j1arnk5yeIQoIEhyh5hMmf4+4Aqo0acbtZqxQncrr92Vnp31Shx+Upb8wF0AKeQAgyBtAqSXCI0oYEhyh5KgzRkJozIOZvAD7H4vSHEI5wWSkaAICmCh3cgdCCyaOz8bAh6FU07ZMofUhwiJKnTMfnakadflTPFpzokLRxVwDBMJcVD2d5hR4AEobVPIGQpB0OCKJQIcEhSp76Mi3CEQ6THnau4ERLpgfsvBhkI4ezrEIHALg06Ym7jycQho7yN8QSgASHKHlWVhnEn2eH1Mw63sMZiJZGZ8PDaSjTQsYAF8fjC062wnkEUWiQ4BAlj616tuDMLRoAIJYtS93aBgDUCjnW15vx9oX402y90RwOQZQ6JDhEyVOhV4niUm1amMMRPZwseRl/vboax/sdsMcpHHAHwlQSTSwJSHCIkodhGNHLiZnDcURzOFla6X/96ipEOOCtnthznKhogFgqkOAQS4KVVXy1WKVhRnBMUYHJtofT3mCBTiXHB5cdMd/3siQ4xNKAvuXEkuAzWxpRbdRAM2txpUIug1GjyGrRAADIZQxaaow4O+KK+T4VDRBLBfqWE0uCzU3l2NxUvmC7RafMyQC0NTVGvHp6dMH2UDgCfzACnYpyOETpQyE1YklTHs3jKOUM1AnGQGfKqlojJj0zIxIEvEG+jxp5OMRSgASHWNKsqORzOwZ1dlvLrKk1AgDOjc4NqwmdoimHQywFSHCIJU1LDS8Esiz3MVsVPc+ZkdiCQyE1YilAgkMsaYRy6UTNNaWg0qBCuV6Fc/MERxhNQCE1YilAgkMsaVpmdSHIJgzDYHWNEWfnhdS8FFIjlhAkOMSSZlm5LmfnWl1rxLlRFyKRmdk4bpr2SSwhSHCIJY1CLkO5XoXbN9Rl/VyraozwsmEMOnziNuHn2R0QCKJUoccqYslz7J+25eQ8q2tnCgcao55V95ATlQbVnC7WBFGqkIdDEDliTa0RChmDY5ft8LIh/OiPF3D0kh2tVhNN+ySWBOThEESO0KsVuGKZBUfOT0CjkOPfXz0HALi5rSbPlhFEbiDBIYgccrWtEk++2oOTA9PitpZqYx4tIojcQSE1gsgh19gqxZ/3/o8rsLHRgutWVSY4giBKB/JwCCKHbG4qwzP3bcLmpnJUGdW4rT371XEEUSiQ4BBEDmEYBreus+bbDILICxRSIwiCIHICCQ5BEASRE0hwCIIgiJxAgkMQBEHkBBIcgiAIIieQ4BAEQRA5gQSHIAiCyAkkOARBEEROIMEhCIIgcgIJDkEQBJETGI7juMV3KywqKyuxfPnytI8fHx9HVVWVdAblGLI/fxSz7QDZn2+K2f5Etvf19WFiYmLRzyhKwcmUjo4OHD16NN9mpA3Znz+K2XaA7M83xWy/FLZTSI0gCILICSQ4BEEQRE5YkoKze/fufJuQEWR//ihm2wGyP98Us/1S2L4kczgEQRBE7lmSHg5BEASRe5ac4Bw6dAirV6+GzWbDnj178m3Ooixfvhzr16/Hxo0b0dHRAQCYmprCtm3b0NLSgm3btsFut+fZyhkeeOABVFdXY926deK2RPY+8cQTsNlsWL16Nf7whz/kw+Q5xLL/m9/8Jurr67Fx40Zs3LgRBw4cEN8rJPv7+/tx/fXXo7W1FW1tbXjqqacAFM/1j2d/sVx/v9+PK6+8Ehs2bEBbWxu+8Y1vACie6x/PfkmvP7eECIVCXHNzM3fhwgUuEAhw7e3t3KlTp/JtVkKampq48fHxOdseffRR7oknnuA4juOeeOIJ7utf/3o+TIvJm2++yb3//vtcW1ubuC2evadOneLa29s5v9/PXbx4kWtubuZCoVBe7BaIZf83vvEN7nvf+96CfQvN/qGhIe7999/nOI7jnE4n19LSwp06daporn88+4vl+kciEc7lcnEcx3Esy3JXXnkl98477xTN9Y9nv5TXf0l5OF1dXbDZbGhuboZKpcLOnTvR2dmZb7NSprOzE7t27QIA7Nq1Cy+++GJ+DZrFddddh/Ly8jnb4tnb2dmJnTt3Qq1WY8WKFbDZbOjq6sq1yXOIZX88Cs1+q9WKTZs2AQCMRiNaW1sxODhYNNc/nv3xKDT7GYaBwWAAAASDQQSDQTAMUzTXP5798UjH/iUlOIODg2hsbBRfNzQ0JPxCFwIMw+Dmm2/G5s2bsW/fPgDA6OgorFYrAP6XdGxsLJ8mLko8e4vp/2Pv3r1ob2/HAw88IIZECtn+vr4+fPDBB7jqqquK8vrPth8onusfDoexceNGVFdXY9u2bUV3/WPZD0h3/ZeU4HAxCvISKXghcOTIERw7dgwHDx7E008/jbfeeivfJklGsfx/PPTQQ7hw4QKOHz8Oq9WKr33tawAK13632427774bTz75JEwmU9z9isX+Yrr+crkcx48fx8DAALq6uvDRRx/F3bdY7Jfy+i8pwWloaEB/f7/4emBgAHV1dXm0aHEE+6qrq3HnnXeiq6sLNTU1GB4eBgAMDw+juro6nyYuSjx7i+X/o6amBnK5HDKZDF/84hfFsEEh2h8MBnH33Xfj3nvvxV133QWguK5/PPuL5foLWCwWfPzjH8ehQ4eK6voLzLdfquu/pARny5Yt6OnpQW9vL1iWxf79+7Fjx458mxUXj8cDl8sl/vzKK69g3bp12LFjB55//nkAwPPPP4877rgjn2YuSjx7d+zYgf379yMQCKC3txc9PT248sor82lqTISbBQD89re/FSvYCs1+juPw+c9/Hq2trfjqV78qbi+W6x/P/mK5/uPj43A4HAAAn8+HV199FWvWrCma6x/Pfkmvv5RVDsXA73//e66lpYVrbm7mvv3tb+fbnIRcuHCBa29v59rb27m1a9eK9k5MTHA33HADZ7PZuBtuuIGbnJzMs6Uz7Ny5k6utreUUCgVXX1/P/cd//EdCe7/97W9zzc3N3KpVq7gDBw7k0XKeWPbfd9993Lp167j169dzt99+Ozc0NCTuX0j2/+lPf+IAcOvXr+c2bNjAbdiwgfv9739fNNc/nv3Fcv1PnDjBbdy4kVu/fj3X1tbG/cu//AvHcYl/X4vBfimvP3UaIAiCIHLCkgqpEQRBEPmDBIcgCILICSQ4BEEQRE4gwSEIgiByAgkOQRAEkRNIcAiCIIicQIJDEARB5AQSHIIgCCIn/P8S7kUOO/sbZAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f59875f351f4cd0a69e8fe449a641f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66aa2998baa1429a9819230f2c54df07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.02\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.02\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "040223c8493d435c855f2aa3d9718db9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all -0.02\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*(-0.02)\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe5b30ac6b8846d38c868320264c72a6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c42b3d87e30b4799baad74eaa162a0b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ebcdfc85e2e490cb7884d4ad2a56f83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37d9f9e2d6024fcaab0bbf03f40d8b5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25aecbc9c74a4bfc9ab2651bee631ddb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aebaf04973b64c27b2a11afe5d45a4e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4350d38ff6224082ab2ee492233428c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33abfe05579546d495b1b44ebe2efd87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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3D4MGDVKr4/79+wEA48eP58pSU1Ph7e2NsLAwtG7dGhMnToRCocD58+cxatQo3L9/H99++y23P2MMw4cPx4kTJ+Dm5oZp06ZBKpVi9+7duHv3btl+QQBmzZqFTZs2wdHREZMnT4ZYLMaJEydw7do1SKVSSCSqw7cePnyIr7/+Gl27dkX//v1hbW2NqKgonDx5En/88QdOnTqFvn37AgBatmwJPz8/LF26FM7OzpgwYQJ3njefiX766ado0qQJunbtCkdHRyQlJeHs2bMYO3YsHj9+jOXLl5e5XnqFlYGnp2dZdiekwq3/Kpg1EW9mTcSbWRvbH1n442QWE5Gm9dfdO/dYXq6sWn9169qdAWB+X3/P7obEsayYO4wl3ijy9zBq1CgGgC1fvpwr27NnDwPAALBTp06p7L9x40YGgHl7e3NlJ0+eZADYrFmz1M6fl5fH0tPTue2LFy8yAKxDhw4sJSVFZd+C9337PM7OzgwA69mzJ8vMzFR7j4YNGzKJRMISExNVynNzc5m1tTVzcHBg+fn5XPn48eMZALZmzRqV/XNyclifPn2YQCBgYWFhXPmBAwcYANa+fXuWk5PDlSclJbF69eoxAKxbt25qcRUlODiYAWBubm4sKSlJ5b3bt2/PADBnZ2eVY1JTU1lCQoLauV6+fMkcHR2Zu7u72mslxfTs2TO1sry8PObt7c0MDAxYdHR0qepTbm7cKPwqwYMHD7R+O7qFS/TWq5cZ+PmN1VamLvSExFBUKe8tWr4UEiMDnf0SLV+qdR3j4pWdsmrWcFJup1iiuAn+6tSpAwCIjY1Ve83b2xsDBgxQKZs2bRrc3Nxw8eJFRL65BBUAY2NjvE0ikcDc3Jzb3rRpEwBg586dsLKyUtl3woQJaNmyJQ4cOFBkrOvWrYOpqala+fjx4yGVSvHbb7+plJ86dQopKSkYPXo0N71jUlISfvnlF3h5eWHevHkq+xsZGWHNmjVgjOHXX3/lyvfs2QMAWLlyJYyMjLhyGxsbLF68uMhYi1Nwrq+//ho2NjYq771q1aoij7G0tISdnZ1aee3ateHj44NHjx4hKiqqiCOL5+amfltfIpHg888/h0wmw19//VWm8+kbuoVL9NYmv6vIy1WOU6zhZIreQ+rzHFHVwv6bD1f437OwrFwjZOYYwfwd+xY1RVq3bt3UykQiETp37oznz58jLCwMzs7O6NatG5ycnLB69WqEhoaiX79+6NSpE1q2bAmRSPWD0ZUrVyAWi3H48GEcPnxY7fxSqRQJCQlISkqCra0tV25kZITmzZsXWd9x48Zh8eLF2LdvHz7//HOufN++fQBUb9/euHEDcrlc7Zlggfx85eLkDx8+5MpCQ0MhFArRuXNntf3LOlQkNDQUQNE/2y5duhQ7j3NwcDD8/f1x5coVxMfHQypVXTggJiYGdevWLXUcUVFRWLNmDf766y9ERUUhJ0d18o2qPsSJEijRSw9vJeDUgcfc9hcrO0AorD5z11YGx5o18fjxI6RmxHFlcSlWMHNiaokyOjpaeYyj+tjbGjVqFHn+mjVrAgDX6cXCwgJXr16Fn58fTp48ifPnzwMA7Ozs8Nlnn2HRokUQi8UAlC1AmUyGpUvf3dLOzMxUSaAODg7FzoNau3Zt9OzZEwEBAXj48CEaN26M+Ph4nDt3Di1btkSLFi24fZOSkgAoE+mNGzeKPF/B+xdIS0uDjY0NV4eifhalVfAzK+pnKxKJVOpc4NixY/Dx8YGRkRF69eoFNzc3mJqaQigU4tKlSwgKCipTZ6YXL16gbdu2SElJQZcuXdC7d29YWlpCJBIhIiIC+/bt06hzlD6hBEr00vqv/kXBgiENm9qidcdalfr+8sV+kC/2q9T3rGwdO3bCpaBLuB5yGQP6DIeCCZErlSA9JQ+WNoW3IOVyOS5dugQA6NSpk9p54uLi1MoA4PXr1wCUtxYL1K5dGz/99BMYY3jw4AEuXryIrVu3YtmyZVAoFFynFEtLSygUijKv6VjSJOLjx49HQEAA9u3bh9WrV+PAgQOQyWQqrc83Y549ezbWr19fqve2tLREcnIy8vPz1ZJowc+itArePy4uTqVjFaD8fSQlJcHJyUmlfPHixZBIJAgJCUHjtxabnjJlCoKCgsoUw/r165GUlIQ9e/aodDICgIMHD3It96qMnoESvXPlr5f498JLbpsmTagYY8eOh0gkwslTJ5AQF8qVx8WqLne2e/duxMbGolGjRkXeUizqwiyXy/HPP8qZo1q1aqX2ukAggIeHB6ZPn46AgAAAwPHjx7nX27dvj5SUFNy/f1/j+hVlyJAhsLCwwC+//AKFQoF9+/bBwMAAo0aNUtmvbdu2EAqFuHz5cqnP3bp1aygUCq7ebyr4AFKWcwFF/2wvX74MmUymVv7s2TM0adJELXkWFxMACIXCYpf9evbsGQDlUKa3lTUZ6ytKoESvMMbg/03hmLV2PZzg0sCax4iqrnr16mH+vAXIz8/HxE8nIzxC+TxPmqdAalIuAGVSmzlzJkQiEbZt2wahUP2ScvHiRZw+fVqlbMuWLXj+/Dl69OgBZ2dnAMC9e/cQERGhdnxBC9bEpHCGpNmzZwMAfH19i+y4lJWVpTa2sTSMjY0xfPhwxMTEYMOGDbh9+zb69esHBwcHlf0cHBwwevRohISEYPny5UUmrOfPnyM8PJzb/vjjjwEoO/7k5uZy5cnJySrDXUqjoMW3YsUKlVZ4bm4uFi5cWOQxLi4uePr0qcrPizGGpUuX4sGDB0UeY2tri5cvXxb5WsFcu28n//Pnz2PXrl2lrIl+o1u4RK8EngrH3Rv/TdknBGYsbc9zRFXb4sV+yMrOgr//RgwZ+R46tu8Ot3ruYEyGh0/CcO3aNRgbG+PgwYPw9vYu8hwDBw7E4MGDMXjwYNSvXx+3b9/G2bNnYWNjg23btnH7XbhwAV988QU6duwId3d3ODg4IDo6GidOnIBQKMTcuXO5fXv27InVq1dj4cKFaNCgAfr16wdXV1dkZmYiMjISQUFB6Ny5M86dO1fmOo8fPx67du3iEtHbt28LbNmyBU+fPsU333yDn3/+GZ07d0aNGjUQGxuLhw8f4saNGzh48CBcXV0BAB999BEOHTqEkydPomnTpvjggw+Qn5+PI0eOoE2bNnj+/HmpY+zUqROmT5+OzZs3o2nTpvDx8eHGgVpbWxf5LHr27NmYOnUqWrVqhaFDh0IsFiM4OBgPHjzAwIEDcerUKbVjevbsid9++w0DBw6Ep6cnDAwM0LVrV3Tt2hWfffYZ9uzZg2HDhmHo0KFwcnLCvXv3cO7cOQwfPhyHDh0qdX30VlnGvNA4UMInuVzBPmh5gBv3OX3o6XIZ80njQEv+uvbnXjZ2eH/mVKsOMzQ0YsbGJszdvQmbM2cOe/nyZZG/r4LxmHv27GGnTp1i7du3ZyYmJszS0pINGTKEPX78WGX/Bw8esNmzZzNPT09mZ2fHJBIJc3Z2ZkOHDmXBwcFFvsfly5fZsGHDmKOjIxOLxczOzo61aNGCzZ49m914ayygs7Oz2tjI4tSvX58BYDY2NiwvL6/Y/fLy8tjmzZtZhw4dmIWFBZNIJKxOnTrM29ubbdiwQW1MaV5eHlu6dClzdXXl6vfVV1+x3NzcMo0DZYwxhULBNm/ezNzd3ZlEImGOjo7ss88+Y6mpqcXWdc+ePaxFixbMxMSE2drasg8//JDduXOH+fn5MQAsMDBQZf+4uDj20UcfMQcHByYUCpXjgv38uNeDg4NZjx49mJWVFTMzM2OdOnVix44dY4GBgWr7VopKHgcqYIwVN7RLjZeXF0JCQiomkxNSgtMHH2P+eOXzMJFIgIPBPrC2VR8zWB6S01+iYUP3Cjm3PpJkhgEAEtIsEJdiBUA5bWIDD1vq/Ux0x5v5ycur+P0Arqe1NugZKNEL+flybF1+ndvuP7JhhSVPUjwb8wyIRMrP3PlSBVKTc0s4gpCqixIo0Qsnfn6EqGeFE8Z//IV6z01S8URCBlvrwg4zCbFZUChKfROLkCqFEijReXm5Mmz/tnCwus/EJjCzkLzjCFKRrC1lMChoheYX9sglpLqhBEp03v923sfraOWMLobGBvjo02Y8R1S9CYUMNjaFy5YlvMqkViipliiBEp2WlSnFjtWFHQPGfN4cxibqU6GRymVtKX+jFcqQkphTwhGEVD2UQIlOO7DlDpITlBdnU3MxhkzQrtccKR9CIVRboa/pWSipfiiBEp2VlpKL3esKp5D7+ItWlbZcGSmZtVVhK1RGrVBSDVECJTpr7/owZKQpl1uytDHEgJENeY6IvEkoAGxtCudJTXhFrVBSvVACJTopMS5bZbHsKQu8IDKgP1ddY2WlgIHBf61QGUNKArVCSfVBVySik3Z+dxM52crxhnY1TdDzg3olHEH4oNYKfZ0FhVzxjiMIqToogRKdExuVgUM/3uW2p33TlqaL02FWlgqI32iFJifSuFBSPVACJTrnh5U3kC9VtmJq1TVHx/fq8BwReZe3W6GJ1Aol1QQlUKJTIp+m4vi+h9z2zGXtIBBQ61PXWVIrlFRDlECJTtmy7BrkcuWF2LWRFVp3qsVzRKQ0imyF6lGP3AkTJkAgEBS5oDdfXFxcuEWriW6iBbWJznh8JxF//O8ptz1rue4uli287wdBdhTfYRSLmdSFwmOp1udp2NANkVGRRb5Ww8EeMc8Kx+laWirwKi4Pm7evx9lzR5GekQZPz9ZYt24dWrdurXZ8QEAAevfujdOnT6N///5ax6pvunfvjqCgIJRhRUmiYyiBEp2xeck1FFxLGre0Q5NWDvwG9A6C7CjA1IXvMIolyIoot3NZWlpi1uTh3LZCZAJACDMzE5X9hAJg24/LsXP3TvTyHoCaNWvhzLkj6NGjBx49egRHR0du38zMTPj6+mLMmDE6kTxXrVqFBQsWwMnJie9QOH/99RffIZASUAIlOuHO9dcIPB3Obc9cprutz+rGytIKS+ZP5rblYjtAoH7pYIxh/6+/YMgHI7F0sT8AwMdnCD4Y0hc///wz5s2bx+07f/585Obmwt/fv+IrUAqOjo4qCV4XuLm58R0CKQE9AyU6wf+bq9z3LTvUhFtjGx6jIZpISExCdnYOOrRtwZXVraWcuzgysvA28N9//43t27dj69atsLHR7Pdc8HwwPT0dX3zxBVxcXCAWi7FkyRJun0ePHmHChAmoU6cODA0NUaNGDYwaNQqPHz9WO9+7noFeu3YNPj4+qFmzJiQSCerUqYMpU6YgNja2yNiSk5Px9ddfo2nTpjAxMYGlpSVatGiBBQsWICsrCxERERAIBAgKCgIACAQC7qt79+5qdXxbXl4eVq9ejebNm8PExAQWFhbo0qUL/ve//6ntW/BeEyZMQEREBEaOHAk7OzsYGRnBy8sLp0+ffvcP+i3Hjx/HmDFj0LBhQ5iamsLMzAyenp7YtGkTFAr1ntcFP9cXL15g8+bNaN68OYyNjVXqmZycjIULF6Jx48YwNjaGpaUlevbsiT///FPtfGlpaVi7di28vb1Ru3ZtSCQS2NvbY9CgQbh69ara/hWNWqCEd1cDo3H1YjS3PXMptT51SZ40D7/87yyiYuJgamIEj+Ze6Nq5I0Qi1XmJ7e1sYWxshIeP72Bgf4Z8mQC3794CADg7OwMAcnJyMGnSJAwdOhRDhw7VKi6pVApvb28kJyejd+/esLCwgKurKwDg3LlzGDJkCPLz8zFw4EDUr18f0dHR+P3333HmzBkEBgYW+Vz2bXv27IGvry8MDQ0xaNAg1KlTB0+fPsWuXbtw6tQpXL16FXXr1uX2Dw8PR48ePRAZGQlPT098+umnUCgUePLkCTZs2ICpU6fCysoKfn5+2Lt3LyIjI+Hn58cdX1KnIalUij59+iAoKAju7u74/PPPkZ2djSNHjmDEiBG4desWVq5cqXZcZGQk2rZti3r16mHs2LFITk7GoUOH8MEHH+DChQvo0aNHqX7mCxYsgFAoRLt27eDk5IS0tDRcvHgRM2fOxI0bN/Dzzz8XedzMmTNx+fJl9O/fH/369eP+diIjI9G9e3dERESgS5cu6Nu3L7KysnD69Gn07dsXP/74I3x9fbnzPHz4EF9//TW6du2K/v37w9raGlFRUTh58iT++OMPnFq3Dn07dixVXcoDJVDCK8YYNn1zhdvu0LM2arta8BgRedvr168x9jM/lTJXl7rYte17dOvcgSsTCATwnTAam3/YjcSkDFhY1MLps0dgamqGjz4aBQBYtGgRkpOTsXXrVq3jevXqFZo0aYKgoCCYmppy5SkpKfjoo49gYmKCv//+G02aNOFeu3//Ptq1a4dJkyYhNDS0qNNynjx5gilTpsDFxQVBQUEqz0cvXryIXr16YebMmTh27BhXPmbMGERGRmLlypVYuHChyvkSExNhZmYGIyMjLFmyBJcuXUJkZKRKq7kk69atQ1BQEN5//32cPHkSBgbKS7ifnx/atm2LVatWYcCAAej4VhK5dOkSlixZopKsR40ahb59+2Lt2rWlTqBnzpxRu7WsUCjw8ccfY//+/Zg2bRratWundlxoaCjCwsK4DzgFxo8fj8jISBw8eBAjR47kylNTU9G9e3fMmDEDgwYNQo0aNQAAjRs3RmxsLOzs7FTOEx0djbZt22L2hg2VmkDpFi7hVdDZCNy+FgcAEAiUsw4R3TFu3Hic++NPvH5wDllRl3H38kFM/ngUIiJfYsDQcbh994HK/quWLcTc2Z/h9t1bOHbyV7i5NcKubUdgYmiDa9euYePGjfD394ednR2WLl0KR0dHGBgYwNPTE8HBwWWOb926dSrJEwD279+P1NRULF26VCV5AoCHhwd8fX0RFhaGBw9UY3/b9u3bkZ+fD39/f7XORd7e3hg0aBBOnTqFjIwMAMDNmzfx77//omXLlpg/f77a+QpunWpj9+7dEAgEWL9+PZc8AcDBwQGLFy8GAOzatUvtOGdnZyxatEilrE+fPqhbty6uX79e6vcv6rmsUCjEzJkzAQDnz58v8rh58+apJc/bt28jKCgIQ4cOVUmeAGBlZYWlS5ciNzcXR48e5cotLS3VkicA1K5dGz4+PngUEYGo169LXR9tUQuU8EahYCrPPrsPcIFDLTMeIyJvW7ToGwCAJDMMANC0cX1s27gCpmZm2LB5B5atWo+jvxZesA0NDbFyyQKsXLIAyalCxMUrLzExUSmYOHEi+vXrh9GjR2Pjxo1ci6hTp05YsWIF+vbti2fPnnGtjZIYGRmhefPmauVXrijvaNy+fbvI1t2TJ08AKG8Hvp1gizpPUFAQbty4ofZ6fHw85HI5njx5Ak9PT+4ZXJ8+fSAUln/bJCMjA8+ePYOTkxPc3d3VXvf29gYAhIWFqb3WsmVLtVvuAFCnTh2unqWRlJSEtWvX4uzZs3jx4gWysrJUXo+JiSnyuLZt1T8YF7xvWlpakb+nhIQEAMrf05uCg4Ph7++PK1euID4+HlKpVDWG+HjUrVmz1HXSBiVQwpvzR57hyd0kAIBQJMCnX7XhOSJSWlMmjsGGzTtwOfhasftYWSqQlMwgkwmwZdtaREfHcB1D1q5di549e3IXzkaNGsHFxQVbt27FsmXLShWDg4NDkbNUJSUp/6Z27tz5zuMzMzPf+XrBedauXVuq86SmpgJAhQ2FSUtLA4BiewsXlBfE8SYrK6sijzEwMCiy809RUlNT0aZNG4SHh6Nt27YYN24cbGxsYGBggNTUVPj7+yMvL6/IY2sWkdAKfr4BAQEICAgo9n3f/D0dO3YMPj4+MDIyQq9eveDm5gZTU1MIhUJcunQJQUFByMvPL1V9ygMlUMILmUyBLUsLL759ferD2s6Yx4hIWTjYK2+jZWUXv3xZwexEl/5+iD37t8Jv0Vo4OtZCeno6YmNjMXr0aG7funXrws7ODvfv3y91DMVN8WhpaQlA2QItqoVaWgXnSUtLg4VFyc/lC5JUca0wbRXE87qYW5SvXr1S2a+87dq1C+Hh4fDz81NrMV65cuWdQ5KK+l0VxOnv748ZM2aUKobFixdDIpEgJCQEjRs3VnltypQpXM/mykLPQAkvTv7yCBFPUwEABmIhJs0tuUck0R1Xrt8EANRzqfvO/cxMpfBbPgttvDrhw4GjkJJYmHDfbq3k5pbP/Lnt2yt7cV++fLlSz1Ow//nz50vVqiu4pSqXy0vYU8nc3Bxubm6IiYnB06dP1V4PDAwEgFL1LtbEs2fPAKDI3tOaJC5Nfk/Pnj1DkyZN1JKnQqHAP//8U+YYtEUJlFQ6aZ4c274tfKb04Vh3mFsa8hgRKcqDB/eRnJysVh4ZFY2ZXyo7rIwaMfid5/hu/RZERYdjyaJ1AJTrhZqZmcPJyQnnzp2DTKZc8zUoKAgZGRnw8PDQOu6PP/6Y64RSVAcZhUKBS5culXieadOmQSwWY/bs2dxz0zdJpVKVi7+npyc6duyIW7duYc2aNWr7JyUlqXxIsLW1BQBERZV+SsiJEyeCMYa5c+eqJN7ExEQsX76c26ciFAyxeftnFxYWhlWrVpX5fF5eXujSpQt+//137N69u8h97t69i/j4eJUYnj59qjIGlzGGpUuXltgprCLQLVxS6Q7vuodXUcqeixJDEcbNaFHCEYQPR48ewdrvv0O3bt3h5mQGczNTPI+IxpmAYOTm5uH93t6YM2NKscfff/gYK9duxtqVi1G3Tm3IZIAsnyElMQfz5s3DzJkz0aVLF7Rr1w4HDhyAmZkZPv/8c63jtrW1xZEjRzB48GC0b98ePXv2hIeHB4RCIaKionDlyhW1ZFYUd3d37N69GxMnToSHhwf69u2Lhg0bIj8/H1FRUbh8+TLs7e3x6NEj7phffvkF3bt3x1dffYWjR4+ie/fuYIzh6dOn+PPPP/Ho0SMuEfXs2ROHDx/GkCFD0K9fPxgbG8PZ2Rljx44tNqYvv/wSf/zxB06cOIEWLVqgX79+yM7OxuHDhxEfH4958+ahc+fOWv8MizJu3DisXbsWs2bNQmBgIBo0aICnT5/i9OnTGDJkCA4dOlTmc/7666/w9vbGJ598gk2bNqFdu3awsrJCdHQ07ty5g3v37uHKlStwcFBO6zl79mxMnToVrVq1wtChQyEWixEcHIwHDx5g4MCBOHXqVHlX+50ogZJKlZ2Vjx9X3+S2R0xuCmNTMY8RkeJ069YdT548wa3bt3DtagyysnNgZWmOTu29MGakD8Z8NLTY55ByuRyTPvsSbb1a4jPf8UhJlSMuQXm5SXidhc8/n4b09HT88MMPCA0NRYsWLbBhw4ZS98AtSc+ePXHnzh18//33OH/+PC5fvgyJRIJatWrB29u71JM4jBkzBi1atMC6desQGBiIP//8E6ampqhVqxZ8fHwwYsQIlf1dXV0RGhqK7777DsePH8eWLVtgZGQEFxcXzJkzh0sEADBp0iRERkbit99+w3fffQeZTIZu3bq9M4FKJBIEBARg/fr1+PXXX7F582YYGBigRYsW2LhxIz766CPNfmClUKtWLVy+fBkLFizAP//8g/Pnz8Pd3R3btm3De++9p1ECrV27Nm7evInNmzfj6NGjOHDgAORyOWrWrIkmTZpg+vTpaNasGbf/lClTYGhoiI0bN2Lfvn0wNjZGly5dsGfPHhw9erTSE6iAlWEpAC8vL4SEhFRkPKSK27kmBBsXK7v7G5sa4Mi1EZAYqnev51ty+ks0bKg+VKBAdVmNpUDBMBag+Llw30XBgOfhYshkyoTrWMcMtg4mJRxVeUaOHIlDhw4hNjZW5+bEJWXwZn7y8nrnrg8fPlR7llpW1AIllSYtJRe71xVeiMdNb6GTybM0yjM5VQdCAWBrXdgKTXydDWs7YwiFurFY+pMnT2BoaAh7e3u+QyF6hDoRkUqz+/tQpKcqe16aW0oweLx2n/6IfrGyUsDAQHnDKz9fodIjly/+/v4YOnQowsLC8MEHH6jM7kNISeivhVSKhFdZ+GXLHW57ygIviAzo81t1oout0I0bN0IqlWLixIlYt24db3EQ/UQJlFSK7StvIDdHOWTBxsEYvYbQWofVkZWVAkkpytmJClqhfD4LDQ8PL3knQopBTQBS4aKep+HoT4VjtGYsaaczz75I5SpohRZIfJ0NhaLU/RgJ0SmUQEmF27rsGmQy5cwstZzN0fG9OjxHRPhkZaWAgUi3noUSoglKoKRCPbmbiDO/Fc7i8sWKDsWOHSTVQ8EcuQWoFUr0FSVQUqH8v7mKgpHGbo2t0aJd5SwzRHTb263Q1CRqhRL9QwmUVJiwK69w6UwEtz1ndeWtFE9029ut0IRX1Aol+ocSKKkQjDFsXFS4UG+zNjXQoIktjxERXUOtUKLvKIGSCvHPn1EIuVy4YsKcVR14jIboImUrtHDZL2qFEn1DCZSUO4WCwf+/+W4BoEPP2nByLnlBYlL9WFnJqRVK9BYlUFLu/jz6DA9vJQAAhEIBZi5rz3NERFe93QqNf5VFrVCiNyiBknKVny/HJr/C1mevwfV0atUNonusrOTcHLmyfIbkBGqFEv1ACZSUqyM/PUDkszQAgIGBEJMXvHtJIUKEAsBOpUduFuRyxTuOIEQ3UAIl5SYzXYpty69z2z6fNIGFlSGPEZHy0mvo5xDYtUFQ8M1i9/ls1kIYWNTBrr2/lvn8VpYKiMXKVqhczpAUR61QovsogZJys3tdKHf7zdjUAGOnt+A5IlJeQu88hkgkgmeL4pegux6iXOvVs1XzMp9fIADsbd+YnSgui5v+kRBdRQmUlIu4mEzs23iL2570ZWu9XSybqHr+/DmSU9Lg4V4PZmZFP8/Ozs7BvQePYWhoiKZNGmn0PhYWChhKlK1QhUI5xR8huowSKCkXW5ddL1yuzN4YA0ZpdhEluic0VHnbtl1rj2L3uXnrDmQyGZo3bQyxWKzR+wig2gpNjs9GvlRe/AGE8IwSKNHa03tJOLbvIbf9xYoOtFxZFXIzNAQA0M6zabH7XLuhvH3rpcHt2zeZmStgZKi8datgQAK1QokOowW1idbWf/0vN3bPpaEV2vWozXNEpDyF3lS2QA8dD0DgPyFgQkO8/dmbe/7ZWrsEKgBgb6fAyxjl+VMSc2BXw4QeBxCdRAmUaOVqYDT+/iOS2563thOP0VSOVubb+Q6h1MIyPtXqeMYYbt2+BQAIuHStxP016UD0NjNTBUyMFcjOEYIx5bAWJxeayYroHrqFSzSmUDCsXxjMbXt2cqQJ46uYJ0+fIC0tDR3aNANLvAGWeAOytHDI0l9yXwmRdwEAJibGaOLeUOX47Tv3oX6zjjC1r4+2Xfvh8r8lJ2EAsLcrfPaZmpTLPV8nRJdQAiUaO3voCe6HKqfsEwiBOatoubKqpuD2batmxXcKC719DwDQopkHRKLCW63/O3oSs+cvwYI50xDyzx/o0NYTA4aOQ9TLmBLf18SYwcxU+SyUAYiPydSiFoRUDLqFSzSSk52PDV8XLlfW16c+7B1NeYyo8mh7W1SfFHQgat3cvdh9wv5LoG93INqwZSfGjx6GSRNGAQD8v1+O8xcu4YeffsbKJQtKfG97Ozkys5Sf8dPTpMjKkMLUXKJRPQipCNQCJRrZ/X0oXkcrWwUSQxGm0JR9VVJBC/RdCfTWfwn0zeefUqkUobfuopd3V5V9e3l3xZVrIaV6byNDBkuLwskUXsdkgjGaaJ7oDkqgpMxevczA7nVh3PbHX7SklkEVpFAocPvObUgkEni41yt2v4IW6Js9cBOTkiGXy+HgYKeyr4ODPeLiEkodg72tHEKBMmnmZMmQnpJXlioQUqEogZIyW//VvyqTJgweX/z0bkR/PXr0EJmZmWjSxAMSSdGTI2RmZuHp83CYmZmiUQM3tdcFAtXxwIwxtbJ3EYsZrK0KW6FxsZm03BnRGZRASZmEXXmFs4eectvzvusEkYj+jKqi0NBQAECrlq2K3ef2vQdQKBRo1aIphMLCvwM7WxuIRCK11mZCQqJaq7QktrZyiP5bdFuap0BKIk00T3QDXflIqSkUDKu+uMxtN25pB8/OtXiMiFSkMWPGIi9Xhh9+2FHsPp3at4Es/SUC/ziiUi6RSNC6ZTNcCLysUn4h8DI6tCvb83KRELB7a9FtOU00T3QA9cIlpXbil0e4fzMegHL1jIXru/AcEdFls6f5YvzkWWjj2RId23thx0+/IPZ1HKZMHFPmc1lbyZGcKkR+vgByGUNiXDZqOJlVQNSElB4lUFIqWRlSbFxUOGylj099ONYx5zEiouuGDx2EpOQUrFy7Ca9ex6Npk0Y4dWQfnOuWfapHgUA5rCX2lfKSlRSXDRt7Y4glNMUf4Q8lUFIqO9fc5JaXMjQW4bOv2/AcEdEHn/qOx6e+48vlXBbmCiQnK5CbJ4SCAfGxNMUf4Rc9AyUlevkiDfv8b3HbvnM9YWyq2ZJVhGhKAMDBXnWKv5zsfP4CItUeJVDyTowxrJj1N6R5yguXQy1TDBxNa30SfpiaqE7x9+olTa5A+EMJlLzTheMvcPlc4WorX2/sQmt9El7VsJdDAGXSzM7Mp8kVCG8ogZJiZWVKsXpO4TCEju/VQZNWDjxGRAggkTBYW6tO8UeTKxA+UAIlxdr+7Q1uvluxRIQvV9NqK0Q32NnKYfDf5Ar5UgUS47J5johUR5RASZGe3kvC/jc7Ds1vDXNLQ/4CIuQNIqHqmqGJr7KQL5W/4whCyh8lUKKGMYZl0y9BLld+wq9V1xwfji1+NY6qijqn6DZLSwWMDJW3chUM3N0SQkpSXv+3KYESNcd/foTQ4Ffc9jdbupVpAvCqQCAQQSaT8R0GeQcBlB2KCqSl5CEzQ8pfQERv5Ofnqyz+rilKoERFanIu1i0I5rbf+6Ae3Brb8BgRP8QGRsjISOc7DFICExMGC/PCDkWvojKoQxEpUXp6OszNtZ9JjRIoUbFx0RWkJOYCAIxNDTBjWTueI+KHkcQcScnJSE5OQn5+Pt3O1WEO9nIIhcrfT16uHEnx1KGIqGOMQSqVIjExESkpKbCx0b5hQFP5EU7I5Rgc3nWf2561vD2MTarnjEMGIjHMje2RnpaBpKRkMFa9O6iI8hK57xWiXOjaZ++sbCEyMpQxxScCDgmmEBnoVoykEiQW/p3i4UO1l0UiEczNzVG3bl0YGmrfKZISKAEA5ObI8M2Ui9x2g6Y26DHAlceI+GcgEsPMuPrdvi5KreCm3PdZNSdBIS7bmp4VTSYDxs90wPNw5Qe+XoPdsPHQ+zxHRSpdkyaF31fCXSP6iEYAANu/vY7IZ2kAAJFIgCXbule7jkNEfxkYAHNnpHLbAcee4/L5yOIPIKQcUAIleBAWjz3rw7jtCbNbwsGR1lok+qVlcyn69Sp8/rl8WiCys2iyeVJxKIFWc/n5ciyefJEb8+nkYo7hvk1LOIoQ3TRtShrXKzcmMhNbl13jOSJSlVECreb2rg/Do9vKB+8CAbB8hzdNFk/0lo21AjOmpnHb+/1v4d7NOB4jIlUZJdBq7PGdRGxZdp3bHu7bFHVcLXmMiBDt9e+TDa/WyqFYCgXgN+Ui8vOrdy9qUjEogVZT0jw5FnwcAFm+8naXQy1TfDy7Jb9BEVIOBAJgwexUGP43zd+jO0nYt/EWv0GRKokSaDW1bfl1PLmbBAAQCIGVP/WkcXOkyqhdSw7f8Rnc9tZl1xDxJIXHiEhVRFfMaujW1Vf46ftQbnv0p83hXN+Kv4AIqQAjfTLRsL5yblxpngJfTboAuVxRwlGElB4l0GomOysfX028wM0XWtvVAmNntOA5KkLKn4EIWDQ3FaL/1g29fTUOezfc4jcoUqVQAq1m1s77R2XChFW7e1KvW1JlNayfj0/GFd7K3ex3FU/vJfEYEalKKIFWI3/+/gz/21k4163vfE/UrK39igSE6LJxH2WgSSPlrdz8fAUWTgyAlBbfJuWAEmg1ERuVAb+pgdy2R2t7DJnQmMeICKkcBiJg8fwUSMTKW7kPbyVix6oQnqMiVQEl0GpAJlNg/vg/kZ6aB0C5TNm3O3vSXLek2nB1lmHqJ4Xru/64OgS3rr56xxGElIwSaDXw48obCA0uvFgs3d4DZhYSHiMipPKNGJKJls2UHyIVcoYvx5znPlQSoglKoFXctUvR+GFl4e2qD8e5o1UHRx4jIoQfIhHgtzAF5mbKoSyvojKx9LNAWiydaIwSaBUWF5OJuWPOqwxZmfqVF89REcIfxxpyLPyicEKFc0ee4fe96gsvE1IalECrKKlUji9GnUNSfA4AQGwowtr9vSAS0a+cVG/e3XLxYf8sbnvFzCA8f5jMY0REX9HVtIpatyAYt6685raXbOkGu5qmPEZEiO6Y9VkaXJ2Va4Xm5coxZ/Q55GTT2qGkbCiBVkFnfnuCX7bc4bZ9PmmCtt1r8xgRIbrFyIhh+aJkbmjL03vJWPIpPQ8lZUMJtIp5eCsBflMvctvuLewweb4njxERopvq15NhzvRUbvv0wSf4dftd/gIieocSaBWS8DoL04aeQU62DABgZinB6j3v0XhPQorxQf9sDHy/8Hnomi8vI/RfGh9KSocSaBWRlyvDzGFn8fplJgDlPLfrDvSGqTmN9yTkXb6ckYrG/031J5cxzB75BxJeZ5VwFCGUQKsExhi+mXIRt6/FcWVfbeyKeo1seIyKEP1gKAFW+iXD0kI5P27i62zMGv4H8nJlPEdGdB0l0Cpg55qbOH3wCbf90dSm6NrXmceICNEvjjXkWPZ1CoRCZSeiW1dfY5HvX9SpiLwTJVA9d3z/Q/h/c5XbbtfdCR9/0YrHiAjRT+288jBjahq3ffbQU2xbfp3HiIiuowSqxy6fi8Q3Uwp73NapZwG/rd2p0xAhGhoxJAuDB2Zy29u+vYHTBx/zGBHRZZRA9dSdG3GYPfIPyOXKW0zWdkbYfKQfxBIRz5ERor8EAmDOtDS09czlyhb5/oWb/8TyGBXRVZRA9VD44xR89sEpbriKsYkBth3rTz1uCSkHBgbAim+SuZmK8qUKfPbhaTy6nchzZETXUALVM1HP0/BJ3+NISVR+QjYQC7HpyPs0TR8h5cjcjGHdiiTYWCt75mamSzG5/wlEPkvlNzCiUyiB6pGYiHRM7HMccTHKMWpCoQBr9vWCSwNrniMjpOqp5SiH/5pEmJkqlz9Lis+B7/snEBeTWcKRpLqgBKonYqMy8HHvY3gVlQFA+axm6fbuaN6mBs+REVJ1NXCTYd3KJBgaKpNoTGQGJvc/ieSEHJ4jI7qAEqgeePUyAxP7HEdMRGHy/GZzN7T3rsNzZIRUfS2aSrHKLxkikbLD3rMHyZjY+xiS4rN5jozwjRKojot4koKxPX7Hy+fK8WkCAbBwQxd07kMTJRBSWTq2y8OSBYUTLTy9n4yPex2nKf+qOUqgOuzhrQSM8/6du20LATB/bWf06O/Kb2CEVEO9vHPg90YSff4wGR+/dwzxsfRMtLqiBKqjbgbH4uNex5AUr3zWIhAA32zqhp4f1OM5MkKqrz49c7D0q8IkGv4kFeN7HsPLF2klHEmqIkqgOijg2HNM7ncSGWnKFSJEIgFW7X4PXWh+W0J416tHDr5dXPhMNOp5GkZ1PYL7ofE8R0YqGyVQHcIYw87vbmLWiD+Qm6OcJEFiKIL//96HZ+daPEdHCCng3TUXq/ySIREre+cmx+dgfM/fcfl8JM+RkcpECVRHSKVyLPL9CxsXXeHKzCwk+OHkADRqbsdjZISQonTtlItNa5NgbqacbCEnS4bPPzyNo3se8BwZqSyUQHVAwqssTOp7Asf3P+LKajmbY9+FD1GnniWPkRFC3qVlMyl2bEpEDQflHSO5XLk274pZQcjPl/McHalolEB5du1SNIa2PaQyWbVnJ0fsOjsIFtZGPEZGCCkNV2cZdm1OQIN6Uq7s1213ManvCRorWsVRAuWJQsGwc02I8j9ZXOF/Mp+JTbBqz3u0qgohesTeToEfNyXCu2vhDEUhl2MxvP3/cOf6ax4jIxWJEigPXkdnYsrAk9i4+CoUCmVPPrFEiGU/9MCUhV60nichesjEmGHFN8n49JM0CATK/9evozMxpttR/LgqBHK5gucISXmjBFqJGGM4deAxBrc+iH8DXnLljnXMsP+vwejQk6bmI0SfCQTA+FGZWLciCWamymegcjnDJr+rmNj7OGILJkUhVQIl0EqSFJ+N2SPPYcHHAUhPzePKew2uh93nP6TlyAipQjq2y8P+HQlo7lH4fz3kciyGev2G3/c+AGOMx+hIeaEEWsHkcgV++/EuBjQ9gIBjz7lyEzMxVu1+D/O+6wwDMf0aCKlqatWUY9uGREwanw7hf7d001PzsHjyRUzscxyRT1P5DZBoja7cFeh+aDxGdTmC5dODVFqd7XrUxsHLPvDqQpMjEFKVGYiASeMysH1jImrVzOfKr1+KwYetD+LHVSHIy5XxGCHRBiXQChAbmY6FEwMwosP/cC+kcHovMwsJlmzrjm93eMPETMxjhISQytSiqRQHdiVg9PAMrjUqzZNjk99VDGh2AGcPPaHbunpIwMrwW/Py8kJISEhFxqPXUhJzsGNNCA5uv4t8aWGPO4EA6P9RQ3z6VRtIDGl4CtE/tYILJ/TIqjkJCjHNjqWpR0/EWLXOCo+fSVTKm7etgS9Xd6JpO7Xx5giGSvhAQgm0HCS8ysI+/1s4tOMesjPzVV5r4GGDr/27wsnZgqfoCNEeJdDyJZMDx06ZYudeM6RnGKi81ra7Ez79ug3adHWiIW1lRQlUf0Q+TcWeDWE4vv+hSosTAGo4meKLFR3QuhN9miT6jxJoxcjIFGDvAXMc+t0UMpnqE7XWnRzxyZet0fV9FwiFlEhLhRKobpPJFAg6E4HffryLfy+8VHvdytYIn37thR4DXOnTI6kyKIFWrNhXIvy03xx/XDCBQqF63ahTzwIjpzbDkAlNYGFlyFOEeoISqG56/jAZpw8+wclfHuF1tPoK9A61TDFpXmt0o0+LpAqiBFo5YmJF2H/QDGfOm0AmV22RGpsYoNcQN3wwxh1tujlBJKI+oGoogeqO6PB0XDjxHGcOPsGDsIQi92ngYYNPvmyN1p0cqcVJqixKoJXrdZwIh4+b4sQZY2RmGai9XrO2GfqNaIheQ9zQzMuBrj0FKIHyRy5X4P7NeASejsCl0+F4ci+pyP0MjUTwHlQPoz5thpq1zSo5SkIqHyVQfuTmCnDuL2P873cTvIgo+vZtDSdTeA+qhx4DXOHZuRaMjNUTbrVBCbTyyGQKPLufjJv/xODqxWjc+DsGGWnSIvcVCoFGLeww9OMm6OBdh4ajkGqFEii/GAMePxXj7HljnPvLCOkZRY8jlxiK0LqTI9p710GbrrXQuKU9DI2qUUKlBFoxGGN49TITd6/H4W5IHO5cj8OD0HjkZBc/C4hQKIBbY2v0HVYf3gPrwcxCUuy+hFRllEB1h0wGXLluhMC/jXD5X0NkFHGLt4CBWAj3FnZo0a4mmretgeZta6K2q0XV7adBCVQ7+flyvH6ZifDHKXjxKAXPHyb/95WCzPSiW5dvMjY1QFNPB/T1qY82XZ1gbEozBhFCCVQ3yeTA7TsSBAUb4ep1MaJijEo8xthUjHru1nBrbI36TWxRv4kNXBpawbGuOST6vg5xJSdQvWnbM8aQmS5FcnwOkhJykByfjeSEHLyOzkRsZAZiozIQE5mO+Jgsbo3N0jAxU/4xdepVB2271Uadehb0QJ4QohcMRIBnKyk8WykbB/EJQly/aYSQUDHu3jdAzGv1hJqTlY/7N+Nx/2a8SrlAANRwMkMtZ3M4OVuglrM5HGqZwsbeGLY1TGD7379mFhK6Rv6HlxZoTEQ6XoanIztDisx0KTIzpMhKlyIrI5/7vqA8M02K5MQcJMfnQJon1+p9xRIhnJwt4OFpD68uteDewh52NUy0rg8hVR21QPVTWpoQ9x+Jcf+hGHfuifDkmQRpGdo9ipIYimDjYAwbO2OYWxnCzFwCM0sJzC0lMDVX/mtmaQgTUzGMTcUwMTVAfQ/byrnWVocW6IFtd7Bv460KO7+xqQHsHU3h2sAK7i3sUM/dGnXrW8HWwZg+ORFCqg1LSwU6tstDx3aFq0GlpArxIsIA4ZFivHghwrMXAsS8EiM5VQLGSr4+SvOUj8lev1QfD1+cVbvfw6Ax7hrVQZfxkkDNzDX7BCQUCiAxEsHEVAwzCwksbQ1hX8MENWubw7aGCWzsjWFtZwRxEffxkxNykJyQo23ohFRLb05ImZ6UCplAUey+RLcJALg5Kr/QvrA8XyZAQpIYcQmS/74MkZwiRHKKEClpBkjLECMtwxDS/LKnjaral6RMt3Dt7Ozg4uJSbm/+9F4S8qUKCESASCiAQCiEsAIn18iTZcDQwLzi3oAnVbVeQNWtm77Wy1iSAyNxXrGvZ+Tkwdy4ak43V1XrVv71Uvx3+5Qp/zUwhU3t2uV4/tJJSEiAvb291uexs7PDuXPninytTAlU3+lDL2JNVNV6AVW3blQv/VNV60b10hxNpkgIIYRogBIoIYQQooFqlUAnT57MdwgVoqrWC6i6daN66Z+qWjeql+aq1TNQQgghpLxUqxYoIYQQUl70PoG+fPkSPj4+sLS0hIWFBYYMGYKoqKhSHZubm4u5c+fC0dERxsbG6NChA/7+++8Kjrj0NK1bSEgIJk+eDHd3d5iYmKBu3boYPXo0wsPDKyHqkmnzO3vTqlWrIBAI0Llz5wqIsuy0rdfDhw8xbNgw2NnZwdjYGI0aNYK/v38FRlx62tQtKioK48ePR926dWFiYoKGDRti0aJFyMrKquCoSxYdHY3p06ejQ4cOMDExgUAgQERERKmO1eXrh6b10vVrhza/rzeV27WD6bGsrCxWv3595uHhwY4dO8aOHz/OmjZtyurVq8cyMzNLPH7UqFHM0tKS7dixg124cIENHjyYGRkZsbCwsIoPvgTa1G3OnDmsY8eObOvWrezSpUvswIEDzN3dndnY2LCoqKhKqkHRtP2dFXj+/DkzNTVlDg4OrFOnThUYceloW68bN24wc3NzNnDgQHbs2DF28eJF9uOPP7J169ZVQvTvpk3dMjMzWYMGDZiLiwvbu3cvu3jxIluzZg0zMjJiw4cPr6QaFC8wMJA5ODiw999/n/Xu3ZsBYOHh4aU6VpevH5rWS5evHYxp9/sqUJ7XDr1OoBs3bmRCoZA9ffqUK3vx4gUTiUQlXnhu3brFALDdu3dzZfn5+axhw4Zs4MCBFRZzaWlTt/j4eLWyiIgIJhAI2OLFi8s91rLQpl5v6t27N5s8eTLr1q2bTiRQbeoll8tZkyZN2IcffljRYWpEm7qdP3+eAWDnz59XKZ8/fz4TiUQsKyurQmIuLblczn2/c+fOUl+Qdf36oWm9dPnawZjm9XpTeV479PoW7smTJ9G+fXvUr1+fK3N1dUWnTp1w4sSJEo8Vi8UYMWIEV2ZgYICRI0fi/PnzyMsrfraVyqBN3YqafcPZ2Rn29vaIiYkp91jLQpt6Ffj1118RGhqKVatWVVSYZaZNvS5duoQHDx7giy++qOgwNaJN3aRS5SohFhYWKuVWVlZQKBRgPPdhFGo49ZmuXz80rZcuXzsAzetVoLyvHXqdQO/fv4+mTZuqlXt4eODBgwclHuvq6goTE9UVAjw8PCCVSvHs2bNyjbWstKlbUR4+fIj4+Hg0bty4PMLTmLb1SklJwezZs/Hdd9/BxsamIkLUiDb1+ueffwAon6m1b98eYrEYDg4OmDFjBnJy+J+/WZu6vffee2jQoAHmz5+PBw8eIDMzExcvXoS/vz+mTp0KU1PTigq7Qun69aM86cq1Q1sVce3Q6wSanJwMa2trtXIbGxukpKRofGzB63zSpm5vk8lkmDp1Kuzt7fHJJ5+UV4ga0bZec+fORcOGDTFhwoQKiE5z2tQrNjYWADBixAj07t0bAQEBmDdvHnbt2oVRo0ZVSLxloU3djIyM8M8//0ChUMDDwwPm5ubo2bMnBgwYgC1btlRUyBVO168f5UWXrh3aqohrh94sqF2copYnK81tIcaYxsdWlvKKb9q0afj3339x5syZIv/TVzZN63X58mXs378foaGhOrksnab1UiiUK5uMGTMGy5YtAwB0794dcrkcCxYswIMHD9CkSZPyDbaMNK1bbm4uRowYgfj4ePz888+oW7curl+/jmXLlsHAwADbt2+viHArnD5cP8qDrl07NFVR1w69TqDW1tZFftJLSUkp8ZdtY2NTZDf8gk/UfN8e1KZub1q4cCF27NiBffv2oXfv3uUZoka0qdeUKVPwySefoHbt2khNTQWg/IQsl8uRmpoKY2NjGBrys1qGNvWytbUFAPTq1UulvHfv3liwYAFu3brFawLVpm4//fQTLl26hGfPnsHNzQ0A0LVrV1haWmLy5MmYOnUqWrRoUSFxVyRdv36UB127dmijoq4den0L18PDA/fv31crL80ndg8PD4SHhyM7O1vtWIlEotJhgg/a1K3AihUrsHr1avj7+2Ps2LHlHaJGtKnXw4cP8cMPP8Da2pr7Cg4OxtWrV2Ftbc1ra0bbv0VAvZVX0JrRtuOEtrSp2927d2Ftbc0lzwJt27YFoPyd6iNdv35oSxevHdqoqGuHXifQQYMG4erVq3jx4gVXFhERgeDgYAwaNKjEY/Pz83H48GGuTCaT4dChQ+jduzdvLZkC2tQNADZt2oRFixZhxYoVmD59ekWGWiba1CswMFDtq0WLFmjatCkCAwPh4+NT0eEXS5t6vf/++zA0NFRbc/D8+fMAlMsy8UmbutWsWRMpKSlqnWquXbsGAHBycir/gCuBrl8/tKGr1w5tVNi1Q6tBMDzLzMxkbm5urGnTpuz48ePsxIkTrHnz5szV1ZVlZGRw+0VERDCRSMSWLl2qcvyIESOYlZUV27lzJ7tw4QIbOnQoMzQ0ZDdv3qzsqqjRpm4HDx5kAoGA9e3bl125ckXl6/79+3xUh6Pt7+xtujIOVNt6LVmyhIlEIrZw4UIWEBDAVq1axYyMjNj48eMruSbqtKlbeHg4Mzc3Zw0aNOAmUvjuu++Yubk58/T0VBnXx5fDhw+zw4cPs6lTpzIAbNu2bezw4cPs0qVLjDH9vH4wplm9dPnaUUDT39fbyuPaodcJlDHGIiMj2ZAhQ5i5uTkzMzNjH3zwgdrA2vDwcAaA+fn5qZRnZ2ez2bNnsxo1ajBDQ0PWtm1bFhgYWGmxl0TTuo0fP/6/5eDVv7p161apdSiKNr+zt+lKAmVMu3opFAq2bt065ubmxsRiMatbty5bvHgxk0qllVeBd9Cmbvfv32fDhg1jtWvXZkZGRqxBgwZszpw5LDk5ufIq8A4l/V/R1+uHJvXS9WsHY5r/vt5WHtcOWo2FEEII0YBePwMlhBBC+EIJlBBCCNEAJVBCCCFEA5RACSGEEA1QAiWEEEI0QAmUEEII0QAlUEIIIUQDlEAJKScCgQACgQBCoRDPnz8vdr8ePXpw++7du1fltQkTJnCv+fn5FXuOffv2cft1795d5bVLly5xrxV8mZiYwNHREV27dsXcuXMRFhamTVUJIaAESki5MjAwAGMMP/30U5GvP336FEFBQTAwePdCSAYGBti9ezfkcnmRr+/cubPEczg7O8PPzw9+fn6YNWsWBgwYgKysLHz//fdo3bo1Ro8ejczMzNJVjBCihhIoIeWoRo0a8PLywp49eyCTydRe37VrFxhjGDBgwDvPM2DAAERHR6tNMA8oV5YIDg7GwIED33kOFxcXLFmyBEuWLMHKlSuxc+dO3Lx5E2FhYWjWrBl+/fVXDBs2rGwVJIRwKIESUs58fX3x+vVrnD59WqU8Pz8f+/btQ8eOHbklzIozevRoGBsbY+fOnWqvFZRNmjRJo/hatmyJCxcuwN7eHufOncPx48c1Og8h1R0lUELK2UcffQRTU1Ps2rVLpfzkyZOIi4uDr69vieewsrLCsGHDcObMGbx69Yorz8vLw/79+9GtWzc0bNhQ4xgdHBwwZcoUAMCBAwc0Pg8h1RklUELKmbm5OUaOHIlz584hOjqaK9+5cycsLCwwfPjwUp3H19cXMplMpaPRsWPHkJSUVKokXJKCzkfXr1/X+lyEVEeUQAmpAL6+vpDL5di9ezcAIDIyEgEBARg9ejRMTExKdY7OnTujcePG3HNTQJmEra2tMXToUK1jLFjMOiEhQetzEVIdUQIlpAK0a9cOzZo1w+7du6FQKLBr1y4oFIoytxwnTZqEFy9e4OLFi3j27BkCAwMxduxYGBkZaR1jQVIWCARan4uQ6ogSKCEVxNfXF5GRkTh37hz27NkDT09PtGrVqkznGDduHAwNDbFr1y6uJVoet28BIDY2FgBgb29fLucjpLqhBEpIBRk7diyMjY0xZcoUxMTEYPLkyWU+h52dHQYPHoxjx45h9+7d6NChA5o2bVou8QUGBgJQtpYJIWVHCZSQCmJlZQUfHx9ER0fD1NQUH330kUbn8fX1RV5eHhISEsqt9RkfH48ff/wRgHLIDCGk7N49lQkhRCvffvsthgwZAnt7e5ibm2t0jh49euDEiRNQKBTo06eP1jHdvn0b48aNQ2JiIvr164dBgwZpfU5CqiNKoIRUoLp166Ju3bpanUMgEGiU5CIiIrBkyRIAykkcEhMTcfPmTdy8eROAsuVZ0AolhJQdJVBCqqjIyEgsXboUAGBkZAQrKys0aNAAX375JUaPHo2WLVvyGyAhek7ACvqyE0IIIaTUqBMRIYQQogFKoIQQQogGKIESQgghGqAESgghhGiAEighhBCiAUqghBBCiAYogRJCCCEaoARKCCGEaIASKCGEEKIBSqCEEEKIBv4PleST6VMzfJ8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2trbM39+f/ec//2EPHz5Ue0zNeMyIiAh2+PBh1q9fP2ZjY8McHR3ZpEmT2J07d5TK37p1iy1dupQFBQUxV1dXJhAImJeXFwsLC2Px8fFqPyMuLo5NmTKFeXh4MAsLC+bq6soCAwPZ0qVL2aVnxgJ6eXmpjI2sTYcOHRgA5uLiwqqqqmotV1VVxTZt2sT69+/PHBwcmEAgYG3btmWhoaFs/fr1KmNKq6qq2MqVK5mPj4/s+3300UessrKyUeNAGZP+v960aRPr3LkzEwgEzMPDg73++uussLCw1u8aERHBAgMDmY2NDWvRogV78cUX2bVr19jy5csZABYdHa1U/smTJ2z69OnM3d2dmZmZMQBs+fLlsvfj4+NZSEgIc3JyYnZ2dmzgwIFs//79LDo6WqVss2jmcaA8xhpeV9erVy9cvnxZN5mc6Myu76/ii6Xytppewa3x+c+h4JsrV7dViyT4YulpxEVKe12aW5jh97jJ8O/pDvyu8CQ6w/ird2t6YRJCDIhifurVq86i2vg/Tm2gRi456SnWvB8v2w7o5Y7Pt6gmT0CaMD9YG4yOXaVd4KtFEqx4PRpisfr5TQkhxJRRAjVi5WUiLJsdCZFQmgBdW9ngq+3DwefX/mMXWPLx0fpgWAikZW4lPlVpNyWEEEIJ1Kj9+L9LeHC3EABgxufh6x3DIbDk130QAE8vB0x9NUC2ve6jcygqozGihBCiiBKokUq9lY8dG67ItsPf64m27R1rP+AZ0xYGoFUbOwBASWEVdp1QHd9HCCGmjBKoEWKM4fMlsbJxnp7eDpg0169R57C0Msfct7vLtnf+OxRllYLaDyCEEBNDCdQIxRxNw6VY+STO//1uMMzMGj+ec+gYb9lTaFGZLfbGqk7TRgghpooSqJERiyXY8Mk52faQ0V5o38mljiNqxzc3w0sL/GXbO46HoFpMtwwhhACUQI3O4V13kHpLOrUXn8/DG//t06TzjZzcAU4tpGsXPilwxulrjasKJoQQY0UJ1IhUV0vw4xfypZXGzegE5xZN6z0rsORjZJh80dy9MQOadD5CCDEWlECNyL/7UvHwfjEAwEJghnnvqK5yoYkXXnpO9jrumj8eZ5bWUZoQQkwDJVAjwRjD1jWJsu0x0zrCxk514V5NeHo5oE+XuwAACTPD/h3JWjkvIYQYMkqgRuJMZAbuXMsFIJ00YdYb3bR6/slDzspe799+g5Y7I4SYPEqgRmLrmgTZ69CxPnB0sdLq+Yf1vAYbK+kaho/Sy3D7aq5Wz08IIYaGEqgRuHI+G5fjpIvk8njAvP9op+1TkZVAhJDu12Xbx/ekaP0zCOHK3LlzwePx1C7ozRVvb2/ZotVEP9GC2kZg53fXZK+DBrWGu4etTj5nVN9EHD3fGwBweFcy3v68v+kuuH31v0B5BtdR1M6mHRD4WZNP4+3tjfT0dLXvtWzZEo8fP1baJxQK8dlnn2HXrl3Iz89Hz549sXbtWvTs2VPl+KioKIwYMQJHjhzBmDFjmhyroRk6dChiY2OpOcSAUQI1cE8flyFqv3wl+1d08PRZY1DAbdhZV6C0whpPsipwIyEHXXu11Nnn6bXyDMDWm+soaleWprVTOTo64u2331bZb2dnp7Lvgw8+wPr16xEWFoY2bdrgt99+Q0hICG7fvg0PDw9ZudLSUoSHh2PWrFl6kTxXr16NDz74AJ6enlyHInPy5EmuQyD1oARq4Pb+cgvVopo5b+3Rwb+Fzj5LYFGN0J7XcCi+LwAg6u97pptATYiTkxNWrFhRbznGGH766SfMmzcP27ZtAwBMnDgRQ4cOxW+//Yb33ntPVvb9999HZWUlNm7cqKuwG8XDw0MpwesDX1/f+gsRTlEbqAETicT4a8sN2faM17Xb81ad4UFXZa9P7L+r888jhuPp06coLy9Hnz7y2a9qXitWA58+fRo//PADvv/+e7i4aDbNZE37YHFxMd555x14e3vDwsJCKdHfvn0bc+fORdu2bWFpaYmWLVtixowZuHPnjsr56moDvXDhAiZPnoxWrVpBIBCgbdu2WLhwIbKystTGlp+fj48//hgBAQGwsbGBo6MjAgMD8cEHH6CsrAxpaWng8XiIjY0FAPB4PNm/oUOHqnzHZ1VVVeHLL79Et27dYGNjAwcHBwQHB+Ovv/5SKVvzWXPnzkVaWhqmTZsGV1dXWFlZoVevXjhy5EjdF/oZBw4cwKxZs9CxY0fY2trCzs4OQUFB+PbbbyGRSFTK11zX+/fvY9OmTejWrRusra2Vvmd+fj4+/PBDdOnSBdbW1nB0dMSwYcPw77//qpyvqKgIa9asQWhoKNq0aQOBQAA3NzeMHz8e58+fb9R30QZ6AjVgpw49QE5WGQDAytocIWO8df6Z/fzvQGDBIBTxkH6vFJkPitHGx0Hnn0u4U1VVhZ07dyIjIwO2trbo1q0bBg8eDD5feW1ZNzc3WFtbIyFB3iP88uXLAAAvLy8AQEVFBRYsWICwsDCEhYU1KS6hUIjQ0FDk5+djxIgRcHBwgI+PDwDg+PHjmDRpEkQiEcaNG4cOHTogMzMTf//9N44ePYro6Gi17bLPioiIQHh4OCwtLTF+/Hi0bdsWKSkp2Lp1Kw4fPozz58+jXbt2svIPHjxASEgI0tPTERQUhNdeew0SiQR3797F+vXrsWjRIjg5OWH58uXYvn070tPTsXz5ctnx9XUaEgqFGDlyJGJjY9G5c2e88cYbKC8vx969ezF16lRcuXIFX3zxhcpx6enp6NOnD9q3b4/Zs2cjPz8fu3fvxoQJE3DixAmEhIQ06Jp/8MEHMDMzQ9++feHp6YmioiKcOnUKS5YswaVLl/Dbb7+pPW7JkiWIi4vDmDFjMHr0aNm9k56ejqFDhyItLQ3BwcEYNWoUysrKcOTIEYwaNQo//fQTwsPDZedJTk7Gxx9/jMGDB2PMmDFwdnZGRkYGDh06hH/++QeH167FqAHNN1saJVAD9seP8l6xIyf7wkJQ/2LZTWVjKUSvnlU4e0E6TCb22APMfCNQ559LuPP48WPMnj1baZ+Pjw8iIiIwZIh8nVgej4dXX30V3377LYqKiuDp6YnffvsN9vb2mDlzJgDgk08+QX5+Pr7//vsmx5WdnQ0/Pz/ExsbC1lbeca6goADTp0+HjY0NTp8+DT8/+fzNN2/eRN++fbFgwQIkJiaqO63M3bt3sXDhQnh7eyM2NlapffTUqVMYPnw4lixZgv3798v2z5o1C+np6fjiiy/w4YcfKp0vNzcXdnZ2sLKywooVKxATE4P09PQGVY/XWLt2LWJjY/HCCy/g0KFDMDeX/gpfvnw5+vTpg9WrV2Ps2LEY8EwSiYmJwYoVK5SS9YwZMzBq1CisWbOmwQn06NGjKlXLEokE8+bNw6+//orFixejb9++KsclJiYiKSlJ9gdOjZdffhnp6en4448/MG3aNNn+wsJCDB06FG+99RbGjx+Pli2lTUVdunRBVlYWXF1dlc6TmZmJPn36YOn69c2aQKkK10Cl3S2QL1nGA6aGBzTbZw/qXyF7HbWfhrMYs3nz5uHkyZN4/PgxysrKcP36dSxcuBBpaWl44YUXcPXqVaXyX331Fd5//31cvHgRv/zyC/z9/XHy5El4enriwoUL2LBhAzZu3AhXV1esXLkSHh4eMDc3R1BQEOLj4xsd39q1a5WSJwD8+uuvKCwsxMqVK5WSJwD4+/sjPDwcSUlJuHXrVp3n/uGHHyASibBx40aVzkWhoaEYP348Dh8+jJKSEgBAQkICzp49i+7du+P9999XOV9N1WlTbNu2DTweD+vWrZMlTwBwd3fHp59+CgDYunWrynFeXl745JNPlPaNHDkS7dq1w8WLFxv8+eraZc3MzLBkyRIAQGRkpNrj3nvvPZXkefXqVcTGxiIsLEwpeQLSdveVK1eisrIS+/btk+13dHRUSZ4A0KZNG0yePBm309KQ8UzPcF2iJ1ADpTidXqeurnDT0dAVdQb1q8TX//86Mf4xSouFsHOgxbaNkeITCwAEBATgxx9/hJ2dHdauXYsVK1YoPYFZWlpi9erVWL16tdJxQqEQ8+fPx+jRozFz5kxs2LBB9kQ0cOBA/O9//8OoUaOQmpoqe9qoj5WVFbp1U233P3dOupzf1atX1T7d3b0rbbtPTk5WSbDqzhMbG4tLly6pvJ+TkwOxWIy7d+8iKChI1gY3cuRImJlp/9mkpKQEqamp8PT0ROfOnVXeDw0NBQAkJSWpvNe9e3eVKncAaNu2rex7NkReXh7WrFmDY8eO4f79+ygrK1N6/9GjR2qPU2wXr1HzuUVFRWp/Tk+fPgUg/Tkpio+Px8aNG3Hu3Dnk5ORAKBQqx5CTg3atWjX4OzUFJVADVF0twcGdt2Xb0xb611Fa+9zdJOjYQYi7qQKIxcDFmEyEjm/frDEQbi1atAhr167F6dOnG1R+5cqVePTokaxjyJo1azBs2DDZL85OnTrB29sb33//PT77rGHjV93d3dWOQ87LywMAbNmypc7jS0vrXhSh5jxr1qxp0HkKCwsBQGdDYYqKigCg1t7CNftr4lDk5OSk9hhzc3O1nX/UKSwsRO/evfHgwQP06dMHc+bMgYuLC8zNzVFYWIiNGzeiqqpK7bGt1CS0musbFRWFqKioWj9X8ee0f/9+TJ48GVZWVhg+fDh8fX1ha2sLMzMzxMTEIDY2FlUiUYO+jzZQAjVAccfT8TS7HABgZWOO/qFtmz2Gfr2rcDdV+tR5JiqDEqiJcXd3BwCVJxB1kpKS8PXXX+PHH3+Ep6cniouLkZWVJWsXBYB27drB1dUVN2/ebHAMtU3i4ejoCED6BKruCbWhas5TVFQEB4f6O8rVJKnansKaqiaeZyevqJGdna1UTtu2bt2KBw8eYPny5SpPjOfOnatzSJK6n1VNnBs3bsRbb73VoBg+/fRTCAQCXL58GV26dFF6b+HChbKezc2F2kAN0P7t8iqN0HE+4Js3/4+xT1Cl7PXpo/fqKEmMUU31W/v2df/hVF1djfnz5yMkJASvvPKK0nvPPq1UVlZCG/r16wcAiIuLa9bz1JSPjIxs0FNdTZWqWCxu0Pnt7e3h6+uLR48eISVFte9BdHQ0ADSod7EmUlNTAUBt72lNEpcmP6fU1FT4+fmpJE+JRIIzZ840OoamogRqYHKflCP2WJpse/L82ttwdKmbvxCWltJfEtmZFXiUVsxJHER3bt68ifz8fJX96enpWLx4MQBpr9O6rF69GqmpqUrVqQ4ODvD09MTx48dRXV0NQPoLuKSkBP7+TW+OmDdvnqwTiroOMhKJBDExMfWeZ/HixbCwsMDSpUtl7aaKhEKh0i//oKAgDBgwAFeuXMFXX32lUj4vL0/pj4QWLaSTnmRkNHxKyPnz54MxhmXLlikl3tzcXKxatUpWRhdqhtg8e+2SkpJU2rwbolevXggODsbff/8tm3jjWdevX0dOTo5SDCkpKUpjcBljWLlyZb2dwnSBqnANzOFdt1FdXTPzkAPattdNdU19BAKgRzchzl+S9io8e/IhprzSvG2xRLf27NmDL7/8EiEhIfDx8YG9vT3u3buHo0ePorKyEqNHj8a7775b6/E3b97E559/jnXr1snGgdZ47733sGTJEgQHB6Nv377YtWsX7Ozs8MYbbzQ57hYtWmDv3r2YOHEi+vXrh2HDhsHf3x9mZmbIyMjAuXPnVJKZOp07d8a2bdswf/58+Pv7Y9SoUejYsSNEIhEyMjIQFxcHNzc33L4t74+wc+dODB06FB999BH27duHoUOHgjGGlJQU/Pvvv7h9+7YsEQ0bNgx79uzBpEmTMHr0aFhbW8PLy0tlyJCid999F//88w8OHjyIwMBAjB49GuXl5dizZw9ycnLw3nvvYdCgQU2+hurMmTMHa9aswdtvv43o6Gg899xzSElJwZEjRzBp0iTs3r270ef8/fffERoaildeeQXffvst+vbtCycnJ2RmZuLatWu4ceMGzp07J2syWLp0KRYtWoQePXogLCwMFhYWiI+Px61btzBu3DgcPnxY21+7TpRADcyhXfJZVMLmdqmjpO71CaqSJdAzxx9QAjUyISEhuHPnDpKSknDu3DmUlZXByckJgwYNwuzZszF79uxa2yHFYjHmz5+Pvn374vXXX1d5/80330RxcTF+/PFHJCYmIjAwEOvXr29wD9z6DBs2DNeuXcM333yDyMhIxMXFQSAQoHXr1ggNDW3wJA6zZs1CYGAg1q5di+joaPz777+wtbVF69atMXnyZEydOlWpvI+PDxITE/H111/jwIED+O6772BlZQVvb2/85z//kSUCAFiwYAHS09Px559/4uuvv0Z1dTWGDBlSZwIVCASIiorCunXr8Pvvv2PTpk0wNzdHYGAgNmzYgOnTp2t2wRqgdevWiIuLwwcffIAzZ84gMjISnTt3xubNm/H8889rlEDbtGmDhIQEbNq0Cfv27cOuXbsgFovRqlUr+Pn54c0330TXrl1l5RcuXAhLS0ts2LABO3bsgLW1NYKDgxEREYF9+/Y1ewLlsUYsBdCrVy/ZzCKk+aXcyMOLPf8AAJiZ8fD35amwtW+e4SOt4+VPuiVtPwAApN43x6xw6S88W3s+zj9dBDMz41idJTk5WaWdRYmJrMZiKqZNm4bdu3cjKytL7+bEJY2gmJ969aqzaL3/xxuAnkANyJE/5E+fXXu7N1vyrI2vTzWcncQoKOSjrESMlBt56NRNdZCzUaLkZFTu3r0LS0tLuLm5cR0KMSDUichASCQMR/6Ud2SYxHH1LSBdvLtHN/kg5stxuum+T4iubNy4EWFhYUhKSsKECROUZvchpD6UQA1EYnwWHj+UDigWWPLRe7B+rFvYI1A+FOHcST2u0iREjQ0bNuD8+fOYP38+fvrpJ67DIQaG/twyEId/l1ff9h/Wplkmjm+IHt3kCfTy6UwwxmrtWEKIvnnw4AHXIRADRk+gBkBYJca/++STFbw4h/vq2xrtvavhYC8dVlNSLMb92wUcR0QIIc2DEqgBiDuejuJC6ZOenYMA/j31p6ODmRnQvavCU2ic+kWGCSHE2FACNQCK1bfDxvvoXRWpYjvo+ZPpHEZCCCHNhxKonistFipN3TduZifugqmFYk/cS7EP0YihxYQQYrAogeq5mCMPIKySznnp2soGXh2cuA1Ijed8RbC1lbaDFuRXI+NeEccREUKI7lEC1XOR+1Jlr4e/qJ9LhvH5QGCA4nhQagclhBg/SqB6rLRYiDP/ysdWjgjrwGE0dVMcznIpNpPDSAghpHlQAtVjMUeVq2/beNe/qC9Xuik8gSbE0YQKhBDjRwlUjymO/dTX6tsanTsKYW4u7TyU9bASBbkVHEdECCG6RQlUT5WVCBEXKR8SMmKS/lbfAoClAOj0nEi2ffXCYw6jIYQQ3aMEqqdijqYpV9/66G/1bY2ufvJq3CvnsjmMhGjb8OHDwePxEBsbW2uZRYsWgcfjYcuWLc0YGSHcoQSqp/5V6H37/AT9rr6t0dVf3pHoYjRNqGBMEhMTwefzERQUVGuZCxcuAJCuG0yIKaAEqofKSp+tvvXlMJqGU3wCTb6aj+pqCYfREG25d+8e8vPz4e/vDzs7O7VlysvLcePGDVhaWiIgIKCZIySEG5RA9dDpY+moqpRW37ZoaYO27R05jqhh3N0kaOleDQAQChnuXs/lOCKiDZcvXwYA9O3bt9YyCQkJqK6uRmBgICwsLJorNEI4RQlUDylOnjBsnA+HkTSeUjvoeepIZAwakkDPnz8PgKpviWmh9UD1THmZCHHHFapvwwyj+rZGV38hTsTYAAAux2VixmvdOI6INFVNAt29ezeio6PVlqH2T2KKKIHqmbh/0lFZIa0GdXGz1su5b+ui+ASadOYRh5EQbWCMISkpCQAQFRVVb3lKoMSUUALVMycP3Ze9HjrGm7tANNSxgwiWlhJUVZkh53EVnj4ug1srW67D0ip/wXdch9BgN4WLm3T83bt3UVRUhP79++Ps2bNqyxQUFMDFxQU2Njbw8/NTem/z5s1Ys2YNsrOz4e/vjw0bNiA4OLhJMRGiL6gNVI8IhWKc/idNtm0ovW8VmZsDnRUmVLh+KYfDaEhT1VTf9ujRo9YyiYmJAIDu3buDz+fL9u/evRtLlizBRx99hKSkJAwYMAAvvPACMjJoqkdiHCiB6pHLpx+hpEhaBWrnIED7zs4cR6QZv87yatzrF6kjkSGrSaA9e/astUxNAn22+nbdunWYO3cuwsPD0aVLF2zatAkeHh744YcfdBcwIc2IqnD1yMlDD2SvBwxrCx6Px2E0mvPrLH8CTTiTAaA/d8HoQFOrRQ1JQxJoTRupYgIVCoVISEjAu+++q1R2xIgRtVYFE2Jo6AlUT0gkDNGH5e2fhtb7VpG/whNoclIeJBLGYTREUxKJBFeuXIFAIIC/v3+t5dQ9gebm5kIsFqNly5ZKZVu2bInHj6lWghgHSqB64mZiDp48KgMACCz5CAhy5zgizXm0EsPJUToRRHm5BBmphdwGRDSSnJyM0tJS+Pv7QyAQqC1TWlqKlJQU2NnZoVOnTirvP1uLwhgz2JoVQp5FCVRPnDoof/rs0b8V+OaG+6Ph8ZSrcW9cpo5EhighIQFA3dW3V69ehUQiQc+ePWFmJr9nXV1dwefzVZ42c3JyVJ5KCTFUhvtb2sgotn+OmqLfS5c1hGJHoqsXaGUWQzRnzhwwxrB169ZaywwcOBCMMZVVWgQCAYKCglTGjkZFRWHAgAE6iZeQ5kadiPRA2t0C3EvOBwCY8XnoFezJcURN59dJnkAT4mjYgil65513MHv2bPTp0wcDBw7Ejz/+iKysLCxatIjr0AjRCkqgeuDUYfnTZ+durrCyNvwfi2IV7r3bxRAKxRAI+HUcQYzN1KlTkZeXh88//xzZ2dkICAjAsWPH4OXlxXVohGgFVeHqAcXZh0YacO9bRU6OErT2kE5JWF0NpNzI4zgiwoXXX38daWlpqKqqQkJCAgYPHsx1SIRoDSVQjuU+KcdVhVVLBg5vx2E02qVYjXv90hMOIyGEEO2jBMqxmKMPwP5/mGQ7X0c4ulhxG5AWKVbjXjmbxWEkhBCifZRAOXZKofr2+RfbcxiJ9in2xE06m8lhJIQQon2UQDlUUS7C+VPyxDJ0tDd3wehApw4i8M2kj9ePMipQViKs5whCCDEclEA5dCE6E1WV0hl7nF2t4NHOnuOItMvamsHHW9qRiDHpbEuEEGIsKIFyKPZYmuz1gGFtuQtEh5RWZqGlzQghRoQSKEcYY0oJdNgE42r/rKGYQBPjH3IYSeMxRpPgE2KMtPV/mxIoR25fzVWaPN6vhxvHEemG4sos1y4YziocfD4fIpGo/oKEEIMjEomUFn/XFCVQjig+fXbt7W7Qk8fXxce7GpaWEgBAfq4IT7PLOI6oYezt7VFcXMx1GIQQHSguLoa9fdP7nBjnb20DoJhAjW34iiJzPtD5OfmT3HUDWZnFxcUFBQUFyM3NhVAopOpcQgwcYwxCoRC5ubkoKCiAi4tLk89p+JOuGqDcJ+WymXl4PKDPkDYcR6RbXTqJcPWGJQDpjESh43w4jqh+lpaWaNeuHfLz85GWlgaxWMx1SISQ+uTmyl8nJ6u8zefzYW9vj3bt2sHS0rLJH0cJlANxkemy2Yfa+jrCwanpP0h9ptgOmhT/EEA/7oJpBEtLS3h4eMDDw4PrUAghDeHnJ3/dDLVGVIXLgdijabLXIWO8OYujuXRRSKA3E59SdSghxChQAm1mQqEY8VHy9TGDRxn/0k6eHmI42Es7EpWXSZBxr4jjiAghpOkogTazhLgslJdKO9U4OFmina8jxxHpHo+nPB70hoF0JCKEkLpQAm1mMQq9b/uGtAGPx+MumGakvLSZ4YwHJYSQ2lACbUaMMcQefSDbHjZe/3ujakuXTvKhLInxtDILIcTwUQJtRg/uFOLhfengfHMLM3Tt3ZLjiJqPYhXu3RsFqK6WcBgNIYQ0HSXQZhR7TP702aW7KwSWTZ9KylC0cJGgpbt0ZRaRkCH1Zj7HERFCSNNQAm1GMQrDV56f4MtdIBxRrMa9fvkJh5EQQkjTUQJtJoX5lUg6my3b7j/MuGcfUke5IxElUEKIYaME2kzi/82AWCydQMCjrR2cXa05jqj5KbaDJp0xrKXNCCHkWZRAm4ni5PGDXzD+yRPU6fycCDye9I+ItNQSVJTTcmGEEMNFCbQZVFdLEBeZLtseOtqbu2A4ZGfH0K6ttCORRCJdE5UQQgwVJdBmcPX8YxQXVAEArG0t4OvX9GV0DJW/YkciagclhBgwSqDNQLH3bdBAD5OZfUgdxYnlr1+kGYkIIYaLEmgzUGz/HD7ReBfPbgiljkRnH3EYCSGENA0lUB17lFaMe8nSSQPMzHjoMcC015Z8rr0I5ubSjkTZmRUozK/kOCJCCNEMJVAdi/0nTfa6fWdnWNtYcBeMHhAIgOd85e2gNxNoZRZCiGGiBKpjp/+R974NNaHJ4+vSpRMtbUYIMXyUQHWovEyEC9HylUeCR5rm+M9n+Sn0xL16PovDSAghRHOUQHXoQnQmhFViAICzqxVatbHjOCL9oNiR6NrF7DpKEkKI/qIEqkOnFdo/+4WY3ty3tfFqWw1rK+lyZgV5Ijx5VMpxRIQQ0niUQHWEMabU/jnsRdMevqKIzwc604QKhBADRwlUR+5cy8PjTOmTlYXADAE93TmOSL/4UUciQoiBowSqI4rVt126u4FvTpdaEU2oQAgxdPRbXUcUx38On2h6i2fXR3Fx7VtJOZBIGIfREEJI41EC1YGC3ApcPS+f57V/KHUgepZHSzGcnaQ9lMvLJEhPKeQ2IEIIaSRKoDpwJjID7P8fqDza2cHRxYrbgPQQj6f8FErtoIQQQ0MJVAcUq28H0+QJtVJsB71+mVZmIYQYFkqgWlZdLUH8vxmy7dDxNHylNoo9cRPPZNZRkhBC9A8lUC27ci4bxYXSxbNtbC3g08mJ24D0mGIVbsqtQohEYg6jIYSQxqEEqmWxx+STJ3Tv38qkF8+uj7OTBB6tqgEA1SKGlBt5HEdECCENRwlUyxQXzx4ZRsNX6qNYjXv9EnUkIoQYDkqgWvTs4tk9B7bmOCL9599FXo2bGE8TKhBCDAclUC1S7H3r09kJVtbm3AVjILr5V8leXz79kMNICCGkcSiBapHS5PHU+7ZBOnYQQWAhHTT7+FEl8nLKOY6IEEIahhKoljy7ePbgUTT+syEEAqBTR3k7qOIMToQQos8ogWrJs4tnt/SkxbMbqpu/wsTy5yiBEkIMAyVQLVHsfdt/WFvuAjFAXRUS6KXYjDpKEkKI/qAEqgUSCUP0kQey7eG0eHajdPWTJ9Dkq3kQCmlCBUKI/qMEqgU3Lj9B7mNp5xdLKz669HDjOCLD0sJFAk8P+YQKt6885TgiQgipHyVQLTh1SP702a1vK/D5dFkbS7Ea9wp1JCKEGAD6Ta8Fpw7LE+joKR04jMRwKSbQxDM0oQIhRP9RAm2i9NRCpdmHgoJp9iFNKLaDXo7LBKtZUJUQQvQUJdAmUuw85OvnAmsbCw6jMVztfUSwsZYAAAryRMh+WMpxRIQQUjdKoE0UrVB9O2IS9b7VlDkf8Oui0A56LpvDaAghpH6UQJugILcCifHyX/RDXvDmLhgj0M1PcUIFSqCEEP1GCbQJYo+lQSKRttV5tLWDs6s1xxEZNsWORBej0+soSQgh3KME2gSK1bdDx/hwGIlxCPATgseT/kFy73YxSouF9RxBCCHcoQSqocqKasRHyaedGz6R2j+byt6O4Tlf6fqgjAFJZ6kalxCivyiBauj8qYeoKJfOnuPgbIm27R05jsg49OimUI0bS+NBCSH6ixKohhQnTxhAk8drTY9A+QLb5048qKMkIYRwixKoBsRiCWKOpsm2R9HsQ1rTvav8CfTO9QKUlVI7KCFEP1EC1UBifDbynkgnj7eyNkfnQFeOIzIeTo4S+PpI20ElEuAKrQ9KCNFTlEA18O/fqbLXvQe3psnjtUyxGvfyaWoHJYToJ/rN30gSCUPU/nuy7XEzO3EYjXHq2U2eQM+eSOMuEEIIqQMl0EZKOpeNp9nytT+79W7JcUTGp7tCT9zkK3moKBdxGA0hhKhHCbSR/t0nr77tOdADfHO6hNrm4iyBj5c0aYrFwFVaH5QQoofot38jPFt9O35WZw6jMW6K7aAXYqgdlBCifyiBNsLVC4/x5FEZAEBgyUePfq04jsh49eouT6Cnj92royQhhHCDEmgjRCpU3/bo34qqb3UoqEcVzMyk8+LeuV6AwvxKjiMihBBllAEaSCJhiPpbsfqWet/qkqMDQ5dO8nlxL0RnchwRIYQoowTaQNcvPcHjzFIAgIXADD0HtOY4IuPXJ0j+1HlWYeJ+QgjRB5RAG+jY7ruy1z0GeMDcgi6drvUNUmgH/ec+GGMcRkMIIcooCzRAdbUE//yVItt+cQ71vm0OAX5C2FhLAAA52ZXISC3iOCJCCJGjBNoA5089RF5OBQDp3Lc9B3hwHJFpMDcHeir0xo0/QdW4hBD9QQm0AY7+Ia++HTi8Lc1924z69pIn0DORadwFQgghz6BMUI+KchFOHLwv2544twuH0ZgexXbQC9GZEInEHEZDCCFylEDrEX34AcpLpcMpHJwt0TGgBccRmZa2barRqmU1AKCyQoLE+GyOIyKEEClKoPU48scd2evhE33B4/E4jMb08HjAoH7y4SynDj/gMBpCCJGjBFqH3CfliP/3oWx7Ai1dxongAfIEeuLvuzSchRCiFyiB1uHwrtuorpYOo2jtZQ+PdvYcR2SaegZWwcZG+nN4/KgCqTfzOY6IEEIogdaKMYa/I5Jl22HUeYgzFhZA/z7yp9DoI1SNSwjhHiXQWlw5/xj37xQAAPh8HoZP9OU4ItMW3F+hGvdASh0lCSGkeVACrcX+7fKnz6Dg1rC2teAwGtK/TyX4/786y83EPDzNLuM4IkKIqaMEqkZZqRD/7JE/5cx4rSuH0RBAujpLYFehbDvmWBp3wRBCCCiBqhW5N1U29tOphRX8erhxHBEBgOABFbLX/+6jalxCCLcogT6DMYY/f7oh2x47vSON/dQTQwbJ20HPR2eiMK+ijtKEEKJblECfce3iE9xMyAEAmJnxMJFWXtEbrVuJ4d9FWo0rEUNpikVCCGlulECfsev7a7LXPQe2goOzFYfRkGc9P7Rc9vqowixRhBDS3CiBKnj6uAyR+1Jl2/Pe6clhNESdYUMqwONJe+NeOp2F3Cfl9RxBCCG6QQlUwZ4tN1Etks5449HWjiaO10PubhIEBkircRkDIvdSZyJCCDcogf4/oVCM3VvknYdmvE5DV/TViGHyp859225yGAkhxJRRAv1/R/+8i9zH0l/MVtbmeH4CzTykr54fWgGBhbQa9871fNy5lstxRIQQU0QJFIBYLMEvXyfItsfN6AhzC7o0+srBnmHwQPkQlgO/JddRmhBCdIOyBICTB+/jwd1CAADfnIeZb3TjNiBSr7Gj5NW4B3+9BaFQzGE0hBBTZPIJlDGGLV/Jnz6fn9AetvYCDiMiDdG7ZxXcXKVJs6hAhFM0JpQQ0sxMPoHGR2XgVtJTAADPDHhlGQ1dMQR8PjD+BfmE8ru+v8phNIQQU2TSCZQxhh+/uCzbHjSiHZxbWHMYEWmMF8eWgc+XdiZKPPsYd69TZyJCSPMx6QQaczQNSWezAQA8HrDow14cR0Qaw81VgqGD5J2J/vjxOofREEJMjckmULFYgvUfn5NtB4/ygntrOw4jIpoImyCvxj3wazIKcmmCeUJI8zDZBHpo523cS84HAPD5PLy5vC/HERFN9OgmRMcO0pmJhFUSpbmMCSFEl0wygVZWVOO7lRdl22OmdYRTC5o03hDxeMDsaaWy7d82XUFZibCOIwghRDtMMoFGrEvC40zpL12BJR8LqOetQQsdXIE2rasBAKXFIvylMCUjIYToiskl0MwHxdjylbzn7ew3u8Ha1oLDiEhT8fnArKklsu0tX11GaTE9hRJCdMukEihjDF8sPY2qSukAfNeWNpiywJ/jqIg2jB5RDo9W0qfQogIhdmxI4jgiQoixM6kEemx3CmKPpcm2P94QDD7fpC6B0RIIgFfnFsu2I9YlIi+H1golhOiOyWSPp9ll+HxJrGx7wPNtEdCrJYcREW0bEVoBXx8RAKCiXKw0TIkQQrTNJBIoYwzLXzuF4oIqAIC1rTneXzOI46iItvH5wOJXi2Tb+3ckI+lcNocREUKMmUkk0B0briD2WLps+6P1wbCxo45Dxqh/nyoMUZid6LPFMRCJaKUWQoj2GX0CTTqXrVSVFzLWG/1C2nIYEdG1t18vgqWlBABw93oefvzf5XqOIISQxjPqBJqVXowlU46hulr6y9S9tS2WfTWQ46iIrnm0FGPhPPmwlp+/vISrFx5zGBEhxBgZbQItKarCGxOPIi9HWp1nbmGGb3aOgIWAz3FkpDlMCytFz0Bpm7dEAvxn5nGaJ5cQolVGmUDLy0R4bcIR3L2RJ93BA1b9HAKPtvbcBkaajZkZ8On7BbC1kdY+ZGeU4j8zI2W1EYQQ0lRGl0DLSoV4M+yobJkyAFj0US/0GuTJYVSECx4txVjxYYFs+0J0Jj57IwaMMQ6jIoQYC6NKoIV5FVgw6iDOn8qU7Zu+KABhc/04jIpwKXhAJcJflk+wsC/iFr75IJ6SKCGkyYwmgabcyMO0QXtx7eIT2b6JL3fG/P/QRPGmbt6sEoweIV83dPv6K/hi6WlIJJRECSGaM/gEyhjDwd9uY8bgvXh4Tz6IfvabgXj9kz4cRkb0hZkZ8NG7hRgyUN6J6PfN1/HO9OO09BkhRGMGnUCzMkrw9kv/4KNXTqC8VDqFm5kZD++uHoA5bwVyHB3RJ+Z8YNUn+Xh+qHx+3Kj99zBtwB7cvprLYWSEEENlkAm0rFSI7z+7gLEBO3Hi4H3ZfjtHAb7fPxojJ3fgMDqirwQCYOVHBXhponwB7vt3CjC1/1/Y+N/zKCulp1FCSMPxWCN6U/Tq1QuXL3M3q8vT7DL8vvka/vz5hmxe2xr9Qtvgo/XBsLahKfp0oXW8o+x1SdsPOIxEO/6JssaX65xQJZT/DeniZo0F7wVh6qsBsLI25zA6QohGeDz562boKKj3CbSkqAqxR9NwdPddnI16qDKOz8XdGh98Mwg9+ns0a1ymxtgSKACkPzTH/9Y44dpNS6X9Ds6WGDu9Iya97IfO3V3BU/xPSQjRX6acQBljyM4oQfKVXNxMzMGF6Excv/QEYrFqiHYOAsxa3A0vzulMa3o2A2NMoIB0lqIjx22wdYcdcnJVay882tkjeGQ79Atpi259W6JVGztKqIToK1NIoA/vF+Fecj6yMkqQlV6C7IwSZGWUID21EEX5VXUe29rLHnPeDMTQsd6UOJuRsSbQGkIhcOCoLX7/ywaPcwS1lnPzsMF3f49BQBCtJUuI3mnmBMpJQ8+WrxOwb9utBpd397BFyDhvjJnWkabjIzohEAAvTSzD5AllSLhiicPHrHHmvCXKK5T/izzNLkfrdg4cRUkI0SecJFBPr9p/AfH5PLi4WcO7oxO69mqJToGusHOQPhGUFAlRUpTXXGESBa0VXhc+yeEsjubwnCfwTjjw1jwgOcUWCdcccPO2FVIe2MLahg8XN2uuQySE6IFGVeG6urrC29tbax+eeisfEgkDn6/dNqWq6hJYmtOTqiJtXBMri0pYCyq1FBG3SiqqYG9tWX/BZ1jYOsO+hbMOIuLe06dP4ebmxnUYeoWuiSpTuyaurq44fvy42vcalUANBdfDbfQRXRNldD1U0TVRRddEFV0TOeqFQwghhGiAEighhBCiAaNMoK+++irXIegduibK6Hqoomuiiq6JKromckbZBkoIIYTomlE+gRJCCCG6ZjAJ9OHDh5g8eTIcHR3h4OCASZMmISMjo0HHVlZWYtmyZfDw8IC1tTX69++P06dP6zhi3WvKNeHxeGr/XblyRbdB61BmZibefPNN9O/fHzY2NuDxeEhLS2vQscZ6jzTlmhjjPbJ3716EhYXBy8sL1tbW6NSpEz788EOUlJTUe6yx3iNNuSbGeI80CjMAZWVlrEOHDszf35/t37+fHThwgAUEBLD27duz0tLSeo+fMWMGc3R0ZD///DM7ceIEmzhxIrOysmJJSUm6D15HmnpNALC5c+eyc+fOKf0rKytrhuh1Izo6mrm7u7MXXniBjRgxggFgDx48aNCxxniPMNa0a2KM90jfvn3ZlClT2M6dO1lMTAxbv349c3R0ZH379mVisbjOY431HmnKNTHGe6QxDCKBbtiwgZmZmbGUlBTZvvv37zM+n8/Wrl1b57FXrlxhANi2bdtk+0QiEevYsSMbN26czmLWtaZcE8akN/7HH3+syxCbneJ/9i1btjQ4WRjrPcKY5teEMeO8R3JyclT27dixgwFgJ0+erPU4Y75HNL0mjBnnPdIYBlGFe+jQIfTr1w8dOsgXyvbx8cHAgQNx8ODBeo+1sLDA1KlTZfvMzc0xbdo0REZGoqqq7snr9VVTromxMjPT7HY21nsE0PyaGCt1M+j07t0bAPDo0aNajzPme0TTa0IMpA305s2bCAgIUNnv7++PW7fqnpT+5s2b8PHxgY2NjcqxQqEQqampWo21uTTlmtT44YcfYGlpCRsbG4SGhiIuLk7bYRoEY71HtMEU7pHY2FgAQJcuXWotY2r3SEOuSQ1TuEdqYxAJND8/H87OqvOPuri4oKCgQONja943RE25JgAwa9YsbN68GSdOnMDPP/+MvLw8hIaGIiYmRgfR6jdjvUeayhTukUePHuG///0vnn/+efTq1avWcqZ0jzT0mgCmcY/UhZPVWDShbhFj1oAhrIwxjY/Vd035Xr/99pvsdXBwMCZMmICAgAB88sknOHPmjNZiNATGfI80hbHfI6WlpZgwYQLMzc0RERFRZ1lTuUcac00A479H6mMQT6DOzs5q/8IrKChQ+1ehIhcXl1qPrXnfEDXlmqhjb2+PMWPG4NKlS9oIz6AY6z2ibcZ0j1RWVmL8+PG4f/8+IiMj0aZNmzrLm8I90throo4x3SMNYRAJ1N/fHzdv3lTZf+vWLfj5+dV77IMHD1BeXq5yrEAgUOqEY0iack1qU9tf2cbOWO8RXTCGe0QkEiEsLAwXL17EsWPH0LVr13qPMfZ7RJNrUhtjuEcayiAS6Pjx43H+/Hncv39fti8tLQ3x8fEYP358vceKRCLs2bNHtq+6uhq7d+/GiBEjYGnZ+DUh9UFTrok6xcXFOHr0KPr27avNMA2Csd4j2mYM94hEIsHMmTNx8uRJHDx4EP369WvQccZ8j2h6TdQxhnukUTgaPtMopaWlzNfXlwUEBLADBw6wgwcPsm7dujEfHx9WUlIiK5eWlsb4fD5buXKl0vFTp05lTk5ObMuWLezEiRMsLCyMWVpasoSEhOb+KlrTlGuyZs0atmDBArZr1y4WHR3Ntm/fzgICApiFhQU7ffo0F19Ha/bs2cP27NnDFi1axACwzZs3sz179rCYmBjGmGndIzU0uSbGeo/UXIOPP/5YZfD/w4cPGWOmd49oek2M9R5pDINIoIwxlp6eziZNmsTs7e2ZnZ0dmzBhgsqA8AcPHjAAbPny5Ur7y8vL2dKlS1nLli2ZpaUl69OnD4uOjm622HVF02ty6NAhNmDAANaiRQtmbm7OXFxc2Lhx49iFCxea9wvoAAC1/4YMGcIYM717hDHNromx3iNeXl61Xo+a729q94im18RY75HGoNVYCCGEEA0YRBsoIYQQom8ogRJCCCEaoARKCCGEaIASKCGEEKIBSqCEEEKIBiiBEkIIIRqgBEoIIYRogBIoIVrC4/HA4/FgZmaGe/fu1VouJCREVnb79u1K782dO1f23vLly2s9x44dO2Tlhg4dqvReTEyM7L2afzY2NvDw8MDgwYOxbNkyJCUlNeWrEkJACZQQrTI3NwdjDL/88ova91NSUhAbGwtz87pXEjQ3N8e2bdsgFovVvr9ly5Z6z+Hl5YXly5dj+fLlePvttzF27FiUlZXhm2++Qc+ePTFz5kyUlpY27IsRQlRQAiVEi1q2bIlevXohIiIC1dXVKu9v3boVjDGMHTu2zvOMHTsWmZmZOH78uMp7ycnJiI+Px7hx4+o8h7e3N1asWIEVK1bgiy++wJYtW5CQkICkpCR07doVv//+O6ZMmdK4L0gIkaEESoiWhYeH4/Hjxzhy5IjSfpFIhB07dmDAgAHw9/ev8xwzZ86EtbU1tmzZovJezb4FCxZoFF/37t1x4sQJuLm54fjx4zhw4IBG5yHE1FECJUTLpk+fDltbW2zdulVp/6FDh/DkyROEh4fXew4nJydMmTIFR48eRXZ2tmx/VVUVfv31VwwZMgQdO3bUOEZ3d3csXLgQALBr1y6Nz0OIKaMESoiW2dvbY9q0aTh+/DgyMzNl+7ds2QIHBwe89NJLDTpPeHg4qqurlToa7d+/H3l5eQ1KwvWp6Xx08eLFJp+LEFNECZQQHQgPD4dYLMa2bdsAAOnp6YiKisLMmTNhY2PToHMMGjQIXbp0kbWbAtIk7OzsjLCwsCbH6OnpCQB4+vRpk89FiCmiBEqIDvTt2xddu3bFtm3bIJFIsHXrVkgkkkY/OS5YsAD379/HqVOnkJqaiujoaMyePRtWVlZNjrEmKfN4vCafixBTRAmUEB0JDw9Heno6jh8/joiICAQFBaFHjx6NOsecOXNgaWmJrVu3yp5EtVF9CwBZWVkAADc3N62cjxBTQwmUEB2ZPXs2rK2tsXDhQjx69Aivvvpqo8/h6uqKiRMnYv/+/di2bRv69++PgIAArcQXHR0NQPq0TAhpPEqghOiIk5MTJk+ejMzMTNja2mL69OkanSc8PBxVVVV4+vSp1p4+c3Jy8NNPPwGQDpkhhDRe3VOZEEKa5PPPP8ekSZPg5uYGe3t7jc4REhKCgwcPQiKRYOTIkU2O6erVq5gzZw5yc3MxevRojB8/vsnnJMQUUQIlRIfatWuHdu3aNekcPB5PoySXlpaGFStWAJBO4pCbm4uEhAQkJCQAkD551jyFEkIajxIoIUYqPT0dK1euBABYWVnByckJzz33HN59913MnDkT3bt35zZAQgwcj9X0ZSeEEEJIg1EnIkIIIUQDlEAJIYQQDVACJYQQQjRACZQQQgjRACVQQgghRAOUQAkhhBANUAIlhBBCNEAJlBBCCNEAJVBCCCFEA5RACSGEEA38H4XOs1HCif53AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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b0+m9kstvLZ1jJFROdUhTac77K12YGxyXZ3NsVlMppUL14Mn2slTL2Jm2mpkyzGihwjP2Fi3YJITPNH2jP3j2EAD7wkXfk2ZGE7xbVel20IyNgx/SbkT9RG9I+LufTsM00IITO01/NROnIg3wjQ5M2zQa6ov3LYyAZXN7AQAH2hYqdP1p9HKbwIwWKnW2Uy064ZsJmJtK1CwjnvpPHjhkNJzmtzMhaMr7qwOUUjRJGxK+AE6nzQJpEjWN/uq091cnI+rz6C/+nBsFREdRjGVDiVBpV1Np12NzqjGjhUrIqCyivRpNeHncxBsh2qEubDQe3V5N01im0+IzWWimkBU/ol2X4smMU5lO71V4f6Vjs78SAOicpkJxKk1lKS54nNH7qwmPrjCOsWiwB4HvdUCodDe7MKOFCtdUhAbSaMdRUFP59pP72vYj55doNjWMVagI21GkHNfK+Jzu2lojmN7lyHgdn/zOtszzjtnH6U5/8UX1mf0n8fS+kxN6vaKgRZ/mHdFVnRMqqiNAHugJ0YZirBbib7+zLVPul44zLeD8u0ZlgqMoRinwsHCg0r5NJadP3YCZLVTInhDFhb266mJRth934MQYbvz7R3DzPz3aVv/44nOqQfwMgSaubeekV36stqGpdGskPcG0yD/47CH8n3ufwc+1hbgThvpJi6hnzX/km0/hw998ckKvVxTULRLYNOc65f0lbSqNj9UzS3zme8/hr+59Bv/44+fFMXEc5yYRVTSVBkIljGMEvofFc3pwsM1y0s5QP43BfdCLenUVMe6S4Hli9/GO9A8ATowWFSrkAWMe5PqCVm9DqHR7cjyT95fYNGjvl39s16W4SMR3O+D01+lqiLFpkhlXj9MhQd25OJXimgqEsEj+oVLPPIaEv+a26a8oRuB5WDKnBwdOjjXuXw6K2oOmK2a0UJEuxTzZXzGhknccLRojbdYiV4RKwd1co1QV+kBsx+hX6/I8RqYNQt2yaVAM9a1eLyeQrpPgfQ+juJAjwmSA1nrpcZh8PjneaUN9gb5omgoJYtL0+TH6/4RmNJUoiuGTptIhm8p0chdvBqWp7sBEQuyY4lgMxE5oKtUwESaNBlrR/gHAiYL2Gc9Cfx0/XYPv2ydcKwO0Nl1WqxZh8v6yUVTK627xtgVtMYk2lXoUTxvXU0k3JoOQnken07QUsqmkj0TYVqgNSKnCn1qjNC1FDPWB52HRnAoOnaoKIdMKxEawS/d0M1qo1EMpIMIm6a88e0K7aRjEwsaaKaqpBL5ZU3n/7T/BvP4yLlu1wHzNVjSVLqe/TFHVVk2FLTGt0g6TFePEhUg9jKZNTJXueUiPsePeX4XijtTNA/XFa0JTiZrQVMIo8X6b01tGGMUYq4for7S2vHaqcNhUYUYLFR7w2FFNpU2h8t5/fAQLB3vw3157rvjuRMGJJ2IBtAX/yEhVicfRMZttKipdREZknf4y/98MJsuleLpqKsKmogXcdmpz0ko9Fd224jGpoqTmMXRR0VQa0V9xjMAHekuJRWG8FqG/0rCbRuQ5D3QDZrRQqQs1nAUFFvX+mkChsvPIKE5XQ81QX9SmAmP/KBWNTRjOSk0lz6YS5wiVVq83aRH18v/EpjI9Vh/qRU0kck0+d8pxoZV6KvqxnJDiv/Fn+On7n0PJ9/Bf1iwU3zXWVBL6q7ecxOaM1Vu3t3a7TWVGG+r5TlXSX40NbvxcE/gOvpUXH8VJMCbfYRYVKkR/6Qt+FCcCxdbtlmwq9e4c1ITQsLDY3m8jKqQIioydTkD3Zpsu8US6UKVedYqe0ytL5iFjU0m/4GYOm/fXPU/uw7ef2qcI7/ECEfW+z4RKrfUNGfWkmc3CU3tOYP+J9rzOOoUZLVT4rpTGRHFNJcIjzx/Boy8czRzDdy1FaSv9GvUoUgbNWEHtx0Z/EfVlo0JaWXi6nf7K01R0wcE/tUt/TWY9lVau99jOY7j2r7+P000UrysCXTORLsYRntl/Ev+590Rb7QtNpZBNBWkf1M+c/rJV+xyvhRithcr8bOQJSYb6npT+asfNu5US4Bv/9gG89H99p+VrdhIzWqhw76+iu0ie++sv7nkaf3nP05ljuFBpJc11GMWoheoOsyil5llcigX91UGbSrfTX/QsTDtS/dZUTa41oTBpLsVxe0LlP/eewNP7T+LQyfZStOuQbvtqbFQ9jPFnd/4nPvRvapDmptt+oAQjNkLgN2NTUTcP9Fl1KZb/82c6WgsxVouaj1NRNJV2hEp6D47+mn7gnj5FbCV0LB1XCyPjYOLfHTndmlCpR5GqXhcUKlSoKKupJK6zdkN9091s23Y01RCaA+fOrXEq5v+bwWS5FOvtNytURAqfDvusxkyIJJ/T60UxRg1Bms/sP4nnDjRf3bApTUX7rLh/KZsN+f9YLcSoZvO0zYUDJ8Zw098/gvF6BN/30FMmTaWNZ9vl3l8dESr33HMPzj33XKxZswa33npr5vf7778fc+fOxfr167F+/Xp85CMf6cRlG0LaURi/njMg41hqD2GY2CdMObn4ADvWqlAJVU+tolqBpL+yNoEoxxOotTiVLhcqzdBf7GOrMoHW6AmvUa8LlSbfbXWCyiHoqZC4jaUeRRntOooKRsfT8UxI6fjot57CVx7eKT7Lyo+qgLMZ6vm8IQFYhEn46/u24Z4n9wFABw311Kfmzx1tMyC7E2jb+ysMQ7zvfe/Dvffei+HhYVx22WXYtGkTLrjgAuW4K664At/61rfavVxT4NxukbTk/Kd6FAuDug5FUxlpPlqYNKdmXBYJtjQt0vvLfN5s9P4SaVoUl2KzpqLWqFd/Gxmvoxz4qJTy92CSOp3Y56YLkWY1I9ooFX29tTBC4HlKMN9jO4/hojPmCkoKkM+trnl/1aJI2BGVfsfNea4JG43hnM/9YDsA4K2XrUyPhfIXgv4y21R0+2aobdBs9FeJ3b/ve+gtJUJlvA36q504lZ1HT+OcpXNavnYn0LamsmXLFqxZswarV69GpVLB9ddfjzvuuKMTfWsbPE7FFvTGwQd9nosuFwCtLLz1KEY9jJoKriLI6nraBE2FYGfjVLpT/SaYbBytGOp/7TMP4W+/s63h9fIWvU5Cb75Z76qipbUJm257EJ/+3nPi88/3ncCbP/Ug/uLff64cl3EhZgt7tW7QVOLmPNcEnVlgXOo2FbpVz2JT4VpVtR5lDPW2+VkKZIOJptI+/aU7GTQCf4Y7j9hLjU8W2hYqu3fvxsqVK8Xn4eFh7N69O3PcQw89hHXr1uH1r389nnzSnlV18+bN2LBhAzZs2ICDBw+21TcuSIpoKipNkuxWTMWw+ABrxdMnilNDfQv0l3Qp1ieo1K7M12y6m12f+0vSX6bv7JqK/gwPnBwvlCSQl6yeSBoi0/cmX66gv9LnMloNsf/EGD76rafwq5/+Yeb4PcdGsefYqPg8kmbU3rL9iHKcsKkYBPd4PcrMpYReTo55eMeRhs+MWitUTyWiPtG5pKnw9rL013hKW0WxKhhsNs9yIJfQwEeHDPXNaSp8PXphJggVE1fP3fYA4JJLLsHzzz+Pxx9/HL/7u7+LN7/5zdb2brzxRmzduhVbt27F4sWL2+qb9P7iAsa+UOqJ+qz0V5tCJUmtUcy7ZNv+k9h02w9E+nBbQskwpfhs/ZmNNhXp/ZXVVPIM9Tqi2E4rqsclf/95605c8Rf/MWGuxRnvrybfraC/0vPe8bkf46X/6zv43A+2Y+vzRzPHJ1qw/EzpR06N1fHTXcew6pY7sW3/yYzNgy+KY7VQGbM8o/GzB07h1z7zED5+93/mdzw2vzvjodpf4VIMc0Q9PQsuSEZYOQrbXMjQXx0QKoSiw4evHbuPjuYcOTloW6gMDw9j505pINu1axdWrFihHDM0NITBwUEAwMaNG1Gr1XDo0KF2L90QiqaieaaYoGgqqcuv0VAftidUhKG+gPfXU3tP4Ke7jmPPsWSn7ImI+iz9Fcb24Md2XIq1PULXwOz9pbq7EvLKCYdRXEgoU5vHTtdw6FR1wlLS65pJq/QXPYtHDIJEuZ4l/unUeB13P5EYqf/9yX1sjqWZLNixY7VQ6Sf3sty2P6lts/d4vjYo3mch7y91QyFeny33F2lt7J3xGkdW+su30F8tavk7j5xWaHuOKKXmdHCB146DQKfQtlC57LLLsG3bNmzfvh3VahW33347Nm3apByzb98+8XK3bNmCKIqwcOFCU3MdRcReTpGcXrqXEFFKOtrVVMI4cVcu4rJINBddJ0hXeP14GVFvob9aGONEk5SayLZaCyPsONS8m+hEQKe6fnHwlFIOgSNW/jdw/00IFcJEuWS3S3/JMVXwepqtjq5/cqyO/nRnPloLM4s+79ZYLVI2aPzY3Sm1dsa8vtx+0HspNOeI9srQX9xQD/Y/aSpyUR4pIFQCn9Nf0lDfyobiR784jCv+4rv4+qO7076r97n5gV/glz/5QOa8dtejTqNt769SqYTbbrsN1157LcIwxA033IC1a9fiM5/5DADgpptuwte+9jV8+tOfRqlUQl9fH26//fYMRTYRoAUk5ob6nMVBTzwYsfM4qmGEcuBl7CJFEUYxQk+eW/I9q3otPXXUSWHK/ZVHf7WjqfhNvKs/v/vn+OwPtuOHt1yFFQ0WiYkG9/76wbZDeMfnfozBnmTIZ3aBhl2r/GzXANXj1M/tZrO2XqdN+qumjanG1zMvwKfG6+hL69CfroZiEaT2+aI4Vg+VzQk3jO9KKZuFA/kZGKm5IhkFrFmKlfbk/KM2uF2H10uyaYPcUO+nHnKVwG/JUP/kniTjwNOp5qZvFvYeGzVqc3ztmOgCcUXQkYSSGzduxMaNG5XvbrrpJvH/zTffjJtvvrkTl2oKPN09vaBaGOHbT+7DNRcszQg2PnAop5Jpsa/WI/SWA9TCesv0V82TfeorB9adkC4MfZH63hT8aNdUbOvOrqOn8d2nD+I3XnZW5jcy1DejqTycUil7j49NvVBh3ljPHkgmKlEauVmKDe0U0Qb0xX18gqgIvStxLHNPFUFd034bXi8yayoAhFAZrYaZRV95pjEUQz39VmeaSiMaz0Z/meZorP9NL6jEPqY/Br4n7o+/M66p2Dzlytz7K33+PWW/JU3FFNCsfzbNY37edEguOjsi6mP5sB/efhQ3/v0j+MnOY5njM4b6yG6o708nU7NChXa99VAa6nvKgdVQn9VU0j4Y3TPtVIhN2Nzx2B78yb8+YcwDJW0qxYVKfwcNle1CFuSCEk8BNIqo1+mlgsbhKaK/gOYWk5pmqG8E3VWd/9/PNBWZ6j7OHKf3m8f0vHD4tNIvADh4chx/8NXHlXFkc9k2zR09f5bpTumYcuAzTcVsqLe9/xKjv0io95aDljYUJkpb/2yax+PTjP6a0UKF0x/0Pw3A0+PZl67bVLgthqMaRsLLo1kjacgGO53bW/at7rum6GQgq6mQPcW2UNi+J3XflJG4UeT10ZEqrvo/9wstAIBCh0wkfvjsoYZ5o+jVmHbxpglL0G83L/6HQx8rNNl/cfAUHjNsYlpFo9K3jVBj86IR4tT5Q91wyd+JGuVCxWRTARJhowf21cIYOw4nNjg+lz76rafw1Ud24d/TaPWkL8gcB5hd34UtRRcu7FT6rhR44n/FpsLprwK0Etk7e8vF6a8wivHBrzyGp/acEEKVtJ/sOLRpKuq6NdWY0UKFL8gZldmgzuo5osLIfFy1HqES+PC95o2kptQPfXmaihAi6mTUB0/j1Pfm72kSme6TBrltQd19bBS/ODiCZ/afEt9JoWLPgPuVh3fiO/+53/p7EfzXz/4Yf/yNJ3KP4fRX4OVrKhy6oT6PVuTQm6Td6ifu24Y//NrjDc8vijwbgn79d31+C57cc1z5nhbhplLIK4uxOk+A5H1Tc7KOkV34xelwOz5aE8KX79Tp2VVYHIgIaMzQX9nr6Nc2Ralzmwq1OWox1BexVVJXe0tBYU39yEgVX390N3743CGxBpD2Y/JQNL1nhf6aBjaVGS1UuMeXPihMOw/dpTiK46TAl3ZutR6hUvJR8v2mOUzeFk2mvkpgNerWtYVdaFwZVRm5ub9siyJNIhMvLYVK8vn3v/I4PvvAL5QYA0BdnIQ3UI6m8of/8lP81he3Wn/vFOhZxHHW2SDPUK9TYXFcLGZAX8jonY6mqdSbxfHTNXzivmcKGaZNAuLQqSq+98xB/OSFY8r3zRjq82rSAHI3zw31dfbcbf002TC43YLGN0+NY9VUcmwquqGen0nNBL7H4lSyLsU9JV9od7b7AeQY6y0XFyokSKphJNgCMv5nbSqx8ZnytWA6aCozu/Ij291nhUp2IHJBE7LgxFoYIfAD8Vs1TISK7zfPYfLFjCZUb8luqNddivXdoOyvna5LzjN/T2q6if4SQiV1WPiXR3cBAOb1V/Crlw7LaprsWRLH3soi2mnkvZuihnqbcdh4PYtQqbEFoxl8f9tBfOK+bdh40XKRz+nLW17AH339Z5ljTZsJ2rXqi25NW9jzYKpmye+TSj+M1UIEfrKciJx7iIWXpLh2Sh1LoSL7xp8RLbZcqMiUK9omz2RT0QQbaTlqPBJpKtKmwoUBaduVkm8NmubPJfCbp79o3tfqMaphmPaHhEp2jOpaNKDHzU19wPIM11TkTrsZ4x6gpjwxceWVwFcGownf+MkuXPd3D6p9YhOMJlRvJbC6FOuBUDwViOg308hs3bGNNZpExhT/dbn48Lox2w6cxK995od4NPX04ruj3gY2FX03//KPfwefuO8Zc+faRN6iqd+uzRAdNrMA6y7F6cJSD81ehI3ANzUEncrSj+WgjUdGqDRBf9E98UP5tY6k48LkUhzFahoTfq5JM+D9FJqKQn/RfbWuqdCp+0+M4ctbkqDtxKZC/eGG+qRvPSXfalOJjEIlKByESPdZDUMhVCn2RReekUVjrrFnNR00lRktVISqbaCFTINEj/iNDAs4IOkv35OT5Kk92ap2T+w+gZ+8cMy6y5M2FV94hOkgW0eoaSxcCHCffLtLsU1TKUZ/HTw1Lr4/eGIcD+84isd3JQscfz795WS3aqO/eJQykLgef+K+xskaW4Gy0GoObJka9fx/9qGZPEyZ4MdQLuqtVNE0BeyOW3bARo8wIZS0sU+brVbpL/b/YSZUdK0uiuOMOzr1hZrgizinmGhu+Gqyrsz1AbOWrRvmdeHyzcf34PMPbgdALsXJ96aI+kpg3zzycUT0V08pKKyp1MQYiTOG+ihO1pUPfuUxkdXBNA5pbPWW/Zbi0TqNGS1U+K5Il+Amv3OT9xeQNWLXwgg9JR+ldLD9dNcxbPzbB/DTXceU42iA8oFqsqmQJ5lp4RExBdrkVqg6psXY6S/j12JHlSdUgISf1++Ldkhc5aY1wKapHB9tvlSADpPwNSFvzczSX4wWUdowa6vm62kabfqc6pE5vUYjiPHH3rVNOJlsezTm9WtXw+bvSdXe5O9UT2i0WldsKnHC1WTKBehVIblNpaYY6snYn+1LZpNn0lRIiIjPahv8mZY1+qvkewh8TxjqKyW7BmDWVHyM10IcOjWueEaawJ0UqqH6bKI4xoPPHsLXH92NwyPjVpsKzdO+SjAtgh9ntFDhRsEiHiOqoV5WZrRrKh7qUYyjp5OF8vAptWAX7dZHLZG5NNkptYNpxyUM9dqulQtFTlE0W05YuBQ3ECoHT0pNRTfu83sy7fg4uFBptVzqibFitdVz6S8DX23ql8091nw99TMtGPUwaov+MnkM2o41fadfm8c+NRLQkeH++bVI0z3N0rTQMVEcK/RVcm21PcWmwukvg+ch/ae/OyP9JTQV9XqiDTZ/hGE8ijFWi9BXDtBXDsTGqKcUWIMfTZoKGer/5r5tuPFLjxjPE/cp6K9I/C+DR2PxHMaqEdO+zJpaf6U0LeJUZrShXsapZAei0VDPBg7XVPQBVQ3JppK4ItaYlw+HSahw4Ua7NHLDHQ9DAGWtT2ZNhQtFZRdp435tmkqNdkomtVp+R6nfA9/L2GHqhr6MWlyKuVDhfTo+WsPcvrLhjPw28sAnWDagUfusCBV+nPl4EzIGZGGol7EeehBmHkwbiGaESt0iVHjwY17iw3/9yW5BP/F7M+Ws070kKXdeSRcqjJKm80S/DMLTdN1iEfV6+3KhTtqQxxJFF8YxRmshelLm4BTXVCzzirejGOrrEUbG6xm6V4fw/qrLjQffyIwLoV1nGgzAAvkxLuivwNlUJhqmGvX6bxxcdnAqyaapBL4natkD2Sjy0wb6i183Q38ZJrieUsO0+yxSl6Ulmwrrz8GT4yj5Hub3VzBKHmMGTYWuc8oQXAoAJ5hA4ItlM8WFipZw5ouPrplmI+rNAqgdmwr3/uJ/i8JE91jpL6OmIoUaBx9Tea6vt333WXzphzuUvujX4hsPPs7Ha5Hw/lKvndVACDWD1mIS9jahwgU2HaJH1vMks4SAeVuN10L0ln2RbRhIDPVWmwobw3qciu6NGUXZbNdVNkaynp6xEBg8DU5WU5G22Wo9wqpb7sSnvvussb+TgRktVHia8yL0l7Ij5EnadE2FCZUojsUOS9dUxoQPvzmIioyuNICNaa0jdfeS5/2l95vD7lJMvH9jm8rCwQoqgSd2T/QMTdlnbcGPXMvgz+L5w8WFShFNRU+1nslAkGOoV2gc2tkW2ABmPAw1L6tmjfVGQ71FszC9Xxoj+nWrbEdsEiqCMork2FYWR0VTkefzDcNItY4oAiol6YoPJGPmN7+wBX9jqKRp0shM8UOmuDFAFSpiAdb6LD6zNkibiqJkDhP9RcizqZiyC5RLfpqFXN1wXfChe/D6v1GzDFcNNhVVU5FCRReQBG5ToXl3239MnVCZ0fRXs3EqfACrvvUGTSUIpKZSJ01FbfN0LXnBNkM9+aWTppJn59G5/ZphIde/V+/N+LXom4n+OslsFwdOjGHBQA9GxuuS/jK4ptIzHLGo/SQQyoGnPIuiFet++OwhfOCfH2t43Hl/co/yOZOBIEN/scXLcFyheiraox/XnCCaraRpCi5tlHjUdL5+3ZoiVEzzIKFXwliObX776hiW558Yk0LldGq4z2gqUYTvPp2t6FoOPEXrMQuVrIBL+pB8NmVBlnYIKJ/586I+hnEiZHvLqiCslHxlLnDw/lEkfOB5wntUtT9F+Pk+1XBPa0CN21RiOebo+Y7WwoyzgWiDZeagNaQJlrXjmNGaCudgi/CwCs1QNwsCIOEwyyVPpGmx0V9mQ312N9ZXgP7S+WSF/sqheQgNgx8Nz2PvcVlF7uCpcSwarKDkewZDfVbA2bhkKVRUSiEvrQvHH/7LT3HsdL6mYhIAGfpLP0ahWeLMcUUMoLaIenqH1IevPbILP9jWuEidpL86a1PhY8qkqfCxZkrVY3McqIWx0LpPjYeIkY1Tse34e8tBQ+8v+jczlw2aijxJ1VBM6VpIGBBNXgo89DCvtXLgN4wjAwDKLemnLspJe/kbCXp+4wabShjFQlPhudW+/dR+fP4H2+X9p+f1sPQwRTNWTwRmtFCR3l8Gj5GcnR2gTxb5fxwn7qE9afBjPYoaCxX2vanao6C/wuwE1+0WptxfqoZls6mYvoutaVpOV+s4MVbHkjk9AJLYlIUDFZQCTxr3c2wqIxabCgmEWhgZKwE2wiVnzlc+mwzopqb0+8sGP5qvR9+3ElEvNTlVcP+3rz6Od3zux43bS/uoaCo2TdTwtS1Ohb83UzZd6X2UdRSh70VbmpAb7EmcLU6PJ4blrE3F/Bz7K6qHlanss6C/LN5fXIAJDVOcK+9Jb5c0HIoD8T0PPemcDHwvo1VzKLaZlP4KRM2jqOG4UW0qWfqLayrU1Pu//BN85FtPyTbCOEkbFcgNXzM1kDqNGS1UpPdXMfqLF81SbSpx5v8kTYuHMJLqtx7wJ+JULJqKtKmQpmIXdJEmVGzeXzZNxTS4be6cALAvLQZENVFOjtcxp7eMku9nBFFo6MtIA5sKD/YCkmcfxzE+/4PtOJ4KnjiO8fN9alDposEe9b4M0tJsX1DvLxOnAvPzNHkpVesRfvfLP8FzB2UizeQ89Zq0YPPdaDMweTvZ6S+Dlmux5aiG+ux5fOGVNJT83RTAS5jTmzDqI2kwZEZTsQjF/krJOHZNQZdZ+6jBpqKdo1NHpvQqCWWVCIWe1BYUeB6CnMwZpjgV6kYtbFxxVolT0Z51FMeCMRllGQt0UIJb7pnp6K8JAg8czNJf9gW8UvKtmgq1WUpdikOuqWi7vtMmTYUNDOFSnBP8qBtKG9NfxQ25XLPS67PsO5EIFV7itRR4KAWekq8IMMepjIzXjZOAG9l1V+sHnz2Mj3zrKXz0zmQX9oUHd+B1n3gAD+84krkPWrzy4jM4dM00m1BS/m+yH/Dj9x0fwzcf34Mf/0L2i+6BQ9BfFhqqEUzv2iZUzOnwzdQmz+mWS3/Fkr7h92azqQAQlTUTmwoycSq2xIxJ0TuzfYdA/+oUWp5NRRcmdCa/BxJ8YZyEEXgeBP3l+0m7NtpOSShJQsWXmkoUJ8/OtsmqMWpUX5PiOFaSktoo7FqYVKItscwAzbiudxozWqhw17yMpmLa2aUvtUcTKqY0KyU/KR0axpLT5QV+oogNCEtdhmIuxcl3//7kfrzkI9+WhkZuqGenNUN/KSkytGtLTaVXfFfyPWXimm0qUnU37YK5rUV1YJCxMHRvj6cZCnYdlUb8OI4xr7+M9716jXK9RvfaUFOxGeoNHDx55OkLss2AKm0qzQkVEwVkdym2n0/X/c+9J7DqljtlOQWLpiJiohRNpZhmTEJlZDxMDfXqEjNuEGJAQn+ZgxjjzP9FXIp1w3xGU+G0lYjFSa4R+NKmkmgqCf01Xg9xzV99T7GH8XHkae3RpiuMY5yyGPq595euyUax/J2nweHP4+RYDcdHa6k3qnzWk1Gu3YaZ7f0ldlnZCW9MfR9LTYW/YCUlSvp/kC6wNk2FL5g8ZYmJOigSp/KLQ6dE5D4gJ/NdP9srFl/bfQGNNZUM/ZVqKsvnSk0l8H2l0p0p+JFfZrweisBOec2I/a9qcGRvmdef1CqnxYPzw1GcfCbe2rSBNFFijQz1/GMjyoXu17QIcIg4FYvGEMdx7uQXJbBbpL+k91fy919/slvrr8WlOG0qjGPoEfC8XRMGif4im4qWpsWWaYF7Lin3YLDl2Az1JUWoxOpfLRiSU7Yy1Xyy+ewpSfrLT+d5PYpwfLSGbQdO4en9J/HKsxdl+kf9orHJwwFs3pDc+ytbTlhuTMcMmkotjHHRn34bADA8vw9cfuv1gyYTM1qo5OX+yqO/ekrqrkmhvyI5gMl1kCb9WNUsVMYsLsWinopwKZY7ynOXzoGfDmZ+rN6P/+cfH1W+p2h/fUdrEiqjeULl+BiGekti50n3XApMmop5F2taALlhWNHs4hjHUmpsKI2sp7YCjdbwPVlrvBH9RanXTaUCOLhNRaW/6LryO7pv3cidpb9ChJHM16TbzE5XQwz0qFPw6X0ncfaSwVQLTsdvEfrLpKlocSr6tepMm1baMnl/FXSqEJpKtZ56f6mLmy0nXDP0l81QzwW03kOpsWQ1lRK3qcSJIKGcZUE65uuMnmrkeUndoOdfj2KctAkVZm8z1UiSmko9o4Hz+UtZ0wmO/pogcPogk6U4Z2fXU/Kthnqxe06TzkWR3CkpmkrVrKmYYg76KjL48We7juP1f/MAPv2955Tj9QFnM8jXwkhZ+AmmdUDVVNQD9h0fw7K5veAbnmSC+ZlzbIFxpgVrvBaJNvWg0ONppDxRD/rOT96HxygLE/3FhUrSlq7B6ULW5vlpMuzSO8lqKln6y7Y5ASCEKGHrjiO49hPfx5ce2pH2SS5KdF/NJJTUbTK6UGlkU4ljHtlufsc6Sr6H3rKf0DVRNveXbcfO6S/d21K/rk6hkU0lNxOCRocZgx+F9xcU+ivx8pR50jhVzJ8FrSmC/mKOLFZNxeD9xe+dfh+tRpnnztcYCsYm+FO4ss9ooWJaCAimXZEQKmVfSTGuCBVmUwlSTYLaGrVoKqpLMV90ZcI6IInB+EwqTCiVfs1Ctdj4+Xpozy+lG87zNJXjozXM668obek2FZMhmT9mo1Cph5iTLm66A8OxUelunLSf/OZrtIbvMY+dnMUUkEJFX4yzmgr730BpKIsHaSqaPcJUpCtXqGjpZn68/QgA4IUjo8q1i0Tkm4SrblOZowmVMIqNdT94bA41q9I81m7A9zwMVEqpo0bW+8umqXD6S6dFCfTviOYJlZfSJWOoTz/zOa1qKqpLMW0eudZm01SoTRqb0nU7sttU0mOqmos99ZnWiNFavqYyXo+UuelciicI/CVlPWDsk7CnpNaMryv/08Dx0zKkcqfE7QWKgLFoKrqhHgDu/NleADJleMh4WYLnqdQERzWMMhOZkOH8a/YFrxom6f354AwCVagQ1FiCfPprrBZhTm9Cb/HnEkaxyPKs7465phLHyYShfkVRjJ/tOq7Us+H3KTx7tJvXH526ywX7PytUpKDXDfVqm+O1SNGQamGkXOe4FsT5QpqqZslQ4jatB17mpc83R9TTIig3S/o5uS7FhgUdyNdUfD/RiE5Xk+BHXWu2CpVKIBgF3ieuQXJDvWlDFBneYSY9i5H+YppKlIwv2ujFMYRNpSboRLOAPX/ZUPIMKE6FafJEf+lrvcmZh7ddZb/naSqHTo2rmooTKhODvEhzY5oWTn/ZDPWRqqkohnqLcf7ep/bjsw/8QrkGwOkvKVQoDoO4aJPhnaLR9VT7yX1GxoUfUCfdqfG6NjGzmlw58JVJUPZ9o8BSYmbYYzUF1o3XQ+EOrHt/HUoLgekpwE02Fa6pvPG2H2Dj3z6gHCP6bHmOerS6aZfLv+fDpS68vzT6S1vYq2Gk1OKphqoX4lFNqOw4PAJAjh29SFxenEteRH2eG3JeuiJbZmK6lm4vARK7Rn8lEIb6wPOUmAlb9mqaA7UoUm2QmmCjzRbf+dP84+9NGOa1PseGe+M14XX6qx5FDTSVCBedMRc7bn0Dls1NPCWlUJE2R6K/+rQUMHkxTNyl+DRLKEk4ydLinByra5pKprlJw4wWKnkpw007Ox7YqMSpGCJ9pVosB5nJOF9J7TMfv/vnODFWM2oq/eUA5y2bAwA4PpoIClq89QJhANCTChVe44RAOysTaGF4eMcRvOQj31aSOGaSDrKAKkLge0ZqjWdqzTPUJxMzxhBpKhrNQfeTsdWwS0ZxsnBRN0y2otCwYOj3NzJex9s3/wiXfuy+VPixxQvZBZRrGHWrpiKPoUJNauofleI4Nio3BUmg50nRN0AuqDSO8zQVc5yKSn9ltbUkLkN/pabYHMWtnuaJYYPhIdFURqqJUEm8p+RxIzn0F5A8W/5c4zhZkLfuOIIYwFC6IeGGb172Wp6n/s2LU9HpL+5SXA9jlAJfCdZVg3azKVHosfBiaCQE9bxijd4p/W7y/joyom4quUux01QmCKFGPXCYPU2kpmJLIaJoKl5jTWXhgHSP/eGzhzTvrxC+lwzKez5wJZYN9YoFVdA2Jk2l5COMY7Gz16HXsCDQmNxzbBS1MMbuY4lQCXzPkHQwcQflg5OCH3WYgh+B7EJOi4XQVNgCU61HOJLaGDLZWjV6zfeh0F86+OTzoO4aCT/efgQP/eIwjo/WUmqBn59ty1ReIM+leLCnbLSp8Gf1x994Al95OKmTfvDUuAgMFUIlPVXXON5yyTA+cPXZyrXzNkm5QiWMBdVDiOPkOSs7f+X/JEDQtMHwhaaS7Kw9T6XAbGWm+yvSA1Khv2LgPV/ail/9zEMYqdbFhsSkqZiST+qxLSbHCy5UwohsKqmQi6TmP27IkxdFMfQpITSVSGoqp1INTX9kebFLUQxFU9GFyuGMUOH/O6EyIcizqRhr1Kff6buJmoH+CnwPQZoTiHZKJuM878P3njmYoeT4y+/vUdNtA+YI5ErgI45lsKAO08IPyAnFPUoAc+AZaSqKTcX3UDa4lahxKkxoavQQfSahwinCwyPjYuESZYoNCwAZUnlqjcx9ssvqxm4TaqFWptVkqDcYZLlQ0YXbYE+Qen+pY1DfJHwhrVfCF0kKENWrfZKwffV5i/HbV6xW2jEJVz33l74okaZS0jTQg6fGcUDTgvUAYN/zjJsX3wMGKiXhAutBbdsWWU6aQTWMMhrsD587DCB5LTR2eBCtyVBP/2fpL3kPBO79FaeaB9dUqP9jIu2OOj70BZw+07w4NVbH3mNpYK+BIrUhjNUsxfor1ulvF/w4CdAXcB6/YU4omfyWl68oq6nECsdOAW3EHROlM7+/jEeeP4qXrFQTIvJFe6AiX0dFuMJmBx0JnP0nzJqKaeEH5ESTBsfUpmMIPKuGESollQ8vpYJUh82lWJ8wY0JTydJfnAYQpWSj7GIYxVCFioH2Mbl55tEMRHsQeIt6CnWAaSoWLyUgucddR0czFKxOZ64bngtAtc+QpqJrGnQPibDP3oMOPU5FH0phnBjGg8BDEMqkiX/0Lz8TzgKm+wsjGWlO/aFreJ6H/p5EUyH7BKdjTYb6ciDjQuqh6uasC0uKYTpptKlk6S/d0ULayOSxskgXBB1I2ls9ikX/aROm56zTqSb6TM/kbZsfEvetv6e8cTmuOf7o3puHR5L5/8Z1K/COl56Jn+46zu4pvf8oxj/++Hn82oaVmc3yRGFGayqhtsBVWHSvLaFkKc1KyqEmlEzOo0jbMFa9sHiuHo5Lz5qP09UwswjyAdnPDPaB7+GR549aDPXJORT1rsOmqegce5WVM7bZVFRNxUfZoFbXFJuK2gaHrqlwKkTf0QNcU5FtRHEMD/n0F3/GeuVME2phpLkUs+ul11ZcR8nbr64uLhwLBioJ7aEsfjJC/QNXn40FAxXpRsvsCEJT0TQ1GlsVjZa03R/P/RXHcaaPyYYoMabz93p4pJrZBeuUoO9Lrzw+r3zPw2BPqqmkn7lGY6K/AmZ3qYURTozKZ6bflklTqYZx5ljdhVi3jeXZVBLvr2wgoalKamTQVMjGQsed1rwcOfKECk8OyTUVmv/0jt62YSVeunqh0fvr3x7fgz+548lJrQQ5szUVpp1U61ES7Ztu7o31VKKsYRGwe3/5vocwjEUaDACiyA/tPD/965fgP35+AL7noRYez6i/fCDw4LR/e3yPsToeIDWpAxZNxWaopwml73x7S0FGyJL3lx6nEhi0IF1T6S37GKtFGUN2RlNRhArf0asTP9S0CM7nmzQVUwyDLZEhXccUOMfbN2k/XFPRu0G2NM57JzaV5NwV8/ow1FvK2OPm9JREKWadvqoyoZK5B8NzkEGTMAYAi0VUBLWyglHaeNC9v7imwjdhvpdkHCZNxdM0FRP9VfZ9kc6lFsaKA4M+hqRNhaUsMuQnI0iNJf3M7oGgBD9GUOJUkvtL/jeViahHMfp0+itd0OnaPCGlPv/zPPpoTAz1lXF8tCburxL4qIWh0FTIc45vJon+Irurrb7RRGBGayr1KFYmYI+iqRhok1TV1Xf6pjoPAdFfsRrlLGwp6Xevu3AZ/vLX1qFcSrL76hObj0euqWw/NGK9L7onW31x3fAq7k+jv3g9F2N1y5Kfiag3uZHWFJuKPZdZRlNJ+19mmY+T9hoY6rn3l2FOqu6/6cKcG+MRZYpBHTo1jsOnxnPjVESyxSjGljRwkbBwMBEqR3ShwjLqlgOfCahInKd7f5HGQeNMjx9KjjFpKmqf9WMo11Xgqe+1boju1oWKz+YJp4spxUk1jFJDvRQ+Jd/DaUOdnVLgoRLI3T0vwrbr6KhyLI0dE/1lcoHOGOoNlKqo/BjxiHqVNQDkfONxKuQtxqGbmupRjAUDFbzmvCVZTSXHpkLzY6i3jDiW85WuR5rKQGqLVTw103/pHNuaMBGY0UIl1IQK/9/kqltPVVndpqLGYdCi4AtDPZ+AopJilBQooh1DJQjSHFTJ+fT+FU2F2VTynDfI3mKKAwGyQW6i7xZNpaes0l9RSouUNfrLJHCBrEtxbzqA9V2Y2I1r9Jeeq0yPU1E0oUgLfsxxpQXAdoh5QkWzqcTAH37tp7jl6z9jixA7XvP++vyD2/HOz29R2lwwkNgkuFCpMk2lFPgoBb7QyujZLBzsEUJFT9NSZQtEIZsK+64aZtN81NPxqLuK18I4syHQtVF+Dp8vHtRgVd+Tv/eUfGOFzxLLW5XQX1Ko6JurSslHT8k3GupN3mq6DYU+82cjbSpZl2IAzKaSaipaBnM9eaPJnXf9ynlYe8bcjFZcrUdK7Eovm7s0Juh3/R5IC+4vl5R+8nuSQmXylvoZLVRqUaTswLhfvTFLcTrBdPqorqm7ANNUUqFC44gGXj2MFBqNYl9oAabdPJ/MfZXs7sgEkXrEsvu2GeQyQiWMRKCXQj+FkmbJxqkYvL+0BYfuQxcq9FmPU7HVr9H5cGpfob8Mi6kpUM+WvZl+011mj52u4vjpmlGwSe+vpP9Psmh+wvz+5B65baJWl5uKkp/szoWTR126oJ8SLsVq36tN21RUGjCT74wM9RrlWzVoKvxUQX+lfeALVqKZyGPJ+6uUUmymLMVl31Ppr9M1kVJmt6apePAwp7ck4lReOHwaO9J4K1N+Moo5ynw22FSiKNFyPY3+0r2/9DQtepyKSajwFEf8vVTrkcjsDKgCmjaovczdmp9PGxaab74izEmopKmgLBvNiUBHrnTPPffg3HPPxZo1a3Drrbdmfo/jGO9///uxZs0aXHzxxXj00Uc7cdlc1FP1m+8CSFMx0T0ARASw7ippilmhnVo9SgL6iEMnPrgWxsquvhJ4Sn4fmojc9W+AuRR7sAsVug8bH6tH7RJozlWZphL4XsrRZhf1ihZRXwo8o6G+rtABzD3UIlSkS3Fd3I/p+oL+MXh/0URWI66z1IawSzTQVPhoiEFG7EiLtFc1B6KsTMJ9XipUjoxIu1c1DBX6qxT40uiftrVoTo+Ib9EN9ZQmXX8v+j3L+5L92vCx+/DYzmMAgL94y8W4KN01h7GZ1sxmzFUXbAr+BXRDvRoMSN5fpcATGaN1BGxc1cIIx0armJ/OJ10I+V6SCZkcID70b08AAK46b0nmHQImQ336mbsUU436OE7rqagbUJrHJu8vWjOU+zHMkTLboOkORDwTOBcMo0JTUVMN6fYzWjdKgUGo1LqQ/grDEO973/tw991346mnnsKXv/xlPPXUU8oxd999N7Zt24Zt27Zh8+bNeO9739vuZRuCFs5eg1AZqJQsWYqTRV5fOE02FfLtp0JGZ8zvByBdiOuRmoNLCAKthDAfkP2M/jIZXjNtWTUV9bUK+wNpKnW58yW6jzsb2HbEge8bYxP03F8kqGz0F+3MaCHlmkrge+I8k5ZAwY+inopB4Jts8vmaSqTRX7GoFmrWelTtwkRDUk0YxVDPNJVyWjlUxCGkGu6idDEdGa+zGBtVeCW2LruHot5fwl0/2wcAeNNLVogNkdBUcihfvS3SVIw2FabBADLzREmrxcOhGuqTuiUkVDJCyPMw2FsS2tze42O4/EULcPbSQWPwI3U7a2ORTYo0LVEsXIR72LpB/R412FRMcSq65gJQanqzpsI3k/xUnf4yFW0DIOjmwJD6XngMWjxCJwJtC5UtW7ZgzZo1WL16NSqVCq6//nrccccdyjF33HEH3vnOd8LzPLzsZS/DsWPHsHfv3nYvnQtaJPngoN1Hf09gXGRol5LRVJRBJBc/8uqoRxGG5yfFrPafGBPncBqNJh4NFBP9NcDorzwX2EoD+kvXVGTCvPR+WI2WUjqhi9BfZQM1CGSzFPuel6G06HrJfZYQ+B4z1EvNsY/V1dCjoOl/HlDHH5Meia/2MUeoRLESnBLH0t6gB/3xtsj4bUrKOM9Ef4WREEhBatDWBdTCNPfbqfE6qMsm+gtQF6C8OBUCOYKQ51YUJ2M3Se+uaSq59Jekf3l/qE/6IpvYj8wpfpLfpR2zntJfC9Lnp2/+dE1lpFrHYE8JvudpWqX4L+2zKmS4HVBoKqmQ1V2K6dkYXYrj7P2aCmSV2f3rQdlkSw18z6ypVCiFTXZ895UDIcSUImVQKdq84N9Oo22hsnv3bqxcuVJ8Hh4exu7du5s+ptMYD1XVEUhiBwBg8WCP0aWYUjToVIAaUZ/8JZfiKE7or/n9ZfRXAhGQWAtjo6ZyuhpmamAT+nuKeXhLTcVe8AiQXi18J5b0TQqVxJAqd8zPHx4RnkxlLciOChbpyEa8U84zzaW4Rqn+fVQCabTlhnpOTRoN9TGsRbpqhhxQi+f0KPdsQj2MNcElPaNM+aT0gEb+HtYsGQQADM/vRyXwBe/te2SrSDXdNONzLUzS95B7+AKhqYQZQ/14RqjId5EXUU+YmwYOEnWbpCWB4sllO5eewz1P7MWhU+OJtmhwKfY8L8Ptl1JNxeQ5CCDVYpLfqpqmogtGDx4Ge8rCpjIyHmKgJ4AHWz0V9X5MaVq4oT6OkREq9Lsw1Cs21iirqRhus8Tc8ymrQpRS50QHD1QCtdBY2sXekqqpcHAtR0m8mnZR1/onA23HqehRnkA2RUCRYwibN2/G5s2bAQAHDx5suV80eTn9df7yIbzv1WtwzxP7BL/MQbtsfdcWRuogAljwYxSjVk+orqVDvVJTidRiWdzXPUjzIwHqroZ7f+WBJqeN/uoRQiXxZ6fBJmwqgv4KUQp8lP2E29919DR+6S/vF+3o9FeS+8uUpVjduXnppOQRwXuOjeI/954Q/SsHHk6MSUM9gVcANC0AMVJjsMH7K9lIlMUCe8MrXoQ3v2QFNt32oOJs4XnqzrseacGPSAWK19joP14PFY3st1/5Ilx/+ZkAgKG+kshnNlBJYlKojXKQZHweqdbxXz/74+S5lHwluE/mjlJdiklTTd6NmWdPzlO/KwUePE8+v8RmlE3TYkIYx9h3fAw3/UNiDz1rYb/Y4SveX566qJLmUraMneR8NaL+2Oka5qf0oa4xeV6SAufUeOIhdmqsjoEcTUWnwegQfizNpyiOMxH1gNyUCUO9kp4na5hvRH/p9XEoPm2wp2SMpyJDvUkwcOcepdZRet8yX1kXaSrDw8PYuXOn+Lxr1y6sWLGi6WMIN954I7Zu3YqtW7di8eLFLfeLB/YRAt/DhWfMRSlIBqC+uxORwjq/bODtS6mqGsXAeJhEny+Z0yN2nDr9RZNmtJYs8iaPDZ77Kw9FbSo02Uu+ugBzTSXwPZRLHmphhM8+sF29TuBlbSqGCZOxeQhNRfbvv9z6H/jHH78AINVUSllaEkjUed2lWOfKOcWix2IAckJdc8FSpqnI4+h6PDOurpFQckE9ml9va7weKe+BP6+h3rJS3qAWxsJhgGxZvCJgbzmQ5XjH62K3maG/6Hk1oL9M0dskjElToYBfmxbBnwnPNRd4ntCy+fvjKXSARAgQ9WMLyuW/jaQlGch7Tg/K9b3kOY3XkoDmahhhsFKCr20AdCGiR9RzbZOEXRhJd2n+PIRNpSptKk/vO4lVt9yJ3cdGM3EpRkN9ID0nRYaEmvo+E+GYfT66TYVj5xHpHcevS/dJWnSY46jSabQtVC677DJs27YN27dvR7Vaxe23345NmzYpx2zatAlf+tKXEMcxfvSjH2Hu3LlYvnx5u5fOhalWCU14W1p58uTIGOotLsVCZeeaSjrxKCKdQANnrBqi5HtioPBdTXFNpYFLcSlQjguETUUVKtUwSgPfEgFwUqtOl9FULAuD7lLse4mh3ta/hP7KClwgWTBOjtfxF/ckpQIAZBZ2m/cX7SDpKx5LwQWcsK1V5GTVXYrD1N6gBNTRIs/GzVgtVIUKez5zmKsoJe0kAVFO6aARFgzYW/aFZj1ejzIJNalWDl2joU1FG9+1ULq/UoE5yrJbJKstdzrgmSfU9CC691eymJZTu4oJpUDW6aFrzE2rjuq3JbTgeiQE8kBPCZ7niezKQNZ1mJ6liF/hhno2lqLUWcfTxj0g0/LUwghbth8Wv2fpr+x9lrihXlvwKcPEupXzjOfKzY/s9BVnL8ocx/uhC67J1FTapr9KpRJuu+02XHvttQjDEDfccAPWrl2Lz3zmMwCAm266CRs3bsRdd92FNWvWoL+/H1/4whfa7ngj1BhHL/qqGbTqYQxuxiCbSkkTBrbgRz55EqHSg/0nxhDHyQ6wZFg4R2shfN9DHxnnLLm/8mBK08FBgpR4YaneJ7/zOBDuUqwvQuXAV2w+psBQQBMqkUxzYdOkPM27ht8PaSp/d/9zss04277J+0s31Pue2WhaKfnAePqcRiiiXqXYwjBJDGqKfdA1laqiqcjrUPJDIPHs43FK5HHF05b0lgOmgUmqjDRlsn/Ia2XpDg5d0NQymkokvJds1UI5DpxQNZWACSgCpybpM21GbNcoB1JQkgv23L4yfI8Sx6joKQcYr4fCA2ywpyQ2RHGs0puC7sqxqdB6EMcU/KheT8SpMJtKSdPOOEzymac8IpsKOXhcsGIIX7rhclz+ogV4zf/5XuZcWsP461wwUMGf/PIFikal2FTSY0/X1JinyUBHcn9t3LgRGzduVL676aabxP+e5+FTn/pUJy5VGKb0BMJLgnmacMQxMlTAYG9JoShkkS6VwyyXPCzt6cVYLcKJsXq6q8xqKqM10lTIUM80lYKG+h42oInG4JD0lzrpY8OiSJ43vAgR77OuqZiLdCVeM0/tPSGCE/M0FUANmOOLTa9BsJqCH2XNCiZUtBxQpjxugBRi/YyrVmwqcbpIx2oSRlE0iz2n8VqkCAad/iKQpiLr5STjjMuC3lKg8O5yAZT3pUarM6FiKuUQxcoCSzQtnRtG9oBfE/YdlzE3PE5FF3T8swdg6VAPxuuh4jLPwQ31VA1zqLeUagvqffmeh17SVKpSU6FLRnEMHx6rp5J8L3O4yWcjrs/StJiyDos4Feb9xZ+X/uxMc6TCahFJ70vyBPVx5TkJ1W8yNZtiTDwAv/XKF6n9ZGOdxg5pwnlxWp3GjI2oFzaVctaQRYut/qATTUV9OUO9JWXRCJmmwgdPJfAFf3/w5DjqYawIJ/LDH60mNpV+4UYor79sqBcXnjHUUGPhi7Ap/YJOf4mCXxr9BUDQX2GUTc2hF+my5f4CgD+54wn88id/gOcOngLV+OZeUfpkMbl6A6q3HkG12dDChcy9iJT56X1y3p9DCpVS2oZMtuh55PklhSVB98YCEuMtpw35gjrUJxfRuX3lxE2Yxk+QLc3cW2a7WebOzDMtB9qCLZ5REU0lzaAAJHOB4jJsmRJ08KzYAfP+CjxPvF9dO/Q94ENvXIv/329swBnz+oztlpntTlRMNaT3B5L301MOEMfA0ZFEAA30SLdaumVhU0kDGqXhnn7nmoq0dSSaji4k1DgVPbC5UEQ926DRexkzBCaazjUxE6bjTPSXyCPXTYb66QoT/SX9uc2aCvH1fMDM7Ssr6cuVNC0a/cWNrEnFOLOmEvieEHZ8cPRVAnzrd6/AZasW5N5bWbNB6KDvaDDyNBSAJlRSQz2QrXWRlBOWnxNBah4yZIQfGQ/h+9nUK4sH1focPaxvfFKa7icTp8JsAEp6F2HgTz4nnnwGTUWzqYTM+8v3vDSiPkq1Bd6P9Drs+R0dqSqLN19flg71iv9XzOvD/hPj4lxys+XoKQfK2Aw1IUbu2gS+rphciuuaWztp4gDS4MfI6kZvwn6N/iqxtuhsXxPkfjrWB3tKIpZLh26fpPNM1KXvybFD7tqDPSXxLPSywVGcdSQBzC7FYm6njd36Kxfh73/r8kycSqb/GSGU7TcFu/LrcE2F358O08bR5D3LNSa6Pb02z2RgxgoVYahnixS9fBIapkys+gQb6iuLVOSApCJ0Kqgc+IK+GqnWs2la0oV7tBqi5PvWVCpJW/kTvJGmQnl+SKhwP3xAjQgOfFnNUa8frkduB5Zywhz1KBLBj9ymMqhRez2sb7xJ03PhEyKGalPJ01R4LAVHWRMqvPJjkBp8wygptWsKfuSbkUN69T32vFYvHhT/L5vbi+OjNTHJS4EU5oTeciCKoPEYGZ5hV6Ga2P+2iPpLz5yP//1r6zL981MjOLkU29x9OfYdl0JFob9YbAqnJgFVm7JpKqVA2ierTOiaduMePCZUEjqOXIoB4HM/2I7Hdx5Tcn2F2qbk6EhVEcIy8DIVaOllr7/8TFxx9mLpUmwpdZDNUmymv3SPxfY0lcxXWpxKomWfFhVoHf3VNkxpWnT6S5+IUawWDAJIqMisqbSGZTUVT3hvnR4Ps2lagqQfiaFeLmgmu4MtnYVoy5LOn9BbVukvmhQ0EbifPae0RrUMspXAVxbJEhNANtRCGZHM70032lMfC2kqJpuKWITkb3oKdN29lUBBgOQswRfw5PZi4W6rOgmQ5iA5db2kMxfCqxcNiP+XpNTonnRhLvl+5ln2lOTz5tH8tCCEOTYVW+6vUuDhly+WnpY8+jqMYlEu15TTTYeuqUhDPZhQ0V2K5f9nWDQVnqmBp+sx2Rc8Ty7Ch5mmQpf8y39/Gm/61INCVYlj1dPrXx/bg5d89F4RPEnXAqStUaezStrvgLp2ZOmvbL8pqwDAPLMMmorpnovSX3zDF8UxRmuhDNh19Ff7MNFfFeEN5SvHEIRNRaO/xmoRS5EgBz2f4OXAF3EmI9V6Nk1LSe52Sr4vDNJGodJAG+DuuCYjHtlUiOaxuRQDqaBg0f5cUzDZVIq4nvoeMpoKCflFKQ0mMwqoz7GvoaFe1VSUMsTCUC/7y7v7okUD+OcbX4Z1K+cBAPrLavZXIFksowjG3F/c+4s8u/Ra7vr1CMvmJlQYZd01eUNxmwn3/pI2lWyyRvpLx/7fB7fjJy8cVdrj70xoKp4n7s8mfHXoLsVSqPhCJcnaVOT/K3I0FZ3ODLTNBoFnECb6i2sqBF75MS+PHsA3maSp6JpHdpnkY7tI6vvE+yvViJrQVGxu/Ka9He9HGMeKLdjRXx3AuMFQT4ZTXruBg7Kv8slOHjzkRcF51wz9RZpKNbTGqSQ7eUnzmNxuG7l3qnEd2WNpYabJQrtQmmj6LktE+1dDsYunPvMxTrEVjSA0Fa1Gyq+/9Ez86I+uAiAnkk5zmOgvPROxzygWlf6KleN9T7q00uek7Gp6Leb9xT3GYiTxConNQfZDeg5FIgZFr76p5HJjlB/ZV3YdS4WKgUqs1iOFdw+190X3LkGaty80mz/95lO47u9+KM4raUKbaxfkDFAKPLzm/CV41bnFg41Vmwo0mwrXVOQ5lMlbB8+LRWNGz4PF75g2JIdHZJGqbBaP9C+yxcky96JpIvr0My3qvOqnLvxswY96QkmzTUUTKoFntJ+YvlODH6HEQJnKp08UZqxQqRnoL1owyb6hBwSZdm0kiE5Vpb+376k7NSAZNEJTGa9n4lS4oCj5vqC/TEKFBt95y+bgk29/CV63dpnyO1FpgEVTsdpUkt/57j6p60E2lboqVPR6KjmpNjg88v5iE4+KEdH51MdAi/cxCUmd/uLGYD5ZRFlZRn8BctILiiZdAk3Bj4Gf2FQo7T2PRJZBiLHYbBwe0TUVs9BdOkfXVHzF6w1II965TUVE1Mv4G92zCkjGVhTFineWOD7d8QvvLJ/u0xfamO95eN2Fy/E/Np5v7LsJXANq5P1FsKVm4lo/jc2Sn60Zk7Qhx/yRU1VUAt9YtIxiWKLY7MTAoTMXNpdiDl4KWxc6RrsI8/4SNXQMmop+atn3rQ4LOjhtHkYxjp6WmqXTVDoAk0sxLQS0KGdqRkSpi62f1VTIAyzZ/amLNV2H6JTT1TClv8y2j8D3mKZiLq9Kbb5x3YrMoObagqn4Tk8pSNNj6DYVg0sx01TGalFGUykSUa+D6C+uqdTCSLMFSU2Fr62NNBWKeDZ5f2Vcipn9IOmXp7SnpmmBOCYxYFObWfqrHkpN5bBmqNd3rb/5ilVYu2IIQ30l9JZ9UTPcpKmMh5qmonl/hbFmqPdIU0mi419Ii1WJFCdsrJaYAEiejYzLkAZ3FAb3wPN9bqjXbCpaXaD7Pngl3vnys5TveCCxLJlr7g9P9nh4ZFwkVNQXcqoYSdkR8iC8v9J3nXUpznZkjM3bTES94fiiNhWTQDNRXUaXYjaewihWxuZk2lQ6Evw4HSFzf6lGd0Du4OmYe5/aj83ffw4ekh2XblMBIIz1iQdO8hsfTHP7yigFSanTkfF6Sn/J3yu6UMnVVFJNI8gKL70tk6Ge0qvQ+Xrq+6xQ4ZpZWWsHyrGN7D2AnPjj9UjECNQjtbRzT1neG9+J9ZiEipamxWO7YZOhPtQ0Fe7xxH9PvNvS2jeQgodrJ1xocfowyV/m4/Apu00FSGI0CEuHevF8uvCbPK6ovg31UU/TQrVP9GslcUYRdh5N2qZ4KW6joeSTPhMuRH9xO0tRcIeWQPP+4s3oTa5ZMgcr09pDBJFN2/eYTSVHU2E2FaIY9ee+O6UZYzTWVIQhnuylnv57do7xcgcZG4zRplLQ+0un3gLLc8i5DyARpjQ25/WXnabSCdACww2/pHXQIKbU7E/sPo6HdxzFeBhlFlkpVKRNhU8mAtXPGOhJgiXz6C+uqeQZ6sm4rw9STpuY6C8KHOutJLQA0TymNCMBK5AEqAF7urE04dEbDxnfT557HKdpTFh9FgI5E3BPLsCsqfBFIQl+ZN5fBkM9bUzFAhqoO/G64M5lTRxJf5mj9AGV/ioFCYVJbtiCasxZmMkDLPFs8jKFk6r1UIlTEd5mlCo91tx1haaSBK/uOpIIFUqfz73UspqKL9xOVcFTDFyQ6HEqquAzUDeaBCDh6vuyQFvSfva6HlTvL3JVt1FrRQz1eoaNPBsJ/c8rUuqMsDHgNsgW6RKlfpV6NBorYbMtNbCphHEsbE5L5/Q6l+JOQC9oBGRtDXo23HoYZQLmaOdO9Bff/emaCpAs4KfHw2yaFq3oj8nLiaBHwuuD3LTjV89PAs4WDfbgH377pXjLJcMAeJyK5v1loekAZpdIF/Ii9JfneWKynxqvs+pz2X7rdgKTk4JiqEfcME6FG+rpHvm9qIXW/DSSWjpg1A3aD8DqqaRaaL9CreYvbgAwt6+i9EcX0K86d4nocxhFzFAv70u1USR/KyUfYQTsTO01tH6EIRMY2jMg4VlvUajwGiy+71m9v0xN6kOoLJ6Hh2q60AY5Bmoao3EMpqnY+96I+pEuwxabCuswbdB4zEoRTYV7uHHvr0rga44NmlDR7Jq2a+r9jOKEmh3sKWGgJ+i+3F/TEeMph68ntwPkwkmLXV3sQKNMwSISFjIyVe7++MumTKMDlZJwKVbTZ7PdG9NUTJDxNCofTig30FQ8z8M//NZLceaCfswfqIiiW3xRFH3xVM1MX9RFwChx8xb6a06vTOrnezJmh+pdJH3NCq9aqFI6pmJaqqFeDbZThIqW+0undcSCnf5ONqKkHnzym+d5mSJcBG7j4G7hfeVAbgBy1mXSAsWmgT2PH/z3V2PZUK/wVuM2FTI2Z+kvGicJZfdCqqmM1UN8/5mDOHhqXLyvki5cfC/JysvsNE3IFMX7kXvweZr3V146Ec8D3nPFarz6vCWiTR78aFpMuaEekMI877nbFtS3X74SZy4YUDwzTX3mY76/EuDkWF2pFZSxqVjoL54OBkg0FX1TqN9HyTfH6xjtTVyoRDEOj4xj4WAFJd83zquJwowVKrV6nNkFEMhQLysMpsIldfdVXYqTR0TBUlRKFTCrxf09QWKoj9RMpp4nS+wO9ZZzNRU6r1JEU0n/9zw1ypdiMQA5AGU1Q3Yt31MWN3337Gn2Ixv9Nb+/woSK3EGeGq+L9pXEkWWZIoXfn7EipyFNC3VDdwYAmPeXRv3oNpXAT4ynlPOJ7pPTg7x9HudTCmShtYEeKVTy4j2IfqVjymwcDTM7AyUJ5cKUhAxfsOhSS+b0Yt+JcRxMAzFPjtXxzs9vUa4VCOcSiGcSkUuxYTw3AreF+Yr3V7aeig6fncc9zoLAw0hVagvGhdPzlIWYmIRcTcVC/ZyzdA5+8xUvkjVWhDuzehwf88lmaVyhvzLeX4aOl1nKI66p6MG+GforsHh/Ga6haiqJoX7BQAWlwJtUoTJz6a8wzGgqBJ3+4pXY9HrdQ5qmErLyoaZJOFAp4dR4kqZFj1ImIbF4TiU3aSSdJ7IM6zYVk8E7Z1KRhhZG2UzEQeAptJSeOoQGuaBs0j7p7rBkU6JzyDtqZLxupCJJGNZDlf668pzFWL1oAFefv0R8Z0ooqccWAFk6U1A9gad8rrN4hMBPMjRTnmLKiaW3CXDvrxhl30d/ObnHvkognkse/UUbFF0T1Z+l0FR4huR0M2CyqZy7bA6e3ndCpIzhzgMlTZgIm4rnCfqLL/KNQONWDX7kNhVzHzls3maBJ+uncO1HofygarxzBO1o77PNSC01pkSA0XF6n/n1+0z0V44NhsCDH2kTO14PDXSzel4psAWB2u8nuUaMQ6fGsXCgJ7NRmmjMeE3FmIdHCBWqiiZ3oJ4nvXK8VGvpLfvC751z0Ka2+yuBiBfQvXto4Vk40KNUpNRB55Usu1++CFE7vu+pBRcY6PQ4zpZnpSzFBD11SKAtzPS77jLMXZE9pqkkedBMQkXGiPDbWzhQwX/8t1fhdLWOT/7Hs7jrZ3szqe/5wsVTztSYQVvpu05/MU2lHHj4l0d3CeHhecjRVJD2OdFUaIHhxdVyNZX0GRHtKgS0trCQphJGyRiuhhFqaelfkxZw3rI5om+rFvZjR+phxvtDwoXTX/QsmnEp7q8kmnjgQdFwuGAyeahx0Hd5rrs8tqbEyihk6a8CmoplQdWZhqrFpuJ5Hub1l3HsdE28a8VQX9D7Sxrqk+/GDZqK/kxs8Tpmm4ocR1GceMetXzkPB06OT6pNZQZrKhHKJTMfKYRKqGoqkv5Sd26DPWUhVBS6wLCTGugp4fho4n6s2x8oC/DCwYpx90EQkfAWocLbJU0lb0GQwY+xsggDlC5Enqz3WUzstI2BngDrVs7DhWcMAUgq0F33kjPw+689V5zjex4G0/iBk2NMU1HoL8kv80qGNKn6KyX899edh75yYBAq5oqOepyKTt3pcSrE23NtxIOqGdm8v8qBDGBVNBXYIbIzpIG09H713Wop9eYKmRs2JbfU7RWel2gqhPWM9qR7BCRdKG1knrg/3Zifhz6uqbB5Qu/N89SxmLcg5mX35TYVPm6IRiaQoG5JU/HUZxkyDVbHi9PkoFJTYXbJDP2VPV8p0pWjqei3kZQFyLbX6Lt6FOHISBULByuppuLor7ZRrUeKpsIfOA1SQX/x8rq+GikMqEZorqnQQsIz8PZXAiFU9F0/XW+RlgZeR0lMJrNGZDLU59FfNImjOJtFgAc/UtvnLZuTEazynn3c8b5X4FXnJvTUUF8Zf/229UoKDm5TGRmX5XZNmkotUqsRmvquV36EJ++3iKFeFyq8fIHumOCntJDeZnJtyb1zD76BSkm6Aue4bhJVQ7cj6C+DpkIR/fQbJbdUF+ykv2uWJAveGfP6lHT7/N5tmkqz3l9E+QWex56vXNQ9T3WBNbVopb80zUFQr4Hanuog09j7y1afXRfQttxfALAmFSoUbDmWo6nYcn/pqe+L2FT0uk151+ApicZqSemGhQM9qSOHo7/aRjWMUCkFTNuQtyriVOomTUW62JJMmNtXxrE05YHJr38Oq/A30FOSqSYsnlILB+UC/PLVCzO/E+0lPYqSdv78LRdh9eJB5V54YkYbaPxFscGm4qs7v3Lg4c73X6H8DmQN9Dq1pHslDfQ0sKmUpVuo7u6q90+fENwwrNRTEcGPat8D7X2GLE5Fn7Cep6V+MdBftSiJUyEPvr5KIPqRx13zwFLATn8lrrVJO8L2RPSXtrsOvKTg2+rFAzhnyRxj8GhybPJXf2/8/yL0FwlS/ux8T7WpqBSd+Z3Seabv6X86l489vT3S/vLkoe2dlLTrVS3eXwCE4CYNZbxmj6i30V8ExftLt6lo23w9qSvBdru3vP48PLDtEL73zEEAyboX+P6kBj/OXKFSj1AJJLXBF+Ik8MzHuBaBTS7FSdoJOTgWDlSEnSTZ2am0FG+b8+u2PFmkqfz8o68zxn0IQ67I3ZV8P6+/gstWLVAWOlOxLx30W2wRKko2Zc0ORf/qApIXe+J/geT5Cpfi8bqgpfgE4jYl7pqa6bufpCAh0G6djKt5mopO3Zk0Ff2aHtQ4FU6v8XLCZeb91V8JpMt5nlDpVYUKacy6oZ7z+70slYxOf/F7/Py7LkN/T4Cvbt2l/H6MqFhtzOr2i6StAppKhYJW5bNTjepFbCp0PfV7Rah4kvbhQaJ6e0W8v2y7dL2fdYtNBQBevCTJOE252/i4yNJfBqFS8jPBrGO1SNmQmq7dTPAjAPz2Fatx9HRVCJVyKSlr4IIfO4BqPYlTWTynB5WSjz9+wwXK75WSj5Njdfz4F4elpsKMxrxw0PyBCo6m0ancpkK0zmAvEyo9LHW8ZetHVFEvS7DIoWsqJMRM3mB60kgT8uivjEux1h8v1QqyuzG1bd3VNfCTRfcU01QU2o65heppVPTrfP+Zgzj3f96N46M1YVOhe+b3Q8ZTmrx6u3Te2UuTXefSoV6jDamW4/0VpV5YJd8X9Vj6KyWZlDBn8vJsBYAU1Lp2wYMAeUwV5aYj8AV81aIBLJnTm6FTyBOMx4bwz3Q9/TsbRHYGzWHFE+OhsfeXb3nfXGPi+cRU13y1rSL0F9HRtn7QPdAGxbQXXLUwESpHUsaCx6mYkKGsfbVU9FgtxOlqPWtT0emvJnJ/iWt7/N0m13W5vzoASmDYWw7wzMden/m9UvLxTz9+Af/04xdwHjN00gspM/V+wUAFh0eqiGM1Apmi7AcUm0pjTWV+vzkFOIGER0UTIpwX97yEOqICR/maSvLXRH/5vupSrKcOSc7P7ujlgqJ+puOBNGVNA5di6kNyf3aqZLwe4eHtRxDFcuL5ntRE5/SWcPR0snjIiHq1XWr+g9ecg1edsxjrV87LxuV4dk0limIhNEqBh5703P5KgEvOnI/vPXMwUzaZQ9dUhKHepKlQ5VJRnTKhv/hGxfezVIue5Zlqjugaip42CCjq/ZUWNoulxu77kv7SE0qa2tTdm/V+6I4D5SA7tgjS+8veZ8q3dtbCfvE/vw61SxsUkyBctXAAv/mKVbh27TJcv/lHSs4+U1p53wN4qtjkHlKnizjGeX9yDwDg4uF5mfM4SrY4lZz75f0vp5nFHf3VAVTDSNEgdPCF9BSrAuex3RG9zAUDFYzXI4zWQiVOZe2KxAPqPVe8SJw/pzdru9HRyMum5OuaCn2v7i4pGj3wzR4i+j1FBpdivViUKbgx0Tz0hS+HTkn/HeyhmB2T9xcv84xMG7It+d3zR04rNUX47nLxYI+0e6Xzx0bRlQMfL01tWVn6y+79FcWSulAN9QF+96o1uOaCpbggHRMm6OORu2dzlHzprk00YbUepWla1IVQX2/0DAsy66+6QeHXbCZOhTTxSPOCFN5fQEab0sGj7zkyQiX9Oa++EGl/edTdC0dGUAl8LGMJPfl1gGSs5Bnqfd/Dh964Vhjo+TwyaQFJG5KGDXwZg8OFkC0tEqHsm9PV5K0h/L7IQWAy66nMXKGSen/ZwCcVf2d0SpnlHlqQahaHT1UVv/4lQ73YcesblHa5MbZI8kUTREJJLaKeaz40aEup/zv16cWLB6CDBu5otZ5xKQ5S9dj3kkWzXDIJFZOmwvthNs4OFtRU8uJ++HcvHB5RAgB9T9oeFg5W8PS+kwDSTNL8nVoWMcDsQm2y0wAQ5XeT87ihvgTf93IFCpBdHAX9ZfD+MmoqWpoWD9lnxjWVm37pxXjbZStFm4AcS/ya9G4LyBT0laWmwj3JJP2lCTqjpmLWjLKu+lmtymaob6SpDC/oyzWo85xvOcuGcYyaKE99YU9sUMnnU6x41iG9dIJJUzFcM+9dKdRmmnLfaSodQBKnUkyo8IVDUia+iLKen9pAjp5OhEruzilHU7nz/a9UXBFtEHEqaR9NnHfJ9zCORChQeuyff/R1xl0WpUI/eHIcZ8xT045LIZqkqjfZgXTjK33H+2TanQ70BIlLcU7wI8A0ihxPIQD4xaERkaYluY4UAIsGe/Dw2FHUw2yQoEh8aJiIuuDXXYpV768Yzx9J6nQsHZI0F7ejNQObS3GJOZiQkCBNRc9lp79vXkr6ltefJ77PaCp8g5Lz/HWQTUWP15J2EjMVymHTjHTNUmoqWS2YILwfc/r+/OHTOHvpYEaj0L3N6F3naT0mitakqShJUjWBzm08z+w/qZyXSShpjVOx95H/RPS4q1HfAVTrUYar5uCTildxk1qBp9BfQMJP17Xdog6uqejCZ+2Kubj0rAUN+y7rqeg7NnUSJMdKTaW3HGQWKCChmub2lXHg5DhOjCUDek5qBwrEtVTKjcP3spNJdwvlazOnv04yTaVHqViZpV8Ciz2H8NyBU6kWIhcf4sHJo+7YaM1IE+ltEZrRVKI4xqPPHwUAXHLmfBn8mJMcNA/CdmbQVPRy2NUwEgXK5H1laZBe5ubMoW9MlPo+TMtoBGqXC25uqC9iU9GFh95HmdEiq6HrRb88w/jTsfvYKM5c0J/rbeb7aqlwG/T7A8z56pSFXdMKT4zKOfgXv3qxcp7+vMqBLaLe2kXVUB/4Iph2sjBjNRW90qAO/tuYoqkkf8uBjxqS77lQ4Ts0E7gxtkhBKxN0+stED/HvGtlUgKSWx4ET43h85zGUAw/nLZ+Dh3cczeT0MvVZL51s6pOiqfikqdjpL71Oi94GgT/rPcfHUAl8xYOJ4gYo9ufY6aoieHgbjZLwAQabShoQSxHuj75wDMuGerFiXp/g57lzRiN87M0Xio2HNaLe55qKTH6aLOTyOFPSRfKqG9CEik5/6UXjgGL0F9dU+BjgglsNfjRon575errwF04zPPjR0kcubMmJhWPlfDV9DfWbX7ueE6fCUfLV2CkTtSSEd+BnnCJIU/nfb12Hl2lxaqbgR9O4LRJCAEDQ43leiZ3GjBUq1XqUS1NxTYXvRnkwXxSnmkq/KlTyNBVuqG/VpqLXUzF77EhqrOSbk85xLBnqwf6TYzg8Mo4Lz5grFkK9bbOmkq34qCf7U20qyV/dptLIccFoqNe+q6Y1b6gPVUZ/AcCRkRqi2CyATRNRXxC8DP0Vo7dMNVeAR54/ikvOmgcgiWAPfA9nLlApxTy842Vnif/zcn+RpsKLueljj4IfOYj+6u9Rp7YU/snnimEsUexP3qZWChV1Y0G98D2z0wYHTYvMAppJ0598z8cktff5d2/AoZNV9r1sq6fkK2lUAGD5vN5MXxSXYs/D6bRoX6NpS1QxYXh+X+YYrhFySrnEhMpgT3b5NT2TIrSk0ga7XlIczEccU9Xa1ja6zWBGC5WimgqHjOL1EEXJ/3N6Swh8T9hU8oQK92qyLaKNoGexNQUHclfeoMDAWzKnFz987hCOna7hHS87S9TwLiZUTN5f6s6XL0jcpTgJfgwTCs3Q9stWLzC6JYvr5Kj+nAcnoULvSHW+UAUgh05deF7WRbTs+xhDhNPVELuPjeLtlyfG7zMX9uOnH3qt4lLeDIT3V6BTVdL7q68sDfVhHKucu5fl4Httmoqnvi9VU5HH6WlxdND4jmKpsfuex4p0eRm7jw59Q6J/r28C1M1Z8t1V5y3VzpX/V4KsUOkpBRmdKeP9lRP8aDpv+dxe3PZfX4JLzpyfOYZT1uWSWVMxCRWT9tasTYUfXwrkhrAWRejxW6Nqm8GMFSr3fODK3JolNqHCF1nasfq+h/n9ZRwZqSnlhBvBFqfSCGctHMD5y4dE/Iwep8L/L/k+ypZMphxL5vRg/4kkEO6SM+eLgk66i6mJ/gr8rPeXTlHQcVEoPa/6ygHG6xHGa2YB/8SHr0Ul8HH3E3uVNvVr6+CaCq1/ixj9FWuGehNFRyAtat3wXCye04MjI9XMTp2eCUXNcyHSqkABknHVW/ZF8k3eX937q1qPEMe6Q0T2+ZADhD72i7gUy//tQoWoOhv95UEm8gTyU7RnqR5VqHiir/maj95WTzkAxurK76Ug69SgjBGPe3/lzyWeF89mI6U25vVXFOFRCnwhVExjJ9tHM22ba1NRNKNsGeOJxowVKisbUBI2d2N6H6XAQ1DnKnWA8XrYUFPhKFJ614QFAxXc/f/K/FsXnjEXl69agCXM48jnmkoh+ksmGnz5ixfim4/vEecDPOAy+1x4Sg6COT4lWZBoYtDCdnKsbmx3sEel4Gz2HFN/kt/kd1JTqWW8pEraIsVB+Z5++4rVeOO6FXjLp3+YOYY2B5Rl2lRts1V84d2Xi7xSvL/CuYFl1DbSX9rzkZqKmf6id6O4FDewWXAbBd27aqiX88bTNJVc7y/LmJJeZWn/cnJ/mfptGmtl389Nte950jW40bTNG68Eusc/ev15OG/ZkPL9kdGEtptjiKPTr80965TjcjrJjy8H5tpDE4kZK1QawaapiChe34fvSxW6FCQ7mToLfmyEPJtOM7jwjLn4yk0vV77j5X0TwZLfxpLUrXhObwkLBirC00p3CjBqKgabip5PC8juMIm6OT5aQyVnIc4z1JvmraC/2PGDvSVUSj6OppqG4iWVQ3/R4i2KTxmOoUXqdC3Z/epR6+3g5S/OJhQNfGkr4ppKtvKjIfixnG9TEfQXo9xM75CjzOg4Gif1UE2sSlaVIjYVGx2p91GmaWGLf7a5zD3oJXqpjYz3l3bf9ZyIeqUtCvzNo6DSLiwd6sWZC+UGl2/Oimkq5oj6vD6qQkU6CkyWpjJjXYobwSpU2EDmgz6hw9L8S0U1lRZtKkXANQWTaq+DtIKXvihR13Wh0KxNxWRc17PdcqGiezg1akv/jYNulf9WSinKo6n3F7+NvNxWlGOLFm+TtxK9x9Np0JqeX6vToBLHgDS8U5oWfguel13Y6Dn3G/KJAdJ+YvL+Ahq7XdOxYRzL4M9yoLwTNfdX9v7ocjY6Stcs1ZiabHt6WyZN0hTvoVOkRVyKARb3lLMO6AGcpmvq7wgwZRlQ6URCPv2l9pWuOVlR9U5T0UCDoa8cKIGKlBalHkWFaS29nkonwYstFbHxvPzFC/EbLzsL73/N2ZnzARanYkrTYrCpGFPea4KqN12oj56u5gqVvHoqZkN9VrPxfQ9z+8o4PlrDUG85V4PiIEpAuAUbXi3du6S/JnYvxgU411SylR+zm4meUuJy3a/ZafQAx4oWOyHbzPZHTxEPJJ5E61fOw+bfuBSXrVrA8rHptJL9/WUXUDWQUWikvD2LrsKHjmlumyoo2oRpEZdi/tfYH6F1aeeyGkxFbCW+b3bCyQ9+ZJpKWt0UsBcr6zRmr1BpYFP5w9edp7galwMfdQOvbWu7GkaTp6n4Xo5pNUFvOcBH33yh8XyA0WClbJ9/7+pzFBUeMLuF6rQG7WQPnapi+Vy1eJTtXmy/cXgGIRR4Hub0JhU6B3vMQqUI/WV6Y6S9jQr6a4I1FdZRisuphRHCSN0d+152t+x5Hq46dwk2aAZkPVbHZqjXd+9hWjtGfpf8JU+0165dllyXXV/Rpgz3x20xyvfpwSVmCKfjfS91dbZMKb6QmoS+kf4yBOwCjV2KS5rwM8GUvgiQmqctC0NG8NlsKnmaCrcnBp4Q1pMVVd+WUDly5Aje9ra3YceOHVi1ahW+8pWvYP78+ZnjVq1ahTlz5iAIApRKJWzdurWdy3YEtt0mTTpeohWAyJ/TKKIeSIs2jU6OUCn5CWca5riB5p2vuxSbtJ63XDqcPd/LLgw2m8qRkXGsWZLNSUagOVCU/qKvPG0xnNNbwpGRKpbM0dK0aMkvOWqa665pApc1Q30nbSom6At7OUhq/+hFugLPbEv73Lsvs7ZpStNi27FTxUBTlmCdnxf0l+cZ3ctNfTHZD5S+sr/Ss7CxpmIUKgZDvT5+9Hu0geZ13jpgsxO+eMkAnt5/0uo1qE8/3zOnvs+1qbDjE5sKaSqTQ3+1NTtuvfVWvOY1r8G2bdvwmte8Brfeeqv12O9+97t47LHHpoVAAexGdNuAKvt+YiwNGwsViu6eyHfIJ9wHX3sOPnjNOS2fD3CbSjFBaFoYdB65r5K0GcVZbyRzX7LvxBRV7VsE2mBPUvY5W3bXvIgBBk3FcPvCpjIB3l8m6HRTJfBRq6dFulj/3nPlavze1cXee/Z9s+epaT8E00ZjWapxXnH2IqV97lLMP+ftsk32A0C+c7HZ8NQsyCYU0lS07yImGBs5Kyht5WjWoj0L5XrO0jlKG1mo3+s2KvNR2rU1Q72wqXQD/XXHHXfg/vvvBwC8613vwqte9Sr8+Z//eSf6NeFoFKeioxQkUc5hnJ+mBQD+77svx9d/sktJOthpcKPmZavMvvLFzk8D8EqqcGkEo6Feo5k4TZQXzyF3dfZ+9pYD1MK60r6+a5/TW8bJsXqmQiIJBZPAIK8msqmYjiE70+gUaSqVko9qGCbCkv2mp/jIg16jni92qk1FXZCSv/K75XP78MNbrsLSIZXOpCZo2fJTVcVsqFfHCUHXVHT6i3+XbVP+bzTUG2wqXLtv5LHGQXRgrqZiCeg9NxUqe4+PGc/LPBMv365ovrY6fkqTTH+1NTv279+P5cuXAwCWL1+OAwcOGI/zPA+vfe1rcemll2Lz5s25bW7evBkbNmzAhg0bcPDgwXa6lwt7RL35eLKp8HLCNpy5sB8fuPqcQuVZW4XYebZIsem8dp5LsfF8o6E+/avRX0C+UJECyqCp+Nm2ZJyKFBa+72Got4STY2p1SN6GaXJ+8u0vwSVnzhOCwkh/pQJ3pJoItcnUVHzPQznwUKsnVSebTdnB2wHMz8BUr57O8b1sEO+Kedk08jRxKBrflt8LyKO/kr96VmlurLbdPm/LVA1V9+YE1EVW9VgrqKkUsaloxxCtflILzjT1AwAClutOOS5nCbIFlU4W/dVQU7n66quxb9++zPd/9md/VvgiDz74IFasWIEDBw7gmmuuwXnnnYcrr7zSeOyNN96IG2+8EQCwYcOGwtdoFjZDvW2glIPE+yvU3FWnCkU8UIqcL0sV272/TMijv+grHtWtpw0xtWV6rvRbf4ULFfV69HewpyQi+E0Lpck+89q1y4Sx2YZSRlOZWKHChavUVCKlhkmzyEv/b6O/Al86gjQCHUGbf5vg4N/pc003gPPjdHotc31FU5FxJCGo/o2k0IZ6S3jl2YsUu6kpwakNRWwqvkUInrXQbltMzlM/E/UnHBW09vOurfd32tBf9913n/W3pUuXYu/evVi+fDn27t2LJUuWGI9bsWIFAGDJkiW47rrrsGXLFqtQmSw0ilPRUfJ9EVGv52qaCuR5TBWBXsKXuNeii5Yxol5bSAprKhZPGaUtZpPJLDrpeRShfHy0Zsz9VWSTb9qllnWbygTTX2pcSPJuqmFkNXwXgS2KHbDTX7SYF6FE5WmU6Vf/XsL2PmSuL7WvPhuXNi3CpKn4PkRN3ySiPvn/ghVD+Ltfv1TtE2u2sUtxEfrLPKYD38NH37QWZy+dYzrNamfSc7LlBz+a+9sV9NemTZvwxS9+EQDwxS9+EW9605syx4yMjODkyZPi/29/+9u48MILM8dNNhpF1OsoBZ7YqZrcbicbuk2kWWRcTAOvqQSYJkpJrwWv2lSKaCom+iv528cWckmLqOfPScsOHB+tWWw9xXfcHPSMT6f0V+8E01+6J1IlSJxE9KDOZpBH2fiGZ0Xfk+dVI9CzjZrQVGw7at02F3herpDS27Ib6u19MtG4NuSVUhDt5WyUfuPlq6z2ML1vts1A3ivRryk0lUkKfmxLqNxyyy249957cfbZZ+Pee+/FLbfcAgDYs2cPNm7cCCCxu7zyla/EunXrcPnll+MNb3gDXve617Xf8zaxaLBipgIsL6sc+BhJhUpemeLJgh693ur5NEFevGQQZy8x755MMA12/TsKxANaN9TTIsWpNF1DEfQX01RMQqWIPcL0PMluVUuTZbaafboolDiVko9KyRdZilu2qeQshHqSSvF9assocr/CUJ8RKqa+qH9lG7pQoc9Z7zLb9QFp8+Je9kmxK7Vvpmvr/5ugp+g39scQx1UEWecF83Nshv6i91ebLvRXHhYuXIjvfOc7me9XrFiBu+66CwCwevVqPP744+1cZkLwqnOW4Du//yq88ZM/wKlxaTSzen/5UlPJS6k/WUjiF7yWnQF0T5t3vnwV3vnyVYXPH+otwfeAeazSpR6n4nke+soBTlfDXJdiqXFkn6ueroS3LxadgDQVKVSWz5M1LnQNKg/8eVIQa08poQbDKE5SqE+gAwagjsGh3rLQVOK4sRHZhjw3WFOMRjlN/RMExTI2kBZAFI3sp12I2QzKdD3ukJEnFNXryfnJl9CSrxYS09GcS3Hx4MdmN322JJv6ZiKvXb0NSX9NE0P9TIXve3jRogFDOVmLUAlkUr1poakUpCVsKGJszMOSoV58+/d+CasXScOjaVclhEqeppJjqKfgxB4D/aVPuDk9iYAbr0eK1qMLuzzwI0qBh2oILBtKCjyFmHh3YkDdAQ/2lFAOfJEyqNX3laet8TbpZ9rZm5KJmkDnCU0lZ1G1UWO64OBxK4L+slyfX0ds+phUCXwZUd9IsDYaJnLu2I9p1eZpszPpzypvLOvvmGxiprLHE4GpXx2nGPouzEYvVNjE6lT24XbA/c9bQZ5xvCjWLBk001/sGRJtlef9Jfti11SUpIKa95BuqNf70MwE55OVDPPL5/aK9iba8wtQn8NAT5J9mYpOtStUjPSXgSqslKTjRjOG+phcinNsKp5F4OgejXyMSs9C8/0rhvqAgm5Vw7au4SrnG8axDUXsmXmOEXmw5SfLpOPJbUP9XGb07WRg6lfHKYbOF9vGAPfVnw70V6ldTUWbwJ2ATVMBinp/ZX8jN0geGyKNtjlChXWiOfpL/QsAy+b2iT5OdDJJIGtTKQc+xtJsyq16f+k1Sjh0e4LvSboo8Iq5FFM9G9Ioab3N8/6yLqA6vemziHpLV7gLb9lAfyXHqH+Va3PNtsEzJrf7XEN9jlDNAzV50RlzAQAXrpirfC+Ps7er92uyNZVZS38RMrXXbTaVaaapmDIHN3s+0PoiZYKJZhKaSgH6y7TzC9PdlSLIaYHXAhYHmVDRMxjr/bKBjgg8D/V0p7tsqFf0cXI0leRatOHpKfkYq5JQaa3NPM1UdykmajXwPZRLXqFN1P/6lYvw0tULRWldm92E/2YrmiVT38vvhZCyXJ+OLfmeqAkfa/nw8uivZjQVQX/lHCYo2qaFSnL8xouW45u/+0rxvd7npoIfhfeX01QmBbqAyMv9RZiM3WojlPziMSW284HO1nwxLSS9QlNpXKTL9OypGl+PklU3+TucGuPp1fSUAjVGIUWRXE0E6gJ/tsvm9gotqGcShAq9E3p25cDDWL09+kumqjEs8gr9BaGh+J6HD29aixuvXN2w/aHeMn7jZWcZsx3okI4ZWj80xwuuseSNEX4935MavO7slEdJKZpwQZfivMwa8lr5bemwUYN5yTB16M+V6MDadImon+nQI8htSsh001TedtlKsStsBc242TbbZtP0l5aeg0PYVAyp2qlk9OFTVfHbUG8Jh05VjS6iRdZjHoBHrS4cqGD53F7sPT42KRsKPTVNYlNpj/7Ksyvpu/SSn3h9+b6Hq85b2pLXUJ4NxGZ81sck77MtQl22KduwbZS4FqqDB+o21lTIc9J+TLv0l35a1vvL3q7+zKm/tboTKpOCDP1l01SmmU3l0rMW4FKtZkYzaDci3wQTpUaT1VTlTu+L6dmbhUry98xUqJBBHUi8pXShotfnyANVyNTps+Vz+wAcmxT6i3bCRB1y769WX1deyWad/qJcWwFbqJtFniBvVBVRj7hPUpWk/bMW6ZLn2jQIUx0eAvfqa+xS3Hju6EXRisImcLM2FXsbWe+vlP5y5YQnB3qyPHuaFq6pdG4hniq0G5FvbNNAHfVVAvSW/cxzVs+jSZr9zSRUaHE4SyscBsioepXSaRxXQJg/UMncAwBRZGxyNJXkGn1lKVT0SPXm27RTP6pWJ93Vm3HFzrSZs1GwUWO6TYXTVY1S7XB7ic3WmBenoiYsNV+D0Ezur+a9vyzf6zaVXPpLFyrJeKo6Q/3koNKlmkq7ePW5S/D+q9ZgeH5f44MLwkR/zesvY0F/Jfe8vCzF5AZpcileMS/bd7J98D40k31gftrXaj3C7/zSasxJNRcKpuQlpicKtCiSVqTYk9oUKkr9ct/LFJ0LhMdXNlV8c9dL/tpaMMVZ6ZqKpLRU7y4TuJu5TagIe5nhZ1PGBhtE4HDewu61tgGwuT1n41Ty2lA/09rVFZUfZwIycSoWecG1k+kQ/NguFs/pwQdfe25H2zTtBG9+9Rq8/fIzc8/Lo2ZCMtSXsy7FJtsW0VemOJUiC/KCgUTTGa2F+KPXny++X5FqKkdPV43ndRK6TYXfZ6s2MBPFSDVP9HT3FJ/SDjXaOK7EQH9p9BQfT3laBv8+z6bC7S46OK3Z2KW48XhKimvlNmPpo3kDlPH+KqCp8Pv1PBf8OGloJqKeMB0M9dMRpvTqCwd7RLU7G0TGZMNisCEtQPYiljI8b86b6C/polqA/rJoVaSpHB2pNWyjXQihUpGGekInDfUyMaI8zvMkhWSzeRSB75sXR/G7Z6oZbxYqikuxtT3ZRiObikkYNEN/kXdanku/rbZ8I0hDfb5mkteyyX287PuTFvw46zUVXUDYdil6QJpDFn6D3an9PPV8jvf+0oux8aLlQgPRj3v4j69WIqcl/ZXVVIrs8hcMWIRKqqkcGZl4TYUMqiZNpeV6KoZnsGJeH549cEo5jjytFs/pwVyW1+2v37YOFw/PK3y9RppKkCO0MkLF4/VUbNQW01Qa0F9G769KNrecDUWDHzvp4JDx/ipgz1GreHou+HGyoA9Am9267DSVhmjVjXKgUsJbNwzjFWuy6cApRxtfzPlkWTxHLdkshIqfFSqFbCoWoUIR41dfsLRxI22CAh1NmkqrQy8wLIT/8FsvxYPPHhLaHSDpr0++/SXKe7zuJcNNXa+RGzfP5yX7qGoAYrPhew3b45pKI0O9abHva8Krr8gmxWtRUxFebg1sKnljWTwLdk4prVw7GZj1QqWsaR1W7y9uU3GaihGtpuP3fQ9/8avr8ttmjea1bzTUNyHsbE4Fge9hyx+/Rtm9TxRGU2cAHqdCaJ3+Sv+y85fN7cVbLlWFhecli3peXFERSLrKrjXYDPWZxJJe43oqQlPxGsepmNpopvBauQj95bfm/m1LR5PJ/VXApqLQX4GPqqO/Jgdl7WUV8v5ymooRE5H6haAKFXv7g2mmYqWccBOG+qEcobFkTm/D8zsBIVQqBu+vloVKNtOACb7XXqYGeT3z4sh/b5SmRWgWAaeSbFqIbEO3qUivL7uG0YymQvbVvOd08RnzsO/4eOE2CTYqWL9Ubu4vg2AqB57TVCYLevyEjQctO02lIfQa9RPRNlBQUzEZpAv0q5PBoK1i7YohAMDLVidOCj0K/dWiUGngPcXb70SWhUbeWq8+d0kmI4TeR07j2NKX6Ncz0V/6uDTSXzlZtHUUob/eetlKvPWylYXbJEjbkfma8ricNiyayrSpUT/ToQcy2saJLMwzPRae6Qg9Z1MnoXoo2ds30V8TqUFNBK44ezG2/s+rZebfUvEYChuKZlDoKwdNLbCNrme72l+9bX32HI1WUry/LLYGAtdGbLRaUe+vRqA1I5iAIGibptLIxsJhctFPagM5TWVSULSeCvG0Tkuxo+huuJ22G7VPQsXsOtsdQgWQjgGAuUBZszhn6SAuf9ECnLss3737v7/uPJFmvx20MhZkQsnkfk2CwtaayNkWZMsf644apmfYnKG+cfBjq7DZVHT5lXdp8dy4x6oz1E8eMlmKrfSXbzzeQaIZL6tW2wbyffTJk4nv7Jrt1weuPlvYNaYDOkF/LRzswVd+5+UNjzvTkPqmFeRlKbZBd3tWNZV8IWUKlCQI+gvqX47eJrSzckGtrxXY3PIbRdhzkIs9714p8FycymSBe3LUo9ju/ZW+oemQ9n66otU4lSLwUg+gKM43NgtNhfVhbl8ZngfM68tPF0P4wNXntNXXTkNNy94d2lYjby3jOZqhnqeWaSZLcWYBznhOZc/vLbVgU5kQoaL+Fd8XuAdCpJV0BpLNsItTmSRwWqteDa27WaepNAY9mola9wLfQxTGuUJLZBlmnRie34/7PvhLWL1owHbatIaiqXSJXaiVmCWdpuT2hcIuxX7WpkLtxmktSFMTzSSJpTVgYjUV/XvzcSYQlXfpKpnFPImod0JlUkA2lUrJx+lqmOP95YRKI+jxBR1v3/MA2LVJIAmk9LzspHzx4sEJ6dNkoLfcvkvxZKMRXWU8R9NUuKAoogX7XhrTYjHUU+IFUxvNaNetBvkWge7+nL1mqq3nXHrBQAV3vv+VypgvBR6qk1RPZdavkGR4p9iTRsGPzlBvRzOuu62giG3E9z1cPDyvq4WIjkpQvCrhdEEjw7oJOu2l2EkKtOengY+6NieFSqqptDk+SwWCH1tFI5tK0XIEa1fMVZJkOvprEiFsJeV8oUL5fpymYkejfE9tt19wh3jH+14xIdefKnDvr66hv1qIWdIXTtVOoh5jO59TZaIvgv5q3EYRlAsEP7YKkYlA/549m1qYr62bUJ5EQ/2sXyFXLx7EGfP6MJR6Ddl2gk5TaYyJjgdpFKU9U9EJ76/Jhu97KQ1ZvL8vXjyIN65bgUvPmi/aAFQ7SSNX2sD3xBzVgx0F/dXMjRjQTILSZtGo8mOrqZCS4EdHf00KrrlgKR685SqhKjaMU5kBVR8nCq0O+Gbbt2Wqnang3l8TpQV2GoHX/FvqqwT45NtfgqVDSTocvjsX8Rs551M8y7z+Cv7qrevwxd+8XJwPSEN9uyDWwpZjrB3YMgdk8qI1OQ5KweSlvp/1QoXQiLopM4O+gxkTrak0qtExU1HpSk2l/XEwkMaO9FeCpgz1APArlwwL4UTfkabSLm01kYZ6m5cb3XepRW19MlPfuxUyBdFeVu+vkrOpNMJE01PdGBnfCejlfrsBpiDEZnH1BUvxtZtejuVz+0REeV6TeoqWMA3YoO86lfrqzIX9uHzVApGjrZOwCU9ZFTPf9muDcymeAjTyLKIFzWUotmMi07TwdmeZTFHQJTIlmU9t9rUc+KLyZxEXZbKpECiyXKe/2h2fgz0lfOWmxtkJWoHQVPTvxaY3/b1ZTaXkuRr1k41Gg1bEqTj6y4qJpAV4+91iV5gIdA39ZfDCaqu9Ii7FWuBjXdNUZJyK+fwt/+M1U56ex7OsQyK3WYuaSsn3XULJyUajmhtB6s3S4zQVKyYy95fafncsrBOBbhEqge911KGiSNqXRvRXIywZmpx6OXkQm1vf/H2rFHNST8VpKpMKU7poHWXfdzaVHPgTrEnY8iLNJnSLQH3z+jMwPL+vY+01a6gHmFARhvpsosXpBkl/aTYVS2BoUbjgxylAEc+iUuChXJrGI3KKMdEuxa2q/jMJ03lB5LhoeC4uGp7bsfaKZD1eNNiDJUOyZIDNUD+dXdLp/rLeX8nfIkGgJpTSIl1xnJ87rxNoa9v91a9+FWvXroXv+9i6dav1uHvuuQfnnnsu1qxZg1tvvbWdS04YxIKYM2tfvnoh1g3Pm6QedR9kQsmJdSmezegW+qvTsBmwOb5608tx81VrxOfVi5MEom9LKzA2sqlMB9hsKkHGptJcuxRfNxmxKm1pKhdeeCG+/vWv43d+53esx4RhiPe973249957MTw8jMsuuwybNm3CBRdc0M6lO44iauXn3n3ZZHWnK0EBpBMVyzPRQqsbMFvvvYj3F9XSISwd6sWOW98gPossxdP4GdqzFKub3mbvgcqm16MIlQmOJGlLqJx//vkNj9myZQvWrFmD1atXAwCuv/563HHHHdNOqMzWwLpO4tq1y/DZd27Ainmd49I5pDY5Ic13BWavptKagZqjU2laJhI2ekvP4NxKmhZgcjSVCZ+eu3fvxsqVK8Xn4eFh7N69e6Iv2zRma2BdJ9FbDnD1BUsnrH3n/TV7772VVPo2TOdHKIRn5vvkb+uGeqK/Jt5Y31BTufrqq7Fv377M93/2Z3+GN73pTQ0vQB4XHHmq2+bNm7F582YAwMGDBxu23yl0ctA6TAwm2mW5GzBbtbROOF1SMOR0Hj79aWqaPq28caBpKs27FKf013Swqdx3331tXWB4eBg7d+4Un3ft2oUVK1ZYj7/xxhtx4403AgA2bNjQ1rWbAQ3abkmDMRsxkeWKuwWzdXx2lP6axs9w/cp5+Kf3vBTrV85TvtdtKs0HP06epjLh+57LLrsM27Ztw/bt21GtVnH77bdj06ZNE33ZpjFb06p3Exz9NXvv3eYV1QxkmpaOdGlC4Hke/suLF9mLdLUoXMl5ZtoLlW984xsYHh7GQw89hDe84Q249tprAQB79uzBxo0bAQClUgm33XYbrr32Wpx//vl461vfirVr17bf8w5Dj1h1mH5opZrgTMNstfmJnFdttCESSnahYG7XpkKuyNPepfi6667Dddddl/l+xYoVuOuuu8TnjRs3CiEzXeF2wdMf7h3N3k1PJ6jPbvD+soHSRMkgyGZdimcQ/dUtcBlwpz9aVf1nAsh7Z7aOz3Lgo/3Ex52pUT8V8DwqldzaOKgEk0d/uTQtKeROoAtH3CyBP4vdvlcvGsTT+09O6xQjE4lf2zCMs5cMtvXu4y5I02LD6sUDyf1TZoEm16mXv3ghtvyP12D+QGUCeqfCCZUUge/NWs+absFE5xabzviH334pHnn+SMbVdLZg+dw+LL+ovaBaUfmxC8fPWzesxFs3rMQN//fhlvrfWw5ExouJhqO/Uiwd6sWSOT2ND3SYMgSCAurCVaFNLJ7Tg9dduHyqu9HVEHEqXTx8fG/6j3+nqaR418vPEonnHKYnZrNNxaF9COevLqS/CJ7nTfvx74RKilLgY9DVSpnWcN5fDu3gltefBwDYtN4efD3d4WH6232dUHHoGtjyIjk4FMGiwR78719bN9XdaAudLtM8EXBbc4eugUt97zDb4fvTf/w7oeLQNQhc5UeHWQ4P3rQf/06oOHQNRKoON2odZil4VP10hZueDl0DGacyzWeVg8MEgUfVT1c4oeLQNXDVOR1mO5I4lanuRT6cUHHoGpScS7HDLIfnNBUHh85hNub8cnDgcDYVB4cOwtlUHGY7fM+b9sGPTqg4dA1cjXqH2Q4P03/8u4h6h65BbzlAyZ/+nLKDw0Thv6xZiP5pnqnaCRWHrsHbLz8T61fOc7YVh1mL614yjOteMjzV3ciFo78cugYLBip4xZpFU90NBweHHDih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMTih4uDg4ODQMXhxHMdT3QkbFi1ahFWrVrV8/sGDB7F48eLOdWiS0c397+a+A67/U41u7n839x3I7/+OHTtw6NCh3POntVBpFxs2bMDWrVunuhsto5v73819B1z/pxrd3P9u7jvQfv8d/eXg4ODg0DE4oeLg4ODg0DHMaKFy4403TnUX2kI397+b+w64/k81urn/3dx3oP3+z2ibioODg4PD5GJGayoODg4ODpOLGSlU7rnnHpx77rlYs2YNbr311qnuTiGsWrUKF110EdavX48NGzYAAI4cOYJrrrkGZ599Nq655hocPXp0inspccMNN2DJkiW48MILxXd5/f34xz+ONWvW4Nxzz8W///u/T0WXFZj6/6d/+qc444wzsH79eqxfvx533XWX+G069X/nzp149atfjfPPPx9r167F3/zN3wDonudv6383PP+xsTFcfvnlWLduHdauXYsPfehDALrn2dv639FnH88w1Ov1ePXq1fFzzz0Xj4+PxxdffHH85JNPTnW3GuKss86KDx48qHz3B3/wB/HHP/7xOI7j+OMf/3j8h3/4h1PRNSO+973vxY888ki8du1a8Z2tv08++WR88cUXx2NjY/EvfvGLePXq1XG9Xp+SfhNM/f/Qhz4U/+Vf/mXm2OnW/z179sSPPPJIHMdxfOLEifjss8+On3zyya55/rb+d8Pzj6IoPnnyZBzHcVytVuPLL788fuihh7rm2dv638lnP+M0lS1btmDNmjVYvXo1KpUKrr/+etxxxx1T3a2WcMcdd+Bd73oXAOBd73oX/vVf/3VqO8Rw5ZVXYsGCBcp3tv7ecccduP7669HT04MXvehFWLNmDbZs2TLZXVZg6r8N063/y5cvxyWXXAIAmDNnDs4//3zs3r27a56/rf82TKf+e56HwcFBAECtVkOtVoPneV3z7G39t6GV/s84obJ7926sXLlSfB4eHs4dsNMFnufhta99LS699FJs3rwZALB//34sX74cQDIRDxw4MJVdbAhbf7vpndx22224+OKLccMNNwgKYzr3f8eOHfjJT36Cl770pV35/Hn/ge54/mEYYv369ViyZAmuueaarnv2pv4DnXv2M06oxAZntjxJPF3w4IMP4tFHH8Xdd9+NT33qU/j+978/1V3qGLrlnbz3ve/Fc889h8ceewzLly/H7//+7wOYvv0/deoU3vKWt+ATn/gEhoaGrMd1S/+75fkHQYDHHnsMu3btwpYtW/DEE09Yj51ufQfM/e/ks59xQmV4eBg7d+4Un3ft2oUVK1ZMYY+Kgfq4ZMkSXHfdddiyZQuWLl2KvXv3AgD27t2LJUuWTGUXG8LW3255J0uXLkUQBPB9H+95z3uEmj8d+1+r1fCWt7wFv/7rv45f+ZVfAdBdz9/W/255/gAwb948vOpVr8I999zTVc+eoPe/U89+xgmVyy67DNu2bcP27dtRrVZx++23Y9OmTVPdrVyMjIzg5MmT4v9vf/vbuPDCC7Fp0yZ88YtfBAB88YtfxJve9Kap7GZD2Pq7adMm3H777RgfH8f27duxbds2XH755VPZVSNoUQCAb3zjG8IzbLr1P45j/NZv/RbOP/98fPCDHxTfd8vzt/W/G57/wYMHcezYMQDA6Ogo7rvvPpx33nld8+xt/e/os++kZ8F0wZ133hmfffbZ8erVq+OPfexjU92dhnjuuefiiy++OL744ovjCy64QPT50KFD8VVXXRWvWbMmvuqqq+LDhw9PcU8lrr/++njZsmVxqVSKzzjjjPizn/1sbn8/9rGPxatXr47POeec+K677prCnicw9f8d73hHfOGFF8YXXXRR/MY3vjHes2ePOH469f+BBx6IAcQXXXRRvG7dunjdunXxnXfe2TXP39b/bnj+jz/+eLx+/fr4oosuiteuXRt/+MMfjuM4f65Ol77Hsb3/nXz2LqLewcHBwaFjmHH0l4ODg4PD1MEJFQcHBweHjsEJFQcHBweHjsEJFQcHBweHjsEJFQcHBweHjsEJFQcHBweHjsEJFQcHBweHjsEJFQcHBweHjuH/A9ICA3Z/aNbyAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2e3c38ae2ca4218a0d528e7d4337a8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b21973fb4894111ac5477fba683b295",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c182e7d51ce640d6b80e0f0f13de6cd7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87c07bfbc96045c185ee45ced05acb3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c2e23af5e8d48bfb762b8c47496a67b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d14965443064220a7bac2e12c4dec5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff4811e1bbff4aa596eec62d9ff01dde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe263409",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "c62bc332",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "222ccbe2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "a2f19479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a80ee2a02a6c491abf6406a7fd9faf95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f706ab2e28d4ddda4d48a683d4abff4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fb5e49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v3_Aug23_bf_model_misspecification_1000_data.ipynb b/bf/v3_Aug23_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..95b6c2d
--- /dev/null
+++ b/bf/v3_Aug23_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2585 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    avg = 0.004647741\n",
+    "    maxAbs = 0.073834606\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "        temp = dpl[0].data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "        temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "#         temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "#         temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "237c7cd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(x)\n",
+    "x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "# maxAbs = np.max(np.abs(x))\n",
+    "# x = x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(x)):\n",
+    "#     x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "5f4c9183",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65d71e5e96a14aaeba674358c4624cf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.513466</td>\n",
+       "      <td>0.822846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  9.513466  0.822846"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "a52c330d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42d1cf715efa4de1a1cd5619f898f502",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kt5GtX3yF3b8Rw16Gp6j8PL4bx/ZuvMs+GniINdf/SeU4N8k6Nvf9Uyw5Mav0F69i1M9U1dWrV9nYsWOZs7Mzk8lkzNDQkLm7u7PPP/+cvXjxonDH//qYFu1nunnzZnb48GHWrl07ZmBgwExNTdngwYPZo0ePVF7jwYMHbObMmczT05NZWloyqVTKnJycmL+/PwsODi42rgsXLrChQ4cyW1tbJpFImGUdM9bCoyGbOWUku3btmsq+Tk5Oan0vS9KgQQMGgFlYWLCcnJwS98vJyWHr1q1j7du3ZyYmJkwqlbJ69eoxX19f9uOPP6r1Wc3JyWGLFy9mLi4uXPm+/PJLlp2dXa5+powpP3/r1q1jTZo0YVKplNna2rKpU6ey5OTkEsu6efNm1qJFC2ZgYMDq1KnD3n33XXbnzh22cOFCBoAFBgaq7B8TE8Pee+89Zm1tzYRCIQPAFi5cyG0PDg5m3bp1Y2ZmZszIyIh17NiR7d+/nwUGBqrtq20q4zMuYKxiD6q8vLxwvZhvnqT2YYxh5bxL2LwqhFsnlgjx0Vde6DuiEUSi0p8mJMRm4p/Nodi/LRR5uQpuvbWdIZb87otOfk5VEntZFLTmJOVU9Prgpd4AqMqV0gCJkAKV8RmnZ6ZEI4wxLJt9USWR1q1nhC2n38WAUU3KlEgBoI61ASZ94YnNp95Fp56FfdtiozMwud9hrF98FXK54i1nIIQQ/lAyJRrZvCoE29fe5pYbN6+DTScGwsbOqELns7E3wsL1Plj4kw9MzPW49b98ew0fDTyC5ISstxxNCCH8oGRKKizwSBhWfXmJW3ZrZYXVf/aGRCp6y1Fl08nPERuPD0CrDoUd2INPRWJou79w/2asxucnhJDKRMmUVEjEk2TMGXsKBU/c7RyNsWK7H8SSyvsnZV5HH0s3vYMRUzy4ddERaRjjsw8B+8ve2pEQQqoaJVNSbnl5cnwxPgCZ6coxR/UNxVj9Vy9I9TS/I32TSCTExM9bY/EvPjAwUo4Uk5Mtx8wRx7Ft7a1Kfz1CCKkISqak3H5feh13r/03LJwAWLHND+Z11Gf3qEwd3nHET/v7wt5ZOVsIY8CyWRex9LPz1DCJEMI7SqakXO7fjMVvSwu7HAyd6IbGzdUHNK8KDs4mWLOnN9xaW3Hrdqy/g0+HHUdWZl61xEAIIcWhZErKTKFgWDI9CHK58kGpvZMxJs5SH3+1KplayLB8aw907lXY7/Ts4TC83+MAEmIzqzUWQggpQMmUlNm+zQ+46l2BAPhmo2+Z+5FWJj2ZGPPXdMHQiW7curvXYjCy898If5xU7fEQQgglU1ImyQlZ+PGrwm4wfUc0goOz6VuOqFpCoQAfzvXCtIVtIfjvX3FUWCpGdd2HkMuveIuLEFI7UTIlZfLb99eRkqic3NfQSIIpX2rH8GwDRzfBop+7cS2JkxOyMbHnAZw+QF1nCCHVh5IpKVV0ZBp2/3KXW56+yFurJvXu0L0eVu7sCVML5YhJOdlyfDr8OHb+dLuUIwkhpHJQMiWl+nnJv9zg8zb2hvAd4MJzROqatLDE2r/6wM6psOvMdzMv4Ie5wTQ/KiGkylEyJW/1LDQRB7c/5JZnftMeAoGAx4hKZudkjDV7eqNpy8KuOptXhWDOmFPIzZHzGBnRJePHj4dAICh2cnK+ODs7cxNwE+2kPXV1RCttWHaDu7NzaWwGz052PEf0dmZ1ZFi+zQ/ff34BwQEvAADH9z5BfEwG1uztA1NzWeW/6O3/AZmRlX/eymLgCLT4WuPTODs7IyIiothtNjY2eP36tcq63Lw8fL1xI3aeOIHE9HS0bt0aK1euROvW6t2pAgIC4OfnhyNHjqBv374ax6prfHx8EBQUhArOiEm0ACVTUqIXz1NwbM9jbvnTb9rzGE3ZyfTFWLCuK37+5hoO7XgEALh2PhpjfP7BLwf7wd7ZpHJfMDMSMHSu3HNWpozwSjuVqakpPv30U7X1RkbqswTNXb8eP+7aBX9fXzg0a4bt27ejW7duePjwIWxtbbn90tPTMWnSJIwePVorEunSpUsxd+5c2Nvb8x0K58yZM3yHQEpByZSUaPOqEG6ABqeGpnBraVXKEdpDJBJi2v/awsbOEBuW3wSgrLIe3uEvrPmrj9bfYWsrMzMzLFq0qNT9GGP47Z9/8H7//tj0v/8BXl4YNGgQfHx8sH37dsyZM4fb94svvkB2djbWrFlThZGXna2trUqy1waurq58h0BKQc9MSbHiXmXgny0PuOUp89rwGE3FCAQCDJvkgS9/7MzNZpMUn40JPQ+olI1Uvri4OGRmZ6Otuzu3rm3btgCgUlV8/vx5/PLLL/jpp59gYWFRodcqeJ6YmpqKzz77DM7OzpBIJFi07Hdun4cPH2L8+PGoV68e9PT0YGNjg5EjR+LRo0dq53vbM9OrV69iyJAhqFu3LqRSKerVq4fJkycjOjq62NgSExPx1VdfwcPDAwYGBjA1NUWLFi0wd+5cZGRkIDw8HAKBAEFBQQCU/2YLfnx8fNTK+KacnBx8//33aN68OQwMDGBiYoLOnTvjr7/+Utu34LXGjx+P8PBwjBgxApaWlpDJZPDy8sKRI0dKeadVHThwAKNHj0ajRo1gaGgIIyMjeHp6Yu3atVAo1MfLLnhfnz9/jnXr1qF58+bQ19dXKWdiYiLmzZuHpk2bQl9fH6ampujevTtOnTqldr6UlBSsWLECvr6+cHBwgFQqhZWVFQYMGIArV66UqyyVge5MSbF2rL+t0oLXs5N2fVMvj279XGBtZ4hFH51DcmI28vMUWPDhWTx9kIjPl3bgZRQnXZWTk4MdO3YgMjIShoaGaN68Obp06QKRSHXGICsrK+jr6eFGaCi37vp15ZjOTk7KoSCzsrLwwQcfwN/fH/7+/hrFlZubC19fXyQmJsLPzw8mJiZwcVJWPZ84cwmDx89FXl4e+vfvjwYNGiAqKgr//PMPjh49isDAwGKf475p8+bNmDRpEvT09DBgwADUq1cPT548wcaNG3H48GFcuXIFjo6O3P5hYWHo1q0bIiIi4OnpiY8++ggKhQKPHz/Gjz/+iClTpsDMzAwLFy7Eli1bEBERgYULF3LHl9bgKDc3Fz179kRQUBCaNGmCjz/+GJmZmfj7778xfPhw3Lp1C999953acREREWjbti3q16+PMWPGIDExEXv27MHAgQNx+vRpdOvWrUzv+dy5cyEUCuHt7Q17e3ukpKTg7NmzmDFjBq5du4bt27cXe9yMGTNw4cIF9O3bF3369OH+7URERMDHxwfh4eHo3LkzevXqhYyMDBw5cgS9evXCb7/9hkmTJnHnCQ0NxVdffYUuXbqgb9++MDc3R2RkJA4dOoTjx4/j8OHD6NWrV5nKUhkErIJPvL28vLgPB6lZsjLz0L3+Fm6Qhq9Wd4FPX2d+g6oEMS/T8b8pgXj+sHDIwc69nLBiux+MTfVKPC40NBRNmzYt+cSXx2v/M9P2WzQ+TUkNkFxcXLB582Z07dpVZf2n772HtXv2YEj37rD38MD27duRm5uL0NBQ2Nvb4/PPP8fWrVvx4MEDWFtbaxxX9+7dcfDgQRgaGio3JFxHUnIq6nu+C5FYivPnz8PNrXAIyvv378Pb2xuNGjXCzZs3ufXjx4/H1q1bERYWxiW0x48fw8PDA46OjggKClJ5nnr27Fn06NEDAwYMwP79+7n1HTt2xKVLl/Ddd99h3rx5KjHHx8fDyMgIMpmyQVxpDZAK4ih6t7x06VJ8+eWX6N27Nw4dOgSxWHlvFBsbi7Zt2yIiIgLBwcHo0KEDd6yLi7Jb26JFi1QS98mTJ9GrVy/07t0bx44dK/U9B4Bnz56pVT8rFAq8//772LZtG65cuQJvb29uW8H7amdnh4sXL3KxFPDx8cH58+exa9cujBgxglufnJwMHx8fPHr0COHh4bCxsQGgvDPNy8uDpaXqRBtRUVFo27YtTE1NEVrky9zblPoZLwP6Sk7UHN71iEukRiZSdO7lWMoRusHG3gir/+yFjj3qcesunIjAyM5/I+JpMn+B6Yj3338fZ86cwevXr5GRkYG7d+9i8uTJCA8PR+/evXH7tuogGcumT8cXY8fi3/v38ccff8Dd3R1nzpyBvb09rl69itWrV2PNmjWwtLTE4sWLYWtrC7FYDE9PTwQHB5c7vpUrVxYm0v9s23MUySlpWLx4sUoiBQB3d3dMmjQJISEhePDg7dX+v/zyC/Ly8rBmzRq1hkm+vr4YMGAADh8+jLS0NADAjRs3cOnSJbRs2RJffPGF2vkKqlc1sWnTJggEAqxatYpLpABgbW2NBQsWAAA2btyodpyTkxPmz5+vsq5nz55wdHTEv//+W+bXL+45rlAoxIwZMwAoE3Rx5syZo5ZIb9++jaCgIPj7+6skUkD5nH7x4sXIzs7Gvn37uPWmpqZqiRQAHBwcMGTIEDx8+BCRkdXXyp6qeYkKxhh2rr/DLQ8a17RGVYPqG0rwv/U+2LL6Fjeq0/OHSRjRcS9W7uyJDu/UjC8OVaHonQwAeHh44Ndff4WRkRFWrlyJRYsWqdyZ6UmlWDptGpZOmwZ4FQ4/mZubiwkTJqBPnz4YNWoUVq9ezd0pdezYEd9++y169eqFp0+fcnchpZHJZGjevLna+svXlH/j27dvF9tw6vFjZWv10NBQtWSrcp7LlwEAQUFBuHbtmtr22NhYyOVyPH78GJ6entwzu549e0IorPzPT1paGp4+fQp7e3s0adJEbbuvry8AICQkRG1by5Yt1arlAaBevXpcOcsiISEBK1aswLFjx/D8+XNkZGSobH/58mWxxxU8Oy+q4HVTUlKK/TvFxcUBgNqdZnBwMNasWYPLly8jNjYWubm5ajEUrXqvSpRMiYorZ6Pw9EEiAEAkEmDweM2qPrSRUCjAhM9awbmBKX6Ydwl5uQqkJuVgcr/DmLWsI8Z+0kJrB6bQRlOmTMHKlStx/vz5Mu2/ePFivHz5kmtUsmLFCnTv3p27iDZu3BjOzs746aef8PXXZesfa21tXezfLCEpBQCwYcOGtx6fnp7+1u0JCQlcrGU5T3JyMgBUWfealBRluUpqdVywviCOoszMzIo9RiwWF9twqDjJyclo06YNwsLC0LZtW4wdOxYWFhYQi8VITk7GmjVrkJOTU+yxdevWVVtX8P4GBAQgICCgxNct+nfav38/hgwZAplMhh49esDV1RWGhoYQCoU4d+4cgoKCSoyhKlAyJSr+/K1wDN6Ofo4wMpHyGE3V8h1QH/bOJlj4USASYrOgUDAsn30Rj+7EY+FPPlo1/rA2K3je+eadSXFCQkKwfPly/Prrr7C3t0dqaiqio6MxatQobh9HR0dYWlri/v37ZY6hpC8/pibKRki3b98u9s61rExNlTMkpaSkwMSk9H7KBQmrpLszTRXE8+ZAGQVevXqlsl9l27hxI8LCwrBw4UK1O8nLly+/tZtTcX+rgjjXrFmDTz75pEwxLFiwAFKpFNevX1d73jl58mSuhXR1qTn1d0Rjca8ycO5IOLc89pMW/AVTTRo3t8RP+/uqDEF4cPtDjOu+H7HRb79bIUoFVXT169d/6375+fmYMGECunXrhokTJ6pse/MOIjs7u1Jia+fpAQC4cOGCZudp165c5ynY/+TJk2W62yuodpXLyzbspbGxMVxdXfHy5Us8efJEbXtgYCAAlKmVckU8ffoUAIpthV2RJFbe97cgBjc3N7VEqlAocPHixXLHoClKpoSzf2so8vOVH3x7ZxM4NTDjN6BqUsfaAD/s6Ime/oUNKu5ei8Gw9n/h9tXiv/nXNvfv30diYqLa+oiICEybNg0AMHr06LeeY+nSpXj69KlKlauJiQns7e1x4sQJ5OfnA1BejNPS0uBepI9qRb0/sj/MTI2xePHiYhvXKBQKnDt3rtTzTJs2DRKJBDNnzuSesxaVm5urkgg8PT3RoUMH3Lp1C8uWLVPbPyEhQeULQ506dQCgXA1mJkyYAMYYZs+erZKE4+PjsWTJEm6fqlDQuvjN9y4kJARLly4t9/m8vLzQuXNn/PPPP9i0aVOx+9y9exexsbEqMTx58kSljy9jDIsXLy61QVlVoHosAgBQKBj+3lRYrTZisuYXMl0i1RPh86Ud0MDNAr98dx0KOUPcq0yM6/4P/gjsxHd4vNu7dy++//57dOvWDS4uLjA2NsazZ89w9OhRZGdno0+fPpg1a1aJx9+/fx/ffPMNVq1axfUzLTBnzhzMmDEDnTt3hre3N3bu3AkjIyN8/PHHGsddx8IMf2/+HoPGzUW7du3QvXt3uLu7QygUIjIyEpcvX1ZLbMVp0qQJNm3ahAkTJsDd3R29evVCo0aNkJeXh8jISFy4cAFWVlZ4+LBwUogdO3bAx8cHX375Jfbt2wcfHx8wxvDkyROcOnUKDx8+5JJS9+7dsXfvXgwePBh9+vSBvr4+nJycMGbMmBJjmjVrFo4fP46DBw+iRYsW6NOnDzIzM7F3717ExsZizpw56NSpav7tjh07FitWrMCnn36KwMBANGzYEE+ePMGRI0cwePBg7Nmzp9zn3LVrF3x9fTFx4kSsXbsW3t7eMDMzQ1RUFO7cuYN79+7h8uXL3GOFmTNnYsqUKWjVqhX8/f0hkUgQHByMBw8eoH///jh8+HBlF/utKJkSAMCl05F4Ga5s1i+RCuHb/+1VdjWRQCDAu2ObwrGBGb75JAhpKbnIy1UgOSEbr16koa6DUa1tmNStWzc8evQIISEhuHz5MjIyMmBmZoZOnTphzJgxGDNmTInvjVwux4QJE+Dt7Y2pU6eqbZ8+fTpSU1Px66+/4ubNm2jRogV+/PHHMrfkLU33Lm1x584d/PDDDzh58iQuXLgAqVQKOzs7+Pr6lnnAiNGjR6NFixZYuXIlAgMDcerUKRgaGsLOzg5DhgzB8OHDVfZ3cXHBzZs3sXz5chw4cADr16+HTCaDs7MzPv/8c5W+tR988AEiIiLw559/Yvny5cjPz0fXrl3fmkylUikCAgKwatUq7Nq1C+vWrYNYLEaLFi2wevVqvPfeexV7w8rAzs4OFy5cwNy5c3Hx4kWcPHkSTZo0wc8//4x33nmnQsnUwcEBN27cwLp167Bv3z7s3LkTcrkcdevWhZubG6ZPn45mzZpx+0+ePBl6enpYvXo1tm7dCn19fXTu3BmbN2/Gvn37qj2Z0qANBADw2XsncHKf8jnIOwPr44sfavfd2KvINPzvo0CEP07G2kNtYWPlDCNjCRzqm0IsfuPpSC2ZNabcil4finSNqTYJRV6/Ttlff8SIEdizZw+io6O1boxeUjUqY9AGujMlSEnKxtnDz7nl4ZM9eIxGO9g6GmPtX72xbHbh4AHpaXkIe5QEpwZmkOoV6afHR6IiVebx48fQ09ODlZXuTOxA+EcNkAiO//WEG4fX2s4Qzg3N+A1ISygHeOgKQxMJty4nW47njxKRlZHHY2SkKqxZswb+/v4ICQnBwIEDVUYVIqQ0lEwJDm4vbDQxYFRjHiPRPkKhAEbGUtjWM0LBI8H8PIawx0lIS6m+DuGk6q1evRpXrlzBhAkT8Ntvv/EdDtEx9NWrlnsWmog7/8YAAARCoPfQBjxHpJ2MTfUgFgsRHZkGuZxBoQAin6bAzskY5pb6fIdHKkFYWBjfIRAdRnemtdyhHYV3pW4trWBirtng2zWZvqEE9eqbQPLf3KgMwMuINCTGZfIbGCGEd5RMazHGGI7vLRw9ZcjEkgf6JkpSPTHquZpCT1bYACk6Mh0JMZRQCanNKJnWYnevxaj0LfX2ceA5It0gFgtRz8UEMv3CpySvotIR97r0sWkJITUTJdNa7NhfhXelXp3tIJGqT8tEiicUCeHgYgx9g8KEGvMyA3GvKKESUhtRMq2l5HIFThSp4h04Wn1ORPJ2QqEQ9s4mMDAsklCjM5AYl8VjVIQQPlAyraVuBr9C3Cvlcz6Zvhgt26nPMUhKJxQKYO9kAgOjIlW+kWlISaycWU8IIbqBkmktdWxP4cwXHd6pB9GbQ+SRMhMIBbBzLHyGygBEhaciPTWX38AIIdWGrqC1UF6eHKf+ecYt93uvEY/R1AxCoQD2zsbcMIOMAZHPUmikJEJqCUqmtdCVs1FITlBWQxoYSeDuaV3KEaQsRCIhHJyNIf6vH6pCwRDxNBm5OWWb8JkQorsomdZCx4u04u3SywlCYe2cVqwqiCUiODgbQyRSvqf5+QwvnqdAIVfwHBkhpCpRMq1lcrLzceZg4QwxVMVb+aR6Ytg5GnNj+WZl5uNlRBoqONuh1unRowcEAgGCgoJK3GfKlCkQtGmDDfv3V2NkhPCHkmktc/FkJNcwxthMikbN6vAcUc2kbyiBta0ht5ySlIP4GjJK0s2bNyESieDp6VniPlevXgUAeLnRqFqkdqBkWssUHT6wW18XCARUxVtVTC1kMLPQ45ZjX2bo/Ewzz549Q2JiItzd3WFkZFTsPpmZmbh37x70pFJ4uLpWc4SE8IOSaS2SmyPH+ePh3HLvYQ35C6aWsLI15EZJYgCiwlJ1ukHS9evXAQDe3t4l7nPjxg3k5+ejRcOGkNCcoKSWoGRai1w5+wIZacquGsamUrg2Nec5oppPIBDAztGYm2lGLmeICkvR2eenZUmmV65cAQB4NW1aLTERog3oa2MtcrpIw6MOPRypireaiMRC2Doa4cXzVDAGZGbkIzY6Azb2xVeTarOCZLpnzx4EBgYWuw/3vJSSKalFKJnWEnK5AoGHCyc/7jWEJgGvTjJ9CepYG3CNkOJfZ8LIRApDYynPkZUdYwwhISEAgICAgFL3p8ZHpDahZFpL3Lr8mhuAXWYghlsrK54jqhlaGf/Cdwhldj93mkbHP378GCkpKWjfvj0uXbpU7D5JSUmwsLCAgYEB3FxcVLb9/PPPWLFiBV69egV3d3esXr0anTt31igmQrQFPTOtJU4fLBw+sE1nOxqogZRbQRVvq1atStzn5s2bAICWLVtCJCqc0m/Pnj2YMWMGvvzyS4SEhKBDhw7o3bs3IiMjqzZoQqoJJdNagDGG0wcKn5dSK15SEQXJtHXr1iXuU5BMvby8VNavWrUK48ePx6RJk9C0aVOsW7cOtra2+OUX3bmzJ+RtqJq3Fnh4Kx7REWkAAIlUSNOtVaKQtI8qdFzsqwxufGSRSIAGbhZaPzl7WZJpwTPVosk0Ny8PN27cwKxZs1T29fPzK7G6mBBdQ3emtUDRVrzN29ho/UW7NrC0MYBEWthd5lWkdg83qFAocOvWLUilUri7u5e4X3F3pvHJyZDL5bCxsVHZ18bGBq9fv66agAmpZpRMa4EzRZ6X9hpKrXi1gVAoQN0iXWNSU3KRmqS9oyOFhoYiPT0d7u7ukEqLb4Gcnp6OJ0+ewMjICI0bN1bb/mZXLMYYdc8iNQYl0xou4kkyntxPBAAIRQJ4+zjwHBEpoG8oURluMPpFGvLztHN2mRs3bgB4exXv7du3oVAo0Lp1awiFhZcWSzMziEQitbvQ2NhYtbtVQnQVJdMarmgVbyOPOtA3lPAYDXmTZV3DwtGR8hlevUjjOaLijR07FowxbNy4scR9OnbsCMaY2mwyUokEnp6ean1TAwIC0KFDhyqJl5DqRg2QarizhwuTKVXxah+hUAAbe0NEhSuTaEpSDkyTc2BiplfKkbrls88+w5gxY9C2bVt07NgRv/76K6KjozFlyhS+QyOkUlAyrcES47Jw+0ph1VqnHo48RkNKYmAkham5HlL+e2b66kUajIwlEIpqTsXR8OHDkZCQgG+++QavXr2Ch4cHjh07BicnJ75DI6RSUDKtwc4fD0dBA1EHFxOYWsj4DYiUyLKuAdJTcyGXM+TlKhD3OlMnx+59m6lTp2Lq1Kl8h0FIlag5X32JmnNHw7nfffo68xYHKZ1IJIRlXQNuOT4mEznZ+TxGRAgpD0qmNVRujhzBAYVDtfn0ceYvGFImpuZ6hXOfMmh931NCSCFKpjXUtfMvkZmunLvUxEwPjg1MeY6IlE4AaztDFPS8TE/L0+q+p4SQQpRMa6hzRwunW/PuZk+d43WEnkwMszqFz7ZfRaVDLtfOvqeEkEKUTGsgxhiCjoVzy++868pfMKTc6tjoQyxWfvnJz1Mg/nUmzxERQkpDybQGeno/ES//67cokQjRzMua54hIeQiFQljWNeSW42MykZtDjZEI0WaUTGugolW8bq2taGD7SlDdDYFMzKSQ6Rc2RnodlVGtr09IbVFZn21KpjVQ0S4xPQZTFa+mBAIR8vOr+85QAGvbwq4yqck5yEjLreYYCKn58vLyVCayryhKpjVMQmwmbl8tHPWofTca2F5TErEMaWmp1f66MgMJTMwKZ2h59YK6yhBS2VJTU2FsbKzxeSiZ1jDnT0Rwox7Vq28CE3Ma9UhTMqkxEhITkZiYgLy8vGpNaJY2BhD+1xA7O0vOTShOCKk4xhhyc3MRHx+PpKQkWFhYaHxOGk6whgkqUsXbrZ8Lf4HUIGKRBMb6VkhNSUNCQiIYk1fr66dn5yIjVdlnOC4RsLYzglCoA12d4uMLfw8Nrf7Xzyjy+rE8vD7RaiKRCMbGxnB0dISenuYTS1AyrUHeHPWoS28aRLyyiEUSGOlr/u21Igwk+Zgw4gDi/usiM2mOJz79pj0vsZSLm1vh73xUT+8q8vojqXqcVC2q5q1BVEY9MteDoyuNelQTyPTFmDircFLurWtu4XVUOo8REULeRMm0BinaJaZdNwca9agG6dbfBQ3dlXfGuTly/PT1VZ4jIoQURcm0hmCMqXSJ6T6wPn/BkEonFAow6QtPbnn/tlA8vhv/liMIIdWJkmkN8eReAqIjaNSjmqxVe1u06WoPAGAKYNVXl3iOiBBSgJJpDVF0LF631tY06lENNWl2axTU3l84EYkrgVH8BkQIAUDJtMYoWsXrN5iqeGsql8bm8CsyqtUPXwRDoaCWqoTwjZJpDfDmqEftaNSjGm3cjJaQ6ilrHkJvxeH4X094jogQQsm0Big66pGDC416VNNZ2Rpi8PtNueUfv7qE3JzqHUiCEKKKkmkNcO5IYZeYbv2c+QuEVJsRH3rAxFw5asurF+nY/etdniMipHajZKrjcnPkuHT6BbfctY8zf8GQamNoLMXoj5tzy7988y9SkmjcXkL4QslUx6mMemRGox7VJv3eawTbekYAgLSUXGxYdoPniAipvSiZ6riiox55d7OnUY9qEYlUpDLM4I71txEdmcZjRITUXpRMddibox698y51ialtuvR2QuPmdQAAebkKrFt0heeICKmdKJnqsMd33xz1yIbniEh1EwgEmDSncJjBwzsf4eFtGmaQkOpGyVSHFb0rdWttRaMe1VItvOtyfYsZA1Z9ScMMElLdKJnqsMAiXWKKjopDap+Js1tzE4YHB0Ti0unIUo4ghFQmSqY6Ku5VBu5ei1EuCID23evxGxDhlXNDM/QcUviFauW8SzTMICHViJKpjgo6Hs797ljfFMamevwFQ7TC2E9aQk+mrOp/eDsex/58zHNEhNQelEx1VOBhGvWIqLK0MYD/+27c8uoFl5GTnc9jRITUHpRMdVBWZh4unykc9ci3P3WJIUrDJrnDtOgwg7/QMIOEVAdKpjroytko5GQrBzY3s5DBzsmY54iItjA0lmL09Bbc8q/fXaNhBgmpBpRMdVDRVrztutN0a0RV3+ENYeeo/IJFwwwSUj0omeoYhUJ11KOe/tQlhqiSSEWYMKsVt7xj/W1ER6TyGBEhNR8lUx1z70YsEmIyAQBSPRGatrTiOSKijbr0ckKT5pYACoYZvMpzRITUbJRMdUzRVrzN2thAJKI/IVEnEAgw6YsiwwzueoTQW3E8RkRIzUZXYh1TdCLw3sMa8BgJ0XbN29qgnS8NM0hIdaBkqkNehqfi8b0EAIBACLTtYs9zRETbfTCrcJjBS6df0DCDhFQRSqY6pOjcpS6NzKFvKOExGqILnBqaodfQwhqMH+bSMIOEVAVKpjqkaCveHoOoFS8pmzHTW0CmLwYAPLoTjyO7HvEcESE1DyVTHZGWkoN/g15yy117O/EYDdElymEGm3LLP86/jMyMPB4jIqTmoWSqI4JPRSI/TwEAsKprACtbQ54jIrpk6AfuMLeUAQBiozOw6YebPEdESM1CyVRHFK3i7dyL7kpJ+RgaSzHhs8KBHP744SYN5EBIJaJkqgPy8uQqU6698y4NbE/Kz8+/ARp61AEA5ObIsXIedZUhpLJQMtUBNy5EIzUpBwBgYCRBAzcLniMiukgoFGDq/Dbc8om/n+L6hZdvOYIQUlaUTHVAwIHn3O9tu9pDIBDwGA3RZR6e1irz33438wLkcgV/ARFSQ1Ay1XIKBcOZg8+45X7vNeIxGlITfDDHE3oyEQBlV5l/toTyHBEhuo+SqZa7ffU14l4pB7bXk4ng4WXNc0RE11nbGmL4hx7c8o9fXkJyQhaPERGi+yiZarnTBwrvSlu1r0sD25NKMfQDd9jYK7tXpSTlYNWXl3mOiBDdRldmLcYYw+kiz0v7UhUvqSQyfTE+XtCWW963+QFuXnrFY0SE6DZKplrs4a14RIUp+wKKJUJ4drTjOSJSk7TvXg8d3qnHLX89LRB5eXIeIyJEd1Ey1WIBRap4PTytIZGKeIyG1EQfL2gDPX3lv6sn9xKxY91tniMiRDdRMtViKlW8IxryGAmpqaztjDB2egtuef3X/+LVizQeIyJEN1Ey1VLPHybhWWgiAEAoEsC7mwPPEZGaavB4Nzg3MgMAZGfm47tPz4MxmqaNkPKgZKqlirbibdLcEvoGNHcpqRpiiRAzFntzy2cPh+HYnic8RkSI7qFkqqWKJtM+w6mKl1QtDy8b9B1R2Fr8mxlBiI/J5DEiQnQLJVMtFBWWivs34wAAAgHQsYcjzxGR2mDSnNawtlP2PU1NysGSaeeoupeQMqJkqoWO//WY+921qQWMTKQ8RkNqC0NjKT7/rj23fPrgc6ruJaSMKJlqoaIXsIGjG/MYCaltWne0o+peQiqAkqmWeXo/AY/vJQBQTpnVpbczvwGRWufN6t7FUwOpupeQUlAy1TJHi9yVNm1pCQMjasVLqteb1b1nD4dhz+/3eIyIEO1HyVSLMMZwfG+RKt4xTXiMhtRmrTvaYUCRRwzff34BD2/H8xgRIdqNkqkWuXcjFi+epQAAxGIhteIlvJo81wv1m5gDAPJyFfh81AlkpOfyHBUh2omSqRY5tqewFW+ztjaQ6tFYvIQ/Uj0R5q/pApmBGAAQ/jgZ3844z3NUhGgnSqZaQi5X4MTep9yy//tNeYyGEKV69U3xyaLC0ZEObn+IQzse8hgRIdqJkqmWuBn8CrHRGQCUdwQ03RrRFj0GuaLHoPrc8uKPzyE0JI7HiAjRPpRMtUTRKt42Xe0hltCfhmiP6Qu9Ua++CQAgOysfHw8+grjXGTxHRYj2oCu2FsjLk+PUP4Vj8Q4eR614iXbRN5Rg0c/dYGis7KoV8zIDnww5hpzsfJ4jI0Q7UDLVAheORyA5IRsAYGAkgYeXDc8REaLO0dUU89d0hVAoAADc+TcGCz48SwM6EAJKplrhny2h3O/d+jpzFytCtI1XZztM+dKLWz7652P8/v0NHiMiRDtQMuVZ3OsMnD8ezi0P+cCdv2AIKYN3xzZB3yLTAq5deAV7/7jPY0SE8I+SKc8O73wEuVxZTWbnZAwHZxOeIyLk7QQCAaYt9EbLdnW5dYunBuLwzkc8RkUIvyiZ8ogxhv1Fqnj9x1PfUqIbxBIhFv3sg0bN6gAAGAO++uA0AvY/K+VIQmomSqY8un31NZ4/SgIAiEQC9BjsynNEhJSdobEUS//oDpfGZgAAuZxh1uiTKo8tCKktKJnyqGjDo1YdbKFvQDPEEN1iYi7Dsi094OCifDyRn6fAjGHHcfbQc54jI6R6UTLlSWZGHo7/VThDzMipzXiMhpCKM7fUx/KtPVDXwQgAkJsjx6fDj2O/wLuUIwmpOSiZ8uTUvqfITM8DAJiY68HD05rniAipOCtbQ/ywww92TsYAlFW+88Wj8bOwN6gXKqkNKJnypGgVb78RDSEQUN9Sotts7I2w+s9ecG1qzq37SdQHc0TjkJWZx2NkhFQ9SqY8CH+chBsXo7nld8dSK15SM5hb6mPlzp5o3dGWW3dM6IWRnf9GxNNk/gIjpIpRMuXBn7/f435v3LwOzC31eYyGkMplaCzFtxu6Y7j8Arfu8d0EDGv3F/ZvC6XhB0mNRMm0mmVm5OHA1sIq3rGftOAxGkKqhlgixALFX1iUvxsSpqziTU/NxfwPzuDjQUcR94pmnCE1CyXTanZk9yOkpeQCUDY88upsz3NEhFQNAYCh7BJ2yH+EnaMxtz7oWDgGtNyFI7se0V0qqTEomVYjxhh2/3yXWx48vikNak9qPA/2Ar8e7od3xxROLZialIMvxgdgtM8+3LryisfoCKkclEyr0fUL0Xh8LwGAcsSjgaNp3lJSO+gbSPDx/9pixXY/WNsZcutvXX6NUV32YeaI49RAieg0SqbVaMuPIdzv7XwdYGQi5TEaQqpfy3Z1seHoAPhPcINIXFgrc+qfZxjQfBfmjD2FByGxPEZISMVQMq0mzx8m4dzRcG554qzW/AVDCI8MjCSYMs8Lm0++i659nLj1+fkKHP3zMYZ6/4XxPfbj7OEw5OcreIyUkLKjZFpNtq29xf3eyKMO6tU35S8YQrSAraMx5q/pirV7e6OFt43KtmtBLzHd/yh8XbZg+ZyLeHg7nqcoCSkbAatgczovLy9cv369suOpkRLjsvCO6xbkZMsBAMu39UCr9ralHEWIbrNzLvzCGB2eUur+j+8lYO/G+wg6Hg5WzA1pI486eOfd+vDtXx9NWlqWPmrYriLbR1KrYVK1KJlWgzULLuP3ZTcAAFa2BtgZ5E/DB5Iar7zJtEDsqwwc3P4QJ/9+ipSknGL3sXU0Rrd+zujwjiPadLEvvv0BJVNSjSiZVrGUpGz0aLAVGWnKjutzVnREj3dp3lJS81U0mRaQ5ytw89IrnPrnGS6eikR+XvHPT0UiAZq1tUF733po5+uA5t51IZWKKJmSaiXmO4CabudPd7hEamohQ/cB9XmOiBDdIBIL0aaLPdp0sUdGWi6uX4jGxVORuHI2CtlZ+dx+cjnDrcuvcevya/zy7TXoG4jRupMdvM26w7vpYzR1ioKIx3KQ2oHuTKtQRlou3mmwFan/VVV9uqQd+o5oxHNUhFQPTe9MS5Kfp8CdazG4fuElrp57icinbz+3sX4m2rzjjrY+DvD2sUcD9zo0WAqpdJRMq9Cv313DukVXAQBGJlL8fXUYRGJqQE1qh6pKpm9KSczGrauvceNiNP499xIJsVlv3d/CSh9tu9rDu5sDvH0c4NjAlNowEI1RMq0iyQlZ6Nl4O9JTlePwTp3vhUHj3HiOipDqU13J9E2vXqTh9pXXeHx4Da6GNkJsstlb97exN0THHo7w6euC9u/Ug4GhpHoCJTUKJdMq8sPcYGxepRzxyMRcD39dGkp3paRW4SuZcq8fbArGgPDX1jgfNQbX7ljjRogUqeklNxWR6onQztcBPn1d0LWPM+o6GFVjxESXUQOkKhDzMh27fr7DLU+Z50WJlBAeCASAi20srFvEYvBgCRQK4FmYGDdu6eHaDSlC7kiRmVV4GczNkeP88QicPx4BAPDwskaf4Y3Qe2gDWNtRYiUlo2RaBdb87wo3QINlXQN0H0gteAnRBkIh0NA1Hw1d8zHCPwP5cuDBQykuXpbh/EUpwl/oqex/73os7l2PxYo5F+HVxR59hjeE3yBXmNXR56kERFtRNW8lu3cjBsPb7+WWv9ngC28fBx4jIoQf2lDNWyDDegwUeqXPHRz9SoQLl2W4EKyHkDt6kCvUa5TEYiE693KC/wQ3dO7lBDHVOhHQnWmlYozh+88vcsuNmtWhREqIDrGzlWP44AwMH5yBlFQBzl3Qx6mzMty8LQNjyha/+fkKBB4JQ+CRMNjYG2LQODf4v98Udk4mPEdP+ER3ppXoyK5H+GJ8AADls5pNJwfCwYUGtCe1ky7emZYkPkGIM+f0cfKMDA8eydS2CwRAhx6OGPaBO3z6udDdai1Ed6aVJDkhC8tmF96Vdh9YnxIpITWEZR0FhvtnYLh/BiKjRDh83BCHj+sjOUV5CWUMCD4VieBTkbBzMsaoqc0x+H03mJjplXJmUlPQ16dK8sPcS0iMU3YW1zcQY/pCb54jIoRUBUcHOT6elIrDe2Lw3cIEeHtmQSAorOCLjkjDii+C4euyBd/MCEL44yQeoyXVhZJpJbgSGIX9W0O55c++6wADI+r4TUhNJpEAvl2ysWZ5IvbtiMG4kWkwNS4cMzgrIw+7f7mLvh47MfXdw7h+4SUq+FSN6ABKphpKTc7BVx+c5pbdWlnBp68zfwERQqqdXV05PpqYioN7YvDl50mo76w6dVzQsQiM674fo332IfBIGBQKSqo1DSVTDX07IwivX6QDACQSIf63vivPERFC+CLTAwb0ycTOjfFY/0McOrVTrQK+dfk1pg0+ikGtd+PQjofIy5PzGC2pTJRMNXBk1yMc2f2YW575bXvUsTbgMSJCiDYQCACvVrn44dtE7NkSg4F9MyAWF87H+vRBIuZNOI3eTXdg50+3kZWZx2O0pDJQMq2gJ/cSsGhqILfs2ckWPQbRpN+EEFWODnLM+ywZ+3fGYNSwNOjLCu9GX0Wm4buZF9CjwVb88u01JCdm8xgp0QQl0wpIT83Fp8OPIytT2djA2FSKBWupepcQUjIrSwWmT07FwT9jMGVCCsxMCxsrJcVnY/3iq3jHdSuWz7mI11HpPEZKKoKSaTnl5yswa/RJhD9JBgAIhQKs2OYHQ2Mpv4ERQnSCiTHD+FHpOLA7BrOmJ6OudWEVb1ZGHrauvoWejbdhwYdn8PwhdavRFZRMy4Exhu8+PY8LJyK4dR//rw1c3Sx4jIoQootkesCQdzPw9/ZYLJqXiPpOudy2/DwF/tkSigEtdmLG0GO4cy2Gx0hJWVAyLYffll7Hnt/vccu9hzXEgFFNeIyIEKLrxGKg1ztZ2PlHHFZ+G4/m7oXPTRkDTh98jvc67sX7fvsRHBBJfVW1FA0nWEabVt7EukVXueUW7Www85t2PEZECKlJBAKgY7scdGyXg1t3pdi+2wjBVwunevv33Ev8e+4lmra0wsRZrdFjsCuNAaxFKJmWgjGGjStuYvX8y9w650Zm+G7jOxAIBDxGRgipqVo2y0XLZol4FibG9j+NcOqsARQK5fUm9FYcZo0+CTsnY4z8qBkGv+8GU3P1wfdJ9aJZY95CoWBYPucitq+9za2rV98EPx/oB5k+fQ8h5G1q0qwxfIt+LcLuv41w6KgBcnJV70b1DcQYMLoJRk5tjgbUfoM3VEdQgvTUXMwYekwtkf70T19KpISQamVXV47Pp6XgwO4YTBidCpOiYwBn5mPP7/cwsOUuTOpzEGcOPqeRlXhAd6bFeHIvATNHHEfY42RunXtrKyzf5gepnoi/wAjRIXRnWnWyc4CAswb4828DPAtXn+atjrU+BoxugsHj3VC/iTkPEdY+lEyLUCgYtq+7jdXzLyM3p/CbnW9/F8xZ0REiEd3IE1JWlEyrHmNAyB0p9uwzxIVL+lAw9XYcLdvXxeDxbug1pAH1h69ClEz/8yAkFkumB+HOv4X9uQRCYPpCb/Qf2ZjHyAjRTZRMq1f0axEOHjXEkeMyJCSpTwGpJxOhcy8n9BrSEF37OsPAkKaJrEy1Ppm+DE/Fz9/8i4PbH6LoO1HHRh/f/dEd9RvTA31CKoKSKT/y5cC/1/Vw8Kg+Ll7Wh1yhXqMm0xejU09H+PRzQZdeTjRBRyWotS1pHt2Jx7a1t3Bk92Pk5xXO5iAQAH3fa4SpX7WBRErPRwkhukUsAjp456CDdw4Sk1Jx4rQBjp6QqTxbzc7Kx+kDz3H6wHMIBEDztjbo1NMJ3t0c0KyNDaR07Su3WnVnmpyQhdMHn2P/1lDcuvxabbtrU3PMX9MFDi6mxRxNCCkPujPVLuGRYpw5p49TZ/UQ8UK90VIBfQMxWnW0RZvO9mjuXRfNvKzpWWsZ1OhkqlAwPLmXgPPHw3HuWDjuXI0pdoZ7e2djzPi6HVq1t+UhSkJqJkqm2issQozgKzKcvyjBvdDiGy4VEAgA16YW8PCyRgM3CzRwr4MGbhao62BEA9cUUWOqeXOy8/HieSqehSbi/o1Y3L8Zi/s3YpGWklvs/gIB4O5pjQ9mt4Z7a+tqjpYQQvjj4pQPF6d0jB4OpKQm4+p1Ga7dlOLaDQlex6retTKmnMz86YNElfVGJlK4NjVH/SYWsHM0hq2TsfL/9YxRt55Rrasq1spkKpcrkJ2Vj+xM5U9WZj7SU3OQGJeNxLhMJMVlISEuC0lxWYiJzsCL5ymIiUpHWe6xbesZwW+wK/q+1wjmdfRLP4AQQmowUxMGP98s+PlmAVC2Cr55Sw9370tw554I4S9kYMXcuaan5uL21Rjcvqo+o41AANSxMYCFpT7MrfRhXkfG/d/MUh+GRhIY/PdjaCyFgWHhsoGRBBKpCCKRQKfufHlPpn9tvIctq0KQnaVMmtmZ+Sp9PDWlJxOhSQtLvDOwPtp3rwdTCxrDkhBCSmJXVw67Xpno10u5nJklwMPHEjx9LsGz5yI8eSpAxAs9ZGSV3LWGMSD+dSbiX2dWOA6BAJBIRRBLhJBIhUV+F0EiFkIsFUIkEkIoBARCgTL5CgUQ/vdT8Pv7M1uhcy+nCsdRVrwn08z0PEQ8rZznKTIDMczryODS2BxNW1rCqaEZLKz0uW83MdEZiInOqJTXIoS8nV2R3x/fS+D19dOSUpEnoH6VFVW/rvIHHZTLjAGJyWJEROkj+rUeYuMleB0jQkycGHEJMiSlFX83Wx6MAbk5co1vrvqPbKTR8WVV4QZIlpaWcHZ2VlsfFxcHKyurcp/vWWgSsrPySt8RgEgshFgsRHXUAOTkp0FPbFz1L1RNalp5gJpXJipP5dIT58BAL6tSz5mWlQNj/ZJbxOqa6i2PQpkpmaLUPVmRXxiKv+Az7j+F29NzcmGkJ4VAIoNlvXoaxlvI0tISJ06cKHZbhZNpSXShlW95UHm0X00rE5VH+9W0MlF5NEeDzRJCCCEaomRKCCGEaKjSk+mHH35Y2afkFZVH+9W0MlF5tF9NKxOVR3OV/syUEEIIqW2ompcQQgjRUJmS6YsXLzBkyBCYmprCxMQEgwcPRmRkZKnHXb9+HR9++CGaNGkCAwMDODo6YtSoUQgLC9M4cE1UtDxvWrp0KQQCATp16lQFUZaPpmUKDQ3F0KFDYWlpCX19fTRu3Bhr1qypwojfTpPyREZGYty4cXB0dISBgQEaNWqE+fPnIyODvz7GUVFRmD59Otq3bw8DAwMIBAKEh4eX6djs7GzMnj0btra20NfXR/v27XH+/PmqDbgUFS2Ptl4TAM3+RkVpy3VB0/Jo2zVBk/JUyzWBlSIjI4M1aNCAubu7s/3797MDBw4wDw8PVr9+fZaenv7WYz///HPWoUMH9tNPP7Fz586xnTt3siZNmjALCwsWGRlZ2ktXCU3KU9SzZ8+YoaEhs7a2Zh07dqzCiEunaZmuXbvGjI2NWf/+/dn+/fvZ2bNn2W+//cZWrlxZDdGr06Q86enprGHDhszZ2Zlt2bKFnT17li1btozJZDI2bNiwaiqBusDAQGZtbc169+7N/Pz8GAAWFhZWpmNHjhzJTE1N2e+//85Onz7NBg0axGQyGQsJCanSmN+mouXRxmtCAU3+RgW06bqgSXm07ZrAWMXLU13XhFKT6erVq5lQKGRPnjzh1j1//pyJRKJS39jY2Fi1deHh4UwgELAFCxZUIFzNaVKeovz8/NiHH37IunbtyvuHRpMyyeVy5ubmxt59992qDrPMNCnPyZMnGQB28uRJlfVffPEFE4lELCMjo0piLo1cLud+37BhQ5kvBLdu3WIA2KZNm7h1eXl5rFGjRqx///5VEWqZVLQ82nhNKFDRMhWlTdeFipZHG68JjFW8PNV1TSi1mvfQoUNo164dGjRowK1zcXFBx44dcfDgwbceW9xISE5OTrCyssLLly/LdutcyTQpT4Fdu3bh5s2bWLp0aVWFWS6alOncuXN48OABPvvss6oOs8w0KU9urnKWIBMTE5X1ZmZmUCgUYDy1txMKK9Y84dChQ5BIJBg+fDi3TiwWY8SIETh58iRycnIqK8RyqWh5tPGaUKCiZSqgbdeFipZHG68JQMXLU13XhFKju3//Pjw8PNTWu7u748GDB+V+wdDQUMTGxqJp06blPrYyaFqepKQkzJw5E8uXL4eFhUVVhFhumpTp4sWLAJTP5dq1aweJRAJra2t88sknyMqq3CHYykqT8rzzzjto2LAhvvjiCzx48ADp6ek4e/Ys1qxZgylTpsDQ0LCqwq4S9+/fh4uLCwwMDFTWu7u7Izc3F0+fPuUpssrD9zWhMmjjdaGitPGaoInquiaUmkwTExNhbm6utt7CwgJJSUnlerH8/HxMmTIFVlZWmDhxYrmOrSyalmf27Nlo1KgRxo8fXwXRVYwmZYqOjgYADB8+HH5+fggICMCcOXOwceNGjBw5skriLY0m5ZHJZLh48SIUCgXc3d1hbGyM7t27o1+/fli/fn1VhVxl3vZeFGzXZdpwTagM2nhdqChtvCZoorquCWWaNaa4OeUqcms8bdo0XLp0CUePHi32AlFdKlqeCxcuYNu2bbh586bWzbNX0TIpFMrBpkePHo2vv/4aAODj4wO5XI65c+fiwYMHcHNzq9xgy6Ci5cnOzsbw4cMRGxuL7du3w9HREf/++y++/vpriMVi/PLLL1URbpVhjFXa508bacs1QRPafF2oCG29JlRUdV0TSk2m5ubmxX77TUpKKtc//nnz5uH333/H1q1b4efnV74oK5Em5Zk8eTImTpwIBwcHJCcnA1B+s5bL5UhOToa+vj709Kp/JglNylSnTh0AQI8ePVTW+/n5Ye7cubh161a1f3A0Kc8ff/yBc+fO4enTp3B1dQUAdOnSBaampvjwww8xZcoUtGjRokrirgoWFhbFdgkquEPX5SpFbbkmaEpbrwsVpY3XBE1U1zWh1Gped3d33L9/X219eb6dfPvtt/j++++xZs0ajBkzpvxRViJNyhMaGopff/0V5ubm3E9wcDCuXLkCc3Nz3u56NCmTu7s7APU7wYI7H00bZVSEJuW5e/cuzM3NuQ9NgbZt2wJQ/g11ibu7O8LCwpCZqTrJ8oMHDyCVSlUaaekSbbomaEpbrwsVpY3XBE1U1zWh1HdlwIABuHLlCp4/f86tCw8PR3BwMAYMGFDqC6xduxbz58/Ht99+i+nTp2sWbSXQpDyBgYFqPy1atICHhwcCAwMxZMiQqg6/WJqUqXfv3tDT01Obo+/kyZMAlFMZVTdNylO3bl0kJSWpNcy5evUqAMDe3r7yA65CAwYMQF5eHvbu3cuty8/Px549e+Dn56dTdzwFtO2aoCltvS5UlDZeEzRRbdeE0vrOpKenM1dXV+bh4cEOHDjADh48yJo3b85cXFxYWloat194eDgTiURs8eLF3Lrdu3czgUDAevXqxS5fvqzyc//+/Urp21NempSnONrQn0zTMi1atIiJRCI2b948FhAQwJYuXcpkMhkbN25cNZdESZPyhIWFMWNjY9awYUOug/by5cuZsbEx8/T0VOmrVt327t3L9u7dy6ZMmcIAsJ9//pnt3buXnTt3jjFW8t9n+PDhzMzMjG3YsIGdPn2a+fv7Mz09PXbjxg0+isGpSHm08ZpQVEX/Rm/ShusCYxUvj7ZdEwpUpDzVdU0oNZkyxlhERAQbPHgwMzY2ZkZGRmzgwIFqnWXDwsIYALZw4UJu3bhx4xiUc6Cr/XTt2rVSClARFS1PcbTlQ6NJmRQKBVu5ciVzdXVlEomEOTo6sgULFrDc3NzqK8AbNCnP/fv32dChQ5mDgwOTyWSsYcOG7PPPP2eJiYnVV4BilPZZKKk8mZmZbObMmczGxobp6emxtm3bssDAwGqP/00VKY+2XhMKVPRv9CZtuS5UtDzaeE1grOLlqY5rAs0aQwghhGhIt54kE0IIIVqIkikhhBCiIUqmhBBCiIYomRJCCCEaomRKCCGEaIiSKSGEEKIhSqaEEEKIhiiZElIFBAIBBAIBhEIhnj17VuJ+3bp14/bdsmWLyrbx48dz2xYuXFjiObZu3crt5+Pjo7Lt3Llz3LaCHwMDA9ja2qJLly6YPXs2QkJCNCkqIQSUTAmpMmKxGIwx/PHHH8Vuf/LkCYKCgiAWv33yJrFYjE2bNkEulxe7fcOGDaWew8nJCQsXLsTChQvx6aefol+/fsjIyMAPP/yA1q1bY9SoUUhPTy9bwQghaiiZElJFbGxs4OXlhc2bNyM/P19t+8aNG8EYQ79+/d56nn79+iEqKkpt4HFAOeNFcHAw+vfv/9ZzODs7Y9GiRVi0aBG+++47bNiwATdu3EBISAiaNWuGXbt2YejQoeUrICGEQ8mUkCo0adIkvH79GkeOHFFZn5eXh61bt6JDhw7clFclGTVqFPT19bFhwwa1bQXrPvjggwrF17JlS5w+fRpWVlY4ceIEDhw4UKHzEFLbUTIlpAq99957MDQ0xMaNG1XWHzp0CDExMZg0aVKp5zAzM8PQoUNx9OhRvHr1ilufk5ODbdu2oWvXrmjUqFGFY7S2tsbkyZMBADt37qzweQipzSiZElKFjI2NMWLECJw4cQJRUVHc+g0bNsDExATDhg0r03kmTZqE/Px8lUZK+/fvR0JCQpkScmkKGi79+++/Gp+LkNqIkikhVWzSpEmQy+XYtGkTACAiIgIBAQEYNWoUDAwMynSOTp06oWnTptxzVkCZkM3NzeHv769xjAUTJMfFxWl8LkJqI0qmhFQxb29vNGvWDJs2bYJCocDGjRuhUCjKfUf5wQcf4Pnz5zh79iyePn2KwMBAjBkzBjKZTOMYCxK0QCDQ+FyE1EaUTAmpBpMmTUJERAROnDiBzZs3w9PTE61atSrXOcaOHQs9PT1s3LiRu0OtjCpeAIiOjgYAWFlZVcr5CKltKJkSUg3GjBkDfX19TJ48GS9fvsSHH35Y7nNYWlpi0KBB2L9/PzZt2oT27dvDw8OjUuILDAwEoLyLJoSUHyVTQqqBmZkZhgwZgqioKBgaGuK9996r0HkmTZqEnJwcxMXFVdpdaWxsLH777TcAym44hJDye/uwKYSQSvPNN99g8ODBsLKygrGxcYXO0a1bNxw8eBAKhQI9e/bUOKbbt29j7NixiI+PR58+fTBgwACNz0lIbUTJlJBq4ujoCEdHR43OIRAIKpTwwsPDsWjRIgDKASPi4+Nx48YN3LhxA4DyjrTg7pQQUn6UTAmpBSIiIrB48WIAgEwmg5mZGRo2bIhZs2Zh1KhRaNmyJb8BEqLjBKygTTwhhBBCKoQaIBFCCCEaomRKCCGEaIiSKSGEEKIhSqaEEEKIhiiZEkIIIRqiZEoIIYRoiJIpIYQQoiFKpoQQQoiGKJkSQgghGqJkSgghhGjo//Sl4ZcxxQ5yAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2835dc5b0>]"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7bbcd74cc3d46b1a187ddb6347761ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11a3058a26254d78a55d12abc46fa461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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x1oq/Vemnh+NvbMOSYBZ6JZbFRKSx2Mh0pZ/H9xLZ/3aHscWT/2WdrH9ROb6NeBv7au4FlpaSW3EQtYT6kSq7fv06mzhxInN2dmYikYgZGRkxd3d39umnn7KXL1+WekxRf85dX37JTmzcyDp37swMDQ2ZmZkZGzZsGHv8+LGiDylLuskePnzI5s2bxzw9PZm1tTUTCATMycmJDR8+nAUHB5f6GpcuXWIjR45k9vb2jM/nM2tra9a2bVs2b948dvPmTaV9nZycVPpWlqVJkyYMALO0tGT5+fll7pefn8+2bt3KunTpwkxNTZlAIGCNGjViPj4+7Pvvv1fpk5qfn89WrlzJXFxcFOX7/PPPWV5eXpX6kTLGmEwmY1u3bmUtWrRgAoGA2dvbsw8//JClpaWVWdZdu3axtm3bMkNDQ2ZlZcXeffdddu/ePbZ8+XIGgAUGBirt//r1azZ27Fhma2vLuFwuA8CWL1+u2B4cHMx69+7NzM3NmbGxMevWrRs7cuQICwwMVNlX12jiGucw9l979Srw8vLCrVu3NJvRiV6Ifp6OD/xOIPJpmmKdqYUQ81Z3RjffxpWecV5aKMOVcy9xeOdDhIUkKm2zsBZh3tddMXRSS3C5mpnBvrqKWm0SNZX8vPDyUt2eXGK7VSnbCakhmrjG6RkpqbTH95IwvsffSknUx88Fv18cju79nSqdRAGAZ8CFd38nbDo4EBsO9Edrr+JnT6lJefhy5nmM8/4bYSEJmiwCIYRoHCVSUilRT9Mw/e3jSEmUd7TmcID5a7pgyQZvCEXqPWpv09EOG37vjy9/6Akb++KGJPdvvsbYboewedlVSPJLbzFICCHaRomUVOh1bBamDjyG5Nc5AAAej4Nv9/pi4MimGnsNDocD7/5O2On/LsZ/2BoGfPl/TamU4ddvbmNU54N4GEp3p4QQ3UOJlJRLki/F3FGn8So6EwDA5QJrd/VFu85VG4GlskRiA0ye9xZ2nPZDm47Fo7U8DUvBmK6HsG3VdRQWUv9TQojuoERKysQYw1cfB+H+zdeKdcu29MRbXVSb1Guag5Mp1u/zxUfLOkAoko+GJJUy/PjVTUzsfRgvX5Q+1yIhhNQ2SqSkTEf3huPwruIO2uM/ao3u/Z1q7fW5XA7endgSv570g0eJxkh3r7/G8A5/4vj+R6hGo3NCCNEoSqSkVDERGVgzr3gUmnZdGmDS3HZaiaWhkwm+2++LqZ+1B48nbxmcnVmAJVPOYsGEf5GRVrcHxCaE6DZKpESFTMbwxbSzihGLTMwEWPVz7yp1b9E0Ho+LMTM9sPnQQDg4Fc9Hefqvpxjm9SduX1YdHo4QQmoDJVKi4vcf7+HWpeLEtPLn3hAbqk75pA3NW1vjp2ODMXBUE8W6V9GZmNz3CH5ec5MGwieE1DpKpERJ4qtsbFlePMj322OaobWX6lyH2iQ24mP+113x5daeMDYVAJDfRW9dcR0z3j6OxPhsLUdICKlPKJESJesXBSM7U16la2ouxEfLOmg5orJ5D3DCryeHKDVEunY+BsM7/Ilr50ufqYIQQjSNEilRuBEUg1N/PlEsL1zfDXyBbk/EbWNvhO/2+WL8h61R9Ag3+XUupg08hq0rqM8pIaTmUSIlAORVo98uCFYst+5gi069HLUYUeXxDLiYPO8trNvVF+ZWIgAAY8DPa25iav+jSHxFVb2EkJpDiZQAAP45+AThd+SzsHC5HHy+sYeWI6q69t0a4pcTQ9CuS/GoS7cuxWFk54MIvfpKi5ERfTF58mRwOJxSJxbXFmdnZ8Xk2UQ30cTeBJJ8qVIDowEjm8C6gWE5R+guSxsx1u3qiz9+eoC9W++AyeQTiE/qcwSLvuuOcR+01mw3nrtfAjnRmjufphk2BtquUvs0zs7OiIqKKnWbnZ0d4uPjldZJJBKsWrUKBw4cQEpKCto3bYoNn3yC9i1aqBwfEBAAX19fnPz9e7zt213tWPVNr169EBQURIOL6DFKpAR/bX+A2Ej5WLp8ARczFnlqOSL18HhcvDe7DVq2s8bX8y4iM00CaaEMaz65iAc3X2P5j70hEmvov35ONGDkrJlz1YTsSI2dyszMDJ988onKemNjY5V1ixcvxvfff4/hw4fD0dER+3bvRu9Zs/Do779RcoDJrKwsTJ8+He+NHKgTSXTt2rVYvHgxHBwctB2Kwrlz57QdAqkAJdJ6Li+3ENu/va1YHjPDA0YmAi1GpDme3Rvip6ODseKjC3gWlgIAOH7gMaKepWHr4bdhZaufd93aYm5ujhUrVlS4H2MMv/zyC95//33s3LkTADC0ZUv0mjUL+/75BwsHDFDsu2jRIuTl5WHzmk9rKuwqsbe3h719zY8lXRVubm7aDoFUgJ6R1nOHd4YhKV4+PZrI0ABjZrXWckSaZedgjE1/DkD/4cUfRnevy+c5ffYwRYuR1V2JiYnIyclBx44dFes6ursDAKJeFT+rvnjxIn766Sds27YNlhZm1XqtoueHGRkZmD9/PpydncHn85US/qNHjzB58mQ0atQIQqEQdnZ2GDduHB4/fqxyvvKekV6/fh0jRoxAgwYNIBAI0KhRI8ycORNxcaWPqpWSkoIvvvgCHh4eMDQ0hJmZGdq2bYvFixcjOzsbkZGR4HA4CAoKAiCfSrDop1evXiplfFN+fj7WrVuHNm3awNDQEKampvD29sZff/2lsm/Ra02ePBmRkZEYM2YMrK2tIRKJ4OXlhZMnT5b/h37D0aNH8d5776FZs2YwMjKCsbExPD09sWXLFshkqi3li/6uL168wNatW9GmTRuIxWKlcqakpGDJkiVo2bIlxGIxzMzM0KdPH/z7778q50tPT8f69evh4+MDR0dHCAQC2NjYwM/PD9euXVPZv6bRHWk9lp9XiB3rQxTLY2Z4QCDU7e4u1SEUGeDTtV3h2sISP6+5CcaA2KhMjO/xN77/cwC69m2s7RD1Qn5+Pvbv34/o6GgYGRmhTZs26NGjB3g85f8zNjY2EIvFuH27uKbjVng4AMDpv7u93NxcTJs2DcOHD8fw4cOB5FvVjksikcDHxwcpKSnw9fWFqakpXFxcAABnzpzBsGHDUFBQgCFDhqBJkyaIiYnB//73P5w6dQqBgYFo3759ha+xa9cuTJ8+HUKhEH5+fmjUqBGePn2KHTt24MSJE7h27RoaNy7+fxQREYHevXsjKioKnp6e+OCDDyCTyfDkyRN8//33mDVrFszNzbF8+XLs3r0bUVFRWL58ueL4ihoXSSQS9O/fH0FBQWjRogU++ugj5OTk4O+//8bo0aNx584drFmzRuW4qKgodOzYEa6urpgwYQJSUlJw8OBBvPPOOzh79ix69+5dqb/54sWLweVy0alTJzg4OCA9PR3nz5/H3LlzcfPmTezbt6/U4+bOnYtLly7h7bffxqBBgxT/d6KiotCrVy9ERkbC29sbAwYMQHZ2Nk6ePIkBAwbgl19+wfTp0xXnCQ8PxxdffIEePXrg7bffhoWFBaKjo3H8+HGcPn0aJ06cwIASNR81jRJpPXZkTzgS4uRdQ4RiHkZMbaXliGoOh8PBsMktYd/IGGvmXUJebiGyMiSYNeQEvtreB37vqTaCIcri4+MxYcIEpXUuLi7YtWsXevbsqVjH4XAwY8YMbNmyBenp6XBwcMC+XbtgYmSE8f99uC1duhQpKSnYtm2b2nG9evUKrVq1QlBQEIyMjBTrU1NTMXbsWBgaGuLixYto1ar4/3dYWBg6deqEadOmISQkpLTTKjx58gQzZ86Es7MzgoKClJ6fnj9/Hv369cPcuXNx5MgRxfr33nsPUVFRWLNmDZYsWaJ0vqSkJBgbG0MkEmHFihW4cOECoqKiKlVtXmTDhg0ICgrCwIEDcfz4cRgYyD/Kly9fjo4dO2Lt2rUYPHgwunbtqnTchQsXsGLFCqWkPW7cOAwYMADr16+vdCI9deqUSpWzTCbD+++/j71792L27Nno1KmTynEhISEIDQ1VfNEpMmnSJERFReGPP/7AmDFjFOvT0tLQq1cvfPzxx/Dz84OdnXyUtZYtWyIuLg7W1tZK54mJiUHHjh0xb968Wk2kVLVbT0mlMuz+PlSxPGqqO4Siuv+9qkufRvj+j/6wtpM/H5VKGT6fehZ/bX+g5ch02/vvv49z584hPj4e2dnZuH//PmbOnInIyEgMHDgQd+/eVdr/m2++waJFi3Djxg389ttvcHd1xblt2+Bga4vr169j06ZN2Lx5M6ytrbFy5UrYtxoAA7vO8PSZgODg4DKiKNuGDRuUkigA7N27F2lpaVi5cqVSEgUAd3d3TJ8+HaGhoXj48CHK89NPP6GgoACbN29WaYTk4+MDPz8/nDhxApmZ8gZ7t2/fxpUrV9CuXTssWrRI5XxFVarq2LlzJzgcDjZu3KhIogBga2uLZcuWAQB27NihcpyTkxOWLl2qtK5///5o3Lgxbty4UenXL+25LZfLxdy5cwEA/v7+pR63cOFClSR69+5dBAUFYfjw4UpJFJA/l1+5ciXy8vJw+PBhxXozMzOVJAoAjo6OGDFiBB49eoTo6NprTV/3PzlJqc4de4GXLzIAyFvqjpzuruWIak8Tdyts/XsQPp92FhGP08AYsPKjC8jJKsDkeW9pOzydVPIOBgA8PDzw888/w9jYGBs2bMCKFSuU7siEQiHWrl2LtWvXylfcklfdSgoKMGXaNAwaNAjjx4/Hpk2b5HdIC6ajW6e2+HrjTgwYMADPnj1T3H1URCQSoU2bNirrr169CkD+QV3a3d6TJ/JRvMLDw1USbWnnCQoKws2bN1W2JyQkQCqV4smTJ/D09FQ8o+vfvz+4XM3fq2RmZuLZs2dwcHBAi1K6E/n4+AAAQkNDVba1a9dOpSoeABo1aqQoZ2UkJydj/fr1+Oeff/DixQtkZysPehIbG1vqcSWfmxcpet309PRS36fERHn/9vD/Hg8UCQ4OxubNm3H16lUkJCRAIpGoxFCyur0mUSKtp0rejfoOc9OZ2V1qi3UDQ3y3vz8+n3oWj+8lA5CPM5yTVYAPlnbQ6pRx+mTWrFnYsGEDLl68WKn9V27fjtjYWEUDkvXr16NPnz5YsWgGAKB5Eyc4v+WHbdu2YdWqyvV/tbW1LfX9Sk6Wv6/bt28v9/isrKxytxedZ/369ZU6T1paGgDUWBea9PR0ACizdXHR+qI4SjI3Ny/1GAMDg1IbCZUmLS0NHTp0QEREBDp27IiJEyfC0tISBgYGSEtLw+bNm5GfX/ocwQ0aNFBZV/T3DQgIQEBAQJmvW/J9OnLkCEaMGAGRSIR+/frBzc0NRkZG4HK5uHDhAoKCgsqMoSZQIq2HQq++wt3rrwEAHA4wYU5bLUekHabmQnyzux+WzQzE/Zvyv8e21TcgkzHMXq76fIeosrWVTxjw5h1JaUIfP8a3e/fi519+gYODAzIyMhAXF4fx48cr9mns2ADW1tYICwurdAxlfekxM5O3BL57926pd6yVVXSe9PR0mJqaVrh/UbIq665MXUXxvDkIRpFX/7WMLtpP03bs2IGIiAgsX75c5Q7y6tWr2Lx5c5nHlvZeFcW5efNmfPzxx5WKYdmyZRAIBLh16xZatmyptG3mzJmKltC1hZ6R1kN7N99R/N6hh0O97k9pZCLAmt/6wMu7oWLdT1/fxP4f7pZzFClSVC3n6upa7n6FhYWYsmoVent5YerUqUrb3rxzyMvL00hsnTt3BgBcunSpVs9TtL+/v3+l7vKKqlqlUmmlzm9iYgI3NzfExsbi6dOnKtsDAwMBoFKtkavj2bNnACBvbf2G6iSw6rxPz549Q6tWrVSSqEwmw+XLl6scg7ookdYz8TFZOHfshWJ56gJ6JigSG2DlT73RoUdxMl07/xJOHFDtZ1gfhYWFISVFtc9tVFQUZs+eDUDeSrU8a3fvxrOYGGz/4gvFOlNTUzg4OODMmTMoLCwEAAQF30ZmZibc3dV/Zv/+++8rGquU1pBGJpPhwoULFZ5n9uzZ4PP5mDdvnuK5akkSiUQpCXh6eqJr1664c+cOvvnmG5X9k5OTlb4sWFlZAUCVGsdMmTIFjDEsWLBAKQEnJSVh9erVin1qQlHXnDf/dqGhocXPxKvAy8sL3t7e+N///qcYwONN9+/fR0JCglIMT58+VerDyxjDypUrK2w8VhOoaree+Wv7A0il8jE9G7mawrW5pZYj0g0CIQ9f/tALiyYH4GGIvHHD59POYm+w/g3er2mHDh3CunXr0Lt3b7i4uMDExATPnz/HqVOnkJeXh0GDBuGzzz4r8/iwsDB8tXMnNn7yiaIfaZGFCxdi7ty58B48A5083XHg7zMwNjbGRx99pHbcVlZW+PvvvzF06FB07twZffr0gbu7O7hcLqKjo3H16lWVpFaaFi1aYOfOnZgyZQrc3d0xYMAANGvWDAUFBYiOjsalS5dgY2ODR48eKY7Zv38/evXqhc8//xyHDx9Gr169wBjD06dP8e+//+LRo0eKhNSnTx8cOnQIw4YNw6BBgyAWi+Hk5KTS1aikzz77DKdPn8axY8fQtm1bDBo0CDk5OTh06BASEhKwcOFCdO9eM0MuTpw4EevXr8cnn3yCwMBANG3aFE+fPsXJkycxbNgwHDx4sMrn/P333+Hj44OpU6diy5Yt6NSpE8zNzRETE4N79+7hwYMHuHr1quJRwrx58zBr1iy89dZbGD58OPh8PoKDg/Hw4UMMGTIEJ06c0HSxy0WJtB6RSKT4e2fxt7X3ZtfPZ6NlEYkN8NWvPvh0vD8iHqdBJmVITcxFdpYERsZ1Y9jE6ujduzceP36M0NBQXL16FdnZ2TA3N0f37t0xYcIETJgwocznlFKpFFOmTEEnDw98OHKkyvY5c+YgIyMDP/+4FSH3HqGte1N8v/XXSrfYrUifPn1w7949fPfdd/D398elS5cgEAjQsGFD+Pj4lFo9WZr33nsPbdu2xYYNGxAYGIh///0XRkZGaNiwIUaMGIHRo0cr7e/i4oKQkBB8++23OHr0KH744QeIRCI4Ozvj008/VSQEAJg2bRqioqLw559/4ttvv0VhYSF69uxZbiIVCAQICAjAxo0b8fvvv2Pr1q0wMDBA27ZtsWnTJowdO7Z6f7BKaNiwIS5duoTFixfj8uXL8Pf3R4sWLfDjjz+ib9++1Uqkjo6OuH37NrZu3YrDhw/jwIEDkEqlaNCgAVq1aoU5c+agdeviUddmzpwJoVCITZs2Yc+ePRCLxfD29sauXbtw+PDhWk+kHFaNKQe8vLxw61b1RyIh2nHqzydYOFHeWlJsZID/3RwDAz7V7r8pOSEHn4w+g/iYLGw53hEO9i5wbWFZ+qhP9WT2F7WV/Lzw8lLdXnJkI6tStteSMWPG4ODBg4iLi9O5MXdJzQgPD1d51lpVdEdaj/z5y33F74NGNaUkWgYrW0N8s7sf5o4+DQAoLGR4+SIdLs3MweW98TfThSRFNObJkycQCoWwsbHRdihEj9AnaT3x4lEqQoLlzeI5HGDktPozAEN1NHQywfIfegL/1Vjm5hQiNiqT5oysozZv3ozhw4cjNDQU77zzjtJoQYRUhP631BOHdxX3y2vR1rped3mpLA8vO8S8Ku4fmZ6aD5FhDmwaGJVzFNFHmzZtgkQiwZQpU7BhwwZth0P0DCXSekAikeLYvuIWhWPr2FRpNcnQiA9zYyHSUuR9HRNisyESG8DETKjlyIgmRUREaDsEoseoarceCDwRgdQkeRN/QyM+OvaqmaHL6iobeyOIDeXfORmAmMgMFEgq13meEFL3USKtB/63u7jLi+8wN/DebDBDysXhcNCwsYmicZa0kCE2MoOelxJCAFAirfMSX2XjSsBLxfLwKXV3ztGaxDPgwt7RuKjtEbIyC5D8OkerMRFCdAMl0jrun7+eQiYrHsmogaOxliPSX2IjPixsxIrl13HZyM0p0GJEhBBdQIm0jis5Xuw7E1TnLiRVY2UrhkgsH5iBMSDmRQak0spNP0UIqZsokdZhz8KSEX5HPm4sl8tBH7/yZ+ggFeNwOLBvZIKi+Zrz86WIjyl/PktCSN1GibQOO/F78d2oh5ctjE3r73ixmsQX8GDbsLiKPDUpD1kZEi1GRAjRJkqkdZRMxnDyz+Ipn4ZNVm8sSaLM1FwAkxJfTOKiqIqXkPqKEmkddetSLOJfyqschSIeOvakvqOaxYFtQyPwePJ2vBKJDIklRkEihNQflEjrqJKNjLr2awS+oJSZS4haeAZc2DQoHmox+XUucrKpFS8h9Q0l0jooL7cQ//7vuWL53QlUrVtTTC2EMDTmA5CPehQbmaHoblQX9evXDxwOB0FBQWXuM2vWLHA4HGzfvr0WIyNEeyiR1kEXTkYoGr+YWgjRsp21liOqyziwczAqbsWbJ0VSfN2t4g0JCQGPx4Onp2eZ+1y/fh2AfN5iQuoDSqR1UMnWuv3edQWHwylnb6IuPp8Ha7viKt7E+Bzk5xVqMaKa8fz5c6SkpMDd3R3GxqUP7JGTk4MHDx5AKBTCw8OjliMkRDsokdYxGWn5uPxvtGLZbzwNwlAbzK1EEIn/G9ieAa9eZtW5sXhv3boFAOjUqVOZ+9y+fRuFhYVo27Yt+Hx+bYVGiFZRIq1jAk9EoLBA3g3DtqERGjqZaDmi+oIDu4ZGxWPxZkiQmZav1Yg0rTKJ9Nq1awCoWpfULzQfaR3jf/ip4nffoW5ajKT+EYoNYGZZPHfpq5gsGJsKwK0js+0UJdKDBw8iMDCw1H3o+SipjyiR1iEZafkILjHTi+9wSqS1zcrOEJnpEkilDAUSGRLjc2DnoP8TBTDGEBoaCgAICAiocH9KpKQ+oURah5Ss1rVzMIJ9I6rWrUlvmfyk7RAqLUwyW63jnzx5gvT0dHTp0gVXrlwpdZ/U1FRYWlrC0NAQrVopT9f346FDWL9/P14lJcHdwwObNm2Ct7e3WjERoivqRp0TAQD4H36m+L0fVesSDSqq1n3rrbfK3CckJAQA0K5dO/B4xQOAHDx4EHM3bMDnkycjdP9+dO3aFQMHDkR0dHRZpyJEr1AirSMy0vJx5WzxBxM9HyWaVJRI27dvX+Y+RYn0zWrdjRs3YvLgwZg+dChaurhg69atsLe3x08/6c8dPSHloardOiLwRAQKJMWtde0bU7VuTQvN/KDc7a9js5CeKm94JBTx0KSVpd726a1MIi16hloykUokEty+fRufffWV0r6+vr5lVhETom/ojrSOKFmt6zuM7kZ1gbWdodKIR6lJudoNqJpkMhnu3LkDgUAAd3f3Mvcr7Y40KSkJUqkUdpaWSvva2dkhPj6+ZgImpJZRIq0DMtOpWlcX8Qy4sLQpHvHodWw2pIX6N9VaeHg4srKy4O7uDoGg9Dlts7Ky8PTpUxgbG6N58+Yq29+8E2eM6e3dOSFvokRaB1C1ru6ysBaBz5dfZlIpQ2J8jpYjqrrbt28DKL9a9+7du5DJZGjfvj243OKPFWtra/B4PMQnJyvtn5CQADs7u5oJmJBaRom0DihZrdv3XVctRkLexOFwYF1yqrWEHEjy9Wsc3okTJ4Ixhh07dpS5T7du3cAYU5kVRiAQwNPTEwH/DdRQJCAgAF27dq2ReAmpbZRI9Vx2pgTBAcXVuv2HNdFiNKQ0JmYCiA2Lx+GNj6m7s8OUZv78+dh98iR2HD2K8IgIzJ07F3FxcZg1a5a2QyNEI6jVrp677B+tqNa1sjOksXV1Egc2DQwR/SIDgLyrUnamBEYmpT9vrGtGjx6N5JAQfLVzJ14lJcGjdWv8888/cHJy0nZohGgEJVI9d+74C8XvvQbRB5OuEhnyYWomQEa6fJ7Y+JgsuLawqDcNbj4cORIfjhwpX6DhA0kdQ1W7ekwikeLi6UjFct93qbWuLrNuYAjuf3kzN6cQ6Sl52g2IEKIRlEj12K2Lscj87w7H2FQAt5YWWo6IlMeAz4O5tVix/Do2GzKp/nWHIYQoo0Sqx84dj1D83qWPY72pJtRnljZiGBjI36eCAhmSEvRzkAZCSDFKpHpKJmMIPFH8fLT/cGqtqw+4XA6s7Iq7wyTFZ6OgQKrFiAgh6qJEqqfCQhLwOlbejUIg5MHD01bLEZHKMjMXQiiUz44ikwGJcfWrOwwhdQ0lUj11/ljx3Wj7rvbgGdBbqTc4HNjYF9+VpiblIS9XvwZpIIQUo09fPVXy+Wj/EdRatyYxxjR+TkNjPgyN+fLzA3gdk6Xx1yCElE9T1zYlUj0U+SQVz8NTAAA8Hgde3g5ajqju4nB4KCysibtF+SANRc3DMjMkyMrIr4HXIYSUpaCgQGkS+uqiRKqHzp8ovhtt3tYaIjGNq1FT+AYiZGZm1Mi5hSIDmFoIFcvxMVk1cvdLCCldRkYGTEzUHw2OEqkeKjma0QBqrVujRAITJKekICUlGQUFBRpPdFYl5izNy5UiLZkGaSCkJjHGIJFIkJSUhNTUVFi+MVduddCtjJ5JjM/G3WvFEyJ37dtIi9HUfQY8PkzENshIz0RycgoY03xXlaxcCbIzCgAAiSkc2DY0Apdbx/oEJyUV/x4erro9u8T2hFK2E6JBPB4PJiYmaNy4MYRCYcUHVIASqZ65cCoSRTdFTk3MYGYp0m5A9YABjw9jsfrfWssiNijAglFHkfLf4AwffdkRHy7tWGOvpxWtWhX/Xtpd/e8lto+j6m2iX6hqV8+cK9Htpd9Qaq1bF4gN+Zj8STvF8m/fhSDxFfUtJURfUCLVI1kZElw7/1Kx3HOQs/aCIRrlO8wNLs3NAQB5OYXYuvJ6+QcQQnQGJVI9ctk/SjH3qHUDQzRwNNZyRERTeDwuZiwqnl7sf7sf4sn9pHKOIIToCkqkeqRka126G617vLwbokOPhgAAJgO+W3xFyxERQiqDEqmekM89GqVY7veuqxajITVl+iJPRYvd4IBoXP43qoIjCCHaRolUT9y6GIusDPnco0YmfLi2oLlH6yKXZhYYMLK4b/D6hcGQ0pylhOg0SqR6ouRoRp160dyjddmkue0gMpT3THv2MAVH9lC/SkJ0GSVSPcAYQ+BJGqS+vrC0EWP0dA/F8pYvryE7S6LFiAgh5aFEqgce3UlC/Ev57CB8ARdtOjTQckSkpg2f0hJWdmIAQHJCLnZ8c1vLERFCykKJVA+UvBt197SFAZ/etrpObMjHlPntFcs7N4Yi+nm6FiMihJSFPpH1QMnnowNHNNViJKQ29X3XFS3aWAMACgtkWL/wspYjIoSUhhKpjouLzkT4nUQAAIcDdOpNc4/WF1wuBx8u66BYPn8iAlfORmsxIkJIaSiR6rgLp4rvRp2bmcPIRKDFaEhta9nOBr7DihuXrZl3CQUFmp+BhhBSfZRIdVxgiWpdGqS+fpr6WXsYGvEBABGPU/Hnzw+0HBEhpCRKpDosMz0fN4JiFcu9aFjAesnSRozxH7VWLG9dcQ3JCTlajIgQUhIlUh122T8ahQXyUW1sGhjCxt5IyxERbRk6qSUcnE0AANmZBdj4OY3DS4iuoESqw86fKB6kvsdAJy1GQrSNL+ApTfZ9dO8j3L4cp8WICCFFKJHqqIICKS6dKTFIPT0frfc69nSAd//GiuWVHwVSwyNCdAAlUh11+1IcMtNpkHqibNYXHRTj8D4PT8W+LXe1HBEhhBKpjio5CIOXd0MapJ4AAGztjTDx47aK5W2rbiAuOlOLERFCKJHqIMaYUiIdNIpGMyLFhk5sCZfm5gCAvNxCrJt/SbsBEVLPUSLVQY/vJePVf3cZBnwu2nSkQepJMQM+Fx+v7KxYPnf8hdIXL0JI7aJEqoNKttZt1d6GBqknKjw8bTFgRPEE4KtnX0Bmer4WIyKk/qJPaB0UqDRIfZNy9iT12fSF7WFuJQIAJLzKpr6lhGgJJVIdEx+ThYehxYPUd+nTSMsREV1laiHC7C+L+5b+tT0MNy/GlnMEIaQmUCLVMSXnHnVqSoPUk/L1GOiErn2Lv2wtm3keebmFWoyIkPqHEqmOKfl8tN+7rlqMhOgDDoeDOSs6wchEPqj9y+fp2LbqhpajIqR+oUSqQ7IyJLhxocQg9YNdtBgN0RfWdoaYschTsbz7+1A8uP1aixERUr9QItUhl/2jFIPUWzcwhC0NUk8qaeCopmjXWd5NSiZjWDLlLPLzqIqXkNpAiVSHlHw+SoPUk6rgcDiY91UXCMU8AMCL8FRsWX5Ny1ERUj9QItURBQVSXDxdYpD6d2mQelI1DZ1MMHOxl2J5z6Y71IqXkFpAiVRH3L4Uh4w0eYd6IxM+3FrSIPWk6gaPbQYv74YAAMaAz6eeRVaGRMtREVK3USLVEQFHi1vrduzpSIPUk2rhcDj4dE1XmJjJu03FRWXi2wWXtRwVIXUbJVIdIJMxnDv2XLH89hgapJ5Un3UDQ8xZ0UmxfHjXQ6Xn74QQzaJEqgPuXo9H4qscAIBQxIOHl62WIyL6rvdgF/R621mxvHT6OSTEZWkvIELqMEqkOuDs0eK70be62oPHo7eFqG/O8o6wtjMEAKQl52HR5ABIpTItR0VI3UOf2FrGGMPZEs9HB49tpsVoSF1iaiHC4g3dUfS4/caFWPy2PkS7QRFSB1Ei1bJHd5IQE5EBQD7PZPuu9lqOiNQlbTs1wLgP2yiWt668jtCrr7QYESF1DyVSLQsoUa3r4WkLvoCnxWhIXTRhdhu4e9oAAGRShgUT/lV0tSKEqI8SqZaVrNal1rqkJvAMuFiywRvGpvIuMa+iM7FsxjkwxrQcGSF1AyVSLXrxKBXPw1MAAFweB519aO5RUjPsHIwx/+suiuWzR19gz6Y72guIkDqEEqkWlWyt26KNNURiAy1GQ+o67wFOeGdCc8Xyhs+v4PblOC1GREjdQIlUi0om0kGjqVqX1LyZi73Qoq01APnz0vnjziAxPlvLURGi3yiRaklcVAbCQhIBABwO0K1fYy1HROoDvoCHZVt7wtRcCABIis/Bggn/orCQ+pcSUl2USLXE//Azxe+uLSwUDUEIqWm29kb4/HtvRf/Sm0Gx2LyMplwjpLookWrJ6b+eKn4fPK55OXsSonme3Rtiwpy2iuWdG0Jw6s8nWoyIEP1FiVQLop6lFVfrcoFeg5y1GxCpl8Z/1AadejkolpfOOIewkAQtRkSIfqJEqgVnDhVX6zZ1t6JqXaIVXC4HSzZ6o5GrGQBAkifFnOGnkPQ6R8uREaJfKJFqwem/iqvQ3nmPqnWJ9hiZCLDq594wMuEDAF7HZuOT0achkUi1HBkh+oMSaS17FpaMp2HFgzB493fSckSkvnN0McUXm3qAy5W3Pgq98gpfzQmikY8IqSRKpLXsdIlq3RZtrCE24msxGkLkOvRwwLQF7RXLh3c9xK6NoVqMiBD9QYm0FjHGlKp1353YQovREKJsxNRW6DfUVbG8YckV/Pu/Z+UcQQgBKJHWqvA7iYh6lg4A4Blw0LUvja1LdAeHw8Enq7ugdQc7xbpFkwNw7+ZrLUZFiO6jRFqLTv9V/O2+dQc7CEU0ti7RLQIhDyu29YSDswkAeUve2UNPIi4qQ8uREaK7KJHWEsYYTh8qHoRhKFXrEh1laiHCV9v7wMRM3i0rOSEXM4ecQFpKnpYjI0Q3USKtJXevx+NVdCYAgC/gokMPhwqOIER7HJ1NsfKn3jDgyz8iXjxKxUdDTyI3p0DLkRGieyiR1pLj+x8rfvfs1hB8AU+L0RBSsdYd7LDw226K5TtX4/HZeH8a4J6QN1AirQX5eYX452Bxa90R01ppMRpCKq/3YBd88LmXYvnCqUis+ugC9TElpARKpLXg/PEIZKZLAADGpgK09rKr4AhCdMew91th1HR3xfLhXQ+xZfl1LUZEiG6hRFoLju4LV/zeb6irYgQZQvTFtAXtlfqY/rruFnZuCNFiRIToDkqkNSwhLgtXAl4qlodNpmpdon84HA7mf90VHXsWN5LbsOQK/vj5vhajIkQ3UCKtYSd+fwKZTP48ydHZFA0cjbUcESHVY8DnYtnWnmjTsfjRxFcfB+Ho3vByjiKk7qNEWoMYY0ofMkMnU99Rot9EYgOs/sUHLdpaK9Ytm3EeZ0r0kSakvqFEWoPu30rAi0epAAAej4N+Q920HBEh6jM05mPNb33g2sICACCTMSyc+C/8/6ZxeUn9RIm0BpW8G23XpQHEhjTTC6kbTMyEWLe7Lxq5mgIApFKGBRP8lbp5EVJfUCKtIfl5hTh9sLi6a8xMDy1GQ4jmWViJsX6fr1IyXTQpACd/f1zBkYTULZRIa8j54xHISMsHIO872qZjAy1HRIjmWdka4rv9/eHUxAyAvJp38fsB1ACJ1CuUSGvIXzseKH73HeZGfUdJnWVpI8b6/b5wbmYOAGAM+GLaORzYdle7gRFSSyiR1oAXj1Jx40KsYnnkVPdy9iZE/1lYifHdPl9FAyQAWDPvErauuE7DCZI6jxJpDSh5N9qynTWsGxhqMRpCaoeZpQjr9/miZbvirjE/r7mJ1XOCIAXVyJC6ixKphuXmFODY3keK5fdmt9FiNITULlNzIb7Z0w8dejRUrDv46wMs4E1GPmgie1I3USLVsDOHnioaGZmYCeDlTfOOkvpFbMjHyp96o/dgZ8U6f257TOHNQTJoZC9S91Ai1SDGGPZtvadYHjK+OTUyIvUSX8DD4g3eeHdi8Whed7iuGGvwGZ6FJWsxMkI0jxKpBt26FIfH95IAAFweB8PfpwHqSf3F5XLw4dIO+OCLDuAy+WTgsRwrjO95GJf/jdJydIRoDiVSDdq3pbi5fxcfR5iaC7UYDSHax+FwMGxyS/wg/RWGLA8AkJUhwQd+J7H929uKCR0I0WeUSDXk5Yt0nD/xQrH8/vy3tBgNIbqlJwvD/sLv0YClAJAP3LBp6VV8POIfRZsCQvQVJVIN2f/DXRR1l3NtYQGnJuZajYcQXdMccfiz8Dt4eNoq1gWejMCoLn/hUTQ1yiP6ixKpBqQm5eLv3x4qlqfQ3SghpbJBJtbv88Xw91sq1r18no5xq+fjwNkeoLEbiD6iRKoBB7bdQ15uIQDAyk6Mjr3o2zUhZTHgczHr8w5YuqUHRIbyvqX5BQKs2T8SMzd8gIS4LC1HSEjVUCJVU3aWBAd+LO7yMmX+W+BwqMsLIRXpOdAZ2/73NlyamyvWBT9ohXfb/wH/wzS3KdEflEjVdGh7GDJSi2d56ePnquWICNEfjd3M8MPht/H+gLPgcORdZNJT8jF/7Bl8Mvo0XsfS3SnRfZRI1ZCTXYCdG0IUy2M/8ADPgP6khFSFQMjDZ2OOYefCrWhgmaJYH3DkOYa0OYADP96DVCrTYoSElI8+9dXw58/3kZyQCwAQGxng3QktKziCEFKWji2f4cjqdRg8IFuxLjuzAGs+uYhx3n/j7vV4LUZHSNkokVZTdqYEv31XfDc6YXZbCIQ8LUZEiP4zNcrF0gVp2LYhEY0dCxTrH9xKwDjvv/HpuDOIicjQYoSEqKJEWk37f7iHtGT5SC2GxnylMUUJIerxbCfB/u0JmDYpA3yD4mrdM38/w+DW+/Hd4mCkJuVqMUJCilEirYbkhBz89t1txfKkj9uCL6C7UUI0SSAApk3MxB+7EuDTI0exvkAiw66NoejXdC82LLmC5ISccs5CSM2jRFoNP351E9mZ8monM0sh3plAd6OE1BTHhlKsWZ6KX7ckwr1l8XCCuf819uvfbC/WL7qM+Bhq4Uu0gxJpFUU8TsWh7Q8Uyx+v7EwtdQmpBW3cJdixNQlrvkyGq5NEsT43pxC7v7+D/s32YsEEf9y7+VqLUZL6iDJAFTDG8M2Cy5BK5eOYNXI1hXf/xlqOipD6g8MBfHrmYf+ORKxdkYwmLsV3qIWFMvxz8CnGdjuE8T3+xvH9jxQjjhFSkyiRVsG5Yy9w6UzxPIoLv+1GoxgRogVcLtDbOw/7tifh29XJeKt1ntL2O9fisWTKWfRqvBNfzQ1C+J1ELUVK6gMDbQegL3KyC7D200uK5a79GqFFWxstRkQI4XCAHl3z0KNrHh494ePg/4wQcF6MQqn8HiEzXYI/frqPP366j2YeVhg0phkGjWoKB2dTLUdO6hIOY1Wfb8HLywu3bt2qiXh01neLg7FrYygAgC/g4s/gkTRxNyGV1NDZTPF7XGS66vbg4u2ZjRar9VrJKVyc8jfE0ZNixMULSt2nXZcGGDS6GQaMaAIrW0O1Xo8QSqSVcPd6PN7reRgymfxP9eHSDhg6iUYxIqSyajORFpHJgNB7Ahw7aYgLwSJIJKpd1Hg8Drx6OKCPnwt8/Fxh38hEI69N6hdKpBXIyy3EiA5/IuJJGgDAqakZtp/yo2ejhFSBNhJpSdk5HFwMFsH/rAg3QsSQyUq/ft09bdH3HVf0eccVbi0tNR4HqZvoGWkFvl96VZFEeTwOvvrVh5IoIXrGyJBhYL9cDOyXi9S0dJy/KIb/WSHuhYmV9gu7nYCw2wnY/OU1uDQzh4+fK/q+6woPLztwuXTdk9LRHWk5zh9/gTkj/lEsT1vYHqOne2gxIkL0k7bvSMuSmMTFxStiBF4UIPSuCFJZ6R0ZbOwN4TPEFT5+LujYyxECGsmMlECJtAxxURkY3uEgMtLk/dSatbbCD4cH0d0oIdWgq4m0pMwsDoKviXDhkhBXb4iQX8ozVQAwMuGjxwBn+Pi5wHuAE0zMqNFhfUdVu6XIyS7A3FGnFUlUbGSAtTv7UhIlpA4zMWYY0DcXA/rmIi8fuHFbhKBLIly6IkRGVvFHZXZmAU4feorTh57CgM9Fp96O6OPnit6DnWHb0FiLJSDaQon0DTIZw+dTzuJhaHEH7tW/+FBXF0LqEZGwuH9qoRS4HybAxWARAi8KEZ9Q3KWmsECG4H+jEfxvNFbNBtp0tIPPEHkLYNcWFvTlu56gqt03bPz8itI8o1M+fQtjZ7XWYkSE6D99qNqtDMaA5xEGuBgsxoVLAjx5LipzX+em8sZKfd5xQZuODaixUh1GibSE7d/cwqZl1xTLPQc5YenmnlqMiJC6oa4k0jfFv+bh0lX5c9XQe6Iyu9VY2Rmi92Bn9H3HDZ16O0IgpMZKdQkl0v/s23oX60oMAdjMwwpbDg2kmV0I0YC6mkhLysiUN1a6GCzC1RtC5OWXniyNTQXwHuCEvu+4wnuAE4xMSh99ieiPep9IGWP46eub2LbqhmJdYzcz/HRsMH1rJERD6kMiLSlfAtwKESLosggXg4VIy+CXup9AyEOXPo3Q911X9HrbBZY24lL3I7qtXifSggIp1n16GX/+fF+xrkEjY/xyfAgMjUv/j08Iqbr6lkhLkkqBB+ECBF0WIfCiAK9el95wkcvlwLO7Pfq844Y+fi5o6EQD6+uLeptIU5NyMX/cGdy4EKtY59zMHFsODYTYkJIoIZpUnxNpSYwBz14YIChYjMAgAZ5Hlt1YqdVbNujzjiv6vuMKt1aW1AJYh9XLRHrlbDS+mHYOCXHZinWtO9hi3a5+VJ1LSA2gRFq62DgeLlwWIzCIj7DHYjBWerJ0amKGPu+4oecgJ7Tt3AB8Pn1O6ZJ6lUgz0vKx+ctrSlW5APDuxBb4cGkH+sZHSA2hRFqx5BQuLl0R4XyQACF3i+dUfZOxqQCdfRzhPcAJ3X2d0MCRBoHQtnqRSAsLZTi27xE2L7uK5IRcxXq+kItF33ZHz0HO2guOkHqAEmnVZGVxEHxdPgDEtZuiMlsAA0BTd0t08mmEDt4N4eXdEOZW1GCpttXpRCrJl+LUwSfYvu4Wop4pX7xN3S3x9Y4+sLCm/3SE1DRKpNWXlw/cvC3C5WtCXLkmQGJy+d1lmrW2QseeDvDydkC7Lg1g08ColiKtv+rkEIGRT1JxbP9jHN71EMmvc5S2iQwN8PHKTuj7jitV5RJCdJ5ICHh3zYN31zwwBkRGGeDqTRGCr/Fx94EIhYXKVcBP7ifjyf1k7P/hHgCggaMxPDxt4eFlCw8vO7h72tKQpxpWJxJpQYEU4XeSEPxvNAJPvEBYSKLKPgZ8Lt6d0AITP24LsRG1yiWE6B8OB3BxLoSLcxbGjQRyczkIvSdA6F0BboUa4PEzEWRvTAUXH5OF+JgsnD32QrHOwdkETVpZwq2lFdxaWqCJuyVcW1jCkD4bq0XvEmlhoQwxL9Lx4nEaHoYkIOTKK9y7Ho/cnMJS9xcbGcBvfAuMmekBY1MaQYQQUneIxQxdO+Wjayf5TFU5uRzcDxPgdqgAIXcN8PS5sNTp4GIjMxEbmYmgf6KU1ts5GKGhkykcnEzk/zqbwMHJFHYOxrCyE8PUXEg1eaXQiUQqyZciMz0fWekSZGb892+6BKnJuUiMy0bCq2wkxGUjJiID0c/TUVggK/d8HA7QvI0VRkxxR7d+jWHAp2H+CCF1n6GYoZNXPjp5yRNroRSIiDRA+GMBwh4a4EE4DxHRqnetRV7HZuN1bDZCr7wqdTtfwIWVrSGsbMWwbmAIcysxTMwEMDYVwtiUD2MzIUxMBTA2E8DETAgjYz74Qh6EIgMIhDwIRTzwhTzw+dw6lZC1mki//+IK9m65C0m+VO1zGZsK0Ly1FfoOdUMXH0cav5IQUu8Z8ICmboVo6lYIv0HydRIJEB1jgIhIPl5E8vD8BRcvIg0Q91pYZoItUiCRKaqK1cHh4L/EagC+kAeBkAcejwMOlwMelwMulwMu778fLqd4m+JfLrhcgMvjgsOBIilzOABKLH+4tAPadbZXK9bK0Goi5fK41UqiAiEPFjZiNHIxResOtnBraanU+jY2KlOTYRJC1NSwxO9PHiSXuz3tdUKNx1PfWRsB1u5AB/fidQWFHCQl8/E6SYDXiQLEJwrwKt4A8a/5SEnjIz1LiHyJZlIGY0B+nhT5eerfRJVnzAyPGj1/kWp1f7G2toazs3Op2xITE2FjY1Ol8z2+l4TCQtXqWg6HAwM+Fzxe7VcB5BdmQmhgUuuvq2l1pRxA3SkLlaNsZoYZ4HLKf3SjaZm5+TAR141WrNorC5P/MAawyr1/isTDOHgzCWXmFcBExJevV2ysWh7gCY1h6aC5u1Fra2ucOXOm1G3VSqTl0Zc+phWhcuieulIWKoduqSvlAOpOWfStHNQKhxBCCFEDJVJCCCFEDRpPpDNmzND0KbWCyqF76kpZqBy6pa6UA6g7ZdG3cmj8GSkhhBBSn1DVLiGEEKKGSiXSly9fYsSIETAzM4OpqSmGDRuG6OjoCo+7desWZsyYgRYtWsDQ0BCNGzfG+PHjERERoXbg1VHdcrxp7dq14HA46N69ew1EWTnqliU8PBwjR46EtbU1xGIxmjdvjs2bN9dgxKVTpxzR0dGYNGkSGjduDENDQzRr1gxLly5FdnZ2xQdrWExMDObMmYMuXbrA0NAQHA4HkZGRlTo2Ly8PCxYsgL29PcRiMbp06YKLFy/WbMBlqG45dO1aB9R7T0rS9vWubjl05VpXpxy6dK2XilUgOzubNWnShLm7u7MjR46wo0ePMg8PD+bq6sqysrLKPfbTTz9lXbt2Zdu2bWMXLlxgBw4cYC1atGCWlpYsOjq6opfWKHXKUdLz58+ZkZERs7W1Zd26davBiMumbllu3rzJTExM2JAhQ9iRI0fY+fPn2S+//MI2bNhQC9EXU6ccWVlZrGnTpszZ2Znt3r2bnT9/nn3zzTdMJBKxUaNG1VIJigUGBjJbW1s2cOBA5uvrywCwiIiISh07btw4ZmZmxn799Vd29uxZNnToUCYSiVhoaGiNxlya6pZDl671Iuq8J0V04XpXpxy6cq0zVv1y6Nq1XpoKE+mmTZsYl8tlT58+Vax78eIF4/F4Fb4ZCQkJKusiIyMZh8Nhy5Ytq0a41adOOUry9fVlM2bMYD179tTahaVOWaRSKWvVqhV79913azrMCqlTDn9/fwaA+fv7K61ftGgR4/F4LDs7u0ZiLotUKlX8vn379kp/SNy5c4cBYDt37lSsKygoYM2aNWNDhgypiVDLVd1y6NK1XqS6ZSlJF6736pZDl651xqpfDl271ktTYdXu8ePH0blzZzRp0kSxzsXFBd26dcOxY8fKPba0EY6cnJxgY2OD2NjYyt0ya4g65Sjy+++/IyQkBGvXrq2pMCtFnbJcuHABDx8+xPz582s6zAqpUw6JRAIAMDU1VVpvbm4OmUwGVstt6Ljc6jU3OH78OPh8PkaPHq1YZ2BggDFjxsDf3x/5+fmaCrFSqlsOXbrWi1S3LEV05Xqvbjl06VoHql8OXbvWS1NhycLCwuDhoTpeobu7Ox4+fFjlFwwPD0dCQgJatmxZ5WPVoW45UlNTMW/ePHz77bewtLSsiRArTZ2yXL58GYD8uVznzp3B5/Nha2uLjz/+GLm5uTUSb1nUKUffvn3RtGlTLFq0CA8fPkRWVhbOnz+PzZs3Y9asWTAyMqqpsDUqLCwMLi4uMDQ0VFrv7u4OiUSCZ8+eaSky9WnrWtcEXbreq0uXrnV16MO1XmEiTUlJgYWFhcp6S0tLpKamVunFCgsLMWvWLNjY2GDq1KlVOlZd6pZjwYIFaNasGSZPnlwD0VWNOmWJi4sDAIwePRq+vr4ICAjAwoULsWPHDowbN65G4i2LOuUQiUS4fPkyZDIZ3N3dYWJigj59+mDw4MH44YcfaipkjSvvb1C0XR9p81rXBF263qtLl651dejDtV6pofxLmzeuOrfTs2fPxpUrV3Dq1KlSPzxqWnXLcenSJezduxchISE6M4dedcsik8kHlH7vvfewatUqAECvXr0glUqxePFiPHz4EK1atdJssOWobjny8vIwevRoJCQkYN++fWjcuDFu3LiBVatWwcDAAD/99FNNhKtxjDGNXV+6RNvXujp08XqvDl271qtLH671ChOphYVFqd+KU1NTq3SBLFmyBL/++iv27NkDX1/fqkWpAeqUY+bMmZg6dSocHR2RlpYGQP6NWyqVIi0tDWKxGEJh7c24oE5ZrKysAAD9+vVTWu/r64vFixfjzp07tXZxqVOO3377DRcuXMCzZ8/g5uYGAOjRowfMzMwwY8YMzJo1C23btq2RuDXJ0tKy1O4+RXfk+litqO1rXV26dr1Xly5d6+rQh2u9wqpdd3d3hIWFqayvyreZr7/+GuvWrcPmzZsxYcKEqkepAeqUIzw8HD///DMsLCwUP8HBwbh27RosLCxq/RuROmVxd5dPQPjmN+2iOyB1G2hUhTrluH//PiwsLBQXVpGOHTsCkL9n+sDd3R0RERHIyclRWv/w4UMIBAKlhlj6QBeudXXp2vVeXbp0ratDH671Cv+Sfn5+uHbtGl68eKFYFxkZieDgYPj5+VX4Alu2bMHSpUvx9ddfY86cOepFqwZ1yhEYGKjy07ZtW3h4eCAwMBAjRoyo6fCVqFOWgQMHQigUqsyr5+/vD0A+fVFtUaccDRo0QGpqqkpjnOvXrwMAHBwcNB9wDfDz80NBQQEOHTqkWFdYWIiDBw/C19dXL+58iujKta4uXbveq0uXrnV16MW1XlH/mKysLObm5sY8PDzY0aNH2bFjx1ibNm2Yi4sLy8zMVOwXGRnJeDweW7lypWLdH3/8wTgcDhswYAC7evWq0k9YWJgmu/FUSJ1ylEab/crULcuKFSsYj8djS5YsYQEBAWzt2rVMJBKxSZMm6U05IiIimImJCWvatKmik/a3337LTExMmKenp1Kftdpy6NAhdujQITZr1iwGgP3444/s0KFD7MKFC2WWgzHGRo8ezczNzdn27dvZ2bNn2fDhw5lQKGS3b9+u9TIwVr1y6NK1XlJ135M3afN6Z6z65dCVa71Idcqhi9f6mypMpIwxFhUVxYYNG8ZMTEyYsbExe+edd1Q60kZERDAAbPny5Yp1kyZN+m/adNWfnj17arAYlVPdcpRG2xeWOmWRyWRsw4YNzM3NjfH5fNa4cWO2bNkyJpFIaq8A/1GnHGFhYWzkyJHM0dGRiUQi1rRpU/bpp5+ylJSU2itACRX9Xy+rHDk5OWzevHnMzs6OCYVC1rFjRxYYGFjr8RepTjl07VovUt335E3avt6rWw5dutYZq345dO1afxPN/kIIIYSoQT+eNhNCCCE6ihIpIYQQogZKpIQQQogaKJESQgghaqBESgghhKiBEikhhBCiBkqkhBBCiBookRKiYRwOBxwOB1wuF8+fPy9zv969eyv23b17t9K2yZMnK7YtX768zHPs2bNHsV+vXr2Utl24cEGxrejH0NAQ9vb26NGjBxYsWIDQ0FB1ikoIASVSQmqEgYEBGGP47bffSt3+9OlTBAUFwcCg/AmYDAwMsHPnTkil0lK3b9++vcJzODk5Yfny5Vi+fDk++eQTDB48GNnZ2fjuu+/Qvn17jB8/HllZWZUrGCFEBSVSQmqAnZ0dvLy8sGvXLhQWFqps37FjBxhjGDx4cLnnGTx4MGJiYlQGHgfks14EBwdjyJAh5Z7D2dkZK1aswIoVK7BmzRps374dt2/fRmhoKFq3bo3ff/8dI0eOrFoBCSEKlEgJqSHTp09HfHw8Tp48qbS+oKAAe/bsQdeuXRVTXZVl/PjxEIvF2L59u8q2onXTpk2rVnzt2rXD2bNnYWNjgzNnzuDo0aPVOg8h9R0lUkJqyNixY2FkZIQdO3YorT9+/Dhev36N6dOnV3gOc3NzjBw5EqdOncKrV68U6/Pz87F371707NkTzZo1q3aMtra2mDlzJgDgwIED1T4PIfUZJVJCaoiJiQnGjBmDM2fOICYmRrF++/btMDU1xahRoyp1nunTp6OwsFCpQdKRI0eQnJxcqWRckaJGSjdu3FD7XITUR5RICalB06dPh1Qqxc6dOwEAUVFRCAgIwPjx42FoaFipc3Tv3h0tW7ZUPFcF5MnYwsICw4cPVzvGoomRExMT1T4XIfURJVJCalCnTp3QunVr7Ny5EzKZDDt27IBMJqvyneS0adPw4sULnD9/Hs+ePUNgYCAmTJgAkUikdoxFyZnD4ah9LkLqI0qkhNSw6dOnIyoqCmfOnMGuXbvg6emJt956q0rnmDhxIoRCIXbs2KG4M9VEtS4AxMXFAQBsbGw0cj5C6htKpITUsAkTJkAsFmPmzJmIjY3FjBkzqnwOa2trDB06FEeOHMHOnTvRpUsXeHh4aCS+wMBAAPK7Z0JI1VEiJaSGmZubY8SIEYiJiYGRkRHGjh1brfNMnz4d+fn5SExM1NjdaEJCAn755RcA8q42hJCqK39IFEKIRnz11VcYNmwYbGxsYGJiUq1z9O7dG8eOHYNMJkP//v3Vjunu3buYOHEikpKSMGjQIPj5+al9TkLqI0qkhNSCxo0bo3Hjxmqdg8PhVCvZRUZGYsWKFQDkg0EkJSXh9u3buH37NgD5nWjRXSkhpOookRJSx0VFRWHlypUAAJFIBHNzczRt2hSfffYZxo8fj3bt2mk3QEL0HIcVtX0nhBBCSJVRYyNCCCFEDZRICSGEEDVQIiWEEELUQImUEEIIUQMlUkIIIUQNlEgJIYQQNVAiJYQQQtRAiZQQQghRAyVSQgghRA2USAkhhBA1/B8gNDqDHrK0OQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54f3ebe4088c452494231dc52030a404",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad1095f079a442bab5d61f6a395a2fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d9177a03c574d6f81dabd077a65ede2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "285db7ce6d67462fbcbc3b40965cb86c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dfd25d94a0544328ae846ce26e53ebf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9991cd4f45a445b0bfb7c8982dc0650c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26605b076e874371a1085c277c2c1aba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6713ec5159044028b3acd3bbe5eefb08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f534e47073b4f71869bbc9b56efd701",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RUsZRIi3Hbp4PR2aG4hmYtZ0hqlYz13FERB8YmwrRY2gN5fK2VTRAAyGaoERajuWs1vWieUdJDv3GeIDHV7TefnznA574ftRxRISUXZRIy6nMDKlKlV2XvlStS7LZ2hujY69qyuXtq+mulJDiokRaTt25/BZpKRIAgIWVCK41LXQbENE7gybUVr6+ciIUYa/idRgNIWUXJdJyKme1bvseLjQIA1Hj6m6JZu0Vc9IyBuxa56/bgAgpoyiRlkMSiQzXTr9RLnfpWy2fvUlFNjjHFGvHdtFg9oQUByXScuj+9fdIildMmWZiJlQOVk7If9VrVgk16yn+fUjEcuzb+FTHERFS9lAiLYcuH8+u1m3r7UzVuiRPHA4Hg3IMG7hv41OkptBg9oQUBSXSckYmk+PKiewp06halxSkTVcn2Fc1AQAkJ4pxbGeQjiMipGyhRFrOPL7zAbFR6QAAQ2M+ajey1XFERN/xeFwMHJ/dgnfHmseQSuU6jIiQsoUSaTmTs7Vuq85VwePRR0wK1rV/dZhbGgAAPr5LwYXDwTqOiJCyg66y5YhcznD5eHa1blcaW5cUksiQjz6jaimXt616CMZoMHtCCoMSaTny3O8TPr5LAQAYiHio16yyjiMiZUnvETVhIOIBAF4+jcW9q+90HBEhZQMl0nLk4tHsat2m7R3AF9DHSwrP3EqEbgOrK5dpMHtCCoeutOUEY0yl20u3/tXz2ZuQ3A0cVxtcrqK71N0rbxHkH63jiAjRf5RIy4mXT2PxNjQJACAQcNGotb2OIyJlkb2TKdp6OymXd6yhu1JCCkKJtJzI2Vq3YSt7CA14OoyGlGU5B2g4dzAYkeFJOoyGEP1HibScyJlIuw2k1rqk+GrWtUGDFoqGanI5w671T3QcESH6jRJpORD6Ih4hQXEAAB6Pg6btHHQcESnrcg5mf3hrABLiMnQYDSH6jRJpOZDzbrROEzsYGgl0GA0pD5q0raKcwzYjXYoDfz/XbUCE6DFKpOVAzkTqPaiGDiMh5cV/B7Pftc4f6WkSHUZEiP6iRFrGvQ1NVHZR4HCBlh0ddRwRKS+8erjCrooxACAxLgOHtwXqOCJC9BMl0jIu50wvNevawNhUqMNoSHnCF3BVnpVu/e0hxJkyHUZEiH7isGIMqNmkSRP4+fkVat/MzEzExcUhOTkZMhn9EZa0mI9pyoubqYUQRsb0fJSUHMaAmE+pkH/+0zW3FsHMXARTU1NYWVnBwMCgdALJOaeuJmMA78txnuE0ljApGXxtnjwzMxMRERGwtLSEi4sLBAIBTTJdgiRiGWRpsQAADoBqHpY02wspcXbW6Yj+mAYAEAg4cHY2Q3JyMiIiIuDk5FR6yZQQPaXVq25cXBwsLS1hY2MDoVBISbSEJSVkKl8LRTxKokQrzK1E4PEUf7sSCUN6CoONjQ0sLS0RFxen4+gI0T2tXnmTk5NhZmamzbeo0HIm0qy5JAkpaVwuBxbWIuVy9MdUMMZgZqa4MyWkotNqIpXJZBAI6JmdNkglcqQlZ3dHMDGnREq0x8JaBO7nq0VmhgzJCZkQCATU7oEQlEKrXarO1Y6khExkNZUQGnDB51O1LtEeHo+rclca9SFNh9EQol/o6ltG5azWNbMQ5bMnISXD0toQn2dYQ0a6FMmJYt0GRIieoERaBsmkcqQmZ1/EzCyo7yjRPh6fC/Ocd6WRKRr1RCGkvKBEqud27twJDoeDnTt3KtclJ2YqL2ACARd8AU2ZlsVAxEeXLh1L5Fzu7m5wd6eZdHKysjFUPivNSJchI52GDSSEEmkp8vPzw7hx41CtWjUYGhrCzMwMdevWxdy5c/H+/ftCnycpPme1LjUy0nclmdx1jcdXfVaanCCGTCbXYUSE6B4l0lLAGMP333+Ppk2bYs+ePahVqxZmzJiB8ePHw8jICKtWrYK7uzsOHz5c4LlkMjlSknJU61K3F1LKLHPclUolcpw/FKzbgAjRMa2ObEQUli5dipUrV8LFxQWnT5+Gp6enyvYjR45g5MiRGDp0KC5dugQvL688z5WSKIb8c7Uun8+BQEjVuqR08XhcWFobIjY6HQCwYYkvug2sTi3HSYVF//K1LCwsDEuXLoVAIMDJkyfVkigADBgwAGvXroVMJsPUqVMhl+deVXbmzBl06twezdq4oJWXO2bPm4DXwa/V9vv06RO+/99c1KlbG5ZWZrCrZI06dWtjwoQvERoaqrb/xUsX0LtPT1RxqARTMyPU8nDH/+Z9h4SEBLV9s54bJiUlYe53s+Hu7gZjExGWLl2MaV9NhYGIj1OnT+Uav6/vPRiI+Bg2fIjK+rS0NKxc+QuaNmsMSyszWFmbo1371jhw4N9czyMWi/Hzz8tQy8MdpmZGcK9ZHQsXLkBmZmau++eHMYaNf/2JBg3rwczcGK7VnDDzmxlITEzMdf/ExESsXrMK3bp1RjU3Z5iYGsLBsTL6D+gLX997Kvvu3r0LBiLFd1Wfmz4wEPGVP0uXLlbZb8jQQahZqwbMLUxgY2uJDh3aYt++vUUuT2mxtBGB+7kJb0RwIs7++0rHERGiO3RHqmU7duyAVCrF4MGDUbdu3Tz3mzBhApYuXYqXL1/ixo0banelR48exblz59DJ6wuMGNYCL14G4Oy5k7jnewvXr99ETfeaABRJqYNXO4SGhqBTp87o0aMHGGOIiIjAqdMn0a9/f1SrVk153uXLl2LJ0sWwsrLCF917wNbOFs+ePcPatWtw/vx5+Ny4pTY6lVgiRjfvLoiLi0Onzl1gZmYKFxdXdOnSFdu2bcGePbvRq2cvtTLu2bsHADBy5CjluoSEBHTz7gJ//8do2LARxowZB7lcjkuXLmL0mJEIDAzA4sVLlfszxjB8xFCcOnUS1aq5YerUaRCLJdi1eyeeBxR98unZc77Fn3/+AXt7e4wfPxECAR+nTp3Cgwf3IZaIIRSotoh+8SIICxcuQJs2beHt/QUsLS3wNuItTp85hQsXzuPo0ePo1tUbAFC/fn3M/3EBli1fCmcnZ4waNVp5nnbt2itffz3jK3h41EbbNm1RuXJlxMbF4cKFcxj35Ri8evUSixYtKXK5tI3L48LSRoQPnxTLGxb7wntwDQiphoRURKwYGjduXKj9AgMDi3P6cqVjx44MANu8eXOB+w4fPpwBYEuXLlWu27FjBwPAALAD+4+yZ36f2DO/TyzwcRRbtWo1A8A6dPBimRlSlpkhZUeOHGMA2Ndfz1Cuy/pJTkpjMdHxyuUL5y8xAKxFixbs08cYlX23bN6W63mcnZwZAObl1ZHFxSaqvUeNGu5MKBSyyPefVNYnJaYyS0tLZmdnx1JTMpTrR40czQCw5ctXqOyfmJDCunTpyjgcDrvv66dcv2vnPwwAa968OUtMSFGu/xAZxVxdqzEArF3bdmpx5fZz/ZoPA8CqVXNjHyKjVN67efPmDABzdnJWOSbqUyx7/+6j2rlCgsOYvb09q1mzltq2gmIKDHiZ62fVoYMX4/P5LDQkvFDlKe2f9DQxu3LBl9UW/MFqC/5gezb4a+8PSTHni+JHE3uR/UNICdFt1e6iRYrpkfT1Z9EijYv44cMHAEDVqlUL3Ddrn8jISLVtHTt2RKsWnZTLpuZCTJs6HdWqueH69WsIDw9X2d9QZKh2DqFQCFNTU+Xynxs3AAD+2vg3LCwsVPYdPXoM6tdvgH//3Z9rrCt//Q3GxsZq60eNHAWxWIyDhw6orD9z5jTi4+MxdOgw8PmKipDY2Fjs278XjRs3wZzZc1X2F4lE+Hn5CjDG8O+B7Bh27d4JAFiyeBlEouzWo1ZWVvhh3o+5xpqX3bt3AQD+9/08WFlZqbz30iXLcz3G3NwcNjY2ausdHR3Rr98AvHz5AhEREUWKw81NvYuNUCjE1CnTIJVKce3a1SKdr7RwuVyV+W//XHofyYlFr14npKyjql0tY587fBZmqMT89m3Xrh2Sc45mZCkCj8dD61atERoaAv8n/nB2dka7tu3h4OCA31atxGP/x/D27o5WLVuhfv0G4PFUq918fe9BIBDgyJHDOHJEvcWwWCxGdHQ0YmNjYW1trVwvEolQt269XMswYsQoLFq8EHv27MbUKdOU6//ZsxsAMGpkdvWm38MHkMlk4HCg8swwi0Si6KP44sUL5Tp//8fgcrlo3bpNLr+j9mrr8vPY/zEAoG3bdmrb2rRpq0z4/3Xnzm1s+PMP+N67h6joKIjFqiP8REa+h5OTU6HjiIiIwKrVv+Hatat4+zYC6enpaufTV0bGAlRyMMan96lIjMvE9lWPMHNpS12HRUipokSqZfb29njxonB3Ke/evVMe818W5taQyRSJlsfjQGSoSIqVKlcCACR9bhxjZmYGnxu3sXTpYpw+cwqXLl0EANjY2GDypCmYN+9H5UQCsbGxkEqlWLZ86X/fTkVKSopKIrWztcvzi4GjoyO8vDriypXLCHoRBI9aHoiKisLFixdQv34D1KtXX7lvXKxiCi4/P798J4pPTUlRvk5MTISVlVWukyFUrlw533L8V1aDokqVKqlt4/F4KmXOcuLEcQwdNhgikQidOnVGtWrVYGxkDC6XCx+fG/C56VOkRk+hoaFo07Yl4uPj0aZ1G3Tu3AXmZmbg8XgIDw/HP3t2F6sRVWnhcIAvv22IFbNvAQB2/u6PIZProrKjiY4jI6T06DaRLlpUItWn+qxNmza4du0aLl++jIkTJ+a5n0wmw/Xr1wEArVu3Vtv+NuKD8rWiOk2RyD59VLT2MDM3V253dHTE339vAWMMQUGBuHb9GjZt+gvLf14GuVyubLxibm4OuVyOjx+ii1Smgu6uR40cjStXLmPPnt1YvmwF9v+7D1KpVKWRkSJmRSOmGTNm4reVqwv13ubm5oiLi4NEIlFLph8/fixCKRTnAhStnHM2wAIUn0dsbCwcqjiorF+0eCGEQiHu3PGFRy0PlW3TvpoKn5s+RYph3frfERsbiy2bt2H06DEq2w4c+Fd5J6/POvR0xeHtgXgdEAdxpgwbFvti2ZZOBR9ISDlB3V+0bOzYseDxeDh27BgCAgLy3G/79u2IjIxEzZo10b69ehXlrds3la8trBSDMMhkMty+cxsA0KB+A7VjOBwOatf2xFfTpuPsmfMAgJOnTiq3N2vWHPHx8QgMzDuu4ujbtx/MzMywf/8+yOVy7NnzD/h8PoYOGaayX9MmzcDlcnH79u1Cn7tBg4aQy+W4ffuW2jYfnxtFirNhg4YAgJu5JL9bt25CKpWqrQ8JCYaHR221JCqXy3HnTu7l4HK5eU43FhKiGMygX7/+atuKmpR1hcvlYOL3jZXLx3cH4dWzGB1GREjpokSqZdWqVcMPP/wAiUSC3r17IzAwUG2f48ePY+bMmeDxeNi4cSO4XPWPxff+Tdy4eRFcLgciI0VFwsa//kRoaAg6tO8AZ2dnAEBAwHOEhYWpHR8VpbhzNTLKboQ04+uZAICp0ybn2sApNTVVrW9kYRgaGmLggEF4//491q3/HU+fPoG3d3fY2dmp7GdnZ4dhQ4fj4UM//PzzsjwSVwjevHmjXB4zeiwAYOGiBcjIyFCuj4uLw4pffi5SnFndUX75dQXi4uKU6zMyMrDgp9wbLjk7uyA4+LXK74sxhmXLliAoSP2zBQBra2tltX1u5wOAGz7XVdZfvHQBO3ZsK2xRdK5hS3s0ba+4e2cMWD3vjo4jIqT00DPSUrBo0SKkpqZizZo1qF+/Prp16wZPT09IJBLcuXMHvr6+MDQ0xP79+9GxY+5jsnZo2xXfzBmHbl17wrNOTTx99hTnz5+DlZUV1q3foNzvytUr+O67OWjZoiVq1qwFW1tbvH//HqdOnwSXy8W3s+Yo9+3YsROWL/sZ8xf8CM86teDt3R0uLi5ISUlFREQ4bt70QatWrXH61Nkil3nkyFHYvmMbFixQJKScjYxy+v339QgOeY3FSxZh7769aN2qNezs7PDhwwe8eBkEPz8//LN7L1xdXQEAQ4YMxaHDB3H69Ck0bFQfvXr1gkQixbFjR9C4cROEhoYUOsZWrVpj2rTp2LhxAxo1ro9+/QYo+5FaWlrm+qx6xtczMf3raWjeogn69u0PgUCAu3fvICgoED169MSZM6fVjvHq0BEHDx1Av/590KhhI/D5fLRp0xZt27bDlMlTsHv3TgwfPhT9+vZHFYcqCAgIwMWLFzBw4CAcOnSw0OXRtYlzG8HP5z0YA25djMC102/g1dNV12ERon3F6TND/UiLx9fXl40ePZq5uLgwkUjEjI2NmaenJ5s9ezZ7+/Ztrsds27Zd0bd04Tq2Ye0/rGmTZszIyIiZm5uzvn37sWfPAlX69vn7P2MzZsxkjRo1ZjY2NkwoFDJnJ2fWr19/dv2aT679Aa9euc4GDBjI7O3tmUAgYDY2NqxevfpsxoyZ7M7te2r9SP/btzKvHze36gwAs7KyYslJaXnul5yUxtauXcdatGjBzMzMmFAoZFUdq7IOHbzYb7+tVuuTmpyUxn5asJC5uLgqy/f9d/9jSYmpRepHmpkhZRnpErZ27TpWs2YtJhQKmb29PZs8eSqL+hSbZ1m3bN7G6tWrz4yMjJi1tTXr3bsPe+j3mM3/cQEDwC5euKyy/9uISDZk8FBmZ2fHuFwuA8Dm/7hApT9rh/YdmIWFBTMxMWGtWrZiBw8eYRcvXFbbV99+nj19zt6HJSp/5ow8r+xX2rn6TpaeJimZPx7qR0r0GIexos8o2KRJk3xbWWYJCgqCh4dHgfuRvKUkiRH2OgEAwOUC1WtbIauhESG69urVC1iZZfeRTozLwLiux5WTfn+1oBmmLWim+RvlbOCmySSo+3KcZzhNpkpKBj0j1XNJ8dnPAXO21iVEH5lbiTDu24bK5c2/+uFtaO7jFhNSXlAi1WOMMSTlGITBwkqUz96E6IcvhtRAjTqKPrgSsRy/zL5ZwBGElG2USPVYarIEUqmi+onLBQyNqW0Y0X88HhdfL8yuzr1+JgzXz7zJ5whCyjZKpHos590oVeuSssSjgS26D66uXF7+jQ/S0yQ6jIgQ7aFEqqcYYyrPR6lal5Q142c3gqm5YlD7yPBkrPup6H2SCSkLKJHqKarWJWWduZUIk3KMeLTnjyd4eEt94A9CyjpKpHoqkVrrknKg28DqaNquCgBFr5UfJ16hKl5S7lAi1UNyOUNSfI7WutZUrUvKJg6Hg1nLWsLIRDHBwNuQRKriJeUOJVI9lJIkVpkyzdCIqnVJ2WVrb4ypPzRRLlMVLylvKJHqocS47GpdEzOq1iVlX25VvKkp4gKOIqRsoESqZ2QyOZITs6t1Lalal5QDuVXxLp1+A8UYoZQQvUOJVM8kJ4ghlyte8/kcCEVUrUvKB1t7Y0zPMVDDqX0vcXRnkA4jIqRkUCLVMzlb65paGOgwEkJKXpe+bvAemD1Qw7KZN/DyKU0CTso2SqR6RCqVIyUp+7lRWRuEYcKEL2Eg4uc6sbiuuLu7wd3dTddhkBy++qkZXGpYAADEGTJ8O+w8UpPpeSkpu3Rbb/jkJyAtQqch5MvICai/ROPTuLi4IDw8PNdtlSpVwsePHwEASfGZYAyQSMTYvH0Nzl08hvj4ODRs0BC//vobGjZspHb85cuX0KNndxw7dgJfdO+hcaxlTZcuHeFz0weZGVJdh0IKSWTIx/z17TC9/1lkpEsR9joBi7+6jl93dQGHQw3rSNmj20SaFgEYu+g0hHylhpXYqczNzfHNN9+orTcxMVG+zqrWXfvHMvyz72/069cfDg6O2LdvD7p264ynTwJgb2+v3D8lJQXTpk3B8GEj9CKJLl26HHPmfgcHBwddh6J07txFXYdAcuFc3QIzl7bAr3NuAQDO/PsKterb4MvZ6l8WCdF31JKllFhYWGDRokV5bpeIZUhLloAxhsNHd2PUqDHYumUbAKBP7z7o0rUT9u7bgzmz5yqP+eHHecjIzMDq1Wu1HX6h2NvbqyR6feDmRtW6+qpzn2p44vsR5w8FAwBWz7sDR1czdO1fvYAjCdEv9IxUTyTEZYABiIuPQXpGOpo1y27d2LSp4nVERHY1+M2bPti8eRPW/f4HrKysivWeWc8Pk5KSMPe72XB3d4OxiQhLly5W7vPi5QtMmPAl3NxcYGpmhKpOVTB69Ei8fPVS7Xz5PSO9f98XQ4cNhpOzA0xMDeHm5oJpX01FZGTuHfPj4uLw00/z0bBRfVhYmsLWzgpNmjbCj/PnITU1FWFhYTAQ8eFz0wcAYCDiK3+6dOmoVsb/yszMxG+//YrGTRrCwtIUNraW6NixPQ4fPqS2b9Z7TZjwJcLCwjBy1HBUcagEM3NjtGzVHGfOni7wd53TiZMnMHbsaHjW8YCllRmsrM3RomUzbPjzD8izmmznkPV7DQ0NxZ8bN6Bxk4YwtzBRKWdcXBzmz/8B9erXgbmFCWztrNDNuwsuXVK/I09MTMTqNavQrVtnVHNzhompIRwcK6P/gL7w9S3dUYe+XtgcdZvYKZf/N/YSnt7/WKoxEKIpuiMtJZmZmdizZw8iIiJgbGyMevXqoV27duDxeGCMISFWUa1rZWkDQ5EhHj96pDz24UM/AICTkxMAID09HVOmTka/fv3Rr19/jeISS8To5t0FcXFx6NS5C8zMTOHi4goAuHDxPIYMGQSJRIIePXrCzc0N79+9x/ETx3Du/FlcvHA51+e2/7Vr105MnTYZBgYG6NmjFxyrOiI4OBg7dmzD2bOn4XPjtrJsAPDmzRt069YZ4RHhaNSoMSZNmgy5XI7Xr19j/fp1mDhhMiwsLDD/xwX455/dCI8Ix/wfFyiPd3Z2yb/MYjF69uwOn5s+qFmzFqZMmYq0tHQcO3YEI0YOw5Mn/li6dLnacRER4WjTtiVcXV0xfPgIxMfF49Dhgxg4sD/Onb2ADh28CvU7nz//B3C5XDRr2gxVHKogMTEJ169fw+zZs/DQzw87duzK9bjZc2bh9u1b6O79Bby9vcHj8gAA4eHh6NK1E8LDw9CmdRt07doVqalpOHfuDHr17oE/N/yF8eMnKM/z4kUQFi5cgDZt2sLb+wtYWlrgbcRbnD5zChcunMfRo8fRrat3ocqiKaEBD4s2dsCMwefwPiwZmRkyfNX/DP69NQgOLmalEgMhmuKwYvSIbtKkCfz8/ArcLygoCB4eHnnvcHes/j8jbblT49Pk1djI1dUVO3bsQNMmrRD6Ih4AwOEAf21fho0bN6B//wGoUqUK9u3bC7FYjCf+z+Hg4IDvvp+DPXv+gf/jZ7Czs1M7b2G5u7shPCIcXl4dceTwMRgbGyu3xcfHo5ZHDfB4PFy5fA0eHrWV2wIDA9CmbSvUqOEO33sPlOsnTPgS/+zZjZcvguHi4gIAePX6FRo1qo+qVZ1w+dJVleen165dxRc9vNGzZy8cOnhEub5Dh7a4e+8uli5Zhu+++59KzDExMTAxMYFIpGjRXFBjo6y70VevQpTrVq78BQt+mo9u3bxx9Mhx8PmK75NRUVFo06YlwiPCcf2aD1q2bAVAcUdas5aiunHB/J8wf/5PynNdvHQBvXr1QLdu3jh5onB3piEhIWpVznK5HBMnjseevf/gps9tNGvWXLkt6/dapUoVXL1yA66urirHdunSETdv3cTuXXswePAQ5fqEhAR06doJr169xKuXIahUqRIAxR2pRCKBjY2NynnevXuHNm1bwszMHE+fPC9UWV69egErs6qF2jc/78OTMGPgOeUcvG4elth9tT8srA0VO+RshKTJIA77cpxnOA0GQUoGVe2WgnHjxuHKlSv4+PEjUlNT8ezZM0yePBlhYWHo3r07bvvcV+5rZCzAip9/wZzZc+H34AF27tyB2h61cf7cRTg4OOD+fV/88cd6rF61FjY2Nli2bAmcXRxhZGyAFi2b4c6d20WOb+Wvv6kkUQDYs/cfJCQkYMGChSpJFABq1/bEl1+Oh7//YwQFBeZ77s2bN0EikWD16jVqjZC8vDqiZ89eOHPmNJKTkwEAjx49xN17d1G/fgPMmfOd2vlsbGyUSbS4du7aCQ6Hg5UrVymTKADY2dlh3rwfAQA7dmxXO87ZyVm5PUvXLt3gVNUJfn4P1PbPS27PbblcLqZP/xoAcq2OBYBvv52jlkSfPn0Cn5s+6Nevv0oSBRTP5RcsWIiMjAwcO35Uud7c3FwtiQKAo6Mj+vUbgJcvX6g8RigNDs5mWPxXB/AFiktSSFA8JnQ/iYQcw2USoq+oarcULFy4UGW5Tp062LRpE0xMTLB69Wr8+tty/P7bTgCAla0hDAwEWLbsZyxb9rPKcWKxGJMmT4S3d3cMGzYc6/9Yh6XLlmD+jwvQsmUr/PrrCvTq3QOBAS+Vdx8FEYlEqFu3ntr6rGdlT58+VXlmmuX169cAgBcvXqgl2tzOc/OmDx7mUosRHRUFmUyG169foVGjxvC97wsA6NKlC7jckv+el5ycjJCQYDg4OKBWzVpq27OqZ/2f+Kttq1e/Png8ntp6R0dH3CvCs8XY2FisWbsK58+fx5s3oUhNTVXZ/j6P58ZNmzRVW5f1vomJibl+TtExisEOXrx4obL+zp3b2PDnH/C9dw9R0VEQi1X7cUZGvlepbi8NdZpUwncrW2PFtzfBGBDkH42J3U9g6/k+MC/VSAgpGkqkOjRlyhSsXr0afo8UF0Mej5PvBN7Lli1BZOR7nDl9DgCwdu1qeHl1xIIFikTt7l4T7jXdsGnTRixcqH5RzY2drV2uffdiY2MBANu3b833+JSUlHy3x8bGAQDWrFldqPMkJiQAAKpU0U4XmsTERABA5cq5ty7OanWc8DmOnCzMLXI9hs/n59pIKDcJCQlo1boFwsLeoGnTphgxYhSsLC3B5/ORkJiIDRvWQ5yZmeuxlSpVVlsX9/lzunLlMq5cuZzn+6bm+JxOnDiOocMGQyQSoVOnzqhWrRqMjYzB5XLh43NDUVWeRwza5tXTFZkZMqz54Q4YAwIff06mMIQZ0nUSEyEFoUSqQ1nPN9PT0wAAZhYGyGumF3//x1i9ZhU2/LERDg4OSEpKQmRkJIYOHabcx8nJCTY2NggsoLo1p7w6wJubK+4B/B48yvWOtbCyzhMdFQczs4Ibj5hbWABQ3BFpQ1Y8nz7m3jL0w4cPKvuVtO07tiEs7A3m/7hA+QUoy717d7Fhw/o8j83tszL7HOfq1Wsx/auvCxXDosULIRQKceeOLzxqqbZhmPbVVGVLaF3xHlgdjDGs+eEuACDgUTQm8r7C37KNsECaTmMjJDf0jFSHfHwUndEdHZwBAJY2uT/7k0qlmDR5Itq374Bx475U2ZaZqVoll5FRMs+Ushq73Lp9q4TOc7NQ+zf/vP+lS5cKdZfH/VzVKpPJCnV+U1NTVKvmhveR7/E6+LXa9hs3rgMAGjZoWKjzFVVIiKLRU26trW8WI4Fl/b5uF+FzCgkJhodHbbUkKpfLi/WMXRu6D6qBWctaKJefc50xgv8t3sJah1ERkjtKpFoWEBCAuLg4tfXh4eGYMWMGAKBn9wEwMOCBL1B//gYoWpmGhATjr41/K9eZmZnBwcEBFy9egFSqaLHq43MDycnJqJ3PM8vCGjN6LCwsLLB8+VI8eHBfbbtcLlcmnfxMnToNAoEAc+fOwavXr9S2i8Vi3LqVnWQbNWqMli1a4skTf6xatVJt/9jYWJUvC9bWigtrURrHjB0zFowxzJv3vUoCjomJwc8rFN1exowZW+jzFYWLs+JL0w2fGyrr/f0fY+Vvvxb5fI0bN0Gb1m1w/Pgx7Ny5I9d9nj9/hqioKOWys7MLgoNfq/ThZYxh2bIlBTYeK01fDHHHzCXZyTSMUwnD+bPxxJf6mRL9QlW7Wnbo0CH88ssv8PLygqurK0xNTRESEoIzZ84gIyMDbVt3xthR0/K8Gw0MDMCKX37GypWr4Pz5Ipzl22/nYPbsWejYqT2aNm2Of//dBxMTE0yZMk3juK2trbF/3wEMHjIQbdu1hpdXR9Su7Qkul4O3b9/C1/ceYmNjkZSYmu95atWshc1/b8WkyRPQsGE9dO3aDTVq1IBEIsXbtxG4ffsWbGxs8expgPKYHTt2o2vXTljw03wcO34M7dq1A2MMwcHBuHz5Ep4+CVB2r/Hq0BFHjhzGkKGD4N3NG4aGhnBycsaIESPzjGnWrNm4cOE8Tp06iSZNG8Hb2xtpaek4evQwoqKiMHv2HLRu3Ubj32FuRowYhTVrV2POnG9x48Z1VK9eHcHBwTh79gz69u2HQ4cOFvmcu3btQTfvLpg8ZSL+3LgBTZs2g4WFOd6/e49nz58hIOA5fG7cUj5KmPH1TEz/ehqat2iCvn37QyAQ4O7dOwgKCkSPHj1x5kzRBpjQpp7D3GFqIcTKr69AzBEgjmOKcV2O4dddXdGlH41aRfQDJVIt8/LywsuXL/H48WPcvXsXqampsLCwQMuWrdC5fT/06jEIXC4n1ynTZDIZJk2egGZNm2HK5Klq27+aNh3JSUnYsnUzHj9+jHr16uG3lasL3WK3IB07doLfg8dY+/saXLp0Ebdv34JQKIS9fRV0aO+FvoUcDGL48BGoV68efv99LW7cuI7Lly/B2NgY9vZV0K/fAAwaOEhlf1dXV9y79wCr1/yGkydP4q+/NkIkEsHZ2QXfzJyl0nf2yy/HIyIiHAcPHcTqNasglUrRrm27fBOpUCjE2bMXsG7dWvx74F9s3Pgn+Hw+6tWth1W/rcGQIUOL9wsrBEVf0Ov4cf4PuHPnNi5duoiaNWth/boN6NixU7ESqaOjI+7dvY+NGzfg2PFj+PfffZDJZKhUqTI8PDwwbepXqFOnrnL/iRMnwcDAAH9sWI89e3bD0NAQrVu3wZbNW3Hs2FG9SqQA0L67C2rJ/sB03iQkcEyQmSHDrKHnMHleU0xb0BQ8HlWsEd3S7YAMFWT2l9y8DU1EYryiZaSJmRBVnEy18j6laeSo4Th06CDC3rzVuzF3iXaU1IAMBaniYo5w2GIKfwoiONlfpJq2q4KVu7vCropJPkfnQAMyEC3Q7R2plpKUvpNIZMoRXADA2s5Qh9GUnNevX8PAwAC2tra6DoWUQ86Ixj7pGkxvtxP+dxXPSR/4RGJA0wNYsaMz2nR1LuAMhGgH1YnoQHxMhnKUM4GQCwNR2a5h/2PDegwZOgj+/o/Rq1dvldGCCClJlkjFLzs6Y/SM+uB8vnrFRadjcs9TWDrjBlKSaIJwUvookZYyxhjiY7I7llvZlv270T/+WI/7930xdsw4/LnhL12HQ8o5Ho+LUV/Xx2+7u6o00vt30zP0abAPN86G6S44UiFRIi1lyQmZkIgV/SO5nKxBGMq2Vy+D8SY0An//vQUWnwdUIETb6jevjL9P90LzDtmjYH18l4JpfU9j7qgLiP6Qf4tyQkoKJdJSFhedfTdqamGQ58hChJCCWVobYunmjpi3pi1MLYTK9WcPvEb32nuwcdl9pKVKdBghqQgokZaijHQpUpKz/6jLSyMjQnSJw+GgYy9X7LjQF537VlOuT0+V4M8l99Gzzh4c3x0Emaxw4yETUlSUSEtRfI67UZFh3iMZEUKKztxKhO9/a4Nfd3aGSw0L5fpP71Px44Qr6NtwP87eawSZnGqBSMmiRFpKZDK5ytyKNpWMdBgNIeVXo9ZVsOlUT8xa3hIW1tmNkUJfxGPupnHoO38eztxrTHeopMRQIi0lCbEZkMkUfV54fA6MTAQ6joiQ8ovH4+KLwTWw+0o/jJxeDyKj7C5ZoZH2+G7TWPSosxf7/npKz1CJxiiRlgLGGGKjsqd/srQWIa/p0gghJcfQWIAxMxtg740BGDGtLoxF2Y9X3oYkYvlMH3R224n1C+8h5hNN0UaKhxJpKUiKz4Q4U1GNxOEAFtbUyIiQ0mRmYYCxsxri4qpFmNzrPMyMs7vGJMZl4u8VfuhSfRd+mnIVIUHqszURkh9KpFrGGFP5pmtuaQAul+5GCdEFC5M0zBhwBpdX/4TZU96isl32SEjiTBmObA9E7/r7MKX3Kdy5HIFiDEVOKiBKpFqWliJBeppivlAOAGs7amREiK4Zi8QY0icGh/dEY/lPsfBwz1DZfvN8OCZ+cRL9G/+LozsDkZkh1VGkpCygRKpl0R+z70aNTQXg8elXToi+4POATu0zsH1jLP5aG412rdLA4WTfhb56HosFk66iS/Vd2Lj0vkpbB0Ky0FVdi9JSJCqDaH85ZSAMRHz4+NzI85ivpk+DgYiPbdu2lkaIhBAo2i40rCfGyqXxOLjrEwb2TYHIQKbcHhuVjj+X3kdnt134afJVBAfE6jBaom8okWpR9MfsBg1Gxnw8eeIPHo+HRo0a53nMgwf3AQCNG+e9DyFEe6o6yDDn60ScPPAJX01MhK1NdvcYcaYMR3YEok/D/ZjY4wRuXQyn56iEEqm2pKdKkJyYfTeakvERcXFxqF3bEyYmuU9CnJaWhoCA5zAwMICnZ53SCpUQkgszU4ZRQ1NwbG8Ulv4Yh1rumSrb71x6i8k9T6F/439x4XAw5HJKqBUVJVItiYpUvRt99vwJAKBp02Z5HvPo0UNIpVLUq1cPAgEN2ECIPuDzgS4d07FjYww2/R6NDm3Un6N+O/y8YgjCA69oxKQKiBKpFqQmi5Gc49moXRVjPHzkBwBolk8i9b3vCwBo1KiJdgMkhBQZhwM0qCvGL4vjcXj3Jwzpr/ocNSQoDnNHXUTfBvtxet9LSKWUUCsKfsG7kKJgjOHT+xTlsrGJAEIDPh49fAgAOHT4IG7cuJ7rsfezno/m8wyVEKJ7DlVkmPVVIsaNTMb+QyY4eMwI6RmKSShCX8bj+7GXsOnnB/hmWUt06lONpkss5yiRlrDkRDHSUrP7jdo5GIMxBv8n/gCAK1cuF3gOamhESNlgYS7H1AlJGD44Gf8eMcGBo8ZIS1Mk1DevEjBz8Dk0bGWPOb+0QoMW9jqOlmiLzhKpp3CDrt66yALE0wu1n1yuejdqYi6EQMDDy1cvkZiYiBYtWuDG9Vu5HhsfH4/K9rYwMjKCh0dtlW2b/v4La9asxsePH1C7tidW/bYabdq0LX6BCCElytyMYfK4ZAwbmIIDR02w/3B2Qn185wNGtDuCLv3cMGtZSzjnmOKNlA/0jLQExUWnIzND8cyEwwHs7I0BQFmtW79+wzyPffz4kWKfevXB42XPU3ro0EHMnj0L33/3P/j6+qFFi5bo3acnIiIitFUMQkgxmZkyTByTjKN7ojB0QAr4vOznpJeOhaB3/X1Y/s0NlSkVSdlHibSESCVyRH/IbqlrZWOoHMUoq6FRwwb5JFL/xwCARo1VGxqtW78Wo0eNwfjxE+BRywO/r12HypXtsXnzppIuAiGkhFiYy/HNtET8uzMKnTtkj4Yklcqxb+Mz9PDcg0PbAqjLTDmhs6rdwlaXlhVRkSnZ843yOLCyy57hJeuOtGHDvBPpE39/AKoNjcRiMR49eoRZ38xW2bdz5y64d+9uSYVOCNESxyoyLFsQj2GDUvDHJjP4P1NMNJ4Qm4FFU6/h8LYA/LiuPeo1raTjSIkm6I60BKSmiBEXk11VY1fFWNlKTy6X48nTJxAKhahd2zPPc2TfkWYn0piYGMhkMtjZ2ansW8nODh8/fSrJIhBCtMizlgR/rY3FL4tjUdkue6Sk535RGN7mEH6achXxMen5nIHoM0qkGpLLGSLDk5XLIkMeTM0NlMsvXgQhJSUFtWt7QigU5nqOlJQUBAe/homJCWq611Tb/t+m84wxak5PSBnD4QAd2mTg3x3RGD8qCQKB4vkpY8CR7YH4wnMPDm59TtW9ZRAlUg3FfErLbmAEoIqTqcr2R48UjYjyez769OkTyOVyNGzQEFxu9kdiY2MDHo+HT/+5+4yKjkal/9ylEkLKBpGIYeLYZOzfHoXWLbLvQpPiM7F42nWMbH8YL57E6DBCUlSUSDWQniZRbWBkZwi+gKeyz8iRo5CZIcWmTZvzPE+rVq2RmSHF5cvXVNYLhUI0atQIl//T9/TKlcto0aJlCZSAEKIrjlVkWL08DquWxaJK5ezq3ie+nzC4xQH8OvcmUpPF+ZyB6AtKpMUklzO8e5OErIkfBEIurG0N8z+oGGbOmIV//tmF7du3IehFEL6dPQsfPkRi4sTJJf5ehJDS16ZlBvZtj8K4kUnK7jIyGcPudU/Qq95eXDwaTDPM6Dka2aiYPr1PUanSdXQxUzwEKWGDBg1GbFwsfvnlZ3z4+AGennVw4vgpODs7l/h7EUJ0Q2QATB6XjG6d0vHbOnM89Fe07v30PhWzhp5HW29n/Ph7O1StZq7jSElu6I60GBLjMxAblf1sw7qSEQRCXj5HaGbK5Kl49SoEyUlpuHf3Ptq2bae19yKE6I6LkxQbVsVi0bw4WJpLletvng9Hnwb78PcKP4gzZfmcgegCJdIiysyQqrXStbIV6TAiQkh5wuEA3p3TcWBXFPr3TlFO2ZaZIcP6hfcwoMm/uH/jnY6jJDlRIi0CmVSOtyGJyoEXuFyOokoX1BWFEFKyzEwZvpuZiK0bolHDLXtS8dCX8RjX5TjmjbuE2Ki0fM5ASgsl0kJiTNG4KEPluagpuDz6FRJCtMezlgQ7/orBrGkJMDLMrtY9ufcletbZi4NbqO+prlEWKATGGD5EJKtM1m1rbwyRkUCHURFCKgo+DxgyIBX/7ohCp/bZd6FJCZlY/NV1jGh3GEH+0TqMsGKjRFoAxUTdqSpDAJpbGsDCmp6LEkJKl52tHMt/isfvv8SgSuXsL/ZP73/C4BYH8csc6nuqC5RI88EYQ/SHVMR8yv4GaGTMRyUHYx1GRQip6Fo0zcS+7dH4cmQS+HxF31O5nOGf9U/Qs+5enN7/kqp7S5HWE2lZ7UisuBNNQdSH7CQqMuTBgRoXEQKg7P5tlxciA2DSuGTs3RqNxg2ya8yiIlPx/ZhLGNLyIO5dfavDCCsOrSZSHo8HiURS8I56Ri5neB+ejJhP2X1FDUQ8VK1mToPFE/KZVCoFh6O9/tOkcJyrKvqeLv5Bte9p4ONojPc+gUk9TyLwcZQOIyz/tJpITU1NkZSUpM23KHESiQxhr+KREJv9Dc/QiA8nN0qihOSUnJwEAZ/aCugDDgfo1ikdh3ZHYcywZAg/zywDALcvRmBQ84P4qt9pPH9I0y9qg1YTqZWVFeLj4xETEwOxWKz3VUEpSWKEBMUjLTX7W52xqQBVXc0oiRICRXWuRCJBXFwsYuPiIBKaFnwQKTUmJgxTJyTh8D+f0Ms7FVxO9jX3+pkwDGl5CFP7nMLD25F6fz0uSzisGL/NJk2awM/Pr1D7ZmZmIi4uDsnJyZDJ9HNoKyZnSE4UIyVJtbWbsakAxmZCeiJKSA4cDg8CvggioSn4vNLpAlbFJXuM2ciwxOKf53b2eVIrfQm5sHxPRxjyho9tu01x7aYhGFO9knk2ssWorxug26DqEGpxiNOKQOuJVJ8xxuBzLhw/z/LBuzfZVdBCAx5+WNsWrbs46TA6QkgWSqSaCQ3jY8c/Jrh8w0gtodpUNsLALz3Rd3QtGhS/mCpsIn364BN+n38XvtdUx6x0cbfArzu7wEoLU6IRQoqHEmnJCIvgY99BY5y7bASJRP3JXpO2VdB3dC107V8dxqZCHURYNlWoRCqXM9y+GIGdvz/GvauqCZQv4GLi943Qb7QHPQ8lRM9QIi1Z8QlcHD9jhMPHjBAbr149LxBy0bJTVXTqUw1ePV1hbWekgyjLjgqRSMNexePswdc4vjsI78OS1ba37lIVM5e2gKU13YUSoo8okWqHRALcvCPCmQuGuPvAEHK5+k0El8tB3aZ2aN7BEc29HNGgpT1EhjSVdU7lMpGmJovhdzMSd668xd3LbxESFJfrfg1aVMb0hc3gXN2idAMkhBQJJVLti43j4vxlI5y9aICQN3l3axIIuajbtBLqNLGDZyM71G1SCU7VK3b3wDL/tSIxPgOhL+Lx4kkMXjyJVvzfPwZSqTzX/QVCLtp1d8GYGfVh70RN9wkhBACsreQYMTgFIwanIPIDDzdui3DNR4jngYaQ52igJBHL8ej2Bzy6/UG5zshEANealnCrZYlqtSzhXMMClauaorKDMWwqG4PLLd9JVq8SqVQqR0aaFOlpEmSkSZGcmIn4mAwkxGYgITZd8TouA1HvU/EuLAmR4UlITix4gGYOF6hV1wa9R9VEO28XCA2oqTchhOSlir0MwwamYtjAVCQmJuLRUyH8Hglx34+Pt5Hqj8DSUiQIeBiFgIfqIyjx+VzYORijsqMJ7OyNYW4tgoWVCOZWIphbGsDcSgQzCwMYGvNhIOLD0IgPkZEABoY8iAz54JWBqSp1mkgXTr2KG2fDPidPKaSS3O8ii8PazhANW9nDq6cr6jWrRHX6hBBSDObmcni1zYBXW8Vob7FxXAS8ECLoBR8BgTwEvTZAckre/YmlUjkiw5MRGa7ePqUwhAafEyqfAx6fCz6fCy7v8+vP/+fyOODzueDxueB9Xsfjc7BkU8dS6dKj0+ySkihG9AfNZnjncAETUyEqVzVBDU9ruHlYwdHVDCZm2U23I0KK/0yFEKJ7VXK8fvU8tkTOkxSXCGn5rnHUCh6Aem6KH/QAGAMSkviIeC/C2/ciRLw3wIePPETHChATb4CUNAON3k+cKYM4s3iD+WSmSwveqQQUq7GRjY0NXFxcct0WHR0NW1vbQp9LJpXjxdOYAvfjcjng8RTfMkpbpjQZBvzy9Ty1PJYJKJ/lojKVPENBBkTCjIJ3LILk9EyYGmqWNPSR9solB5hckYkLwD7/h+UxzhxT/id7u9DEEuZ2NrnuX9Q8BSjy3vnz53PdVqxEmh99b9FbHFSmsqM8lovKVDaUxzIB5bNcJV0m/X+KSwghhOgxSqSEEEKIBko8kU6aNKmkT6lzVKayozyWi8pUNpTHMgHls1wlXaYSf0ZKCCGEVCRUtUsIIYRooFCJ9O3btxg4cCDMzc1hZmaG/v37IyIiosDj/Pz8MGnSJNSqVQtGRkZwcnLCiBEj8ObNG40D11Rxy/RfK1asAIfDQZs2bbQQZdFpWq6goCAMGjQINjY2MDQ0RM2aNbFu3TotRlwwTcoUERGBMWPGwMnJCUZGRnB3d8f8+fORmpqq5ajz9+7dO3z99ddo2bIljIyMwOFwEBYWVqhjMzIyMHfuXNjb28PQ0BAtW7aEj4+PdgMuhOKWSZ+vE5p8Tjnp23VC03Lp43VCkzJpfJ1gBUhNTWXVq1dnnp6e7NixY+z48eOsTp06rFq1aiwlJSXfY2fPns1atWrF/vzzT3b9+nW2d+9eVqtWLWZlZcUiIiIKemut0aRMOYWEhDBjY2NmZ2fHWrdurcWIC0fTcj148ICZmpqyXr16sWPHjrGrV6+yv//+m61evboUos+dJmVKSUlhNWrUYC4uLmznzp3s6tWr7Ndff2UikYgNHjy4lEqQu2vXrjE7OzvWvXt31rVrVwaAvXnzplDHDh8+nJmbm7PNmzezy5cvs379+jGRSMQeP36s1ZgLUtwy6et1gjHNPqcs+nadYEyzcunjdYKx4pepJK4TBSbS33//nXG5XPb69WvlutDQUMbj8Qr8xUVFRamtCwsLYxwOhy1YsKBQAWqDJmXKqWvXrmzSpEmsffv2evEHokm5ZDIZq127Nuvbt6+2wywSTcp04cIFBoBduHBBZf3333/PeDweS01N1UrMhSGTyZSvt2zZUug/en9/fwaAbd++XblOIpEwd3d31qtXL22EWmjFLZO+XicYK36ZctK36wRjxS+Xvl4nGCt+mUriOlFg1e7JkyfRokULVK9eXbnO1dUVrVu3xokTJ/I9NreRI5ydnWFra4v3798X7pZZCzQpU5Z9+/bh0aNHWLFihbbCLDJNynX9+nUEBgbi22+/1XaYRaJJmcRixYQGZmZmKustLCwgl8vBdNjOjsstXvOEkydPQiAQYMiQIcp1fD4fQ4cOxYULF5CZmVlSIRZZccukr9cJoPhlyqKP1wmg+OXS1+sEUPwylcR1osB3DggIQJ06ddTWe3p6IjAwsLCxKgUFBSEqKgoeHh5FPrakaFqm+Ph4zJo1CytXroSVlZU2QiwWTcp169YtAIrnby1atIBAIICdnR1mzJiB9PR0rcRbGJqUqXPnzqhRowa+//57BAYGIiUlBVevXsW6deswZcoUGBsbaytsrQkICICrqyuMjIxU1nt6ekIsFiM4OFhHkZUsfbhOaEpfrxOa0NfrhCZK4jpRYCKNi4uDpaWl2norKyvEx8cXKWCpVIopU6bA1tYW48ePL9KxJUnTMs2dOxfu7u4YO3asFqIrPk3KFRkZCQAYMmQIunbtikuXLuG7777D1q1bMXz4cK3EWxialEkkEuHWrVuQy+Xw9PSEqakpOnXqhJ49e2LDhg3aClmr8vt9ZG0v6/TlOqEpfb1OaEJfrxOaKInrRKFmf8lt5vPiVItNnz4dd+7cwZkzZ3K9GJSm4pbp5s2b2L17Nx49eqSXM8IXt1xyuWIKu5EjR2LJkiUAgA4dOkAmk+F///sfAgMDUbt27ZINtpCKW6aMjAwMGTIEUVFR+Oeff+Dk5IT79+9jyZIl4PP5+Ouvv7QRrlYxxkrs71Ff6dN1orj0/TpRXPp8nSiukrhOFJhILS0tc/2WGx8fX6R/5PPmzcPmzZuxa9cudO3atdDHaYMmZZo8eTLGjx8PR0dHJCQkAFB8g5bJZEhISIChoSEMDHQzA4Qm5bK2tgYAdOnSRWV9165d8b///Q/+/v46+QPRpEzbtm3D9evXERwcDDc3NwBAu3btYG5ujkmTJmHKlCmoX7++VuLWFisrq1y7/mTdnZf1KkR9uk5oQp+vE5rQ1+uEJkriOlFg1a6npycCAgLU1hflm8fy5cvxyy+/YN26dRg1alShjtEmTcoUFBSETZs2wdLSUvlz+/Zt3Lt3D5aWljq9y9GkXJ6engDU7/6y7nQ0bXRRXJqU6dmzZ7C0tFT+cWRp1qwZAMVnWdZ4enrizZs3SEtTncc3MDAQQqFQpVFWWaNv1wlN6PN1QhP6ep3QRElcJwosde/evXHv3j2EhoYq14WFheH27dvo3bt3gW+wfv16zJ8/H8uXL8fXX39d4P6lQZMyXbt2Te2nfv36qFOnDq5du4aBAwdqO/w8aVKu7t27w8DAQG2+vQsXLgBQTDukC5qUqXLlyoiPj1drgOPr6wsAcHBwKPmAtax3796QSCQ4dOiQcp1UKsWBAwfQtWvXMnmXA+jndUIT+nyd0IS+Xic0USLXiYL6x6SkpDA3NzdWp04ddvz4cXbixAlWr1495urqypKTk5X7hYWFMR6PxxYvXqxct3//fsbhcJi3tze7e/euyk9AQECBfXO0RZMy5UZf+odpWq5FixYxHo/H5s2bxy5dusRWrFjBRCIRGzNmTCmXJJsmZXrz5g0zNTVlNWrUUHa0XrlyJTM1NWWNGzdW6XemC4cOHWKHDh1iU6ZMYQDYxo0b2aFDh9j169cZY3l/TkOGDGEWFhZsy5Yt7PLly2zAgAHMwMCAPXz4UBfFUFGcMunrdSJLcT+n/9KX60SW4pZLH68TWYpTppK4ThSYSBljLDw8nPXv35+ZmpoyExMT1qdPH7WOrm/evGEA2MKFC5XrxowZw6CYt1ztp3379oV5a60pbplyo09/IJqUSy6Xs9WrVzM3NzcmEAiYk5MTW7BgAROLxaVXgFxoUqaAgAA2aNAg5ujoyEQiEatRowabPXs2i4uLK70C5KGgv428ypSWlsZmzZrFKlWqxAwMDFizZs3YtWvXSj3+3BSnTPp8nWCs+J/Tf+nTdYKx4pdLX68TjBW/TJpeJ2j2F0IIIUQDZe/JMCGEEKJHKJESQgghGqBESgghhGiAEikhhBCiAUqkhBBCiAYokRJCCCEaoERKCCGEaIASKSEljMPhgMPhgMvlIiQkJM/9vLy8lPvu3LlTZdvYsWOV2xYuXJjnOXbt2qXcr0OHDirbrl+/rtyW9WNkZAR7e3u0a9cOc+fOxePHjzUpKiEElEgJ0Qo+nw/GGLZt25br9tevX+PGjRvg8/OfgInP52P79u2QyWS5bt+yZUuB53B2dsbChQuxcOFCfPPNN+jZsydSU1OxatUqNGrUCCNGjEBKSkrhCkYIUUOJlBAtqFSpEpo0aYIdO3ZAKpWqbd+6dSsYY+jZs2e+5+nZsyfevXunNkg4oJiV4vbt2+jVq1e+53BxccGiRYuwaNEi/Pzzz9iyZQsePnyIx48fo27duti3bx8GDRpUtAISQpQokRKiJRMnTsTHjx9x+vRplfUSiQS7du1Cq1atlNNS5WXEiBEwNDTEli1b1LZlrZswYUKx4mvQoAEuX74MW1tbnD9/HsePHy/WeQip6CiREqIlw4YNg7GxMbZu3aqy/uTJk/j06RMmTpxY4DksLCwwaNAgnDlzBh8+fFCuz8zMxO7du9G+fXu4u7sXO0Y7OztMnjwZALB3795in4eQiowSKSFaYmpqiqFDh+L8+fN49+6dcv2WLVtgZmaGwYMHF+o8EydOhFQqVWmQdOzYMcTGxhYqGRckq5HS/fv3NT4XIRURJVJCtGjixImQyWTYvn07ACA8PByXLl3CiBEjYGRkVKhztGnTBh4eHsrnqoAiGVtaWmLAgAEax5g1cXF0dLTG5yKkIqJESogWNW/eHHXr1sX27dshl8uxdetWyOXyIt9JTpgwAaGhobh69SqCg4Nx7do1jBo1CiKRSOMYs5Izh8PR+FyEVESUSAnRsokTJyI8PBznz5/Hjh070LhxYzRs2LBI5xg9ejQMDAywdetW5Z1pSVTrAkBkZCQAwNbWtkTOR0hFQ4mUEC0bNWoUDA0NMXnyZLx//x6TJk0q8jlsbGzQr18/HDt2DNu3b0fLli1Rp06dEonv2rVrABR3z4SQoqNESoiWWVhYYODAgXj37h2MjY0xbNiwYp1n4sSJyMzMRHR0dIndjUZFReHvv/8GoOhqQwgpuvyHRCGElIhly5ahf//+sLW1hampabHO4eXlhRMnTkAul6Nbt24ax/TkyROMHj0aMTEx+OKLL9C7d2+Nz0lIRUSJlJBS4OTkBCcnJ43OweFwipXswsLCsGjRIgCKwSBiYmLw8OFDPHz4EIDiTjTrrpQQUnSUSAkp58LDw7F48WIAgEgkgoWFBWrUqIE5c+ZgxIgRaNCggW4DJKSM47Cstu+EEEIIKTJqbEQIIYRogBIpIYQQogFKpIQQQogGKJESQgghGqBESgghhGiAEikhhBCiAUqkhBBCiAYokRJCCCEaoERKCCGEaIASKSGEEKKB/wOfZIFxV6tpVQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62c2a44f3f434d83a52796cf1c451428",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c931e4a56af146a38774923c3597adf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "976afafbcc244a0dbf1c318ee2455dd0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6966bfa5da2b4853a977c1792cfb7db6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86e4b212020e431a9bf8ccdb0a2edb62",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffde19a236d34b3882c527fc2dfe984c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8eca26bb952947cc88a1ece4e614fbd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "6a837527",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "avg = np.mean(ori_x)\n",
+    "ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "# maxAbs = np.max(np.abs(ori_x))\n",
+    "# ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db00a69b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "3f47b36c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "03000e92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "yes_data = yes_data / 3000\n",
+    "no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "yes_data = yes_data - avg\n",
+    "no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "# yes_data = yes_data / maxAbs\n",
+    "# no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "3f6bb185",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "b0dfe2e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb91f4e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9234e5e36c0d438289b6612314482da5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b56b95ef8ff74b279c2ba8ffd3e643f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0d70770",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "340368d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ceddb75",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v4_Aug16_bf_model_misspecification_1000_data.ipynb b/bf/v4_Aug16_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..b97854e
--- /dev/null
+++ b/bf/v4_Aug16_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,6098 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # Divide entire matrix by maximum of absolute value\n",
+    "        temp = dpl[0].data['agg']\n",
+    "        maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "\n",
+    "#     print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b8cd8fbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9787d8dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f333229c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "202baf4537204aef8c30a897d9d3acf9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.06918</td>\n",
+       "      <td>0.820124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Loss   W.Decay\n",
+       "0  7.06918  0.820124"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8476e5dd",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2d854e3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da5d1b8a68a14240977fe2cf35ff5fee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0699c7b2dd3944969c24ff6d1faaead6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.2\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.2\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00486b8789674b4a96f32d39ab38e48d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all -0.2\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*(-0.2)\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd895581abd64b88aca6e2522bb65c56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61d9a20ad79e4cb69987816cb3536c5a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3cdd446f29c4e9abc74adcdbac1d7ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7481f2138ca140f5b3e518b7fc040a48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daea56dd585a4db3ad9689715f01b0f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32c6d189f37f4cbdbd0a2e3a9c041d4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75f992b5a12b40ce89c8190966e78e9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b3e746ac41f466ba562c43125ed3aeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jgAshbVRsbm9CK4WnbVkxLpvscYP1vBuPd8GVG0hUCIKQGN4amdtyRK9WCJ/IpWLS5saHY1bc0VKJFeUGXDEtDkvllXNj0KnkCLBcN+HrV5QKj0WzVJxxLJXlZXoAQG9I08j+KQcYhnNTAUChTokZpxcOjw9Tdg/qirXQquRYVqYXYjoA567iM7sS0ba0BADw/OkhfOG/u6PW1vDt8cMsFXJ/EQQhJbGCwTqVXPhELhXDZi7jaWmpHg3legxMOxe8FoZlWVwZt+HONVXCtXs21glfc9Xq3rDOH/agqESzIpaWcqLSNxEuKlUFGiEBokjLub/4Gz8vNs3VBbg4OmupDE45UFesTer3uHpJEaoLNfjWy+dx5PwYfnCsJ2KNmdxfBEHEYsrukcQ9FesGqVPJJXd/Dc9wN9GaQi1WlBvgD7BCvGGhmHF6YXX7sLq6AH9zeyP23b9RuPkDnFXh8QXC4kvOoNhqlZHurzKDCga1IkxUBqYcQjwF4NxPM06v4CKrDQpHc5UR/VMOoX3+4LQzaVFRyGW4/7qlYFnO6nzu5CDGLOE9xqYdHshljJCiXG5QY8bpXXBhnwuJCkGEYHZ4MDIT6XqQmnv2vYlvH74o+nnsMTKZdCqFIDhSMRL8ZF5dpMGKcgOAhQ/W8/GO+hIdvnrHqjCLBZj9VB9arW53x7ZUGIbB8jI9ekJ+r/4phxBPAbhuxmaHF0NzLJV1dUUAgLMDM7C7fZi0e4SCymS4/7ql+PO2enznz9bDF2DxQUgxpdPjx/+eHUFjuQEyGddbkU8rDm3LvxggUSGIIP4Ai9ZvHMFt3/1jWvdxeHy4YrLj0pg18eKEz8XHVCLdX444rd9TYWTGBZVChlK9Ck2VBshlDM4NWxL/YBoZmOJu7KE3/VCKg0Ht0FqVeIF6ALiuoQTdfdOwurzw+QMYt7pRUzhbXFqkU8Hp9aPXZIdCxqCygHusdUkRGAY41T8tWDH1Mc4VjUKtEt++Zx2uWc7FV0LTk//j+GX0Tznw2KdWC9cWa60KiQpBBDnwbj+A9Lc36ZvgbhbRgrHzhb9BRrq/pA/UD8+4UF2oAcMw0CjlaKowLPhMj1BLJRp8oDy0tTz/NZ9FNZetq6vg8Qfw6iUTJmwesCxQGSIqfPPOc8MWVBVqIA9aDgUaJZoqDDjVPy3UutQn6f4KpVSvgl4lF363GacXP369F9taKnFDY5mwjkSFIBY5L50dAcD9UacTfhztsDm5FurxcAiunCiWisSB+hGzU+h9BQBragvxwdBM1PEXmaJ/yoESvSpmJ+BZ99espTLt8MCgVgijAuaycWkxSvQqvHJuTIhr8O1ZgFkxOj9iEVxfPBvqi/HHD0146L+7AWBe7i8ehmFQX6IT4lX/824/rC4fvrS5KWwdiQpBLGL8ARZng72b0t0zi+8X5fEHRFdEz8ZU5qYUy6W3VMxO1ITcRNfUFGDC5sGYZeFuagNTjrjWAO/+Co2pzASbMsZCLmPwiTVVOHJ+VPi3qixQC48XaTmhmnF6hSA9z19cuwR/urZa+L7MkNoHlKWlOsFSeeXcGNbWFqKlpjBsTak+UlT8ARZfOXAab1+ZTGlfKSBRIQgAl8ascHj8qC/RwuH1p/XTd2hm0aBIF5jg/poTH9AqpXV/BQIsxqzuCEsF4IoOF4phszOuNVDIx1Ts4ZZKcYL6kc9sqIXLG8DP3v4YAIS4CRDuNpu79/r6Ijx939U49tVb8NPPb4oYWJgsS0o4UTFZ3TjVP40tzZURa1QKGYp1Sphss1lib12exAtnhvFY17mU9pUCEhWCABdcBYAbGsrAsukdxds3aRduTGLjKkLLEXU0S8UnmThaXF74A6zw6RgAmiq4/lo9GcgA+1X3AD7zozfgDWmfwrIsRmZcwoyTaKgVcuhU8rBOxdMJLBWAc4HVFmlx8uNpMEy4S7SxwoBgGCXMggllRbkBt62qSOZXi8qSUj3cvgC+fOA0WBbY3Bz9uSqMGpzoncKw2YkfHOvB/T9+BwBQrI//+6UTEhWCAHBxxIoCjQIrq7gbZTpdYMNml1BBPRRldsd8sLt9UMqZiP5S2mCFuVTiyLuPQm9WhTolKoxq9IylV1Sm7B5866XzON1vDhu4NeP0wun1h1lP0eDH9Yb+XKJKd4ZhcEOwMr9Ur4IiJP6iUcrx84euhVGjwMalxan8Sgm5dWU51tUV4nS/GV/a3ISWmoKo6/5mcyMGp53Y8r1X8b0jl4TrC+mSlGSeCkFkO1N2D8qMaiE24fT6UZSmvcwOD5aU6GBQK4RhT6nCjcWN/DPm3WF2ty+sGDBV+JsyH0/gWVlpxEfj4lOj4/G9Ix/CHhz/+9J7I7g1aAHwr111YfwMq9AGkAD3uxRpE3+S37SsBL8+OSjMRQnlhhVleO+xO1J2byWivkSHF//6RrAsG3ePT66rQWOFAX/54xO4sbEM91+3FIc+GMWvuwfg8wfCxDBTkKgQBGb97NrgDTgdzRgBbsSv3eNHiV6JAo1CaI+eKna3L6LvFwChS6/D40dpxKPzZyZGGm5jhQG/6h5AIMAKRXlScvySCb98px+fu34ZLC4vjpwfE/biR/pWFyVvqfgDLGacXiGAH49NwXqRWNZeugRlvntcVVWA1//xNihlMu51sbjw7Il+DJmdQtuZTELuL4IA594p1imFT/XpaBsPhNZIcDMxbFE+Bc+HuR2KeXhLRapgvWCpzHEbNVUa4PD4hRYuUvKr7gF87icnsKREh69sacI1y0rC2qPweyZyfxWGWCpWlxcsG/l7RGNZKReEv/2q1GMjmUKtkAuivozvX7ZALXRIVAgCnEuqSKcSPuGnK6YSOhPDoFGIbvpoi2Gp6ARLRZpaFf6mPPcTPj8MS+q4yvFLJvzzi+dww4pSHP7KzSjSqbA6GFc4P8Jlm43OuCBjIIzWjUWxTilU1E8nKHwMhWEYnH3sDvz7/VeL+VUyDi+GfQs0moBEhSAw62efdX+lJ/uLn0JYrFfCoJbI/RXFUtGFuL+kwOzwgGG4qvFQGoM9wHokjKu8dXkSn/vJCVQY1fjXe9cL1uPKSiPkMgbng61hhs0uVBZoEsYNioMzVQIBNmLQVSIKtcqEQ7YWG2UGNVQKmSQdG1KBRIXIe1xeP1zeAIr1qrBAfToIvanpVeLdXza3T+haG4ouJFAvBdMOLwq1yoi4SbFehTKDtBlgL5wegkGtwKEv3xwWhNco5VhRrsf54MySgWlHWDFmLIp0KgRYLi06Vmwol5DJGNQWaUXXQKW8vxRPcvjwYaxatQqNjY3Ys2dPxOO/+MUvsG7dOqxbtw433HADzp49K8W2BCEJob2g0h1TiXB/ibzpW10+GNSRN0i+bkWq38Ps9Mb8dL+y0oBLEtWqeP0BvHJ+FJubKwRXZCirqwvwwZAFgQCLC8MWNFcbEz4n77L71ssXhM7CJWluxbPQ1BRpstdS8fv9ePTRR3Ho0CGcP38ezz77LM6fPx+2Zvny5Xj11Vfx3nvv4Z/+6Z/Q0dEhdluCkIzQG70QU0lT9leogEnh/rK6vDEsFe734Nu8i8Xs8AiNFOfSVGHAR2NWSQotPxiagdnhxbaWqqiPX720GKMWF17/aAJWtw9rawujrguFt0qeOzmIX7zTD7mMSZiGnO3UFGpF10ClimhROXHiBBobG9HQ0ACVSoWdO3eiq6srbM0NN9yA4mKuSOi6667D4OCg2G0JQjKmQ2Z/a9Ntqdg90Knk0Cjl0KvlsLlTr3pnWTah+0uqQD2Xch1dVBorjbB7/KJrboDZor1YreyvXc4lSD/zei8ARPTDikboc10INoFUKXLb819brMW41b0gA7xEv7JDQ0Oor68Xvq+rq8PQ0FDM9T/+8Y/xiU98Quy2BCEZs5lNs3UqUjdj5JkK6TtlUCsRYFNvte/w+BFgEbVDL38tWuFeKpjjzFtfWcEF6+c7H8YU5aY3aedEpSxGRldThQHFOiWOXzJBJZcJ2WfxaKww4tQ/bRWec2np/DsHZxt8rGlsJvOV9aJFJdqnrFgFO3/4wx/w4x//GN/+9rdjPl9nZyfa2trQ1tYGk8kk9ngEkZBQ95c6+Ak2nXUqvDvGEIx7pOoC4wXDqIm0IOQyBnqVXLR7jcccp19WU/DGPp8pkJM2NzY9cRTffCncVT4VnGIYK+YhkzHCTJFrlpckbXGU6FVYUc7Vbywvy3xBYKbhW/IPmjNfqyJaVOrq6jAwMCB8Pzg4iJqamoh17733Hh566CF0dXWhtDR2jW9HRwe6u7vR3d2N8vJysccjiISExjlkMgYapSytdSq8pcKnAqd647e5uXMbori/+Ov8vHQxuLx+2Ny+mHNmSvQqlOpV88oA2/fqZQDA4Q/Gwq5P2j0o0CjiisU3trfgN4/cgJ9+flPS+wFc9T+ABakyzzS8qCxEXEW0qGzatAk9PT3o7e2Fx+PBgQMHsH379rA1/f39uOuuu/Czn/0MK1euFLslQUiKxemFSiETMr+0Snna2rRYXT4UaDkR4F1UqWaAWQRLJYaoSJAIAHA3eiC2SwrgKusvJVmrwrIs/udd7oOo0+ODP2RQ2YTNjdIExYylBjU2Li2OOWQrFiuCNTXL8sT9JWMgTKDMJKJFRaFQYO/evdi2bRuam5tx7733oqWlBfv27cO+ffsAAN/4xjcwOTmJL37xi2htbUVbW5vogxOEVDi9/rAhV1qlPG3uL65XV7iopGyp8KISY+qhUaOUJKYyYY0f5wC4NvgfjdmSSjoYmXHB4vKhtb4Ido8fvROzFs6kzZO2yZs3NZVhVaUR6+uL0vL8iwmVQobaYu2CtGqRpKFke3s72tvbw649/PDDwtfPPPMMnnnmGSm2IgjJcXr8QoAeADSq9ImKLaQCXnB/pXjjjxdT4a4rpBGV4HTKMmNsUVlZaYDV7UP/lCOhe+nDYED/no11ODNgxnuDM2gMzmaZtLvTFvNoqjTilb+9OS3PvRhZVqrHx5OZb9WS23l1BJEETm+4qGiV8rTUqbAsC7vbJ1gofCwk1f5fiWIqRgm6IAOzohLPgrh1VQUYBvjtqdiZnzyXRjlRaV9bDbVChgvBCnmAG0GQyP1FJMfSUt2CWCokKkTe4/IGoA4RFV2aLBWXN4AAO2uhiHV/WZOIqUgRqJ8IZmSVx7FU6kt0uLGxDL/qHoAnwWCwD8esqCxQo0SvwvIyPa6YuE/T/gDLzbXJ8Wr3TLGsVI8Zp1doppkpSFSIvMfl9UOrDJ/sl446FStvWQRTiaVyf82dT89jUCtF9xYDuHoSg1qRcNjXAzcux8iMK2wCYTQujVmF+pLlZXr0Brvpmh0eBFiQpSIRSxeoBT6JCpH3OL3+sD5TaoU8LTPq+ZYpvJjolHIwTOrZX1zfLwXkMYZjGTUK2D3+sOyqVJiwuVFmSGw93LaqAvdsrEPn8ctxra+BKadQgNhQrkf/lANef0DIMsv1vlyZgs9yy3RchUSFyHtcc2IqaoUMnjS0t+DFgxcVmYyBRiGHK0UBs7m9UavpeXi3mNi4yqTNEzfzK5RbVpYjwCJmM0Onx48Zp1fovdVQZoAvwKJ/yoHxYIuWeG42InnqS3RgGKBvgiwVgsgoTq8/LKaiUsjg8UtvqfA399AUYJVCBneK8RurK3rfLx6pRIWzVJK70dckKLobtXD9waoKuGmNDcEq9ysmO0w27rEKEhVJ0CjlqC7QkKVCEJnG5Ym0VNwp9uOKx1xLRdgrRUtlOk7nYABCS3yxwfoJmxtlxuRcUnXFQVGJYamMzBkB3FDGFSReMdkES6WiIP54YCJ5lpbq8XGGCyBJVIi8x+ULhIlKui2VMFFRpi4qEwncUnyqsZhgvdcfwLTDi1J9ctZDuUENpZyJKSpjQUulMigqhTolSvUq9E7YMW51Q6eSx3XpEfNjWZmOLBUiPqf6p/HtwxclmV1BcDg9fmhCsr/SZ6lwbi5DmKUiT7k9+YTNHTf+wLu/rCLcX1N8i5YkXVIyGYOqwtgDovj2+FUh1khDOZdWPG51k+tLYpaW6jFh80iSWp4sJCpZxtd/+z7+/Y+XcfTCeNj1IbMT4xbx8yzyDZZlI4of02WpzLq/5iYFzH8vrz8As8Mb11IxStD+3hRs0VKeRPYXT21R7AFRYzMuGDWKMGutocyAKxM2jFtcqDCS60tKZjPAMucCy2lRycVP83xR3lPHZmsB3D4/7vn3N/GJp17DZZN0s8LzAd71pJmTUuwPsPBJLCyC+0slPqYyaeMtiNg3e759ixj3VzLNJOdSU6SNaamMWlxCPIVneTn3afqyyUaZXxJTV8yJyuA0iYpoNn7zCPYcurjQx5CUGacXH086YFAr8MGQRXBN/OrdAYzMuOD0+vFY17kFPmV2wbe4n2upAJDcWrG5fdCp5JCF1JWoUnS1mZJo8ijEVNypuz6SaSY5l7oiLUYtLnijvH6jMy5UzgnENwR7fU3YPCQqEsO/nvyHg0yQs6IikzGwZNCPmAneH5wBAHzu+qUAgPcGzQCA504NYW1tIR66qQFvXJ7AqARjXfMF3vILrRZXBVuqp+KWioc9pJkkT6oxFaHJY5ybPV9cKcb9lUwzybnUFGkRYGeD8qGYrO4IF1dDsCU9AFQUkKhICT+7hx9+lglyVlSMGoUwbyJXODMwDQC47zpeVGbg8wdwccSCa5eX4DMbasGywItnEzf1Izj4Ub5hKcXBoL3UVfW2kGaSwl4pur9MNj7WEfsmLJMxwf5f4kRFrZBBr4rfoiWU2mBa8bA5XFRYluUy1ua47FaU67H5qgoAXL8qQjpUChkKNIqMWio5m7sn1SyJxcSZgRk0lOtRW6RFQ7ke7w3O4LLJDrcvgJbaAiwv06OxwoATvdPoyJ8O36Lgh3FlzlIJvzmrlam1hJm1IOIH0I2iRYVLW441IjwaQgGk2QGgRLhucfng8QcihJBhGDyzqw0XR61YlcTMeWJ+lBrU5P6SggKJRqkuFliWxZkBM1rrigAArfVFOPnxFM4GXWAtNYUAuE99oUOPiPjMur9CUoqDApNqqm8s7B4/dKpISyUV8ZqweqBXySOeby5GjVJcTMXmnpfrCwBqCqNbKkIL/SiZZAzDoLm6ICzeREhDiV6FyeBrnwlyVlSkGlC0WBiecWHC5ham1rWvqca0w4u9v/8IaoVMCHYuLzOgf8ohuolgvuCOFqiXp8f95fYFIjr9qhSylMTLlOTN3iByporJ6p5XOjEAaFVylOpVGJyTVixkrFEX4oxSqlcJST2ZIHdFRa2ExZm9lsqw2Yl7972F/mB++dkBMwDOQgGAW1eVQyln0D/lwLUNpVAEb4QNZXp4/WzMOgEiHN5SCetSrEyP+8vt9UOtCP+TS7XQcsTsjEjNjYbYmMq0w5NS1+BoacXJJBcQ0lNqUJH7SwoKtNltqbx4dhgn+qbw0zd7AXCiopLLcFU153NWyGV44tNrcW9bHX742Q3Czy3nG/SRCywpnFEsFXWaLBWPPyCkKwt7pdhmf9jsFGIX8TBqFKLqVGac3rj9xWJRW6SNaNVCorIwlAQtlUCGvBc5KypGjRJOrz9qrnw28PuLXMX8b04OwuX14/SAGc01BVArZm9+926qx7/csz7sj57PnuEHHxHxiRqoV6TLUglEtVQ8/sC8/uB9/gBGLS7UJikqqWZBun1+uLyBlESlvkSLgSmHUAcEcDUvMobmpWSaUr0a/gCbsRKLHBYV8c30FooZhxcnP57GhiVFsLh8+P3Fcbw/OIMNQddXPMoMKhjVCvSRqCQFP8tEowyvqAfSY6mEfigAQlxt8/jwM2Z1I8AiSUsl9UD9TNB9nIqo3NRUDrcvgOOXTMI1k41zpcUaKkakBz4xYiJDtSqSiMrhw4exatUqNDY2Ys+ePRGPsyyLL33pS2hsbMS6detw6tQpKbaNC9+iIhtdYM+fHoQ/wOKfPrkaRrUC/3H8CpxeP9bXFyb8WYZhsLxcjyskKknh8kTGVNJnqUSLqcxfwPhYRTKiYlAr4PIGUrLY+ZhkQQqicv2KUhRqlTj8wahwbdLmTrrbMSEdvGWYqWC9aFHx+/149NFHcejQIZw/fx7PPvsszp8/H7bm0KFD6OnpQU9PDzo7O/HII4+I3TYhvKWSLVX1LMvi8Acj2P9mH37yRh82LCnC1UuKcdPKMiFIv6G+OKnnWlaqJ/dXkggpxYrwLsWA9CnFbl+k+0uVwl68qNQWJReoB1Kz2Gec3M+kYqko5TLcsboSh8+NCjezUYuLKuYXAF7IM5VWLFpUTpw4gcbGRjQ0NEClUmHnzp3o6uoKW9PV1YXPfe5zYBgG1113HcxmM0ZGRsRuHZdsE5V9r17Bwz8/hcdePIf+KQc6bmoAAHxyXQ0A4G+3rMSysuSqjZeX6TFkdob5s4noWF1e6FRyIXsOSI+lwrJsjEB9UFTmkQHGB8D5kbzxEDP9UYylAgAdNzfA6fVj36uX4fL6cWHEgtU1BSk9F5E6vPsrUxlgoivqh4aGUF9fL3xfV1eHd955J+GaoaEhVFdXi90+JgVJuL9YloXF6cOoxYVRiwuTNjfsHj8cbh/sbh/sHj88vgD8LItAgEWAZeEPAAGW+1rGMJAxDOQyQC7jv579/+zXgJxhwDAM2NnNYff4MW334OMpB05+PI0/XVeNb2xvgVzGoCjYs6d9bTVOfH3zvKbhNZTrwbLAwJQDTVShHBeL0ye8V3jUaWgo6fWzYFlEDdQD83N/DUw5UahVRvQRi4aYD1diYioA0FRpxI71NfjZWx/j+oZSeP0srl6SnLVNSIfQ/ytbRCVae/m5LR2SWcPT2dmJzs5OAIDJZIq6JhnmigrLsuibdOD1jyZwfngG54ctuDRmE9wf0dCr5FApZBGCIZMBMoYBywL+AMv9FxQeP8t9HxCuQbgW7fmL9SqU6FX4evtV+PyfLIdSHmk8zne86vIyPq3YTqKSAIvLiwJt+J+BKgXrIRG8QEUE6hXzr95/+8okNiwpSmqtmPb3YkUFAL5wcwNeODOMr/76LAAkfW5COoT+Xxlyf4kWlbq6OgwMDAjfDw4OoqamZt5reDo6OtDR0QEAaGtrS/lc/Ce0iyMWfO/IJbx4Zgh9wULCQq0SLTUF2HlNPWqLtKgs0KCqUIMygxp6NTfOVKOQS94ygmXZefVQShXeTUZxlcRYXN4ISyUdre/5yv0I99c8Cy37JuzonbDjr25YltR6IaaSgvtLClFpqSnEjY1leP2jCZTqVTSEa4HIZP8v0aKyadMm9PT0oLe3F7W1tThw4AB++ctfhq3Zvn079u7di507d+Kdd95BYWFhWl1fwKyoPPN6LxgG+JMVZXjwxuW4qakcS0t1Gbm5zyVTexZolKgwqnFpzJqR/bIZi9MXMcNDaNMiYUyKd2+JdX/x9Uu3rapIaj0/UyWVLMgZJxdvimY9z4en77sav+4ewFLqQLxglOpVQpucdCNaVBQKBfbu3Ytt27bB7/fjgQceQEtLC/bt2wcAePjhh9He3o6DBw+isbEROp0OP/3pT0UfPOG55DL8/R0roZDL8OnWWlQl0dIil1hdU4Dzw5aFPsaix+LyYkV5+M2OYRiuJ5eElgpvicQM1CchKna3D53Hr2BdXSGWBMfEJkLMnHpLitX0cynUKvFQMPGEWBhK9KqMjRSWpPV9e3s72tvbw649/PDDwtcMw+Dpp5+WYqt58de3N2V8z8VCc3UB3vhoAh5fZMYRMYvF6Y2a3ZRqT65YzFoqMWIqSVhFz57ox6jFhafv25BwLY9RzccWUwvUz3UNEtlJqUGFU/3mjOxFd5scpbm6AF4/i4/GqQdYLFiWhcUVmf0FzLZPkQqPBO6vyyY7SvUqbFxaknAtj0Ypg0LGpByol8JSIRaeUr0a047M9P8iUclRVldz9QDnR9LnAvMHWBx6f0QI6GYbDo8f/gAbkf0FcBaElHUqfHZXtIaS3OOJ95qwuec9w51hmJTb38/EsOKI7KNEr4I/wGbkb5VEJUdZXqaHRinDhTSKys/f/hiP/OIUPvH947hiyj6LiK/diGapqFIc8xuLmIH6eWR/TdjcKXX4TbX9/aTdg1Jq/pgTzBZApj+tmEQlR5HLGKyqNMYVlQ+GZvD4/56LmHuRDFaXF9/93YdorS+C0+vHF39xSuj4my1Ygm1IjNFERS6DR8I2LYkD9Yn3MlndKJvnwCwgtdHagQCLKXvkPHkiOykzqFGsU8LuTv/fKIlKDrO6pgDnRyxRi08/GrfhL/7zbfz0jT5s/d6r+OU7/fN67gsjVlhdPnxpcyO+v3MDPhyz4v++8H7UvRYrgqUSzf2llNpS4f6Y5wbqVUnGVFiWTdlS4ebUz8/tMe3wwB9gafZJjvAnjWU4/f/dIUyOTSckKjlMc3UBzA4vRi2uiMf+8/gVeP0sDnRch/X1Rfj68+/jo/Hk61r6JrnCyoYyA25ZWY4v3d6E354awlPHerJGWITeVjEtlTS4v5RzGkoKNTHx97J7uNkm842pAMFBXfOMqUzQ6F8iRUhUchghWD+nXsXq8uLFs8PYvr4G1zWU4oef3QC1QoZnXutN+rn7JuxQyBjUFXNNDb+8uQn3bKzD94/24K+fPY3RmUghW2zwLqFowWiVIj2ioppTSKiQc9lZidxfE9bUpyamEqjnpzSmImJEfkOiksNcVV0AhYzBm5cnw64f+mAUTq8fO6/hmnyWGtS46+o6PH96KOnGg32TdtSX6ITuvjIZg2/fvQ5f27YKR86N4dbv/gHffeXDlOojMsVsoD7S/aWUyySdGhrLUgGCNTEJBEwYxZvCTT6VQD2N/iVSRZLiR2JxYlArsKW5Es+fHsI/3nmV4L//3bkx1BZp0RriX723rQ7PnujHofdH8OebliR87t4JB5bNqeqWyxg8elsjtq+vwXde+RB7//AROo9fQWOFAQ3lelQVaFBZoEFFgZrrt1agQZlRDb1KviBtc3j3V9RAvUIGj186N55QpyKXRzymViZOXxYsh1RiKhrlvOtUTNbU9yPyGxKVHOfPN9Xj8LlR7H+zD1+4uQEmqxuv9Zjw2WuWhN3IW+uLsLxMj9+cHEooKizL4uNJO65riF6EV1+iww8+uwFfuKkBL703jIujVrw/NIOjF8bgihI70ChluGN1Fb7e3pzRdjoWlw9apTxqxwGps7+EQH0US0UllyV0f/E3+VSysYwaBTz+AFxef9jY5Lj72dxQyWVRkxgIIh70jslxbl5Zjs1XVeCJgxfwxuUJvNYzAX+AxbaWqrB1DMPgvmuX4FsvX8BrPSbc1FQe8zlHZlxwePxCi/1YrK0rxNq62RHIfAX7uMWFMYtbmGEzMO3Ab04O4eTH0zjQcR3qS5LrayUWrkVL9D8BlUIGbxoslbkxFSC5TDO+w2yJLjVRAbhOxcmKyoTVg1KDakEsSCK7oZhKjiOXMdj3lxvxVzcswx8/NKF9bTV++YVro1oZf3n9UtSXaPHdVz6M+5wnP54GkPx4Yx6GYVCoVaKp0ogbm8pwz8Y6/J9bVuBbn16LXz98PcwOD/Ycujiv5xRDtLb3PEo5I3mgXilnoo5TSKbPmNnhRYFGETahMllSGSmcavoyQZCo5AFKuQz/vL0F7/7fLfjBzlbcsKIs6idQtUKOz16zBGcHZzBujZ291d03BZ1KjuZq6QaAraktxIM3LsfL749krLuyxemL2YaEs1SknKcSiKhR4VEr5AndX9MOD4pTrG7nRWU+wfrRGRcqaZ48kQIkKnlEuVGd0J1xUyPn9nrjo4mYa97tm8aGJUUpfWqOx4M3NkAll+G3pwYlfd5YcJZKdPeXUi5xQ0m/P6JFC08y2V/TDq8wYnq+8IkIVndymXiBAIu+STuW0fwTIgVIVIgwWmoKUKxT4rVL0UXlssmGi6MWtM2jU26yFOqU+JPGUhw+N5qRAspYbe+BNBQ/emOPIFArE+9ldnhQlGJzRz5uNONITlRGLS64fQEsLydRIeYPiQoRhkzG4IbGMrx9ZTLiMZZl8Q/PvQejRon7rk2cdpwKd66pwuC0E+cy4AKL1fYeSIP7yxeIaalw2V+JLBUPinWpiUpNIVegOpxkQSo/hjpRIgZBRINEhYhg45JiDM+4MDIT3mjyzcuTOPnxNHZ/4ipUFKQn9XdLcyVkDPDKudG0PD8Py7Jxs7+UchkCLOCTSFjiDUuLFlPpn3Rg6/dexVCw2afZnrr7q0inhFYpT7px6BUSFUIEJCpEBBuXclldpz42h13/rzf7UKJX4TMbatO2d6lBjWuWl+DwB+kVFafXD1+AjWupAJAsrdjt88cO1EdJKX7hzBB6xm348Wu98PoDsLp9KE5RVBiGQU2RJmlR6TXZoVXKUWnMrxHchDSQqBARrK4pgEYpE1KHAWBgyoFjF8bw2Wvqk651SJU7W6rQM27D5TTOaOHb3seKqSjlyc85SQaPP7b7K1pKcUkw0+viqEUYrFSUovsLAGqLdYLVk4jeCRuWlemjpj8TRCJIVIgIlHIZ1tcV4a2QuMrP3/4YDMPg/uuWpn3/W1dVAEDUuI5UxBvQBcxaKlJlgMUN1Edxf/FCcmHEArODK3wUJSrzsFT6Jh1oINcXkSIkKkRUtq6uxIURC3on7HB6/Djw7gC2tVSiOhj0TSdLS3Uo0atwpt+ctj2EtvexKurl3Kd0qYL18QL16igdkXlRmXZ4cWZgBgBSdn8BXLB+wuaByxu/HsbrD6B/ykHxFCJlSFSIqLSvrQYAvHR2GF1nhjDj9GLX9csysjfDMFhfV4gzA+a07cFbKtGaSQJpcH/FCdRHG10cmv57/JIJgDhRqQ2OKEhkrQxMOeAPsFhGokKkiChRmZqawtatW9HU1IStW7dieno6Ys3AwABuu+02NDc3o6WlBU899ZSYLYkMUVOkxfUNpfjJG7340R8v46oqI65ZLn1tSiw2LCnGRyZb2lrnCzGVGMWPs4F6qSyVOIF6hRy+ABuWaTbj9GJ5mR4qhQyv9XCiIsb9VVfM9VPjh6vFgtKJCbGIEpU9e/Zg8+bN6OnpwebNm7Fnz56INQqFAv/6r/+KCxcu4O2338bTTz+N8+fPi9mWyBDf/PQaOL1+TNrc+MaONRltLri+vggsi7TVq/BDq/gWJnPhLRWpRgp74rm/lJHxmxmnF2UGFZqrjJh2eKFVykUNzFpTy83WCU2+iAYvKhRTIVJFlKh0dXVh165dAIBdu3bhhRdeiFhTXV2Nq6++GgBgNBrR3NyMoaEhMdsSGaKxwoDnHr4B//s3N2bUSgGApgoDAKQtA0yYbxLDepDeUokXqI8cKTzj9KJQqxTcVp+5ulZU1p1OpUBLbSHe7U0sKkU6Zcp9xghClKiMjY2huprzvVdXV2N8fDzu+r6+Ppw+fRrXXnttzDWdnZ1oa2tDW1sbTCaTmOMRErCmthAN5YaM71tVoIFWKcfl8fjumlThrQKlIrr1xbeol65OJX5DSX4Nz0ywhcytK7lMuL+UIOvummXFODNojtu8sn/KgaUZGj1A5CYJ56ls2bIFo6ORhWhPPPHEvDay2Wy4++678f3vfx8FBQUx13V0dKCjowMA0NbWNq89iNxBJmPQUK7HlYn0WCreOPNNgJCU4gwE6tVR9uItlT9rq8Pm5gqUStCG/prlpfjP13rxzpUp3Lwy+rycMYuLGkkSokgoKkePHo35WGVlJUZGRlBdXY2RkRFUVFREXef1enH33Xfjvvvuw1133ZX6aYm8YkW5AacH4rtrUsXjD0DGIGanZaVcOvdXIMDGLX7kxYa3IHz+AGxuHwq1SjAMI4mgAMBNTWUo0Cjw21ODMUVldMaF6xpKJdmPyE9Eub+2b9+O/fv3AwD279+PHTt2RKxhWRYPPvggmpub8Xd/93ditiPyjIZyPQannQlrK1IhnuUAcEO6AGkC9byrLdooYSAkphLcyxKce5JqV+JYaJRybG+twaEPRoWU6lCcHj8sLh8q09TXjcgPRInK7t27ceTIETQ1NeHIkSPYvXs3AGB4eBjt7e0AgDfeeAM/+9nP8Pvf/x6tra1obW3FwYMHxZ+cyHlWlBvAsonTYFOBm8QY++2vljBQ707galMr+ZgKJ5584WOhiBTiWHxqXQ3cvgDeuhzZrWDMwnUxJlEhxCBqRn1paSmOHTsWcb2mpkYQjhtvvDEjszGI3KMhOM/j8rgdV1XFjsOlQjx3FCCt+4sXC3WM7K252V+CqEhsqQBc/Y9WKcebH01gW0tV2GOjQVGpIlEhREAV9cSipaEsfWnFHl8gpuUASBuoF9KXY1kqvKgEBUxoIROj2l8MKoUMm5aX4M24lgqNESZSh0SFWLRoVXLUFmlxJQ2i4vUniqlI7/6KFVNRzbFU7HxhZoxqf7HcsKIUPeM2jM4Z2iWISiFZKkTqkKgQi5qGcj0um6SPqSQK1KvmBM/F7gUgTkPJ8JiKNSgqelV6RGVLcyUA4OX3R8Kuj864oVXKYYzRZYAgkoFEhVjUrCg34IrJJnlczpMgUC9l8aMQqE9UUR9cZwtmfxnTZKk0VhjQUlOAF88Oh10fMjtQXaTJaDseIvcgUSEWNSvK9bB7/BizuCV9Xk8m3V/BlOh4kx+BWVHh3V/6NFoMn26txdkBM073z9YBXRix4qoqY9r2JPIDEhViUbOiPD3BeneCQL1cxkAuY6QJ1PvjWyq6oJuLFxOb2we1QhbXkhLLZ69dgnKjGt986TwCARZWlxf9Uw6srpY2y47IP0hUiEUN33dM6mB9okA9wBVASmOpxI+p6FVyqBUyTNo4a8zm9qXN9cVjUCuw+86rcKrfjM7XruDiqBUAN0qaIMRAETliUVNZoIZeJZc8WO/xBaDWxxcVlTxyeFZKe/EV9THcXwzDoMygxqSNGxtsc/vS6vriuevqWvz+4ji++8qH+NT6GgDA6urCtO9L5DZkqRCLGoZhsKLCILn7K1GgHuDcVVIWP8azjMoMKph4S8XliznnRUoYhsH/u2stKgs0eP70EMqNaqpRIURDlgqx6Gko0+PdPmkbSyYK1AOcpSJFTCWR+wsAygxqDAfrRmzuzIgKwFXt//Tzm3D8kgm3riqnzC9CNGSpEIueFeUGDJmdcHh8kj1noop6AFBKZKnMur/ii8pESEwlU6ICACsrjXjopgY0VlDmFyEeEhVi0bOklBsaNWx2SvaciYofAS6tWJI6FW/87C8AKDOqMGX3IBBgYXf70lZNTxDphkSFWPRUF3IjdUfmtBURg8efRExFLos7JTFZhIaSMQL1AGep+AMszE5vxgL1BJEOSFSIRU91sBfViFlCUfHF71IMcGm3Nrd4lxsfl+FntESjLDiIa8LmhtXlo1YpRNZCokIseiqCGUlSWSosyyYVqC/QKmB1iRcVd1DA4gXBSw0qANzkRbcvQJYKkbWQqBCLHrVCjjKDGqMWaWIqvgALlo09NIvHqFFGnZA4X9xJWEXlQUuFH0iWyUA9QUgJiQqRFVQXajAskfvLk6DBI0+BRgGLUxpLRRUnngIA9SU6KOUMTvROAUhf23uCSDckKkRWUFWoiZj/kSp8mnCiQL1Ro4TV5RXdIdnt8ye0VDRKOVrri3Dk/BgAslSI7IVEhcgKago1GJmRxv2VtKWiVSDAAnaPuAywZJICAOC6hlKhLQx1CyayFRIVIiuoKtTC4vIJnXzFkGi+CY8xOM7XKjKu4vYFYs6nD+Wa5SUAgLalxUIjTYLINkhUiKygwsgFsset4ueqJFPhDszOiBcbV0kmUA8A1zeUYvcnrsJ//OVGUfsRxEIiSlSmpqawdetWNDU1YevWrZiejt2fye/3Y8OGDfjkJz8pZksiT6ks4GpVxi3i4yqC+ythTIWLa4i2VLyJYyoAoJDL8PAtK1BqoKaORPYiSlT27NmDzZs3o6enB5s3b8aePXtirn3qqafQ3NwsZjsij+FrVaSwVJIN1Bdog5aKSFFxJen+IohcQJSodHV1YdeuXQCAXbt24YUXXoi6bnBwEC+//DIeeughMdsReQzv/hqT0lJJGFPhLBXR7q8kLRWCyAVEvdPHxsZQXV0NAKiursb4+HjUdV/5ylfwL//yL5DJ6A+LSI1CrRIqhQwmKWIqSdepSBOoTzb7iyBygYTJ8Fu2bMHo6GjE9SeeeCKpDV566SVUVFRg48aN+OMf/5hwfWdnJzo7OwEAJpMpqT2I3IdhGJQb1JK4v9wJZsbzCJaKyFYtXKCe3F9EfpBQVI4ePRrzscrKSoyMjKC6uhojIyOoqKiIWPPGG2/gxRdfxMGDB+FyuWCxWHD//ffj5z//edTn7OjoQEdHBwCgra0t2d+DyAMqC9QYt2YuUK9RyqFSyETHVNw+PzRKslSI/EDUO3379u3Yv38/AGD//v3YsWNHxJonn3wSg4OD6Ovrw4EDB3D77bfHFBSCiEeFUYNxS+bcX4A0rVrcXrJUiPxBlKjs3r0bR44cQVNTE44cOYLdu3cDAIaHh9He3i7JAQmCp6JAGveXM1ghr00iI6uyQIMzA2YEAqm3auGKH8lSIfIDUQ2GSktLcezYsYjrNTU1OHjwYMT1W2+9FbfeequYLYk8pqpQg5ngECsxvbEm7R4As+3m4/GFmxrwlf85g/99bxg7WmvnvZc/wLXZp0A9kS/QO53IGhrK9ACAXpNd1PNMOzxQK2RJWSrb19egplCDoxeiZzYmgne1kfuLyBdIVIisge+HdWXCJup5puwelOhVcYdm8chkDJaV6TE07Uhpr9lRwvSnRuQH9E4nsoalpTrIGOCySEuFF5VkqSnSYsicWodkvnklxVSIfIHe6UTWoFbIUV+iw2WTNJZKstQWaTFudQuurPng9nI/oyH3F5EnkKgQWUVDmR5XMmyp1BZrwbJIaZ6L4P4iS4XIE+idTmQVDeUG9E7YRKX4Tts9KNYlLyp1RVoASMkF5qZAPZFnkKgQWUVDuR4ubwDDKU6B9PgCsLp9KJ2npQIAQ9Pz39PlpUA9kV/QO53IKlbwGWApusCmHVyNSvE8RKWqkJvlIs5SoT81Ij+gdzqRVTSUc7UqV1IM1k8FCx/nE1NRK+SoKtBgYEpMTIXcX0R+QKJCZBXlBjWMagWuTKRmqaQiKgCXzvzx5Pz3FLK/KFBP5An0TieyCoZh0FBhSDmtWHB/zSNQDwRFZWr+BZAUqCfyDRIVIutYISKteMbJtbEv0inn9XNLS/UwWd2wu+fXsZgq6ol8g97pRNbRUK7HyIwLDs/8W9KbHZyoFGrnKyo6AMDHk/OzVlxeCtQT+QW904mso0FEBpjF6YVaIYNmnoHzZaVcgkD/1Pz2pEA9kW+QqBBZB59WnEpcxezwzttKAYAlQUulb56WipssFSLPoHc6kXUsLdWBYVKzVGac3nnHUwCgQKOEQa3AmGV+44zdvgBkDKCQJe6ITBC5AIkKkXVolHLUFWtTSiuecaZmqQBAsV6J6WBKcrJw8+nlSbXZJ4hcgESFyEoaygz4YGhm3p2DzU4vCrXzSyfmKdGrhamRyeL20dRHIr+gdzuRldx1dS16J+z4v8+/P6+fs4iwVEr1KqF4Mlnc3gDVqBB5BYkKkZXsaK3FXRtq8fuL8xvza3Z4UoqpAFzB5HzdXy6fn9reE3kFvduJrGVVlRGTdo9Q0JgIrz8Au8efuqViUGHS7gHLJt92n7NU6M+MyB/o3U5kLcvKuNqRviQD9rz4pByo16ng9gXgDLazTwa3z0/uLyKvECUqU1NT2Lp1K5qamrB161ZMT09HXWc2m3HPPffgqquuQnNzM9566y0x2xIEAGA5LypJNnpMtUULDz+DZdKWvAuMAvVEviHq3b5nzx5s3rwZPT092Lx5M/bs2RN13Ze//GXceeeduHjxIs6ePYvm5mYx2xIEAGBJCVev0hvDUrG7fWGuKl5UClJOKeZEhW9KmQxuX2De1fsEkc2IEpWuri7s2rULALBr1y688MILEWssFguOHz+OBx98EACgUqlQVFQkZluCAMDVq9QUaqOKiscXQMtjr+CbL10Qrll4UdGkJip8u/z5pBVz7i+yVIj8QdS7fWxsDNXV1QCA6upqjI9HZuJcuXIF5eXl+PznP48NGzbgoYcegt0e213R2dmJtrY2tLW1wWQyiTkekQcsK9OhP0pLerOTu/H/5I1e4ZrDw8VCDGpFSnvxojI1H/eXN0DZX0RekfDdvmXLFqxZsybiv66urqQ28Pl8OHXqFB555BGcPn0aer0+ppsMADo6OtDd3Y3u7m6Ul5cn/5sQeUmZQR21dsTinO1g7A9wLjC+bb1OlZo7ShCVeVgqLgrUE3lGwo9sR48ejflYZWUlRkZGUF1djZGREVRUVESsqaurQ11dHa699loAwD333BNXVAhiPhTrVFEtB6trNs34/LAFa+sKBVHRp2ipFGgUMGoUUS2jWFBKMZFviHq3b9++Hfv37wcA7N+/Hzt27IhYU1VVhfr6enz44YcAgGPHjmH16tVitiUIgRK9Cla3L6Jdi9U1a6m8cXkCAGAPur9StVQYhsGKcgM+Gk++OzJlfxH5hqh3++7du3HkyBE0NTXhyJEj2L17NwBgeHgY7e3twrof/vCHuO+++7Bu3TqcOXMGX//618WdmiCC8C4p85yMLEvQUmEY4PglLjbn8PigkDGibvKNFQZ8NI+W+26fn2apEHlFan6AIKWlpTh27FjE9ZqaGhw8eFD4vrW1Fd3d3WK2IoiohGZkVRRohOu8pfKpdTU49MEIbG4f7G4/dCpxHYMbKwx47uRgUt2OWZblUorJUiHyCHq3E1lNsS5YOzIneM7HVD61vgZeP4u3L0/C7valHE/haZzHgDCvnwXL0tRHIr8gUSGymlJDMCPLMVdUfJAxwMalxQCAgWkHHB5/yvEUnsYKTlQOnOiH1x+/7b6LHyVMlgqRR9C7nchqYlkqFqcXBrUCRVol5DIGkzYP7B5fyjUqPEtLdfiLa5fgV92DeO7kYNy1NEqYyEfo3U5kNXwfr7lV7laXDwVaJWQyBiV6FSbtbtjdPuhU4kSFYRg88ek1KDOo8W7fVNy1bsFSIfcXkT+QqBBZjVIuQ6E2csyvxeWDMdiOpVSvwoTNA7vbD71a/A2eYRi01hfhzIA57jp3MM2ZKuqJfILe7UTWU6JXYcoRPlPF4vKiQMNZJWUGNSZtbjg84i0Vng1LinDFZMeMI/Ysl1n3F1kqRP5AokJkPSV6FSZt7rBr1lBLJThcy+b2i87+4mmtLwIAnBk0x1wjuL/IUiHyCHq3E1lPVYEGoxZX2DVriKVSqldj0uaBw+ODXmT2F8+KYGrxQJyWLYL7iwL1RB5B73Yi66kq1GDE7AqbnWJxeoW5KaUGFWxuH5dSLJGlUmZQQcYAY3PELBSXlwL1RP5BokJkPdWFGji9fqEzMcuysLln04fLgrUsACSzVBRyGcqN6riiQpYKkY/Qu53IeqoLtQCAEYsTAOD0+hFgAWOI+4tHqpgKAFQWaDBqccd8nBcVDcVUiDyC3u1E1lNVyPX8GpnhrAabK7zFfWmopSJBSjFPZYEGYzNxLBVyfxF5CIkKkfVU86JiDopKcG4Kb6lcVVUgrNVK2IerskCNMWsS7i+yVIg8gt7tRNZTYVRDxgCjM5z7ixcVfbAmRauS4/dfvQVbmiuwPpgKLAVVBRqYHV4hID+X2ZgKWSpE/kCiQmQ9CrkMFUbNrPsryoTHhnIDntm1SYi/SAHfan88RlzFTQ0liTyE3u1ETlBuVMMULIDkYyq8+ytdVAVFZW6NDI+LGkoSeQi924mcwKBWCGJi94ibRZ8sZQYuq2zCFttSUSlkooaCEUS2QaJC5ARGjUJwe/HiIrbNfSL4qZPTc2a58Li9NJ+eyD/oHU/kBAaNQhghbHNzsYx0iwrfdt8co6mkU4KhYASRbZCoEDmBUR1iqbi9kMuYtBcdapRyaJXyiLb7PHaPT8hAI4h8gUSFyAmMGiVsbh9YluXmpqjkGYllFOuUmI5hqXC9xshSIfILUaIyNTWFrVu3oqmpCVu3bsX09HTUdf/2b/+GlpYWrFmzBp/97GfhcsUuGCOIVDBoFPAHWDi9/rC29+mmSKeCOUZMRcr5LQSRLYgSlT179mDz5s3o6enB5s2bsWfPnog1Q0ND+MEPfoDu7m588MEH8Pv9OHDggJhtCSICPn5ic/lgd/skbccSj2K9Mmag3kExFSIPESUqXV1d2LVrFwBg165deOGFF6Ku8/l8cDqd8Pl8cDgcqKmpEbMtQUTA16RYXD7Y3L60pxPzcJZKdPeX3U0xFSL/ECUqY2NjqK6uBgBUV1djfHw8Yk1tbS3+/u//HkuWLEF1dTUKCwtxxx13xHzOzs5OtLW1oa2tDSaTSczxiDyCFxWb2xfW9j7dcDEVslQIgiehqGzZsgVr1qyJ+K+rqyupDaanp9HV1YXe3l4MDw/Dbrfj5z//ecz1HR0d6O7uRnd3N8rLy5P/TYi8xqDmYig2V6ZFRYUZpxeBABvxmD2DFhNBLBYSvuOPHj0a87HKykqMjIyguroaIyMjqKioiPrzy5cvFwTirrvuwptvvon7779fxLEJIhzeUrG6vLBnUFSKdCoEWMDi8qJIpwp7zOklS4XIP0S5v7Zv3479+/cDAPbv348dO3ZErFmyZAnefvttOBwOsCyLY8eOobm5Wcy2BBEBLyJWtw82V+YshOJgAeTctGKPLwCvnyVRIfIOUaKye/duHDlyBE1NTThy5Ah2794NABgeHkZ7ezsA4Nprr8U999yDq6++GmvXrkUgEEBHR4f4kxNECEKg3umF1e0T5tOnm2Jd9FYtjmD/MUopJvINUe/40tJSHDt2LOJ6TU0NDh48KHz/+OOP4/HHHxezFUHEhbdUhoODugozJCqzrVrCRcXu4VrFZCq1mSAWC1RRT+QECrkMWqUcg9MOAJkTFaGppD3c/eUkS4XIU0hUiJzBoFFgODj9MXOWSnT3l91NlgqRn5CoEDlDoVaJ/knOUilI84AungKNAnIZE1EAyc900SrJUiHyCxIVImcoM6hgCba/L9RlxlJhGAZF2sgCSAdZKkSeQqJC5AwVRo3wdabcXwAXrI9lqVBMhcg3SFSInKHcqBa+zqSoFOtUEZaKk7K/iDyFRIXIGSqCoqKUM9AqM3czL9KpMGWPnlJMlgqRb5CoEDkDb6kUaJQZGdDFUxzF/eVw8+4vslSI/IJEhcgZ+JhKJl1fAFCsj3R/2dw+qBQyKOX0J0bkF/SOJ3IGwVLJsKgU6ZRw+wJCHAUAJmwelOlVcX6KIHITEhUiZ+BjKhm3VKIUQE7Y3Cg1qGP9CEHkLCQqRM5QpFNCKWcWTFRCg/WTdjfKDGSpEPkHiQqRMzAMg5uaynH1kqKM7ltfogUAfBys5geACasHZWSpEHkI5TsSOcVP/mpTxvdsKDMAAK6YbAAAlmU5S8VIokLkH2SpEIRItCo5aou0uBwUlRmnF14/S5YKkZeQqBCEBDSU63HZZAfABekBUEyFyEtIVAhCAlaUG3DZZAPLsjBZuYA9WSpEPkKiQhASsKLCAIfHj+EZFybtvKVCokLkHxSoJwgJaK0rAgC8c2USFifXsoXcX0Q+QqJCEBLQUlOAUr0Kxy+ZUGZQQ6OUCfUrBJFPkPuLICRAJmNwU1MZjvdM4MKoBU0VRshkmWtqSRCLBVGi8utf/xotLS2QyWTo7u6Oue7w4cNYtWoVGhsbsWfPHjFbEsSi5ZZV5Ziye/D2lSmsrDQu9HEIYkEQJSpr1qzBb3/7W9x8880x1/j9fjz66KM4dOgQzp8/j2effRbnz58Xsy1BLEpuaioHAPgDLFZVGRb4NASxMIgSlebmZqxatSrumhMnTqCxsRENDQ1QqVTYuXMnurq6xGxLEIuSMoMaa2oLAIAsFSJvSXtMZWhoCPX19cL3dXV1GBoaSve2BLEg3LKSs1ZWVZGoEPlJwuyvLVu2YHR0NOL6E088gR07diTcgGXZiGvxpvJ1dnais7MTAGAymRI+P0EsJjpuXoHW+mJUF2oX+igEsSAkFJWjR4+K2qCurg4DAwPC94ODg6ipqYm5vqOjAx0dHQCAtrY2UXsTRKYp1CqxdXXlQh+DIBaMtLu/Nm3ahJ6eHvT29sLj8eDAgQPYvn17urclCIIgFgBRovL888+jrq4Ob731Fv70T/8U27ZtAwAMDw+jvb0dAKBQKLB3715s27YNzc3NuPfee9HS0iL+5ARBEMSig2GjBT0WCW1tbXHrXwiCIIjMkcw9mSrqCYIgCMkgUSEIgiAkg0SFIAiCkAwSFYIgCEIySFQIgiAIySBRIQiCICSDRIUgCIKQjEVdp1JWVoZly5al/PMmkwnl5eXSHSjDZPP5s/nsAJ1/ocnm82fz2YH45+/r68PExETcn1/UoiKWbC+ezObzZ/PZATr/QpPN58/mswPiz0/uL4IgCEIySFQIgiAIychpUeFb6Gcr2Xz+bD47QOdfaLL5/Nl8dkD8+XM6pkIQBEFklpy2VAiCIIjMkpOicvjwYaxatQqNjY3Ys2fPQh8nKZYtW4a1a9eitbVVmHg5NTWFrVu3oqmpCVu3bsX09PQCn3KWBx54ABUVFVizZo1wLd55n3zySTQ2NmLVqlV45ZVXFuLIYUQ7/z//8z+jtrYWra2taG1txcGDB4XHFtP5BwYGcNttt6G5uRktLS146qmnAGTP6x/r/Nnw+rtcLlxzzTVYv349Wlpa8NhjjwHIntc+1vklfe3ZHMPn87ENDQ3s5cuXWbfbza5bt449d+7cQh8rIUuXLmVNJlPYta997Wvsk08+ybIsyz755JPsP/zDPyzE0aLy6quvsidPnmRbWlqEa7HOe+7cOXbdunWsy+Vir1y5wjY0NLA+n29Bzs0T7fyPPfYY+53vfCdi7WI7//DwMHvy5EmWZVnWYrGwTU1N7Llz57Lm9Y91/mx4/QOBAGu1WlmWZVmPx8Nec8017FtvvZU1r32s80v52uecpXLixAk0NjaioaEBKpUKO3fuRFdX10IfKyW6urqwa9cuAMCuXbvwwgsvLOyBQrj55ptRUlISdi3Webu6urBz506o1WosX74cjY2NOHHiRKaPHEa088disZ2/uroaV199NQDAaDSiubkZQ0NDWfP6xzp/LBbT+RmGgcFgAAB4vV54vV4wDJM1r32s88cilfPnnKgMDQ2hvr5e+L6uri7uG3axwDAM7rjjDmzcuBGdnZ0AgLGxMVRXVwPg/hDHx8cX8ogJiXXebPo32bt3L9atW4cHHnhAcGEs5vP39fXh9OnTuPbaa7Py9Q89P5Adr7/f70draysqKiqwdevWrHvto50fkO61zzlRYaMks8VT4sXCG2+8gVOnTuHQoUN4+umncfz48YU+kmRky7/JI488gsuXL+PMmTOorq7GV7/6VQCL9/w2mw133303vv/976OgoCDmumw5f7a8/nK5HGfOnMHg4CBOnDiBDz74IObaxXZ2IPr5pXztc05U6urqMDAwIHw/ODiImpqaBTxRcvBnrKiowGc+8xmcOHEClZWVGBkZAQCMjIygoqJiIY+YkFjnzZZ/k8rKSsjlcshkMnzhC18QzPzFeH6v14u7774b9913H+666y4A2fX6xzp/trz+AFBUVIRbb70Vhw8fzqrXnmfu+aV67XNOVDZt2oSenh709vbC4/HgwIED2L59+0IfKy52ux1Wq1X4+ne/+x3WrFmD7du3Y//+/QCA/fv3Y8eOHQt5zITEOu/27dtx4MABuN1u9Pb2oqenB9dcc81CHjUq/E0BAJ5//nkhM2yxnZ9lWTz44INobm7G3/3d3wnXs+X1j3X+bHj9TSYTzGYzAMDpdOLo0aO46qqrsua1j3V+SV97KTMLFgsvv/wy29TUxDY0NLDf+ta3Fvo4Cbl8+TK7bt06dt26dezq1auFM09MTLC3334729jYyN5+++3s5OTkAp90lp07d7JVVVWsQqFga2tr2WeeeSbueb/1rW+xDQ0N7MqVK9mDBw8u4Mk5op3//vvvZ9esWcOuXbuW/dSnPsUODw8L6xfT+V977TUWALt27Vp2/fr17Pr169mXX345a17/WOfPhtf/7NmzbGtrK7t27Vq2paWFffzxx1mWjf+3uljOzrKxzy/la08V9QRBEIRk5Jz7iyAIglg4SFQIgiAIySBRIQiCICSDRIUgCIKQDBIVgiAIQjJIVAiCIAjJIFEhCIIgJINEhSAIgpCM/x96BgPzCuYPawAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ad384ae42d84e728815912aaf0300af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5c4691220b54106af730b5fec565722",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4323dcc570dc40a182b7e174ec1417d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a31493c23ee24f288ceeb243089f40b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9e17cc00667445ebdc23ba67a12dd77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93365f7149ed41ac99fb227646df5a25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb68c60a81544f919ab1154312608faf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe263409",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0581cd01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "4701cf81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a2f19479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "609edc2f39b2462ab4b993e96b432a7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2aef14f7ab27426499fb3584388a3593",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fb5e49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v4_Aug23_bf_model_misspecification_1000_data.ipynb b/bf/v4_Aug23_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..331d5eb
--- /dev/null
+++ b/bf/v4_Aug23_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2619 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    maxAbs = 0.06918687\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # v3: Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "        temp = dpl[0].data['agg']\n",
+    "#         avg = np.mean(temp)\n",
+    "#         temp = temp-avg\n",
+    "        \n",
+    "        # v4: Divide entire matrix by maximum of absolute value\n",
+    "#         maxAbs = np.max(np.abs(temp))\n",
+    "        temp = temp / maxAbs\n",
+    "        \n",
+    "#         # v5: smoothing \n",
+    "#         temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "89434b52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "69bf39ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "# avg = np.mean(x)\n",
+    "# x = x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(x))\n",
+    "x = x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(x)):\n",
+    "#     x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "32809288",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06918687"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "8f463b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8e15fc63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfd9352658034206aae40a8001bc3a9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.61536</td>\n",
+       "      <td>0.821687</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Loss   W.Decay\n",
+       "0  7.61536  0.821687"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036f1165",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "37e0d459",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "52cb730e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "14be1512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53d92b80a90442be8600d341df008234",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21dafcdf",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "bb822ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "20143237",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "89d3f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "08a2fbbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "f63abbb2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x295e13520>]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "7e6247c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "b221affe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "6f13f06b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1401bfe39ccc46b584e57e423f6ce550",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bf02580",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08afe4c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80ae9785091548579f36bf265907a88f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8082c90e5f604620b8dc8bc516cd3d49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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o0YyjoyOjq6vLGBgYMC4uLsx3333HvHz5Uu05+eMx/fz8mJMnTzJt27Zl9PX1GRMTE2bQoEHMkydPlI4PDw9nZs6cybRs2ZKxtLRkRCIR4+DgwPj4+DBXr15V+xrBwcHM4MGDGRsbG0ZHR4extLRkXF1dmZkzZzK3b99WOtbBwYFxqG3DMIm3FT8fUbduXQYAY25uzuTm5hZ5XG5uLrNx40amXbt2jLGxMSMSiZjatWszXl5ezNq1a1XGlObm5jKLFi1inJyc2PL9+OOPTE5OTonGgTIMw8jlcmbjxo1Mw4YNGZFIxNjY2DBTpkxhUlNTFWX9YBwowyh+J66uroy+vj5jYWHBfPbZZ8yDBw+YBQsWMACYixcvKh3/7t07ZtiwYYy1tTXD5/MZAMyCBQvY569evcp4enoypqamjKGhIdOhQwfm6NGjzMWLF1WO1TTl8TfOY5ji16e5u7vjzp07FZPJidaIf52BEV2O4HXMe3bf8K+aYvR0VwgE6lsFcnPysGXpbZw+WDB9W0NXS2z3HwBTC9UxeeWicIeRElYb5/fCJFVMUqHPLwt37uIgnCuPv3FqAyUlIsmVYcaQs2zy5PGAWSs7YNy3zYtMngAg1hVixtJ2mLmsHZvXHocmYlL/k8jJVm0/IoQQTUcJlJTIshlBCL1ZMCxh3vrO6P6Z6mwkRenzeT18t7w9m0Qf3YnHoikXqWMRIUTrUAIlxXb6wFMc3l7QW2/Y5Cbo0tuxxNfp6VMXU+a3YrdP7HuC3RtCyyNEQgipNJRASbEkxWfhl5mX2W3XtjUw7lvV8XvFNWBUQ/Tyrctur557FRH3Ej5yBiGEaBZKoKRYln8bjNQkxdg1PQMhFm32LNYcqUXh8Xj4ZmEbNGymmNxalsdg1tj/kJtD7aGEEO1ACZR8UrB/DM7+U9B7dtbKDuUyhlMkFmD26o4Q6ykmCn8ekYL1P5d88DshhHCBEij5qLw8OX6bVTDNV4sONujY3aHcrm/naIxJswuGE+xefx9hd+PL7fqEEFJRKIGSj/rXLxzPH6cAAARCHuasVl2Kqaz6Da+PFh0UM6cwDLB46iV29iJCCNFUlEBJkTLfS7Bp0U12e+DoRjCrgEkP8ttDdXQU/x0f3YnH4R3lO0k4IYSUN0qgpEi7N4QiKV6xQK6egRBjZrhV2GvZORpjyKQm7PaaudeQmpzzkTMIIYRblECJWpnvJdiz8T67Pf67FtDVq9i1B4ZOaoKadoYAgPdpEmz9laaNJIRoLkqgRK2Dfz1CWrJiNXlDYxH6D6tf4a8p1hViwqwW7PbeTQ8QF51e4a9LCCGlQQmUqMjJzsPOtffY7ZFTm0IgrJz/Kp17OaChq2JsaJ5UTsNaCCEaixIoUfHvznClts/+wxtW2mvzeDxMmtOS3T594CkehyZW2uuT6mns2LHg8XhqF/TmiqOjI7toNdFMtKA2USKXM9i7sWBe2iFfNoFILKjUGJq410D7brVx7dxLAMDmpbew4VCfSo3hk0J/BrJiuY6iaPr2gOviMl/G0dERMTExap+rUaMG3r59q7RPIpFg8eLF2LdvH5KTk9GiRQusXr0aLVq0UDk/MDAQPXr0wKlTp9C3b98yx6ptPDw8EBQURAspaDFKoERJsH8MYqLSAABCHT4GjeFmTczR01zZBHr++HOE34tH4+bWnMSiVlYsYODIdRRFy4wut0uZmJhgxowZKvsNDQ1V9s2ZMwdr166Fj48P7OzssGfPHnh6euLx48ewsbFhj8vIyMDEiRMxcuRIjUiey5cvx5w5c2Bra8t1KKzz589zHQL5BEqgRMneTQV3n579HKFnoMNJHM6NzNGppz2CAxR3eb8vvoXfj/bjJJbqztTUFAsXLvzkcQzD4M8//8S4ceOwY8cOAMDAgQPh4eGBPXv2YNasWeyxs2fPRk5ODtavX19RYZeIjY2NUoLXBM7OxV8mkHCD2kAJKyosib3rA4DR09y4CwbAqGmu7Lqhl05H02otGi4hIQFZWVlo3bo1uy//ceFq4MuXL2PLli34/fffYW5uXqrXym8fTE9Px7fffgtHR0fo6OgoJfrHjx9j7NixqF27NsRiMWrUqIHhX87Hk8holet9rA305s2b8PX1Rc2aNSESiVC7dm1MmjQJr1+/VhtbcnIy5s2bhyZNmkBfXx8mJiZwdXXFnDlzkJmZiejoaPB4PAQFBQFQtPvn/3h4eKiU8UO5ublYsWIFmjVrBn19fRgbG6NTp074559/VI7Nf62xY8ciOjoaQ4cOhaWlJXR1deHu7o5Tp0599H3+0LFjxzBy5EjUr18fBgYGMDQ0RMuWLbFhwwbI5XKV4/Pf1+fPn2Pjxo1o1qwZ9PT0lMqZnJyMuXPnolGjRtDT04OJiQm6du2K//77T+V6aWlpWLlyJby8vGBnZweRSAQrKyt4e3vjxo3K73BId6CEtf/PR+zjRs2t2DGZXHGqb4ZOvRxw+aziw3f76rtYtbcnpzFVR7m5udi7dy9iY2NhYGCAZs2aoXPnzhAIlNvGraysoKenh5CQEHbfnTuKsbwODor5k7OzszFhwgT4+PjAx8enTHFJJBJ4eXkhOTkZPXr0gLGxMZycnAAA/v7+GDRoEKRSKfr374+6devi1atX+PffIzgdeAUXj/2BFl7un3gFwM/PDxMnToRYLIa3tzdq166NyMhIbNu2DSdPnsSNGzdgb2/PHv/ixQt4enoiJiYGLVu2xFdffQW5XI6nT59i7dq1mDx5MkxNTbFgwQLs3LkTMTExWLBgAXv+pzoNSSQS9OzZE0FBQWjYsCG+/vprZGVl4fDhwxgyZAju37+PX375ReW8mJgYtG7dGnXq1MGoUaOQnJyMgwcPYsCAATh37hw8PT2L9Z7PmTMHfD4fbdq0ga2tLdLS0nDhwgVMnz4dt2/fxp49e9SeN336dAQHB6Nv377o06cP+38nJiYGHh4eiI6ORqdOndCrVy9kZmbi1KlT6NWrF/78809MnDiRvU5ERATmzZuHzp07o2/fvjAzM0NsbCxOnDiBs2fP4uTJk+jVq1exylIeeEwJWrDd3d3ZPwhStWRnSeFh74eMdAkA4Ned3dCiQy2OowIiw5Iw5bPTAAAeHzgbPgq165gU7+TCy62VsKNGREQEGjX6SPvv9bGa3wbabmeZL1NUJyInJyf4+fmhS5cuSvtnzJiBDRs2wNfXF7a2ttizZw8kEgkiIiJga2uL7777Drt27UJ4eDisrUvfpp0fV9euXXH8+HEYGBiwz6WkpKBOnToQCAS4fPkyGjduzD4XdvUg2vQch/p17HH3wWN2/9ixY7Fr1y68ePGCTWJPnz5FkyZNYG9vj6CgIKX20QsXLqB79+7w9vbG0aNH2f0dOnTAtWvX8Msvv2Du3LlKMScmJsLQ0BC6uroAPt2JKD+OwnfFy5cvx48//ojevXvjxIkTEAoV90Dx8fFo3bo1YmJicPXqVbRv3549N/9LxcKFC5WSdUBAAHr16oXevXvjzJkzH3/D/+/Zs2cqVctyuRzjxo3D7t27cePGDbRp04Z9Lv99rVWrFq5cucLGks/DwwOXL1/G33//jaFDh7L7U1NT4eHhgSdPniA6Oho1atQAoLgDlUqlsLS0VLrOq1ev0Lp1a5iYmCAiIqJYZfnk33gxUBUuAQD4H45ik6exqRjN22tGe1A9Fwu0+H8sjBxK41NJxRs3bhzOnz+Pt2/fIjMzEw8fPsSkSZMQHR2N3r17IzQ0VOn4X3/9FbNnz8atW7ewfft2uLi44Pz587C1tcXNmzexbt06rF+/HpaWlli0aBFsbGwgFArRsmVLXL16tcTxrV69Wil5AsDu3buRmpqKRYsWKSVPAHBp6IyJIz/DvYdPEB4e/tFrb9myBVKpFOvXr1fpXOTl5QVvb2+cPHkS79+/BwCEhITg2rVrcHNzw+zZs1Wul191WhY7duwAj8fDmjVr2OQJANbW1vjpp58AANu2bVM5z8HBAfPnz1fa17NnT9jb2+PWrVvFfn117bJ8Ph/Tp08HoEjK6syaNUsleYaGhiIoKAg+Pj5KyRNQtLsvWrQIOTk5OHLkCLvfxMREJXkCgJ2dHXx9ffH48WPExlZe73iqwiUAgMPbCyZv7z+8fpkWyy5vQ750wd1rbwAAR/zC8fXPbWBuVf6T2hNVhe9YAKBJkyb4448/YGhoiNWrV2PhwoVKd2BisRjLly/H8uXLlc6TSCQYP348+vTpgxEjRmDdunXsHVGHDh2wbNky9OrVC1FRUezdxqfo6uqiWbNmKvuvX78OQPEBrdL5Kes1nj5TfMBGRESoJFh11wkKCsLt27dVno+Pj4dMJsPTp0/RsmVLtg2uZ8+e4PPL/97k/fv3iIqKgq2tLRo2VB2b7eXlBQC4d0/1S6abm5tKlTsA1K5dmy1ncSQlJWHlypU4c+YMnj9/jszMTKXn4+Li1J5XuF08X/7rpqWlqe2klpCg6PPw4R3l1atXsX79ely/fh3x8fGQSCQqMRSuVq9IlEAJosKScP+6YjwfjwcMGF15EycUR/P2NqjnYo7IsGRIJXLs+/0BvlnY5tMnkgozefJkrF69GpcvXy7W8YsWLUJcXBzbMWTlypXo2rUr+8HZoEEDODo64vfff8fixcUbv2ptba32i15SUhIAYOvWrR89PyMj46PP519n5cqVxbpOamoqAFTYUJi0NMXwsqJ6C+fvz4+jMFNTU7XnCIVCtZ1/1ElNTUWrVq3w4sULtG7dGqNHj4a5uTmEQiFSU1Oxfv165Obmqj23Zs2aKvvy39/AwEAEBgYW+bqFf09Hjx6Fr68vdHV10b17dzg7O8PAwAB8Ph+XLl1CUFBQkTFUBEqgBEd2FlRlubSwrpAly8qCx+Ph84kuWDYjGIBiqM3475vDwFDEcWTVV3775Yd3IOrcu3cPv/32G/744w/Y2toiPT0dr1+/xogRI9hj7O3tYWlpibCw4i9jV1QtiYmJoo08NDRU9Q41qVAfDouPdyLKv05aWhqMjY0/GU9+kirqLqys8uP5cPKKfG/evFE6rrxt27YNL168wIIFC1TuGK9fv/7RIUnqflf5ca5fvx7Tpk0rVgw//fQTRCIR7ty5o9J+OWnSJLZnc2WhNtBqLi9PjtP7n7LbQyc3+cjR3OnU0wE2tRW9gjPSJTiy4+PtV6Ri5Ve/1alT56PH5eXlYfz48fD09MQXX3yh9NyHdwo5OeWzfF3btm0BAMHBwZV6nfzjAwICinVXl1+lKpPJinV9IyMjODs7Iy4uDpGRkSrPX7x4EQDUzvpUHqKiogBAbe/p0iSu0vyeoqKi0LhxY5XkKZfLceXKlRLHUFaUQKu5a4Gx7Ly3uvpCuHfivuetOgIhH4O/cGG3/Vbfg1RavA8eUjphYWFITk5W2R8TE4OpU6cCAEaOHPnRayxfvhxRUVFK1anGxsawtbWFv78/8vLyACg+gN+/fw8XF5eiLlVs48aNYzuhqOsgI5fLcelKiJozlU2dOhU6OjqYOXMmnj59qvK8RCJR+vBv2bIl2rdvj/v37+PXX39VOT4pKUnpS4KFhQUAlKjTy/jx48EwDH744QelxJuYmIglS5awx1SE/F7Bly5dUtp/7949lTbv4nB3d0enTp3w77//shNvfOjhw4eIj49XiiEyMlJpDC7DMFi0aNEnO4VVBKrCreZO7H3CPu7SxwECgeZ+p+rh44zdG0ORmpSD+DeZ+O/IM/QdWvHLrFVXhw4dwooVK+Dp6QknJycYGRnh2bNnOH36NHJyctCnTx98//33RZ4fFhaGpUuXYs2aNew40HyzZs3C9OnT0alTJ7Rp0wb79u2DoaEhvv766zLHbWFhgcOHD2PgwIFo27YtunbtChcXF/D5fMRGheL67YdISklDTs6kj16nYcOG2LFjB8aPHw8XFxf06tUL9evXh1QqRWxsLIKDg2FlZYXHjwuGw+zduxceHh748ccfceTIEXh4eIBhGERGRuK///7D48eP2UTUtWtXHDp0CIMGDUKfPn2gp6cHBwcHjBo1qsiYvv/+e5w9exbHjx+Hq6sr+vTpg6ysLBw6dAjx8fGYNWsWOnbsWOb3UJ3Ro0dj5cqVmDFjBi5evIh69eohMjISp06dwqBBg3Dw4MESX/Pvv/+Gl5cXvvjiC2zYsAFt2rSBqakpXr16hQcPHuDRo0e4fv0622Qwc+ZMTJ48Gc2bN4ePjw90dHRw9epVhIeHo3///jh58mR5F/ujKIFWY+mpuTh/4jm77TOu6B6JmkCsK4T3iAbYvUExdGL3hvuUQCuQp6cnnjx5gnv37uH69evIzMyEqakpOnbsiFGjRmHUqFFFtkPKZDKMHz8ebdq0wZQpU1Se/+abb5Ceno4//vgDd+/ehaurK9auXVvsHrif0rVrVzx48ACrVq1CQEAAgoODIRKJUKuGObw6ucOnv1exrjNy5Ei4urpi9erVuHjxIv777z8YGBigVq1a8PX1xZAhQ5SOd3Jywt27d/Hbb7/h2LFj2LRpE3R1deHo6IjvvvtOaezrhAkTEBMTgwMHDuC3335DXl4eunTp8tEEKhKJEBgYiDVr1uDvv//Gxo0bIRQK4erqinXr1mHYsGGle8OKoVatWggODsacOXNw5coVBAQEoGHDhti8eTO6detWqgRqZ2eHkJAQbNy4EUeOHMG+ffsgk8lQs2ZNNG7cGN988w2aNm3KHj9p0iSIxWKsW7cOu3btgp6eHjp16gQ/Pz8cOXKk0hMoTaRQjR3eEYYFkxXtJpY19bE/2JfjiD4tJTEbIzofgVSqaGPaf3UwmrUq4kO3IidSqCarsVQ5RXQiGjp0KA4ePIjXr19r3Jy4pGKUx0QKdAdajZ05WNARoe/QehxGUnxmlnrw6OeIwKOKO+e9G0Px2+4elR8IJacq5enTpxCLxbCysuI6FKJFNLfBi1SoxHdZuB1U0N2+l692JFAA+Gx0wbdG/8ORSHjz6aEUhKizfv16+Pj44N69exgwYIDS7D6EfAol0Grq3LFnkMsV1Zp2TsawrKHPcUTFV7+JBVxaKu4UZHkMDv716BNnEKLeunXrcOPGDYwfPx5//vkn1+EQLUNft6qpgCNR7OM+n2vP3We+gWMaISxEMdXX/i0P8eUcd4jEqlOVEfIxL1684DoEosXoDrQaSnibiTuXC8ZReXk7feRozdShmz2sairumlOTc+B/SHVgOSGEVCRKoNXQuWPP2epbh7omsLDWnurbfEIdPvqPaMBu71p/v8hloQghpCJQAq2GAg4X3K311sLq23x9Pq/HVts+Dk3E/Rvq5wglhJCKQAm0mkl4m4k7wQXVt579tK/6Np+Jua5S9fO+3x9wGA0hpLqhBFrNBB59xs4p4FjPVOvX1RwwsmDptf/+jULCWxrSQgipHJRAq5mAwwW9b7W5+jZf3cbmSkNaDm+nVVoIIZWDEmg1kvAmEyFXClffOnIXTDkqfBe6/48HtEoLIaRSUAKtRv4rVH3rUM8EZpbaXX2br2MPe7YqOuldNi4cp7F9hJCKRwm0Ggn8V7snTyiKjkigNJfv3t9DOYyGEFJdUAKtJlKTshFy5Q277dFXe3vfqtNnSH0IhIrVV+5efYOnDxM5jogQUtVRAq0mgs7EsJMn2Ngban3v2w9Z1tBHxx727Pb+Px5yGA0hpDqgBFpNXDhZsHC2lxaP/fyYwp2Jju95jHRUrS8JXOrevTt4PB6CgoKKPGby5Mng8XjYunVrJUZGCHcogVYDuTl5uBr4kt327F81E2gTd2vUaWgGAMjNkeEYvw3HEVUdd+/ehUAgQMuWLYs85ubNmwAAd3f3Io8hpCqhBFoN3LjwCtmZUgCAibkY9s4mHEdUMXg8HrwLzY+7n98JcvA4jKhqePbsGZKTk+Hi4gJDQ0O1x2RlZeHRo0cQi8Vo0qRJJUdICDcogVYDhatv23erDR6v6iYVL28nGBjpAABieda4ymv4iTPIp9y5cwcA0KZN0Xf0ISEhyMvLg6urK3R0dCorNEI4RQm0ipPLGVw6Hc1u9xjozF0wlUBPXwe9fOuy2/v5nTiMpmooTgK9ceMGAKq+JdULLahdxT268w6Jb7MAAGI9IRo1t+I4oorXf0QDHPGLAABc5rngJSxQm+OYtFl+Aj148CAuXryo9hhq/yTVESXQKu7CiYJZeZq3qwmBoOpXOtg6GKNVF1vcDooDw+PjIL8jvi/H67uINpXj1SpWmGRqmc5nGAb37t0DAAQGBn7yeEqgpDqp+p+m1dyFkwUJtKdP3Y8cWbUMKNSZ6Ai/HbKzpBxGo72ePn2KtLQ0tGvXDgzDqP1JTk4GAOjr66Nx48ZK52/evBlOTk7Q1dVFy5YtERwczEUxCKkQlECrsJioVDyLUHy48QU8uHeqxXFElce9cy3UZhIAAOk8A5w5GPmJM4g6+dW3zZs3L/KYu3fvAgDc3NwgEAjY/QcPHsT06dPx448/4t69e2jfvj169+6N2NjYig2akEpCVbhV2MVCd5/1m1hAV6/6/LoFAj6Gyq9gpWAgAMVi24PGNiqXHshlrRbVJvkJtEWLFkUek59AP6y+XbNmDcaOHYuJEycCADZu3Ah/f39s2bIFy5cvr6CICak8dAdahV08VZBAewyq2r1v1flMfgO6jAQA8ORBIu7feMtxRNqnOAk0v420cAKVSCQICQlBjx49lI7t0aMHrl27VgGRElL5KIFWUWkpObh3rWDy+MLzxFYXpshCX+YOu/335gccRqN95HI57t+/D5FIBBcXlyKPU3cHmpiYCJlMhho1aigdW6NGDbx9S19kSNVACbSKuhb4EjKZYvL4GnYGVWbtz5IaJrvMPg44EoWEN5kcRqNdIiIikJGRARcXF4hEIrXHZGRkIDIyEoaGhmjQoIHK8x9WmTMMU6Un8iDVCyXQKury2Wj2caeeDtwFwrFGiENz+TMAgCyPwaHtYRxHpD1CQkIAfLz6NjQ0FHK5HC1atACfX/BxYmlpCYFAoHK3GR8fr3JXSoi2ogRaBcnlDIIDYthtj76O3AWjAYbLC+5C//nrEaRSGYfRaI/Ro0eDYRhs27atyGM6dOgAhmFUVmkRiURo2bKlytjRwMBAtG/fvkLiJaSyUQKtgh7deYeUxBwAgK6+EPVcLDiOiFvdmFB2/dOEt1m4cPzFJ84g5eHbb7/Fzp07sW3bNkRERGD69Ol4/fo1Jk+ezHVohJQLSqBVUNCZgrtP1zY1wOdX7zYnEWToM6Qeu72POhNViiFDhmDdunVYunQp3NzccOXKFZw5cwYODtW3SYFULZRAq6Bg/2j2cffPqt/wFXX6Dq0PgVDxRSLkyms8eZDIcUTVw5QpUxAdHY3c3FyEhISgc+fOXIdESLmhBFrFJLzJRNhdxQw8PB6q1exDH2NZQ19pKM/+Px5yGA0hpCqgBFrFFO48ZF/XBAZG6ocfVEcDRhasDXpi32Okp+ZyGA0hRNtRAq1iLp8tSKCe/Zw4jETzNHG3Rp2GZgCA3GwZju2O4DgiQog2owRahUgkMlw7VzBRd3WcfehjeDwevAut0vL35geQyxkOIyKEaDNKoFXI3atvkPlesWyXkYkI9s4mHEekeby8nWBgpAMAePk8HVcDaWUQQkjpUAKtQgrPPtSqsy1NmaaGnr4OevkWrIv6sflxGYbuTgmpisrrb5sSaBUSXKj908ub2j+L0r9QNW6wfwxePk9TOUYgEEAqpUW4CamKpFKp0tq1pUUJtIp4+TwNz5+kAAAEAh7c2tbkOCLNZetgjFZdbAEADAP8vUX1LtTIyAjp6emVHRohpBKkp6fDyMiozNehBFpFFK6+retiDrFu9Vk8uzQGjCy4C/1na5jKkBZzc3OkpKQgMTEREomEqnMJ0XIMw0AikSAxMREpKSkwNzcv8zXpU7aKuOxfUH3b1bsOh5Foh1adbeFQ1wQxUWnIycrDoW1h+OL7glVHxGIx7O3tkZycjOjoaMhkNAF9lZBZaAaqeBrGVN0IBAIYGRnB3t4eYrG4zNejBFoFZGVKcetSHLvdvlttDqPRDnw+D77jG2P1j9cBALvX38eoaa4QiQraRcRiMWxsbGBjY8NVmKS8/d244PFwqlUgZUNVuFXAzYuvIMlV3CGZW+mhhq0hxxFpBy/vOuwqLYnvsnDmwFOOIyKEaBNKoFVAcKHqW7r7LD6RWIDPRhdM77dj9V1q6ySEFBslUC3HMAyCCnUgoun7SqbfsPrQ1Ve0ZDyLSMGVAJpYgRBSPJRAtVzkoyS8fZkBANAR8dG4uRXHEWkXIxMx+nxesFbojtV3OYyGEKJNKIFqucK9bxs3t4JQh36lJTVobCPwBYpZm24FxSH8XjzHERFCtAF92mq5wquvdKPFs0ulhq0huvR2YLe3raS7UELIp1EC1WJpKTm4f/0Nu93G05bDaLTb5xNc2Mf/HYnC88cpHEZDCNEGlEC12LXAl5DJFL1Ga9oZwsxCj+OItFddFwu0LjS939bf7nAcESFE01EC1WKFp+/r1JPW/iyrEV83Yx+f+vup2knmCSEkHyVQLSWTyREcUND+6UHDV8qscXMrNG+vmIRfLmewbWUIxxERQjQZJVAt9fD2O6Qk5gAAdPWFqNu47BMjE2DElIK70KO7IvA69j2H0RBCNBklUC0VdKbg7tOtbU3w+bR4dnlo1roGmrhbAwBkeQz81lCPXEKIepRAtVTQmWj2cY9BNHylvPB4PAyf0pTdPrQtDAlvMjmMiBCiqSiBaqE3L9/jyQPFskw8PuDesRbHEVUt7h1roUEzCwCAVCKH39p7HEdECNFElEC1UOHJExzrmULPQIfDaKoexV1oQVvo/i0P8S4ug8OICCGaiBKoFgo684J97NWfet9WhHZedqjfVHEXKsmV4Y9fbnMcESFE01AC1TLZWVLcuPCK3e7S25G7YKowHo+H8d82Z7eP7AhHTFQqdwERQjQOJVAtc+tSHHJzFItnm1rowsbeiOOIqq4WHWzg2qYGAEAmY7B5yS2OIyKEaBJKoFqmcO/bNh40921F4vF4GFfoLvT0gad4+jCRw4gIIZqEEqgWYRhGKYF2p+ErFc6lhTXaetoBUMyRu2HBDY4jIoRoCkqgWuTJgyS8fVWweHaTFtYcR1Q9FL4LvXgqGqE333IYDSFEU1AC1SKFJ49v5GYFgZB+fZWhTkMzePZzZLdXz70GhmG4C4gQohHoE1iL0OxD3Bk93Q0CoWK6xJArr3Hu2HOOIyKEcI0SqJZITshWqjps17U2h9FUP3aOxug/vAG7vXL2VUhyZRxGRAjhGiVQLRHsH4P8WsNa9kYwNhVzG1A1NOobVxiZiAAAcdHp2LsplOOICCFcogSqJQpX33bp48BdINWYsakYo75xZbe3LLuNpPgsDiMihHCJEqgWkEpluBoYy257edfhMJrqrf/wBqhdxxgAkJUhxaZFNzmOiBDCFUqgWuDu1TfISJcAAAyMRXCoa8JxRNWXUIePSXPd2e1D28PYlXEIIdULJVAtULj61r2jDXg8WjybS6272MK9k2IJOUYOrPgumIa1EFINUQLVAkGno9nHNHyFezweD5PmuoMvUHyRuRUUh9P7n3IcFSGkslEC1XDPH6cgOjIVACAQ8ODW1obbgAgAxTqsA0c3ZLdXfB+MtJQcDiMihFQ2SqAa7vyJggH7DVwtIRILOIyGFDZ6mhusauoDAFISc7B23nWOIyKEVCZKoBruQqEE2tu3LoeRkA/pG+pgyk+t2e1D28Jw7/obDiMihFQmSqAa7F1cBh7ceqfY4AEdethzGxBR0aF7bbT1smO3F319EVIpzVBESHVACVSDXTj5gn3sUNcERiY0+5Cm4fF4mPpza4j1FFXrkY+SsWcDzVBESHVACVSDFa6+7f4Z9b7VVDVsDTG60AxFmxbdRPTTFA4jIoRUBkqgGiotJQe3LsWx2179nTiMhnzKoLGNUaehGQAgN0eGHyech0wm5zgqQkhFogSqoYLPxiAvT/EBbG1jACsbA44jIh8j1OHjh187sEuehd54i13r7nMbFCGkQlEC1VDnjhdU33r0pcnjtUHdxuYYMaUZu71hwQ1EhSdzGBEhpCJRAtVAOdl5uPJfweTxPXxo+Iq2GDa5Keo1sQAASCVy/PjFObYmgRBStVAC1UDXz79EdqYUAGBsJoa9M00ery2EOnzM+q0DdHQUf1phIfHY+msIx1ERQioCJVANdL5Q9W2H7rVp8ngt41jPFKOnu7HbW5bdQujNt9wFRAipEJRANYxMJsel0wXjP3v51uMwGlJagyc0RuPmVgAAWR6D70cGID01l+OoCCHliRKohrkdFIeURMWk5Hr6QjR0teQ4IlIaAgEfc1Z3hIGRDgDgdcx7LPzqIi17RkgVQglUw5w9FMk+buNpBz6fqm+1lU1tI3y7rD27HXAkCoe2h3EYESGkPFEC1SBSqQyBR5+x2wNGNuAwGlIeOvd2QL9h9dnt5TODEfkoicOICCHlhRKoBrlx4RXSkhXtZHoGOmjcwprjiEh5mPyjOxzrmwIAJLkyfDvcH5kZEm6DIoSUGSVQDeJfqPq2fVeqvq0qxLpCzF/XGWJdxYTzzx+nYP6E89QeSoiWowSqISS5MqXhKwNGNeQwGlLeHOqZYtqituz2f/8+w7aVdzmMiBBSVpRANcTVwFi8T1NU6xkY6VDv2yqoxyBnDBhV0K69/qfrCA6I4TAiQkhZUALVEIWrbzt0t6fJE6qoyXNboam7om2bYYAfRgYg9lkax1ERQkqDEqgGyMnOU1o8+7PRVH1bVQl1+PhpYxdY1tQHALxPk2Ca72lkpFOnIkK0DSVQDRDsH4OsDMXct0amYtRtbM5xRKQimVnqYeHvHtARKf78IsOS8e1wf0ilMo4jI4SUBCVQDVC4+tajrwNV31YDDZpZYubSduz21f9isXRaEPXMJUSLUALlWFamFEFnotnt/sNp8oTqovtAZ4ycWrB+6OHt4di+inrmEqItKIFy7Pyx58jOygMAmFnqwqm+GccRkco0eporug6ow26vnXddqUaCEKK5KIFy7OjuCPZxT1o4u9rh8Xj4dlk7NGtdg903d/w53Lj4isOoCCHFQQmUQ69j0nGz0AflgJHU+7Y6EokFWLjZA7XrKBZOl+TKMHXQKTy4RWuIEqLJKIFy6PjeJ+xjp/qm7NAGUv0YmYjxy/ausKyh+D+QnZmHSf1P4smDRI4jI4QUhRIoRxiGwfE9BdW3vl805jAaoglq2hlixc5uMDETAwDSU3Ixse9xxESmchsYIUQtSqAcCbnyGi+fpwNQDK736OvEcUREEzjUNcVyv27QN1QsxJ30Lhtf9DqGuOh0jiMjhHyIEihHju1+zD5272gDkVjAYTREk9RzscDSv7wg+v/qLW9eZmBMt39pyj9CNAwlUA5kZkjgfziK3R72VVMOoyGaqGmrGlj4uweEOoo/0TexGRjb7V+qziVEg1AC5cC5o8+RnamYus/EXIxGblYcR0Q0UavOtlj8pydbO/EuLhNjuv2LZxHJHEdGCAEogXKi8NjP3oPr0dR9pEitOtli6VYvdjHuhDdZGNv9KCLuJXAcGSGEEmgle/UiHbeD4thtWjibfErzdjb4ZXs36OoJAQDJ8dkY0+1f3LjwkuPICKneKIFWskPbHrGPnRubseP+CPmYZq1rYMXObjAwUvTOzXwvxaT+J3Fq/5NPnEkIqSiUQCuRJFeGf3cWVN8Op85DpARcWlhj7YFesPj/l648qRyzxwRi59p7HEdGSPVECbQS/fdvFJITsgEA+gY66NDNnuOIiLZxqm+GDYd6w6GeCbtv5eyrWPDVBUgktJ4oIZWJEmglOvhXQfVtnyH1IBDS209KztrGAGv390LTVgUT0B/eHo5x3Y8i4W0mh5ERUr3QJ3glefIgEXevvgEA8HjA4AkuHEdEtJmRiRgr/Lqhq3fBDFb3r7/FkHb/4OGddxxGRkj1QQm0kuzdFMo+dmlpDXMrPQ6jIVWBSCzA7FUd8eWcluD9/y/5XVwmRnv+i/1/PATDMNwGSEgVRwm0EiTFZ+HU/qfs9vhvm3MYDalKeDweBn/hgl+2dYOhsQiAorPa0mlBmOZ7BimJ2RxHSEjVRQm0Ehz88xEkuYoOHta1DNDE3ZrjiEhV496pFn7/ty+cGpix+y6cfIFB7gdocW5CKggl0AqWm5OHA38+ZLdHTWtGMw+RClHLwQibjvTBZ6MLJueIf52JCb2OYdmMIGSkSziMjpCqhxJoBTu1/ymS4hXVaHoGQnTzduY4IlKVicQCfP1Tayz9ywvGpop1RRkG+HvzQ3i77sO5Y884jpCQqoMSaAWSyeTYvjKE3R4wqiG7ugYhFamNpx3+Ot0frTrXYve9i8vE9M/P4huf03j1gtYXJaSs6NO8AgX++wwxUYo1HIU6fAz9sgnHEZHqxMJaH8u2dcWPazvBxEzM7r9w8gX6NtmLX2ZeRuK7LA4jJES7UQKtIAzDYOtvBXefvXzrwsBIxGFEpDri8Xjw7OcEv8DP0GdIPXZ/nlSOfb8/QK+Ge7Bp0U2kpeRwGCUh2okSaAUJ9o/B49BEAACfz8OY6W7cBkSqNSMTMWYubYd1B3uhcYuC9WezM6XYsuw2ujrtxOKpl2itUUJKQMh1AFURwzDYuPAmu925jwNMLXQ5jIgQBZcW1lh3oBduXozD1pUhiP1/E0N2Vh4O/vUIB/96hPbda2Pg6Ebo0tcRBobc15owDIO3rzIQ/TQVcTHpSHiThYS3mUh8k4W0lBzk5sggyZVBmiuDTCaHgZEIBsYiGBmLYGwmRu06JnCqbwbH+qZwkOhAVyTlukikiqAEWgHOH3+O8P8veMzn8zB5rjvHERFSgMfjoa2XHVp1qYWLp6Jx8M9HiI5MZZ+/FvgS1wJfQqwrQKeeDujhUxftu9WGmWXFzp6VmSFBTGQqXjxJRfTTFLx4morop6mIjkxFdmb5JD0+bxUaObxC64ZP0co0Gi071mInoCCkpHhMCeb7cnd3x507dyoyHq0nlzMY1HI/IsMUVWHdPquD2Ss7chxV9VTLsWDFktfRaRxGotkYhsGDW+9wxC8c1y+8Aor4RHCqb4rm7W3QvL0N6jY2h52TCcwsdUs0rjkzQ4L4uEy8fJ6G6EhFgnzxNAUxkal4F1f5E+ELBDy06mKLPkPqo9tndWBiRjVFpPgogZazU/ufYPaYQAAAX8DDwauDqfqWI5RAS+7Ny/c4f/wFLpx8jpfPPz3URd9QB3aOxjAxF0NXXwd6+kLo6gshy2OQm52H7Ow85GTlITk+C/FvMpH5vnR3kmJdAaxsDGDraIRa9saoaWcAC2t9mJjrQiQWQCQWQEfEB5/PQ1amFJnvpch8L0FqUg5evUhH9NNUxD5LRXJ8JuSM+q4fOiI+OvV0wMAxiuprgYC6iJCPowRajnKy89Cv6T68iX0PQLFk2cyl7TiOqvqiBFo2sc/ScPlsDK6ei8XzxymQyyp2cnoeDzAx10UteyPUbWyOui7mqO1kjNp1TGBiXj5fQo0CayDkqTNuRdTHtaetEBVtCIZRvYO2czLG8CnNMGhsIxiZiNVciRBKoOVq628hWDf/OgDFjDD/XB9MQ1c4RAm0/OTm5OHpwySE3Y3Ho5B4vHyWjoS3mZBK5CW6Dl/Ag6GRCOZWuqjtbIJ6LhZwqm8GOydj1LQzrPCJRmpdLfg/kWk9HG/THXHukj4CzuniSZRqotQ31IHP+Mb44rsWsLIxqNDYiPahBFpOkuKz0LvRHraK6ovvW2DoJJo4gUuUQCsWwzBIT8nF27gMZGcqqmpzsqXIyZZBqMOHWFcAsa4QYl0BjM3EsLDWh5GJiNO5oD9MoHKxPbsd+1KIk/76OHZSD+8zlftXinUF+PzLJpRIiRJKoOVk3oRzOLb7MQDAxFyMg1cHQyCkNhQuUQIlH/pYAs2Xk8OD/zk9HDhigOhY5Roksa4AQyc3xZdz3GFaTtXKRHvRJ3w5CLnymk2eADBzaTtKnoRoKV1dBp/1y8L+HQlYtTQJDevlss/l5siwa9199G60B7vW32eXKSTVE33Kl5FUKsPiqZfY7UZulujQXfVbLSFEu/B4QMd2OfDbkohVS5PQoFAiTU/JxW8/XIG36z4EHIlCCSrySBVCCbSMdqy6h6hwxZhPvoCH+Ru6cBwRIaQ85SfSnVsS8cuCJNjaFAzFefk8Hd8O88dIjyMIvfmWwygJFyiBlsHj0ERsXnqL3R7yZRNYUwcDQqokHg/w6pyDA37xmDElFUaGBdW396+/xfBOhzFr9H948/I9h1GSykQJtJQkEhl+/OIc8qSKbvzWtQwwZporx1ERQiqajg4w1CcTh/e8w1CfDAgFBUN5Th94in5N9uH3xTeRVU7TDxLNRQm0lDYtvIknDxSrrfB4wLJtXanjECHViIkxgxlT0nBgZzw8OxWsq5qTnYfNS2+jX5O9OLnvCeRyah+tqugTvxSC/WOwfdVddnvY5KZwrGfKXUCEEM7Y1ZJh+cIUbF6TgHrOEnb/u7hMzBkXiOGdDuP+jTccRkgqCiXQEnr7KgNzxgWy204NzDBmhht3ARFCNEILVwl2bknAvO9TYGaax+5/ePsdRnQ+gh9GBVD7aBVDCbQEsrOkmOZ7GqlJOQAAsZ4Av+3qDj6fu5lVCCGaQyAA+vfOwuE98Rg97D2EwoL20TMHI9GvyT5sWkTto1UFJdBikssZzPviPMLuJrD7Fm32pJVWCCEqDPQZTJmQjn/UtI9uWXYbfV324sTex9Q+quUogRbTuvnXEXAkit0e960bWnasxWFEhBBNV8tG0T66ZW0C6jkXTMQQ/zoTc8efw6CW+3Hu2DOaiEFLUQIthq2/hSh1GurUywHDv2rGYUSEEG3SvJkEO7ckYv4PKTA3K2gfjQxLxvTPz+Lztv/g8tloSqRahhLoJ+zdFMouUQYA9ZtYYN7aThxGRAjRRgIB0K9XFg7tjsfYEenQFRdMxBB+LwFfDTiFEZ0P4/r5l5RItQQl0CIwDIM/l9/B8m+D2X216xhj7YFeNN6TEFJqBvoMJo9/j6N/x2P44PcQ6RR0NAq9+Q4Teh/H8E6HEXj0GWSykq23SioXZQI18vLkWPFdMDYsuMHuq1nbEJuO9IVILOAwMkJIVWFmKse0yen4d987+H6WodRj98Gtd5gx5Cz6N92Hf7Y+QnYW9drVRJRAP/A+LRdfDzyFvZsesPsc6prgr5P9oW+ow2FkhJCqyNJCju+/ScPh3fEY2C9TKZHGRKVh0deX4OW4E7/+EIzopykcRko+RAm0kAe33sK39UFcCYhl9zVuboXNx/pBz4CSJyGk4tSsIcPsmak49vc7jBn2HoYGBZ2N0lNzsXt9KPo22YcJvY/jzMGnyMnO+8jVSGXgMSVorXZ3d8edO3cqMh5OSHJl2PZbCP5cfgd5eQXf/voMqYfpi9vSRAlaqpajCfv4dXQah5EQTVHrasH/iUzr4ZCLNXft3swsHk6c0ceho/p4/Vak8ryhsQg9BjnDe2RDtOxYiz6nOFDtE+jNS6+wbPplPItIZvcJBDzMWNIWvQbX4zAyUlaUQMmHtCmB5pPLgVshYhw+po9rN/UgZ1QTpZWNPry866D7Z85w71wLOjrUV6MyVNsE+jg0ERsW3EDQmWil/TXtDPDrru6oZW/MTWCk3FACJR/SxgRa2Nt3Apz5Tx+nA8SIeyNWe4yxmRgde9ijQ3fFjxWtUVxhqlUCZRgGNy/FYff6ewg6E6P0HF/Aw4ivm2HElKYQCKhpuCqgBEo+pO0JNB/DAGEROjjznx7OB+kiLb3oPhr1m1igtYctmrezgVs7G9S0M6zESKu2apFAXz5Pg/+hSBzb/RjRkakqz7foYIPvlreHNX1Tq1IogZIPVZUEWphMBjwIE+HiZV1cvCxGQpJqe2lhNvZGaN6uJtza2aCpuzWcG5vDwPDj5xD1qmQCzcqUIiT4Na6dj8X1cy8RGZas9rjGza0wdUFr1HOxqOQISWWgBEo+VBUTaGEMAzyN0sHNO2Jcu6GDRxG6yJN9ukattrMJ6rtYoF4TC9RvYgHnRmawdTKGnj6NPvgYIdcBlAXDMEhLzsGLp6l4HJqIx6EJeByaiCcPEiGVqJ/BQ6jDR/tutTFmuhvsnU3UHkMIIdqIxwMa1JOiQT0pRg8DsrJ5uP9AhAePdHAvVIjHkbrIlah2MHr5LA0vn6Xh/InnSvsta+qjdh0T1HYyhp2TMWo5GsOqpj4saxjAykYfZpZ61br3r8YkUIZhkJsjQ052HnKz85CTlYfMDAlSEnOQkpiN1KQcxeOkbCS8zsSr6HTERacj8/2nZ+jg8YC6LuYYMLIhOvdyoDGdhJBqQV+PQfs2uWjfRrESTF4eEPlMBw/CRHjwSIinkQLEvRVDLld/l5r4NguJb7Nw79obtc8LhXxY1NCDhbU+jM3EMDIRw9hMDGNTxY9R/r8mIujqC6GrrwNdXQF09XWgpy+EWE8IXX0hdHT44PG0LxFzkkD91tzFP9vCFIkyOw+52bJyHxRsbqWH5u1qwqOfI1zb1KSqCEJItScUAo0aSNGogRRDBin25UqAmFgdRD0XIuq5EE8j+Xj1WoiERBFkRSTWfHl5cryLy8S7uMwyxcXn8yDU4UMg5EMo5Cn+1eFDKORDkL8tzN/+/z4BDzweDzw+D3w+D3w+AJ7i8dc/t0bLDhW/3CQnCTQ9VYLYqPJpk+LzeTAw0oFNbUPUdbGAc2Nz2Dkaw9C4oFH85fP0cnktol0K//k8fZTEWRxEcxT+P5GRnAEJP56zWDSJtRFg7Qq0dy3YJ5MBCUkivIkX4W28GK/fiRGfwEdSsgApqTpISRcjK6d8Oh/J5QwkuTIgV/bpg4thxJTKWW6yRJ2ILC0t4ejoWG4v/uRBotLMPzImAwKe+i7WfL7iW4hAoH23+QCQm/ceYqER12GUu6paLoDKpo2KWy4dgRSGumW7a6ps77NzYaSnfuynZmIARv7/f4tOM+k5Uhjr6uSf8f9TefhYYiq4nGo+EIgNYG5bfneflpaW8Pf3V/tciRJoRdOWXr6lUVXLVlXLBVDZtFFVLRdQdcumzeWiGQMIIYSQUqAESgghhJSCRiXQL7/8kusQKkxVLVtVLRdAZdNGVbVcQNUtmzaXS6PaQAkhhBBtoVF3oIQQQoi2qPAE+urVK3zzzTdo164d9PX1wePxEB0dXaxzeTye2p/79+9XaMzFVZayAUBERAQGDx4MS0tL6OnpoUGDBli/fn3FBVxMpS3XwoULi/yd6erqVnzgxVCW31lsbCzGjBkDe3t76Ovro379+pg/fz4yMzVjOERZyvbixQv4+vrC1NQUBgYG8PT01JiekYcPH4aPjw8cHBzYv5O5c+fi/fv3nzw3JycHP/zwA2xsbKCnp4d27drh8uXLlRD1p5WlXD/++CN69OgBCwsL8Hg87Ny5s+IDLoHSlu3OnTv48ssv0bBhQ+jr68Pe3h4jRozAixcvKinykqnwBBoVFYV//vkHZmZm6NSpU4nPHzt2LK5fv670U79+/QqItOTKUrY7d+6gTZs2yM3NxbZt23DmzBl89913kMnKZyBxWZS2XBMmTFD5XZ07dw5CoRDe3t4VGHHxlbZsmZmZ6NatGy5fvowlS5bg9OnTmDBhAlavXo3x48dXYMTFV9qyJSUloWPHjnj06BH+/PNPHDhwAADg6emJiIiIigq32FatWgWBQIBffvkF/v7++Oqrr7BlyxZ0794dcrn6Oa/zffHFF9i6dSsWL16MU6dOwcbGBj179tSIL+FlKdfGjRuRnZ2Nfv36VVK0JVPash04cABhYWGYNm0azp49ixUrVuDu3btwd3fHy5cvK7EExcRUMJlMxj7eunUrA4B58eJFsc4FwMybN6+CIiu70pZNJpMxjRs3Zj777LMKjK70yvI7+9Du3bsZAMypU6fKKbqyKW3ZAgICGABMQECA0v7Zs2czAoGAyczMLO9QS6y0ZVuyZAkjEAiYyMhIdl9GRgZjbW3NDB48uCJCLZH4+HiVfbt27WIAMOfPny/yvPv37zMAmB07drD7pFIpU79+faZ///4VEmtJlLZcDFPwu46MjGQAMH5+fhURYqmVtmzqzouOjmZ4PB7z008/lWuM5aHC70D5/KrbzFrasl26dAnh4eH49ttvyzmi8lGev7Ndu3ahRo0a6NmzZ7ldsyxKWzaJRAIAMDY2VtpvamoKuVwORgP64pW2bDdu3EC9evVQt25ddp+BgQE6deqEU6dOIS+vfOepLikrKyuVfa1atQIAxMXFFXneiRMnoKOjgyFDhrD7hEIhhg4dioCAAOTm5pZ/sCVQ2nIBmv+5WtqyqTvPwcEBVlZWn3xPuKDZvwUAW7ZsgVgshr6+Pry8vBAcHMx1SGV25coVAIr2mbZt20JHRwfW1taYNm0asrOzOY6u/Lx69QoXL17EiBEjIBRqzMI/pdKtWzfUq1cPs2fPRnh4ODIyMnDhwgWsX78ekydPhoGB9i7GLhAIIBKpzmkqFouRnZ2NZ8+ecRDVxwUFBQEAGjVqVOQxYWFhcHJygr6+vtJ+FxcXSCQSREVFVWiMpVGccmmr0pYtIiIC8fHxGvmeaHQCHTlyJDZv3oxz587hr7/+QlJSEry8vHDp0iWuQyuT169fAwCGDBmCHj16IDAwELNmzcK2bdswfPhwjqMrP3v27IFcLseYMWO4DqXMdHV1ceXKFcjlcri4uMDIyAhdu3ZFv379sGnTJq7DK5MGDRogMjISSUkFE+7L5XLcunULAJCcrH5Beq7ExcXh559/Rrdu3eDu7l7kccnJyTAzM1PZb25uzj6vSYpbLm1U2rLl5eVh8uTJsLKywhdffFGBEZaORt8W7Nmzh33cqVMnDBgwAE2aNMH8+fPZuzhtlN+IPnLkSCxevBgA4OHhAZlMhjlz5iA8PByNGzfmMsRysXv3bjRv3hzNmlXOyggVKScnB0OGDEF8fDz27NkDe3t73Lp1C4sXL4ZQKMSWLVu4DrHUJk+ejA0bNmD06NHYsGED9PX1sWzZMrbnoyZVF2ZkZGDAgAEQCoXw8/P76LEMw6hdY1ITqts/VJJyaZuylG3q1Km4du0aTp8+rfbLENc05y+jGIyMjNC3b1/cvn2b61DKxMLCAgDQvXt3pf09evQAAI3oIVhWt27dwuPHj6vE3ScAbN++HZcuXcKZM2cwcuRIdO7cGd9//z1Wr16NP/74A6GhoVyHWGp16tTBvn37EBISgrp166JWrVq4fv06Zs6cCQCwsbHhOEKFnJwceHt74/nz5wgICICdnd1Hjzc3N1d7l5mSksI+rwlKWi5tUpayzZ07F3/99Rd27NjBfjZqGq1KoEDR3yq1iYuLCwColCP/m7EmfeMvrV27dkEoFFaZKumHDx/CzMwMzs7OSvtbt24NABox3KMsfHx8EBcXh/DwcERFRSEkJAQZGRmoXbs27O3tuQ4PUqkUPj4+uHXrFs6cOYOmTZt+8hwXFxe8ePECWVlZSvvDw8MhEomUOk1xpTTl0hZlKduyZcuwYsUKrF+/HqNGjarAKMtGqz6p09PTcfr0abRp04brUMqkd+/eEIvFKmvMBQQEAIDWt39IJBIcOHAAffr0UdurThvVrFkTKSkpKh1Pbt68CQCwtbXlIqxyJRAI0KhRIzg7O+P169c4ePAgvvrqK67Dglwux4gRI3D+/HkcP34cbdu2LdZ53t7ekEqlOHToELsvLy8PBw8eRI8ePSAWc7u2ZmnLpQ3KUrYNGzZg/vz5WLZsGb755psKjLLsKqUN9PDhwwCAkJAQAMDZs2dhZWUFKysrdOnSBTExMXB2dsbPP/+Mn3/+GYBiIO6TJ0/g6emJWrVqISYmBqtWrcLbt2+xb9++ygi7WEpTNgsLC8ydOxdLliyBsbExvLy8cOfOHSxevBhjxozRiG/GpSlXvlOnTiE5OVljq29LU7axY8dizZo16NOnD+bNmwd7e3vcuXMHS5YsQcuWLdGhQwfOylNYacomlUoxa9YsdOnSBcbGxggLC8Py5cvh4uKC7777jrOy5Pv6669x6NAhzJs3DwYGBrhx4wb7nJ2dHezs7NSWy83NDUOGDMGMGTMglUrh5OSELVu24MWLFxrxGVLacgGKHq0JCQl4+/YtAMXELIaGhgAAX1/fyi2IGqUt24EDBzBjxgz06tULXl5eSucZGxtrXt+QyhhsCsVC4yo/Xbp0YRiGYV68eMEAYBYsWMCec+LECaZ9+/aMhYUFIxQKGXNzc6Z///7MzZs3KyPkYitN2RiGYeRyObN69WrG2dmZ0dHRYezt7ZmffvqJkUgklV8INUpbLoZhGG9vb8bc3JzJzc2t3KCLqbRlCwsLYwYPHszY2dkxurq6TL169ZjvvvuOSU5OrvxCFKE0ZZNKpUzfvn0Za2trRiQSMXXq1GHmzZunEZNDMAzDODg4FFmu/HIU9TvLyspiZs6cydSoUYMRi8VM69atmYsXL1Z6GdQpS7m6dOlS5LmaoLRlGzNmzCf/D2sSWo2FEEIIKQWtagMlhBBCNAUlUEIIIaQUKIESQgghpUAJlBBCCCkFSqCEEEJIKVACJYQQQkqBEighhBBSCpRACSknPB4PPB4PfD7/o2toenp6ssfu3LlT6bmxY8eyzy1YsKDIa+zatYs9zsPDQ+m5S5cusc/l/+jr68PGxgadO3fGDz/8gHv37pWlqIQQUAIlpFwJhUIwDIPt27erfT4yMhJBQUGfXGBcKBRix44dkMlkap/funXrJ6/h4OCABQsWYMGCBZgxYwb69euHzMxMrFq1Ci1atMCIESOQkZFRvIIRQlRQAiWkHNWoUQPu7u7w8/NDXl6eyvPbtm0DwzDo16/fR6/Tr18/vHr1SmXBAUCx8svVq1fRv3//j17D0dERCxcuxMKFC/HLL79g69atCAkJwb1799C0aVP8/fffGDx4cMkKSAhhUQIlpJxNnDgRb9++xalTp5T2S6VS7Nq1C+3bt2eXtCvKiBEjoKenh61bt6o8l79vwoQJpYrPzc0N586dg5WVFfz9/XHs2LFSXYeQ6o4SKCHlbNiwYTAwMMC2bduU9p84cQLv3r3DxIkTP3kNU1NTDB48GKdPn8abN2/Y/bm5udi9eze6dOmC+vXrlzpGa2trTJo0CQA0YmUSQrQRJVBCypmRkRGGDh0Kf39/vHr1it2/detWGBsb4/PPPy/WdSZOnIi8vDyljkZHjx5FUlJSsZLwp+R3Prp161aZr0VIdUQJlJAKMHHiRMhkMuzYsQMAEBMTg8DAQIwYMQL6+vrFukbHjh3RqFEjtt0UUCRhMzMz+Pj4lDnG/EXAExISynwtQqojSqCEVIA2bdqgadOm2LFjB+RyObZt2wa5XF7iO8cJEybg+fPnuHDhAqKionDx4kWMGjUKurq6ZY4xPynzeLwyX4uQ6ogSKCEVZOLEiYiJiYG/vz/8/PzQsmVLNG/evETXGD16NMRiMbZt28beiZZH9S0AvH79GgBgZWVVLtcjpLqhBEpIBRk1ahT09PQwadIkxMXF4csvvyzxNSwtLTFw4EAcPXoUO3bsQLt27dCkSZNyie/ixYsAFHfLhJCSowRKSAUxNTWFr68vXr16BQMDAwwbNqxU15k4cSJyc3ORkJBQbnef8fHx+PPPPwEohswQQkru41OZEELKZOnSpRg0aBCsrKxgZGRUqmt4enri+PHjkMvl6NmzZ5ljCg0NxejRo5GYmIg+ffrA29u7zNckpDqiBEpIBbK3t4e9vX2ZrsHj8UqV5KKjo7Fw4UIAikkcEhMTERISgpCQEACKO8/8u1BCSMlRAiWkioqJicGiRYsAALq6ujA1NUW9evXw/fffY8SIEXBzc+M2QEK0HI/J78tOCCGEkGKjTkSEEEJIKVACJYQQQkqBEighhBBSCpRACSGEkFKgBEoIIYSUAiVQQgghpBQogRJCCCGlQAmUEEIIKQVKoIQQQkgpUAIlhBBCSuF/6NUW23FWx9UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eee6aaef5d71408690c2c910cc182321",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fd326fc83b140408ceb4c96dd013826",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b4bc7297baa41e8b9d79f86b3392f57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "525314f325e14ccc8be433bc3a023f07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1995085a6cb4b8786c4a9625ff9b1a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f6a2c22a4cb4ad9b17c62e0a9c063dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c7e66bcfac54c4f99df25cb6107debe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "608ad6cce6c24c429811848097518d9f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YhqH7QP/r9u3bzPDhwxlnZ2dGX1+fMTIyYtzd3Znp06czb9++LfCY3Psxd+7cyZw+fZpp2bIlY2hoyJiZmTFff/018/z5c6XyYWFhzLRp05gmTZow1tbWjFgsZpycnBgfHx/mxo0bBb5GUFAQ069fP8bOzo4RiUSMtbU106BBA2batGnM3bt3lco6OTmp3BtZmOrVqzMAGEsLMyb73U2G+XS3wHLZ2dnMhg0bmFatWjGmpqaMWCxmqlatynh7ezNr1qxRvqf03/KLFy9mXFxc2Pr99NNPTFZWVrHuA2UYhpHL5cyGDRuY2rVrM2KxmLGzs2O+++47JikpqdC67ty5k2nQoAFjaGjIWFlZMV999RXzzz//MAsXLmQAMFevXlUq//HjR2bQoEGMra0tw+fzGQDMwoUL2edv3LjBeHl5Mebm5oyxsTHTpk0b5vjx48zVq1dVymobTfyN8xjm3/HnRdC0aVOEhISUTiYnxZd/QETRf40a9yE6DUPbH8P7qFR2X8evqmHKkpbQNyi8k+PTxwxs+uUurp3Puw+wajVT7Lr0NSo7GJdqzF+SOwqTVHDx+T7vrJpyFwfROE38jdM1UFIi6akSfPfVGaXkOXFBc8xe6fHZ5AkA1pUMMW9dO4yf0xS8f/8lvn2dgjFdTiDufXpphk0IISVGCZSoTSaTY8ZQPzz/5xO7b976dugzrHaRz8Hj8eA72g2L/ucFoVDxzzEiPAnjup9EWorkC0cTQgh3KIEStW1feV+p+/WbH5vAs5uzWudq3aEq5q5rB75A0S0d/iQBs0dchFzOXdc0IYR8DiVQopbH9z7ijyV32O0uPq7oN0Z1uavi8OjsiBnLWrPbAWcjsGFh8W9FIISQskAJlBRbRroUs4f7IydHsfZjZQdjTPu5lUbO3amvK/qPzUvEW1bcQ5Bf5GeOIIQQblACJcW2ZVkIIsKTAAB8AQ8rdnWEQKi5f0qjZzRCs3ZV2O2fRl/Cp48ZGjs/IYRoAiVQUixvnidi55pQdnvcrMao4mT6mSOKTyDgY9ZvHrC0USxrlRCXibljL6EYd1wRQkipowRKioxhGPw67RpypP923VY1xtcj3Urltcyt9DHrt7yJya/7ReH47qefOYIQQsoWJVBSZFdPv8HNS3nT9M1b1w58/pfXilRXE48q8BmVd6PzipnX6f5QQojWoARKikQmk2PdgrwRsW27OKJWPdXV7TVt5LRGsKuqmJUoLVmCnydXjFUeCCHajxIoKZKzB1/gZZhi1QeBgIfJi1uWyevqGwgx7Ze8Eb6XTr5GwNk3nzmCEELKBiVQ8kUSiQx/LM2757PHwJowt9Ivs9dv1MoOXftVZ7d/nXYN2Vmqy0cRQkhZogRKvujk3meIfpMCABCJ+Rg9XXUB5NI2dkZjmJiJAQAxEanYsSr0C0cQQkjpogRKPksmk2P77/fZbZ9RbjAyEZd5HGaW+hg1LS9xb1kegnf5JrAnpCRGjhwJHo9X4ILeXHF2dmYXrSbaiRbUJp/l//crvH2VDAAQivgYNKEeZ7F0H1gD5/4Kx8uwBEiyZdi4+DZ+3d6Rm2AeLgAyorh57aIwdAQaLCnxaZydnREZWfBMUJUqVcKHDx+U9kkkEixZsgT79+9HQkICGjdujFWrVqFx48Yqx/v7+6Nz5844c+YMevQoeNHq8qx9+/YIDAyk+5t1GCVQUiiGYbBtZV7rs1u/6jA0FnEWj0DAx4SfmmLG0IsAgFP7nmHElIaoVb/0RwOryIgCjJzL/nWLKj1CY6cyMzPD1KlTVfYbG6uu2frjjz9izZo18PHxgYODA/bu3QsvLy88e/YMdnZ2bLm0tDSMGzcOQ4cO1YrkuWzZMvz444+wt7fnOhTW5cuXuQ6BfAElUFKom5fe4umDOAAAn8/D8MkNuQ0IQIMWldHc0x53AmPAMMCaeTfx56neXIdVrpmbm2PRokVfLMcwDDZv3oxRo0Zhx44dAIC+ffuiffv22Lt3L2bNmsWWnT17NrKysrBu3brSCrtY7OzslBK8NnB1deU6BPIFdA2UFGrvhofsY48ujmU68vZzxs5sDN6/8zcEXYjCncBobgMiAIC4uDhkZGSgefPm7L7cx/m7ga9du4ZNmzbhjz/+gKWlpVqvlXt9MCUlBT/88AOcnZ0hEomUEv2zZ88wcuRIVK1aFXp6eqhUqRIGDx6M58+fq5zvc9dAb997DN9Rs1G5cmWIxWJUrVoV48ePx7t37wqMLSEhAXPnzkXdunVhaGgIMzMzNGjQAD/++CPS09MREREBHo+HwEDFPc08Ho/9ad++vUod/ys7OxvLly9H/fr1YWhoCFNTU7Rt2xZ//fWXStnc1xo5ciQiIiIwcOBAWFtbQ19fH02bNsWZM2c+/0b/x4kTJzB06FDUrFkTRkZGMDY2RpMmTbB+/XrI5XKV8rnv6+vXr7FhwwbUr18fBgYGSvVMSEjAnDlzUKdOHRgYGMDMzAwdOnTAxYsXVc6XnJyMlStXwtvbGw4ODhCLxbCxsUHv3r1x61bZr9xELVBSoMjwJARdyPvQGzNd9RoWV1xqWaDjV67wP/4KALBy9g38FdwfPF7pzYpUkWVnZ2Pfvn2IioqCkZER6tevj3bt2kEgECiVs7GxgYGBAe7du8fuCwkJAQA4OTkBADIzMzF27Fj4+PjAx8enRHFJJBJ4e3sjISEBnTt3hqmpKVxcXAAAFy5cwNdffw2pVIpevXqhevXqiI6Oxt9//42zZ8/i6tWrBV6X/a+dB05h3LRfoacnQu/eX6Fq1aoIDw/Htm3bcPr0ady6dQuOjo5s+Tdv3sDLywuRkZFo0qQJvv32W8jlcrx48QJr1qzBhAkTYG5ujoULF2LXrl2IjIzEwoUL2eO/NGhIIpGgS5cuCAwMRO3atfH9998jIyMDR48exYABA/DgwQP8+uuvKsdFRkaiefPmqFatGoYNG4aEhAQcPnwYffr0waVLl+Dl5VWk9/zHH38En89HixYtYG9vj+TkZFy5cgVTpkzB3bt3sXfv3gKPmzJlCoKCgtCjRw90796d/bcTGRmJ9u3bIyIiAm3btkXXrl2Rnp6OM2fOoGvXrti8eTPGjRvHnufp06eYO3cu2rVrhx49esDCwgJRUVE4deoUzp8/j9OnT6Nr165FqosmUAIlBTrw5yP2cQ13S1RxMuEwGlUjpzZAwNk3kErkCLsfB7+jL9G1Xw2uwyqXPnz4gGHDhintc3Fxwc6dO+Hp6cnu4/F4+Oabb7B+/XokJyfD3t4ee/fuhYmJCYYMGQIAmDdvHhISEvDHH3+UOK7379/Dzc0NgYGBMDIyYvcnJiZi0KBBMDQ0xLVr1+Dmljdf85MnT9CiRQuMHTsW9+/fL+i0rBcvXmD89GVwdrRD4KnNsK/bnX3uypUr6NSpE6ZMmYLjx4+z+4cOHYrIyEj8+uuvmDNnjtL5Pn36BGNjY+jr62PRokUICAhAZGRkkbrHc61atQqBgYHo1q0bTp06BaFQ8RG+cOFCNG/eHMuWLUPPnj3RunVrpeMCAgKwaNEipWQ9ePBgdO3aFStXrixyAj179qxK17JcLseoUaOwZ88eTJw4ES1atFA57v79+wgNDWW/4OQaMWIEIiMjcfDgQQwcOJDdn5SUhPbt22Py5Mno3bs3KlWqBACoU6cO3r17B2tr5XEP0dHRaN68OaZNm1amCZS6cImK9FQJju8KY7dHTG3IXTCFsK1ijL7D8+bJXT03GBKJjMOIyqdRo0bh8uXL+PDhA9LT0/Ho0SOMHz8eERER6NatGx4+fKhUfsWKFZg9ezbu3LmD7du3w93dHZcvX4a9vT1u376NtWvXYt26dbC2tsbixYthZ2cHoVCIJk2a4MaNG8WOb9WqVUrJEwD27NmDpKQkLF68WCl5AoC7uzvGjRuH0NBQhIWF4XM2bdoEqTQH636ZDns7W6XnvL290bt3b5w+fRqpqYrbqe7du4ebN2+iYcOGmD17tsr5crtOS2LHjh3g8XhYvXo1mzwBwNbWFvPnzwcAbNu2TeU4JycnzJs3T2lfly5d4OjoiDt37qiUL0xB12X5fD6mTJkCAPDz8yvwuFmzZqkkz4cPHyIwMBA+Pj5KyRNQXHdfvHgxsrKycOzYMXa/mZmZSvIEAAcHB/j6+uLZs2eIiiq70fHUAiUqTu1/jvRUKQDAzEIPzdppz8jE/AZOqIvzR8KRmixBTEQKTux5iv5j63IdVrmSv8UCAHXr1sWff/4JY2NjrFq1CosWLVJqgenp6WHZsmVYtmyZ0nESiQSjR49G9+7dMWTIEKxdu5ZtEbVp0wa//PILunbtipcvX7KtjS/R19dH/fr1VfYHBwcDUHxAF9S6e/HiBQBFd+B/E2xB5wm8eR93Q8MAQ+XrhbGxsZDJZHjx4gWaNGnCXoPr0qUL+HzNt01SU1Px8uVL2Nvbo3bt2irPe3t7AwBCQ1UnGWnYsKFKlzsAVK1ala1nUcTHx2PlypU4d+4cXr9+jfR05cUdYmJiCjwu/3XxXLmvm5ycXODvKS5OMYDx6VPlVZhu3LiBdevWITg4GLGxsZBIJCox5O9WL02UQIkShmFwZNsTdttnlFuprrhSEiZmehjwTV32VpvNv4bgq+F1IBarflAQzZowYQJWrVqFa9euFan84sWLERMTww4MWblyJTp06MB+cNaqVQvOzs74448/sGRJ0e5ftbW1LfC6d3x8PABg69atnz0+LS3ts8/nnmflxoKv6/33PElJSQBQarfCJCcr7scubLRw7v7cOPIzNzcv8BihUFjg4J+CJCUloVmzZnjz5g2aN2+O4cOHw9LSEkKhEElJSVi3bh2ys7MLPLZy5coq+3LfX39/f/j7+xf6uvl/T8ePH4evry/09fXRqVMnuLq6wsjICHw+HwEBAQgMDCw0htJACZQoeXI/Fs//+QRAcetKryG1OI7o83oPrYUj254gOTEbH6LTcHLvM/Qb4851WOWera2iS/O/LZCChIaG4rfffsOff/4Je3t7pKSk4N27d+x1UQBwdHSEtbU1njx58pkzKSts0JiZmRkARQu0oBZqUeWeJ/nNVZiaGANWTT9bPjdJFdYKK6nceP47eUWu9+/fK5XTtG3btuHNmzdYuHChSosxODj4s7ckFfS7yo1z3bp1mDx5cpFimD9/PsRiMUJCQlCnTh2l58aPH8+ObC4rdA2UKDm6Pe+6UMNWlWFsWvbT9hWHgaEI/cflJcw/f7lL10LLQG73W7Vq1T5bLicnB6NHj4aXlxfGjBmj9Nx/WwpZWVkaia1lS8VKQUFBQZo5T/CDYpX38/MrUqsut0tVJivav1cTExO4uroiJiYG4eHhKs9fvXoVAIo0ulgdL1++BIACR0+rk7jU+T29fPkSbm5uKslTLpfj+vXrxY6hpCiBElZ6mgRnD71gt4d8x920fcXRa0gtmFnoAQDbCiUl9+TJEyQkJKjsj4yMxMSJEwEoRp1+zrJly/Dy5Uul7lRTU1PY29vjwoULyMlRrKoTGBiI1NRUuLuXvPdg1KhR7CCUggbIyOVyBAQEfPE8EydOhEgkxLR5a/Dipep0hhKJROnDv0mTJmjdujUePHiAFStWqJSPj49X+pJgZWUFAMUa9DJ69GgwDIOZM2cqJd5Pnz5h6dKlbJnSkHuLzX/fu9DQUJVr3kXRtGlTtG3bFn///Tc78cZ/PXr0CLGxsUoxhIeHK92DyzAMFi9e/MVBYaWBunAJy+/oS2Sk/Tt4yFIP9ZoVbTAH1wwMReg31p29Frrpl7voM6w2XQstoSNHjmD58uXw8vKCi4sLTExM8OrVK5w9exZZWVno3r07ZsyYUejxT548wc8//4zVq1ez94HmmjVrFqZMmYK2bduiRYsW2L9/P4yNjfH999+XOG4rKyscPXoUffv2RcuWLdGhQwe4u7uDz+cjKioKwcHBKsmsILVr18aOdfMxespSuHsMQNeu3VCzZk1IpVJERUUhKCgINjY2ePYs7wvbvn370L59e/z00084duwY2rdvD4ZhEB4ejosXL+LZs2dsIurQoQOOHDmCr7/+Gt27d4eBgQGcnJxUbhnKb8aMGTh//jxOnjyJBg0aoHv37sjIyMCRI0cQGxuLWbNmwcPDo8TvYUGGDx+OlStXYurUqbh69Spq1KiB8PBwnDlzBl9//TUOHz5c7HMeOHAA3t7eGDNmDNavX48WLVrA3Nwc0dHR+Oeff/D48WMEBwezlwymTZuGCRMmoFGjRvDx8YFIJMKNGzcQFhaGXr164fTp05qu9mdRAiWsE3vyPgh6D6mlUxMT9B6Sdy30Y3QaTu17Bt/RdC20JLy8vPD8+XOEhoYiODgY6enpMDc3h4eHB4YNG4Zhw4YV+m9EJpNh9OjRaNGiBb777juV5ydNmoSUlBT8+eefuH//Pho0aIA1a9YUeQTul3To0AH//PMPfv/9d/j5+SEoKAhisRhVqlSBt7d3kSdxGNq/OxrUrYlV/9uHqzf/wcWLF2FkZIQqVarA19cXAwYMUCrv4uKC+/fv47fffsOJEyewceNG6Ovrw9nZGdOnT2cTAQCMHTsWkZGROHToEH777Tfk5OTA09PzswlULBbD398fq1evxoEDB7BhwwYIhUI0aNAAa9euxaBBg9R7w4qgSpUqCAoKwo8//ojr16/Dz88PtWvXxv/+9z907NhRrQTq4OCAe/fuYcOGDTh27Bj2798PmUyGypUrw83NDZMmTUK9enk9YePHj4eenh7Wrl2L3bt3w8DAAG3btsXOnTtx7NixMk+gPKYYSwE0bdqUnVmEaIH8H14lXNHh7etkdK3972hDHvDXzX6wsDYo0TnL2uEtj9lWqF1VY5x7OkztVujTp09VrrMoqSCrsVQUAwcOxOHDh/Hu3TvlUa7x+T7vvjCIiOiWL/6NFwG1QAkA4PSBvPlBa7hZ6lzyBJRboe/fpuH0/ufwGVX4fX4lQsmpXHnx4gX09PRgY2PDdShEh9AgIgKGYXBqf14CLbWkU8oMjETwzXcLy9YVIZDJinaPG6mY1q1bBx8fH4SGhqJPnz5Ks/sQ8iWUQAlCgz8oLZrt0aVsZvEoDb0G12RvvXn7OgWXjr/mOCKizdauXYtbt25h9OjR2Lx5M9fhEB1DCZTgTL7u22bt7KGnr7vfwo1MxOgzNG/yhz+X3UUxLvOTCubNmzeIiYnB9u3bC52th5DCUAKt4HJy5Lj490t222dUyS6qa4OvhteBnr5i8NCLR/G4flGLB/sQQnQWJdAK7vbVaCR+UtwPZ2Ak1Jl7Pz/H3Eof3frnLW22eRmNHCeEaB4l0AruwpG8KcHadHTU2onji6vfWHcIhIq6hN58j/s333McESGkvKEEWoFJJDJcOpE3yEbbJ44vDls7I3TskzdP65bl1AolhGgWJdAKLPjSW6QkKSb0NjIRo05D1YVqddmAb+qyc00EXYjEs4efuA2IEFKuUAKtwPJ333p2d9KpqfuKomo1M3h0yZuDddtv9ziMhhBS3lACraCys3Jw+VRe9233ATU+U1p3DZpQl3184Wg4Il8mcRcMIaRcoQRaQV33i0J6qmLlFVNzPdSsa8VxRKWjhrsVmngo5jZlGGDH7/c5jogQUl5QAq2gzufrvm3f07ncdd/mN2hC3moOx/c8xceYNA6jIYSUF5RAK6DMDCkCzkaw2937l8/u21z1m1eCWyPFJOGyHAa71z3gNiBCSLlACbQCunYuEpnpiu5bc0t9VKttwXFEpYvH4ym1Qg9tfoyk+EwOI9I9nTp1Ao/HQ2BgYKFlJkyYAB6Ph61bt5ZhZIRwhxJoBeR3LG/qPq/eLuW6+zZXCy97uNQyBwBkZ+Zg/x//cBuQjrl//z4EAgGaNGlSaJnbt28DUKwbTEhFQAm0gsnOykGQXyS73a1fdQ6jKTs8Hg8Dv8kbkbtn/UOkp0o4jEh3vHr1CgkJCXB3d4exsXGBZTIyMvD48WPo6emhbt26BZYhpLyhBFrBBF9+i4y0f0ffWujBuYY5twGVIc/uzrCrqkgAaSkS/LX1MccR6YaQEMUsTi1atCi0zL1795CTk4MGDRpAJBKVVWiEcIoSaAVz5dQb9nGbTlUrRPdtLoGQj/7j8lpHO1eHIjsrh8OIdENREuitW7cAUPctqVh0d+FHUmwymRxXz+Ql0K6+5Xv0bUE6f+2KvRsfIiE2E/GxmTix5xkGfENdjp+Tm0APHz6Mq1evFliGrn+SiogSaAUSevM9EuIUo08NjISo3aB8zX1bFGI9AfqNcWeXONu6IgQ+o90gFFJnTEEYhkFoaCgAwN/f/4vlKYGSioQSaAVy6WTe1H3N29mXm6XLiqvHgBo4sOkfpCZJ8P5tGs4fDi/WSjTu4o2lGJ1mPZFMLNHxL168QHJyMlq1aoWbN28WWCYxMRGWlpYwNDSEm5ub0nP/+9//sHLlSrx//x7u7u5Yu3Yt2rZtW6KYCNEW9LW7gmAYBlfyzX3bxbdijL4tiIGRCF+PyPug37w8BHI5w2FE2iu3+7ZRo0aFlrl/XzE9YsOGDSEQCNj9hw8fxpQpU/DTTz8hNDQUrVu3Rrdu3RAVFVW6QRNSRiiBVhDPHn5CTEQqAEAk5qNhy8ocR8StPsNqwdBIMVr0zfNEpS8XJE9uAm3cuHGhZXIT6H+7b1evXo2RI0di3LhxqFOnDjZs2AA7Ozts2rSp9AImpAxRF24FcTlf9229ZpUgEgs+U7r8MzHTQ68hNXF4yxMAwJ/LQtChT7UijUouabeoLilKAs29Rpo/gUokEty7dw8zZsxQKtu5c+dCu4IJ0TXUAq0g8ifQijJ5wpf4jHKDWE/xReJpaBxuXnrLcUTaRS6X48GDBxCLxXB3dy+0XEEt0E+fPkEmk6FSpUpKZStVqoQPHz6UTsCElDFKoBVA1KtkvHgcDwDg83lo0d6B44i0g4W1Abr1z/sysWV5CIfRaJ+nT58iLS0N7u7uEIvFBZZJS0tDeHg4jI2NUauW6kCs/7boGYapUPcek/KNEmgFkP/6XnV3SxgY0UwxufqNcYdAqPhADwl6h/s333Mckfa4d+8egM933z58+BByuRyNGzcGn5/3cWJtbQ2BQKDS2oyNjVVplRKiqyiBVgCX8yXQrtR9q6SSvTE69K7GblMrNM/w4cPBMAy2bdtWaJk2bdqAYRiVVVrEYjGaNGmicu+ov78/WrduXSrxElLWKIGWc0nxmXgQnNcK8OjsyGE02mng+LrI7VUMuhCJp6Fx3AZUTvzwww/YtWsXtm3bhqdPn2LKlCl49+4dJkyYwHVohGgEJdBy7tr5SPYexyqOJrCwMuA4Iu1TtZoZ2nZ1Yrc3UytUIwYMGIC1a9fi559/RsOGDXH9+nWcO3cOTk5OXz6YEB1ACbScCzgbwT5u38OZszi0Xf4Ft/2Pv4JUIuMwmvLju+++Q0REBLKzs3Hv3j20a9eO65AI0RhKoOWYRCLD9Yt5a3+27+nMXTBarrqbJVp3rMpupybTWqGEkM+jBFqO3Qt6h/RUxdqfxmbiCrX2pzqGTqzPPs7KyEFWJi11RggpHCXQcizgbN7SZS087en+uy+o4W6FVh3y7pGNe5/OYTSEEG1HCbScYhhG6fpnx77VCi9MWEMnNmAfpyRmUyuUEFIoSqDl1KuwBES/SQEACEV8NGhesSePL6qada3Q0lvRCmVArVBCSOEogZZTV/O1Pt0a2VT4yeOLY9gkaoUSQr6MEmg5lf/6Z6e+rhxGontq1rWCWF8ABgy1QgkphxhGM+v/UgIthxLiMvHwVt7sQ628afL44jIy1gMgBwAkJ2YjM0PKbUCEEI2RSqVKi7+rixJoOXTtfARyv2A5uJjCzFKf24B0kLGxMUR6efeCxr6jVigh5UVKSgpMTExKfB5KoOUQzT5UcvpiE2RmpyFHngYGMqQkZyMjjVqhhOgqhmEgkUjw6dMnJCYmwtLSssTnFGogLqJFJNky3PCPYrfbd3fmLhgdJhSIYGVRGVfPPYFFZSGMjAVITo2GVSVDrkMjZSn9U97j2KfcxUE0QiAQwMTEBI6OjtDT0yvx+SiBljN3r8WwLSVTcz04VjfjOCLdJRSIUKdeNYzpehJymaJPfPuFPmjpXfULR5Jy44Bb3uPBmhl4QsoP6sItZ5RmH/Ki2YdKysHZFF188kYxr50frLERfIQQ3UYJtBxRmX3oK7p9RROGTmwAkUjxp/LobqzSe0wIqbgogZYj4Y/j8S4yFYBi9qF6TW05jqh8sLUzQs/BtdjttfODIZPJOYyIEKINKIGWI/lbRu6NafYhTRo0oS70DRVDBl4+ScCpfc84jogQwjVKoOVI/uufnWn2IY2ysDZA/7Hu7Pa6BbdocgVCKjhKoOVEfGwG/rnzkd1uSbMPaZzvaDdY2hgAAOLeZ2Dv+occR0QI4RIl0HLiv7MPmVrQ7EOaZmAkwvDJeRPNb11xDwlxmRxGRAjhEiXQciLgTAT72KuXC3eBlHNdfavD0VVxb21GuhR//nqX44gIIVyhBFoOSCDEjUtv2W2afaj0CIR8jJ3ZmN0+9OcjRIYncRcQIYQzlEDLgTu86shMz5t9qGo1U44jKt9aejugXrNKAACZjMHquTc5jogQwgVKoOVAAK8e+5hmHyp9PB4P38xuwm5fOvEadwKjOYyIEMIFSqA6jgEQwK/LbtPi2WWjdgNreOe71vzrtCCaXIGQCoYSqI57Bnu85ymW5RGJ+ajXtBLHEVUcY2c2hr6BYnKF8MfxOLo9jOOICCFliRKojrvKz+u+dW9iC6GIfqVlxcbOCAPH57X+180PRnJiFocREULKEn3a6rj81z+7+dbgMJKKyXeMGyo7GAMAkhOz8b+ldziOiBBSViiB6rAPMMcTviMAgMdTDCAiZUtPX6g0oOjApkd4GZbAYUSEkLJCCVSH5R885FTDHEYmYg6jqbg8ujiiQQvFtWe5jMGyH67RmqGEVACUQHXY1Xzdtx37VOMwkoqNx+Phu3nNwecrbh+6dSUaF4+94jgqQkhpowSqo9JTJbjNy7vm6dXTmbtgCKrVtkCvITXZ7WU/XENaioTDiAghpY0SqI66fjEKUp4IAFCbeQvbKsYcR0RGTWsEC2vFJP5xHzLwx9LbHEdECClNlEB1VMCZvLU/veSPOYyE5DIyEWPCnKbs9r6N/+DZw08cRkQIKU2UQHVQTo4cgecj2G1v+T/cBUOUePVyQaPWlQEoBhQtmRQAuZwGFBFSHlEC1UGhN98jOSEbAFCJSUQd0Dys2oLH42HSwhbshBYPb33A37tohiJCyiNKoDroar7u2/byx6Cp47VL1Wpm6D/Wnd3+ffYNfPqYwWFEhJDSQAlUxzAMgyun8xKoN0Pdt9po8Hf12BmKUpMlWP5DEMcREUI0jRKojnn1NBFvXyUDAAyZLDRnXnIcESmInr4QU5a0ZLfPHwlX6jkghOg+SqA6Jv+HsAfzFGLkcBgN+ZymbasoLS+35PsApCZncxgRIUSTKIHqGOXbVx5xGAkpigk/NYW5leLe0Nj36Vj9002OIyKEaAolUB3y6WMGHt7+oNjgAe2YJ9wGRL7I1FwPExc0Z7f/2voEd6/FcBgRIURTKIHqkMBzEcido9zB2RTmoJGduqBdNye06uDAbi8YfwWZGVIOIyKEaAIlUB1yNd/o2w69XTiMhBQHj8fD5EUtYWismHox6lUy1i24xXFUhJCSogSqIzLSpQi+/Jbd9u5Fq6/oEuvKhpjwU940f3vXP6SuXEJ0HCVQHRF0IRJZmYoRtxbW+qjiZMJxRKS4uvpWR3PPvEXP5469hPRUWrGFEF1FCVRH+B/PW1/Ss7szd4EQtfF4PEz7pRVMzBQLn8dEpOL3H29wHBUhRF2UQHVAdlYOAs9FsNvd+tUovDDRataVDPH9fOVRudcvRnIYESFEXZRAdcDNS2+RkaYYtWlirgeXWubcBkRKxLu3Czw6O7Lb87+5guTELA4jIoSogxKoDsjffdumU1XweDR9vC7j8XiYvKQFzC3/nWDhXToWfxcAhqFlzwjRJZRAtZxUKlOavq/HAOq+LQ8srAww9ee8uXL9jr3Eyb3POIyIEFJclEC13J2AGKQkKuZPNTQWoVZ9a44jIprSppOj0hein6dcQ9S/CwUQQrQfJVAtd/HvvNVWWng5UPdtOTP+p6ZwcDEFAGSmSzF7xEVIpTKOoyKEFAUlUC0mk8lx5VRe922vQTU5jIaUBgNDEeasbguBUPHF6J87H7H51xCOoyKEFAUlUC127/o7JMRlAgD0DYVwb2LLcUSkNNSsa4WRUxux25uXhSAkiGYpIkTbUQLVYvlH3zZrVwV8PnXfllf9xrqhQYtKAAC5nMGMoX7slydCiHaiBKql5HIGl068Zrd7UvdtuSYQ8DF7pQdMzfUAAHHvMzBntD/kcrq1hRBtRQlUS4XefI/Yd+kAALGeAA2aV+Y4IlLabOyMMPt3D3b7ul8Udqy6z2FEhJDPoQSqpc4eesE+bu5pD4GQflUVQXNPe/Qf585ur1twC/dvvucwIkJIYehTWQtJpTL4Hcu7feWrEbU5jIaUtVHTGsGtsQ0AQC5jMGPIBSR+ouuhhGgbSqBaKPjSWyTFK+ZGNTASoV7TShxHRMqSUMTH3DVt2VVbPsakY9bwi5DJ5BxHRgjJjxKoFsrffdu2iyONvq2AbKsYY9ZveddDb156i42LbnMYESHkvyiBapnMDCku55s84avh1H1bUbX0dsCQ7+qx21tW3MOVU68/cwQhpCxRAtUyAWcikJmuWLrMzEIP1d0sOY6IcGnY5AZo2rYKu/3jKH9EvEjkMCJCSC5KoFomf/etd28Xmvu2ghMI+Jizui0qOxgDANJTpZjc7xzS0yQcR0YIoQSqRZITsxDkF8lu9xlG3bcEMDXXw8KNnhDrCQAAr54mYs7ISzTJAiEcowSqRfyPv0KOVDHS0raKEeydTDmOiGiL6u5WmLo0b/3Qy6deYwMNKiKEU5RAtUj+7ttu/apzGAnRRp36usJntBu7vWV5CM4cfM5hRIRUbJRAtcTHmDTcDcxbgaNrvxqfKU0qqnGzGqOZpz27Pf+bK/jn7kcOIyKk4qIEqiVO7HkG5t9LWk7VzWBdyZDbgIhWEggUkyw4upoBACTZMkz6+izev03lODJCKh5KoFpALmdwfHcYu52/m46Q/zIyEWPJZi+YmCtmKvr0MQPf9j6N1ORsjiMjpGKhBKoF7l6LwdvXKQAU07h593LhOCKi7eydTLFgQ3sIhIrbnMKfJGByv3OQZMs4joyQioMSqBY4vusp+7iFlwP09IUcRkN0RcOWlTF9WWt2+05ADOaOpdtbCCkrlEA5lpKUjYt/5628MvjbuhxGQ3RNp69cMXp6I3b73OFwrJkbzGFEhFQclEA5du7wC2RnKbrdLG0MULOuNccREV0zcHxd9BxUk93eseo+dq97wF1AhFQQlEA59vfOvMFDvQbX/ExJQgrG4/EwcWFztOrgwO77beZ1HNn+hMOoCCn/KIFy6NnDT3hyPw4AwOMDvYfS1H1EPQIBHz+taQf3JjbsvsXfXaWJFggpRZRAOfT3rrzWZ93GtjA11+MwGqLr9A2E+GVrB9SoawUAYBjgp9GXcOnEK44jI6R8ogTKkeysHJw+kNc6GPRtvc+UJqRojEzEWL6jA5xrmgMAZDIG04f44dr5CE7jIqQ8ogTKkYvHXiElUXHju5GJCE08qnzhCEKKxtRCHyt2dUIVJxMAQI5Ujkm+56glSoiGUQLlyIFN/7CPuw+oAT6f1v0kmmNpY4CVezqhkoMRAEUS/WHQBbomSogGUQLlwKOQj/jnjmICcB4P6DfWneOISHlkW8UYqw90hb2zoiUqkzH4caQ/juUb+U0IUR8lUA4c3PSIfdyotR0srAw4jIaUZ7Z2Rlh9oCucaigmn2cYYMH4K9i1NhQMQzMWEVISlEDLWEJcJs7/Fc5uj5zakLtgSIVgaWOAVfu6oLqbJbtv5awbWPZDEGQyOYeREaLbKIGWsUObH7ETfttWMUKdhjZfOIKQkjOz1MfKvZ2V7hPd/8c/mOx7DhnpUg4jI0R3UQItQ9lZOTj0Z1737dCJ9TmMhlQ0xqZi/La7M9p1c2L3BZyNwMgOfyPufTqHkRGimyiBlqHTB54jPjYTAGBoJEKnr1w5johUNGI9AeaubYcB3+QNXHtyPw6+LQ4jJCiGw8gI0T2UQMuIXM5g99oH7HbfkXUgFNHbT8oen8/D2JlNMGVJS/b2qU8fMjC68wnsWHWfBhcRUkT0CV5GAs9F4PWzRACAQMhDvzFuHEdEKrqeg2pi2Y4OMLVQTCEpkzFYNecmJvueQ0pSNsfREaL9KIGWAYZhsHlZCLvt3dMFRiZiDiMiRKFxmyr482RPuDXKG1x05fQb9G18EDf8oziMjBDtRwm0DARffotHd/+dOIEPjJnZmOOICMljY2eE3/d1Rt8Rddh9H6LT8E2PU1j47RWkpUg4jI4Q7UUJtAzkb316dHKEla0hh9EQokokFuC7ec2wYIMnTMzyekeObg/DV40PIuhCJIfREaKdKIGWsrvXYhAS9A6AYtq+CT815TgiQgrXtqsTtvv1Qdsujuy+91GpmND7NCb0Ps1exyeEUAItVQzDYN2CW+x2s3b2sK1izGFEhHyZhZUB5m/wxE9r2iq1RoMuRKJv44NY9sM1JCVkcRghIdqBEmgpCroQidCb7wEoWp+TFrXgOCJCiobH48Grpwu2+/VBt/7Vwft3saCcHDn2bfwHnarvxqo5N/HpYwa3gRLCIUqgpUQuV259tu3ihMoO1PokusXCygA//NIa/zveA3Wb2rL7M9Kk2LHqPjrX2I2fpwQi+k0Kh1ESwg1KoKXk/F/hePbwEwDFjevfL2jOcUSEqK+6uxVWH+iCBRs8UbWaKbs/O0uGg5seoUutPRjb7STO/xXOzvVMSHkn5DqA8igrMwer595ktzt/7QpLG1qyjOg2Ho+Htl2d0KazI25eeot9Gx/i1dO8QUXBl98i+PJbmFnqoatvDXj1ckGL9g4Q6wk4jJqQ0kMJtBTsWhOKD2/TACjmHqWRt6Q84fN58OjsiDadquLe9fc4uuMJ7t14D/w7A2ByQjYOb3mMw1sew9BYhNYdq8KjixMatayManUs2ekDucQwDDLSpEj8lIXsrBxIsmWQSuWQSmQQifjQ0xdC30AIvQRzWBinQU+cw3XIRAvxmGJMfNm0aVOEhIR8uWAFFvsuDd3d9iEzQ/EH983sJug31v0LR6mnirMZ+/hdRHKpvAYhRRH7Lg1+x17h7OEXiP+YWWg5EzMx6jWvBPdGtnCuaQ6XWhZwrmkOMwv9Er0+wzDISJciOT4L8XGZSIzLRHxcJhJiM5H4KRPxsRlI+M/+4nQ1mxmlo5KLI2ztjeBU3Ry1G1ijdgNrVHezohZ2BUYJVMOmD76AC0dfAlAsZHwgyAcCQelcaqYESrSNXM7g0d2PCL78FtcuRCLufdFG6RqbimFhYwArGwNYWBvAxEwMkZ4AIrEAIrHi70eSLYM0WwZJtgyZ6TlISshCckIW+3+ppOwXBxcK+ahV3wotvauidceqaNTaDnr61LFXUVAC1aAgv0hM6HWa3f51Rwc0a2tfaq9HCZRoM4Zh8PZ1Cm5fjcbD2x/w5H6cVk0LKBDyYGgkglhPAKGID5FYAIGAB5mMgVQig1Qih0D6AYmpxsiRFa2VqW8gRPP29ujWrwY69KlGc16Xc5RANSQzQ4qvGh1kh/M3bl0ZK3Z3LtXXpARKdAnDMPjwNg1hoXGIfJWE108TEfkyGXEf0iHLKfkSagIBD/qGQhibiWFuoQ9LWwPY2BnBprIhzC31YW5lAHMrfZhZ6sHcSh8GhqIvnrPKDTPI5TzEpxgjkjcYH5Ls8CpChOcvBHgRLsD72MK7nvX0BfDs7oyeg2rBs4czhEK66aG8ob4GDVm/8BabPIUiPn5c3ZbjiAjRLjweD3aOJrBzNFHaL5czSEvORlJCtqJLNj4LGelSyHLkkErlyJHKwTCASMyH6N+Wop6+EKYWejAxE8PUXA+mFnql1nXK5zOwMU+FoW0GaullwdMjbxam1DQe7j/Qw+0QMW6HiBHzXo99LjtLhot/v8LFv1+hkr0RfMe4o98Yd9jYGZVKnKTsUQtUA+5ei8GoTseR+06On9MEvqNLZ+BQftQCJaR0VbmR9zeWbjsMcr3PX5J5916ASwEG8Lukj1cReirPC4V8dOrrilHTG8G9sW0BZyC6hPoUSig9VYK5Yy+xybNabQv4jKLFsgmpiKrYyTB8UBr2b/+EA9s/YsSgVJib5d0Ck5Mjx/kj4ejf8i+M634StwOiUYw2DNEylEBLgGEYLJkYgJiIVACKb5c/b/EGj8f9fW6EEG5Vc87Bt2NTcPrwRyyZm4D67soT8N+89BajO5/AkHZHEeQXSYlUB1ECLYGjO8Jw5uALdvv7Bc3o+gYhRIlIBHT2zsSW9fHYszkWndqng8/LS5YPb3/EhF6nMczrb9wJjOYwUlJclEDV9DQ0Dr9OvcZuN/e0R89BtTiMiBCi7WpWl2Lp/CQc3v0RX/VIh1CYd+9q6M33GNXpBMZ0PYEHt95zGCUpKkqgaoh7n46JPmfZmUwsrPWxYKMnx1ERQnRFVXsZfvwhCX/v+wif3mkQCvIS6a0r0RjS7hi+7XMaYaGxHEZJvoQSaDFlZeZgku85fIhWzHUrEPKwcm9nmn2EEFJstjZyzJySjL/2xKJXt3Tw+Xldu9fOR6Jfi78wdcB5vAxL4DBKUhhKoMUglcowffAFPLr7kd03b107OFU35y4oQojOq1JZhrkzknBo50d06ZABXr5rpP7HX6Fv44OYM9ofb1/T7WrahBJoEcnlDOaOuYyAsxHsvuGTG8CjsxN3QRFCyhVHBxkW/5SI/dti0d4jbx5huZzBqX3P0bPufiyZGICPMWkcRklyUQItgpwcOeaOvYSzh/JG3HbxccWwSQ04jIoQUl5Vc87B8sWJ2LUpFi2b5q1uk5Mjx+Etj9Gtzl78Nus6EuIKX/mGlD5KoF8gkcgwY4gfTu17zu5r06kqpi9rzWFUhJCKoHZNKdauSMCfa+LQsF7efaTZWTLsXvsAXWrtwfqFt5CSlM1hlBUXJdDPSPyUiXHdTsL/+Ct2X4v29liwsT1NlkAIKTMN60uwaU081q34hNo18pJlRpoUm5eFoKPrLvz+4w3EvqOu3bJECbQQ4Y/jMcjjCEKC3rH7vHo6Y+kWb/D5lDwJIWWLxwNaNM3Gzk2fsHxxPFyc8paGS0+VYufqUHSuuQcLJlxBxItEDiOtOCiB/gfDMPhr22MMaP0X3r5OYff7jnbDnNVtqeVJCOEUjwe098jCvq1xWDQnAU5V8xKpVCLHsR1h6FlvP6YOOI8Ht97TFIGliG5ezOdDdBqWTgpQGmnL5/MwY0VrdPrKlbvACCHkPwQCoGvHTHT2zsT1YH3sPmCEJ88U65MyjOL2F//jr1CrvjUGTqiLHgNrwsiYFvjWJFrODIAkW4aDfz7CH0tuIz1Vyu43t9LHit0dUa2WJYfRFY6WMyOkdBV3OTMuMQzw4JEYew4YIfiuocrzRiYi9BhYE72G1EajVpWpN00DKnQLNCdHjvOHw/HH0ttK3bUA4NHZET+u8qAZhgghOoHHAxrVl6BRfQnCX6Xi0DEj+F8xhESquFKXnirFX1uf4K+tT+DgYoruA2qie/8aqO5uSclUTRWyBZqanI1T+55h74aHKonT1FwPc9e1Q+PWdhxFV3TUAiWkdOlSC7QgKak8nLtoiGMnDfE2puDu26quZvDq6Qyvni5o3KYKhEIaGlNUFSaBZmXm4LpfJC7+/QpXTr9BZrpU6XmhiI+B4+ti8Lf1IBILOIqyeCiBElK6dD2B5mIY4OEjMc5fMsClq/pIzyi4Z83YVIwmHnZo7umA5u3tUau+NQQCSqiFKbcJlGEYRLxIwv2b73HTPwqB5yNVkiYAiMR89BxUE8MmNYCJmR4HkaqPEighpau8JND8JBLg5h19XLykj+A7+sjMLrzBYGQigltjW9RtYot6TSuhTiMbOLiY0q18/yoXF/gkEhmiwpPw6lkiXj1NwLMHnxAa/P6z01xZWOvDd7Qbeg6qBUNjURlGSwgh3BGLFbfBtPfIgkQC3H+oh2s39BB0Uw9x8crdvOmpUtwNjMHdwBh2n76BEC61LFCttgWca5qjiqMJqjiZwK6qCSo5GEOsIz14mqB1CTQnR47srBxIsmTISJMiJTEbyUnZSEnMQkpSNlISs5EQl4kP0Wn4GPPvT3Q6cnLkXzy3qbkePLo4otfgWnCtY0EXzgkhFZpYDLRslo2WzbIxcwrwNkaA+w/0cPe+CPcfiJGYrHrdNCszB08fxOHpgziV53g8wLqyISrZG8PSxgDmlvowt9aHhZUBzK30YWahD0NjEQyMhDAwEsHAMO+xoZEIIjFfpz6XOUmge9Y/wLEdYcjOlkGSlYPsLBmysxSPZTLN3fQrEvPhXNMczT3t4dnNGc41zXXql0MIIWWFx1OsBuPokIGveiqum36MFSDsuQhPn4nwOEyA1xEiJKcWfi8pwwBx7zMQ9z6j0DKfw+fzIBLzIRTxIRDyIRIpHgtFfAiFin1CkWI/X8AHnw/w+Dzw//3Jffz9guZo0qaKum9FkXGSQBM/ZWl8gVh9AwEsbQ1RtZopqtexhEttC1SyN2b76qVSOcKflK9FafP/83jxOJ6zOAgpr/L/jaUkpiOHF8tZLFzQB9C4luIHfRT7UlIFiIrRR2SMPt5/1MPHWD5i44SIi9dHYqo+GEb9RopczrANqpIY8l39Eh1fVMUaRGRtbQ1nZ+cSv2hcXBxsbGzw/NEn5Ei/3PWaiwceBEIeBEI+dKUhmZ2TCj2hCddhlAmqa/lVkepbUF35PDlMDVKVFrouD1Izs2FiUBaDJxmAkf/7/y+/hwz7Hx4KKp33vKJMfgI9I1jaq7Y+c/NOcVlbW+PChQsFPlesBKopujSat6SoruVTRaorULHqS3Utn0qjrnSDDyGEEKIGSqCEEEKIGjhJoN988w0XL8sJqmv5VJHqClSs+lJdy6fSqCsn10AJIYQQXUdduIQQQogaNJZAo6OjMWnSJLRq1QqGhobg8XiIiIgo0rE8Hq/AnwcPHmgqPI0qSV0B4OnTp+jXrx+sra1hYGCAWrVqYd26daUXcAmoW9dFixYV+nvV19cv/cDVUJLfa1RUFEaMGAFHR0cYGhqiZs2amDdvHtLT00s36BIoSX3fvHkDX19fmJubw8jICF5eXlo7mvPo0aPw8fGBk5MT+/c2Z84cpKamfvHYrKwszJw5E3Z2djAwMECrVq1w7dq1MohaPSWp608//YTOnTvDysoKPB4Pu3btKv2AS0jd+oaEhOCbb75B7dq1YWhoCEdHRwwZMgRv3rwp1utrLIG+fPkSf/31FywsLNC2bdtiHz9y5EgEBwcr/dSsWVNT4WlUSeoaEhKCFi1aIDs7G9u2bcO5c+cwffp0yGQlu3G4tKhb17Fjx6r8Pi9dugShUIjevXuXYsTqU7eu6enp6NixI65du4alS5fi7NmzGDt2LFatWoXRo0eXYsQlo2594+Pj4eHhgcePH2Pz5s04dOgQAMDLywtPnz4trXDV9vvvv0MgEODXX3/FhQsX8O2332LTpk3o1KkT5PLP34c+ZswYbN26FUuWLMGZM2dgZ2eHLl26aO2X+5LUdcOGDcjMzETPnj3LKNqSU7e+hw4dwpMnTzB58mScP38ey5cvx/3799G0aVO8ffu26AEwGiKTydjHW7duZQAwb968KdKxAJi5c+dqKpRSp25dZTIZ4+bmxnz11VelGJ1mleT3+l979uxhADBnzpzRUHSapW5d/fz8GACMn5+f0v7Zs2czAoGASU9P13SoGqFufZcuXcoIBAImPDyc3ZeWlsbY2toy/fr1K41QSyQ2NlZl3+7duxkAzOXLlws97sGDBwwAZseOHew+qVTK1KxZk+nVq1epxFpS6taVYfL+PYSHhzMAmJ07d5ZGiBqlbn0LOi4iIoLh8XjM/Pnzi/z6GmuB8vkV53KqunUNCAhAWFgYfvjhBw1HVHo0+XvdvXs3KlWqhC5dumjsnJqkbl0lEgkAwNTUVGm/ubk55HI5GC0dp6dufW/duoUaNWqgevXq7D4jIyO0bdsWZ86cQU5OjqZC1IiCZp9p1qwZACAmJkbluVynTp2CSCTCgAED2H1CoRADBw6En58fsrOzNR9sCalbV0A3P8PVrW9Bxzk5OcHGxuaL71N+WvOObdq0CXp6ejA0NIS3tzeCgoK4Dknjrl+/DkBxXaVly5YQiUSwtbXF5MmTkZlZ+NJr5UF0dDSuXr2KIUOGQCjUukWASqRjx46oUaMGZs+ejbCwMKSlpeHKlStYt24dJkyYACMjI65D1CiBQACxWHVCcT09PWRmZuLVq1ccRFU8gYGBAIA6deoUWubJkydwcXGBoaGh0n53d3dIJBK8fPmyVGPUlKLUtTxRt75Pnz5FbGxssY7TigQ6dOhQ/O9//8OlS5ewZcsWxMfHw9vbGwEBAVyHplHv3r0DAAwYMACdO3eGv78/Zs2ahW3btmHw4MEcR1e69u7dC7lcjhEjRnAdisbp6+vj+vXrkMvlcHd3h4mJCTp06ICePXti48aNXIencbVq1UJ4eDji4/MWMJDL5bhz5w4AICFBuxdtiImJwYIFC9CxY0c0bdq00HIJCQmwsLBQ2W9pack+r+2KWtfyQt365uTkYMKECbCxscGYMWOKfJxWNAX27t3LPm7bti369OmDunXrYt68eWyrrTzIvag9dOhQLFmyBADQvn17yGQy/PjjjwgLC4ObmxuXIZaaPXv2oFGjRqhfv2xWSShLWVlZGDBgAGJjY7F37144Ojrizp07WLJkCYRCITZt2sR1iBo1YcIErF+/HsOHD8f69ethaGiIX375hR3BqM1dgWlpaejTpw+EQiF27tz52bIMwxS4/KG2dsn/V3HqWh6UpL4TJ07EzZs3cfbs2QK/NBVGK/+lm5iYoEePHrh79y7XoWiUlZUVAKBTp05K+zt37gwAWjuyr6Tu3LmDZ8+elcvWJwBs374dAQEBOHfuHIYOHYp27dphxowZWLVqFf788088fPiQ6xA1qlq1ati/fz/u3buH6tWro0qVKggODsa0adMAAHZ2dhxHWLCsrCz07t0br1+/hp+fHxwcHD5b3tLSssBWZmJiIvu8tipuXXVdSeo7Z84cbNmyBTt27GA/i4tKKxMoUPi3P13m7u4OACr1yv1Gq83f3Eti9+7dEAqF5bab+tGjR7CwsICrq6vS/ubNmwOAVt7aUVI+Pj6IiYlBWFgYXr58iXv37iEtLQ1Vq1aFo6Mj1+GpkEql8PHxwZ07d3Du3DnUq1fvi8e4u7vjzZs3yMhQXhw6LCwMYrFYaRCVNlGnrrqsJPX95ZdfsHz5cqxbtw7Dhg0r9mtr5Sd2SkoKzp49ixYtWnAdikZ169YNenp6KmvL+fn5AUC5vEYhkUhw6NAhdO/eXa21+HRB5cqVkZiYqDKo5Pbt2wAAe3t7LsIqdQKBAHXq1IGrqyvevXuHw4cP49tvv+U6LBVyuRxDhgzB5cuXcfLkSbRs2bJIx/Xu3RtSqRRHjhxh9+Xk5ODw4cPo3Lkz9PTKYh3N4lG3rrqqJPVdv3495s2bh19++QWTJk1S6/U1eg306NGjAIB79+4BAM6fPw8bGxvY2NjA09MTkZGRcHV1xYIFC7BgwQIAihthnz9/Di8vL1SpUgWRkZH4/fff8eHDB+zfv1+T4WmUOnW1srLCnDlzsHTpUpiamsLb2xshISFYsmQJRowYobXfaNWpa64zZ84gISFBZ7pv1anryJEjsXr1anTv3h1z586Fo6MjQkJCsHTpUjRp0gRt2rThrD5fok59pVIpZs2aBU9PT5iamuLJkydYtmwZ3N3dMX36dM7qUpjvv/8eR44cwdy5c2FkZIRbt26xzzk4OMDBwaHAejZs2BADBgzA1KlTIZVK4eLigk2bNuHNmzda+9mkbl0BxejVuLg4fPjwAYBi0hdjY2MAgK+vb9lWpIjUre+hQ4cwdepUdO3aFd7e3krHmZqaFn0sSpHvGC0CKNYIV/nx9PRkGIZh3rx5wwBgFi5cyB5z6tQppnXr1oyVlRUjFAoZS0tLplevXszt27c1GZrGqVNXhmEYuVzOrFq1inF1dWVEIhHj6OjIzJ8/n5FIJGVfiSJSt64MwzC9e/dmLC0tmezs7LINWk3q1vXJkydMv379GAcHB0ZfX5+pUaMGM336dCYhIaHsK1EM6tRXKpUyPXr0YGxtbRmxWMxUq1aNmTt3rtZOGOHk5FRoPXPrVdjvNSMjg5k2bRpTqVIlRk9Pj2nevDlz9erVMq9DUZWkrp6enoUeq63Ure+IESO++G+/KGg1FkIIIUQNWnkNlBBCCNF2lEAJIYQQNVACJYQQQtRACZQQQghRAyVQQgghRA2UQAkhhBA1UAIlhBBC1EAJlBAN4fF44PF44PP5n10T08vLiy27a9cupedGjhzJPrdw4cJCz7F79262XPv27ZWeCwgIYJ/L/TE0NISdnR3atWuHmTNnIjQ0tCRVJYSAEighGiUUCsEwDLZv317g8+Hh4QgMDPziouJCoRA7duyATCYr8PmtW7d+8RxOTk5YuHAhFi5ciKlTp6Jnz55IT0/H77//jsaNG2PIkCFIS0srWsUIISoogRKiQZUqVULTpk2xc+dO5OTkqDy/bds2MAyDnj17fvY8PXv2RHR0tMrCA4BidZcbN26gV69enz2Hs7MzFi1ahEWLFuHXX3/F1q1bce/ePYSGhqJevXo4cOAA+vXrV7wKEkJYlEAJ0bBx48bhw4cPOHPmjNJ+qVSK3bt3o3Xr1uzSdoUZMmQIDAwMsHXrVpXncveNHTtWrfgaNmyIS5cuwcbGBhcuXMCJEyfUOg8hFR0lUEI0bNCgQTAyMsK2bduU9p86dQofP37EuHHjvngOc3Nz9OvXD2fPnsX79+/Z/dnZ2dizZw88PT1Rs2ZNtWO0tbXF+PHjAUBrVxYhRNtRAiVEw0xMTDBw4EBcuHAB0dHR7P6tW7fC1NQU/fv3L9J5xo0bh5ycHKWBRsePH0d8fHyRkvCX5A4+unPnTonPRUhFRAmUkFIwbtw4yGQy7NixAwAQGRkJf39/DBkyBIaGhkU6h4eHB+rUqcNeNwUUSdjCwgI+Pj4ljjF3oe+4uLgSn4uQiogSKCGloEWLFqhXrx527NgBuVyObdu2QS6XF7vlOHbsWLx+/RpXrlzBy5cvcfXqVQwbNgz6+voljjE3KfN4vBKfi5CKiBIoIaVk3LhxiIyMxIULF7Bz5040adIEjRo1KtY5hg8fDj09PWzbto1tiWqi+xYA3r17BwCwsbHRyPkIqWgogRJSSoYNGwYDAwOMHz8eMTEx+Oabb4p9Dmtra/Tt2xfHjx/Hjh070KpVK9StW1cj8V29ehWAorVMCCk+SqCElBJzc3P4+voiOjoaRkZGGDRokFrnGTduHLKzsxEXF6ex1mdsbCw2b94MQHHLDCGk+D4/lQkhpER+/vlnfP3117CxsYGJiYla5/Dy8sLJkychl8vRpUuXEsf08OFDDB8+HJ8+fUL37t3Ru3fvEp+TkIqIEighpcjR0RGOjo4lOgePx1MryUVERGDRokUAFJM4fPr0Cffu3cO9e/cAKFqeua1QQkjxUQIlpJyKjIzE4sWLAQD6+vowNzdHjRo1MGPGDAwZMgQNGzbkNkBCdByPyR3LTgghhJAio0FEhBBCiBoogRJCCCFqoARKCCGEqIESKCGEEKIGSqCEEEKIGiiBEkIIIWqgBEoIIYSogRIoIYQQogZKoIQQQogaKIESQgghavg/9UfixleXMPAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ohT18+JAZO3YsY2Njw2hoaDBCoZCxt7dn5syZw7x7967YYwrux9y7dy9z4cIFpkOHDoyWlhajp6fHDBkyhHn16pVU+eDgYGb27NmMk5MTY2xszKipqTHW1taMh4cH4+PjU+xr3L17lxk6dChjZmbGqKqqMsbGxoyDgwMze/Zs5vHjx1Jlra2tZe6NLEmDBg0YAIyhgR6TE3OfYeIfF1suJyeH2bJlC9OxY0dGV1eXUVNTY+rVq8e4uroyGzZskL6n9P/lly9fztja2rL1W7hwIZOdnV2u+0AZhmHEYjGzZcsWpkmTJoyamhpjZmbGTJs2jUlOTi6xrnv37mUcHBwYLS0txsjIiPnqq6+YZ8+eMUuXLmUAMF5eXlLlP336xIwYMYIxNTVl+Hw+A4BZunQp+7yPjw/j4uLC6OvrM9ra2kznzp2ZM2fOMF5eXjJllY0i/sZ5DPP/8edl0KZNG/j5+VVOJifVXlpKDsa5ncGrZ/Hsvq69rTB3bWdoCmVHHhYIuP8Bf873QdyHTHbfD8s7YMqCNooJrOjAkTL+uheMwiS1XEKRzzsjBf0+EqWgiL9xugZKFCI/X4y5Y/6TSp6jvm+BJVudv5g8AaB1JzP8dW4AmrQsvMl789IH+HfXi0qLlxBCKooSKFGI9Qvv4+7Vwhllvp3vhPGzZEdXlkTXQAO/H+iJ1p0Kr92snOGNO1ciFBkmIYQoDCVQUmG3LoRj34Yn7HYfzwYY+o3syh2l0RSqYtl2ZzRsLrkJXCxmMGfkNUS8TlJUqIQQojCUQEmFJMZlYdl3t9jtxi2NMHtVR7nPpylUxa9/u6KOhWSu0syMXPw0+hpEOcXfi0YIIVyhBErkxjAMVky/jYRYyfyX6poCrN7TA3x+xWYXMjTRxLJtzlBRlfx6hjyJx5/zfSocLyGEKBIlUCI3rwvhuH6m8N61+X92gY6eukLO3cDeSGoU7uFtz/DgVvE3dBNCCBcogRK55GTn4fd5hdN/tXe2QJde1gp9jUGjG6NTj8KltJZM9UJmRq5CX4MQQuRFCZTI5cCmJ3j3NhUAoKrKx7w/ZJdoqigej4eZKzpAR08NABAdkYoty8o/PRshhFQGSqCk3OI+ZGDnGn92e/T0ltDVV0zX7ecMTTSlunIPbn6KkMC4SnktQggpD0qgpNz+XuuHrP93pRoYa2D4t80r9fV6DbFj7w9lGGDV7DsoxwRahBBSKSiBknL58C4NJ3YXLiE1a2UHCFQq99eIx+Ph+yXtIFCRjO4NvP8BV/4NrdTXJISQ0lACJeWya60/ckViAEDdetro6FavlCMUw8pOD1+NLZy38vd5PsjKpAFFhBDuUAIlZRYTmYpTewvXDZy+pB14vIrd81keY6a3hL6RBgDJddhDW55W2WsTQsjnKIGSMtu/6QnyciWtT3NrHbTrblGlry/UUcP4WY7s9q7f/ZGcmF3yAYSU0fjx48Hj8Ypd0JsrNjY27KLVRDnRgtqkTJITs3FqT2Hrc9qitlXa+izQx7MBTu4JxvvwVGSk5WL37/74aU3nKo8DT5cAmVFV/7plpWUFOKyo8GlsbGwQGRlZ7HN16tTBx48fpfaJRCKsWLEChw8fRmJiIlq3bo1169ahdevWMsdfv34dvXr1wsWLF9G/f/GLVtdkzs7O8Pb2pgFx1RglUFIm//79AlmZeQAkt5a0c67a1mcBgQofE2a3wsofvAEAh7Y+xejpDqhrqV21gWRGAUKbqn3N8siIUNip9PT0MGvWLJn92tqy7/n8+fOxYcMGeHh4wNLSEgcPHoSLiwtevnwJM7PClXbS09MxefJkjB49WimS5+rVqzF//nxYWHDze12cmzdvch0CKQUlUFKqnOw8HNr2jN0eM6MlJ63PAl37WKFxSyO8epaAXJEYe9cHYMH6bpzFU9Pp6+tj2bJlpZZjGAY7d+7EhAkTsGfPHgDA4MGD4ezsjIMHD2LevHls2Z9//hnZ2dnYtGlTZYVdLmZmZlIJXhnY2dlxHQIpBV0DJaW6fPw1Ej5lAgA0hSro7dGA03h4PB7G/uDIbh//+wXi/x8f4U5cXBwyMzPRrl07dl/B46LdwHfu3MGOHTuwbds2GBoayvVaBdcHU1NT8eOPP8LGxgaqqqpSif7ly5cYP3486tWrB3V1ddSpUwcjR47Eq1evZM73pWugD/1fwHPCz6hbty7U1NRQr149TJkyBTExMcXGlpiYiEWLFqF58+bQ0tKCnp4eHBwcMH/+fGRkZCAiIgI8Hg/e3pJeFB6Px/5zdnaWqePncnJysGbNGrRs2RJaWlrQ1dVF165d8e+//8qULXit8ePHIyIiAl9//TWMjY2hoaGBNm3a4OLFi19+oz9z9uxZjB49Go0aNYJQKIS2tjacnJywefNmiMVimfIF7+vbt2+xZcsWtGzZEpqamlL1TExMxIIFC9C0aVNoampCT08Pbm5u+O+//2TOl5KSgj/++AOurq6wtLSEmpoaTExM4O7ujgcPqn6WMmqBklId/esF+/irMU2hqibgMBqJtt3M0bC5EUJfSFqh+zYEcnMttBbIycnBoUOHEBUVBaFQiJYtW6Jbt24QCKR/D0xMTKCpqQl//8JZqvz8/AAA1taSeZKzsrIwadIkeHh4wMPDo0JxiUQiuLq6IjExEb169YKuri5sbW0BAFevXsWQIUOQm5uLgQMHokGDBnj//j1Onz6NS5cuwcvLq9jrsp/be+Q8Js/+DerqqnB3/wr16tVDaGgodu/ejQsXLuDBgwewsrJiy4eHh8PFxQWRkZFwcnLCd999B7FYjNevX2PDhg2YOnUq9PX1sXTpUuzbtw+RkZFYunQpe3xpg4ZEIhF69+4Nb29vNGnSBN9//z0yMzNx8uRJDB8+HE+ePMFvv/0mc1xkZCTatWuH+vXrY8yYMUhMTMTx48cxaNAg3LhxAy4uLmV6z+fPnw8+n4/27dvDwsICKSkpuHXrFmbOnInHjx/j4MGDxR43c+ZM3L17F/3790e/fv3Y353IyEg4OzsjIiICXbt2RZ8+fZCRkYGLFy+iT58+2LlzJyZPnsyeJyQkBIsWLUK3bt3Qv39/GBgYICoqCufPn8eVK1dw4cIF9OnTp0x1UQQeU44r2G3atGH/IEjt8OzxJ4zofAIAwOMDJx8Mg66BBsdRSfhcj8KyabcBSJZSu/V2PPSNNGULFu1uLuOve0hICJo2bVpyAd/xyn8NtOO+Cp+mpEFEtra22Lt3L7p37y61f9asWdi8eTM8PT1hYWGBgwcPQiQSISQkBBYWFpgzZw7279+P4OBgmJqaVjguNzc3nDt3DkKhkH0uKSkJ9evXh0AgwJ07d9CsWTP2uaCgILRv3x6NGjVCQEAAu3/8+PHYv38/wsPD2ST2+vVrNG9uDyvLuvA+vxMWzfux5W/duoWePXvC3d0dZ86cYfd37twZ9+/fx2+//YYFCxZIxRwfHw9tbW1oaEj+fkobRFQQR9FW8erVq7Fw4UL07dsX58+fh4qKpA0UGxuLdu3aITIyEj4+PujUqRN7bMGXimXLlkkl62vXrqFPnz7o27cvLl++/OU3/P/evHkj07UsFosxYcIEHDhwAA8ePED79u3Z5wreV3Nzc9y7d4+NpYCzszPu3LmDI0eO4Ouvv2b3Jycnw9nZGa9evUJERATq1KkDQNICzc3NhbGxsdR53r9/j3bt2kFPTw8hISFlqkupf+NlQF245IuO/fWcfezU2VxpkicAdHSrB5tG+gCAnKx8HNzy7MsHkHKbMGECbt68iY8fPyIjIwPPnz/HlClTEBERgb59++LpU+l7cdeuXYuff/4Zjx49wj///AN7e3vcvHkTFhYWePjwITZu3IhNmzbB2NgYy5cvh5mZGVRUVODk5AQfn/Kv+bpu3Tqp5AkABw4cQHJyMpYvXy6VPAHA3t4ekydPRmBgIIKDg/ElO3bsQG5uHjatmgMLM+lk7+rqCnd3d1y4cAFpaWkAAH9/f9y/fx+Ojo74+eefZc5X0HVaEXv27AGPx8P69evZ5AkApqam+OWXXwAAu3fvljnO2toaixcvltrXu3dvWFlZ4dGjR2V+/eKuy/L5fMycOROAJCkXZ968eTLJ8+nTp/D29oaHh4dU8gQk192XL1+O7OxsnDp1it2vp6cnkzwBwNLSEp6ennj58iWioqpudDx14ZISJSdkSU2ZN26mA4fRyOLzeRg1rQVWzboLADiw+QnGz3ZU2JqkBFItFgBo3rw5/vrrL2hra2PdunVYtmyZVAtMXV0dq1evxurVq6WOE4lEmDhxIvr164dRo0Zh48aNbIuoc+fOWLVqFfr06YOwsDC2tVEaDQ0NtGzZUma/r68vAMkHdHGDn16/fg1A0gL5PMEWdx7v+wF4HBgMaElfL4yNjUV+fj5ev34NJycn9hpc7969wecrvm2SlpaGsLAwWFhYoEmTJjLPu7q6AgACAwNlnnN0dJTpcgeAevXqsfUsi4SEBPzxxx+4fPky3r59i4yMDKnno6Ojiz2u6HXxAgWvm5KSUuzPKS5OsmjE5y1KHx8fbNq0Cb6+voiNjYVIJJKJoWi3emWiBEpKdPbAS4hy8gEApuZCNHEw4TgiWV37WMPS9ineh6ciMz0XR7Y/l1q9hVSOqVOnYt26dbhz506Zyi9fvhzR0dHswJA//vgDbm5u7Adn48aNYWNjg23btmHFirLdv2pqalrsaPCEhAQAwK5du754fHp6+hefLzjPH1uLv673+XmSk5MBoNJuhUlJSQGAEkcLF+wviKMofX39Yo9RUVEpdvBPcZKTk9G2bVuEh4ejXbt2GDt2LAwNDaGiooLk5GRs2rQJOTk5xR5bt25dmX0F7+/169dx/fr1El+36M/pzJkz8PT0hIaGBnr27Ak7OzsIhULw+Xzcvn0b3t7eJcZQGSiBkmIxDIPT+wq/+Q2dVPI3dS4JBHyMmNoCf/ws6f7btyEQo2e0hFBbjePIaraC65eft0CKExgYiN9//x1//fUXLCwskJqaipiYGIwaNYotY2VlBWNjYwQFBX3hTNJKupVKT08PgKQFWlwLtawKzpMS7gVdHW3A6MtfzAqSVEmtsIoqiOfzySsKfPjwQaqcou3evRvh4eFYunSpTIvR19f3i7ckFfezKohz06ZN+OGHH8oUwy+//AI1NTX4+fnJXL+cMmUKO7K5qtA1UFKsZ48+4U1IIgBAIOCh9xBub135EteBtuxECqnJOVIzJpHKUdD9Vr9+/S+Wy8vLw8SJE+Hi4oJvvvlG6rnPWwrZ2YqZlrFDhw4AgLt37yrmPL5PylX+2rVrZWrVFXSp5ufnl+n8Ojo6sLOzQ3R0NEJDZVcj8vLyAoAyjS6WR1hYGAAUO3pansQlz88pLCwMzZo1k0meYrEY9+7dK3cMFUUJlBTr9L7CJNSmqzk0haocRvNlKqp8DJtkz27v3/QE+fll65YiJQsKCkJiYqLM/sjISEyfPh0AMHr06C+eY/Xq1QgLC5PqTtXV1YWFhQWuXr2KvDzJ7Fbe3t5IS0uDvb19SacqswkTJrCDUIobICMWi3H79u1SzzN9+nSoqqpg9uINeB0mOxJZJBJJffg7OTmhU6dOePLkCdauXStTPiEhQepLgpGREQCUa9DLxIkTwTAM5s6dK5V44+PjsXLlSrZMZSgYFfz5excYGChzzbss2rRpg65du+L06dPsxBufe/78OWJjY6ViCA0NlboHl2EYLF++vNRBYZWBunCJjMyMXKnBQ19PqdwFsxWh5xA77N0YiLRkET6+S8fNc2/RS4lbzdXBiRMnsGbNGri4uMDW1hY6Ojp48+YNLl26hOzsbPTr1w8//fRTiccHBQXh119/xfr169n7QAvMmzcPM2fORNeuXdG+fXscPnwY2tra+P777ysct5GREU6ePInBgwejQ4cOcHNzg729Pfh8PqKiouDr6yuTzIrTpEkT7Nn0CybOXAn7LsPRp09fNGrUCLm5uYiKisLdu3dhYmKCly9fssccOnQIzs7OWLhwIU6dOgVnZ2cwDIPQ0FD8999/ePnyJZuI3NzccOLECQwZMgT9+vWDpqYmrK2tMWbMmBJj+umnn3DlyhWcO3cODg4O6NevHzIzM3HixAnExsZi3rx56NKlS4Xfw+KMHTsWf/zxB2bNmgUvLy80bNgQoaGhuHjxIoYMGYLjx4+X+5xHjhyBq6srvvnmG2zevBnt27eHvr4+3r9/j2fPnuHFixfw9fVlLxnMnj0bU6dORatWreDh4QFVVVX4+PggODgYAwcOxIULFxRd7S+iBEpkXD/9BhlpkrU29QzVYe8k//16VUVDUwUDRzTGkR2S2272rg+kBFpBLi4uePXqFQIDA+Hr64uMjAzo6+ujS5cuGDNmDMaMGVPidcj8/HxMnDgR7du3x7Rp02SenzFjBlJTU/HXX38hICAADg4O2LBhQ5lH4JbGzc0Nz549w59//olr167h7t27UFNTg7m5OVxdXcs8icPoYf3g0LwR1m0/BK/7z/Dff/9BKBTC3Nwcnp6eGD58uFR5W1tbBAQE4Pfff8fZs2exdetWaGhowMbGBnPmzJG693XSpEmIjIzEsWPH8PvvvyMvLw/du3f/YgJVU1PD9evXsX79ehw5cgRbtmyBiooKHBwcsHHjRowYMUK+N6wMzM3NcffuXcyfPx/37t3DtWvX0KRJE2zfvh09evSQK4FaWlrC398fW7ZswalTp3D48GHk5+ejbt26aNasGWbMmIEWLVqw5adMmQJ1dXVs3LgR+/fvh6amJrp27Yq9e/fi1KlTVZ5AaSIFImNS33PwvfkOADBianNMnFM511QULSE2E6OdT7NLrh256wmH9nUrZyKFWrIaS23x9ddf4/jx44iJiZEe5ZpQ5POulEFEpHpRxEQK1AIlUj5Fp+PBrXfs9sCRjTmMpnyMTLXgMsAW18+8AQDs3xiI9Uf7Vs6LUXKqUV6/fg11dXWYmCjfrVpEedEgIiLl8vHXbCPNuoEeTMyEXz5AyXhOLLzd5r/TbxAdkcphNETZbdq0CR4eHggMDMSgQYOkZvchpDSUQImUC0des4+/Gis724myq9/EAK07SbrgGAY4vJ2m9yMl27hxIx48eICJEydi586dXIdDqhn6ukVYr5/H49WzeACSafJcBtiWcoRy8pjYDAH3JTeV//v3C0yDBrShmHsMSc0SHh7OdQikGqMWKGFdOlbY+mzexhRCneo5m0+bruawspPMcpKVmYfT/A4cR0QIqYkogRIAkpuRr50KY7cHj69+3bcF+HwehowvHF13jN8VYhR/uwUhhMiLEigBAAQHxuHdW8mAGxVVPtp1s+Q4oopxdbeFtq6kBR3JM4UPr/p+ISCEKCdKoAQAcO1kYevTsWNdqKnLLn1UnWhqqaK3Z+FECkf53TiMhhBSE1ECJTLdt9Xp3s8vGTiyETuHwh1eM0RBdiFeQgiRFyVQguDAOLwPl3Tfqqrx0aaLOccRKYaFtS7adpOszcjw+DjOr5w5QgkhtRMlUIKrJwpbn606mVX77tuiBo0pvPZ5mt8RWZm5HEZDCKlJKIHWcgzD4L/TRbpvRzTiMBrFa9PVHPWYOABAKk8LF4++LuUIQggpG0qgtVxQQKxU961TDem+LcDn8zBCXLhm48EtT1GO9RMIIaRElEBruc+7b1XVak73bYHB4gfQZHIAAG+CExHg84HjiAghNQEl0Frs8+5b9xoy+vZzusjCQPFjdvvwtqccRkMIqSkogdZiL/xjER2RBkDSfdu6s1kpR1RfI8R32MfXz77Fp+h0DqOpfnr27Akejwdvb+8Sy0ydOhU8Hg+7du2qwsgI4Q4l0Frsv1Nv2MetOtbM7tsCjfABbcWhAABxPoN/dwVxHFH1EhAQAIFAACcnpxLLPHz4EADQpg0tPE1qB0qgtZjXhbfs4wEja9bo2+IUbYUe++s5RDn5HEZTfbx58waJiYmwt7eHtrZ2sWUyMzPx4sULqKuro3nz5lUcISHcoARaS4W/SkL462QAgEDAg1PnmjX6tjiuzDMY19ECACQnZuO/IrMvkZL5+fkBANq3b19iGX9/f+Tl5cHBwQGqqqpVFRohnKIEWkt5XSxcB7GJo3GNmjyhJKoQY0CR+1wPbaPFtsuiLAn0wYMHAKj7ltQutKB2LVU0gfYe0uALJWuWfsMb4tC2Z8jLFeP5408ICoiFfWtTrsNSagUJ9Pjx4/Dy8iq2DF3/JLURJdBaKDEuC098P7LbHd3qcRhN1TIw1kT3vta4eV7yBeLIjmdYtatHuc5hr7a1MkKrFEGi6RU6nmEYBAYGAgCuX79eanlKoKQ2oS7cWujOlQiIxZLZeOrV14W+kQbHEVUt99GF8+NeOvoayQlZHEaj3F6/fo2UlBR07NgRDMMU+y8xMREAoKWlhWbNmkkdv337dtja2kJDQwNOTk64e/ducS9DSLVECbQWKtp96+puy2Ek3GjqaIyGzY0AALkiMU7vC+E4IuVV0H3bqlWrEssEBAQAABwdHSEQFF5LP378OGbOnImFCxciMDAQnTp1Qt++fREVFVW5QRNSRagLt5bJyc6Dz/V37Ha3PjbcBcMRHo+HQaMb48/59wEAh7c/w7hZjhAIyvZ9sqLdotVJQQJt3bp1iWUKEujn3bfr16/H+PHjMXnyZADAli1bcPXqVezYsQOrV6+upIgJqTrUAq1lHt2ORlaGZEkvAyMN1Kuvy3FE3HDubwNdA3UAwMd36bhzJZLjiJRTWRJowTXSoglUJBLB398fvXr1kirbq1cv3L9/vxIiJaTqUQKtZYp233bpbQUej8dhNNxR11BB36GFo48Pb6dbWj4nFovx5MkTqKmpwd7evsRyxbVA4+PjkZ+fjzp16kiVrVOnDj5+/AhCagJKoLUIwzBSCdS5f+27/lnUgBGNUfD9wffGO4S/SuI2ICUTEhKC9PR02NvbQ01Nrdgy6enpCA0Nhba2Nho3ll2M4PMvaAzD1NovbaTmoQRaiwQHxiE2JgMAoK4hgH1rE44j4lZdS210cLVkt4/tfM5hNMrH398fwJe7b58+fQqxWIzWrVuDzy/8ODE2NoZAIJBpbcbGxsq0SgmpriiB1iJeFwpbn05dzCFQoR//oCK3tJzeF4KMdBGH0SiXsWPHgmEY7N69u8QynTt3BsMwMqu0qKmpwcnJSebe0evXr6NTp06VEi8hVY0+QWuRot23bu71OYxEebTqZMYOpMpMz8XFI685jqjm+PHHH7Fv3z7s3r0bISEhmDlzJmJiYjB16lSuQyNEISiB1hIxUWl4+TQeAMDn89Cma82fPL4s+Hwe3EcVXrs7tPUpGIbhMKKaY/jw4di4cSN+/fVXODo64t69e7h8+TKsra25Do0QhaAEWkvcvlTY+mzUwgha2rRiRoGeg+2gKZTcEv32ZRIe34nmOKKaY9q0aYiIiEBOTg78/f3RrVs3rkMiRGEogdYSUt23g2r36NvPCXXU0OMrO3b7yHYaTEQIKR0l0FogPVWER7cLW1WdelhxGI1yGlSkG/fmubfIzxNzGA0hpDqgBFoL+PwXhbxcSUKoW08bpmZCjiNSPtYN9eHYsS4AQCxmkJGey3FEhBBlRwm0FpCaPKGfDXeBKLmig4ky00XsijWEEFIcSqA1XF6eGN5XItjtbn1pBGRJOrnVg0ldLQCAOB9ITcrhOCJCiDKjBFrDBd7/wCYCoY4qGjQz5Dgi5SVQ4WPAyIJWKIP4Txl0SwshNZCi/q4pgdZwRbtvO7rVo3lIS9FvWEOoqvGRkZ6PrCwRu3INIaTmyM3NlVq7Vl6UQGswhmFwq8j0fa4D6faV0ugbacBtUH0E3ktAvjgL8Z+yuA6JEKJgqamp0NHRqfB5KIHWYG9fJuHdmxQAgIoqHw7t63IcUfUwZFxT3Dr9EVHhH5GYmICM9GzqyiWkmmMYBiKRCPHx8UhKSoKhYcUvZ6koIC6ipIp23zZ3MoWaesW7LGoD28YGsKpviHU/BcF1SALcBqfAyFSD67AIFzLiCx/HhnAXB1EIgUAAHR0dWFlZQV1dvcLnowRagxVNoD0G0+Tx5TFkfFMs/vYW/t0eiYsHYnA7cgKEOsWviUlqsCPNCh+PpF4IIo26cGuohNhMPH3w/7UYeUAHZ8svH0CktO1uAUvbwlVazh6g1gchRBol0BrqzpUIFFy2s7bTg54hdUGWB5/Pw+BxhWuF7t/0BPn5NL0fIaQQJdAayutCBPvY1Z1G38qj52A7aOtKum2jI9LgfSmC24AIIUqFEmgNlJ2Vh/s3otjtbn1suAumGtPUUkW/4Q3Z7b0bAjmMhhCibCiB1kAPvd4jKzMPAGBgpMFeyyPlN2hMEwhUJJNPBPh8wLNHHzmOiBCiLCiB1kBFR9926kVLl1WEqZkQLgMKu8D3rKNWKCFEghJoDSMWM7h9qcjtK3T9s8KGTbJnH984+waRocncBUMIURqUQGuYoIBYxH3IBACoawjQtJUJxxFVf7aNDdC2uwUAgGGAfRupFUoIoQRa43gVmfvWoUNdCAT0I1aE4ZMLW6Fn9ocg/lMmh9EQQpQBfbrWMEWvf/b2aMBhJDVLy3Z10KSlMQAgVyTG4W3POI6IEMI1SqA1SHREKl4/TwAA8Pg8tO1qznFENQePx8OwIq3QI9ufISNNxGFEhBCuUQKtQYoOHmrQ1ACaQlUOo6l5OvWsB3NryRJI6akinNwTzHFEhBAuUQKtQbwuRrCPe3xFk8crmkDAx9BvCluhe9YFQJSTz2FEhBAuUQKtIdJScvDYO5rd7trbmsNoaq5eQ+xgaKIJAIj/mEmTzBNSi1ECrSHuXYtCXp5ksnNTcyFMzIQcR1QzqakL4PlN4RJXO1f7ITeXWqGE1EaUQGuIoqNvu/Wh1mdlGjCiEfQMJIvxfnyfjguHX3EcESGEC5RAa4Dc3HzcvRrJbrsNouuflUlTS1W6FfqbH9v6J4TUHpRAa4AAnw9ITc4BAAh1VGHX1IDjiGo+91FNoKMnWersfUQqLh97zXFEhJCqRgm0Bijafdumqzl4PB6H0dQOWtqqGDyuKbv912+PacFtQmoZSqDVHMMwuF109iFPmn2oqgwe1xRa2pJ7bSPDUvDfqTccR0QIqUqUQKu50BcJePc2FQCgosqHQ7u6HEdUe2jrqmHw2Cbs9raVj6gVSkgtQgm0mrt5vrD1ad/aBGrqAg6jqX2GjG8KTaEKACD8VRKuHA/lOCJCSFWhBFrN3Tr/ln3cd1hDDiOpnXQNNDBkfOGI3M3LHtB9oYTUEpRAq7GYqDQEB8YBAHg8oIOLJccR1U5Dv2nGjsiNjkjDmX00OxEhtQEl0GrM60Jh69O2sQGEOmocRlN7CXXUMPzb5uz2tpWPkJ2Vx2FEhJCqQAm0Git6/bP3EDsOIyGDxjSRmiP32F/POY6IEFLZKIFWU8mJ2fC7Uzh5fPf+NtwFQ6ChqYJR37dgt3eu8UN6Kq0XSkhNRgm0mrpzOQL5+QwAoK6lNoxMtTiOiPQd2hB1LbUBAKlJOTiw6Qm3ARFCKhUl0GrqZpHRty4DbLgLhLBU1QQY+4MDu71nXQDiP2VyGBEhpDJRAq2GsrPy4PNfFLvdczBd/1QWru62sG6gBwDIyszDthUPOY6IEFJZKIFWQ7433yErUzLKU89QHfXq63EcESkgEPDx7fw27PaJf4IQ+iKBw4gIIZWFEmg1VHTyhM49rTiMhBSnbTdzOHUxAwAwYuCPn304jogQUhkogVYzeXliqdVX+tDk8UqHx+Nhyvw24PMlq+L4XI/C3WuRpRxFCKluKIFWM353opEUnw0A0BSqoHFLY44jIsWxbWyAvkMLv9z8PvceLbpNSA1DCbSa+e904ZJZ7Z0t2VYOUT7jZjmyE82/fZmEU3uCOY6IEKJIlECrkfx8MW6cLUygA0c24jAaUhoDY02MmFo4ucKmJQ+QkpTNYUSEEEWiBFqN+N+LQUJsFgDJzDf2TqYcR0RKM2R8U5iaCwEAKYnZ2LTkAccREUIUhRJoNXLtVGHrs213CwgE9ONTduoaKpi6sPC2ln//foEX/p84jIgQoij0CVxN5OeLcf1MYQJ1H0Xdt9VFl15WaNvNHADAMMCK6d7Iz6cBRYRUd5RAq4nA+x+Q8P9p4dQ1BGjRtg7HEZGy4vF4+H5JO6iqSf7cgvxjcfIfGlBESHVHCbSauHYqjH3cpqs5dd9WMxbWuvh6SuGaoRsW30dCLM2TS0h1Rp/C1YBYzHzWfduYw2iIvIZ/2xxm9SSrtaQli7B+4X2OIyKEVAQl0Gog0PcD4j4Udt86tK/LcUREHuoaKpi+tD27ffbASzzwes9hRISQiqAEWg38V6T71qmLOQQq9GOrrtp1t0CXXoXzFy+ZchMZ6bTwNiHVEX0SKznqvq15pi9tB21dNQBAdEQaNtO9oYRUS5RAlZz/vRh8is4AAKipC+DYgbpvqzsjUy18t6gtu3142zP4+8RwGBEhRB6UQJXchSOv2MftXSyo+7aG6Dm4Ptp2twAguTd08eSbyM7K4zgqQkh50KexEsvJzpO6/ukxoRmH0RBF4vF4mL2yA7S0VQEAUWEp2Lr8IcdREULKgxKoErtzJRJpKZIBJtq6amjWyoTjiIgimZgJMWW+E7u9f+MT6solpBqhBKrELhbpvnUZYAMej5Yuq2n6DmuI1p3MAEgGjP087jpSk3M4jooQUhaUQJVUSlI2vK9EsNuDxzXlLhhSaXg8Huas6QQdPcmo3A9RaVg54zYYhuE4MkJIaSiBKqn/Tr9Brkgy4bhJXS3Uq6/HcUSkspiaCTFrZUd2+/LxUFw4/OoLRxBClAElUCVVtPu277CGHEZCqkK3vtbo49mA3V75gzfevU3hMCJCSGkogSqhmMhU+N0tHEwyYAQtXVYbTFvcFhY2OgCAzPRczBv7H3Jz8zmOihBSEkqgSujS8VD2sW1jfRgYa3IYDakqmkJVLFjXFQIVyWCxZ48+YcMiX46jIoSUhBKokmEYRur611djm3AYDalqjVsaY8LsVuz2/o1PcOXf0C8cQQjhCiVQJRMUEIs3IYkAAL6AB5f+thxHRKra0En26NSjHrv9y7c3ERaUwGFEhJDiUAJVMif/CWYfO7avC02hKofREC7w+TzM+70zzK0l10OzMvMwc9gVpKfSqi2EKBNKoEokI12ES8des9ujvm/JYTSES0IdNSzb7gx1TQEAICI0GYu+uUH3hxKiRCiBKpEr/4YiMz0XAKBnqI4WbU05johwybaRAX5c1YndvnHuLbYuf8RhRISQoiiBKpGi3bcDRzamqfsIXAfaSs1C9ddvj3H2QAiHERFCClACVRIvn8bj+eNPAAAeD/hqDI2+JRJT5juhTVdzdnvpd154ePs9hxERQgBKoErj5J4g9nHTVibQM9TgMBqiTAQqfCze1A22jfUBAHm5YswcdpkdrU0I4QYlUCWQlZkrNXXfmOk0eIhIE+qo4dddbjA0kUyqkZYswnfuFxD3MYPjyAipvSiBKoH/Tr0pXPdTTw1OXcxLOYLURqZmQvy6y5UdmRsdmYbJfc8hOSGL48gIqZ0ogSqBE/8Udt/2H96QBg+REjW0N8KiDd3AF0h+R0KDEvFt//NIS6E1RAmpapRAORYWlIDA+x8ASAYPeUxoxnFERNl1dKuHuWs7o+B7VlBAHKa6X0BGOk20QEhVogTKsUPbnrGPm7UyoYnjSZn0GFQfs1Z2YLef+H7E9CGXkJ2Vx2FUhNQulEA5lJyYLTVx/IQ5rb5QmhBp/YY3wrTFbdntR7ejMWUAdecSUlUogXLo5D9BbIvBuI4WWratw3FEpLoZPK4pvvmpNbvtdzcG43ucQfynTA6jIqR2oATKkbw8MY7+9ZzdHvFdcxo8ROTy9ZTm+Ha+E7v98mk8xricQnREKodREVLzUQLlyH+nwvDxXToAQE1DgD6eDTmOiFRnQ7+xx5zVncD7/190VFgKRjufwsun8dwGRkgNRgmUAwzD4J8/A9jtvkMbQE1dwGFEpCbo49kAS7c6Q1VN8mcdG5OBUd1P4uoJWpCbkMpACZQDvjffsS0DPp+HMdMdOI6I1BSde1rht396QFOoAgDIzszDnFHXsH7hfeTnizmOjpCahRIoB4q2Prv0tqJ5b4lCOXaoiy0n+sHi/wtyA5Lfue8GXaRZiwhRIEqgVeyF/yc8uFW4ksakua2/UJoQ+Vg31MfW0/3RrrsFu8/nvygManUU3pcjuAuMkBqEEmgV277yMfu4Zfs6MKun84XShMhPW1cNK3a6YOR3Ldh98R8zMe2ri1gy5RbSU2nmIkIqQoXrAGqToIBYqW//05e04y4YUisIBHxM+LEVmjgYY/0iXyQnZAMATu0Nhu+td1i0sRu697OpkbdQpaeKEB2RiqT4LCTFZyMxPgupSZJJJng8yfgDvoAHA2NNmJoJYWImhKm5EAbGGjXy/SCKRwm0Cu34tbD12dzJFLaNDDiMhtQmHd3qYXcrE2xe9hB3rkQCAGIi0/D94Eto72KJuWs7o6mjCcdRyicnOw+vnsXjuV8sXj2LR2RoMiJCkxH/Ub7JJAyMNdCstSnsW5vCPqMlWjd8C0PddAVHTWoCHsMwTFkLt2nTBn5+fpUZT40VFBCLYR3+Zbd3XhiA+k0MOYyo9jC30WMfx0SkcBiJcrh9KRybljyU6sLl8YBBY5rg+1/awdxal8PovoxhGLx9mYRnjz7i+eNYvPD7hFfPE5CXW3kjjHk8MRzsIuAyYRSc+9vCrqkBtVAJAEqgVWZyv3O4f+MdAMmk8Zv+7ctxRLUHJVBZyQnZOLDlKS4dfQ2xuPAjQCDgod/wRhg70wHNWplyGKGESJSPkMA4BPjEwP/eBwT6fmC7oUvD5/Ogb6QBPSN1GBhpwtBEEwZGGlBVF4BhJMk4Nycf8Z8yEf8xE/GfMpEYn4XcnC8n4/pNDOD5jT0GjW4MfSNa/KE2owRaBXxvvsOkvufY7d2X3WHdUJ+7gGoZSqAliwxLxq61/nh4O1rmueZtTPHV2Kbo7dEAhiZVkyjSU0V48uADAnw+IMAnBs8efUJOdn6px+kbaaBhc0M4tK+L+k0MYGmjC1NzIQSC8o2TFIsZfHiXhtfPExD6IgGh3hfx7I0NxIzseVTV+Og1pAGGf9scrTubUau0FqIEWsnEYgZfd/oXQQFxAIB2zhZYtcuN46hqF0qgpQv0/YBDW5/h2aNPMs8JBDy06mSGLr2t4dTFHE0djaGppVrh18zKzMXr5wkICohFcEAcggJiERaUKNUiLo66hgD1mxqgdScztGxbBw2bG0FHT73C8RTH3EcPSWlC3Hlmj+svPeD7SB3ZObKzhrXqZIYpC9qgSy8rSqS1CCXQSnbx6Cv8PO46AIDHBw7f9oCJmZDjqGoXSqBl9/JpPM4efAnvyxElXlfk83lo0MwQ9k6msLSVtPRM6gphXFcL6hqScYkFOSQtVYSkuCwkxWchMS4L7yNS2UE+BXNBl0ZHTw2NWxqjk1s9OHSoi3r1dassSZn7FP7upNWbj8wsHm54aeLMRS2EvJJN2vatTTBlQVu4uttSIq0FKIFWosyMXAxofgifojMAAL087DB3TWeOo6p9KIGWX0piNryvRODG2bcIeVK1E9KbmGmhuZMpOrrVQ4u2dWBcR6tKX7+ozxNoUa/DVHHynBCXr2kiL1+6i9exQ138tLYzWnU0q5I4CTcogVaiTUse4O81kvdLXUOA4/eHQqijxnFUtQ8l0IpJis+C390Y+N2NQVBALPuFsKJ4PEDPUAP1mxjAoX0d2Lc2RYNmhkr1N/KlBFrgU6wAh/7VxrmLWhDlSifSnoPtMHtVR1g30K/MMAlHKIFWkndvU+DucASiHMkAiGmL22LwuKYcR1U7UQJVrMz0XIQFJ+JNSCLiPmYgJiodcR8ykJKQjdxcMRiGQcGnioamCnT01KBnqAHjOlqwsNGBdQN9WNrqoq6lNlTVlHsVorIk0AIJiXwcPKaDU+e0kJtXmEhVVPkYP8sRUxe1Vci1Y6I8KIFWAoZh8G3/8+xtK6bmQhz0GgI+n66JcIESKJFXeRJogegYAXb8o4sbt6W7ns2tdbBwQze4DLBVaIyEOzQXbiW4eOQ1mzwBYOGGrpQ8CaklLMzz8esvSdi9NRYtmuWw+2Mi0zB9yCXM8LiEmKg0DiMkikIJVMGS4rOwdu5ddtu5vw3sW3N/QzohpGo1b5qLnZviseinJOjpFN7LeutCONxbHsbuP/yRm1v6Pa5EeVECVSCGYbBsmheS4iUzpWgKVfDjqo4cR0UI4QqfDwzsm4nj+2MxsG/h4KuszDxsWOSLYe3/xbNHHzmMkFQEJVAFOnfwJW6cfctuz1vbGZpCGjRASG2nryfGop+SsXNTHOxsC+cgfv0iASO7nsRvs+8gI42Wl6tuKIEqyPvwVPw2+w673alHPXTpbc1hRIQQZePQXIT9O+MwY0oKNNQlE1UwDHB42zO4OxzBrQvhHEdIyoMSqALkZOfhxxFXkJGWC0Ayc8r8dV04jooQooxUBMCoYek48k8sOrTNYvd/fJ+OGR6XMGv4FcR9UMy9tqRyUQJVgLU/3WPnugUPWPGXC93vRQj5InOzfGxYnYgVixKhr5fH7r9+5g0GtjyMf3e9KHVeYMItSqAVdHpfMI7//YLdHjvDAc3b1OEwIkJIdcHjAb1cs3B8XywG9ilsdaaliLD8+9sY63oaYcGJHEZIvoQSaAU88HqP5dNus9st29XB6OktuQuIEFIt6ekyWDQ3Gdv+jIOFeS67P/D+B3i0PYatyx+ys5oR5UEJVE5hwYmYPfwK8vIkAwEMTTTx69+utAIDIURuTq1EOLw7FuNHpUIgkHy25OWKsWPVYwxxOgq/u7LrthLuUAKVw7u3KZjc7xxSkyWzjKhrCLD5ZF+6ZYUQUmEa6sDUiWk4sDMO9k0KZzIKf52McW5nsPS7W0hJyuYwQlKAEmg5fXiXhm/6nEVsjOR6BZ/Pw+8HeqKOuTbHkRFCahI72zz8vTkeP81IhpZmYfftyX+C0a/ZIRz96znbA0a4QQm0HCLDkjHW9TSiIyTzWPJ4wIqdLmjWiqbqI4QonkAAeH6VgWN7Y9Gtcya7PzkhG7/+4A3Ptsfge/PdF85AKhMl0DIKeRKHsa6nERP5/0mgecDCDd3Q3tmS28AIITWeqYkYv69IwprlCahbp3CQUWhQIib1PYep7hcQEhjHYYS1EyXQMrh57i3GOJ9C/EfJN0AeH1iyuTuc+9twGxghpFZx7pKN4/tiMXViCjTUC7t1716NhGf745gz8ioiXidxGGHtQgn0C/LyxNi6/CFmDruMrEzJjc4CAQ+r/+mBrn1omj5CSNVTVwPGj0rHyYOx6N87Azxe4WQLV0+Gwd3hCBZ+cwNvQuj+0cpGCbQEMZGpmNjrLHaseoyCJceFOqrYcX4AnLqYcxscIaTWMzYS45d5yTi0Kxbdi1wfzc9ncO7gS7g7HMEMj0t4+pBWe6kslEA/IxYzOLz9Gdwdj8L/Xgy736qBHg7cHAzbRgYcRkcIIdLsbPOwdkUS/tkWi7atpG9vuXUhHCO7nsRo51O4eOQVcrLzSjgLkQePYZgyT7bYpk0b+Pn5VWY8nPK7G421P91D8GcX478a0wRTF7WBQEDfN6ojcxs99nFMRAqHkZDqxtyn8Hcnrd58DiMpu2dBajhwVBv3fDVlntM30sDgcU3hObEZbKgxUGGUQAE8ffgRO1Y9xt2rkVL79QzVsWRLd7RsV5ejyIgiUAIl8qqOCbRAeIQKDh7TxrVbmsjPl/3y36yVCfp/3Qh9hjZEXUu6j10etTaBikT58DofjkPbniLA54PUczw+MGBEI0xd0BZq6gKOIiSKQgmUyKs6J9ACCYl8XLiihTMXNPEpTk3meR4PcOxohu59rdG1jw0atzSiKUnLqFYl0NzcfATe/4AbZ9/i8vHXSIqXnQ7LsUNdzF3bCaY0s1CNQQmUyKsmJNAC+fnAg8fqOHdJC/cfaSAvr/hLUnUshOjc0wqtu5jDqbM56tXXpYRaghqdQBmGQdSbFAT4xODOlUjcv/EO6akimXI8niRxfr+kHawb6Fd9oKRSUQIl8qpJCbSotHQevO9p4uoNdQQ80YSYKTlBmphpwbGDGZo6mqCpozGaOBjDxExISRU1KIGmp4oQGZaMyNBkhAUn4oV/LF74fUJKYk6Jx2hpq6LnYDt8PaU5jOtoVWG0pCpRAiXyqqkJtKjkFD58H6nDx1cdvo/VkZGpUuoxBsYasGlkAJuG+rBuqA8rOz3UtdRGHQttGNfVgopK7RhwWfo7VcXEYgainHyIcvKRlZGLtOQcpKaIkJ6Sg9TkHKSliJCckI24jxmIi8lA7IcMfHiXjoRPmaWfHJKk6dTZDANGNIJjRzPw+fQtihBSe+nridG3Zxb69sxCXj4QFKKGwKdqCHiighfB6sjMlk0TSfHZSIr/gMD7H2Se4/N5MKqjiToW2jCpK4S+kQb0DNWhq1/wf3XoGqhDU6gKTS0VaGiqQkNLRfJYSwXqGirV5nOZkwR6YPMTnPwnGCJRPkTZeRDl5CMnW5I083IVu7qAqroAVvV10cmtHrr1s4F1Az3qeiCEkGKoCACH5iI4NBdh/CjJddM34ap4+VoVr0JVEPKSj7eRGsjOKXlwpVjMIO5DJuI+lK1RUxx1DQFUVPkQqPChosKHQIVX+FhQ5PH/n1NR4YMv4IHH44HPB2Ys61AlE95wkkCT4rMVPs0UjydpXRqZasHSVheNWhrBpoE+jOtqsQlTlJOP0CCa3qq2Kfpn9PpFAmdxkOqn6O9O8qdYzuLgkqkOYOoEdHOSbIvFQFyCKqI/auD9B3W8j1HHh48CxCeqICFZHanpGhV+zZxsSaNKXskJVbNearmugRobG8PGxkZhL/7qeTzycsXIZ9Ih4JVt1CufL/n2IRBUv1ZkTl4a1FV0uA6j0lE9a5baXE8BPx+6mmkcRVQ50rJyoKOpzsErMwAj/v//S087bAmGh5JKM+x/CvOBQF0IQwtzxMXFwcTEpCIBA5DkvatXrxb7XLkSaGVR5sFJikT1rFmonjUL1bNmqYp61o6hUoQQQoiCUQIlhBBC5KAUCfTbb7/lOoQqQfWsWaieNQvVs2apinoqxTVQQgghpLpRihYoIYQQUt1UWgJ9//49ZsyYgY4dO0JLS3IvZkRERJmO5fF4xf578uRJZYUrt4rUEwBCQkIwdOhQGBsbQ1NTE40bN8amTZsqL2A5yVvPZcuWlfjz1NCo+P1iilaRn2dUVBTGjRsHKysraGlpoVGjRli8eDEyMjIqN2g5VKSe4eHh8PT0hL6+PoRCIVxcXJRyVOfJkyfh4eEBa2tr9m9rwYIFSEsr/baU7OxszJ07F2ZmZtDU1ETHjh1x586dKoi6/CpSz4ULF6JXr14wMpKswLJv377KD1hO8tbTz88P3377LZo0aQItLS1YWVlh1KhRCA8Pr3BMlZZAw8LC8O+//8LAwABdu3Yt9/Hjx4+Hr6+v1L9GjRpVQqQVU5F6+vn5oX379sjJycHu3btx+fJlzJkzB/n58t9AXFnkreekSZNkfo43btyAiooK3N3dKzFi+chbz4yMDPTo0QN37tzBypUrcenSJUyaNAnr1q3DxIkTKzFi+chbz4SEBHTp0gUvXrzAzp07cezYMQCAi4sLQkJCKitcufz5558QCAT47bffcPXqVXz33XfYsWMHevbsCbH4yzOeffPNN9i1axdWrFiBixcvwszMDL1791bKL/EVqeeWLVuQlZWFAQMGVFG08pO3nseOHUNQUBB++OEHXLlyBWvWrEFAQADatGmDd+/eVSwoppLk5+ezj3ft2sUAYMLDw8t0LABm0aJFlRSZYslbz/z8fKZZs2bMV199VYnRKU5Ffp6fO3DgAAOAuXjxooKiUxx563nt2jUGAHPt2jWp/T///DMjEAiYjIwMRYdaIfLWc+XKlYxAIGBCQ0PZfenp6YypqSkzdOjQyghVbrGxsTL79u/fzwBgbt68WeJxT548YQAwe/bsYffl5uYyjRo1YgYOHFgpsVaEvPVkmMLfg9DQUAYAs3fv3soIUSHkrWdxx0VERDA8Ho/55ZdfKhRTpbVA+fzacXlV3nrevn0bwcHB+PHHHxUcUeVQ5M9z//79qFOnDnr37q2wcyqKvPUUiSTL5Onq6krt19fXh1gsBqNkY/XkreeDBw/QsGFDNGjQgN0nFArRtWtXXLx4EXl5eYoKscKKm4Wmbdu2AIDo6OgSjzt//jxUVVUxfPhwdp+Kigq+/vprXLt2DTk5Ja/wxAV56wlUr89peetZ3HHW1tYwMTEp9f0pjdK+ezt27IC6ujq0tLTg6uqKu3fvch2SQt27dw+A5FpLhw4doKqqClNTU/zwww/IysriOLrK8/79e3h5eWHUqFFQUVG6xYDk1qNHDzRs2BA///wzgoODkZ6ejlu3bmHTpk2YOnUqhEIh1yEqhEAggJqamsx+dXV1ZGVl4c2bNxxEVXbe3t4AgKZNm5ZYJigoCLa2ttDSkl7i0N7eHiKRCGFhYZUaoyKUpZ41gbz1DAkJQWxsbIXfH6VMoKNHj8b27dtx48YN/P3330hISICrqytu377NdWgKExMTAwAYPnw4evXqhevXr2PevHnYvXs3Ro4cyXF0lefgwYMQi8UYN24c16EolIaGBu7duwexWAx7e3vo6OjAzc0NAwYMwNatW7kOT2EaN26M0NBQJCQUTsovFovx6NEjAEBiovIu1hAdHY0lS5agR48eaNOmTYnlEhMTYWBgILPf0NCQfV6ZlbWe1Z289czLy8PUqVNhYmKCb775pkIxKGUT4ODBg+zjrl27YtCgQWjevDkWL17Mttyqu4KL3qNHj8aKFSsAAM7OzsjPz8f8+fMRHByMZs2acRlipThw4ABatWqFli1bch2KQmVnZ2P48OGIjY3FwYMHYWVlhUePHmHFihVQUVHBjh07uA5RIaZOnYrNmzdj7Nix2Lx5M7S0tLBq1Sp2RKOydgmmp6dj0KBBUFFRwd69e79YlmGYYpc8VLZu+OKUp57VWUXqOX36dNy/fx+XLl0q9otSeSjnb/tndHR00L9/fzx+/JjrUBTGyMgIANCzZ0+p/b169QIApRztV1GPHj3Cy5cva1zrEwD++ecf3L59G5cvX8bo0aPRrVs3/PTTT1i3bh3++usvPH36lOsQFaJ+/fo4fPgw/P390aBBA5ibm8PX1xezZ88GAJiZmXEcoazs7Gy4u7vj7du3uHbtGiwtLb9Y3tDQsNhWZlJSEvu8MipvPauritRzwYIF+Pvvv7Fnzx72s7YiqkUCBUr+Vlhd2dvbA4BMnQq+5SrrN/mK2L9/P1RUVGpkF/Xz589hYGAAOzs7qf3t2rUDAKW7xaMiPDw8EB0djeDgYISFhcHf3x/p6emoV68erKysuA5PSm5uLjw8PPDo0SNcvnwZLVq0KPUYe3t7hIeHIzNTekHo4OBgqKmpSQ2gUhby1LM6qkg9V61ahTVr1mDTpk0YM2aMQuKpFp/SqampuHTpEtq3b891KArTt29fqKury6wzd+3aNQCocdcuRCIRjh07hn79+ilkjT5lU7duXSQlJckMMHn48CEAwMLCgouwKo1AIEDTpk1hZ2eHmJgYHD9+HN999x3XYUkRi8UYNWoUbt68iXPnzqFDhw5lOs7d3R25ubk4ceIEuy8vLw/Hjx9Hr169oK7OxVqaJZO3ntVNReq5efNmLF68GKtWrcKMGTMUFlOlXgM9efIkAMDf3x8AcOXKFZiYmMDExATdu3dHZGQk7OzssGTJEixZsgSA5GbZV69ewcXFBebm5oiMjMSff/6Jjx8/4vDhw5UZrtzkqaeRkREWLFiAlStXQldXF66urvDz88OKFSswbtw4pfyWK089C1y8eBGJiYnVovtWnnqOHz8e69evR79+/bBo0SJYWVnBz88PK1euhJOTEzp37sxZfUoiTz1zc3Mxb948dO/eHbq6uggKCsLq1athb2+POXPmcFaX4nz//fc4ceIEFi1aBKFQiAcPHrDPWVpawtLSstg6Ojo6Yvjw4Zg1axZyc3Nha2uLHTt2IDw8XCk/g+StJyAZxRoXF4ePHz8CkEzuoq2tDQDw9PSs2oqUQt56Hjt2DLNmzUKfPn3g6uoqdZyurm7FxppU6C7SUkCyVrjMv+7duzMMwzDh4eEMAGbp0qXsMefPn2c6derEGBkZMSoqKoyhoSEzcOBA5uHDh5UZaoXIU0+GYRixWMysW7eOsbOzY1RVVRkrKyvml19+YUQiUdVXogzkrSfDMIy7uztjaGjI5OTkVG3QcpC3nkFBQczQoUMZS0tLRkNDg2nYsCEzZ84cJjExseorUQby1DM3N5fp378/Y2pqyqipqTH169dnFi1apHQTRTAMw1hbW5dYx4I6lfSzzMzMZGbPns3UqVOHUVdXZ9q1a8d4eXlVeR3KoiL17N69e4nHKht56zlu3LhSf9flRauxEEIIIXKoFtdACSGEEGVDCZQQQgiRAyVQQgghRA6UQAkhhBA5UAIlhBBC5EAJlBBCCJEDJVBCCCFEDpRACVEQHo8HHo8HPp//xXUxXVxc2LL79u2Tem78+PHsc0uXLi3xHPv372fLOTs7Sz13+/Zt9rmCf1paWjAzM0O3bt0wd+5cBAYGVqSqhBBQAiVEoVRUVMAwDP75559inw8NDYW3t3epi4mrqKhgz549yM/PL/b5Xbt2lXoOa2trLF26FEuXLsWsWbMwYMAAZGRk4M8//0Tr1q0xatQopKenl61ihBAZlEAJUaA6deqgTZs22Lt3L/Ly8mSe3717NxiGwYABA754ngEDBuD9+/cyiw0AkpVdfHx8MHDgwC+ew8bGBsuWLcOyZcvw22+/YdeuXfD390dgYCBatGiBI0eOYOjQoeWrICGERQmUEAWbPHkyPn78iIsXL0rtz83Nxf79+9GpUyd2ObuSjBo1Cpqamti1a5fMcwX7Jk2aJFd8jo6OuHHjBkxMTHD16lWcPXtWrvMQUttRAiVEwUaMGAGhUIjdu3dL7T9//jw+ffqEyZMnl3oOfX19DB06FJcuXcKHDx/Y/Tk5OThw4AC6d++ORo0ayR2jqakppkyZAgBKucIIIdUBJVBCFExHRwdff/01rl69ivfv37P7d+3aBV1dXQwbNqxM55k8eTLy8vKkBhqdOXMGCQkJZUrCpSkYfPTo0aMKn4uQ2ogSKCGVYPLkycjPz8eePXsAAJGRkbh+/TpGjRoFLS2tMp2jS5cuaNq0KXvdFJAkYQMDA3h4eFQ4xoJFvuPi4ip8LkJqI0qghFSC9u3bo0WLFtizZw/EYjF2794NsVhc7pbjpEmT8PbtW9y6dQthYWHw8vLCmDFjoKGhUeEYC5Iyj8er8LkIqY0ogRJSSSZPnozIyEhcvXoVe/fuhZOTE1q1alWuc4wdOxbq6urYvXs32xJVRPctAMTExAAATExMFHI+QmobSqCEVJIxY8ZAU1MTU6ZMQXR0NL799ttyn8PY2BiDBw/GmTNnsGfPHnTs2BHNmzdXSHxeXl4AJK1lQkj5UQIlpJLo6+vD09MT79+/h1AoxIgRI+Q6z+TJk5GTk4O4uDiFtT5jY2Oxc+dOAJJbZggh5fflqUwIIRXy66+/YsiQITAxMYGOjo5c53BxccG5c+cgFovRu3fvCsf09OlTjB07FvHx8ejXrx/c3d0rfE5CaiNKoIRUIisrK1hZWVXoHDweT64kFxERgWXLlgGQTOIQHx8Pf39/+Pv7A5C0PAtaoYSQ8qMESkgNFRkZieXLlwMANDQ0oK+vj4YNG+Knn37CqFGj4OjoyG2AhFRzPKZgLDshhBBCyowGERFCCCFyoARKCCGEyIESKCGEECIHSqCEEEKIHCiBEkIIIXKgBEoIIYTIgRIoIYQQIgdKoIQQQogcKIESQgghcqAESgghhMjhf+YIJmtY97jCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0ahT0zyiO4fvAcC2pjz7Z7E2GG/Tcqb6t6T3TaDM5ph0Wa/lydj4zklTF99COYhyZaGGkVnyby6xrTJf4lbufxI7HnlnQklQnqw3q+StzGbqvbS60WfcV8SbFcQyPlszrEjT8lx1IoxLrwCg+jPRvGuwnm5H4rri8ecIwhqtkMHobP6LYXAxxUDCwDIMbiTCKRZ6FdXsxc5uZDrgozYYtS2HLrGtM13vzPfvwhZ/uEVFS3cxYPrPjCax/59fmRBRON+gkDyPO/DF3QQOdYBhzQJLKYxjjPYaZ8uOX9knRZ+7kiklS6Qkm0rZOtkLRl8oYDN9LEv1qFb+nAoRcGRhUDKzBCNnsOozjQuqu6//TDSqfcGSiM4OR9zKFsTR6yfUn33dzSX/zjaTSa78iXWYaUdTBACMkqe6efRzH+I2P/xjfKVnssBcIhiF8GLM/HdUHZv6+HetR0+fHL78eRrYdOpRqtT0aDMoKH64GPa2JIfOBnMGYcbSYDyNhGPkPQTCMGeqAi+pJ+YSyDINeytxZU3oNzTBSmFI3sgXNYIMe5YTZQps9/yKIAabLR98MI3z/wYO4e/fR7g7QAeixysoE037KQig5DZGqwR/rQ3mQMgZAbY+UsGz9JY5jIfNKSSoUn9JgFBQfjGMEaUTVcDXARA8+DD3KsQwa7bAvGeb9wsAaDJ6DUM6HkXbAAiZyxxOH8f0H7ZFKZTEmJKlmqe3Fy51zHSGTK3jGejcGQzjQO95zbiCMOssMBnphGHTO6R+9pdObfBmzbzH4fW5FkTLg9YOhdlzenBkYW3eJYpnNTbeQ5KSpkgzj2FQLU80QtOT3cC3AZEEpkTx040t7x+fvxsV/+e0ZSwcowuA6vUMpyXTGMPK3e83/+REA4LEbruypfYs7lKQINnsWs6zuOFZLnHTDcMn4zIWwzW7QTaZ3t5c6kzkR+jXNhefDB7ik3zED0oeBrJvSIOLTcvoojlH1PTQhnxtJUhPNUDq9cwzAlj//JgBg7ZIkB2O4GvRUGkRfMbMMKMx+37EprDtppOtz9wsDyzC4JJH4MAq0yBnWhOtpVEVZSaqorLX+Ne+o3Qwqc3Ht6E5APpwy7eeGtqtzUTTeDAzeulGaaYPx6MFxPH5oXPmON0k3GP0I+6UjlL1UU8isDpVhpJJUK42SaoXiuzJBH0GaFT5c65cPo/w+q8bqAPq39kivGGCDESuz5KKOJTO9yz2tXikgvZBHyhoMS7brvqNT+PvvPpy5vl59GO0ZnDVPB2RV1+Jt5SV2KUnRDHgGBm/9HDNtMF78N9/FC//6u8p3uiTF0Q/JTDCGjhmjPQ8pjmNU04Fed3pPtULx/pdZZpb8fL36MDqV3gBg1eLEYOx1BqM38FpC7TAqnP11WkvqcIdSku18HTMMrXnfum8f/urm+3MdX92MKVKXH0yD0Ymk1mn5bNu5ZoNhTOc5b7lvP/7o83cVbsebYGIYPevrOdp+ox3iySOTps0Rx9nn3wojvH77f+DuPUdF+Y98p3dx231fMox++DA66XPLRx3D6AvaYcxm5cWMoNNaUs+Ml3NWczxxaAJ/dfP9Sq2boyUNj02SEjOhnHb3MvMdUILRUWIb3Z5u5beZDMnWn+V0sprbHzmEr9z1ZOF2us+Ck4ofPXwI5/75N3CY9Zddu4/gUz9OKi/cuGtvYU21PB/T2z53F37phluVfssZhn5/jk22cPsjhwDIAoNSkuo8rBaQlW17DavtZeXHPYfL182bTgyuwYgikbTWjsowjM4SobpZ8P3W+/fj77/7MA6caIjzHCkZJaWv100os45HL9nAg8owog4G8V5Lg3TjrOwWM8kwypbH54aWV1gAkjLsU60Ih8Zlf/lPH/kh3vXlewAAb/3sLvzpl36Wf/yc375+z1NpW3l75H76/eHPqBqoDEPJwxCZ3sUGwO9Sktr+vYfxlzf9HEBSjPSJtFhqJ/2VJLO9R+YGwxjYKCk+65sOH8bBLhgGPwe1p2yUVGiJFKH/87TWXsaUQfVhhBYDa4IMZ+zuXDNqMCL9/2k0GJHdBxBFMba855t41flrcOnGleL7dhgpRoZm3M129+0sUw7HtKZJbAit5tdDzIDuIUlSU1ySskhMnLlUmNO7kyipHzx0CPuPTuG/v2ITXvF33ze2sQikLDhJqkeQ0zeMk5DaovA+MVOPIpxotHHjrr3G7SiPrRuGwfNCIm1WU7xv0v6s0zP5zLu+XiKdBjFKiocYl5FsYsNg0wlmU5KaznOGOaHJ4802TjTa+MyO3ZokpTIMknlMzu8yDmWgnERjSu6LkW0/nxCKKKk0wa8TSYozF+H0rgWY6sCH0WyHRilZv+VxHONff7LbyHboHh7sYjyaDgysweCd2OT80tFm23/jnn1462d3ZZxpAESRsUMnumcYPAu9bKeRDEP9XjIM+/X1whLmSD5QR+DPupzTO/3slmFE5c/VK2YyrJaevcmQHmMJeYqTO4oUCYkGUFONtrIZymUMub7aZPJ39v7w32WUVDJTF0yDGYymRZLix/GZD6MZRqX9oI12ZOz/+vXev+84/viLd+MHDx7MbEv7z4WaYkCfDMa1116LVatW4dxzz+3H4UqBZtxlb2jIypvTrMLkwKLjck22LLi/gQaZssX9bFE/4sXO9WF02tLs8QcJ/FmXk6TIh9Hdtdr8S9MBW9DDdCC2vHOAWiOKX3aGYaQM2jSIlpVRylxhKFii/M6UvMnvF0lSURQrUhKPkrLd39BwHFGxtqTju9GKjP3WFNmVfGbb0ggj4z6zhb4YjDe96U24+eab+3Go0tAH0tI+jCiyhklGUSwe2sEeGAaP4GqF5aqq0jb67EMYoTwfRg8v01won90p1EqkxdsPdOLeND6fvAx2bjDUMFq1NAgNnqbBrmxkTylZ0eDji+N8RkZhtVEcK9LwFHN623ya/FmT03uo1pnBaIYJw8gr3sj/NzEtmhAnSsrs99W+GIwXvOAFWLZsWT8OVQpxLEuBcIaRd0NFlBQbwHW/AJ8NlA2HNZ2D01/9uDbYEumEJDVNUVKDaDAUSaojp3eXDMMS8jwdyDDMadQMTbN2gipJye/1fkYzd1NRz93PJAaDZujivNoxytxVU0hqbAirVQxGICtAc4PBJSlb4iE/LhUGHUkZxlTJNTEa7RDNdpTxk2QNCMlt2WNwyWwuyFID6cNQBmP2MPJYhmQYatFCDh4x0U0GK2cYfHAqE+stw0TV78uF1Xba0uzxBwn80XTm9O7yfOl+M9FhZ1KSygtNtjGMRG6V/0+1SZLKHuOJ1GD4msF49n+/CW9nCYP89PbSOFlZMUYBw2ClQUh+HqtXklpSNAmIzedsGwwGrbo30SoXWttsJ5KULn3r9ztvTXM+UZwLfXXGDMb27duxbds2bNu2DQcO9FYNlj9MToXzOhefUdgGYe5v6Ko+k/CTqAzD5sf46G0P467dR5J9LS8NHSY/rLYXhtH1rrMGPkiUy8NIP7s930wyDMvsswziOMZ/+9wu/OihrPPUBBPzorGdly3ny5jyop/JOen77Pu5L3V6t8KsLPOlO2SUop7nkd9W/l12HRwlD4OF1RLDWLaoJv6uV5Lhz8SOTPWy5Kp7JX0Y7WQcONFQDYytjxsNRptPYme/s86Ywbjuuuuwc+dO7Ny5EytXrizeIQc2hpF3Q23+BY5Gjw+HZ2Xb2sjxt9/8Bb56d5JpK2c8XUhSC8yH0bkkRQyjS0lqJn0Y2mPu5JxhFOPLd+7Ff6SZzoXb0ySFnXO0lq2yzP9uR5HxnTFVgaYyGnGMjITMwQ9nLyaY7R9xnH1/Fae3kKQgEu6WjdbEAE4Gw3ROdVKatHmoQ6c3XeuxSdVg6Lcvr/8qCsocWN99ICUpPqtohowV5NxQHhVh0y8bJeWtwnNo63PYQusSw6Lua9Nkc53eC0yS4m0uJUnRZ9eSlF266TcyiWgdDBI0qdCNzP5jU3jd9tsz5W6EJGW4MYcn5La8gKZtiWPT+8mZCQ26ptm5HrZrgsmHYYqS4sfikhSdd9lITfRHqihtuqbIYDBGUkmqbHkQGk/0enK2wAYbw6il1zEX1kbpi8F4/etfj+c973l44IEHsG7dOnz84x/vx2Gt4DdOZQX2G8p9GBEb2DnoRQp8ryeG0QqjdKUuL9NGgswjUaO89NNK+czengXn9OaSVJcx/B2dL0fr7zcyTu8O2txqmw3AP3zvEfzHI8/gCz/dbTxXZLifhztkGKa+xwdWahvp/3yhR340Wxc2RRIZGQbrJ0KSSidnALBkuCp+J4ZhktNCxWCkklRqMB5+ehyX/tWtwqlvApeldYOht5n+NV17M4zEeeeCJNWX0iCf+cxn+nGY0jA9TAB47NA4At/D0pGaYR+KkpJhtbrMQ76GkVrQlcxD52ilRmmkGuB4o21kGHpUlG0WO+1RUrM/aekY/NmUGsRj5aPz880kw8iJ+imCCDEP9Xco+fS15XiFdm64n0cYw+CrRraYD6MW+OKcpgkNl24aYQigKvwHQ5WAtaMEwzBMqOI4O+kzOb3DSAahjNblkEcSk2kgNklSFO318IET2HN4Eo8dGsepy8yLGvH+qq97Tpd408+ewiUbllsnNBTmP1ILcHSyNScMxkBKUlwv5YPxf/n4DnzwlgeN+9CLpZZFVx8AMYHRWqUnhkFGieK2TQyDXkJ99pqRJHK0X0Kngz6fUXUyg71x1148PQfWF1YlqeLte6kSmpwv/ewjG/v0j5/AA/uOZ8+Vo8kXoaVJUofHm7jif92GX+xPzpM1GFlGQt9xdsDZBo8ArFV89r2JYcjvyKCQNFSvyn3L+DBkLo3a1ry8lSqrVkvNI1kJAJN6DJKUwRFPioEu/e07OoV/+N4jStt4xKW+7nkUxzg21cLvfeoOfOmOPYw9qW1oalKY82F0CX7j+EA63gyVmZGyD5OLeBIfBx1rpBb05MOgTG960EaGwTLPk7ZYZhldMow4jvGBbz5gTJ7qZvGl8UYbb/3sLrzhYz8utf10olNJiraYS07v93zlXnzxjj2Z7zMMswuDQe19/JkJ/GL/Cfz8qWMA5ICnn4ufwuSc5n2qxYoPKgbD0E5Vkkp9GK0swygXJWVgGMhOGPiKmnw9DNqfWAUgjZbJ2LUNYwwZ3JbWd//rJ3+C99/0c6WirJLTZZCkplLDeWyqbWUY0mAkrGje+DBmGjwMTg+NtWn9vJaUkKS0gZyYwEg96CkPo5U6vSkMzxRWKxiGiFTJdghqL1Dk9M5e875jU/jftz6Eb//86cxv/LrLDki01cMHTpTafjphqyX1qR8/jp/tOZrZXsbwd4fpSNyzFczMi/opgl4mhwbshhjwzOfiVQbo9JwV8wFvoimXN6UZPGCe0PBj0O9CkuqQYcgkwwKGwZoR+B48L7k+ajNnGGS0jE5vdlx6TmRwqS+Scdp3NGHdZKAAlWFkDYa8NxONNsvDUNtAz5N8GHMhQGUgDUZeBJItwY07j4UkpT0A4cOoVnrPw4hj8aBNDKPFDBggO0S2llS6fYcMQ2TgmjpyWJxv8ukfP4Gb07UI+DnmwDurSlLs2m/4+v0Zxy4gZ6FdS1LTwDDCKJtDQN/n/Z8H3YcxKQxG8qkn0Om+GTV3iDMMOeCNN9riHaD1JgB7iQ1iIdQHJpsU0sp9GHJ72z02rWkSxyqjSK5B/u95CSsImWHhBoMYRlFYbZEkRX2NGy8evZkxGJFMJBxvhqy4ZYTv/eKAuFaueOhtmi0MpMHgL2cjwzDMA6vCMCI6jlmSGq4FuQ8nimJ89LaHM+VD2ppRkgzDRHnVl842a5aSVE57DD/RTM4081MlKfMxP3n7Y/j8TimZxHZ7JRBGcVcrFXYK270Kozj/uXUtSaWffeqwcsKi3tRkpUZ1204MBi/5D8iwVvpe92HofjN+75RkvfT7iu/hRKMtsq2LfBiAHOxaGsNQfBjgz9AWVkufcts4jqGfVq8BFXgewkjuP1yTTm+RuFcUJZWRpCiAJtlmIr1XXCrn/puM0zuWBVAnmlKS+uTtj+O//OMOfO1nyUQtYzCcD6M78I6WZRgWSitW3JNhgbbEvdF6vg/jkYMncMPX78ctP99vPEcrXQEwz4dB5zaVZ+AvcKnSIIa2ToqFbbL7KZJUHKPRDpUlNum8LYPhysNf3vRzXPAX38pktvYbavFBdSZoem69Or1tA7yOOI5LxejziYVyHkP7unF60z3Q124IdKd3ROdVDQdgnmiMDVUUhlFjDMOUuAfI+ktNzYdRZ8bGxjB0+UnfNkY+I/M9wPeTfekZDnMfRkXO3E802goTMN0LwTA06Y82tYX7myWp5D5MNENxTbTcwuPpyny6D+N3P/VTXPgX38JsYkANhnlwNf2v70P+BdO20qJLSerX/uE/8MFbfqFsd6JBD1sdGMU52inDSB90ng/DVHRQqR8Tm9vKYXL85klSukH6u28/iOf8xbdw/dd/rhzTtI5yHj6/c7dy7umCLdM7tBgMEec+zXkYH/jmL3D2n91ceP22MF3T8W0zbhNa2kCmZySTJHXP3qN45xfvFgOcnjwKmFnx2FAV481Ec/c91Ylu87GNpGGsepQUdz5De4YE3s+FcdMmMbaoQoAzDPleKJKUyPSO8P/eeA/e8uk75LkNJYcCjWFk5TDWh3MNRix8HBNN6cMgxjalTfaozXsOT+LQDDD4PAzkEq29+DC4bGH1YTBJ6qGnT+CkUTWvgwyFvpqeUq8qjsXsyixJqYOGkllqcErnhdWaxkEhSZVgGA/sSxzZX9n1JCabIY5PtRFpGnuZaKQJZqR+9NBB7Hz8MP7wpWcU7tcpIsMAk8gT+Qyj1/Pp92Df0Sm84wt34cOvvwBLRqr48p1JfaQDxxt41nJzfD5vs/6umtrZSbFakmfpndHZDo3v33/wID77k904Y9Ui5bzFBqOCE40QURzD8zyR5wAkfWmqFQoHMMEqSVkYhq1vm+5NFGX7MN/f8zz4vqfkYQwbfBitMMaB4w08fUyugWN6x8inTcZPZ4j8fz5JPKqVBokiKUmNNyTDSBhPKxOswNs82xhIhsE7WllJqh3KTmorb05WnzOMZhiJEDjCREM6rJRzaCxmOC8PQ8vwthVULCVJmRhGmlFrXJRF64hUBqIVxXjo6RN4+MCJJIqHzaDKjLl0DY12hF/72I/xt9/6RekVBztBW/HBqIOdScKhb7qWpOgc2r28Z+9RfP/Bg/jF00mew9KRJIv4sCW0m2BbX/7W+7MRbd0xDFX+IRAjkJKs+g4WlbNZPFQVkpTvyUxqOtbvfeoOvOhvvqvsM6L1ATIYHpPHuA+jXWAwFB8G8subEwviTIRLUhQl1Q6TiDXex0zvkYiSsqzBY7t/WR+GNObchyEZRoTvPvA0rvn7HwFIgnDmCgbSYNj0ftP/+j48rDZTGiSMEPge6hUfYZTU7G+1owyTGCeGoWn1Sh5GHAvanZeHYWQYBikor0S6SSmZTGv25yUNJueV6wW3w0jUwdIZRiezdF7/5omc8gndQpWhkk9T8AAhr3x0qfORZJMJeU1+oM5PZSeK1l82+US+94sD+L1P3ZHZ1ubDmGyGGUm0pb1TtvWn9WRQG4PSsUj4MIhhyEG/HcZGg0fFDOmdo3ulSkvZtgGqNCud3nLbJEpKG7TZNaiSVPKdKUqqHUVosZU49fZdfs5qcTzeLv3Z2HwYuj+IO73HU8YGqJLU//qWlMFHHMPoDUo0R0lJylRLKlvePBnoAlYWuRlGIgqCQDqs/r2S6R3FqPgeaoFfECWVzlYMcd/UhrzrAsxObxpMTPspFTDjhI4nbaKCiIlRbRk6LFCcANdsR0KSefTgeO623YBPuvXZsVmSSj67FaZsPgcaoGlgJoZRZDBMYbo2VmIzcn/yxbvx1s/u0tqjO73V95MOxdlzcg71exM8D1hUryQBDSnD4HkHphLhgJRTpCTVzlwXv0RTwhzfXq8llbceRowYvs4wagand8owmgaG8c9vfi4+/GvPAZBlGHpkWBFD49cind5tmQiZTrKm2pEig4/UncHoCWq12s6ipACp9WaipFoh6lVpMJLkqjgjSZEUlWUYUdqmJDzS9z3UKr7ZjyD0ZtrXfA30DnZafLCs0/vEVFv6HqJIrBeih6hGOUZaR6Md4tSThgEAj0xDop+plIXNLwXwBZS6Mxk2g0HGnjq/ZBj5kpSQz9gz5XkJyjksz33/sSns18q06DNf3WDosg5f/pN/muB7HkbrgWAYvsYwbO+nHik4IXIW5Db8udj8KKbkNlO1WsXvFkEwDHOUlGQYzXZkXIxt5VhdPBvh9G7Ld23KEH6st11HFMvfx5uSYZBK12iFWMbq4TmG0SP44K8PxrZ4cDXGXJ3di2OFEeoVXxQZEzqjtsIWJR9lfBih2hEDL5G3TFFSsiRINuLCJEnlz1iy35UNq306ZRerxupohbFYLyRJLDM7HYvCZpvtSBR5mx6GkR1g5Gf2egXD0O7Tk0cm8a379me212Hzj+gMgyYahQzDwBprFS93Wx3tKM48W90A6NFa+r0Sod00GKeH05dUBRJGMVqvCCet73kKw7DnYaiSFLXJtkSrLWSeDq/vR9dCg63qrE4k5jACS9wz5WFkfRh0HH6NvsGHcZwtZauyI3ukHI+S4oaK9p9qR6L0OgAMOx9Gb8gLq7UluKkzF/Psu9GKUKtIhkGD7qS2hi8ZCluHpGUrAx9WhtFmTu9/uf0xax2aMpKUaVCZyGEY/PhUTHDN0mEhwRkZBjsF7yQEfi8aoSzt/MiB/hsMtV2a4TU8flOWMAB8ZscT+H0WSmkDH2g/8p2H8PTx5J6JDt5SlyktyzBUCcPy3lpYkclgZCQp7XchSYlkUGK56j3kEUwEz/OwqFZBM4zQaIfwPNWwFDIMSnIjhmFhr0r9KcMAzs/CpSZqiZ6hLfIw0q95SRIalMNUSTAtxhZotyLwPcnkwgjHp9TS74Q8hhHHakACTcCET0zzTzmG0SNsOieQ78Ooa2UK9Je8GUaoBpJhTAjDoA6QJEWNN81Ob8qSDXwf9Yqfm219dLKFP7vxXvzwIblKWstAjfPDau2SlOnF5ceiZTTXLB4CkLyswmBYnN4nDAaDa/DNtizwyA1hv2DK9OblFXTY8jB0GcIG2m2iGeKvv/EAvnlvwkqkJKW+TwePl2QYhkmMbdvs95FBjs33Yeh1y4Th0qSqmsFgEMMAkgmDB6hOb4sPY7SuMQxyelsYBl+drmkwGLrvQzIM6XcU1xvFiiTle2p0Fs/0pqKket2wwFfvReB5Sg4VnzyZ5DTPTByNBoNPOMcbzmD0DbbQO8BOjcNIRi012pFx2zCKUfV9BOm0ggbdyVaoDMoTwodhdnpLiSLpfA1DtIregdTfYjx+aBxv+qcd4mXKr1ab/Y6Oa6wlxe7ZfsEwhsR+Uer05oMAnwUeb6hhgoBqMBrtKOPU7ydMMfJ0HpPmT9/odtUUoWaCPsvXDYRgGGkbSju9c8LDCTYfRjs0MQxVOrP5MPRXQvejmfwpvudhETMYvu+VqiWl+zCoT+m+CAJPcuPXJ4w+azvl3iTto2vQGYasJaVX6+XrYVA/0f1Aena876v3WZGktHEpCT02D7GckdMEjN6LqVaIcTa2cBlttjF3WtIBlI5WwulNL1aGYRg06cD3xEsiZ0PJIEEvmDAYLQvDoGJvnod6JRBt/Nmeo6gEHjatWSw6mC1H471fuQ/ffeAAa5vUyfNCCQl5iXtPsxkw/X1yyjAmmiGqgY8ostecMjEMXqCOM4xOEs/KwsQw8qOkSM4w37cojuHDMhU0HFNKUCnDyEhSJcNqlUQvy0QnR5KyVSowrWnBz2sroU8Gz8wwPMEWTky1Ux9GfrVaIDE+vseipNI+QwX4rv7wDxH4nig7wteOMEZJQX0n8xhGO4wQeF5SrTZ11Kttk+th0LmaYdLPBcMI1H0Cz8NUep8OHG/gHV+4i51PZYz1SiDGEM9TJyyKwSCGwXxiXJIaG5o7w/S8YxjNMMpINPQSDWnlxvVyBmEUoRLIjsAfKv9bZHpnGIYqUQRplBSd71Uf/gFe/qHvJ+fWYviVa2hHOKiVAKDrNDkkjZJUTuLek0cmxUt4fKqNkVqgsK8oZRedOL11SSrUBqcv37kH/37Xk5n9uoHCMLQoKdMAqzt1xfc5+3Do91cwVDFBIMaRfB6eaOHxQ3bfjWk9FqvBsDm9w6ycJrX1ZB+dvdKhbBMOag43GPS35yU11oBkQSDf0yQpi8GoBAkTobbRzDmKY+zafQQP7D+O+546lhqNqsIw1ATT5JM/ijiWOVXUEp1h0AQrkaR0g0FhtZHoJ3qdqCzDkP/f9osDeIpltusOe15gcdVYXTkOfzbHhQ9DGvoTjTZecd7JePgvX6GWUZllDKTBsCX3ELIJNcn/NKPQOzzfruJ7Gae3/rcIq7U5vYUPI4mS+uFDh3Dr/Wo0jolheIxWH9JmqfRCV3UvHPLDak0M48kjk1h3kixdUfG1JKwocRLaig+aDAbXnhvtMJNI998+dxf+8DN3ZvbTsfOxZ7D+nV8zrmtB0B2mn/vJE/hOmjRmTtyzHMdiSPLOB/AqsKrRb0cxViyqw/eAf92pllm//qaf42Pff0Q5nhqGafZh2BL32lGcZddalJSeuKf7LAi64eRFBevp37ok5elRUpZ2VoMkF4kmETSxCGMoa2J7SMKSeVa04sMwsKMolt9T3+HtCFMjkYTfZheQkgsoMUmK7mF6Hl1R4segieP7/tO54nwEyukirE4ZvNzXIEmx8WOiGWK0VkHge5lJYrfh4f3AQBoMTsVNGr1tnV/dh6HvmyTb+WLw5LP/CQPDmGyFmRkNP77vSZ332n/eabwG/uxlMbQYh7RIG+o81SDLMEwDXl6U1JNHJkWeBJCsfcy1VlFiQssIJ5h8MtygNpkPo9OS4F+9OyntvOOxZ6zb6IPCn3zxZ3jf136etjn57fM7d4uERLrHWeaZfhZ0QJ2kSR+YOjC3wgjrThrGi85ahX+7U7KpMIrxf7/3iGijKQ/D5sOw3b9kkIuVa9IX5dKj+PQFk+T31M40SqqaZRiq0zvLMGxRUhXfR63ioxVGODLRVJ7FnsMyIML3PCwerlh9GKaKwzFkJB+xB31pVcEw4jizgBSVBmmGUkJtae9txeD0JtBz37RmTJyPt11lGKrBmGy1hfE5oTOMdoTxRlvcb10W67BL9RUDaTCKMir1mZfOMPS4Z75dwBgGNxI248EjpUiDbLC4/DueOGy8BlMHq7FF63U5QUhSBoZhrFZLeRh6WYIoxpNHp5TF6yu+hyrLA+AZwKaZnWk2PKUZDD6LLlPym0Aa9uIc3VbJw9AuPYpjPHpwHO/4wt14++cTfZl0b72jmUrLm5CVpGSiI/+/HcaoBh42rlqEQ+OSIdKa2rLN+ZIUn1HmMQxAfb4Uuqr70ghFVXLpUHxmLA2GJ8p8jDdDeFCd3rboxGrgoxr4aLXVtVLCKMZutnyw56UMg/nHTJUG9Cgp3fgpiXshOb1TP5VmMfTqsEBnkhRhiIXnEqbaocYwNEmqGeKktDKAzjCa7QjjzVBIgDrDoO1+tufotCTG5mEgDUaeDwMwMwfA4MPQDUto92EoDIP5Lrb8+Tdx1+4jyXnI2Uh5GJ6H5WmKv+5INGm+NVaf34aawWDkhdXq9+fQeBPNdjITJlR8VV7gxowGRdVgZNs+1QrTsMU0SorJHHoFUwD43E+ewH1PHst8T5LE2FA18xtByfQ2yIoU+UXhz0L/tjm9CwxG1ultZhjtKEoYqu8p93BnypaIHRqd3oy1qQzKEj1FVQXSZ3HHE4fF+u2yxpXeD9JPi9ObrtPsw/CEPJNIPao8Y/NhVINkMtIMI1Gae7QWIIqhMAzP87BY82Hw8GTjehhxNiQ4wzDSJVopxJaDro33bXq385zeOsSa2+weNFqR4nsY1vwQky2Z3Cojs+T+YRSL4+pSGl3jqz78A7zkA7dl2jOdGEyDUUDls8whpdqaD0OPkgoFw0jDai0+jIlmW5SBACCcuXo4o+97+PRvXYKXnr0qGwJpGKSofaZQW0LFJEkZxjtbaRBapEXxYQS+UeoC5L3k5zAxhslmiKFqIPJOeMjqUwaD8Sdf/Ble8Xffz3xPvhBb5jMdk2CqJUSLQS1LjXVskDP4cYolKTPDEFFSYgISCycvL0fxk8cSlrk0Lfegyx/8GJlzW4wZsVkyTK/5Pz/C9x88mF5Xsk3W6W2TpGLc8PX78Qepj4mH1dYCKUnxSYXneVq1WoskFfiJDyOMBMNYMVZHHMfYq0hSSTVcmjD86KGD+AArwCcnRWyyEMuignRJ6qAbqZKUNvBWUv+AIqcWhNXqgzcgjQE39FPtUDEY+rknm22xn636LfmM9NDcIkY8nRhIg6GUBjG8qFaGkcaE07unz4qk0zv5X42Saqf7xphohlixSNZ6+eZ9+5SYcJrZVXwPa5cOY9v6ZZk2mmZkZDD2HbUnu5mipPgL9LHvP4K7dh8RhRF1Q0UGY+3SIaHp6gxDaadRkjIwjLSDkIOT6+n7jpVP3iNJKi8c1xRWK9srZ7LL02ck5QztOCUZhm5PiA20tOfdjqJUgklLSKTvKTEsSsYyrSlv82HYBoeWxjA4kuqrUWZfaSCz5/jobQ+LcGCe6U06vM8YBpA4g0ke9Tx7vk01zddoteVzWbGojmYY4Un2nnvwsGREMoz7nkrY56f+68VpG9Nr4AwDbCyIoWyX3IdYrOkdRcj4MII02IP3c/Lf0fuuGwhTN6GChook1YowVPXxrGUjOGmkmkngm2iGIkdFL9VCoN9931P2L7M2zXRhIA2G2tEo50H+nvFhpA9iSEtIsjm9TQyDr5FN0TCE3c9M4uEDJzIdlGYViwx6vEnzJYrMZ146TFFSXJL6n994AF+8Y4/1JaREo8VDVeEorASekbkAMDqvTU7vqVaE4WqAejVAoy2DAeIYePJIMmBSB8gboGmGaRsoHzlwAj946KD4PzNbjmRuyeLhqibXWbT7IoahS1Iaw5gSYdrJhIOekR7eOtGUSZGAHiVlk54sDIPp3abfTCxVGE4Dw+BQJCnGMPhs2/ekr2+4GlgT9ypB4vRuhpFgfstHa5hqhoohThhGBY12hKlWiGNTbXgeRD8zsSOVYZCspso6ge+Je25iCxXfV3Ie9NBk3WDoEyuqSA1ko97qlQDf+aMX4SfvuiwT0jvZClmdLfO9I8mKziOuO8ombc4U5k5GSAdQDEYYpREbvtWZTZ1ruKb7EbL6d8B8GFzblHWlkk/dJ8FX6yLQC7pIK0/8+5+6Qyz0zkHHfGYim0lNyJOk4jgJD6Q2jtQCTDSTLHVKbKJKvfWqnxi0KEbg+0ZDBHCGIb8zySeTaaXf5PdIeUZUHoTuR17W+jHNAahD12xNa1RQfaw4VtlBxult0MVNyIbVqu+ZZBxpaRmLwQCSIAleBl8csx3i1GXDuHTjChweb+Hme/clbTM0LnnOqcEw3MvQEmgQikE1a2Q56hYfBh88kwhAaTBsz5QkulYqSY0NVVCvBhllwPM8IfMem2rh2GQLi2oV8b6baknFcaz4Ln7lg9/D/ftkgEEYxahV/JRhZCUpYhi8n2fCarXupv9fZcsh8OdJDCP5zcvsN9kMBTMpKqtCbW0xeZjXsJpJDCbDCGWIHGVn66t/KduTJKUzjEhnIpGSh8E7HS8TAgAXn74MS4areNcrNgHI5mQAsnDZorrqwDUZC4DVw7f4MBJHoyFKikkccSyLzsnlMbPSRz0IxABeDTyrwZDhv/mz4UYrxFAl9WG0VTmEGBMZK9ts+tCJhsx6LUm7TSuukdObFrIi2BI6iyQpfQYuGAVJUlqUVC1QDSOXPMYbIYs8k+dutCMsG6nh+tdsUfxjJobBvzJLUjGmmtnvH376BK79559k1nGZ0iYAqtObZBFVh/cgZ9vDtcD6TGtpbbZ2GOPQeBPLR2sIvGwf9byEEQKJH+v4VBuLhyULpkdgqyUVxVCMBQCctnwkycOIzJneQZpLokdJ/d23H8Tnd+5G4HtK7SkgyzhqrLq14sNI+wNBP3c7ikX/tLGzUVZDijObMIqVaLKZxGAajCgSL7V0VNvLFEiGoRoMW1itZBjyoQgnclpV9OQlw7jr3VfgotMT/4TeCQH5koyWXACFz9BNGKoGMClH+pKblFg2pDnVkmNLhkS3jGZaJoiVAQujpCIM14I0sz1SjBRp9812koVvYihPHJrAhe+7RV5TSceeyen9NFsQyi5I8XDSzgyGZBhR5v8KZxgsm58y60802sr5uC9ChLCyXmlqm7JEsYVh0HF5VN3Ox5/Brfc/LaKpCDo7VsJq0/eCVq8jeJ6M+hqpBVaJhOTOMIrxzHgDy0Zr8FkBP348MhhHJ1s4NtXC2FBFvKOmPIwoNhvUDStH8c9vfi7ec9W5idM7jhHG2cE+CBKWpDCMMMLffusXeOrolDEiSh/4q4EvfAx64h7Pw9D3A+SEzsYweA0p3j+jOM4s+zpTGFCDESuRHEmynb0QGj0QPcXe7MPgmd7ydzII1BGps1AnJ6c4fynp77G6PUSUgzpqw8IwkkE+++JRJyJjJhaPNywRS3/XKr6YMVZ931okje4d9YXA98xRUq0QQ1WfMQzzoGYqyw1kCxqWiQShtQ7U9sbYnw6A7UhlGLb1n4t9GOr/evHBBmMcnK3R85hshViZavHjmsGQOROReKf5rNbUNsXgWCod0LXyct56+QsClWsn8IGO2uQbJCliu3rIKEfFT97ZdpQkoy4brcP3swaD53lMNkMcn2opfja91AyQSlKG9yTwPLzorFUYrgWJ0zuCqFYLyKzwIL2mSYMkBZgjojLZ4mn/r/p+hmHUcxgGINe50Merl569CoBcwRFQfRgJw3AGozTCMFZos84wbFFSep1/3dnUjuJkhuirRqAaeCKmn45NgwIdk2rkDLFz5Dm9TahV8hlG3WIwxLrfIZU0lz4M3mYg6RBkFOlYeQxDX2RnuGqWH4iC0/oftgggW0lxTyv+V0aSqviesWQ5Jc0127GWGayCHn/HkpRWnXaKMwxfjZKK48QBvYIbDCU7Wxodepf4mBQa5Ao9rFyX2qJIZkDzSRJFAOn3f3+GYbCwWtYm/u7xFffyah1VU59gGCXRhaN1Kkao+TAgjdtUK8SxyTYWD1fE4C4zxNl1xub3RJ20QUhSAZsg0TVUA98YVqsfx/YdPWu9KCgvVpqcK3MoxjDUa/jIGy7A53/neVi7VOZKBZrBMK1JMxPoi8G4+eabcdZZZ2Hjxo244YYb+nHIXCQVJVWDUfE9tliLxWBUdUnKzDBosk0v0slLhsSiONRZyWAIhpFuy2WvoENJKpmN2X0Y9UpgDOsLhSSlMYyaWgoFUKUPegkrgVeYh0GD0lAaBaVjqhViKJWkKA+DzpMxGIZZsT4ol2EYFT9bubfRjmTYdBSpIbG607skw9ANSrY0iPRhVDjDCCPRHgrx1SUpLmvxEFbxu+E+tDX2lg0tliHefNCi+67ff32pV3OUlFrTiEtSutTLUQmSqMMwbVMShWiWeqitk60QxxstjA1VxbamxDzAwjA0lk8rSNJ9fdsVZwKQxszk9NaPw9vJQc+6wlgTRTHVDZNHDj6GcdQrPp6rheJzHwaXpPLY3XSgZ4MRhiF+//d/H1//+tdx33334TOf+Qzuu+++frTNikY7mc1yDT7wZfllneLRSzakMwztZWuFkQi1A6Tf4lnLRkTMOHW2iiZJkb+D09BOJSk6d6cMg/oQveykqevLY1L7ebkHAEooqA6R6Z0eYrhmXt9jqhWlTu/EoLTDWBSuUwwWq7LKO6Q+ZtsMxrCWDJVnWNphrEpSFqPEjzHRbCuL1wD2xD2+kE47jGSUlIiakdFKgmE0zZJUM4yUwdl2bjofodmOjNvQe8oHJWEwChiGKUoqycPQGEbaT/IW+Kn6kmEkmfBZRzIAwJPPVjCMoYq4F/fvO44bd+0t5cOoaO3Und6/88Jn47EbrkyNmZaH0SHDEJOvwFPkRUA11qZLrgZ+hnl4Hoz3h583imW+0kwvrtSzwdixYwc2btyIDRs2oFar4XWvex1uvPHGfrTNCpqNiRlyOuAtqmcHSEBq0DrDKPJhTKVLtq5ZMoyn0lwCovXUueuBukYGf4B0nKFq9sUwgQyfzYdRr6oGgzoGzbKE0zsd0IQPQytZwGPrASiOWh2603ukWrFLUlVfJO61o8joxOeSlNoJkuP/1TXnKf/r0O+vbTsguR+5Tm8R2y+/e8fn78bb/nWXsp0+i51qJTKQnkfRDtM8DGJWYSSYJxmMEw21YCVNWhot7sNgbTQyDNVgmHymTcOgpU8sCPr6Hbby5oB8Zr4v37+8NaergY8g8NCOEiYUBNkQUyBdO4ZJUsenEoZB5/3nHz2Gt352l8K+ozg2Gktu2IhhRAanN7WvqUm2vE06dEe4ZBjSh0H9b6jA6c1DcvO2A1SnN5ekTGuXTCd6PtvevXtx6qmniv/XrVuHvXv3Zrbbvn07tm3bhm3btuHAgQOZ3zsBOQi5Bn/ZplV48VmJs0g3BDSo6D4MHpYHUJQUX6K1jVrgY+2SITx9fAptVtVS+DDSl4LqS/HFTuhl8Fhp6DwQ7bczjEB0Br72uFijmRiGJkm12HrRzVAO5IrBtVg0kj+EJFULjJLZVCvEcDWRpKZaEaJYGtVmW9WIqX0Vg8Hg0W8mjNRVyS+XYUSSYZiMi0mS2n9sKlPKxHSKZhhpcfeh8IGRRt4OYzF7XTGWSFITNkmqLfNYAvZeh1Gc8VHw8yZLi2bfF2EwDKvn5dUqA9RBqKqxHmqbBw9bTl2KF5y5EmedvMh6rErgieckKikYBsXEh5G09dB4E1EMLB6uZAbQe1n9sTg2+5/4exWI8ubZarXUPg5uMExVFXRJmO5VxfeUBZAAaE7v7LmrFT/DJmwTS72vkCRV9Cz7jZ4NhqnwnYlSXXfdddi5cyd27tyJlStX9nROchDSgBf4Ht515Tl48/PXA8jOoOilMi1uz41LmEa58PUwahUfa5YOI4qB/ccbWUkq7VAURbWYxdDzl72MwQh8pLMx80tQYxS2zqQP2rwZagZDMAxVo9WljzIMg84xXM1KZuTYpVpSxLbqjOFQu+0MI/kkmcNmCEZZqGGRJNUKpQ/D9wyyl0EX54mP+nYcUy09dDjZJymFkVzXn3zxbpFouHy0Bs+zO7255i0mBekz0S+xDMOgZ1S36OR5qLNBkF4LX//0gFOWDuOT116E01fYDQZJdO0oRpjmTJnGB9/zxPtKYdFjLEqKcNeeI+JvMgQ6lGgu30MUmavVAtk6TTyhsJQkFcjJl15Lrohh1IKs8TTKdVDzr3gehq3o43ShZ4Oxbt067N4tF4vZs2cP1q5d2+thc9FIOxefiQFyNqQPuNRBTdEcPDGNcjrkehjJ4HrykqSW/VNHJsUAKgbdVKOliCpeZZW/XGUipTxkF0vhqFflNVNYbDIQppKU8GGo8liTMYxGO1KSsYDsAkocMqw2laRqlYzTuxUmlH+ompSBoHshGUakhPiaGAZdQ1UMksmKbFd/+AcKo9GDCmxlFZJ2yQgi3/MykpRIoIvUAVxPZDPNYpNFomRnpYRDbnwfPShX3RuuVTBaq+AES9wDbGG1yW9keHQGoURJhaoPg6QvkyRVFtxw0UBFUWyCYbCB7RQWzaODJF6FYRje8cSJnkwCaR2TJKxW3W7XE0eU/4sMBrGbyFAaRN8W6NzpLRhGKrsBZh9GeUkqs1lyfC1KamAZxnOf+1w8+OCDePTRR9FsNvHZz34WV111VT/aZgXpvTRjoJtJg54trJY7dmkw4wu607H4Q6xWPKxdknSIJ49OiWPzAbbGZtV8HQf+go6WYBg89I+Oy1FnFJZyMng4n84wbIl7wlHHnd6WPAy6Pzz5scFCOdthJMpYDKWS1LhgGHKGPFyTpZypnYES+ZF8ykEyxp//+724a89RRYYY0oIKbElPSdtkWG3ge9lMb0OpjIRhmCVNjkbKMKh8PRkHWmFOx3A1wGg9wHijrXTyVipttVlUmc+esd4+QIuSYln1f3H1ZvzhSzcm34dq8mYn4O+HPoGhPse/PuUku8GgCRhFSQW+2Z9H7/VQxWcMo5JhBcdZQILNh8HfK99HJkqKIyNJMTZuamdeWG0+w8gey+T0tvkwdH/fccEwBsxgVCoVfPjDH8bLXvYybNq0Ca997WuxefPmfrTNCtJ7pSSVJs+wGS0H17GpM5N0pC9eEvi+MtDXAh9rlkqGoedhAMlAToMkZxh8DL504wqcrC3TqIPCDvlxOeosMox8GJ7nicG2pUtSNemQ/9/ffhC/9g//gaePNTLSR7mw2uT/4WqAOJaG5BO3Py6WXk0kqWxpbICzHS5JqdeetCU1MulslP8GqGta+L594R4gcSiLZ+95iNPoki/8dE963PTcmiSlBx2YTtFoh2iFES487ST4HvAfjxxK2m9ha4nBqOCEFiXFl1rV8zD4glq0poN+P376+GE8+PTx9H7IyY70YXQjScmFe/Sw1oowGPIaeSkTjmrgiRpUPErKJA3RN0PVAAfSMF9eGsQE3QdJ4LdfRElF5kqzej/mY4dp9p5dUyO5V1XfZz6MdMLEfRhGh3uWbdmut6o5vQ+Oy+TUmURfig++4hWvwCte8Yp+HKoUSJLiYaEAjFUjARkF43vJ4NgMkyzKgycaIjyNMww+aFcDH4uHqhiq+jhwvIHlKeXnM3Iuwywezjq9AeDtV5yFs09ejN//9B3W6woZw/C97GJJ9XSpS7pW34MiSemGkgbpO544jO3fe0R8/8tnrEjPQffPHBuf3BdVkiJpiZgKX/CGGAaB/80lKbF6ILuHUpIiIyHvH+8UvH/kSVLD1QCtdiSMqe8nktTN9+zDH3/hbjx/43ImScn9WmG20quJYUy1kqiopSNVnLN2MW4ngxGYCzkO15IIsqRNqtNb1PfSGEaVMYy3f/4ufPnOvXjshiuVa/76Pfvw9Xv2iftB6J8klRyTMzXexjzwCKJmGIlIpTC2O5OHqoHIPOelQUyIYvOz4QxDRknFRhatyMZ1NQLQNHvXB34Tw5AMX57P5JtIGIbuw8hslmlnFEPIdrRMr8330W/MbExWn0B6r3TIaZKUNnCGTMeml3hpOisiakezg8D3lFW2qPMsqleTSqPEMNgCP1ySUnwY2kO0+QlEO9msOtHC1e15WK2QpDwZ/aNXAKXBQq87Ixx1zAfkeWaW0dKd3jWKlU/uw8oxWeZ9OHV6i/Zyg8GSKskHkuv0ZsaTD+icDZhKTBBG60GSuAc1SooGUp6/oDMMyquQbTP5MJLopErgY9tpy/DQ08lSmTZJaqgapDq3Go7bjmLmoFZLg5CRnWyF+PKdMvLQ5ujnFQ9Mg1Ye+HY88kcGVsg+lDSy+JicjdB9rxocvfy4Q1VfvHOLDU5vjjiOjYM6v/1+6sMIY7Mkxd/5kXpgzckQx9YZBhlF5sMwMgybJFWSYfDJVVI6pS3610y6MQayvHlSp8VXNHj+mQmrjeSgQS8I1Wk5LhiGfKG5bksGZmwocViaJKla4OPIRHYt6swawpZIJEIYxYpfRq+9X68E8LzkPCRJUa0cwM4wxrWon5omSdE9qfg+WqG67WQrxC//z1uxZnGiU+vL3PJLHKr6SmId7zDUls/v3CPYhCmsNvCTY0ZRbGQYfLBMGIbZYAzXAsWH4aeSFK/sa3J6Cx26HWERk4R0NFohWmGMqu/h3FOWiO+TumZmSSpIY/WV84WxSITU82M2rBzF/fuOY8ejzyjXb4uM4QbY5HjNw2itgqlWUs2Avx96tVjOgDk8LxuFVmWDaUPIkD58L9t+LkkRxoYq1hk3tSkvD4faG6X33OjDYH1stFaxlgnhx+PgVRNC9u4k11IiD0P73sb0+ff7UsnulKXDeOTgeJpwPDMJfIPLMKrSOvPIjVrgZ5dejbnBSC55yXDirBTOI+7DMDieyWFJs5+q4msIMN6UCxMRdIdhEcNoc4Zh0MKlkUh+972kSqZerZZAg7eeuVzTtHKi8Kb2fX7nbux+ZhI70nWpR7RyIzzre6gaKB2eS1K03y0/349v3/90et6swfDSDGJ+LxQfBhsgAt8uSY1UK2ktJ9o2+eTLYRqd3ul10Uzzhw8dxO5n1OqudP1UnfYkXiTOUip+uBag6nsIo0jxibSiSGTT0/2nwWXTyYsBAB//waNie71OFwdnGJ1KUjwog5e7oDYRU6O26QPg6y96VuaYFSbXiO98c1itzqpqFT+tzmzvM7aw2rYWGpuXuEfvWOB7qFdVhlFOkkr9p77P1kfJMgzTddQqJh9GZjOlnYBckZN8qzMZKTVwBqOd1s5JJKnkJvJaTdXAy0hSQsf2ZAc4KcMwWJQUe7j0QozWKjgx1ZYMQ5OkSKLhiXt6p7ItgyrayWbVFZZnQSC/DVUApTUKYpvBqJkNRp3NipJrVH1AHHfvOapsIwxGer18FpYYDLMPwzRw8dvDmYDvqxFjSikNLknlMIyReqCUBqFnytfTNlWrpeshWeENH/sx7tpzNHNvplohWlFSO4rn3vAlWjmG0ve1FcbqqnBhrDDg5LqS31YtrmPZaE08AyAbzsvBy3U0NL9IEXgGPZeS6B2OhOE1G4z3XrUZX3/rL4v/lwxXRZCHkkjn28NqAfmeEFPPlaQgC0hy8Alj4vSGNXGPlm2mdUwmCiUp9f98hpFfGqTi+5nv7XkYWYaxJo3eNBWonC4MnMHgHYEGgaUjcn3tipbqD0jJwffAGIbdh8HX0KUXIpGkpMGo+OaBUa+iy1FjRuZPX342/t9XnqP8HsYqw9BnqpTdnqyn7Akfhp7pTRAMo2lmGPRy8iKENtBMXpekOMOo+J7SSeoGhqFcL+vYUiOXsfNSkmIGI0oWr9rxrpemA7DFh1GrKKVBaGbYZgyD3gvOWnhoJG+fbgTGmyHaYYSq7ytRQlWL09tPnydnNsn5pC9Fz3HwPQ/PWjYi7gsgS5CYkAzGyd+dMoxF9WywBhX1BLJrXOvjWiXwsWaJjAL8o5edhU//1iXKPsl29tIgSXvTKMaUqedJUgnDyD5/LtkFaVitaQElANi4Mkk6nGoldcB4Do5p5m5jGEoeRksNK+fXp+9blmHwd2rf0cThTfdbXwhuOjHQBoMeHu+wemgcwENmZb4BZSVTXHdbkwWoo9SFJEUGQ43m4dvQOUx/J8eU212x+WRsXKVmyPJB0pTgVE8XPSKGEXhqWK1uKCXD0HwYaf0rugQezVIEGSVFDENNqrP5MExVNVWDkXxSkTt+L/jzjKIkMmnV2BB837MOnsO1IEko1GbvFBjQViQp2R4xS2xFyjKYek7MM+MNRHHyvigyZM766IHvpQxZfpcwjtRYarP3wPdEUcurt54CIDHQ+ZJUGlreYR7GsMIwpCQl3kHxfOgze418MB2q+ELm0hmGWsQw+aRvyPFPTN3zPKvRSHxS2e8VScrjOSDZA21eu1j8XQnMhTU5dGmJs/VMlJQSVps9Vq2SDQAok4ex/9gUfA9YlTK4mczFGECDoZadADSDUZUGY6oV4qoP/wB3PHEYgCpJ+b6HsaGqGBREHoCvyjXL0sSsRfVK6sPIVtzkBoMPurZSyIBaC4qQFK+jGUu2XAdFSZEPw/Pyw2qLfBh8Jpm0L9/HAkimQJJNI127+CtvuRRnrh5TZlVKWG0tG1/BZ9rShyEjmsTgxw0Gk6oCz74++GgtQJuVBhGSFPdhaHkYnK1MtkIlZJgz04rviVXqqoHGMHIWoxIlMtiMsB3J0h6BYBbp/76H9159Ll62eTVedFZSTieRpNQBgl4znmhHA1/ZKCleckXITr4pD0P1f3HwwU8vz0Eg3xtBT1akd9ZWYofDtoASfyfo3LbEvU3MYFR9z1i6n8OWuEfL0H78B4/ir7/xgPKb7Ro6i5JSJallo3Ux7uTlIvUbg2cwWpJh0EDIV6aqBbLW0YHjDdy95yjuSTOFudO74ntYPFQRNVlaoUa5ocpdi+oVHE+zdHXJwSZD2V4uaqf+Ow8lNRUEpOx2Kssso6TMPoxKqqfTfSJduK5JUjyUtwg6w2ikZT/OW5dECtmc3iaGwZm0lA1lsTq6XQ12XSGLOc+TpIZrFbSiWDprNXmrzXwJdG59xbRjk9LQCudm4OGk0ZrIRk5kODZhCDxloOWQGc/yu3YYC3ZL4dzcsXzJhuX4v7+xTQzoSf6Hes08gomukwbNLacsxX/auhanLR8x3icCd3pzWVTkYaS/0fGLSnCbqion26iRQcK4pl/RvVR9geY2Jz4Mg9ObvVh0rlYUGZPndHY4VcAwrJJUuubH3337QfEbv0e2PIxsLSnzefm9PXC8gZVjdZaz5BiGFVKSCoT/gRsMWo8BYA7OdB+f5RokDKMifRjM6Q3IBZHIOT5ar6DZjjDRbGdm4twhqlfK5OADcs3g1I5SJ2qybXbQqVV8XLJhGV62+WQ8e+UiPHvlonQ2nvyuM4yKn0SNUWHEk1K2ZCoNAsCovesY0qKk+IJMgErDFeZlYC9KQh6TpMiBaGQYUcxm4p5SiZdjtBagFcrEPdqHjkXltgH57HmwxGQrNC6D6Xselo3URHJZJVArjtK7YWJrSdhypDGMrGOeDscHXVkqPsswRNuYz4Guc8lwFR983XMEU7aBB45ww0WMgoc9J79lj2FjFTyRjpgxoVpRGQu9P3wgt826oyipBqz/zCUaakerHWcc1gTy31QC37p4mbgWPQ+DrYfRjiJcfPoy436m+1UJsnKb7VpXsHwnIDGodF9nMtt7AA2GrNNCBd+WDsvOUGfVVKlj0mzL9+WgGHhJdIuehxFobxWXpADgyEQrl2FU2d+6MqEwjEqWjraZzhr4WedpveLj6q2n4PrXnIc/fcUm/P2vX6iF1aoDSeAnazPQJIySFfUoqQqbPZswyvRtIUk15XKwiq+iZjEYhh7DJ8qK0zs1GEJeYTKBwsICey2pkVpSwmTHo0kGNvVD2j4ZqOUxAdV5OJWRpJIDrFk6jJNGq4Jh6PeMBliaOb/t8jNx+5++RLQ34/RmPg09Aok/frrH3On9nT96kVj/GYBYo5q2A+Q7WJSZzZkhr81GbdClPSPDYN/xx53xYXBJKj0BMXp6f8pIUu0oxr5jU5nih6ZFkFphZD3Oj/70JfjJuy4TkmEesqqBVCx4mZfzWG4OYAmr7cDp/daXnoHPXXeJsm9VXJtjGFZwhkFYYpGk6EbSCxR4nhgcAx+5DIPAJSnAbDB4W5azmZzt5QISg6GXNOGDZNXgPDWFSFJCGpDV86u+r7CfJSMqw6B3WE981LGK1cAivZ6MdTNUl6Lk8owtGIBgWg2PksVCVo5aZRh8QDWvTuh50sf1J1/8mXJ+YiQhczbLBah0SUoajNWLh/CBXz0fH/sv27BstIYDx0iSUp8JPTOaOKxdOizCH2VYrVrGRji9NR+GwjAqkmHQRGhsqKJUQfZ95tzXyq/k5TMAquHjhotmsSLs2VfbyKGwCos/w1Tck9oOSMM1Vi+WpPYdnUIYxVinFT/kg76QpEKzJAUkbGblWL1U0IdudBSndxhjvNHG8zYsx1f+4FJlO6skpRsMSxuHqoEoSwRQcEX6DjmDYYfwYbCBaani9A4Yw1ClhsBPYq2B5MGP1auZPAz9AZIkRR3z8ETTmFAHJD4CPlOzGQyaRdPvVF7jzNVjxpBGcW2GxXB4/LcuSQUsiSxIJbjkOGaGYfNh8IGfSp+QoW207JJUXogxoIY/8jwMys41OfOT8Mjk74rvZa4ZSGdf2jMSBoMxDL00CJekplqRwjAC38M1F67DyrE6Thqpieg6/V3gM04gy7LC1OlN90bJFWHOZv4/wCQptg6HPvjymmD6miNFY2FFkY14lFTyXazJZkXRPJ7FYFDiKaGqMQy6X2UYxp7DSQLbqSep/hmjJBWay5tzlAn60LsIf960Ep5pKQOTHTDVkspjgoqhZaWDnCSVAxElxToiz8PgYbXELCiUkrKIgaSjc4bRFuGy6i05KT02OQUPjzczSVzU+Vdp1WhttaT0KKUNK0fxuesuwftffa4iaWRKgxgiXlRJyuDDqEi5iaQluw/D/LIqpdyDpPwHGVoqBEmwOb1N7IWz/4wkFcNoCHl4pC1xr2bIhRA5Haxcu14ahHc83YfBOzL3B2QlKbqXyflVP46fMgr5fYv5UqjJnmFQ5pIUr+yryj3y/Hq9riJJij8rwXB8yTCiDMOwGAyNJfE2JG1Ww2qFJJV+NaSF1fLfdOw9khiMdZrBUCQpT35nm73zthVBN+Y1NtlqRzFONNrGxdJsdaz0JuU1UU0olhNKl+mdA5Mkxes31Sp+1ukdytkWL78wNlTFRJqAJXwYOsMQPozkfEcmW1mGkb40KxapjsUMw0g7X02bhVZ8DxdvWJ6UQuAMo1NJSmcYviyEVwtkXDzlYYgoKVZLymQ0dG2akhjpnHWLo1spjWCY4tryMIhh8MWF5HZqvS1TX6lWsiHJXJoA1IQ5GTSgSlKcYfDB5iSeKCryesgwq/4g3TfQjpJqtfR9O4yV4pjJZ/acXJISpUTYgE770zGEDyM9hInhcXDD46XhuTxMl6LNxHaWw1FzdBmKEPjqrFowDD2s1rAQmZ4Ls+dwUrJFl6RMPoy2JdObo4wkJRYwo+fMCjWGUYRxm8EwHJpKvyvb5Rh2JVggkBNKF1abAxPD4IMDZxg0mxT03JMDaOB7WDaavJTPTDStPgyalS+qJ9tONMOsDyMdLMj5TuXDdd2yqjEMvkSquBbmcDSVBtHhe0x6016cwPOERlyr+CI0UzIc9ZzVwDeuAa1LCosYM+MLMgHmQY6fg8OU6e15SbvbkUxQUyUpNghaZoTcISjaJQyG9FeQoTXdv0ktrJafysQwRmoUaZPDMNI1E5LQ7CRCphVGhtIg6aSBvT9kYG5/+BD+fdeT6fE8bTCWk4ymli9UVP7axMiomgBQXBpE7GdgRxkfhiGqjDYxMQyRBa69/4cnWlg6Us0sTmaSpPR2mFBOklL7sFLePEwZhkGSst3/bHnzcpIUn9zNpA9j4KrVmnwYHElYrZxFAnIgoJIaQPJikxPp0Imm1YdBD1CtV6VJUun/FN67/Te24eCJRqZtGRrrqy8d3yYp/Kaex1hywpPlzflM3PeSziJmQoGPkbomSYlzJf8/f+NyLKoH+Ld0QPrfr38O1i8fxXu/eq9yP8aGqkKu0SUp5b5oPoxPXnsRhqoBXvt/bwegxtDzpVSTTG/5O3fmhyys1mRQG+0I1Ur23tEMj94J3S/CfwOS98wmSZ00mmUYI7UAz4xLpzqdnyeYVgIPrSgSNcNqga8UE8wyDPXaAIhnA6h+MNqfJ+6pTmjkwiSt8URX7mPibdQhJCttcONt1mfKgCQsZ508htOWj2DDSlkFgfpgvRoAU2oS6tolw0pbNq5ahL989XnyfJ56f/LA35lLN67AtZeuz14ftaWiTggqvoeJtJxMWUkKyI43uZIUl/LYOz6TeRiDZzCYJDVU9TOJNvWKj0YrxK3378cz46pDm6+H4fuekBYOnmhkMr11LFKqeapPlQY0iiAargU4dVk2UYrWnOCx2/o5ZR5GVh4yOaUTg5H8zWfI0hjJT7oGPXGP2vHm55+OZ8abYlC6/JzVikxG7+tiTZLSpQICl6QqgYcXnLlS+T2M5OIv1PREkkrrBFH1TxZWG0VSkjKVaWi0I2MBQKpCKvxaPFRXMJlY2Z5LUjyii9dMouf192+4EP/3ew+L5UprQpJSB0tepqKWtpcYBh3L7MPQDKAnpSN+fNqHGIbcvguGwQxSrDvmCwZAqw/Dz8/03rhqEW57x4sz1wqYGfbqxXXleB/5tQtw1sljct9O7gHb9i0v2YhLNizPbEPXsnntYqydaGHDisSwVdI6YQCMBsPmcO9EklKTIaX/aiYlqQE0GFKS+vF/vyxjXesVH8em2rj2n3dm9g2YwUgYhjQYUh4yP7BFhvLPhKO0FoZlqUoOXpwuMJyTM4ysDyTbNqrsCqhhoSbqPKJLUun5eSkLUzikPkgsqlfw1NEkca2h+TA4VEeq+b5GcTL7NRUfbHvmKCm+8JN6vgBA2+j03n9MrlAGaKzFwDCm2mpYLd/+jFWL8LLNq/GNe/cL6eS8dUvw4V+7QGwjGAa7N1VfLqAU+H4in4bZ4oO+4foou1+EApNEFPBBRAZ1NNuROrsudPj6mf99Jh/pCyjZxjXxbHKipPRon+R4xQOlqS5WUv2AnV+zKWo7rKcAoN4Dm3xF3z9r2Qj+6c3nyn3Z9maGYT6nft1lGYYaJeWc3lbw0iBLhquZDNa8cs5JxVA5q17BJCndh/E3v3o+PqINADwjlINmojzj3AYeuUQvAB+wlVpS6d9UR8hkkLgkxQdWPcejGvjCQBIT0g0CoA5AcmCWrAyg/BUmSVnlQbMP47EbrsQfXXEmADm7j4UPI5GkeCVSLrXx/AxbUADPcSHQwC4Zhpo9zn8D5KpmBJ5R7nke/v4NF+Krf3ApnnPqScZrp/dMzUVJkijbUYTAh5CkssUHkX6ar49DYRG+XC2y0Q6VZ1nk8DWFIZtKg8hMb/MBTaVDeBv1kt5VwarsbaM2mK6/WlEz7fV22bLP886j/80hJDnffh7dpwLklC33ym2XbMsNhpwcOKd3DhrtKHXIFevmHHrOQeAltaRqgY8DJxoZH8Z/vnAdrtyyRjkGDbQ17Sm/JM22vfj0LIU1tY8vgZm0ycwwqDP9zgufjcduuNI4w7JJUjRY1Ngg+sIzVuILv/M8PDvVh+n9qxiMhOexEEIxgCWfY0NVnOBO7xLPQu+AMlxTnTH7HkQhNyJM9sQ9bUBlkUqcYXzi2ovw65ecBkA6CBUjFNF5YtHuyVaIiWYoDY0W6+77yUp7tkGIzj+k+TCAZNKTBCQkBkMvDWLzE5gGzEBjh7wEii5X5aEa+Hjz89fjdc89NWmrr9bE0h3ztqPxBYlM57YxjLzm0XtqNBi+p7QlLxGuWJYzGzkOeq0yIfNs+7GSeRjG9uY0kTOpKgupdj6MHNDyrDbY5BEhv7AoKS+VpQ6daGL98nwfBpAYjL1HJjPbXLH5ZDz8l68o7JR0fO5H4W1KfpcGgwaDvOMOVX1MNqU/QT8OHbuWSgzb1i8T25icx4J1KFRe1ZkX1SsYbyYOvtIMIzODTT7bQmKRA5LI9LYwDBndpUWrVeRiOPx8LzxzpQjBNDEMXZJaOlzFiUYb4802Tls2guNTbWOCYB7MUVJs9u97jGHQPVFn27bETw7b4kRJNFkng6WPd79qs3osHlabcXoXzMDZzwrDYJFXnid/83IWCc+TpPTktwzD4O9xkdM7p9K0/r3+bPja9iaGYZtY5DGivG15GX2X6Z2DkVqQqR3DYWMYdK/JD0A3f8WiOg4aGIYJJDlVjTO9YmMBJA+6rjMMw4CdLPWZ3zkBYNXYkNDnm4b4c+701iH8NloUi349ugxBM6jjU60kD6MUw9CdtursiCZJFJseRXEmLDrZLs7MxPXzmRYxogGJDEYjR5I6aaSGZ8abiGMZQtsp7TdlegfCYETC6c3DavWkOP36TFq1Lrmo8k95g6Eb9Dc+bz1edf7ajCQlVuOzjBzlMr3lpMQvOF7S9uTTKMkFqhM9y2TZcQpZljogmyAz59Xfzztlqfg7z4ehN6HbsFr+jrsFlHLwtivOwrfe9kLr7zb2kZGk0v+XL6olBkOspFfCYJSI17ahFvhKLghvEz9/hWV65xmjk5cMYf+xKcRxjGY7YvX5pRQFmI2cyRdAp9JjvvlvlFT1zHgTgBo6ylEPsgXt9P/1Ve98L40mimXJDDKEcZzkTugJh+J8LNRRJs6RUzXZRuR2hKoRAqSfYulIVax3QQaj01lcNV3vhD9b6uDcYJR1egMwVlLVqyMrobQGqdMGXVb8rRdswOXnrGaMRTVqRUuJqg571Yjxd0ywjRIMw6QeJAwje3593+Rv6ynSdpp9bhwmBg4AZ6yWYcBmSSo7OTO3194+NeGRZXo7htE9bPKIlKTUF3rForrq9M4xBkvSxDzbAjllsHKsjlWL60ob9ExY+o3njNiwaqyORjvCnsOTOHiioRS6A6S/Rfe7JNsgc36a4ZsibOiTEpMOkcGwhdXmLFHJM3ABNQrH91KGoSXuhRoLzPgwGMOQGdhBetxkG6oXZZKkaKa2dKQqytuTwTAVOcxD1fcyiWbU3iYZjCAJqxVh3+nmz11/Eq65YB1OXzGq7G8yGPrsvWrwRwH5TmWgeIDUq9XaBjbTTFpnAPz56SXdTRA+DEPfThIg7UxKuT+FklS2H+qwOb05ozVKUpZJQNkFlPR9+Ts+k1FSA+fDKEItMM92dX+BYBijNSVxL8+HQQyjTM0ZG/7hjdsUKWrlWB1rmcQmM4W9QvoPJAwDAL5y95OIYuDSM1bg0z9+QkZJsUFUhynTHMjOVEU7NEnqYFri2yYDKuuEBOaOknV6e4JhxOkYTaHUYawaDFsmPA85pMx1msE2DU5vIUm1yYchI++WjSbGvVNJqhr4GeZFg3mjHcL3PNQqHo5PtaVDOb2/qxYP4QOvPT9zTNO4oMuZ/J53MliaGGhyfDU4oXweRpah0t988CxiLPyYvAoBlROvBmrUVa7Tu1CSUgMI8tpi+n3NkiE8dXQKI5bgFGq3cjzPvJ0JfNcKD6t1eRjdwzbbpZut50AMVQM0w0ipaGsDVcXtJYyN18jxPA/fe8eLlTbz0t26fGbC6rTg4Y13PolaxcdF65fh0z9+okMfRnZWZtK/6SuqWHuwgGHklWXQIzzo0/OSbduhdG4TGyhyvJJkUatIfVckzqWbykxvnrhHv6WS1Kh8RlQ+ptNZ3AvPWpkpEUGz1kYawRT4Hg7xYoJdMNdAG+R4vkYZJy7Bxprp62x5c/PxTM9GZ0HcqW/T9k3HzDLWOMkXyXFsd5bpzY5j82GI/pn97d/fcike2HfcaJjENVsmTvp2JnCjWuNOb8cwuodVktL0euoINDueSgeQPB8GhdWON/NX5eoEfMEhfn7OMPJmh6vHEoPxwP7juGTDMrHAUTZxz+6oz0Yw5TMMKsT4zImmcg7Ca55zCr50515xLL7OB4GOdehEE6sXDymlQQIvzS0R4a6qJEWHUo2aZFvVwBe6LhkROp/NkQ5Iv4aJYXQaJfXKLWvxyi1rle942Q7uw5D+gXLH/qMrzhRSm5qHkXzWKz4mmmHu7JonAQJyXQodZHSo+GARUynjw+Cz9KIwXYCVBmHvGbHNqq9WfNXvoSl4wwaVCVkMoibPcqwcqyvRUsp+Fh+GbsSKjBpvKxl5l+ndA2w5AfQgdIczyQSTzUjJPTCBJKmJRtu6Ta/gHU4aN3ubyB8CAC89e7WyxCvAwmothQv5OXkbjD6M9LvhNGP8yCQxDNXo/fWvno/rr0nq+QSehxCx8RwA8KoP/wCvv+hU4XvxPTmYRekuYn0TTRbhs7V//e3nYfPaJfj4Dx5FLZDJjOSDoC2bhigpuW6KdHoTaEEsfXGqbsALA/qel0ncKxtp98ZfWi9YninfgQyGblA5aoGPyUhOfGzsRmZ6q+fotjRIwKKauNO7jBTDw2rpnumJezYmW3QOfVvbs7DlYRRBVvHVjpcxGOWOV60kYfKe59b07gm2iB16AU5bPoLRWoCVaZY3dZTJVljozF48DQxDBx/oqT15LyfvRFdtXcuc5jL/IvnMHkP4dQyRG2qUlDoIkEZ7JC2JoktSge8JIyL9DfbokM/s2K06vf0kGVGuU5GsXSHCTw1S2tKRqmBr1cDH5rWL8d8uOxMfev3W9FqT7doFeRi+p0a5yCipPhgMJsNV0ppizXaUKW9eBKo6zI8JcIORsqqcAVCfQNgi/8goZ30Y5raZnML8/eW1pLjByKMYesE/Dh51xbclcAbfkSRVILmVNe76fhmGYWHeRaiyvjswS7R+/vOfx+bNm+H7PnbuzNZumg3YfRjJDX7Os07Cve/9FVGplpx9U62w8CUYEyXOZ4ZhnLN2MTatWWyluXyfkVqA1YuHpKRVRpKil98gSZlmrmLNgrQTHp4wS1Lm6zF/T5B5GIkjMGRRUgBS6Ubdl3cuatvioQqWjlTheR7eetkZWJVKduT0pmOYnN7NMClcyKNcyGD0YxKn+xSqQSpJdcgwbIZArNWg5fnQ+TiyBsPm9E72i/V7b52BZ59NNg9D/l1UaoSfyxRWW6v41nMBcn0N0286FKd3UR5GpwzDYmj0217kmCeIlf4Cb3Cc3ueeey6+9KUv4bd/+7f71Z6eYRu8bOSBZt5TrTDXfwFAlAcfb0wjwxAz8qT0xNff+suF+/zkXZdlwoUzTm/L4kuAgWF4ZoPBJY/A93BYMAwzq0vOQcfInoMjTpdeTcJ6/QzNnkpLR/Nj6jkIAPCl33u+8GVw6P1bX8UPSPwbvKovgEytsl6gFwoUDIMyvQsGoW+//YV44pkJ5TtT0qW+BC9gMBiZYoP5M2r9f1tTTQxE9WH4yvskoqTMh1OONWTwT+q1qfR7qDCMgv5tS3rksBW+LIJN/s3e33LHk4ueeYPj9N60aVO/2tE3WBP3LG84l6RsswrCKUuHMVwN8Pa0cN50gAbWIuPFsUxZnyF9kbRaUsYoKd/88vNZIP+dvvM8DyPVQITVmjJb9X1NkVgcE82QzcLS0ueIRYc4OtmSORXCh5EdLDeuWgQT9LBNNXEv+WyFUbqUbSXdByKI4KVpvbBeoBcKFIl7USS+y8OzVy4SdcAIkv3xdSOyUqb++PWJlc33l31uafttPoyCKCmftbO8D8POMKpaprd+D0eYfFfUpXh4eVFxxbJMQD+efthund7q0rADwjA6wfbt27F9+3YAwIEDB6btPLbZrrVIXNpxdCehCUPVAD//i1/prYEFkAN+d2qhnL2RoUgNh8EYPmvZCE5ZOmypVJodjPnLPFwLcCA1GKbMVrmvOTRY///xQxMKbQ/TrO6VY3U8dXQKz4w3RQixqQZWUT/TfzZFSbVSSYoM4GitAs/zsPN/XJZ7jWWh3FPPQz2QPoxOZ6zymJ44HoH6gJrBX8Qw8kOj//OF65Tz2FprkqwqbBLkeUyS8sol7tH2pkgunodhuoeKJFUUWlym+KCXvd9l4FvuWxHjsIGeFxXqnCkU9oLLLrsM+/bty3z//ve/H1dffXXpE1133XW47rrrAADbtm3roImdwSpJWR4El6S67bT9hG1GXhYVjaHUcxjGf75wnRgI9DaYnJb8Fo7UAjydGgxTZqs8FrVL6xja/48dGhfHp0zvKI6xdukInjo6hSMTLeHLMS2gVPTs9MdvjJJKJSmSHoldUBn8XqFIM2whLape2w2IFfP7qUcC6r8D2cE3r9zNve95WYbdFc3A89ZVMUpSOddvWlBKtpslAhp+5zJWsSRlTnrksPnkisDf7ReftRKvvmCd8TxlX4MqC46ZSad3ocG45ZZbZqIdfUOnkhQNpOONdq7zdqbQP4YhXygg3zFtOgY/vZlhyFeHL1+bOZZF89UNyJ7DWYYRRrEYrJ8Zb4KYt8mBWDTg6oMNX8UvVBiGJxlGjiHsBroTmp7JZA+TFVMBS5MkpR9eZxh5EYL8PhSVBtFL4QPZSZAaJZVsU4ZhmEJ/efFB0yUoIbclGUbge1YDVmQwbeD7/dObL2Lt07YrGyUVSAVhJiWp2R8h+4wyWcccFWEwwlzn7UyhZ4YRqPvnZXpb2+DJ0uq8TXxQo9l3reLnO73F9eQ7vRvtiGX9pnkYUYwV6aJPhyeaLA8j2Y7P2PNmqECBJMWq1VYCXzj1R2r9fR/4PaiyIpS9GAyeBEcwOb31+71l3VJsO+0k8X9ZTb5s8UFT4qc+8eD5PnmnN71/hBqXpIomDUWJe0HW0Gba0qPTWz9013kYzIcxMAsoffnLX8a6detw++2348orr8TLXvayfrWra3ieh1OXZcuf28ZLmlWcaLStjr+ZhJ541yn0ziUWUOrEYPieUuNGD6sFpMEYK5iFywFE/V4foGK2fkPF90RY7UkjNQS+h2fGmyxKysAwCiUp9XdliVZNkvI8D6O1QMl36Ad4GxfVK+mSsokc2q0aKioJc4Zh8GHo1//yc0/GF373l7o4X4EkleP0pgFZJrF5pWbsebkPCcNItyu4iWUT9/LeJd72TmAryd+9D0NOCPla9NONnkbIV7/61dizZw8ajQb279+Pb3zjG/1qV0/4zttfhDf90nrlO9usgQbS8ebckKS6iZLi0CUKGVZb/ni2PAzeJDIYer0k07GS9mhOVsP1cedlGCWSVOB7OGmkhsMTTaV8CJAvudiOTYhZHyOn9y/2H8eaNCR3tF4Rvox+gfsJRusVKUk1+8AwuA8jPW7+OhG9nc8qSRl+12fl3HFscwZzeJ76TnOQgefHL2q7DTo7Nx6jRFSXCdTGIgmqiCkTeELuwDCMuYpK4GcGf9uDoFlPHHem808XKpYBtiz0KKk8p7cNvq+GKppmlRSumBdSC9gpvGk2yEMWozhGGCdhtctGqzg83sqWBslx6urI+zWMYjx6cByPH5rAC89M1k8/ZemwUkW4H1AZRiDet4keDIbp2egLdPFzFyXeFaEoD8MUduqnvgqxuh6XpNLXMm+gpEOZ7hEPq7VNCusGA2qCia3pyJPH8sDlVtPx9O2KwBnGvAyrnWmYQkWLtstb+nWmIF7ILiUpPUrq1GUjOPeUJGO8LMbqFTW6xMsOApQQVWgwLD4ZU+fmHZ8iPwLfT1bAm8hKUmVKOejHNrUvjIHvPvA0AODFZyX5Fh9/03P7LlFyoz1Sq4jjJ5JUrwxDfmcqDUKHpzpTdL9u/n9+GQ/uP9Hx+aw+DMsMnCfscQNQZID4scwGQy6gZBvo6xU/9ZHl3+NqCYbRtdPb4qvR/y9riLhvsjWDktQ8Nhj5TlYCHxTmgg9j9eIhBL4npJFOoRucJcNVfPUPirPFOW645jyVYQTZl53qSRXlJ/ieZyzqaOoYxjpEfpKY+NDTJzLlKbhjvqgD2whbxfcQxzHufOIITlk6jGctHwEgKxP3E/yaE0kq+b+nKClthUVARkmZMuGFwUh/O/vkxTj75PKTiaIB0+SET/bL+gg6Tdwz+jD8/LBaIK0vN9UuIUmZc4Y4yK/VaUCENIz5TLusJEVRbdWKj8nJVkdt6QULxmDYBgy+na00+kzi9BWj+NmfX6FkqHYCU5hlp1h30ojyv6lTj3TAMExt0bN/o5iHT3KD4WNp6sPILW9e8OiGLEUpa0FShmS80Vaq1E4HeHv5fZvsgWGYFtkySTCiWnNJecaG4hX3zIxBZRhckkq3zzmn6XkTahX+rhRJUjknQbm+c+bqRfjEtRfh+RtX5B9Mgycy5NXvu69Wmz5P58PoD3RJytZBuKwxFxgGgK6NBSCZRT+TEPVqtYDMwyjj9Dbde94+ET1jmEkKH8ZEiy1lmjVgRZKUzbBVK4nBmGyFSlbwdIDn1owwH8ZUK+ohKi4bCkrH5TWG6J7VDCG3nZ0vPZ6tP1k0fl49gCf3FclJ/FymbSq+z/wm5v2FwSi45mpQvJ3neXjhmSu78GGYGUbXUVKiksMAhdXOZWQkKcsDViSpOeDD6BX9YBg6TAM0JestqufPysswDN0g6dE9S4drCKMYx6YS6i1Kg+SUvtBhS8KrpmtnTLXCzGJW/UY2rDY1GMyn0Ckkw5D7kw+DF3Ckn2XZ+a5OJwxUodM748PwGMOgY5UsPmipFgCk60LQ8axO7yD3d0KSsNffviOObWFmGYmqLMOYpUzvwR8hLchIUpaXpTrPDIZJ3+8Vpkic4ZI+DL3yLf9e/1tGz7DtfA+Lh5NzHE2r43YTJWVlGAExjMgqW/ULnPWO1AIlca/bqKW8xD1uMGi7shFD9vMhd3/5e7adIpOaS1KWmTdHXths1feMzJSDpOYy11xl0lk/QafOMIwCA2I/njQYna4G2QsGf4S0QJeXbC+BKknNfqZ3r9CjpPoBU/Jd2bBa3zeXOVHyPLRlc3WGQeugH0nX36CfyyRaEWwMoxb4COMkUmm6JSkbw2hHcc8Mgx+7Lo4rBxIaYHqVpArLm6dBDibpRY+w4uHbeZefl11dVHwQKO/DAJLxoNuQ9jzYoqS6laQIVefD6A/0RDXbyzLfGIbvAeevW4KzTh7r2zF1ZyVQ3uldsczYTGUrbD4MWo6U1t/Qty8zEIwwY8CNaTXwEUUxJpuhcb2FfoIPRCO1ihLG3TPDYPsLQ8SkCh4lBfTCMMySE0HP4eH70fXzUGspYdnPmef0rgSeWBzL1iaSpBolBlYuk/UTJrkVyD73Tk890z6MeRslVbTYOkGJkpoHBsPzPNz4lkv7ekxTbP1wyUxvv4QPQx+E9HUjSJIihiFLTeQPFHo7CPWKj3a6zG614s2Y01sf1PkEpVufgikU1OTDoOPrS+d2iuLig56RLRl9GB5L3MvxYsiJRPYmcUmmiGE0WsUDazXwp8WHYWNS2fDjThmG82H0BWWd3pwqzweGMR0wlXvYdPJivODMldh66tL8fT3zgG4qW0Ff6YXrhCSVxpvLgoblDQbHkMI2fERxYjCGptnprYPLpj07vQ0+DB4l1TdJSpOVdKwYqxtXKQx8Txh48wJK9nOK7Q3nVMqb2wJb0mtulpiJc8PWT4h3O+Pk7lGSqsysJDVvGUZNl6RyXgIqdjcfGMZ0wCRDLBmp4pPXXmTbRdnXFDJqKlthSsBKnN7kw2ip21kWZyoCf861wMfxqRaa7WjaGYaO2jRJUnS/FYaR3rNfevZy1Co+lne59GxRLaU3P389ftWwxoq+NCt9iuPkJu6l5za8R2WS/+qsyGMRqsF0Ob3Nvh0uyfFcpLKoubDa/qBslBTACnk5g2FEUTmIPPiWKCnfYDDEYKT9RpFYQpLSDEvZTkbNrzPDUK14mEzlqZk2GNxw9ez0NkSd8YGE1JzNa5fgI792Qc/rrdjueb0SYLlhwamhWoAhKllCBsAgU5nApcqrt67NLMNLSXG2gf7yc1YDAM4pUR6nEkwPwwDUvBPxnQgZ7s63VA18RLE6OZhOzFuGkS0NkrNtxQcacydxb67BVK+oLE5bPmKUAkw+DBHtwjpNxfdQDXwMVwPh9BYlpg2rzeWhXvEx1YqUgboa+DjRaAPAtOdh6Kiks1mqytsNTAyD/lbzMPJlm07P16l9+8tXnysMsnEBpTwfBmOgH3rdcwAA69/5Nfl7jmQFAL9y7sn4+Xt/pdTzrVh8bv2A72UDApTnEnbn9AaSyUHgT//7O48NRmeSFOAYhg1FkTF5eNeV5xi/N9U5Mq9zkTyTxcOVrNPbMLvOQ70SJAaDM4zAx3jKMKY7D8PcJr+narVeyuBMmfNhPJ0Go7PjbF67JNMWPoB2GyUFyKS/vGjYspOB6ZKkgMTI6kfWn0vRff2VzSfj0YPj4n8a55rh9OcRAfPaYJQrPsi3dQbDjG7XAMiDyeltWpyGHuPioSr2H2so+8r9yrVrqOrj6GTWh0Ez8ZmWpIDeDQaQDQWlgVUJq9WMbLcoUyywCLwtZZ6heN4WmaBfxhBInsd0KQ1JIU61jTpDLLqvH/2NC5X/acxqzVDy3sIxGDkvE930ubBE61yEjG7p3zHzwmrNDEOWINEliLLjBM3AeCIXLzg5UwzjgmctFX8n71yrpwG44qsrJNJ18ByZ1YuHUPE9LF/UnbObUMbnUATuwygVEm2QKjlsEUjd4M9eec60vQe+l71veih595KU82H0BMEaAh/NMMqlekTrHMMwo58zOHlM+be+sI5p8SZegkTMyrRSE0UgpytNDC46fZkym5wJhvHA+37FWCiwV4bB9z9z9SL8jys34aqta8V3F552Ena9+4rCRMvicyWfvQzOSlhtiZl10Yp6/Xw/t61f1vMxbDD5MDIMo8NroP4xU5FS83aEpIGgLjqkfVuKUHBObzO6jeDIg+d5WDZaw7tfdU4m21eJHkq/pFwMQBoIU+G9PFAm994jkwCAXz5jpbYo1PQ//3olUCKUes28BtKMaba/53n4r7+8AavG1DVVejUWgD0BrVNctH4ZNq1ZzGpJ2bctktOKGMhcgedl/Sz0KtAEt9NL6CTHpB+YtyMkySgkOeT6MCrOh5EHesn73R/v+LPL8ebnn57xkZy6TK7HIQzGsBzsRDRVh854cnYPp+/EFeesVpjOrDi9q8UTmiIEvt91efTOz9Wd01vHv/7O8/Cq89eykhn2bX3teevgtanmMgI/68PQ2VM3YbXAzDGMeS9JmZar1FEj4+IMhhHTwTA4dF38WQaDMcYZhphxdibn0PP97Rc+G6eeNIIzVo8pE4nZcXr3VqoDyDKM6UQ/fBgcsrx5vtPb9+xGalAYhu9lr1Jny51eA41z7RnyYczbEZLKR1OZgrwOJSQpZzCMoNnvdHVIfdbKn0OeD0PE8Jds1lmrk4KMy0ZrOGdtksTFl2Kd6TwMoD+SlO7DmE7QEqWjPSzyxVFG4hquBbnnK/JxzBV4JXwYnb4GPKx2JjBvGcZovYKv/uGl+NFDB/GzvUdzX6aqiJJyBsMEilSahqrP6fHtsyvqYGP1rCQlchBK9rI//pWzcfGG5Xguc2w+m2UNz1ZYLdBbuOtLN63CphJZzP3A+hWj+Nx1l/TNOSyKD+Y8wzf90npctml17nF8b+5LUnlRUt1KUuR3dWG1fcCZq8ew64kjAIpKg7goqTyUWeSmp+NreRgAcPLiIew7NiV+41Vx9VpTZWeWtYovykQQnr1SGozZSdwrlkyL8N6rz+1Xc0rh4g3L+3asMol7S0dqWDqihgN/5NcuUGbjvuflVnOYCzBneief3Up9NNl1YbV9glg/OMcW8BBchyxECY5p6pCmiqtnrF6EfcemRNkOvhSsvl5GL4bs9BWj4u/ZYJhlgjLmM4Qk1eF+V25ZkznOIDAM/UL1CLBuypsDzundN5QpgV1xmd650Iv99Ru+gcH87Wu3Yvv3HsZz0vLpPCRUKU7oebmTgSKMKlLXzA84NEmZ6/r7dKHb/IMMvLlvdH/zlzdkCifKCW13LN75MPqMMolBLnEvH/0KpbShYmAwK8fqSh0q7vRWyooE5X0YcxFivekFajC6ZRjZ48x9o/ubl56e+U5GScnqA52gNsMMY96PkGXWfa45SSoXvRQfLIMy9YnGLD6MXiUpAPiDl2zERadPX4ZvHkRY7QAbvV7gl3B6lzrOAEhSJnRaS0pHxUlS/UWZsgGVwEOt4s+KJDEI6HfsvY4ydXRUSUp+34+Q0rdfcVZP+/eCeh9KgwwyymR6l0Hi9B68e0hNNrHsMiB1pNUegDyMd7zjHTj77LOxZcsWvPrVr8aRI0f61Kz+oUx88/LROlZ0uQLZQoBpzYV+omjZTyAnSsrLLkozSFjoBS/LSMZl4A2AJGVCrwyDVJGBKA1y+eWX45577sHdd9+NM888E9dff32/2tU3lMmg/O0XbsAXf++XZqpJAwe9OOB0HT+vv/OBVfdhzFSW83SAfBjtaOaW2ZxL6JcP449fdhZec8EpvTdohpGpi9Z1pvcAGIwrrrgClUoy87vkkkuwZ8+evjSqn9CjEEwYqVWwZsnwTDVp4FAmVr4XdDq7Utd+mL4Fb2YCJEnN1BKbcw39WmvlN563HlvWLe1Di2YWOsPqNDl2pvMw+ubl/cd//Ee8/OUv79fh+oZuLbeDxHTfw15CK8uuqTBXQcxppjr8XMNJo1UsGa5i3bKFOWE79aQR/OqF63BJmgzZOcOYY2G1l112Gfbt25f5/v3vfz+uvvpq8XelUsEb3vAG63G2b9+O7du3AwAOHDjQbXs7xnSs5bDQ4PseViyqYeVYfVqO38ssMxjQ6BgChXLPlKQw1zA2VMVd775itpsxa6hVfPz1r56Pb96bjLEd52H4cyxK6pZbbsn9/ROf+AS++tWv4tvf/nbuxV533XW47rrrAADbtm3rsJndY7ojfBYKvv22F2GkPj0O2qDLGPRk37lfEiIPIsplgUpSDgm6lX1930PF9+aOwcjDzTffjL/6q7/CbbfdhpGRkeIdZgF9yyRd4FgyUi3eqEv0sorbVVvXYunw9LVtujHTTkuHuQm/hz5QDfzBqCX1lre8BY1GA5dffjmAxPH90Y9+tC8N6xfyKqE6zA0Qw+jmEf3OC5/d59bMLMg/tFCd3g4JvB4CS6qBh+YgVKt96KGH+tWOaUO3Rb0cZg5lGcZwNcBkK5yBFs0cZPE4ZzAWMkz11MoiYRgDYDAGAWXKTjjMLsr6ML7zRy/C7sMTM9CimQNl+C7UPAyHBPTudzNOfe63n6csYTydmPcGQ2ZSznJDHKwoGyV18pIhnLxkaCaaNGOo+I5hOMglarsZp/QKuNOJeT+MUtG6sfrgOkbnO2iWvRBredG1Ox/GwoZcbnhu94F5zzDWLh3GV//g0hlbwtKhc0x3JvlcBvnYXJTUwoY3INL5vDcYAHDuKUtmuwkOOShTgn6+gtYU/+0Bj/Zy6A3ShzG77SjCgjAYDnMb/apYOohYPFTFYzdcOdvNcJhlnLZ8FKevGMX65aPFG88inMFwmHXIariz3BAHh1nCyUuG8J0/etFsN6MQ897p7TD3sZAZhoPDIMEZDIdZR5n1MBwcHGYfzmA4zDr6tSaCg4PD9MIZDIdZh1xG1xkMB4e5DGcwHGYdrgS9g8NgwBkMh1lHp0u0Ojg4zA6cwXCYdTiG4eAwGHAGw2HW4Ra5cnAYDDiD4TDrcFFSDg6DAWcwHGYdQeAkKQeHQYAzGA6zjqCH1cYcHBxmDs5gOMw6Ki5KysFhIOAMhsOsw3dRUg4OAwFnMBxmHY5hODgMBpzBcJh1+K68uYPDQMAZDIdZh2MYDg6DAWcwHGYdZCgW4hKtDg6DBGcwHGYdFZeH4eAwEHAGw2HW4fIwHBwGA85gOMw6XLVaB4fBgDMYDrMOV63WwWEw4AyGw6xjqBoAAOpV9zo6OMxlVGa7AQ4OqxcP4WP/ZRuev3HFbDfFwcEhBz1N6f7sz/4MW7ZswdatW3HFFVfgySef7Fe7HBYYLjtnNYZrwWw3w8HBIQc9GYx3vOMduPvuu7Fr1y688pWvxHvf+95+tcvBwcHBYY6hJ4OxePFi8ff4+LgLi3RwcHCYx+jZh/Gud70Ln/zkJ7FkyRJ85zvfsW63fft2bN++HQBw4MCBXk/r4ODg4DDD8OI4jvM2uOyyy7Bv377M9+9///tx9dVXi/+vv/56TE1N4T3veU/hSbdt24adO3d20VwHBwcHh36j7JhcaDDK4vHHH8eVV16Je+65p2+Nc3BwcHCYfpQdk3vyYTz44IPi73//93/H2Wef3cvhHBwcHBzmMHryYbzzne/EAw88AN/3cdppp+GjH/1ov9rl4ODg4DDH0JPB+OIXv9ivdjg4ODg4zHG4WgwODg4ODqXgDIaDg4ODQyn0LUqqE6xYsQLr16/vev8DBw5g5cqV/WvQDMO1f/YwyG0HXPtnG4Pc/ry2P/bYYzh48GDhMWbFYPSKQQ/Lde2fPQxy2wHX/tnGILe/H213kpSDg4ODQyk4g+Hg4ODgUAoDaTCuu+662W5CT3Dtnz0MctsB1/7ZxiC3vx9tH0gfhoODg4PDzGMgGYaDg4ODw8xj4AzGzTffjLPOOgsbN27EDTfcMNvNKcT69etx3nnnYevWrdi2bRsA4JlnnsHll1+OM844A5dffjkOHz48y62UuPbaa7Fq1Sqce+654ru89l5//fXYuHEjzjrrLHzjG9+YjSYrMLX/z//8z3HKKadg69at2Lp1K2666Sbx21xq/+7du/HiF78YmzZtwubNm/GhD30IwODcf1v7B+X+T01N4aKLLsL555+PzZs3493vfjeAwbn/tvb39f7HA4R2ux1v2LAhfvjhh+NGoxFv2bIlvvfee2e7Wbk47bTT4gMHDijfveMd74ivv/76OI7j+Prrr4//+I//eDaaZsRtt90W//SnP403b94svrO199577423bNkST01NxY888ki8YcOGuN1uz0q7Cab2v/vd747/+q//OrPtXGv/k08+Gf/0pz+N4ziOjx07Fp9xxhnxvffeOzD339b+Qbn/URTFx48fj+M4jpvNZnzRRRfFt99++8Dcf1v7+3n/B4ph7NixAxs3bsSGDRtQq9Xwute9DjfeeONsN6tj3HjjjXjjG98IAHjjG9+If/u3f5vdBjG84AUvwLJly5TvbO298cYb8brXvQ71eh2nn346Nm7ciB07dsx0kxWY2m/DXGv/mjVrcMEFFwAAxsbGsGnTJuzdu3dg7r+t/TbMtfZ7nodFixYBAFqtFlqtFjzPG5j7b2u/Dd20f6AMxt69e3HqqaeK/9etW5f7Qs4FeJ6HK664AhdeeKFYcXD//v1Ys2YNgKSTPf3007PZxELY2jtIz+PDH/4wtmzZgmuvvVZICnO5/Y899hjuvPNOXHzxxQN5/3n7gcG5/2EYYuvWrVi1ahUuv/zygbv/pvYD/bv/A2UwYkNA11xfR/yHP/wh7rjjDnz961/HRz7yEXzve9+b7Sb1DYPyPH73d38XDz/8MHbt2oU1a9bg7W9/O4C52/4TJ07gmmuuwQc/+EEsXrzYut2gtH+Q7n8QBNi1axf27NmDHTt25C4INyjt7+f9HyiDsW7dOuzevVv8v2fPHqxdu3YWW1QMat+qVavw6le/Gjt27MDq1avx1FNPAQCeeuoprFq1ajabWAhbewfleaxevRpBEMD3ffzWb/2WoN1zsf2tVgvXXHMN3vCGN+A1r3kNgMG6/7b2D8r9JyxduhQvetGLcPPNNw/U/Sfo7e/X/R8og/Hc5z4XDz74IB599FE0m0189rOfxVVXXTXbzbJifHwcx48fF39/85vfxLnnnourrroKn/jEJwAAn/jEJ5S10ecibO296qqr8NnPfhaNRgOPPvooHnzwQVx00UWz2VQjqLMDwJe//GURQTXX2h/HMX7zN38TmzZtwtve9jbx/aDcf1v7B+X+HzhwAEeOHAEATE5O4pZbbsHZZ589MPff1v6+3v9+eulnAl/72tfiM844I96wYUP8vve9b7abk4uHH3443rJlS7xly5b4nHPOEe09ePBg/JKXvCTeuHFj/JKXvCQ+dOjQLLdU4nWve1188sknx5VKJT7llFPij33sY7ntfd/73hdv2LAhPvPMM+ObbrppFluewNT+X//1X4/PPffc+Lzzzotf9apXxU8++aTYfi61//vf/34MID7vvPPi888/Pz7//PPjr33tawNz/23tH5T7f9ddd8Vbt26NzzvvvHjz5s3xe97znjiO8/vrILS/n/ffZXo7ODg4OJTCQElSDg4ODg6zB2cwHBwcHBxKwRkMBwcHB4dScAbDwcHBwaEUnMFwcHBwcCgFZzAcHBwcHErBGQwHBwcHh1JwBsPBwcHBoRT+fyhHloAbtiGUAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b573eaf64e354c09a5791fd44908a028",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f98673ad19634143874530cb43447917",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8a602d0bab941f8a83ec990e87c203e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c4c3269c9114c39b2bbfe46212d9976",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31046e7a4d174ebe9d14672354161675",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa04826aeaa4427bb84d13b39bd946c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce2628d8282a42a1987090b054aab66d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9dbfe51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "5df15be1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "d08b239f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "7b61eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "56af1c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the 1000 simulated data\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "# avg = np.mean(ori_x)\n",
+    "# ori_x = ori_x - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "maxAbs = np.max(np.abs(ori_x))\n",
+    "ori_x = ori_x / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "7a3e1c12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.06918687"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maxAbs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "b0da79df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64eff746",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "67a85a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "c5cec2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "yes_data = yes_data / 3000\n",
+    "no_data = no_data / 3000\n",
+    "\n",
+    "# Center time series by taking the “grand mean” (mean of all entries in matrix) and subtract from each row\n",
+    "# yes_data = yes_data - avg\n",
+    "# no_data = no_data - avg\n",
+    "\n",
+    "# Divide entire matrix by maximum of absolute value\n",
+    "yes_data = yes_data / maxAbs\n",
+    "no_data = no_data / maxAbs\n",
+    "\n",
+    "# # Smoothing\n",
+    "# for i in range(len(ori_x)):\n",
+    "#     ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "3c2d5f04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "475911c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb57df44",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7459ed8d4ce240f0b18236ba95f3f7d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bc847cf643941fab817b914c9e82a14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "fc640bc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e27b8203",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v5_Aug16_bf_model_misspecification_1000_data.ipynb b/bf/v5_Aug16_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..ccfd6e3
--- /dev/null
+++ b/bf/v5_Aug16_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,6143 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72\n",
+    "\n",
+    "from hnn_core.utils import smooth_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    param0 = params[0]\n",
+    "    param1 = params[1]\n",
+    "    param2 = params[2]\n",
+    "    \n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "        # Only smoothing \n",
+    "        temp = dpl[0].data['agg']\n",
+    "        temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "        \n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "\n",
+    "#     print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "38ab20ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "fa03f4e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "9787d8dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only smoothing\n",
+    "for i in range(len(x)):\n",
+    "    x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "882bfe3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "45d11636",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98c4d2fa91bd43d48848564eb158becb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.770267</td>\n",
+       "      <td>0.816772</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  9.770267  0.816772"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "097f385e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "80a2ec47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fef942072e384fec9aec074b4270028f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "da2c69dc2c9a4741b0325d2858304b8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.01\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.01\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WECAAgBUECABgBQECAFhBgAAAVhAgAIAVBAgAYAUBAgBYQYAAAFYQIACAFQQIAGAFAQIAWEGAAABWECAAgBUECABgBQECAFhBgAAAVhAgAIAVBAgAYAUBAgBYQYAAAFYQIACAFQQIAGAFAQIAWEGAAABWECAAgBUECABgBQECAFhBgAAAVhAgAIAVBAgAYAUBAgBYQYAAAFYQIACAFQQIAGAFAQIAWEGAAABWECAAgBUzClBHR4fy8vLkOI4aGhomHTfGaPv27XIcR36/X93d3UnPPXXqlFatWqXCwkKtWbNGly9fThx75513tGrVKvl8PhUWFuratWt3skcAQCoySYyNjRmv12vOnDljRkZGjN/vNz09PRPWHDp0yKxevdrcuHHDHD9+3JSWliY9NxAImGPHjhljjNm7d695/vnnjTHGXL9+3RQWFpqTJ08aY4y5cOGCGRsbm3bG4uLiZNsAAHxMZvqanPQdUFdXlxzHkdfrVWZmpqqrqxUOhyesCYfD2rBhg1wul8rKyjQ8PKx4PD7tuadPn1Z5ebkkKRgM6sCBA5KkI0eOyO/3a+XKlZKkz33uc5ozZ86sRhcAYF/SAMViMeXk5CQeezwexWKxGa2Z7tyCggK9+uqrkqT9+/erv79fkvTuu+/K5XKpsrJSjzzyiH72s5/dwfYAAKkqaYCMMZOec7lcM1oz3bkvvviidu/ereLiYl25ckWZmZmSpLGxMb311lt6+eWX9dZbb+mVV17RH//4x0nXaW5uViAQUCAQ0NDQULJtAABSTNIAeTyexLsTSRoYGFB2dvaM1kx37vLly3XkyBGdOHFC69ev14MPPpi41he/+EUtWrRI8+bNU1VV1YR/1PBvdXV1ikQiikQiWrx48S1uGwBgW9IAlZSUKBqNqq+vT6Ojo2pra1MoFJqwJhQKqbW1VcYYdXZ2asGCBXK73dOeOzg4KEm6ceOGdu3apS1btkiSKisr9c477+hf//qXxsbG9MYbb2jFihWzvW8AgGUZSRdkZKixsVGVlZUaHx9XbW2tfD6fmpqaJElbtmxRVVWVDh8+LMdxNG/ePL300kvTnitJ+/bt0+7duyVJTz75pDZt2iRJ+uxnP6tnn31WJSUlcrlcqqqq0uOPP/6RbB4AYI/L3OyLmjQTCAQUiURsjwEA0Mxfk/lNCAAAKwgQAMAKAgQAsIIAAQCsIEAAACsIEADACgIEALCCAAEArCBAAAArCBAAwAoCBACwggABAKwgQAAAKwgQAMAKAgQAsIIAAQCsIEAAACsIEADACgIEALCCAAEArCBAAAArCBAAwAoCBACwggABAKwgQAAAKwgQAMAKAgQAsIIAAQCsIEAAACsIEADACgIEALDCZYwxtoe4U4sWLdKyZcvu6BpDQ0NavHjx7Az0MUvn2SXmty2d50/n2aX/3vnPnj2rCxcuJD3/vyJAsyEQCCgSidge47ak8+wS89uWzvOn8+wS8/MRHADACgIEALCCAP2furo62yPctnSeXWJ+29J5/nSeXWJ+vgMCAFjBOyAAgBV3fYA6OjqUl5cnx3HU0NBge5wZWbZsmQoLC1VUVKRAICBJunjxooLBoHJzcxUMBnXp0iXLU/6/2tpaZWVlqaCgIPHcdPPW19fLcRzl5eXptddeszFyws1m//GPf6z77rtPRUVFKioq0uHDhxPHUml2Serv79eXv/xl5efny+fz6de//rWk9Lj/U82eLvf/2rVrKi0t1cqVK+Xz+bRz505J6XHvpannn9X7b+5iY2Njxuv1mjNnzpiRkRHj9/tNT0+P7bGSuv/++83Q0NCE537wgx+Y+vp6Y4wx9fX15rnnnrMx2k298cYb5sSJE8bn8yWem2renp4e4/f7zbVr18x7771nvF6vGRsbszK3MTeffefOnebnP//5pLWpNrsxxpw7d86cOHHCGGPM5cuXTW5urunp6UmL+z/V7Oly/2/cuGGuXLlijDFmdHTUlJaWmuPHj6fFvZ9u/tm8/3f1O6Curi45jiOv16vMzExVV1crHA7bHuu2hMNh1dTUSJJqamp08OBBuwP9h/Lyci1cuHDCc1PNGw6HVV1drblz5+qBBx6Q4zjq6ur6uEdOuNnsU0m12SXJ7XbrkUcekSR9+tOfVn5+vmKxWFrc/6lmn0oqzS5JLpdL8+fPlyRdv35d169fl8vlSot7L009/1RuZ/67OkCxWEw5OTmJxx6PZ9r/wVOFy+XSV77yFRUXF6u5uVmSdP78ebndbkn/+xd3cHDQ5ohJTTVvuvyZNDY2yu/3q7a2NvERSqrPfvbsWf3lL3/RF77whbS7//85u5Q+9398fFxFRUXKyspSMBhMu3t/s/ml2bv/d3WAzE3+AeB0hU8Vf/7zn9Xd3a329nbt3r1bf/rTn2yPNGvS4c9k69atOnPmjE6ePCm3263vfe97klJ79vfff19PPfWUfvWrX+nee++dcl0q7uHDs6fT/Z8zZ45OnjypgYEBdXV16W9/+9uUa9Nl/tm8/3d1gDwej/r7+xOPBwYGlJ2dbXGimfn3jFlZWXriiSfU1dWlJUuWKB6PS5Li8biysrJsjpjUVPOmw5/JkiVLNGfOHN1zzz3avHlz4mOGVJ39+vXreuqpp/SNb3xDTz75pKT0uf9TzZ5O91+SPvOZz+hLX/qSOjo60ube/6cPzz9b9/+uDlBJSYmi0aj6+vo0OjqqtrY2hUIh22NN6+rVq7py5Uriv48cOaKCggKFQiG1tLRIklpaWrR27VqbYyY11byhUEhtbW0aGRlRX1+fotGoSktLbY46yb9fPCTplVdeSfwLuVSc3Rijp59+Wvn5+Xr22WcTz6fD/Z9q9nS5/0NDQxoeHpYkffDBB/rDH/6g5cuXp8W9l6aef1bv/2z+q4l0dOjQIZObm2u8Xq/ZtWuX7XGSOnPmjPH7/cbv95sVK1YkZr5w4YJ57LHHjOM45rHHHjP//Oc/LU/6/6qrq83nP/95k5GRYe677z7zm9/8Ztp5d+3aZbxer3nooYfM4cOHLU5+89m/+c1vmoKCAlNYWGjWrFljzp07l1ifSrMbY8ybb75pJJnCwkKzcuVKs3LlSnPo0KG0uP9TzZ4u9//UqVOmqKjIFBYWGp/PZ1544QVjzPR/V9Nh/tm8//wmBACAFXf1R3AAAHsIEADACgIEALCCAAEArCBAAAArCBAAwAoCBACwggABAKz4H+g6aQErlYh1AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47c5e5dcfb364e06a16972f2bb7fa796",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all -0.01\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))*(-0.01)\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4cbdad3c379541c1a2055c00b648e627",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BF/Cxf/8RAHm80jOr2u+nHtqGrz27G49uO2BKTE8E1EwaaLGGQ02pRcMRO/sM5zz43F585huviP+3QsNJ2gSeGKmEv9f+bED6QsWdue1KbUN3KSsPrNNwdofZLGZMbm4BsXoxFhpOVnp70rkVz2/6u6XL5aqZ1IT2nH69f/7Bz3Hf5l0A5HlAz6xuRsg0X3G9xDgckX3kNLGpTUgfzs7Dg9h/YgQVz6tpRywGQ2vkDbMV12BSI3ZWhnMqnoeS64maNSPl9pEGiBLa05V9yHEhmvZ83Bnbrp1g3rFi9waSBE6g4XTKosE1HM/zW0aC4aB3WZeGw3b/ThObmjXTQBSQXYNJjV2PxkhZEba5sMZPxY00OLUdaRkIxiMmpMB562e/BQCY3VOoa3Fv1S46q+rOUYuGUw4zK1Q8H3nHaglNN6ntx4ZDgVPMPuSkXWKqwGGCqU0Ts5ALFgtVwJTceJ+ShtNojE6zxt3gaNTGsuehaCeX7m4WIi2htj7wvCgbSNn14DSxrTzwM23c0J6iFtIAD1SlZy8pJmzatFSYpSUmcE4zDWdCm9T4i84Cevmt2m14YmDX3qYsk8FVnJetEDhJ7Tg+VLvAyarhVLyx0HCCqaOaSbSkgZCl1mjcU7PWHG5SU3fdrUK9JrVaYrFqBV06qw+nfg2HBI58PpnmA0sLWSr0bTxdMg1MaIHjurXRLVtdr0ZU/Kzh+mmlaVXQZCcVf7gFAifJpHZsqASgNpMafzdZfTitFDg/3ncCV//Ft3B8uIx8mE9vVDFLqgLI83wcDLOLlyqNta1ZMU7cpFZucWYEQr2kgax+vHrgI9Jw0oZNPbnUdDFk6oYjF46hsusnxvqksdfGIyakSY1Q8Xwx6LIeD7RuUauFcSba5GbfOZaFak8aTvsyDdRjUuOTNj0wj2tCmS9fM35y4CReOzSIAydGkM8Fu1OV6afuYk+OVMRi1rCG06RnkwROm5KN1ktbryX4t1ZEmQayZouu4dpcw0mgRQuTWuhXDc7TX8eY1E4DBCy17Md7Ld5tiEwDCdf/1y27YpHatWk4wYCnXThpOM30GVc8Dz947XU88zM5yJEC/mrpOr6jTROoWUsFNwpuZxcmNUVoq0Ll+HAU6NioNlGvhvP/fvAzXPHpJ8T/uUmt1clGCfQuaxVwMr24+rk7Dg/ius99G0cGS1WPzUwaqMP/5GrGbimJNMBo3+ra4gnNJ/OtOxoTXMPxYNcQU9Muk1rSbub3/i3g+L/j4nniu1oCP9V4APLh0OLZDHi+j88++gqKOQf/9BuXie/JpFbLoillGshgY1c/J8ENKba5Gp9bmD087sMJ+tD3A+c27WJpWB0bjha+Rhf3ene5f/DAiwAiRtqpMfDh1LtT92rUcH5y4CR+cuAUdh0ZwszJhdRjaagEyTuTj6sn04Csmek1HKJF8xRbSRoO0D5GYSsxoTUcz68v5qVVu+gkLn76OaTuZ9dwVNJAM1P1VFwfZTee+4pMarU4jbOYU/7p+z/Dz49EaX2yrGf/7e6n0P8Hj2Ruh7g27ULho5iTSQO/8sXvY9HvPyz6lnw8pOFM6co1zFJr1IdBY2Qwg0nN933c+72dkobW0L2FhlPbM2TVcgm1VM2VfThpGxr6W9/YLQnSgGx+JR8Or8ulttvNuOmqF/uPj+BEShbrZmNCCxwg2OVkNZHR7rxVVpt67Ny1xeHIPhwK/GzmQKZiUerkJJNao45XjpGyiz988EU88FxkZsyyKLxQZ2llzlIkIU0azuYwTxr1LTGQ6LnnTCk27C9p1I5P50u06IQ2ff+1I/jEf7yUuWJl1ntnMUu9uOc4/mULBVByk1oW7TX4m2VO0+WyFmCrabPE7j8SzjNVw6V9XqpJrcXm4lv+fjP+fNPLTb9uEia8wAGym8jq2enU1g4SHtnPqUVIRQFooYYTLpZNLSng6XNfRSa12rU39TOhLDQ2lx1XU3NrAtdwyaSmEi9oUaHFhDSEOT3FhjWcpgmcUnWTGgXqptVwqefeWd7/V3+4C3/68I9jx2cRVrWYv4QPx6pCGqgh1g0IBBQXlFTps6ywFEUwrOslWk/4I7eCELPv+LAITG4HjMBB9onc6qjfejj3lRomg5q1djgsT9BMAUrZG1SNi0xqrufjpwdPZepzneNVupegeUe/tZI+yplEeTKplfUsNWq6EDhTimPmwyHQWBkqJWs4vu/jc4/9BD85cBJA8/x7tcTh8B1/rbToWlLBCB+OY6VaCGplj5Y9T2orZedW3z9dr8RZauEhu44M4YXdx1uq4Xiej5OjFRytI4N3vTACB8Cb1j+BG+/6r6rHtd6HU7tJrZbJQJNgVNFwmm5S8+IlcU+EC+8rB07i2r/8Nj732E8yXUv3mUB0U1o0c7bV0KJ859d+hEdeSK4rxN9/ISHwc7QiC/FjQyV05x30FDvAhxOeP1xyMbkQROyrzLkjgyX81RPb8dlHg/dTK7Gi2r2zCg1d7EsWYVXLHOXZotMOp9+y9n/ZlZONkulaff9iPpY5aSD4++Y/fxLvuOu/ZLNySiN3Hx3CJf/nMeyoIRv7qVJA2T+agdHXLDRlNG3atAnnn38++vv7sX79+tjvvu/jIx/5CPr7+7F8+XI8++yz4reFCxdi2bJlWLFiBQYGBprRnJpx+FQJz++ubtfXsdRKFQ+HwsC+RlGPQKMUGlnMy2qadLIt1+LH8n0/lXJacQNh40qmgIh+vit08D/786PV28seSjfZ1aSIOSfdFl8ND/1oH55+7fXE33lVRoqhiGUaUArBHR8uY1p3HoWc3QQNp/bzK+ye9P4HSxVM685L7SVYCmsz3yRCSS3lCVy2Yak100AtZm/Zh1PdpJZ1XpYrnnTsUILAoWcbqbjM9yTPRX7LtE3lriPDeH2wJBFoquFkmFB3XAkc13Vx++2345FHHsG2bdtw3333Ydu2bdIxjzzyCLZv347t27djw4YNuO2226Tfn3zySWzduhVbtmRPXd9K+L6P//HPz+LJVw5K39Ok4QPiy0/twHWf+3ZTTDnCpFYLaaAGZ6xqUuOLZdZ7fvsnh3DZnz6eKGQFaSBhZ0baSJaM29UWm4j5FDHDkt7Dhu+8ijf+n8dS71ct1ZHHNhwWZNIAQSViHBsqY/qkPPKO3YQ4nNrH2DHGMqMhMlxyMW1SQBlWfThqPzfLpFaL9u76UWFEPo5uvPspfOS+59LvU4NPU/bhpLcn6zWBkOas8eFw0xkQbRZHy9H3ru9j15HIpyKld0prY8I68G/P7MZbPvOk9hyyOpwcrbQtALjh0bR582b09/dj0aJFKBQKuPnmm7Fx40bpmI0bN+IDH/gALMvC5ZdfjmPHjmHfvtpLIrcLoxUP//mjffjQl3+oxAHEWWo7Dg/i6FC5KVH7dZnU6iANkMDhu00a2Dd8/jv4ytM7AQSC9UvffQ3Hhkr4wWuvw/d97D02grLr4+DJkYT2BJNeurYXnzRZ4p/43NE9n0qCyOeSSwX/6cMvVw0GVDUz3e9A8P7pPmrgJ+0a6ffjw2VMbZqGU4fAGYqemd7JUMnFtO4gBE9daFQNJNek9MxCw8lEbWYOdeWZ/6NKiWrVNFXtPgBpxtU3Gln7v+TqNRxAFvDU1tGKK7X7uV2R9p8100JUxlo+5nf/9Xn87PUhrTn3BNuMHB1qj5bTsMDZs2cPFixYIP4/f/587NmzJ/MxlmXhuuuuwyWXXIINGzY02pymgA+Wx398QHzWqdaHTwUv6uRofY431/Px5CsHA7t1DZNFnF8DK0fN6aRjwLy8/yQ+vjGgwv5gxxF86qEf451/+z38yobvY/OOIyI7wVBCLR3XC+zRfG7onqcZGo6IXg/ZP9Wy/iZdh/+W6jxm758OU1PbEKuLTCPHh8uY3p1HwbFRrjF3Xy1tT8KRwWhcch8OmdRiAkdZsJqm4WQwqW0/cBL3fPtV8IJrNQeK1sNSy1wPJ6uG40v9yOcK33TQ9UbKnKUGvMDM+zzBrmxq8/GXj74ifDZJbaR4MV3tK9ocAair9Hc9aHg06UwYqh047ZinnnoKzz77LB555BHcfffd+M53vqO9z4YNGzAwMICBgQEcOnSo0Wangr80XVAhf6evDwamJf7yasFXf7gLH/ryD/Hg1j1RapuaNJzsTLOyYHUlazgcNEgPHA+0mZMjFTEBkop4BYt29Z1ZFtdAtSjzmIbj2FUX9LRAR+47SGuP50fFwJI0nOC4QMOZPikvyhmoBduyYs+xYew9Vjt99aik4QQBuSXXi3w4CY5sArW7Hnzl6Z3YvCOIT+KkgUrCO3johX1Y/8jL4h3x0uFZUYs2IjQcO1kzBqqnnFLBMwcA8mJf0pixRyuuaIvv+xJrjGeF4Nc8MVzBF775Uzy2bT8A7iOT29iVD8ghQ+X4fOUBn1lSATUDDQuc+fPnY9euXeL/u3fvxrx58zIfQ397e3tx0003YfPmzdr7rFu3Dlu2bMGWLVswZ86cRputhaC0svkgRTprFvfDpwKBc6pOgUM74h+xXU0tc4x2UtlS28gmNb6ZTfOR8PMjgaPXcMgPUk3gqJsSHaoxdKIcVZw0kN4PSX4QsZlI03B0JjVlwZaKm/k+To1WMLmYE6y2eplqV67/Jm79p2erH6iAm9Q8zxe77UjDkZ9X9QFUy0Lx9ef34mP//oL2ty88sR3//uzu8LrBfV7ZfxL9f/CIlg1IXV8WRJj0DYAOHlu4qx8bHGNXicMRWlPGwM9SxZPWkCEW96QTOJKG4/mSX5CvK3L2Ato0yvNffX+k4egsEtykdmy8mNRWrlyJ7du3Y8eOHSiVSrj//vuxZs0a6Zg1a9bgK1/5Cnzfx/e//31MmzYNfX19GBwcxMmTAd9/cHAQjz76KJYuXdpok+oG7dqTTDn0Lvmwez00qdVbJ753ahEAcOBE5BPRLdBJE6i25J3yIskHJ6eiimsr1yy7fiRwSvLzkvwgYeMl9CHBySJwsprUGC26qkktYdHQbSZUcA2HLqOSBngqGM/3Uap4KOTsSMNpU+6ykyNlfPWHP5faU/F8sdtONKkpHViNFv29V1/Hphf1/thAo4qEBxAtfI9tOxA7nsY4tamaiVOHWoI06Yhq7MZq1/zWKwdx/+afi/+rGs5QgoYjaNEVl5nT5U0MX1f47Wm8imzxYi7oNZxqJjVuem0lGk7emcvlcNddd+H666+H67pYu3YtlixZgnvuuQcAcOutt2L16tV4+OGH0d/fj0mTJuHLX/4yAODAgQO46aabAACVSgXvfe97ccMNNzTapLpxarSCGZPlKqAyNZdIAzR5KmIw1RuRXXCCAbHveCRwaPAdPjWKI4MlnDd3SqIGo+5w0qCSBvhaozMnqZO94nmRD0cRsLZlCae750NS03QTNYsPp5pJrSJMasFvecfGaCVd8CeZtKJFJflcnmAxScPhZgrPC3aiBccWvpBGY3Gy4tGXDuB/fe0F/MpA5Dt1PV9sFCKWWroPpxpcz0sce64bkUfigiz+/lXWoefHM1ZUQy3EG+7DASBKr8faRe9dueaP953Ai3uOi6S6BNVXx+tOcR+O50VjiPtv+Zg6OarXcMhvqbJU9x8fwf/6tx/hj29cgq68k67hjJSRdyyUXb9tpIGmZItevXo1Vq9eLX136623is+WZeHuu++Onbdo0SI8//zzzWhCU6AyjABZC1DNLqTd8HNrBU3IfceYwAmv/4UntuMbL+3HD37/2hRTUPYJppIGXMWHo15DFRQV1xfZCQaVAUzT1A2rF8rxA/G2ZMl6W6kmcARpIDKpVdvYJi2o3KSRBG6uoU2HWjWV379U8eD7QMGxE0tStwq0aPEdsk7DUZlz6jirpmFUPD+xzwN/jb5fdZoTHcLNxLVqOLUQbyIfjiXuryPl0btW++bdf/u92DwAAoEpZRqoouHIpAFfyl4xqJhoxT2EhiNrOk/99DB+sOMIbnnTQlw0b2rkwynF16eTIxXMnFzAyZFK22JxJmR5AsvSp6eh3V+1dBr01evsJdVrUqN77ecmtbBxB0+M4sCJ0YA2maTh1ODDUQuwqebCmMCJaTi+YGWppIGA5hxoOK7ng28UdcykTKSBKiY1obEJk5qdepz6mSMLEymqhxQJUVXD4aC+KuRsESjavvozRIGWd8j07kngqAJYdehXM02lES1c32cEFfkYXUApLeycRVl7hVCIc6uB3idR9D3fh4NkzStmAUhoWkmNw0moP8Rp0WIzG5rUpnblcGKkIvlw+HxQ573wBylzuyufruFM6crjjWfNwJkzuvUP02RMyNQ2Sf4DernVHN7C5MWCH+vXcOLXpzFJ5pmDJ0ar7sxrKU/AbeTiOhrzBf1OZoaK64nsBGm0aJU0oNVwaiQN6PtJ3nnmE0gDJxQ/hg5ZctLJtOhwsUiJvyLtp5CzhWmjXRoOmRn5e3I9XwignmIOtlXdh1NNw+AUZt1vokquKnA0Gk70PonUohdmb//r7+IfntqhvWct5TroiEjDSXqO8NrKM5w9a5L2+FimgXICS82Na8lkUpsR1vKRWGrsVZUVc7LQlsL3TRpQpOHoSAMVTO3K4W/fdwk+dOU52mdpNiakwEla7MheKu0ktA784C9RooHGNRwOHjQIBP6dpPiFpAkdu6YXxY5ELDVf+l2n0XCU3WQNh090NVsu3YcznmoVOLrnKyu7vLyjL6R1gm0Gkii5SdVcSxUPf/rwj3FipJzgw4lPZMpTRsHAnDTQbA3nW68cxHFNDAU9J19oKp4n/j+56CDvxINRq2m5KlSCCIFo5kkaTlaTmo7k8erBQex8XZ/Cxcs4H4L7Bcc4Dvlw0o9Tn+GsmQkCR8mlpppZCTSe5OSzwZiaPikucCSTmmIWjzQcV/p/FIejM6kFQcntxMQUOAlPnabhSAMo3BsRs6OnmGuyhhN8RxrO/hMjseN+cuAkXj81mnlHx88XAsf1hRDQ7VS1pIGSnqXGJ6W6M6VJwWM6ssQTVhP88ah4fWobruEkscSSyBcPPLcbG77zGv7y0Z9ILDU6jExq3EI0uRhYqoWGw0gDzUwhcnKkjA/9ww/xtZB6rHse1aRGAmdSPqBqqynz1fZlGVdplHrhw1Guk2fOEs/zcZiNZWFSS9BwdP5Gtb1ZNBy6hGOlazhR2ZDqWhoQT22j/ibaqttsej5Gyx6mh4JgMIk0oGg2kT/Ile5TTNFwhkouJoWbo3ZhQgqcXILEORVmC5AXOtkMFfwe/KWFe1ZPoX4NR5ngOTtKlX5iOLjmgeNxgfOhL/8Qf/utV9luMP0+fHGmbNGuH9V1yUIaKLs+hsNduxqHQ0eSJiUL7eBvURI4WTSc4G8hIWVNLCo+IQkjZ44laYpJLDV6jJFylH6EZ4WIEodGz9bTpQicnN1wHI4OQ6UgYFDnEC5rNBzX88VOt7vgIJ+z4wKmDpOa58c1wyg1jezUJvA5+PkntmPgU4+L0ADOUtPdn5vqVHBfSDUErDTuw9Efl2S2Tto8qD4cjmr5Cz0/OH/GpEDgJNKiFbIAbepELr/w2jTudAKnHDIo24kJKXCS1rpTo/Syou9UPwEQCaSy68GyAgdsvbRoHV2UzFKShqMM7lOjFZwarYiBVj3CXqPheFHWY1+zm4xpOG7EoOGLnM9YaZRLTaclFnPRbipT4Gd40aJTnQwAhH2nOY4Ed3BOws44wRRDu99AAwy+C0xqwWcSKtwJ3hNqOMNMw2mFSS0pCzEQLURxHw43qVmxPowFgmbUnD0/oOTuPjok7sWvp16H06K/HuZHO66UIX9530n8IMxUILXJS2av1WJS8/1A2PAYMu01E5igSUJPjcMBoCWNaDXDkKVGpI6kTANlZV3ijDcgLuCHyzqB4zctdVFWTEiBk7S71pnURJEvNqnpvZc9D3nHxpSuXN2ZBtRBlw+ZVlSrAtCb1Lxwl5e1ABsXWFHyTl8shK6HmL2cFgvqLR6Hc4ppOPzWPJdaVLAs3G1xDSec5dv2nsCFf7QJ+47H07aQ9pfP6QWO+l1gUos/uxT8mLDgJ5nUaDFy2W47YKmFGk45ruFMLpCGE5kSWxGHQ7vZUc0zlZUdLxA8I9F4u3LN8uFEm5ePb3wRv/uvz4t7BX/1GyJuUiOzUU4xO37oH36If/jeTv19U5hxunb/9OCpWICq5/uwmYbjJ7yaJAZj2fXwhrOm47+/9Vz5+4oXe95J4ZjQZRrg8EPSQFfBQXfeScw0UBZzODKPA8kZCHRacMn1RCHBdsEIHAa9SS0UOMwUc2yohH/+wc9RcX3kbQs9xVzdJjWdhuP5vuR3OHB8RGvukkrTZjB9EDhLjVRqHb1V7H4t+n+UaYAPYLW/6L90S3pGLnDoFfzz5p9huOzi0Zfikeck/wpJGo7yXcHRm95kk1r0u5QqnvlnOKKgwGgx4yw1mvB8ASWT2iijRbeCpUbCX/XDAPG0P0AwRoZLFXTnHdi2FZRMUGnRXroAUsH77fhwWeTkogVQjYQncJPakDADhTT3DH2U1K6kOJyvPL1TSsGz/cBJ/OTAKViwxFhM1HA8/T3LbrDhVNP/lN34XJoSjgnVp6Z7rtGKh2LOwaSCI/lwfGmeyYIlzjCNzJLBffUmtWbVO8qKCRqHo+/kwVHZ/gmwF8om5v/97g4AwMULpiPn2Ogp5usmDcQ0nHBxpV15MWfj8KlRra2dO2yrxSvwxVkyqYULYUBlVs0rCmXWjTQc7sPht+b95Ho+HFaFk/tw6B2QNqCbECRE8zkroQCbxv+l6YakksqeHwX6VRM4rucLTY8LHNLk+ALa0ybSwIgoXSxnFH7t8KDUN915B8NlF5XQpDa5GJg2844Vq9GjmmKqNZf3W8XzozmkMLvUfuVTkAgoqiM8y31VJCXvHCm7kiD7+MaX8PRrr6OYs0UQ8g1/9R389rXn4es/2ovfWXUeLjl7ptT2uMDx0ZW3Y4y7kuuh6NniOX0f6J1SxN5jwyK7fPCc8c4lH08xZ6O74EjaOb9/SQj0eIgDtY23fUiT+7Bc8YxJrR1IisMZ1ZRcdjWkAcKJ4bIwqTXLh0PUXhpoc6YUMVqJOyEp1iVrahvJpJak4ZBCY9Fx4TXJhOh6wkyUpOHoWDg6kxr9NqkQ7fxe2H0c//P+57Dn2DC+9cpB8R6SNJeYI9qxtaYWHrnNnba6wFK1H8nc4jIh4/uykHU9X2GppdCim6Dh0PvR+XA2bt2La/7i23ji5ah4IAX/BaQBF90FEjgZSAMZfTiuF2jcQ0rwdMX18Mv3fE/4aaLrRp/pFmqcWBqSNZz49YGgj/imi8qJBD6coEMPnBjF4z8+iKd++jp+71+jdDVJzLeK6yFn2zGLCWepEduskLMxc3JBKlyocyfShq6Ys2MajryxkwWNmrIpsnwE/x9K8uEYk1rrkWRSowWCm6eED0czOkbLLvKOhUIubprIilh23nDLTbEVs3uKwWTRVGbkfP9qC0MyaSBiqdGOyxLnhAtA+D35bYo5G0MlV2vG00VS6zQc+o0W58FRF//108N4cOtefO6xn+DWf3omYtrknARatCqsLS0tmgfWqUF20bWIuSefKxhMXpSwk5cnoGfhi1FPMS/dq9lxOPR+hEmNNfqlvUHW8d1HI59Yd0iNrYTmmq5cJHDU9iQtXEngC1vZjXxEUWJKDz/ceVTKFQjok9EmJaDksBWNNLE9yvVV5hjN9cCHEx03HKbx52tEEhGhFDrdVZNaiW0QKZ4m79iY3VMU2eWDtnqx8g8UuFnMO+gu5CQho9vYCVJGzP8aMVGBwCy/M6ydAwT9X3KNhjOmGK3ETWpq9DPHSMVDzrGQsy3t779133P4zDdeTr1n3LYdDF6qiTFnSjE2WXyfqMdedg0nbJ9jW1LgJw34IFgzOJYWWTK30KAmH9fsniDDNe2aqmk4XHDwZwB4nEBF9P++48MYKXtC2Bdy+jo3cZOaPvCTV2PlmQF8zWSOsdTs6HdeYloOSpV3nz2qhtPk5J30fkY0Go4uoJaizSn1fTHUeCYXnZgpsxo1XoWg5fphrZ2KF+zww++T6iZl0Vh1oOerNbfgaJivjMYdkSksy5L6jL7nZrKk6qMV10PesbQaDrWDAisd28KcKUVZw/EQi4Oh91F0bEzKy7/p4nCSTOr0PT3vKwdO4q2f/VbUdpqTTaromhUTUuAkaQOjFXlXAOh9OISRsou8Hdhwy64f27V9/fm9uPvJVwEAC+98CH/y0LbYNdSdCS1MR8IsBqTh8MWVO+N5MGIaqP2T8k6QndbzRWJJuqbQcARJQFbbyU81qyfYtQ2JzAzRffjip9rTixqTGjHRBkuu6P8DJ4Jnp6JRBUcv0FXSQFJqG54NgFfo1OWmUs8XJjVPnsSSSU2JF5ms0qI5aaAJGg61ia4/WvFwfLiMd/7NU5JmQygqGg7R03uKcXalOs6zklG4eXeoFMUs6ZJbAvqYl7RqoNF56eM9SeCUFI2AxoSlaDi04OckDQfaa6aRBqgvyKSWs/UajipUhEktb8eEkTxeZfNjEmW7WgCq0XDagKTBSiYQnYajWyhGQ6dbnjmWdaDBQWQD6TdPTnRJbJZ9x0dgWcDMyXmUXNn+zKO4s2s4ocApOoHGFPZBPhfPNEA2bfWZSeCQmUCYICUNRzY18b+yD0du19BoRWgfB8MAwKFRF5YV7A51a1FMwwmzRauCf6Tsickrm9SiY5J8ONGxkdlMvYfryuldYqQBRovWMcpqRcyH43r42euDePbnx/DKgZOx4yMfjofRsieEX08xH2NXqot+NQc+Z+5FwaZRfFg10xdHFrM032zprxv8VcfAaEVuT2RSsyQSES34kkktQciVXR85x4KjIQ2QoJ4eBnDmnUjD8ZlQ7FKEygjz4ajmNt08S9JwhI8noUt5SY92YkIKnKRNpjoo82xnrdNwXC8YcKR+J00CncOOX4Pvcijga/fRYUwp5tCVc4L8SuwafHGsNrEJNAC78o4UmBlpONF3NNdKyuJIixNFQdPk5PELWtIAaThscKtpTAZLFaF9UO6zwVIFjmUhZ9va3W8sDseOtDWOkYrL0s0kkAYSFhW6Fg829DQaDm8L3UvQop3AsezYlsQoqxe0Pka0aE/0u843IvtwXCFwdGSXepJ3AiFLTZjR3KqCyhfCm1sTsmt/Se3ylU0OgTRv8lHRws7jcABmUmMCJ3FR94JI/ZiGU/HEGjOdmdRm9xQwWvHEPHI9X7wbQiRwnJgw4N1Dz1EWa4F+o5BUuFFoOIY00HroXkLescQOW7CqWPxH0mTIOVHq+SR2DdmxdWkkKp4vWENAZPPdc2wYU7vzYpfDI4WjwRb5cKrMb7GjKeZsaYHMSyy1UMOB/nlocZohNJy4D0cXSa2Lw+FaGhDs1tXMy8MlF3a4UGfZESdl/R0te5iiaB2ALCiTNEW+u9Ul76Rz6L/FXLQAcZYaELz/Zvhw6F4RLdoTmwPdMCAfDsV4FISGE8SPSYt+2KeUgLR6poFow8NLIlTzx1A3c9ZgLcXfqmlO6lTk+QN5vZrAhxMdR/OMZ0JIKsBGGk7cpBZZEFQfDgDhx3H9uMDhLDVV4EiZBiqyoIkLw2g8TynmMLUrJ5m0aW4bH04boDOpTSrkWKbV4LtCzmbZmPXXKrABlzRhjg1FMTUqXE9mipCGs+foMKZ2RQJHyvrLC1QJh2b6QuayRZ/b20WmAbag2ooPhxCZ1BQNhx2TquHk4hoOaV6nRiqxzMtDJReOlRxYy5/ZtqKibur7zaThuJG5jEMSOF78Mz0n9d2kghM59ZlJDYCortgoqI3DjDQg+kJzeU6Llnw4XQELim9maFy88MnrsWLB9OosNTeaH7SoD4662jnGQb/z+LVa+iZR4CRoqsKH43nSpiNZw2FjlRbvmEkt9OE4cR+O5wVUeTKRlyqeINtQLI7ryptNQPbhFHLydbWBnwn1sPhYvXDeVLz/irMloUQCKymvZKswQQVO/LvJBUfssNXFGUje6eVsO5aSQ8We0JFL7CCOiudL9mISOMNhPiWdwOGUyAqb8GkgragQpn+hSSRMakwIkU1bfR7ajaZpONxHoWayLrLdXGRSC/4eHy5LwiB45goc28LU7hyOD8cFDl+gyGQFxIXGSNkTfpXRBNJAUqwFN19GcThylUseozOpkBPtiPKshRpOzkkt2JYVavR42fUSxx4QaTgVL8jTVWQaDgAlfYqHnG0JzTJzHI7PSQOVqiY1+vlkhsSqOiTNR/o6RmGuRAs0H2cWy6UWtCE4T/bhhPfUUI/zjg1HWbRLoQaVs21M6YrmcyRwUjScUrJJjb9iNYVNvJQIhXgEQtUJU2apRe6MSa0N0E2i7oKD0YorZQIu5OzEErmEnGNFJrWEYyihIU9eSXA9X1LJp7H6FFO7c2LQ8aqBvBZGNYFIqAiTmqPXcJiPIhb4qYA0HK3ASaNFOzqTWnD88eGyVsOxrcAscUITWMtJA7aVnKJktOKyDM5xtp+urYRoAYuEumpS45Ty7oIj+VjyjiU0r2KuOSY1urcwqbE4Ld3YVk1qtPGh3fdJXobajTZASaZMDomlRj6cUnKFWvEMXlzDqcWklkwa0M8HHnvGNRwLeiq5zoejK7mes/UmNc/3YdsskWvJFcKFh1+oTLRI4KSb1GIVPzVxekAwHhzbkohN+44P42evBzE5xqTWBujW5snFwLzAgynJh3N8qJw4gQJaZEgaSNhlElW1K4OGM7UrL32m3SgnHvAUILVmGqBU/7Qw5RlpQGg4dJ+ExXGGwlLj/anz4XCNMfoN4hno7xGlrvpwyQ00nK48ShXZFKI+s2NbjMKsCBzuw2FCjXZ7r58aFZkd1I0F19J05QnofvTb4t4eZlKT07/rsjPXA2oip0UL0oDm+GIuwaSm0XAqXhQM7FgWhsse/vLRV6QkoBw8dQ1p0UOjleoCR2tSa5w0kORviWjRHtTgX53A0bHUePP8cL7knXimAc8P+tqxLCHURyqeOI7P30STmkbDkX1tMi060YcTPh8Vmat4Pv7wgRfx21/dCqD9LLUJmUtNNxlopxHUF6cF0sGP953Axf/7UVx9Qa/2WnnHEjbcJBv0nmOhSU2n4bhyHi6u4Uzrzkf1LKRdaGQekAMQfbGbVkEDsBgKnKgsc8TsssiHY+tp0QQSOMPVNBxG/6R7i7Yqqj0AHGRBcUDIUrMt0Scnhstitw7IGqXD6K3q6x0pu0LzGNVoOL/xlS14Zf9Jqc3iGZjQtNh5/LBKaG77rav7ccd15+Opnx4W9+VCttAkDQcIFp9hRotOiiOi+5K2wllqQuAoY4trOM/vOobndx3DG86egV88Pz4HaGEvVTzRJ6cyCRw6Vp8rrBqSNJxqLLXArBi9g7IrpyUiSKQBMQbk84Bw/isXoGqntm0Jk9pIyRXX5GNKXRPosYIaSvJ1ef+UVA1HZamRSc0PNDheaPHwYEkwQQ0tug3QTUpKIsnzlvHF4pssNxVHzomKayXZoHczH86uI0PSoqxqOIWcLVTvqYk+nGiwSc7rFLMatS3QcKKFgsfhxDScBIETN6lFv0lxOJ68CBZSSANAoGlwDI2GGg4JHJXCy01qtiUScepiMIo5B3nb1vpwXj9VEv2rrmN0KV4viBhrfBL7fmSaoQ2zTuA0K3mn50dab9n1hODWCbR8mO8r8PX4EmkAUMxazMTLNy8jVTQc7psaKmWnRcslsJPPOXN6t/T/RA1HjDn5e8mHw8aA6/naZL4SacCXrw3IgZN8/loW0eaDeU0aznDZFcdxq4QqrAg6kxp/pkjDibQlDl56nnw49D0vN20EThugW5cnMdqs2JGzl5GUxTtvszicBA2HfDgV18eb//xJ/NGDL4rfXM+TdlM2U8OndjEfTllHGpBT3qTtEGlgqiy1IjOp0QJMC2fSbnzGZFnD4Qs8N8NFEdoQ91bbyvtMbf5QOWCpTQ37g2fO5c8EhCY1O1nDCbL6WtpcalIGaeVkTiaQ6uH4kemJzqcFhfqPU5ABfe6yeuH5vpTaRpQj0AmcMFZEpE0hH05RU+TL88V45BvskYpe4IjAaHbfQRb4mdZ+fn41PHXn1bhj1XnRfRM2V3Q5/h4pbxgQbLxGJFaep9dwtIGf0e/U3zlHFgyU7Z0ENxc4RB6h7Bqu5wtTl4pi3o459D2NwEuKw+Mar2PLGg7P9K4y4VqNCSlw9BoOmdQ87Y48qaQBp0UmLVyUF43w8AtRIaiKJ9uQbSvaeU6bxOJwSvIkAeTYh6TnEuewOJzg3GiHJq5FBdcSWGpAMBEnh+apqj4csah44b11LLXkhcn1fHTlHWZSqyi/y6SByKQm27or4XVytiWRBugwnRlQbafryZRbz4tq4ND5tEZxlhr34RQcuyksNWrDsEQaSNZwco4Fx7JEGQBhUusiH040PsvMxMt37iqDkEDjj2uOQ6NuLA4m3v7gby1mNK5xJWaE0Pg0+RgrKyy1ilvdh6OLxYtYn3IutXxYIsML5zVlQx8puZIfha6blLm+4FSJw1E2bGlxOJZlMe3KkzK9Gw2nDUhiqQGU5C/4TlcwTEXOscXOpeL5+OOvv4Tb/ukZrXmAJqVa9jcnqeSR3VeOw4kGCQX5pWk4wyUXX3l6p5iAFWXRp2twlpoI/BRxOJp+yjuwLAvFnJ2BpSbvvnS51Koxk6Z05YRJTdVweB/zrL9c2I+w+iJ5x07QcJK1RJ4pmB4t8OFwDSfUDIWGExwXsNQaN6npApV9P9IwK54vBI1OoBUcG45jiZooNAYoU7es4UQaN1+IVcIGELLzfPq9uoajzaVXhV3JoRMCKnT0drXKphp3pAtF4VaHaAxEv9N7zCmZBoKqs8FcCggvOVx30Vx88f2XRPF67Nl1JrVcaDVRfThy+IFMFohrOJ44x7HkWEG+/hjSQBtA74bTPsmH85eP/UQQCPjuNIi+jw9ymTTgYcfhQezXVOgE5AWCQD4c2wraRYMUUEgDCRqOXLsn+vzkKwfx8Y0v4Y1nzcDSM6dJmZcBxsOXTGrBuUmBnwBE7qfuvKP14fDPdLqo3Cn5cKJnmdadjwkTQk9XLtJwRlSTWtQ+zlLjbaCUQF15JzCpST6c+HXiLDV6Bh8QAifoK3rvFaHhkA8nYqnxRbbeTAO6tdVjpAEgGh+6jQ5Fw6saTjHnoJCzZVo08ynyRVen4fCxp2o4unYUc5GGJ8qP16DhcG0gUcNhJrWRsoufvT6E2WGyWSB4V6rwrO7DiQsxYVKzFQ0nNKkRO8yyLGz4wAAAmZpNyXN1JJ8oUDjZpKaW8E7KFk1rCrWxVPGkTYnRcFoM/tJ4WeBJ4W7v8R8fwH+ExaKk5HlJGo7NU9v4IkBQW85AN2k9OR+bbUXsoandjBatCfysMDMYXYtA6XQoy4EgDSh+B3rGTz30Y/zLll3hoyb7cIja3ZV3xIKXZMpTMw1IAseLtAsiIegwpZgXVPHjQyppgGs4lliQeHtIw+nKB/R1/g6y+JGk7AJcuDMNh4Q33Z8vjM1gqen6l3bq9D4GNTXrCeTYpvfF2zRFyRhNsSVAdQ2Hj7fRmA9HI3CkwN/wfi0yqXk+8MBze/COv/4vyaRNwa/SdTUCh8sBzrZUAycLOVsSTkHGcggNh4NrGXRNnYZTTBI47JFVOnRSLjXPC5ID07XUysT5BB9SqzDxBA6bvLMmF8Vn0nA4JB8O/VXeTz5nSXE4lPaE3j8JD0A/aYMdZZRx2pJIA3kxUIY1LDVeYhqQd2C00JJWUFZ8OKSSF9hAfO7nxwCw5J0aDYcYdN15R9zDT5g8Udqd8N6aGiNl1xPZp3Xo6coJ5l6Mpcae3bGjiHFJ4EhxDTJpIMgY4Gv9TuozeJ4vmdd832fCWzZF2gkCR1dhMwt0AoeoxzSGk+rOAKFJzbJEqQCudfV05aRzKUIeUHw4GtJAksAZSgj81NHia/Hh8LWxaj0cP4jrKrmexH4MSAPyO9CZy/nV+VouBKXQcGSWWi4sAqhjoNnhGK2wgG2dhkNzXs0WrWPJ+T7wzM+OxPIQ0gaTSAPURnUOGQ2nxaB39uE3n4P/G6q6QLwQEqCSBoK/ecXgG9TDiTScihuYW+iFnz1rkjhWHRRAsHiRzRZAmH8p2NHLqW10mQZ8aeLxiUEMphOhqSrRpKZhqaSRBigOpsg0HFoP46k4Iu0gOCeu4VRcH915m5kRohgQIIqGD9LbqAJHb1Lj6zP1OdWeV+NwqmVH5gsYZytxk5rKUuObkoLTuIajUyCpguaCmQFdWC2kxhGk0LfEGOKaRnfekWrWVJgPh2tqurHL+45rDYOj+tQ2vC+EqbIWgaPJNq6Ca9XU11zDKbuyDwfQazi6tEf8+hEt2pJMj/mwzHmQaUDvn+EbRZ2Gk2hS05j0AOBdf/s0th88JR3LC+PxOBw1O/i4FDibNm3C+eefj/7+fqxfvz72u+/7+MhHPoL+/n4sX74czz77bOZzmw16aTMmFzBvepf4fnJRo+HEfDiyXZv+Ty+t4kUaDg2ohbMni2N1GgPZzGlAOLaFN541AysXzkAXW4ilRUFTLhqQJwZNKlqkXc+DZUWVDNPqYaSRBrqEhhPFtNCakbQji0gD0UInNJwweSlpgm89vxerl50h/DaUIWBadz7GUpNNahDOX76I0c68GLLU+DvgKfXVdkXHhH9VlpofaQJlxYejxlURAlp09gWWt1PFgVDgzJ8RbGjSNBzKhjE4Gtdwco4lxTPxnbkUh5PRpDa54CRqOHxdT0qImQYuAJOyenDNidp0fDjKYKGmtlHbFV0n+ux6kTYbFzi2wlKzg7Hi6RloOVsOS9AJu4LQcPQWg3u+/Sqe+fnReKMZeNJhm7HU1Dmky2DfSjR8N9d1cfvtt+ORRx7Btm3bcN9992HbNrmy5SOPPILt27dj+/bt2LBhA2677bbM5zYbNL5ty5ImlJpiApB35HSouiPhFf8o8p8PqHNmTYYOEX3TCzWcyG7+tuV9+Ndb3wTLsiKNhAkWrnlIgYx8oS1TbZnQpOb5QQCgQgjQDTjLigeVEsikpvPhVBM4XLiRckIJEIkxdfUFvfibX7sEXaJmS178PTmalTQQ74cuXX0RxZwGaIqrMS1NJGQOY3LyyruJWGryjpcQ5FLTayIHTozgy0/t0P6mUwL2CYETaDjpPhwr9OHIpAEAYa0heecsTGpVfDhcw6TFffqkQmLyznnTu/GxX7oA3XlHMlVmBX99ZdfHL/zZN/HQj/ZJx4gNgh+9l2OShqMzqcUXfTKL/UP4TkjzVstN5xlLFQgEeLDp1JvLcmEArk7Doc9JGs7gqIuy62H9Iy9rtV4OnnLItqKNcsykNt7icDZv3oz+/n4sWrQIhUIBN998MzZu3Cgds3HjRnzgAx+AZVm4/PLLcezYMezbty/Tuc1GFNwoTyidD4f7HMjMlFMGQZ5pODSQePp6WhBUEDMo0nDs8D7ycTqBwDWP0bInUZsJpOHQjqbiBqYSmgRpBZhsKznnFzmpAx+OInAS4gZcTx70/BxyUveEQYh0fTL7UKxIMFHlWSbTohNYakSLzsfTyPu+fqd83+ZdeP/f/QDP7zomVWfkzB/PjxIfUrtoPPF1JkYaSOjX//HPz+KPv74Nrx06FftNp+HsP6FqOMkmtXzMh+Ow3+R3XfGi1DayhqMnvBBo4zO1Ox+UJ9AIEs/38ZtvORczJxfqIw0olTl3Hx3G7z/wgrZNwWYiaJNEGlAyDajX5df50e5j+OTXgw1wXplj5AMlcyUh79jh/PegUx4cx5LGEteOnCoC56+e2I7PfOOV+EU1kGjRtiUyDZyIkQbGmYazZ88eLFiwQPx//vz52LNnT6ZjspxL2LBhAwYGBjAwMIBDhw7V3d6o5oslDTSdD4fbuulIVcMJWGpRHA6laReO8ryND7/5HEF1JpwQpq5gwc0zDYdDX7QtmvwjFVcIRlezsyeTWtn1JS2Adn9FnYaD5LQ2XMNRAz9jJXGZ2SRny/EK3DQRmNRC31CO/pKGEwocx4otYtwcJvtw4r6FrjC1jdQ+39eaDX//gRfw3e2H8d3th1jsDUveCdmkRu+DHo8v1EXJpJZcD4fYQ7qF3de8iv3HR9CddzArzPowlIGlJtrENHcy8RAqXpRpgL+vqqSBsN3TunMYLrtaXxW9L9uukxadoDFwSCa1Mmk4ikmtpAqc+L08X16cYyY1puHwNhScIHWU60fpZOT22pIPx5EYbrb2L8drhwbjjdUgYq8Fm2Vq4wnFD5qUWqdVaFjg6ILSVF570jFZziWsW7cOW7ZswZYtWzBnzpw6WxtN3sCkFn2v8+HkNTvymEktZ0vxGK5HQYLRgPqDt12E65acIZ13bKiM/3h+L0bKLhzGdFEFjm3HkwPK5jU/tvsCooWLVOhK6Cuh6wsfTkI9jCTndleaSS1BwwmSiiqBeyzoMu9Yov9FjAhpOOH3tmVpoqlVk5p8bd4PXRoNx/PTMxQfPlWSFjBOufX8aGFOM6nJmQacRFMlXWvj83tww+e/I80NrYZzfARzpxYjH1+KhpNTHNuqD4cLQU6LljMNVPPhBL+T7+2kxqfEUyfpYluqQSdwbNvCwZMjOOdjD+GZnx1lxI7IXMpNai/uPY5/f26PZEnQrTme70t08bwqcCoRaUDy4eQswWjUMY5ztgVXoUULc314gvDhaASOrkyHDlzTc9jc4+cXHDtxvW0VGg78nD9/Pnbt2iX+v3v3bsybNy/TMaVSqeq5zUaSSU0tHcDVUCBaoGMmNdsSO+cgDseTSjjT5FWFxtee3Y1/+N5O8Zs6oDkKORuVlCSHfPdVcT38w/d2Cs1G1aSo+eRL0A1qH8DRoVLse4ALnIg0IFhqij2Y+3CCaGcdS81DzrGFwBEsOMWHoysGpsbhiNQ2TIaMctKAxoeTLnBGhYZVcX04TnReYFKTCRh2BpNacLwHx5Y1auqbL377NQDAz48M4ezQ/6cTOPtODKNvajcjlcQXeApsLqgajmRSsyXBHWg4xJhMN6nJLLXQh9MdaFzqThqIxoNtWSIYuJpJ7YYlZ+Bdl8wXzxN7RsvC06++Dt8H7v3eTkkjFT4cRhr4ytM/AxBo6sTs09KifTmTNW0+I6p/aFJTNPecTSa1eBwOPUPZ80T9GjtkqJYqXtyHo/Gv8H4lxpsOaqYBaj8nDbQ7BgdogoazcuVKbN++HTt27ECpVML999+PNWvWSMesWbMGX/nKV+D7Pr7//e9j2rRp6Ovry3RusyEEji2b1NTBwdNBABHDLM5Sk3OpRaQBmSqrXp8SegKBXVdoSZqAUdVUpdap4T6cZ352FJ966Mf49k8CsyM3qeXZjiaNpeb5Pg6cCGIXqEohoZtlGqiq4ZBQ8QKKqByHET6LF2g4UxQNhwTPFObDURllKmmA+tjTaTi5KNaJ4CeY1AivMw2nzGIniFgQp0UH5yXH4QTf69LPqJP/R7uPi8+6NeXA8VH0Ti2yAn1xDWRS2Ie50IdDkE1qcr8SiYU/D5CdpTYtDOJVAwyBKOOEbcnxTWl4y/lzsOqiuUF7EnKeRZtBS/K5jWpIA4S7f+2N4nMSLfqUlOQyMpsDUXmAQk7NNBAFfmoLu5EPh2k41N9ZTGq8X1NzJ7LNXpBLjXw4TGC12X8DNEHg5HI53HXXXbj++utx4YUX4j3veQ+WLFmCe+65B/fccw8AYPXq1Vi0aBH6+/vx4Q9/GH/zN3+Tem4rQePbUlhq6mBWTUCEWByOJCyCnS83qSVpODxuIsdIA7oFUF3I1cqiNBk83xc7Xbo/2aGpzgk9Z5IABQINYX/IguLUcSDwhQChD6fihcXIqC/0AocclzMmF3DP+96IVRfNjflwIpOa3ofDzTD8+mJxtCLTBD+MZ1RQn/WPHnwJH/v3H8Wen/D64KjYMVOd+qB/gvcrfHcKLZqPq4ITaRP0TNoEm+H7nzs1EPAv7IkEjs70XHI9zJlSFGNDG6Qbbg5Us4/sV5KDUXnFT/4ce44O486v/UjOtMzJK4pJTWf6cb2on3yhKaQLHN5uHeuLyi4AwdyM4nCiflbjty49ZybeeNaM6LpagQOtSY1r5gBpOHJ/kkDRza0oDicyw6qphOj9VDOppXUdvRvfhxR2cVLzTO1EU3KprV69GqtXr5a+u/XWW8Vny7Jw9913Zz63laCBLhy8VhjEpwgSVcMR32to0ZFJzQuiiH3GsxeDSb4+X0P4gNCxptSBoZqBuDlODQAkFbwcOoOjqoOyBia3zRcsqL5pXdJuu7tAGkgwscpulO4jRhpgu016vhuW9uGJHx9UWGrcpBb6cJQiYbpyx2XPRzE0N9p2tHBIAXJC8Nuxd7xt34nYsxO68jZeP1WSdswR1RSSSa2UYlLjZhG1nAEHLTb0/n60+5j4jR77zYtnY960bnw1TEHUnXdifc7RzXIC8sWPL2Q5RzbLqBU/CSdHK7j/h7vwzjfOx6XnzBR9QiBtYiorlqeCHtu2rKhcdxWBw+egTsOxLfaOHUtK3hkFfsrm4e68IwldfRyOr5jUFB8OY3nyvuXJO/Wlq23h66XnizalpNnIGg/HqSoZJXgZBmqvbTEfznBZrHntLi8NTMBMA5ylBrAaJjbwzx++DOfPnRL+34otxpYVX6BzthWa54LFk4K+PLHQ6TUc9RofuvIcAMC5vT2x34vKoqIKJa7hqAJnNCzN7LpBHE4U1Ek7NI3AQRAXMrUrFyNTcJYaELCXaM1Q2ymSdyqTjwuPQMOxNCy1oAQC0dVVgeOH/hciFwQ+nPB+mjgax7a0O84knDG1C0eGSpLmUGJ2cSCeaUCMqSTSQIqGo+a6ejmsQMrv9/blfbji3Fni+5xtpQbudXOTWriYFXKyozhYADlpwEs0AwPAvuPD4rOUvFOw1EjDiRZG2kTQrt5iJrWkjMkEHW1Y/Z3mQ96xxUYuMKkFc0H1P3Xl5dT/SXE4fHFXiywKkzSzGtD/iVSS1F5ucreZuSseh6NrV+wrAT7/pDgcW840QBV7kwhDrcSEEzi0ZkUxE9Hu4k3nzkb/3GDB1y1Q3GxDoIGbc2xh5+e74aTJyxcyx7bxtuV92Ln+bTGfCaDx4Shmt6jGDbS1548NlUXKErWuepL9+sCJEZwxrSu2qywqAuePHnwR7/ni01JfEHh5AtU0Qu+BdtTzpnej4NhiwZpUcNBTzAkN0bGtWCYF34egmyfRorlgVTWcNJwxrQu+DxwZjHbHfDEDeNYG2YfDF3QpW7SSVohDXXTlFDy+uC5/HY5tS/4YFZO4Sc2Kt4d+UyvQqumFOA6Emi8gJ4ykxX06sdSY6YdirHh0Pdd+00w7/J1VM6nl2KbE9/3E2kPdeUcZj/FjPA+SD2dmSD8/MkipoiIhRxtO+j+RhnQaWT7UKD2NhkPjNyIN1LY88zAOXoBNyjQwUhH5CcfCpDbxBI5Hkzf4f2R3D/5PjmXHkllqdKxKIxQOvtD5Sn4cntwv+CufN6z4cNJQUMxLqkmGsimrJrUL+6bCsoBPPbQtKKzFaNElFrimwvOB/SdGMXdqV2zR4ck7AWDj1r2xviAIlpofLzLnhdH6bmjq+6WlfXjy994qnM4fvHIhPvvLF4tzHEvWcMi+TgKKTypupQmcpsFiVQsrp29aELB78MRo7DcRZa6YJy2xiYmOVVPbAOkajmg3E5r0UY0dyzmWNn6MQMW/8kzD6c4r7DjFpMY3B7oFkzIcAKoPR9FwQtMNEPnhaO7ZtpxLLc0syLtFN0+CIOVoA8A1J1Ww0/ldSh8kkgaY0KScdfd8+1Us+fgmEUQbxSyFZkiWvDNJQKqkIroGtV0k76wiEN62rE/6f1R2gvmyfMQYot15JyDRGIHTevDJC0S7uMh+GlJC7bgPx7bj9l4x4BxbZIsG4j4S1YfDEwjqdpIcJEQGFgaOTpWtRbv8QOBEZoDLF83EuqsW4T9/tA8HToyEnH/Fh5NgTjhwfARzp3bFJk2XouFwxFLbhM30PNmBSsKDqiZSYCKvW3/unB5cz2KXVJMaxXmQz0DKFq36JEjo1yBw5k4NyBKHTmkEjlJLiIrZOcqYAvS0aK2Go7SNPyun8ksCx7a0GTIIKxZMx8ULpmP6pLwYy6oGnbNl0kDZ9cRc0C2Y+49zDSdF4IxUhHmUNkokRB1Jw0l3XvNNX1JlzopgXEbBwa4na4mOHaWJiguc+H09X/aXLJgZZHT45ssHMVhycejkaHhPW1zftujZZL8lB/U3N/WSYFFTRKX1y3/+1i/g/VecLX1H2m53wYn7cNj46so76C44xofTDkS06OD/nDwARILBseI+HEfZYQKyg6/M2Gk0AZNMalwwVNNwfhpmgh04OxA4qoZDO0hX8eF05x2cOzswEZ4arUhBZmmkgbLr49CpUZwxNW5SExpOIT50kjINVBTzgm0HCwNfKKpBFTg6DUeX2obv2ClGJAv6pgUC5yAzIfFrApHJI54tmvtwGEuNTHApLDV+DxGNzzZJfA1yQv9hjyZoGQCuOHcWNt5+JYo5Ryw4vVNlgZN3VFq0PvCTsJ/1hy5bNL0PIFoAKU8emZEsy2Iajpe68EmkAe0CbokUNoAcbMwF+9wpRXG+KnB0wY++70uMrsmFHGawuk2ULFWY1EOfr21BsNTSNBwucL50ywDWXnkOzgnnapEJsSQUc3Zs3SAGaXfeCc12EXGJhwQUw3IfRsNpA3i0MxCRA2jQcfu1rp6FOgY4s6TCdi6qU15Nf8EFg5Nxp7FiQShwFGYPBUd6XlzgkBAYLrlSihOR/0szqIfCAlpTu3Pi994pRVhW4NsAosHNkZhpQJl8jhX4Y6I+qj4M1cBP2n1OExpOAkuNRc6fxUpFVAMVhdNFzKsmtQpzhgMKS43n2arBhxM8B/2NzMB8caQFY0qXXuDoGF69UxQNRwn8LHu+GI867beahjO5GI2ZILNFZNrjKYAE+89PjweRSQPx323bEmO+7PpSn3HT5aI5PWJ8qGZFbS41hTRg25GZFQjIHZxE5IT+UTscp16CD4dMmFzgLJrTg4+/4yJQFZ4sgqCYiwcy01zvzjthmZRorZNTGznoMgKnPeAOWCDUZBTWDqCPw+GCiRCRBixR8ROI7PQ6Dacrb0vxDNU0nCv7ZwVMrnBhUXfIU5hJbZhpTt2FaFANlVwpeWepioYT/Bb5fJadOQ3P/OEq9Icsuq4q9YOoPfSX97ETLhIr/vdjALJrOHxHTbtPEgxB4Gfwm0yL9sQCujAhc7cOtAvWsYLU7NfCpKZ515JJLcWHo6PmRjEl0cKhC1ZO0nD4gkSnkamQkLctidrONRzdDv3gyVEpoJcwylK9kF+J6jzRAi/Ropkmkvb+Jee+lhYdFdUru56Uv49nUj9n9mTRbjWrSJJJjZd8sC0L85jJ9/hwWaaX27awgHihhpOkkamBnwTqzzSfFqGg0XDofsW8I8Iz6Hu+qSvmbPRN68KcKXGCUqvRlDic8YTIPBH+teORwkDc0UbfqWNImNTINuvLJjU1ijgX2pI5bVSX5I/jn379MrieL+JG1GwEpOGoJrVi3hHtGy67yDEBUq4kC5zoeVn0vG0Jpg6QTcPZdWQIRwdLcD35PqrQzhLxbKukgXAxIDOZlNqGCYkKW0DPrkHDUXfBHEkstSgORy9w0uJwdCmNPI1Jjb8ueq6eBA2HL+QUba/TcOj+Pz8yCNfzhUYSL8URCKfdR4O0O2outVy4IZtcyOHkSAW2ZeHi+dOwcuEMPPTCPrzrkjPFc0RCK/AZWZZeuGehRdOYr7D5xwuwAcCiOZNFv6ulSLSkAU/WcAIfYySsjw6VJMIGhUcEmjjguvo4HMe2g0KNzNxFiEzMWTQcO9YfNF668gHVnfuruRWlmLPxl++5WNu+VmMCChzZwWtbUARO5DCNx+HEfTg5puFUJA0nmATqrpcXbBPXqKLhWFbAZBEMM4U0wFlAnIzQnXckamXOjrS5NB8OgUwEQNy8oqsfpD7X/T/chU0v7ccbFkxPNY1kmWCc8gpAMIimMdKAMKlx1hWr76IutmnQkSIIFS/azQNxgcO7SkeL1tF1+bPRws5jKQDZbBj8P13D4TnTXh8MnNxzpsgaDs+ScefXXsC07jzeHeYuUzWc1cv68PAL+3DXN3+Kz/zyxVKby64vNIdJRUdc+19vfRMA4FdWniX6QmapRWmmdIk8q/lwHMsSjM8y81uUKp608Vg0p0f0p7pZ0q27I2VXCj9wNBoO35RQaiWKMUqKL8o7FlzPExmreVtoXGXRcIp5O6YZ0rOTDyfKXSf3YzHniE1quzHxTGrMcQnEyQG8vK76Qh0NS42OCdKORz6ckuKfEJqOHacjVmOpEWixUQM/SeDc9v+exXe3Hxbfd+cdqfxAILSCz2oNF+39mIBS26iaJQD9RDk2VIbrI+bD4aiHNEAmNcFSsyzRJm5KoXpDQHImch10z8evGbQ7OEak3Q8vLwd+xlPb/OEDLwqWE4EvthRs6yoCR43DSfLhvOW8OfjTm5bhvLlREPGRU0E80dypKkstEprP/Pwobl65QJjd6DmIBXnLmxbi/ZcvxL89uxvHw9guDmIDTmHZIQjdBUfKph2Z8DxpI6Qii4ZDm6wyEzJqGelF3KQWbpbed/lZ+J/XLtZm91ZT81iWhasv6BX06KNDJcmsnHOsyKTmJxdgI9MwpduZxogIahn4NBRYiANhwcxJWDCzG4vmTJauR8KQkBa71WpMPIGjpLaxLNWkFjFEsrHUItIA37mKwMqwh7mGE0sAmlHg0DVUWnTSbqW7YEsaDgWpAek+HN4uXX4wQG9yShIcaqp29VpZSAPkjCWcGq2gmIsCH23bEm3ii40bBrzWiiwmNSKRqH2ZZFKbEZokT45W8I2X9kvX5P4QojrzZKF03SwaTk8xh/dedpYkYA+HAay9ig+H+n5w1A2JItFYor3KLyyejVf/dDXeeNYMnDe3B74PDJUrMTMgjTW6RtK4tjlLLdyMJO0F+NhISt5JGk4QAxdcmMbA5Ytm4tw5kyXthKrJfuq/LcP/vPY8qU//9dYrsPTMqbHko2XXw+K5U/B3t6wEEGQvUDUc2qB5flQWIP48gSVECBzW38KHU0Xjd0LfmBoAPm96N7770asFuWE0ZO9Zip9aDf5tJyacwNHF4UgaDls01IXQTiUN2JLAiXw4duw4dUBl13CCvyrLSS3uRgu/WlZZjcOxrfRdPy/Dra7ZOpNT0kAOHNHJ8RRZNBw1FfvJ0QqmdOUiDcxiAqckR87z/v3qusvxG79wTtX7pZrUmEbDI92pL/njcYHTU8xh+5/8EvKOhT3HohQxgGwG7EnQcGwrHocTHC9vOHTjifwZc3ritGggSnA5me3aeRYO1TRcYSY/tT2cqq4DmZ3ouXN2fHMn2mDzz1U0HMYSpXQ2ay4+E0/c8daYtiXdg1324gXTMaWYF36homIG5SYw1YdDPl7PD8qE6AVkEJR5fLgMx7ak/qa2V9NwqE3qRqpUkU29grhkyQzbosb/2i5MOIGjxuE4ijqfquGwl7b0zKn4w7ddKLj5OVvWcJJYanlbkzInq8Chya6YMtTF8YIzpsK2gL7p3dJiHuTUCj7zAL8k8L5RJ7tOuCRNlCDqWr4uRxYfTmCGAQ6eHMGJkTJOjVTQwyi4tm0JEwePceK0aAC4bNEs3HzpWVXvV8jZWvYSPQ+1iZfj5qY7OlcVppTGZ/dRWeDwxZt8IFVJA2G/UR46gm48/dXNK/DmxbNj74iuIQQO05a4Vs7bT+2NaTjhbyRwkjRLYnIF1/FiTFGpfVU0HNuKsnaUWeA1QTcmq8Xh2HZktqb+IDMtN7V2SRqOLVisvk9jPt7eILWNh+PDZUzrzkv3FglBq8wHeqZz5/Tgr3/1DfhfN1wAgGvaMhvSVtYgo+G0ES6zhwd/VdJAtIDpc6kF382b1o3fePMiFr9ji+A3IBqgahxOzon7cLLWdY8YZvLx6sBeuXAmnv/EdThn9mSFuhlpaOWKLAR04FRjdbJbzGdCSJooKkVU3fnqYl10bQGAD335h1j/yMs4NRrkhOIZmknDkVLoK9oVHVsNQengyFzKweNJAg0n7g9T82JxzJ/RLdVDAvQajkoasBSNlMZUMV9d4Ny44kz8469fFvuerpEmcPgYijQcLyZwaL5MY341HYjJBQQ+VV0GD92z6J7LsiINp8LicAi6xVUlDaiXtS1LxLrRmKKsBbyvuUkt71hSKEXZ05MGyBdJAocjTcP53K9cjP/+1nNjz/SOi+dh1UW9ACAyc9B9SwqZJaKFGw2nbfAVllqcNBBqOFZ8gFsWQN+ov+UcS1rohIajJELMO1aspk5SOWcVdMuyouGo9VImFSIWCh+85NgEgsFIC+pHrlmMX7ssvuuXWGqayaMO3ESB46nZouXf1XolOtD9D50cxdHBUkzDcWwLVF++mg9HFXjaeAmW6HRql35hoIA6en/8snSPohOf3AtmTErVcMiHQ0IoKqkhazhJkfO11KnPKxoONxPpNZxwQXXjGo5qUktKnklmJ4CyRccpvrpn0R3j+xEzs6zRunSLt5olQx0PlmUxDSfKig7Ii3230lc8bKLieloNJxduUI4PlyV/GZDuw7npDfNxzYVzwzbI77u/dwp2rn8b3nLenOAeiklNzcBhNJw2gpsnAAqKiu8adZkGeOCnjjzA06BH9tNIA6K/aunYtDLHHGoeNMLsniIWzpqEs8J8T3wicCHAMw2UKp6YHL+z6jxRHoGDmzp0k0d1rCeZ1DyFIsr77pcvmY93vfFM7XlSW8LzRyseyq6Pk6MV9BTzUZxQeM2gEmmyD0e9PxBVxuTIs9LBSQsDCRwdxZz2FEkazqGTo9IGxdOw1Hj9HbqfrRmr6gKie1dJyKk+HK7hKGMXiMw1PCM6QTWpJdVu4altqBpskr+nmobDE9aWKm7sd93iqi7Y6r0DgRh8fvPiYBFfsWC6uB4dzsc/xeHwWDB9poHAh3NCo+FEtOjovC99YABffP8l0rNU8/Hk2FwB4hs8w1JrI6Ko7eD/auXPNB8O32GqkzpgqUUDXl2EJJZavRqOMGfIE70r7+Bbv/eLeNvyPukZ+fMAZFKL2scDLnUTk8ci6SaPurPWTYRCzpaqSALyBP+zdy0XgYZpoPuPVlxUPA+nRsuYwkxqYrdfcDBcdrE3dMrrkiiqj6Jrd96JguUmKz6SiKUWtIsLIPUZ9QIn2BhwLYe/U7pfPNOAbP6h99eIhkNjMSINRO+Cxps0hkjD8Typvg1vRzWBY1uR1kYMxiQhWS3TQIUFeOrmkV7DUXw4yiH8Pm85bw5e+OR1eNO5s4NjrahUepcah6NYS5IyDXAfDodLtGimFV970VxhKlOLEyahO3yHlG9Q7TdDGmgjaC0WcTi2PEG5wOmd0oVrL+wVEfY8uFAdSzlbZqlFcTiW8jfuw8kqcNSgTRUUj6ErHEVtEDZm10uk7/L7qQs6h7rQFTWmgO68I2pyiOtKmkC2xZFrOBXXx6mRCiYXnSgtvB1pON/5ySFc+WffxM7DgzFhp7unzrkdaLh0bb3PTWg4mp0k/abrt/kzAtoq9+Po4nBU0kAQhxPvx5iGk8I8VCFYamFVTC5cabxJ80Oj4RSE4JM1nMFEgcMzDfgIUijp21fNpMbvoRZaAxJ8OFVyqalMQDXsgM7vllhqtkQqSmqv7MORN1rkN0rSYNTy60mYHa5XFOsVFzhGw2kb1BLTamwNLT62FaSg+dItK3FR39TgWNuK2G1WfNHS+nAUDSfvWJLKDCQ7TFUIH47ra8+hgDueNkf24bDUNgp7S2c3dpgfQ3e/GGlAt5vMO9oCbLWCzvf9QFhSHESU9TsSOHuODcP3g2JhlDqFQ729zvdkWbwwlvxbhdGgHdsSmwsuDGwrOZ6C4nG470pLGhB16dNp0bEaNzWZ1FQfTpw0IJvUgu8efmEf/nzTKwCi56QFkUyQapwIgcdUeV4Qr5Ip8FNzDN9ckYWBH6bbzXfFBDQS/68bqzwrM28n35AmtZd8OCdGKrHs5VGOPn1fZDWpzQqp7wdPjoi26a4zFphwAkeUmBYLqaV1iuoWSJ6vKxaPY9sSQ2a04kk+H5ECx47YT2fPmoR1Vy3C2gxxIbwdFdfTLmZvXz4PKxZMx61vWRR7nqANPD2OJz1jooaTYlKL+XAystTSshskQco55flhBVMeHxL8xqO/h8uuKPAmXUvjf9MhyZwoqrmqtGhLHjNJCwdpojy4UCYNJNCibZUWHfznqvPm4FcvXYB3viHwhWXNPg5EGa8jHw6Lw2GbJHF8+PnLT+2MvhMmJlnDSQJR3IGINBCPb4ssAgR6Lj5Wee5AEvyclKMb12ruvjQNR6/ZB+fz8b9k3lQsnTdNNqvqTGosd53aTzSOqms46SaxWT2BIDsYajjqdFNZje3EhBM4OtKAvGsklhpfICGOpW9j+cAUrYUETnRdEjxRLrWeYg6/v/rCTD4M3uayxkwEBDvnB2+/EmezrMgO89vkmeminEXg2MmpbYBsLDUKxpNt20lPmAwpq64bEAfyji2xDQGgm2ldwyVXSt5JUCdg3rGw8fYrsSF0zop7KtougftUOMVX9eEUEhaGKUUqwxwJnHTSQJKGE+14P/3O5cJUV4tApwXw2HAZlqXs2rWkgeA7HmwcmfZCH86kagIneKaRcEOgy1vIK2iq7eGmW26OJuoyF5DVovaB+HiQgk1TfJd8c/PRGy7An717eSaTGiGRFp3QZnL2V9NwZkwqwLaiarVGwxlDqKltbEtezMQio7Ed8wlfLXixVJEjjbl5Quzealx5hQ/Hi659hpKqRIVlRQKOU319H1qByMELftXLUiuRwGF9YaUIsSTw84kGW2DlFugvb9NQyY0JO3oujpxt4+IF04UzX9yTmRM5XZgEATe7BW2Q75E0sbvyAQPuJMvXxUkDSZkG1Fxq8cBk0qJrETiRhjO5kNP6iPg4pbE0fVJkDqJ3Qzv/niobKNuysPP1ISz5xDew4/CgNgSBZ20X52mYf9xXKrJ7cDJMBkaWjhYtnk3Tl/RedemPqmny/N3E2I9VcqlFpsv0Z3LsILM7mdQ6yYcz4bJF85gGAJjWXZDiWOil6oL4OEtNNQGou5Igkj8uyHJ2JADyNSwMQMSm8cP8U19ddznOmVO9xkvRsVGqeFJqG0CeHLoUN9wEp5s86mTW7cwqbpDBV5e8s5aFkbd1SNST12g4iklNl1EhZlJjWYyl7xkhoTvv4BgCAcGz8PLnkjWc5IXDsixM6crFNJylZ07FzSvPigrq+em51NT+E4lnM+SmU5/xxHBZEqrBdWiTFB8zuhLppOFU89HRI0RFyOyYlkF9p1vAJYFDKWfyjvDh8PbyGKovf3AlXjlwMtaeVJOadtzHfTjRs6ULKy4Mk2nR+vdn20E56iwss9k9RWFSMyy1MQTFTNJLWP+uZfizdy8Xv4vAT42Gw81v8YzHGg3HiS8O+ZwtFoZaK+6pGtNli2ahd0q6hkP3BELSQA2+FGJaAfVrOFQjSMc2qlfgEDmDxxVR+7iZbzhBw4m9uwRfTaTZWpIg4+UIdFos/ZZmzpnSlZc1HM/HWTMn4X2Xny0EH+14E01qCSmSahlWkoajJAGl68ixXMHxIyUXthUku6T2qiSSC86Yor1nPPBWQ8LRaGv0fHyc0bso5mxWBC76nQvRX7ygF7e+5VxNe5L/r5PdEUst/mM1/w9/HlXgkGk2bV0o5uxM2aRn9RSYwFGuMYZxOBNOw+FpQoBgJ8Chq3bIo+2jwE/5uuogKKkaDqVJsaOFqNYsxro4jyzIM+3KSVmwVHBab5Y4HD7B/vnDl+Hv/2sHHv/xQVRcX9uftZgU+fmk4eSdeCYE1aRW0ZAG1LgLXgOJg5tXJ2mSLKq0Z3WxSVsYpnTlJIaVF9KDgWisVC/AJl8/ClquQcNhmQPUeKMoeSd/ruDaw2UXfdO6sXLhzMi3wnbOL3zyumQyhkawx7XOwHzI3wmZFLkgJyZcV94RmiC9tynFnFZzVxHLpabxk3F0CZKEzqTGrlPNh6P4umb3FHD4VCl1I/b2i/twZf+sxN+jaxVjTFmCMam1EZ5iUlMh4nDYz1GxtsiGHg/81Gg4ul29U7+GI9vvs5/HsxxIC5Zykcd/5y146Ef78LnHfxLcg7HydPdTd7S8fW86dzae+/kxPP7jgwGBQjE3Be3JLjT5JBzmGk6MNKBhqVXx4ZAGmKzhAJPy0VSJfDiqVhOda1npz9dTzEn09Qor4UB9XS2XWtyHU7vmyBdUlbyi0yjo2lyQk0Dk4yGtwJe6hgfhBvH5pHuOXIIg5/em8a76SLKi2jzrympSq+LDUTWcf7/tSjy362iqkPz0O5cn/sYxa3K0ke4kk9oEFDjB3ySHdRppgAd2VSsiNlrxJL5/JGSYD6dGDadeanFBmNSUrApKH/T39mBmD3MG2+mpbd68eA52HB7Ewy8EtV2Syg6MlF1t22shDfBri52sHcVFCdIA9+GUKiKwUL6WfO2FYenpOFMqEmbdGg1HTTUTM6mlajh5qUQBzy5M7f3pwVOYXHASc6nFCwQmv6skcO1vckLpZS6U1FRJQBSXlHUhUxdUbel2O15gjNpULQO0KASXUHq7GqpZEgQtWlP1Ni1nICCb7NX+PmvWJJxVQxn0NMyeEs3jWKZ3Y1JrH1SWmop00gD7rFxAnQTliidHbdvR5OW1cWpBNY5/EoQJz9YHucr3iD4HJqPws2biXb5oFt5w1nQ8/MKm2LmAnMpeF9eUpfAab4uKQi5woFpWtAh0aVhqaRrO539lBf5bGL+iPiK1z7IsTGL2epcJAN6FqvaRRIsGAlrxy8yHw9tJff1//nMbAOBvfu2N4n5pPoJGNRzVh7NozmRcv2Qu3nDWdO09o6zEZNbK9j7V5lFqf458Lp7LkO6v841Jec3Cl6ImXc0KdQ6oKGoCP6Pj+ed4O3mC01oq0NYKrj2pc3fc0qKPHDmCVatWYfHixVi1ahWOHj2qPW7Tpk04//zz0d/fj/Xr14vvP/nJT+LMM8/EihUrsGLFCjz88MONNCcT1NQ2KiKmT3ynwgM/qwUPjirsKNp1BXE4obZTw8IQ3DP6XIt2IAScbUuDT2fSU3doaaltADnITu1TLlD5YiRYanWa1MS9HRvTuvO490OX4sYVgdBQTWpqvFHQzuhzkobC2+fY8nUj1pgSmCj5G9JjQFSWGg+OVdtRLQ4n+n/tmiN/BypLbVIhhy++f0CqlinFuIQLV7lGDUdHGtAVNtTGsWTRcJw2aTgagVNNw3GYwGklJkmF9OTfssQmtQoN3Xn9+vW45pprsH37dlxzzTWSMCG4rovbb78djzzyCLZt24b77rsP27ZtE7//9m//NrZu3YqtW7di9erVjTQnE9TUNipoEssLUaQG09dJu3lCzIfD/Dbcp1ILeBxGLSa1KO7HkhzmeoHDPzOWWsL9ZFqw/FuBLU5qGhD+NwuSUr0DQaQ9xa5MkkxqgYajmp6SqMVpLLU1K+YBAHqnMNs4M+kFx0WfZ0wqYHaPnLqEY0pXHqdGK3ISy0SBE/xV43DiLLU4w7Ia+IaBx9YkQafhCIGTWcNR3octa4rULp0FwLb18U38O1pQ6/XhpG1CAH3gpzi3irCisdhqgcPnmzp3WqlZVUNDAmfjxo245ZZbAAC33HILHnzwwdgxmzdvRn9/PxYtWoRCoYCbb74ZGzdubOS2DaE6aUA2awBMw7GTk1nq/DG6sgc524rS3NSx00jzqSSBdoTcya62j2ApO+i0ejgq0rS+buaQpsPUukBp0AlYbe62gmxS0/tw9ItCjKXGfCJvPb8XO9e/DReEefXo3CSW2j3vuwS//7YLE59nSlcOrhfVcqmw4Fh1WHgJcT9xKnFjGk6agCTofThB++rVcHQlpvM5feLTJPZflxSYGwjA+jUc+X4qzpo5CbN7itqyFtVo0TQWWy5weNbvMRQwKhoSOAcOHEBfX5ASv6+vDwcPHowds2fPHixYsED8f/78+dizZ4/4/1133YXly5dj7dq1iSa5ZsJV4nBU8GzRBM6EotOqBX7GrmFHGk6BEQhqRVogZhL4M/HnrmZSo/T7wefq94v5QLjA0Wg4tZjUdJNXJ7C6MrHUWBszaji6c1UTF/88Z0ox1YfQo+RT47RoVUBS3JFKGlDfSeQbq1fgFFOORNi2+Pgp1azhKNe04uEG6uaI318313gVT6LN1+/DSRca775kPp7+2NV6DSxhbKnftVPDGUMLWgxVm3Lttddi6dKlsX9ZtRS1GiUQLda33XYbXn31VWzduhV9fX244447Eq+zYcMGDAwMYGBgAIcOHcp0bx2EhpPw5Dx/FoHv8rMGftLxhIJj46rz5mDFgulRTE4dI0FlZWVBQZjwZBNQNtJAdgGnCvFCgn+g0UwDBJ3A7maU1aTAzyRqcWznraHI82PU1Da1aBZEG6bgz0qYNTm4n3wditdRSQMq6tFwuNCelUHgyKU85PuopZuToBOUat62GZMKmK7JyXbbW87Fuy9ZEPue+wipv6Z216bhvOncIL6lWqYBni5KRVLmCXG9MfDhjKUJTUXVN/L4448n/jZ37lzs27cPfX192LdvH3p7e2PHzJ8/H7t27RL/3717N+bNmyfOJ3z4wx/G29/+9sR7rVu3DuvWrQMADAwMVGt2ItTUNiqidCbRdzToLCvaxceTd6YLHMuy8JW1lwIA/uP5vQBqD/zk7a6njLBKGtCxxHi3yPVwqt9H7VI+Kbs0NuWacqlp7q/r8zeePQMfuOJs7D8+ghf3HAeQlCcurMrIFwjlclF5gmQNJykOpxrI3EOxOAFpIBx7yrgYHI3S7qetHY36xrKY1KgkQ5CFW+6wrBpOnA0YbYTyTpAx4HevPx+6p1j7C+doN7F8fFGNnLRYIBXPf+I6IbSSSCVZUDXTQJt8OFJxuLBND33kF0RCz7FCQ8rWmjVrcO+99wIA7r33Xtx4442xY1auXInt27djx44dKJVKuP/++7FmzRoAwL59+8RxDzzwAJYuXdpIczJBl9mXg1LAa+Nw2AKclE6dI0koEDutFh+GaF8DPhyeGy34XrcQy89djTSQdC6QnGIkigivhRYdP1bXfz3FHP73jUsxq6cozFW6dP06X1wSzVjua/l4W9qMZH8nVFlzODT/uCkazmCpItqRRcOpZTPCMSeDhsOvr5q2smo46vPpfKMzJuUldhyHZVmi9pO4NxN2FDBby6I+rTsf5YKrIjTSkGbyBKJNXut9OJylFrRjybxp+MUL4kpBO9GQwLnzzjvx2GOPYfHixXjsscdw5513AgD27t0rGGe5XA533XUXrr/+elx44YV4z3vegyVLlgAAPvrRj2LZsmVYvnw5nnzySXzuc59r8HGqo1ocDgB88E3n4K3nRS9GNqlBfOao5sPhqJelBkTtrkU5kjINSEQGnYajCpzoc/W2qSap6P/dGqduLT4sPWkg+fxJBQcnw52uTjDpBI7OmR18z8+Tr5HTkEyygPrV9Xz4vh8KHGJIysdGJrV0wV/L5kAHKgxXDUmBy/Wy1PhGTqQZqvIMD9x+paj/A8jC7o/edhE++KaFWHXRXN2pGdont62mc1N8gkAbTWqSRaGlt6oJDQV+zpo1C0888UTs+3nz5kkxNatXr9ZSnv/xH/+xkdvXhWpxOADw8XdcJP2fO86TcqnphEdSYGOeaRy1oh5zFGkyPFWL51f34aSx8rRtUw7hQpg7MUUBsxpmgu7QtMBRHUmBIzKNpmk4cQKJ7MNhGmetC1N4vOv7UfYLYS6Vn2tQ8uEkX1MkvKxjXAHZN0A8TROHLreYDjHSANOk8xofqg79vT0SC43fu3dqEe9ZuSRTW/TtY++7xsU6LZs3EBW4m5XBfNkI+AavVi2tlegg2dcecIppVvCqlzSeqgV+8vNU5JXJVQucOha4qByCvIBWDfy0ajOp6YL3CFJqmJBGW0vgq064pAWwSfXmM5rUkjQcK2EBkrNpp7U+DjrPdX2WDDT8TTWphQKnmtlOlD9v8ZZWBC6rPpyMEeyx1DbMcqALvE4Cn19dTVxg1dCAWlDNpHb+3Cn40gcG8Jbz5tTdvizg76KTaNETNrVNTQ5rtjgl7fh1/pCkCZhncTG1op7iZWp26uAacbowEGep1aLhqMgnajj6LLZp0OelSjepRecmP6cuqah6fV3xuOB4q64NAG+T6/tSXRgg7nM6lVHDmRmaxGZUqbjZKFRthJBdw4kL9rhJrbbrdDVxgU0yoWZBUrkKgmVZuLZOU18tUMdpp2ACCpzgby0OXp7ahsZQFg1Hl/oCYM7degI/a/CpiLYpJrxoN1ndh3PGtC7MnFzAWTOzJRX86rrLWV2S6FqTpHoyoYZTS3kCnQ8nTcPhubW0AicuSGOpebQ+HN4/MqGkFtB5nueL3GzJGg6Pw0m+zwVnTMU373gLFs3pqakttSKJ1p9Vw1FfGy8xnQ+LsWWZn/zdSSzIhgVO9N5rpRR34kLfQRa1iSdwqqW20cFmC08SS0y3+CUlM6y34mfQhsZNaqLQU8pCDAQL3+yeIp79o1WZ73XZoqhWRxIt+qrz5uDCvqn4n9cuznzdhkxqKfRvneD+yNX94W/kxNdrQUQR5tfLChIqru8LE2NSahrBUrOqv/d6hM2/3XpFphgcgqhe61SfAzros0VH18wqvCUNh42vmRnJD8nXDdtVx/xM8wmOFTqlHcAEFDjVUtvowE1qtZAGkkwMjbHU6jGpheeEf0no6uJYqtmgawG/Pt/9TuvO45H/7801XUtLGkgxqfEoc60PJ4FCvHP926LzNP4rfrRtxQvAZQWd53INJ2Ghi3w4Vs2CLQsGFs6s6fgkWnRWxJN3Rv2oVqVNv070mcbXGVO7cH5CpdHM7WuA7cfbVC89vdnoFE0LmJACJ/hbk8ARjmFLLDjqLkybbiNB4MyZUkRX3saCjGYqDpFpoIb2X3DGVJw/d4qgSpLPIM3U1IzJwk1qjUY767SUNIHNGUx6H45eU+XQBVKqQaA6oZQFQsPxmA8nwX8RmdQ6Y/HggcT1QMdSo++uuaAXl5w1I9N1+Hu5eMF0/Pe3nou1v3BOXW2S21ffJgLoUJNahwg+YAIKHJrctYwFns4myaSliwlJEjgzJxfw0h/fUNeAriXyn3DtRXMlR2Va7XTBwmuGwGkiW0p3qTSWH88UnEaOSBOseUGyiL5TFxTyFb0+WEq8jg6CNMAFjkX3i6L5geykgXZB+FtS4qDSoNNw6Nmv7J+NS87OJnD4dSYXHHz0hgvqak/8umG76hAYnWhS65BmAJiAtGi/HpYa03DshAVZr+Ekd2+9g7GeaplJSKULN2F3pjPZ1QtdZuQ0rUnWcJLJEemBlHbs3qrJ8f1XnJ3e8MRrBxfy/MiklpZIlNraCbvmnOIT/OwvX4y1V2bXLNRHSDNVp6GRjABpEEzQulJPRZ87JWlmM+ZyszDhNJx6TGo8Diey2cvHaOtmtKB2uNBAmiEQMkbg1339OgMQdVDNN9X8X1V9OFbyb9E940JJNan1TZ+Ehz/yZuw+OpTaHhWRhhPFJcXSKbnyOQF7q6bbtATUL/R+333JfOCS7OfHAmyt2jJaRNcJ/tbDJktDI5uupLEyljAmtTFEltQ2Khy2+0pa8HUDXlfzvFGklS2uFWkLcVMETgtNatWi6ScVMtKiUzWcuJ9HDfwEgIvmTcVF86aiFvBMA1oNJ6HNnZD5txFaP5BAGqiDfVlPqfJM123ArCxnGugMFadTBB8wAU1qDcXh2BaINpDlJWat8V4L1l65EACw++hww9dKi8NphsBp5s4qS+46jqQSBIQsmpwatxRemd0jtQmpiDINeHDDQNi06qlJ340FqF/qZ6nJ/+cstbp8q03umIY0HFv/eSxhTGpjCN/3a564fGBbyndpaIVJ7ZcHFsD1fCye2xj1E5Dr1RDE7q6DBikQX1RqyRdWaxyOel6iD6eBPooyDURFAaXSEVXSDo0lROBnnaQBdbPH6+HUkwGk2fTjLGMjCa3yK9UD2wo22FaHCD5gAgoc1/PrTkOiKzudBl3N82bg5kvPasp1dAtxko8qDefMnowdhweb0qYkZCl4l3huvRqOzqTWJBs9zzSgS/Wju3aHyBtWBqE5JjX49W10xFhtoq+Qt6Gex1MDp8cSxZyD4bKrrSs0VphwAsfz64iZYAtPtQJuHK3QcJqJVGd6DbPtP/7HlTg+XG5Ws7RQBUMtAkdHXsjiq9IlLuWHFxpg4fFMAx5pOOzi6q49IAx0xtJB46aewGUgvpmpeL6UzSPzdcRYbbbAoevXo+FEn8daw1lz8Tx8dcuuut9TKzDhBI7v+3WnHHcswBtjH04zoY/DqX13N6Urn1hd8bJzZuKt5zde9MkKY1CiGKJafHDN8+HwRb+rAYFD/esyDUdHGsg7Fspu7Vp5KyFo0XVqFqrgdD2vIdJAs/um3uwR6jljLXD+5KaluOP68zInVW0HJpzA8fz6TWo2W/GyDKZOetE61MveqgVf/c0rmnIdIOhzr4bEn935wKSQ6sPJwFLTmbpytlU3S4vf1/X8iDmpWax6ijkcHSp3DGEA4LTo5pjUKp5fUxkM9TrN13DqFzhyLZ2xfWk5x0bvlK4xbYOKzt6CtwD1mNT47ot2Z1kukZQtulOgd0yHfztphQthW5bYVWdZ7KnYVf0+nOAefEdOHxvdTMiZBuh+GoETBrB2ijkNaLzQm9rlrufXFV8mNgSt8uHU0ef8lLH24XQiJqDA8Wt2vvKdbi1xKp2u4eh9G63ZNTYDOdsSfjEdw04FxUHpKcZW1YBB6gN+K1sInMamTmQijExqOl/R5EJO+n8noNnJO13PZ1V1a7kOtac1cTjj3aTWiZhwAsevizQQ/rV4Co7T1YcT/O0knwHBti3B/MuyyJCGSfV5OCyr+jVo56xjqRWbQAihfGlEGuAaA7WNUvRYHcQ1ytWgZeqgas/zpnfX58NpdRzOOKdFdyI6e0VsAT7+9ovwzB9eW9M5PLNwLRNjPGs4nThZHNsSQjxLnrZ3vnE+AGCWpj5KEGyYfr6uxDR9bsZmwrYsuEkaTnjvycVA4JCfBwD+/oMD+OYdb2n4/vVCTW1TK2hoveW8OfjP3/oFXL5oVl0EABqjrYrDMSa15mPCkQZs24Jd425RBADatanbWSsgjhW0cTgdLHBytoW8Y8OxrUwmtd+8ahHee9lZUl41gm1Z1TUcTfxVZFJrkobjRqQBvnDS555Q4JTdSEu7+oLWlyhOQ8RSa8yklrMtLD1zGgDUaVKr39eStX21wtFsGgwidPaK2CHgNWii3U/18zrJ0atDq3OpNRu2ZSFv28g71YUFEPS/TtgE16r+DukeuuwCTRE4pOG4ceajrQgcz4+fP1ZolKVGT6lL5VNXLrVmkwZYKquaz+3gDVsnwAicDHDYTiqKUxn/AyotDqcTzQE5O6gTk7fthksfWFZ1WnNO48OJWGqNTx3HseBxWrQUpR78JYHTSRA+nAbngE4bqLcSbzPRjGzRnTh/OgFG4GSAzFJLnhj3vO8S/OHbLmxr2xpBWmGyThSoth3QonOOhXyD7Qs0nPRraEtMk4bTDNKA8OGEJjUNaaCnqwMFTti2ejMtuCLuKPqunoWa9gvN9+E0QBqoo8zCRIIROBnAd19paS9uWHoGfuPNi9rZtIag0xI6eYfmhMGWXXkHxQZNWoEPJ/0Zo40GPy/4W2wGacC2gno4XlzDoYWrIzWcBk1quuelS9WSaLKZmc05Gtl0GZNaOjpvNHcguOreSGK/TkNaAbZm28WbASc0qf3Fey7GmdO7G7qWbVlVF4XIh8N9DcHnptCiLQuu54kFWCYNhBpOJwocp7HAT09TdfeKRbPxzjeciZ5C9ueNskU3Ow6HNl21nxtlTGhmi04fnAbLZutxYd9U/OZVi3DZopkdHadSK3QLRic/nxNmGnjTubNx9qzJDV3LsqrvQs+Y1oVzZk9Gf2+P+I5OqTfokcMJNZyKJl2SIA10oEntyv5Z+OVL5tckHDh05RiWzZ+Gv/yVFTVpFboCec1AI8QZq4FzJwI6bzR3IAo5Gx9bHfhmsqjxn3jHRRKNtVOh9eHY1Z9vrODY2dhpWZBFw5nWnceTv/tW6Tt6/41kiiY4tgXP93HwxAgAYHZPUfxG72ZyB2o4y+dPx2d+eXrd53tePHdcPaDFvRNzqTmngwmkBei80dzheMOC6bj6gl7Mn5Fs0vnQlee0sUX1Q0fbroee2i5MKjgiP1qjsO3GdrDNiLGiTAO7jgxjdk9RKklO/T+lAwVOoxAmtQbHmC65ajPQiMCJzO9NbdJpg4a65ciRI1i1ahUWL16MVatW4ejRo9rj1q5di97eXixdurSu8zsJC2ZOwt9/cCUm1WlO6HRQCpVOzKX25+++GB/7peawAG3LqmvBo915MzQc2woYW7uODmHBTHkDQwtWJ5rUGoWOpVYPWlXxk8fd1QphUuvADVsnoKFXvn79elxzzTXYvn07rrnmGqxfv1573Ac/+EFs2rSp7vMN2odODvzs7+3BgpmTmnItK4NJTYdSmJetWSY11w0Fzgz5uXK2DcsCJuVPP4HjaVhq9aBVLLVGrmuFweGdGFbQCWho1mzcuBG33HILAOCWW27Bgw8+qD3uqquuwsyZM+s+36B9OJ0CW9OQt626coGNus0TOLZloeR62HtsBGcpgtQOA1w7PQFsPXBrqCmVhlblUms0NMCpczMzEdDQ9unAgQPo6+sDAPT19eHgwYMtO3/Dhg3YsGEDAODQoUN1ttigGqI4o7FtR6vxW9csxnDJrfk8oeE0wUifcyzsOToM1/NjJrVcKBCbQb/uNISZfBrWcOgVtIqlVu91sxBSJiqqCpxrr70W+/fvj33/J3/yJy1pUBLWrVuHdevWAQAGBgbaeu+JhInCslmxYHpd55HAaQppwLKw/fVTABAzqdlh6p1mBJh2Gt4zMB9bdh7B/7i6v6HrWK3y4TSo4ViW8eEkoarAefzxxxN/mzt3Lvbt24e+vj7s27cPvb211a5v9HyD+nD9krn41it6LdE2LJtURAKncc3Dti1Rq2fOlKL0G2k4zdCkOg1TuvL42/dd0vB1nBZtjoTjv041nwKUDeJo6E2tWbMG9957LwDg3nvvxY033tjW8w3qwxffP4BXPvVL2t8otYiZMHqMNpM0wHbB6vXWrJiHW99y7mnvS2sEnerDCVJgmfemQ0Oz5s4778Rjjz2GxYsX47HHHsOdd94JANi7dy9Wr14tjvvVX/1VXHHFFXjllVcwf/58/N3f/V3q+QZjh1bVGDldMFoJ/D5NIQ3YyQLnyv7Z4yov31igEyt+BuebDVsSGiINzJo1C0888UTs+3nz5uHhhx8W/7/vvvtqOt9g7EDzpBPjcDoBTSUNsD6uNxHmREarMjM3GvxsG5NaIswoN5BgTxBadL0oNZEW7RiB0xBaFfhpNejHNCy1ZJhRbiDBREqng8gCkwpNIA1wH44RODWjVXn/Gk0KWm8Wi4mA0y+M2aAhmHoe6fizdy3Hv2zZhUvOntHwtWQNx/R3rbBbRotGQ9e1rdOjfEkrYASOgQQjcNIxZ0oRt/9iY/EjBOrrLKUSDOJoFS26GSw18z71MHLYQEIn51I73UBWtIJjazN3G6SD5EyziwWKmlB1zoEgDscsrToYDcdAgmVZePPi2Vg+f/pYN+W0B9X2qea/+craS0WGZYMIraLwNyfTQDNbdPrACByDGP7x1y8b6yZMCNAOOl+F8XbVeXPa0Zxxh1YHfppcas2H0fsMDMYItAs2hIH60LrATzR0Xcc2mQaSYASOgcEYQWg4hhJdF6Z05TBjUr5pNZIIVjNMakbD0cKY1AwMxgi0oJkYnPrQlXfw3Meva/p1G9VwPnTlOeib2tXEFp0+MALHwGCMQOyqZmQtMGgeGjXVvf/ys5vZnNMKZqQbGIwRaGEzJrXOgknv1DqYkW5gMEZwhA/HLGydBJPeqXUwAsfAYIxgNJzOxNSuPAo5O1YUz6BxGB+OgcEYgTQc48PpLEyblMfTd16NmZMLY92U0w5G4BgYjBEoYNGw1DoPs3qMdtMKmJFuYDBGMHE4BhMNZqQbGIwRyCldLbWNgcHpAjPSDQzGCELDMfRbgwkCI3AMDMYIjqk9ZDDBYASOgcEYgTINNLuei4FBp8IIHAODMUKr6rkYGHQqjMAxMBgjEDnNCByDiQIjcAwMxgitqudiYNCpMALHwGCMYTQcg4kCI3AMDMYInu8DiExrBganO8xQNzAYI7he8NekwTeYKDACx8BgjOB6gcQxafANJgoaEjhHjhzBqlWrsHjxYqxatQpHjx7VHrd27Vr09vZi6dKl0vef/OQnceaZZ2LFihVYsWIFHn744UaaY2AwrkAajiENGEwUNCRw1q9fj2uuuQbbt2/HNddcg/Xr12uP++AHP4hNmzZpf/vt3/5tbN26FVu3bsXq1asbaY6BwbiCG/pwDGnAYKKgIYGzceNG3HLLLQCAW265BQ8++KD2uKuuugozZ85s5FYGBqcdPI9IA0bgGEwMNCRwDhw4gL6+PgBAX18fDh48WPM17rrrLixfvhxr165NNMkZGJyOcH0jcAwmFqoKnGuvvRZLly6N/du4cWPDN7/tttvw6quvYuvWrejr68Mdd9yReOyGDRswMDCAgYEBHDp0qOF7GxiMNUjDMSY1g4mCqhU/H3/88cTf5s6di3379qGvrw/79u1Db29vTTefO3eu+PzhD38Yb3/72xOPXbduHdatWwcAGBgYqOk+BgadCNczcTgGEwsNDfU1a9bg3nvvBQDce++9uPHGG2s6f9++feLzAw88EGOxGRiczjCkAYOJhoYEzp133onHHnsMixcvxmOPPYY777wTALB3716Jcfarv/qruOKKK/DKK69g/vz5+Lu/+zsAwEc/+lEsW7YMy5cvx5NPPonPfe5zjTTHwGBc4YypXQCA3vCvgcHpDsv3w23WOMLAwAC2bNky1s0wMGgIFdfDo9sO4JeWngHLaDkG4xhZ1+SqPhwDA4PWIOfYWL2sb6ybYWDQNhh3pYGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBW2AEjoGBgYFBWzAuc6nNnj0bCxcurPv8Q4cOYc6cOc1rUJth2j92GM9tB0z7xxrjuf1pbd+5cycOHz5c9RrjUuA0ivGe/NO0f+wwntsOmPaPNcZz+5vRdmNSMzAwMDBoC4zAMTAwMDBoCyakwKFS1eMVpv1jh/HcdsC0f6wxntvfjLZPSB+OgYGBgUH7MSE1HAMDAwOD9mPCCZxNmzbh/PPPR39/P9avXz/WzamKhQsXYtmyZVixYgUGBgYAAEeOHMGqVauwePFirFq1CkePHh3jVkZYu3Ytent7sXTpUvFdWns//elPo7+/H+effz6+8Y1vjEWTJeja/8lPfhJnnnkmVqxYgRUrVuDhhx8Wv3VS+3ft2oVf/MVfxIUXXoglS5bgr/7qrwCMn/5Pav946f+RkRFceumluPjii7FkyRJ84hOfADB++j+p/U3tf38CoVKp+IsWLfJfffVVf3R01F++fLn/0ksvjXWzUnH22Wf7hw4dkr77vd/7Pf/Tn/607/u+/+lPf9r/6Ec/OhZN0+Lb3/62/8wzz/hLliwR3yW196WXXvKXL1/uj4yM+K+99pq/aNEiv1KpjEm7Cbr2f+ITn/A/85nPxI7ttPbv3bvXf+aZZ3zf9/0TJ074ixcv9l966aVx0/9J7R8v/e95nn/y5Enf932/VCr5l156qf/000+Pm/5Pan8z+39CaTibN29Gf38/Fi1ahEKhgJtvvhkbN24c62bVjI0bN+KWW24BANxyyy148MEHx7ZBDFdddRVmzpwpfZfU3o0bN+Lmm29GsVjEOeecg/7+fmzevLndTZaga38SOq39fX19eOMb3wgAmDJlCi688ELs2bNn3PR/UvuT0GnttywLPT09AIByuYxyuQzLssZN/ye1Pwn1tH9CCZw9e/ZgwYIF4v/z589PHdCdAMuycN111+GSSy7Bhg0bAAAHDhxAX18fgGCSHjx4cCybWBVJ7R1P7+Ouu+7C8uXLsXbtWmES6eT279y5E8899xwuu+yycdn/vP3A+Ol/13WxYsUK9Pb2YtWqVeOu/3XtB5rX/xNK4PgaQl6aBO8EPPXUU3j22WfxyCOP4O6778Z3vvOdsW5S0zBe3sdtt92GV199FVu3bkVfXx/uuOMOAJ3b/lOnTuFd73oXPv/5z2Pq1KmJx42X9o+n/nccB1u3bsXu3buxefNmvPjii4nHjpf2N7P/J5TAmT9/Pnbt2iX+v3v3bsybN28MW1Qd1L7e3l7cdNNN2Lx5M+bOnYt9+/YBAPbt24fe3t6xbGJVJLV3vLyPuXPnwnEc2LaND3/4w8Js0IntL5fLeNe73oVf+7Vfwzvf+U4A46v/k9o/XvqfMH36dLz1rW/Fpk2bxlX/E9T2N6v/J5TAWblyJbZv344dO3agVCrh/vvvx5o1a8a6WYkYHBzEyZMnxedHH30US5cuxZo1a3DvvfcCAO69917ceOONY9nMqkhq75o1a3D//fdjdHQUO3bswPbt23HppZeOZVO1oMUCAB544AHBYOu09vu+j1//9V/HhRdeiN/5nd8R34+X/k9q/3jp/0OHDuHYsWMAgOHhYTz++OO44IILxk3/J7W/qf3fTJbDeMBDDz3kL1682F+0aJH/qU99aqybk4pXX33VX758ub98+XL/oosuEu09fPiwf/XVV/v9/f3+1Vdf7b/++utj3NIIN998s3/GGWf4uVzOP/PMM/0vfelLqe391Kc+5S9atMg/77zz/IcffngMWx5A1/73ve99/tKlS/1ly5b573jHO/y9e/eK4zup/d/97nd9AP6yZcv8iy++2L/44ov9hx56aNz0f1L7x0v/P//88/6KFSv8ZcuW+UuWLPH/+I//2Pf99Pk6HtrfzP43mQYMDAwMDNqCCWVSMzAwMDAYOxiBY2BgYGDQFhiBY2BgYGDQFhiBY2BgYGDQFhiBY2BgYGDQFhiBY2BgYGDQFhiBY2BgYGDQFhiBY2BgYGDQFvz/KsVs0TmqqD8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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pAR7eeQjff2LvmFy73uC4LPh+/QLnuQODAICKYcKQKt8KGjNpNfOmdo+JSY12pxPdscpNL2nvaWC0ajTv/PiZA2JRJA2nFc/Ld+kkcFqpOeVP3inbQwtzHtLA/uMj+MmOgw21EWALehO0aCBbyyEB3lSszwTfTAFW4LQE1SAYs5ddL5MnC7TLqqet+09EtNq5U7oS35F/YMhgbqsXQuBM6xoTDafSQhPTWIK3L+2Vr/j4D/Der2xVPvvhr/fh2n/Zik0P/gYAMFqJBU6DmsivXzou/F60BgahXOBbwXZs1IcT1slS23DLQ3jPl3/eWCPZfRsxqfH3maXlCA2nAYEjBeLEHtuAFTgtgZ/TsV/xA1z2uQdw/9P7c1+73knZ6msdODEKwGzeIZNaK2jMJGTmTe0aEx8O7RyzJv2N/+8RfPOR7HLeIy3wV2XBz2lSe+jZQ8r/vz0U0Z9fOhZtEJoxqe09NozLb34Qn/huVLeKR8sTK6wVMR+8vk2+4+WBtDAXcvhwXmoyFonuWvED7D4yhC/c/2xuE3eYQ2MFmgv8lBrOxNbeAStwWoIogr/2ABwYqeLpfSewY98J4/cjFR+/eOGI8hk3IzQL4cOpY7E4MDAq2gZESQZv+eEOBEEoNJzBFmg4I/ECOauvC8MVv+UBqnlIAw/uOIhHtf7n+I9Hd+Psv7wLOw80H4OShqqi4eTvA9Iye0pRHq5mqLxUefXnv4mEGr2LMAyFGbUV76febNH8MDIZ1sOKbrTN3A/zvv+7DZ/5wdPYczRfZmZ+y6yx1whLLQzD+AeifRMdVuC0AJGGU/s4cuqlDYw//+bjuPIffor9bEdGR7ZC4NAOqB5ziNgxxwLn/qf342/vfgYvHB6SJrUWaCTUJd3Fxsv5ZqFcra3hVIMgc4d51y9fAgDsaEHQYxoU0kAdr5w2BL1FNR9vIxqOLDgmNZuoba2Naq8/l5r8mxbmelrRaJu5D+fIUBSHlEezAvKb1MjXltcsNlz2ccZf3IF//PFO68OZbPBz+nDInu6nDKqtzx0GoDoOwwwz2N1PvoRddUSSyzicfANztOoLc8RIVfUJVJkDuRUaDi20pcLYBLEJDSdjQlf9MNNpS00ay+D2ag2Bk9YvpOH0ltRMw40ssqQ1EAGBa0tjQxrIe7w8kjYG9WiBjRIoaGxW/ED0Z142Nm9zloZVb6YBMmP/4492slxvVuBMCtB7rjX4KzX8CLRL5bunrF3g9bc9gjd+9kf528kC+PJg//FRsehR20jz8JlJbbgFPhx6vi5vbDLf1mKpRbEdYeaEp/c7ltHtqg8n2QdpTmUSON2awGnEri8WP2I1BrLvSOA04tzWkbWZMoHPAdJw6nm8RscUX9DFczfQ5mwNhwROvs0bXenESJWRGqwPZ1LAZ4twFoSJIuU4CqDkg1Q4VlPGUi0VPAxDEbQpWGo5d4V8ANM1eEGpotc6DYeuSxpOq3dr5RosNfo4S+DQexnLbCoqLTr5fdqiQhuCnqKnbHwaWWR9JmCidsT/+6EItGzEua1DaDgN+XDi8ViHhpOl3WbeF1KrrzdjhUKLzmirZKnlu7CJzWhNapMEeVPGVGv4cEYqyUWRR3lz5J2kdz+1Dxf89b0YHK3WXV+dTxDdpBaEIRyH4nBap+EIgdNiH04t7ZK+z2dSG0MNx+cLiUnDSTOpRe/Agep4bmQRoj6qaj6cih+IQMtWUHCpbfXmUgOYDyfl5AMnRvEPP3q2aeEb3Vf+LTSrnI3O7cOpMw7HRC7pBNJASyp+Tnbk9Y0I/0eNQaVqOOZr88HlM9u6jhePDmNgtIoBLnByTxb5N+2g5e5XpohviQ8nbhKZ6Vqu4dRgqeVhCY2HhsMFLW9pGIa47OYHsGbRDPHZaNVHVyEyoZFJLQhDJS6qkX6UJjVa1KltUsNphfmGJ+PMd7z8m+KM0tbYP/m3X+ChZw/hoqWyZHOjmxiTUMs7hxRadAt9OHxjIjalHSBwrIZjwEjFr+vl5V3Ia+2yCfzrtMBP3r6sOANuAqs3eSe/BwkcHjwqywkkBc59v9onSBD13GusSQNpfU8Lc9ZCSq/AdRxU/QCHYsp4K5EW+PnoC0fwzL4B/Ps2GSfEA2RHmDmWxzE1ssiKTYUmpKt+ICpstsaHE/2uN7UNUHsDcXy4mjin0U2MaV7n1cp4N2WN6XpzqfH3Kmnb1ofTkTj7L+/Cn38zyo0WhiFu3/pCZjBiNedCXsuHQ1BNamazA5/wLxxKZ6rxe9abaUAxYQhznzQp0OQwBX6+79ZtePs/PZzrPoDcCZZaQBo4ODCKD//bdqWQm+wH86QUEz6HhuM6DjY9+Buc/6l7sftI8/VmOHyDdgsAX334twCAOVNK4rMTI7I0hPT/qbntGvLhaM58HmlPmnRDSSY11EuL5v1BzvW0c2mMc/Nno5sY02n1lsUG8mUayK3hGNYIy1LrQNBC/o044vzBHQfxF//xBD59569Sz8nLg6/m1nCSg0m/Nv8/ixrNCQ1BUN8EVzScalLDoXQxrUhFQ+tXVzEyETWSKJGw7fnD+I9f7FGCaMuaD0pHnkhvKXCA38Q55r704HMNt9PYDu7DYZ8/d3AwboP87MSIFCzcpMbfhz5untl3omZeMf0czlKjBbxSbX5xkya1vMfLv6UPJ/vaXDA2G4eT1pbsc5Nz2YR66+HwZ8kTYzZRYAWOBt08dHgwCvTik1sHLRI1adFisa7Dh2P4DFAH19MpmQv4cX4YMk0s38Ck43uKnjSpMY0pS8OpF4I04DUfWEh03j1Hhtln2SYYIXDykAZcB3OnRrnlvr5tV0uLuqWZ1KjfudbG/x4pSw2Hvw9dwP7D/c/iv3/7icw26OdwWrCI0WlJUtDod0MstYrUtLOO5amI2uHD4RprlgbSjIZDa1QnwAocDUT/JV8CDdjugpd6DveTZCGvhmNadPTxzQfvz36j5tUyHcd9OPWa1Pq6PMGgM/lwWkMaCOE6sr5JM+YBahelHwnDsKYPh5yweeJwHMhkoINlv6GiWWlIyxYtduzsXgNcw4nHaVhDwxkY9WtqpAl/IZnUgkBooq0wqdWbCb0eHw4dqwiclvpw8s6h7OsQ8mx4OLgmZAVOB4Ps3xSASAO2q5jeVbrNOw0i+LDGwOcbsTQ7N+3WFs3qwVN7j+PokHnQcQEh4ipyrhU0mXtLBcZSk+lxaHJkBX7WQ8H2XEdWL2xiBy0ETqzh+IHMN5W2QFVy+XCi367rKAtDK5ONpmo4hnZzDUeY1IJQWYz080Yqfs3y0Po5gnbrh7L6ZUtMavQ7r4Yjj6NnqGVSG8gQvnlh9uHkPFdjk6ahGQ3nkBU4nYuEhhMPgO5ibQ0nL0vNNPD4IqE6jhFf23zP1y6ZizAEfvYbMyOMM41kmvmcQiAgDacgnNJCgPlMw8nYMQ/kNLf5AeA4jshArffRv/3nC/j3/9wl/v+PR3fj6ZfMpkRyKO+ONRweM5Kq4dThw9GPa2U5BdWHk71YcdIAD0j0lUVOfZ6hclWYo9KgbxJ4UGJWGv2tzx3GQ8/mrzvTjA+nXCMeho7lWmDjcTjJ8xoh3uTJpZZX4PBrHbECp3NBu9WkSS1Dw8kpcGTgZ3JQ8SJmeUgDtIi+PI7L+M1Bc0JJruHUm7yTjpvS5TGWmlxwqElZPpxjQ5XU7ziCMITnOCx1itrGP//mE/jYN2VV1Q//+2O47OYHjNci7YM0nLISCGme0LSAjubw4QSBmnNtoIUajinLBGAeMyfi++qO6axgw+FKgLKfnftPP0f4cAL5zk0C5+3/9DDe/aV8dWd4luPc2oKi4dQSOKThyPGnE1Hu//X+XNR9M2kg5xwyWCuMx+Wg5avXZQInxboxEdESgXPXXXdh2bJlWLJkCTZu3Jj4/mtf+xpWrVqFVatW4dWvfjUee+wx8d3pp5+OlStXYs2aNVi7dm0rmtMUyBbeJQRONAC6cmg4zaS24ZqAKWOwbjOma/R1FeC5TqpZR7SNkwZyznBuUqNFiq4xzGzjWTv8Y8M5BU4Q+XAo8LOZOBwSMC8dH0HVD3IxlbiGk2afD9mOXPGlaH3/9/ftwP996Lm62/3bQ4P4pwd+w+4nvzPJSepb/i7ChIaTNKkByDSr6UJZsNT80Mj+ysL+4yPGGkL82Rrxh4hURTVMapzso/fF39z1a/yfH+6oeV9T+xrxO2WZ0uv14SgsNUOy34mKpgWO7/v44Ac/iDvvvBNPPfUUbr/9djz11FPKMWeccQZ+/OMf4/HHH8df/uVf4vrrr1e+v//++7F9+3Zs27at2eY0DdI0dA0nC3lTdPBMy0BEdaUBwtPDqHZ80p7Ua4kCVJ6DvpKXuuhLvw2nRdd8pPg4EmqRsB2t+mKXSIvcrL4Syn6QuoAdzanh+GEI12UaTjM+HCbY950YVc2VNVhqQHraFu5Pq2T4cO5+6iX88OkDdbf7O9tfxAPPyPO4Sc2k4fzmwCCqfoA/vO0RpY38WXQBS9roSIZZja95VV/Weqoof+cbRG/9wkP4ZyZEeTsJeddI1YcTJD5Tj41+n8gwqQ2MVnNtiIJQllOXn9VvUsuVSy2nb4xvCqo5TMYTBU0LnK1bt2LJkiVYvHgxSqUSrrnmGmzZskU55tWvfjVmzpwJAHjlK1+J3buzqyq2EzQhKWVInsqJ+TUcaZb67aFBvOFvfyR8L3yXrDJb6LdZwyl6DqZ0FVLNOlzI1Rv4SQtPbynKgDRSkenZydc1qy8KRBxIoY3n1XDCMArSo4mdlWix1i6Oax+7Dw+pTvS0OBz2edouU8zxMLpHT6z16n1f9cOGin3p1+GPqb+zFadOw1MvHsf+E6N4kMXVRD4c/rzqs9B7y9pIceE2Ug2EFlFlTMdKTl/DocEyjhg2HXnZW+o5Sc0t1aQWkEktPevC4Gg114YoCEP0dRW0z3I12dhmE2heZZl0leN983UnesbopgXOnj17sGjRIvH/woULsWfPntTjv/zlL+NNb3qT+N9xHFx66aU4//zzsWnTpmab0zRo504ajsi0nPEi/TB78BMENdcPBZXx0GCUHoXH/+Tx4dAA9VwXfV2FVJMaN6PVn0st1nDilPc85Q/tMIXASbn/0eF89mXKB0elGfJEZaeBC5w9R4dzajjymBUf/4HRJBay32U/wMzeIoCkhqP7UfJisJy8jmyfer3VC2dgz9FhHIzT61z18lPFOVkaDmk2WQJHT2nEx2C9qfCDMDSOt0Y0HCNbLKUZRtKAttkYHPVzazhFzxVZMKLP6jcDjhVLTRE4LWAPjiWaFjim3aaTkt3w/vvvx5e//GX8zd/8jfjsoYcewqOPPoo777wTX/jCF/DAA2ZH8KZNm7B27VqsXbsWBw7Ub65Iw1/8xxP4HywQjkxT5MMhWmWeTK+1SQOkbQTCJEEDbCDNpEa/tUuTACy4DvqyNBxxz/oDP+m43i7ScHxxDdIEZ8cCJy0wNrcPJ47DKXi1adG1zAZl3xeFyPYcGVbiZGrlUiN8/r6kbZ9naqhUQ8xMEbZcE6gHulmUX0G/3uqFMwAAj+0+BgB4zZI50TlM2HmuoxIImD8r26SmCRzuL4j7spLz+aqBWeAo/qnctOjkZ7VJA+Z5Va5GfXF8pJJDG43GZm+X9OPm9jtl+NM4SIA3kmkgzZ8zEdG0wFm4cCF27ZJ01d27d2PBggWJ4x5//HG8//3vx5YtWzB79mzxOR07b948XHnlldi6davxPtdffz22bduGbdu2Ye7cucZjGsHtW1/A//vZC+J/iikpeCRwogUza7BI2372vXjgp0iHLxJgcpMa3/2ZhQS1p+BGJrVUDUfcs/YuX0dSwwmEqWu4nK7h8ElWD0tNManVwaLSUa4GmNpdwJwpJdz2s9/izX//E/ZM5gmp34/MiHobgWjhG/UDzOyNnl0XFEEQ1lWnhaC/Q92kxt0Iq2N24hO7jwKQGnlkUotO7C64Sl9xckE2acCs4UTnxQtjjp04MdFqajjx7/t/vR/X/svW1MW81nXUz6Pf5ZTNxqBg+GVnEQEiLcp1HPQy4lAjJrWxyqXmTyaBc8EFF2DHjh147rnnUC6XsXnzZmzYsEE55oUXXsBVV12F2267DWeddZb4fHBwECdOnBB/33333VixYkWzTWoKg6LQmBpjkuUkzesb4Sw1vf4Kv75qUkt+xu9Z8Bz0dWWRBpIDuW4Nh3w4VanhUG41k4bDJ1Z+lhoUk1pWf9cqpFWuBigVXJw6owf7T6gZnXnbntl3As/EaYH0xaCn5OHYUAWv/Zsf4pd7Ii2CkwbK1QDdRQ/dRTdhCsuj4VT9AP/68xdU8kFZFzjqYtXDFrxTpnXDcYCDA5HJkmfZpmfpKnpKX3GBk6bh/Pu2XXju0KByHF/DhMDJsbBJ32byO9Om6ufPHcaPnzmQ2jZzihnzvc251MzMwlpmX9K+e5kfJz/TM9kmHVRtFlDn6b7jI6l5ElWSizwnr28NAH7/X7bi41t+mfv4VqBpgVMoFHDLLbfgsssuwznnnIO3v/3tWL58Ob74xS/ii1/8IgDgk5/8JA4dOoT/8l/+i0J/3rdvH1772tdi9erVuPDCC/HmN78Zl19+ebNNagpDwmcTvVDaCWXlP6s38DMqVasOMFX1luek18OJDvJcF32ldJOa7nOJ7pXZzMS5xFKLfDgxS00nDbB4B77Y5k1748cajicCP9MbWYvBVvFDlDwXC2f2Ju/D2nbp5x7ApZ+LTLi6j6635OGnOw9i95FhQZ0VFHVE77JUMBM2/BwC58s/eQ7/7VtP4JuPmMsN0H34NXuY1lUquCh6rtCMSwUXjqOa1Lp0DYe9ixGDhhMEIT72jceVANuRim9kh+UROHTvWoJCMsqiMaQLXtM58rNsbSiNMMLvYdoUnRip4IP/+ih2HR5CEEZugt5S/RpOngJw/GPe3k9+7yn88eZfGM/xUywW9ZAGHnjmAG6Ns5CPF1pSgG39+vVYv3698tkNN9wg/v7Sl76EL33pS4nzFi9erMTkTATQBKaXSIsJ2az3HR/B3939DD751uWCySYETo13raSZoUBDQ8laPoCkhqNdy5cmtb6uQuok1U13QB2Bn/FNaaEbNbHUpkRJLNWI7tqBljqCMITrAkU3O3lnkGMxH60GKBW8KOmZhlQfjq7hFD3RT0TVlia1SMMpeWbCRh6B80SsNZVYQHGaSY2ESF+XB4rvLXgOSp4r3kPRdeE6DoJQPkvRc5X+V0xqBtIACXJdE+LjhcZRuQaL0HEcRSM0HUPQ/S2Do1XMiccVh9GkljFOAKQSRnhfE1PND0Ks/MQP8InfXY4DA6P4/uN7sXBGT/w8UAROmtnvKw89h75SAW+/ICJSKbnxUseebCOfp0cGy6l50tI0nLIf4NhwBd/ZvgfveeVpqf70dmFSZxowLQpCw9EEDpkm/se3f4l/27YLDzwjqah1aziBdN6SwOED82s//y3WfPJuhIzhk5ZuJDKppftwhG3Y5xpOfSY1yrJQ8QMh6BImtRTnbN54jSjwU8bhpNGXy35Q24fjRya1OX2lxHd5WGpAtLhI57v0jwCRIKjE9+grFRKUcM7mSsOuOAvC1O6i+EzPVB6EIW6+9xnc9M2I1MJNagU3SgNE5xQ8B65DDLlAfJ+q4RjMVvS8/LvIhyOPId9P1k5aD3DWD/3FC0eUdtFfZJZNMw+bNRxzG6j709Ia8RxrR2MNZ2C0iqGyj//5vafwn89H4Qqz+koIEftwmIaZtmn7q+8+pWTECEK5YUkbe/S546jzdLQaGIsb6tfS59tf/Mfj+MstT2L7rqOJ8w6cGBVJbduBSS1wTGYoruEEQZgQQPR9N0vmyaP5s8AzN1c1kxrfMT244yCODlWiiZ+i4ZDGFZEGPFRYbjPTPVUfTmYz2XNFvymPHM+lRYvXlK4Cip6T6sPJTVAIAc9xBFkjTaiMVgPF/PX47qMJYVuu+ujyXHzs8rPx9+98ufJdnjgcIPJbCYETbxIpEDMMo/4seq7RpMYZgWnYHdvm/RS/At3n8d3HsDVe/GiHXfJcOI4Tm9RiDSf+jDQcN06E6qcKHIOGY+ibkWoKS80P8J/PH8anvvdUQvjQ//RofGy/cGgIV/7DT/EjFhhLlyfBnZYqqZ4yAabs2nzc8DFDJjVxrAM88tsjACIhKHw4DZjUgiBEMSU/oGiXIHl4Sl+OVPxUgaPE4bA+qPgB9h0fTb3f//zeU/jQvz6ar/FjACtwNHABo1ZOVH0XXaxcgcw0UEPgBJIgoJu6TIPj6HBZMeNw0EJViONwAHPWYlNSwHpzqUmBE4jrjbB4pSld6i6/EZtyEJstRLbolPPKVVXD2XDLQ0qkPR1TLESa34bVKmMyveKnZlJjGo5LJrVAtpWICVO6k+ZMntUhDZThl99XX2hDRIKL+ronXvBoASsxH04x1nDCMITvhyi4Djw3naVmEjimMViuqnnXOEvttod/iy/95Dn81XefVM7hmjygjjeac8eZ34TG9nHhw0nTcPKb1ERJBT8Q7D5Vw2ECJ85FRn1yYqQqNlDRHCQNhwuc/FYCka4p5RyynnQVXQSh7I9Iw6ka1xWV7q5uCGjueG7SnHZ4sIz9x1UizTce2S00urHG5BY4bJH88k+ew12/3CtS2/hBYNy1D1NdGEN251q7nnJVajikoZR9WThLx9GhivhcH6w0yLzYhwOYBaiJbpnbpEY7r1ib40kfeYDs1O6iFtFdv4YTBFHgp1fDh2NKPPlwXA9o95EhnP2Xd+Lx3ceUID2OtEmv3y9avKO/vdgOruRS87kPR10gq0E2LdpUo2W06ic1jDDqF1rke4rRey7GJs4iN6kJH050byr1wMepInCM2rDZzGbKkl0JQkEY2bx1l6I9iVo1hnlB1+ImTLo8jaGhFPNwHpPa4cEy/uI/HhdmwYofJHytgFnDMQnhY8ORhgNHpcrX2lzS9YMwrFk2XbAKGdOQ2hOEMNZb4sQZft2RihxHxPjkGK36iXXiU99/Ct997MXM52kVJrXA4endv/az3+I7j72IoUr0MqpBiEMD0mFHk1EkP/SlKSxvahuZLVqWZzaZ1AjHhivCjJMgDTDH8BTScAymiIpY0OqnRYt4DkXD0QROfH8lSSK3m/shNm99AU+9eDzzXhSHUyt5Z7kaJLQmOvb5g0Mi/U4pJbt3egG2IHEcLZhUVpn+D8NQ+ImmdHlJllqYTRp4gVFdqT0mv0VkHgvEmCO2IC0kRc8V77VUcARpwA9Iw3E0DYqTBgwCx9BmPY6GByjSM1aDEI/H8UDRd/GYZRqhfKZQOYZ/Jnw4KRqOadjqY/nv7nkat2+VLDvSRPXnI4Ewu68kSAPDBoFzdKiMMB6bioZTQ3EnOr4fyGDmVG1MCJxYMIYkcKKbmMxqaaEBwywbiGmej1QCDIyqWtPgaFWsIWONyS1wuKM7XiSGRRxOKNKG0P+ANKnx+iOEmpkGhA8nSJi6jCY1puGkxeFwDcdkUhO5r3Kkd0k7l6qdVqqSNECBn2RW4sKb714rQYBPfu8pfOOR7Px5fhAt7ML8kWFSS2s/p7eWUiq0pk1UfbHlTDOiatMhFT8KaCx5LrqLnijvbDrXBL6A0HOa3l2IEEEgNznShxO1p8i0uIIb0aIptY3nugkfzoii4SQXMZN/yw9DZYyXmUmtGgs2AHjkhSPiGNpMCQ2HXYD+VOr+xGYkzlIzQd+Uea6T2Ig5GjWx4svNB7/nwKiPkudi0axesQEwESmOD1fiPH9Q8qnRfPzr7z+Fyz6XzI5yIBY4YSjvnxbUSfOlpGk4RNAw+bRovOqa/HDZF9qPKVsHpafiwrXih5jSbQXOmIOb1Cj/1agwmYU4EAucmb3FxM6eBg9/qbV2PbzMsYjD8ZOCi3BsuMzK8OomNfLhOCITwIBhl0yDryENR/hwZDBmwofjuZiqOc51Bk20G87unDCmRUfOcCdVEzGanmIoAsdgUuM7/kR/Bsn/JWmATGrUhuhZioXIjMXPJI03O29W0hxi0k7DWMOhS5GmyU1qBNGWEMKHk8ZSK7iO0XxkWqDS6utEBJIAc6d2YfHcPjz6WyZwyKRmSPlkMqkFYajszNPjcNQ+LXlu4jPdb0GmT0Dt98HRKvq6PCxfECVBDbRFGACmdhdwdLgitO8eQ6aBf37wOTy9L1kIkASOH4aY0VPC7L6S8bioXWaT2miWhhOboHWrGc93aNpA0EZDT6hqNZxxAC2SpYKLIFDNKNUgEBrOKdN75M5eEzimctBp4HE4OmnAdO6xeHcVnaN+p9OigWwNRxU4mc1MnNsdCzTuP6EFq1hwMbW7kOrDqcRBrrWICn4YioVdNwVxZGk4x0e4hiOH9stmRQGgPUVJBNBTgOgale+rOckA7syVwpaoyIQ0jVS5NruV1HBMJjV1se8VpAFX+Q1E8UuuouFEWRv4+bRwTe8pGnfzpj4PgqistC7AyzFF3nMdLF8wHc/sG1C+A+SGxRTIrG8a+OZvKCctulSoLXDoM30TMzhaRW+pgBWnTseJ0Sp2HRlKCOG5U7qElcFxHMyf1p14DqV97Pr7T4yINruug9WLZuAxA00ZYNoKEzh+IIv8yWD0QNYmIoGjxdkMV/xEGRQOeu+HB9T4HitwxgE0yPtKnpjcXCgcPFFGb8nD1O5CwgSmM3GA2uwvVcNRNQ+TfTca7GZ7rEht40ofjok0YKJF1+PIB5hJjflwpA8pTh5qYKkVXEcEGNa6J01qIApi5LsznZabltiTazhdTOD8+x++Cn//zpejp+QZU4jw5xHPwGKgaFLT/7TzLBZcJcAxuo58x2momjSc+N39jzefg9csiXINhlDHFDmtyYylmNQ8RhoIAkHA0B3KXQUXPSXPGPhp2hEHYdQGrk0RRqvRfUqeqwhsPQ6HL850GDfxBmGI42z85NZwCm5i3uh1awCIvuBjcCD2W6xYMB0A8OSLxxMCZ86ULhwfqcAPQjgALl9xCr74nvPjtqj34AICkBpOEITwHGDVwul49sBAJrGnxPyXfHwSiWLJf78Tv/cvP4+PiWKtdIET+TDTxyC998NDVuCMG4IgivQnH053vPOtsl1tNfbhzJ3aFbN91JcndnHspdZirsjUNkFCEJiEVaTOm6/NqY+08x02qN5CUGoTPA+oTYWY8cQdxYSi66JUcI1VNbuLnpjENQVOvGMDIp9JmiAfZX2ng1Nt+QJ5yvRubFi9IPZpJE2MpvYpPpx4ltAhdG6XR34T/hzm66Xdq+wH+PVLx4Wd/tVnzsFHLzs7+jJUhYDw4ZBJjQnVoucocTj0zvRMAz0lL3ovJh+OQZBH8WgwkjCGK75gw+mxIPw5jSY1Nl7CUN0spWk4+rAteW7iszQNp+C6ilY1WI5MamedMgUF18ETe44lBc7UEsIwGleuG11nybw+8Rx8Tg5XfGVMEWmAzHGrF85AGELk5QvDUPxN75iqCvtBqLSFm9QeejZiZEoNJ/q8SJknFJOawYcTt/GoFTjjh5vvfQZv+6eHNR+Ousj4fiRw5kzpEjtFPghMzv5aISeyjIFctDJ9OMzOqn8vNRxH2PazYivKbIGplxbtuhF7LPLhyHMjGzLtcHk/xAtywRUmyFqBkH4gTWoFbWeuLNC5SQPZPhxd4FR8dRcfpdWP/hZxOLpJLfabcCcOr7CaBv5s//ijnbj85gfx053RQtLX5Qm3t15LRjeplbgPJzbvUXYKoeGw93J4sIzpPUV0F92aJjWZfTpUYkk4Rqt+tMt2HWXs63OD7w8ES43dKwhDhXQyUIeG44fR5vHv7nkGx4YqZg3HifxZXPiOVKLkq10FD/OmduHgidHEho3S6xwZKgtNwmHaLh9vQ+WqopXIHIxREO6qhZEmRWa127fuwlv+z0/wo6f3JzWcMFTGp0njIyYijU3HcdBdjFIdcT+b0n9Mc9JT5ljSwBjihcND2HV4WAxyijUY1XbpkcApxTmpQmUHPWoQODVT22jmDSCbFn2E7UJM5QlcJ1oMSeCYaJ16klBAXQCyQPEcQLSD4kFl9Fn02zUmSYw0nNoLMD0fWQcKrquwyXSBkxZMqpIGkiw1ztrSTWqRicLFf33jErhO9D/1eSppwODDyZN1gj8bUYF3Hoh8IL2lguiHMFSFgCANGFlqkXmFNHXPpUVWnv/SsRH0T+9Gd8HL3JwAMqddpsCpBPBcF56r9oHcRCVNavQnjSMnjneizV/JczPicDSBE5MGntk3gL+/bwce2HFApCHioKwLev4xEqqFeH7rsUlS4FSEoOHm1QMsE/nQqK9o+TTviOE2e0oXFs7sweNx/aKnX4rCBJ47OCg2KV1Fyabj72e47BtJLl4cexW1S1oUqppJk8CFWELgWA1n7FCOI+YHRHBWNIC4XTuiRZeFhlOJk+KJaxhMYbUWVZ46XBc4pl07HxT69xU/FPEYZEfPyo/VSC41P5CLbangKj4cIDKnARBsKLouZ93k1XACJtw811EC2/TkkemkAblQ1dZw1AW34ocoeA4+cukyXHD6LFR9mX5IT97Jsyy4mg8nT0yWqS/4LpeovSHUdyUFjkoacBzppyBNveBFmQZ4O/YeG0H/9B50Fz1zMCHfTGj1dUz9OVqN/Aieowq2ijamTf0jgxMjlh8J3nnTujIyDaj/FwvR83Iijyne13OTxegijdYVbagEYaqGc3iwLExXNB+CAKrAKfta7R0pdGn8rF44A4/F8UqkmXCrSpcntUpu8hwq+0kTcMxEdJkg7Cl5GK74in9595EhEdTJhZjVcMYR5WoUeEm2UaKy0qJMPokjQ5HAKcY7Rc6CKgt/DNdwsu9b1ezp/Dqmc3m9dX3D7AeBCCgDot0RDahf7jnG2EAq2SG6V06TWkxVBhDbwNXFXtJzY9q05qzsKuZPBRKEcvdY1HbmXCNIS96pa6CmBZJrTkkNR8aUkGagB35KkxppONJvwq/Df5tg+o66J6KG02eqCbPoOaIsAbUTiAS/4zjGOJwKM/HtOz6C+dO60V10U6m28l7SgR2GZpr5SOzDcQ2LOaCyMgl62QAS2ORPnT+tOyOXmvp/0XMRso1i2Q+MwaFRX6g+nAqjS3uxr0v3a01lizDNNHo3QRgqtZaGK1UjMYfKbgDA6kXTsfvIMA4NjCpas5wvMkCVB+YOlasJjVT34TiIWJjDZV8R6tds+hn+6+2/QNUPFKF1xPpwxg9lP0AlkEwxPwxFQkZAMpzCMIrBoZxUfAdtojPnTd4JSHoiv45ufs4yqdGAI/TE6vQPf70Pb/k/P8HX40BLkwmpnvIENDGKBSfhw6EFmiauzk7iCU7THP38Xi7TcNKSE6ax1IbK1aZ8ONUgFIlD6X2TdiE1HNkGuofLhANdh45NI5Fk9b/nOmxRUzUc13HQVXCVXGqAFDwiDieQmQZIwB4aLKMahOif3o0FM3qw6/CQgYgi/6drh2EkeIsFM0vNqOFoJjUuX6VJjWk4bO7N7C1lkAaSJjUexFiJ57UOz0HCh1Px5YatEPsn9QBeznQUmoQrBYWu4XCtucI2knTuqrgs+ON7jkkNh1Hfqc+DIEka0M3ltOHkGk53MdJwpKCXoR0Do6rQOjIo54rrqJnIxxKTU+BUI7WTFz/zA+mo44k5u4tebP9V2VEy8JPbp2uY1EwaDmtDQdtFqtRR9VpVX7Wrk/12z9GI//+LF44qO2S1AFt+gUMTo+i5iTxm0rSjJtzUc0NF7c++Z8gEbsF1VZYau+do1TcKr8FRX9Fwugw7cr7oJGjRfiA1HFfVcHgbozbIjQmZv6iJetCrCVlBsK7jsGh5XcC7scBRNUv6LeNwgshRzgTsS8eicXHK9G4snTcFA6NVkVVY9AG7Fy9ZzfOB0X0AqeF4GktNZ3CqmQZIMEuWJVG5AWBaT3oxQSMtOlQ3byZmlokiXomzfQPyfesm6W62CHNfCbXlAMtEMjjqGzWcIJDjun96FMdzeECSEHgoBlkEqpo/aajsJ8x9tOGkzZDjyE2neEY/RF9MpT8xUlU0OG5Sm9JVGLe6OZNU4ESqN72AMIyZIRXKBC27pasYpwjxw8QOCVAnUy3qr6rhJFPkkKOWwC8XBFG+ql/tjZyNuobTXYz8JVPifFuDo9UE/dZ03Sxw+3PJcxP+kyLbIfJ7UD/xCcsn+9MvnUjSkEPGUtNNavw5qmaT2sGBUeVz0/zJ8uGQ34MfR+9W36nTOCHSACCFkdLuFCGbpe3x6PEwVBdZLyaI0IZIF/g8l5pOGth7LKqB0j+9G0vmTQUA7NivRr7zxZqEL9Gi+eaGhBHF4Xiuo8wDfW5k+XC8WMOh/6f3FFNT2+ivneYpN0+bxobrJEs1lLkPx4t8tLoWwdcBGk/SvKpSublJLfJ3ymenc+i9lVkG62izG5/HzJijioZTNWg4kRYr2uVKHw6h6gfojdeDY8MVRaDyOJzxMqcBk1Tg0GAgO7Yfc+rFzpWZgroKXjQgA3W3adJwshbybzyyG3uPjYjFgXYso8z8YIohkNcOseGWh/Cmzz8YtTkIFApoT8wIo+DAwdGqsb3RuWpDL/zre/EHX9mauKdiUvPchB1Z0nNVk5pJw6F77jwwgMtufgCf+cHT6vMFknIaxfzUJ3Be1IpKmdLf8EVHT15ZCSQJg+JXuHks+h39McJNapq5TSWRJJqQeB4CfeIxDScI1fFV8Bx8+qqVuOF3FgOAMHNRu8mHQ8KzwMoTvHScaTjzpwAAdrDsAIB+L1myOghV0gC979GqWcOpaHND2TiRSY2VSA9JK4sFqp4FQp6r9huNP/L5pDEYiUChFmMLBK1cajjq+DZpONyHM1L2Rb8MlX0xl/tKnng+biXgOdVEUbZQklO6hFYpNRzHiZKZmn04rkoaiH04/BhFw1FMakzgjBNhAGhRielOAy2+ZCumSVqJB3Q3M6l1FVyRIoQWCseRgkJxiGZInD/7+mPi2hVfqreRtiVjJ1zHLLhMJjWVNKCq0wOawMnKpbb/xCj2s4JY/DhpUnMSaj1pNrTwiYWG0aIJ1E800PX6G9Hzy+umaTijfpDI7AwA+2J7+qy+Eg4PlhMaDKBqOHxRC0MZnQ9Eu0WeDULms4vbwPLI8QVIb2utUgjc5FX1AzgxzV2QBqCmtvFcB69bOlf8X9L6P/LhhELD4cysl46NoOA6mNPXBceJfJM79usCR9VwXMcRPoZSioZDx/FHFb48oRkmzc46S4009mK82aAy1Rx6d1I7KC0Qr0jL4cb5+RQLBTOp0bjQtQg+fnUNJwyj42f3lbD32AiGGUutt1QQ7SBatNpvvqIpCR8Oy2pNc3l6TxHDZV8my2VaUEEjDXQXPcXPXA1CEbt1YqQiTHZ6HJbVcMYYMkcRp0XL73UNh2jRNIh6i56RzkwT69/+8wX8689fMN67W2QFUDn7UVyNk6rlmEgDvN5Fj8bBHyxXFXaXnkokD3QNR5+QetbiSoYPh/qpoB3L20STkPpbnJsgDSTbT/4bmmCmzLy0cfj8vTvwx5u3s3sTzVzd8eokCIIah6PG6ChkhxTTmSRVyAWt4su+lmY69d6etgALk5pLPhxH5ASkTAMkRI4MlTGjtxQLNAdnzp2C3xzQBA5rrxuTAWhumDScMEQch6O2q5xhUhMaDsuUEcS7/KIrK76atEBTHA4ADDMNx0QoMfpwfOkzLcapeXSt10gaYIJiuOKLmkDch9PLUihxy0WXQcMJuA+HNJxAWltm9ZYwOCpNavQeKn4gGIJAZB3oKbmK5lL1pcWDaziz+7qU5+yzAmdsITQcVoqAg+/muosyzTsd11PyMk1q33xkD77xiKzJwUGTkNtoy36UEdh1k7mR5LV1gRMoE512LTThBkd9ZfKpJjWwv9OFjx9IhlapkBQ4fMICnKWW9OHQfcikaKIlu9qCb2pjqsCJKetL50XmorlTuxLH0KLzuXufSdxb9+FEAZRmyjonDXAnMv8NpGs4JhZfxQ/E80OY1JIaDodkWUlzT5Ci4RwbrmB6j1xYpnQXjGYace1Y0wqCZOAnFz7EhuPQ54Yy3jQ2H8UOVf0ABc8Vz2JMJKp9RLR8ituJTGpmUyofU6Kekfa+Exsqow+H2hJpIX1dBZQKLoaYD6e3S5Yn95mmRn2qCBzOUitI0gCtD/OmdeHYcCUhcKSGIzcp3QVP6Tddw6H3PbOvqDzn1HE0qU1OgePrE0IdpDx+JPLhRLZwmixc4JgWmEoQpAZaUmlhPrjL1UCwWVIFjtbGKtuRA5CUyHjCnRjJZ1LjzK7EPUMZh1P03ARtVEa8xyY1P4gns8GkFpIJJU3Dkc9O/S3O1QWOwaR2fDja5b77FafhK39wAa58+amJY/TcYvw5ucZIi1NaKQNKE1RgGk6gLaR6uzlkYKzsn7IfCA2GDwF+DW5CBZhJzWMaTii1X26yiwSOXGgi1mFSayZ4jiO0jyyB43nJTZJOGlAzDRhMaqHUyoop4wNI13Bo41j2Q+PYIMuBnkVZstRiWjSbkyXPVeZXMrVNVP23p+ihtxT5TmjO95U8Zo4Fe69RgPZoNRDv2GdM0pJBw+mf3oMjQ2Ux93j1UB6HQ4GfHFVfCpzjI1Whwc3sLYljLj57Hl6/bF6iz8YKk9qHkwYTS63KGDC9xYIx8FPEYvhq4JZpp6s79MmklG5SU//nO3KAmdSEhlNNXfy48DqWIXBUk5qT2AHqtNyKH+Diz/4Yvzk4CADoNpjUqIKpvoMNwiirLgDFFKS3PY2JROZRz3PwhpQJpMf38OtzWjTF4ejxJHp7+LuiJuWhReuBfoA0kQAyyJAWYoKeJ0yyrGKB48ogZtd1Yod89L6PDVcwd4rU+kpaOiJAZalR6ns/oEwD8t5JDUd9Ph7fBugmNZojQdzmyO9EPkmh4fghdh0ewsBoFef0T4v7Q9sYCoc9N6kl+zwqT+BisFoV1wakhlQwaDgFz2EaJ9jCHj9HEJEGeqZ1o7foRaQBViiPp5fhr62rEAkcboqlvujSfDiOA8yb2oUjQxXxjFzD6Sl6idQ2HJVA3ufESAUzeqMNB5kBAeCTb12BU2f0JPpsrDA5NZx6BE4hUvODUDrFu9M0nIB2b6qGQ5+vWTQD//vqVcb20A4/jaimL3qVIFTyRlEcDu0cI23H/Jx+GOKvv/8UPvDVbUr2BNNxPA6nlsAp+4EQNoCqKeoR+NSXuw4P4Q++shUnRqqKSU1NBqoKZ9NCTouc7ufg0OnW/Dk5zVxoOCzZKgctag7XSE0CJ9Wkloz3KlcDZbeqX4t/TpA+HOlfoDgcKsBG7U1qOE5C4OjalBub6MJQNTPzv71YsHHovi91swPRJkCSBipxLjvu43vT5x/Emz7/oELa4PODnr8maUAzL9Lc5LToqJS3SprgY4k0G24Ko+zbPSUvSt7pM9IA8+FwwVUqeLEJXfaNruH4sbmuu+BhZl8J5WqAw3HWEU4s4PVwHMdJBG/6LFD7O4+9iP99V8QM5RqOqezEWGJyCpwaaZ27CppJLR4wpOb2Fr2EgxyQC1NFc0DS4Lp8xSnonyGLONHLHq36wnyVpuHoa5cfBErcDiXKNNUl0REEIf75wedwz1P7hIZjWqd95sg25WrT67Lo9zORBug5KCJ8452/xv1PH4jzVUmfRDVl4X7x2IjxuWgDkEUtp9xi+rNSuQqFteQHqRqOyKLNNgh6QCM/ToeJVMHt+tQ+3QFe0BZ2PYmn4zjww2hR50GB1SDAsaEKpmkmtYq28eL96jrRDp98DKkmNceBvmbpgZ+8+4TZWSMNkMZOY7oSyJLTv34pihcKQpUoQ+0YrmSTBiSBQr23iCNzk9ngSwVXlBaP+kP2S9SWaFPXXXTR11VQcqn1dUladGQql9fpKrgYrchCatw3LCp++pFJravoYmaslRDtX6YcihmCcXc4DpImNeYG2Hd8VPQn13BMKYvGEpNO4PAXDJgXKJWl5oodHFFtFR+OwVRVDUKjhuNpJjNih4zGJjXPSScN8EWXTBA6aaCsaVYmc1nkWEbiGBM1Us0WnRwqeqljXaPK1HB8yZ4iyIqfZlr0q8+cjcd2HcXzTIsikD8irf8AubDM6FGdptVAOtrpOD8IhVDUhX1VCJxk6QK+3mX5cBxHnewVX2qsFIejC1ZP9+EU5A6d2hNyDYcW72qUq0zRcApJH44fJHf45MPxPClcS2xDFsW4aD6cRHmCpNmZ5k8xrmlDPkkaZ1U/wGmzo0qtDzwTUfaDUJ2vJg3HtBmheUfaT0XTcLy44B8XOAXXNWo4ShxOxUd30UNPbFKjZ+oueqy8tqp1d8V5Grl/UKdFKxpOrI1Q4C4323tMC9NLYAPRRsO06ZnGSAKmeT2WaMnd7rrrLixbtgxLlizBxo0bE9+HYYg/+qM/wpIlS7Bq1So8+uijuc9tNXQzAvczEPjOs7voJZhVPSWZbVfVcORgKFcD3PTNx/FPP94pFqEoTbq8NgVlUcoY2lWawHfZlNNMT20DqKV6Dw2oqUuAOP8Uu1aWwOFF0Uy5tIqaMMrqW5Hqg/URoNZWFwSFFB/OW2MiwF1PvpRoSyWXhhMJEm5SoOeM+jMWeLGGVWUajik9i+s4ihMZULWStAzZgrbMBEjZl5mOhYbj6xqOo/2vmjRJQHCWGgAcHS4jDKEIHJMPhy/WUU63yIdD/kW6n25S08dsMpda0gpA84dKDFRiDZP6pOLLjcGDOw4CiNYR3gcl3YfjmzUc13ViIorqd+WZGigTOl2+WFAFqa7hhLFQ4KSBUT8QyVUpjZXPiDfU5nLVF+OpEsiaR0qmAdJwYm1kb5yyim/cCp4cfyYfTlXLfUggqjSQJKKMNZoWOL7v44Mf/CDuvPNOPPXUU7j99tvx1FNPKcfceeed2LFjB3bs2IFNmzbhxhtvzH1uq6EnbezSXhKgmtRKBRlnQCalXhYNbbLZV/wAo1Ufm/9zFz5956/F55REkNDXJWNGwlDGPpjAd9mjVT+R2oZ2NydYuo2DWt1ygKok5hM4NTWchA9HM6kZWGrUXdR/vPIgj8Mx+XBOm9WLxXP7Eu3g18vasBEZgdJ98LZxjVH34fiBmldN9eFEn5lS26TFOwWaQABi0oC2i9YXC12Y6ixBPQ6HFmRKMjmtDh+OF5MBoqBkVTvvUkgDbmLMSh8OPW/yHrR5K3gy4Sg596Nnl6lmtu86Cj8IRegAgUf50zXTNJyC62DvsRE89eJx5sOR442CmmkTWGRR/NS3/Dfdq6foYWp3EYcHyyhXA3R5ruKD1ANYI4EjNZwK80lyQgDNbzKp7YlNaj4bZ1GmAdk+0gjPP20mZvWVUPFDo4bDx3/R7TANZ+vWrViyZAkWL16MUqmEa665Blu2bFGO2bJlC37/938fjuPgla98JY4ePYq9e/fmOrfV0CdZVw0Nx3NlMBqZ1HpL5sDP7S8cxd1PvhQznJILpsfMHADwslnR4km5xVxHdYpy8IEzWg2gp7ahmA6u4ZwwEAKKBdVcRQLHlF2ZtC7AbOulfhGpbTRhzitSSgd8ukmtFi3acx1M61bNYQTpw0kf0qTh6JvgaIIHiWzRIrV/aGaf8USb9HVellrBdRUhHrLFlBaohMDRSQOsgFh0XhzXEZvnaBNBi5VOi06w1DSBQwXdaGyK7OCFbA1Hz6lnyjTAHfdhGJkvC4yKHJm4omMGRqvYeWAg9uHIe9Hfw4IWnZK8Mzb7DZV9rP/7B8Vzc1r5UEWay+kzfi89DoeEXHfRw+pFM7Dn6DB+e2gIpYKko3NhTSCWGt+c0t+8JEQQRCZ20sZHNRM+bSp4yp1z+qdh5/9aj2/e+GpM7S6Ap2fi6GW+njSLylihaYGzZ88eLFq0SPy/cOFC7NmzJ9cxec5tNXSGmlHgFNXPaOCNVCImEe1SANU+ffdT+/DpO39tjKIHJFuGsHT+FCyZNwV3P/WSMFvwVDIc/D6UUYBrS8KkpiQUTKZ3iVK6y/8pDictstuk4ciyBKq5TX9uvvhLH4fqF+FEBK5h8IVDaIiuk5qGIxdLzaUSC9r7YVoBvwYRP7idnT+DiTSQR+DoJi+CSK2TYlJL+HAMLDWiUnuuDOjbfSRN4ISKtmukRfOxq5VFAJJsLoDRojUzavQ3lO+KnisCP4tMw6FkmheeMQtApOXoPhy672AtWrSjshNpzMkNhvye/Ko6LVr6cOiescApeXj1mbMBAD96en8kcJjg0GnRtHYIkxrTQhSBE8+96Zq/UdVwnMQmhfrHcx3FXAcAH71sGd6xdpFiUhtvNH1nU0r+ZA4k8zF5ziVs2rQJmzZtAgAcOJDM+5UXSYGTbVID5EuMari7okAboDpagSiDQJqpwnNUH07RdXDpufPxTw/8Bue9bIYShxMtCGpwqLhHPLEKGi0aqC1wIhOG7PcDJyINw2T+UeNwVHZQteyLiSWCObXJzgWiCLJNMTMBcheppyEhB6znOsIMqYP6J8tCQGQEfVFKmNQ82mCwXHsGk1qkkcY2fXYtcd1UDSeIzUeawKFFLYU0kBaHIwI/XZkmKdJwVHOM4sMRKVJkjE1Cw3GlZkoZl/m5dJzuB6AchdR83g3JEgMOwrgMgxKHE0Q+knP7p+FXe49j+66jCR8OLbYkQCp+gCBMDgDPdbDt+SPifyrVwFlqBDJN68JINa/JdDo9RQ/L5k/F7L4SDg2WY4EjnyFBi/ZcJSibsozQdwAJqmgtLHgupvcUhSVC0sopj51sE0fRjdL1+EGIC06fia/f8Grx3eNx1dF2oGkNZ+HChdi1S6Zx2b17NxYsWJDrmDznEq6//nps27YN27Ztw9y5c43H5IFOie4uGjQcTeuR9GVaKGQyT533H0U7awsaLVDarrbouVh7+kz4QYgd+wfgunIB001cXIiNVgKFVRU9h4woJlB0Mh3WXYz8Ub4icFRnJEcQMEc+Iw1Q/ySyRWvCnE9kXbMBkEitwoNMTb4x13FS8z5JH066hkN+Cz8I8bqlc/Df158TXT8IExUgAWbGCGEkDVCVTf58aXE4FT/Alf/wEB569qB4dzrNWe5Wo/91TSzNhyMCP4k0EC/MwqRm1HCSWqkpeSenL7uGzQc36xCODpfjvpBmoC3b9+DAidHEOCu4UsMpxJU5qV0jcZzLWfOn4vmDg4nFW3/VaVkoXMfBy2b1iv9JAOsF7ABpbhJpb5hTnl+PNJyeogfXdfCaJXNEfwizYGwa4/1T0mjRvIouzTHKXk/dTH6cqE81DUfzLYl+9WS2DP273pJ50zYeaFrgXHDBBdixYweee+45lMtlbN68GRs2bFCO2bBhA7761a8iDEP87Gc/w/Tp09Hf35/r3FYjn4ajdoukRQeKM7bCArjk9f3EDpqO8RzVh1PwXJS86P6VahDToqPvdCc9b/dIXEBOzzQAAAPMb0MaDj1jT9FLmBeIWGDajHPSgClbcLGGSY0vqKY0Qgc1Fp2s+OkaSQMFrzmTGvktqn6IeVOjypd0faK4Ru1Oajh6jirurAXMyTu5kDoxUsUvXjiKX7xwRFCAdY1FajjyPqbv+fNEv6VZxWcOeDKppflwAPWdJUgDjtQ0nRQfjr6J6il6gnlIlz4xUsUfb96O3/vyz41lohGbAQueo2zuyClPJI5QM6klU+pEm8D1K0/Blg++BovnRD7SgufgH99zHv72basBALuPDEX3LkifnWh/icaAuvnQCQTEjOspRcddunw+AGDngUFxbjXWXnibu+LATx4TFIRh3L9EmAiV0tRnzp0izue56HgeO90yROZjzjQldLRJrVAo4JZbbsFll10G3/dx3XXXYfny5fjiF78IALjhhhuwfv163HHHHViyZAl6e3vxla98JfPcsYSu4ej+mugzVQiRjXy04sPzHLH4/iAmCHCYzFjc6a1qODJwq+JHbBbTAq+3mzQcI2mAmdTIsdkVF2ej3Rif9OS054vNT3YcxDn9UxVTAheAyQJgZlo0NxmZMgfzmvAA2L3MtGgvS8MRJrUMH06cMaLsByh6MiVLJHACIXA8TeCEmobDFz56f8KHYzC9AdI/MjDqSw1HN6nppAE9DqeWSc2R99E1nKLnKDtbnh2CkKRFs6zODvPhaKlt+GI8e0oJ+46PiJIbgKx0++z+AYNJzRXZonmmASK/kFZe8QM4usDR+mO0GsB1o/G5etEMpaTAjN4SXhX7Wkjj0zM1AHL3L4WRugkAIuE7EJsNqZQJz0fGTWp+qAYaCx8O9U01FKZrnkGaz73Pvn01bvnhs9jy2ItSk46JIZwWzUE0cL5p1J+xHWiJqFu/fj3Wr1+vfHbDDTeIvx3HwRe+8IXc544lGtNw4gVI03D+ePN2/NmlZynHmmiZnDTAJ2vEhJETn/sEdJPaqOLD8ZVAQUCa1E5wk5pWwbSn5MF1VOHCM2a/cGgI03uKeM+Xf46FM3swq69kJA3oAYciwDBjgawaBM6xoYp2vDyPzFgU7U6fp2s4UiilgZ6BSiNTX1fjZKs9uobDmIj6xiKZrj6pwf3Z1x/DOy98GT74hiViYR8YrQiTl86oS2Qa0Jz4+i5WmtRkW4Sm50nhPFzxMX9al0rPNWSHUFLbxGUHeFZnGqsqi1MtTzC7r4TdR4YxWPYTmQYin4byCCh6VA9HDValcdxTjMqDjFSiDZmaUFO9VsUPUPTlMdKBHn1PueRI4xPH8VAFQYtWF3Led57rYCje2FG5kSldBbxu6Rz0T+9mGk6KSY2VSS/HLDUeElHVtLkZvSX8j7eci7If4DuPvSiOofRDUTuTm5eqrwY0E/SMBOOJ8SVhTwAkBI5Bw9EXe5rQoxU/ZibJF0g7nSzQuuE5DuZN7RILStFTJ6tqJ0+aCwgjlYgWzY+hndZQ2Zd1QrSU5pHAcYyO+yAM8Xv/8nORun/3kWGNNJBcrOi34zjGuA4upEQcDjvkhFZKWNemZHEyRhpImSx5fDjcVMbNEaQVkpZIgoCzrHTfgy7XdPYVEPXhZ37wtJLjKyobQTEn6kWEDyf+n5MwTM+VrIejZmEuFVwhHGZpNVD0onmAqqHSOKfP0rRvPXnn7HhRPzJYNjLGTFU7hYbjSZYaUfq7ih4cJ9qARH4NZs7ThADFxnCfVvRdPF4LLmb1laQPp6D2H6DSoum60bVkmyOTmvThEG573yvwv/+/1Sx4NTKdcQ3KRIsmCjQJPqnhqH1HNHVAEk9MGcaj9jsig4Zuuh3vdDYck07g1BuHA0jbajnWcK4671RsWB2RG4itkgVpUosmLmUz1mvaKJNaa0OZVbA0BX5y8gP9TfEJig/HNbMD/SBK8Mj9Kn6QLI8LQASY8YFsiuvg7TOlOeExQ4Aa+Gk6J5M0kCPTAM/5VvBkeWhiVUn7vXoNCkw0tVUuekkNh/DAMwdE3wyMVmNziIEWre1WuYajtwngvjQp+IVpMb4G+XHmTFGzK9Ty4RTisgNC2DuyDVGEe9xmzaRGebqODlWMQYf6ZyK1TRAqmQYUDceJsz2ESNVwpnYXRKYBHtAZtV0eOG9qF47GmrUuVACVFh19pwovIFrchxhLTYeee7GgWQd4AtqINBA9C7XTj82RJq1Fj8PR6dqyDZKlpl8njQk8Hph0AkfXcPR0EEDSzMYHkOc56C0VcMm5kZOQtIishY6nQgGA313dDyBafPgE8hzJq0/4cHRatK+mtuFtJqegDE4jDaeg7JLUNkrnOeHESDVBi3YcuagUNfOgblLjO/jI6avGfei53kTgJ5noWF14IJs0QKjFUiPw+BGK49B9OASdFh21VW0zdalpV//gjoOibwZiSqynBX7y+0qTGtNwDIsEz3ZMbaowHw4gM0jwhI38XN2HU2LagetKLYuTA1xH1q0peCrbbnYs2I4MlVM0afX/EtMGPFf6R6XW6QlqcpKlJv+e1l2EH4TxplDXcOT95k1LJs81kW+kMIrvxa6hsNQMGjcJGJpLSil4L9Zw4o4QpUlY/4pnNQmc2NRMJjc5DrU2uFLDyZoT441JVw8nQRowaDg6VZpnGpgaR7rrxZ9KnovhwGxe46QBAHjrmlPhOg4uOWc+Xjg8JI6LaNHR3wmWmkIa8BMDiZsGySlIC2lvMXrNPUUXx1wHBjeTMBsNsSJre44OY82iGVHb4lu9fNEM6dfhMUWek+hb3UcRhOqCc3hwVDs++k2LJcXf1CINuI68bmbyTi0HmO4vECw1Lylw9J05r0NCxwDmDNEVluNrsFwVVWT1hUBEjVMcDjepGXJedRddFD1HJGNUfTjRs07pNgscsw8nyt9V9gMhkEXdGuY/EYujL+N1CLP7pMAxajiaEKIxUomJHNT3VDajh5nUkhoOEzg9cpMlUv246m8AmGPIlMwFpiANaLRoThtwHbkB7Db4gAXJiAr1KfM0Ol7GDskgTy5w/CBEVyE5PnjBNrXip6bheLEPx0AaaCcmnYaj51LLo+FI1pI0gZGgol1MVl0JHq0NRCrtFWtORV9XIUHzFOUAEiY1PfBTTW2jJxwFZBZdcmySeUI3qfEKlzrLjtpMVNdXnTlbChw2IaguPB/buhmIJhLh0GBZ0VhEtDQthlpqlCjwMylwipogSYMSP8JMaoOjqnkkqeEkNRea3yJ5pwjIMy+y3KQmfDi6SY3GR9zMWia13lIB37zx1bj6/IUASOCEyvHUv3OmaD4cg0mtGoRKHzjMpMaDkl1uXnPVTAPkKzo6VKnpw+GBi6SZ0OKvsNQcJ97Vq4snF3Q85ZEwhznyPuK4Hn6cm/ieBI4saqduLKgvCN2l5BLK14vo+sykJvyrMuEo+UrpHkSu0Fl4ngtR8htQTfAJDceLEohaDafNaMyHQwPITwgE0giy0nwHbIeuQxc4vOAZB0+MOVz2492ePMZxHGEf1vOq9caLSJpJbUp3AX7MqBku68GY0e+3rjkV+4+P4g9/ZzH+9gdPx/2imtSokBxB1xT8QE21cWigjKndBWE+kZqT3OkBKmnAVH+dyvZG7a3DpEYCR3MAJ304SdKAvhBJlloy8LDqh4pJjRy5Be0d6xRcnaZswqqFM5Q26eQJyjaQNKnFWhTbyFT9UJiIaMdNQt9zWWyK4yjMOJ2lBkQCJy1dEn8mel0VXyVScK2TgpWphAeB+yIUgZMRQ2MKflVMarE5uqQJI9dwX8cxO+CLGSY1fd2gOBw3FvDkp4lMaup1PUf2Az2f2PhAPZg2kZz4w/HZt602brbHGpNO4OSiRWsmNc6a0p36w3Ep2KxdhG5S49CdoLzgWVq7B2JTmb6gd8cCJwoolel3epiG47oGgdNVwOGYVZSm4fSUPPzxJUuV5+BC0VSCWo+k55MFiAI/uYZDXUHX1xN+umkaTsEFRqm9ia+N7eHO7kGNpaabJ8i/xaHXuKfHMgS6w2cazuBotWYuNbomF15ZglS2KenDmZpiUiP/W1nRcAJFw3Ed+Q4cR5rOIiEjfTh6BuepXYXIpGbw4QyX5f2KnowjKQuTWtKH4ziy8igf87xPyKTGn10noQAQZZbp/vr3fRpxxKzhxM/K2s+RpeHQZpY2dpQt2mNtpZQ4+junGLoq28Dy5J0chTh4WtcKCaQVjzcmnUmNFm568cbUNp466JRANy2B4eBoFUXPTSxSHJxlpUOndtLYzGKpEatKH0hdRWl/5nmiCkJoROYJfR2Y2l0UC1VSwzG0WQgc1aQ2nFIRlBBpUfL/QwNlRYDwip8Ao0Uzm/UUQ5Q0b0cmLVppL9NwRrN9OAdOjOKt//CQ8hndJo+GE1UUjWnRZZklIj2XWgQlt1mOuiUue7dSw4n6a3YOH041CBXihMt8ODwPoOPw1P56oTJgRl8RR4fMtGhKe0P3oFP9QM0WTbTonpLHdv3JiH+CalLTSQNpGo7K8qP7ASzw08AC08ktOvRcfKYaPrQ5i7IjqIy6yJ+aZJOJOB1fbsBMWlz0TFHwtM5mbTcmrcChiWhKy0++CVPcga7hHB+uorvgZr5UWoOMGo7HJ6s5XxWg7kRpgdQHPAnRguuKCdNVcIWZq7dUiJlH6qI4tasgFp6ElmJY6PTS0vT3SDn7XF9jex0aHMXU7kKC8UULm6+x1CJatImGKtuRJfjV9ygXSp1xpJMdnthzLCGkZYR3rOGwZ9ShV4A9PlJVtATRdm3xqCg+nNpTlT+6EDixhjM7jw/HDzGzt4gPvWEJLjlnfuQTEiw1HkgpFzqd/OA5UQmJ4yNVI2mAB/vqWQp4xU+h4RRcYUpKJO9kz6vX+uF9kCZwTN/TBqiofcf7lg7XTaLy/rFJrVrbpFZmqW2oDyhgVL88jQ+ZxomVTdCOpcDPIEXDaRcmnUmt4gdwHLmb1U1qrhOZnq56+al4xwVR6QQ9lQf/7PhIBVO6CgmVlqPK7OA69MkqBZp6QcWkliJwupmGI6ozxpHN9L3rOAmz4hTmF9ETamZpZbrGMFLNNqlVg0AhLFT8EFO6Ir8Snxh65oKAaTimSW56Pybo7aXmJTScHBOUDqHuEZVMU/wWnOJ8bLiCRTN70jWc+ONqDh+O2iZ18QbkAjs7EYcTff+z3xzCmXOnYNkpU+PodRd/dtkycU8jS81xFHOUonW4UQqdoXIVfqDeEwCOMio8p/VGbXaF1kNJaHtKkRk4CJBYPFUfjsGkxrQGAjep6ccD5mzR9Mz6fdOIQrpJTWGTxusNtySUq5KMRJk1TIKC/ucxZ2ksNYqL03PdtRuTTuCM+lEp21KKSa0Q5yf6u3esEZ/xgSVSzzBa9JwpXamp6AEkgvE4PGWyJq9P4KYP2h152jFcw6H4hlJBBmRS4KdOnOB+lKz0NPpnJU3DOTESmUvesXYR/uC1pyfpxUHSxzGlqxAtDCyymtcTAZjNOmXi5DapaT4cnRGVxlIzQZ/oouKngXMekQbkgx8bqkBPCWO6Lxde9QjB6FrRs7197SIsmTslUbiOBMZXH/4t9h0fwT/93tqoJo2iccPMUtM0nII2P3pKBRxLicPhFV6Lnqs4u+k6RddVaMfR8IgDP1PetYl9JgJpUzQc/XiA06J1gSOPN2lGHBQuIBmsjKWmlcWm41QfThDnYDNvSGgs8XQ4puSdfhAiRLbWP96YlCY1KgMLpFOgOYw+HIWGLOulmzBaTe50xPU0plmqSc2g4ei0Wu6DkCY1WZ20t1RbwyGk2Yb5d3oENZnjVi2ajrNPmZZYJKtBMrv2FG5SS2g4khbtOHJSff6aNfjd1bKMBQ9KzYqi5lojT8kyqEWN51vc1f4h2ZCWNogLHIpz0cv76iY1GlNFL1kCIKtN/Blm9ZVEkDIHH780nqiODkFlqXFmmrx+QsNxIlbkEMulxnFM03D4Y/FMBkC0oSHhZgqGVExq3UmTmsnXMs0kcNj3FDStX8N03zQzp9Rwkr5W2qTxrhmp+ixTugM/iBPEGkgDgJo3kJqgjw7PiwuwGVLbtBOTTuBcePosvOdVp8laLikUaA41ziM+T4vyN7GTCJkajsa6ocumxeG4jnkgR+2QTlD+fCTw0kxqUw3MrxnxxMzScHSase4k5XECAIRZRL+32N1pTl6u4fD3csWaU7F64XR5b7GjzZ5YfIEosJr1ZFKj/uNtNvVN9GzRbzrUVA+HoJccByDKHqvto36Lz4vP6S54Rl9ask35ND1AHdODoz4++LVH8ZuDg8omhqe2cR059jktuuAm8wH2ljwMV8wC56jmw+Ft1uNiOGuQWGr8eP73VMWkFp9n0ESMGg7r91l9JaxeOB3nLpimnsu6My3fIYE+N8XomXI3jlQCZQ74AaWkUY+jy3ANJy1bNBVgm2ikgUlnUnvTyn68aWU/frrzUOzr0NRWwyDSkxUCSQ3HRAElZGk4nrZzqkUa6Cl6QpPQj+ELpmgny3EWMX6S2RZMVOPpPUUcGixntlknDZBdWt8l84SDuiM50nC0HanGUtMz7gLqYkPmw1r12fmiXWAL/sCoj66CyzQs2f45U7sSSUZ5W2kh4qltokSm0gwYBdUmGXy6EJGZBiLQe+sqZpNS5Pnq9bPAF8Hhso/vP7FXtJe3R1Y3dbSNBC3q+hh20FPy4lgxs/Al6D4cUV/JI3O3J65J1HTeZXRuqeAqWroM/ExqJ6YwiAK7b6ngYsuHXqu0Ub+GcPCnkAZkahua98wSYDhnpJLUcHgeQ/G85MNhzEFhHdBNap4jApZrzYvxxKTTcAhUP13fFJsmqusmU56rAsfL9OGQ0z5LW6D7mJJlcvSUCmxhV6/HSQN0flfRTZjUeLaFEvNncUyPnatZJjVF4BRckc5fSerJzCZRTrLob3LeTukqJlKQ8PTuAIwZbz1locqn4egZCejZhspVJSeWKZARAO798EVih6xP9BBSOCp57YpebFLTNBzmeJf3Va9Ji/O07mKuGiY6xT4L/N68hpLqj1GvzX0XRfau9DxjPRkmNY6C5yqmIH1c0UaIhLa+CNPx3QUXZ8TF1vj5tXwt8jpJq4X4zqBB1KRFayY1PVu0jpGKr8Sg+TG5JmFSIx8O28Bm+XAA89xpJyavwInNTmkv1XQ8wOzMbCGNTGrm1CeA1HBM19ZzQ6UFfhJ6Sq6R3x+1g9GiPTmJ6P4U+MlNaj0lz7hQTxcmtWQbdDYZ3ccU4FrwZIr8aiCTdy6PTRaRVgfxd3QOLbiBOC9tt0f30O9rgloaWWoNQ2VfyfqrlBzW4oR0QSNJAxBt5QK8t8tLkAaoremBn/G1YiH1yStW4JNXrMh8tqgtyWulgfcFzxC+77j8WxdgkqVmpkjT/2RS0wvI6Si4atAoTwgKSDOZ68joe1MutZ6Sp1Sx1PuxVl9Imn/65soU/5Nm5hRxOAbLhknD4imzCm5ctTUleScgfThZJjWufVnSwARAsRAtyvpClrYboEWE50MrCdXfTQicPjYBRg30SIKyY3M4LTpF4BQ9GcGcMKkx0gAjN5AKTnmp+OLXG9NOdZAPx+Sb4tqT+MxgdqS/SfOiuAAA+J2z5kbPz4gSuiDjmQb0vuPpRfRiWWnQ28X/52k+VLMSP99V7su/58k7+cLVWyrEtGjNpOY5iU2Fnr2A3tOSeVOUMsNpqMeHw7/nGu+v9x43HuM4UDRRvgHTzcKUHmawRukOz1UTshQ0kxoRAVxHlvnW2wTId0clGLLKE5hgIgKJ53HVd8Lvm5c0oJNrxH2ZiUxsYGINx5hLTRsX2ZkGHOPf7cak8+EQiq6DkuckFqm0iG5dwwGkQ7676CXYSb0lT5gqhEktZeBTKnHXBZxAvZ+OnqInBEgyDidJGuAstZJHFFO1naZHJg1Hj8sBgPUr+tFVcDFvqkz1zicSXxRet3QOhso+7n5qn4igBoD3vPI0LJrZi3Xnzsdn7o5yszma4OGZBvRn5ZqGMMVl5LMDNJOalpKFC5y0jMQ8m7cuHIIUDaen6MWBnzKWiBbOV505Gze96Wx8fdsu7DwwmBASIuA157aQtzVtw1ILM3qlCZEPV1XDcVI1HIrDAVRTnQlFzzEKST0tj+fGOcQ0Xx4dT9rpGXP6cHCgLKjWOgmF8N/Wn60UTiTBYZpzNDf4FWqZ1HRatCnTABBZJKplX4nDITpzYCAN6IGfkaaptkk+k5M4byJg8mo4XhSNn3xR5i4xMZjos66Cm3CG/+HvnCmKtAmTWkpv812UqcImR9pOHJCpbXjEdldB+nBKhaRG11sqGHfDVIbBJHCm9xZx1XlqLiYTdRwAbr7m5XjnK14GgLLgSrPbm1b2R1mbyfShkRFExU0DaUBoRazPapkOlMBPLSVLD9PW0qi30dhQ78U1nJ/uPIg7f7lXWbh6S16c2iaKcSHndsF10V30cMPvnIlSwVOuSdeVedHyTVO+XjZS1fG615yBf3nvBex6fJfsKhoDpzDrRAPyh+kF9pLtVSnVsr5P9FuY1Nwo8NNPMal1MYEDAC8eGxbn8d+E6y86Ex9eJ0vDc4KNDiMtOj6spknNVA+HzROaM+VqoIxhPzCXFaDmcZNaWuBnqZC+TrQTk1bgrDh1GlYtnJFqJ9XBc5MRpEnNS0SYv33tQrx5VVRoLYs0wK+Zy6RWMvsagMh5Gn3uKia1P7o4Sro5s6+oLLIXnz0Pb1u70LgDooXRJHBM0Ovcc9DzBYGsK6PsVLXFW2o4MrVNkjQgTQmeMKFktzFBGkjTcLR0QwSu4QiSmvDhhHjXP/8cFT9MlCyOaNFR+v0pzBEu2xLf11Xvy5M05oFp8a4Hl5w7D6dMl1qrej3Vh1Ng80E3C+fVcCITJf9f3WxNZSY1Pwzh+5Ev7/zTZuJ/vnWFeBc07j+8bhkuOmsu1q/oF23h101th2bK49BNp9Hf6cfTcwEp5QlMAscPFCEmNRzzJoub1HQTr+k+1qQ2AfChN0aL8BO7jymfp6rJwjGdHDCmBKBFtuiLVBQpCwePZqYBxHdb3AymOLe1hV0k79Q0nPe88jS855WnxdeSbXjXK16Gi8+Zj81bX0i0iXaXem61NGQNcJF0ME7AyD8DkjvRooGlpgtF7hDW43jSoMQNaTvzHoPm2FVQtSC+QCY0HOai4QstMRijstayYmlBW6T5fYFIjxJlGXLE4ACqcCw2YFKb2qXGqCiBpJ7LqNAyS4OevNN1ZbT+iVwajvyfFn56DvLhiISWMXPrmze+GgCw88AAALkJO2V6N7563YVKW/TnMEGY1Aqm45JaklNDkGVpOEQ2CkPpc+U1a1wnIg3oiUoBOT54CYo0kxrfAFrSwASCKemdCSVhUkt+Zqr6V2CTUprUaggcNoB0RhXBtDASuhQNx1XaSDDRSk3tot2lngE6DV0ZKjz9HwQyeafuHwCYmUxjqZnMC9yUkPUcHGrgp+rs5hoOCY9pPUVlfHiOI/wD+mLG9ds9R4eV+1CmgZLnCqqvUirZ0P4ocWbtGj8c/PGzCgKmQU+MqpoTVR8O13B0kxr1pV7sUEfRc9TUNqQZxLR/blKjtPyKZhj3i2n+UVuA/AQKk8aia7TRfem8NA0nFjiGip+O44h5WjIIhciHE8REGa2dmoajmtTUY9NM3O2GFTi5TWqSckyQGk5ywHuunJSCpZZBGqC2CD9GihO+m5nU9EVF9eEkNaXo/+QEN+2AKMI+r0ktU8MRJrKIFs01OX5/3bTESQMJgSMW/PwLS1F7dr5e8MV27tQurF95Cjb93vmqU9tL0qLp67SYE8+NTGPVPBoOfw8OEqUGakEhDTRgUpuiBQDz+xY95sNx1c2K7sjvNZSQMMFztdQ28fshrZrTooFoodWFG2C2MPD21za1Zgmc+D0jed80oU4ZNtLYpCVmfdCvSfWqfJNJLX6eStVkUusMDWfSmtQIeQWO1HCSk1of8FSYiQYw+XBqajiOPMZEM/ZclUqbruEwk5rWtm5DgKNpQk5tyodj7lM/TJlIYhepmiq+//heHDgxGqVqT7FnK0G5NSaWEzu7SYDxzQPPtuC5Dv7h3efH93lefm6Y4NSO4yMyZYv+7EHswyl6Zh+OScNxtGvkgarh1C9w9IwTatoZNdOAiJnRNBzHQWqQKjcNR+eqBczofUiBo6ZXCsMkVRtQ/Zrq/Wh8Z/cFN6HqyPLhZLEiTameCJFDv2pkdnqOI3IOptOiJXvRpIFF90ifj+3EpNdw9DGTGoeTQosGksFc3NwAZKe2AZjTnO0WFXXblddTovhTUtsoJjVPbRs3yekJCjmI8XPFmlONbdahajhqu4TA8c212jnjjJ//052HsHnrLmNddn5OGhvJBOkgVv0HfSm7cn4M35E74vvor0MDMgvyOf3TlHOqQYhKECoCp5aGk8aUy4Liw6lT4JgEBb9E0XUlOcNxGFFD7UfPcZQxxmFKlKuY7cj3EZvUqIqnKegSkO/bFEzJj82bgSKbNJAUdJmlMFxHWDbSArRNWoggDRiSdyZ8OE46QzPLxN1OTHoNJ5ECvB4NJ36puhYh2TaqDyeVNMAWfpG806DhRPm30ncu3QppICm4ALMPyKSRzJ7ShWf/+k25B2teDcdYq11rB7c5l/0gU+A4rM/ysHGKrosRBLHwkMeb8snx+9D19R0v/aZo/X9571q8Ydk8nPEXd0TP5DgKLVqmazHsbtmr4qSIrAzYaW2t14fTVyok7qOSBjQfDhuTZEIi7TVNw+kqugoJpcBSMAGSLELH8MBPcY5BUJtM2oDsz1qscunDSfaZiQVWKw4HiDZ9g+VKfJx5Y6hSl+W1pUlNvWaiAJvLNlvascp8nEAmtaY0nMOHD2PdunVYunQp1q1bhyNHjiSO2bVrF97whjfgnHPOwfLly/H5z39efPeJT3wCp556KtasWYM1a9bgjjvuaKY5DUF/GWkxD3r0MqDSogm9JS+Ry0ma1MxtEIwfx6zhCI3JczUfjHnnxDUc3UzQbWC5JftA3i/vYteV4aQUAieF7qlPat7HlWqQQhqgY7N9UTrIN6a/5ymGSqK8TYDupFXbfCgWOLP7uhLBjAotujup4cjEjbJNDjs/L7h5Jc97++VfXYYf/dnrASQJA9H1VIEjzZhs7GjmTFfz4fDn1MdiwXWUDAp6wC8P/BRt4n/Hf6ZpVKZkrCboG0QOXZONrhufl6FF6oxIjpJBw+GBnyJhaqpJjWeLTrYv7doTAU0JnI0bN+Liiy/Gjh07cPHFF2Pjxo2JYwqFAj772c/iV7/6FX72s5/hC1/4Ap566inx/Z/+6Z9i+/bt2L59O9avX99McxpCvaQBU+CnKnAKTEWPVeAaGg7fLdPfSlEx5qTkg1wf8Dx5ZxpLzaTh6AO7EYdzlgrPEwmaTAVCeDjJiT/qByhXg0SbuA8lL2mAt0VfBGppOMJ/oX1O7TgYm9Rm9alVLj3XEck7iwVXCDYlF5xRw1EX8jzQ6wnVwpSuAuZMjUpPm55f0ZhcV9VwWHmC6N50jurTNBUfI3iui8VzedJNtd1Sw1EFOIH6KJU04CT71QTS0LICP+vVcNQ4K7Mw4P3hMIEtMwlo8yQ+nNfD0avEEk5KH86WLVtw7bXXAgCuvfZafPvb304c09/fj/POOw8AMHXqVJxzzjnYs2dPM7dtKfQXVSuXmupDiQc8e7lTurzEZKzlw+H1O0wUTW4vLxpMbQSl4qdhUANmlltCw2mARpnFUqOJQ0W09D7XBR8/vUICJ7FYycVPmk5yaDgGXxyAVGYV37nzZ9F3lmRSMwkc0nCKrjSpmSLm6/UT6DBpiLVAGxCdoRZdR/5d8BxRYZab1HQNh0yAPWzzE52DRMG5gusoGxV9AzXFoOHwsdrX5aG35GHBjB7js6VF4Zugzy2C2Ydj3rRw8OfSrys0HD7HmRArp6wXiVxqrpPY+Jjuf9IInH379qG/P4rq7e/vx/79+zOPf/755/GLX/wCr3jFK8Rnt9xyC1atWoXrrrvOaJIjbNq0CWvXrsXatWtx4MCBZpqtIM3foKMkNJzkji1Nw+G06CwzB188RboOgxM+ygKdvnNaMKMHpYKLRbN6xIKgmzHy+HBq5SQzIUuFp/aTwNG/1zPeOmxBC8Mom3MinogWAle+kzxysmAwjQLmBZeuHz0DNY7ur7b54EAZXQXX4HiP6+H4KmlA3QEj8ZnQpOoyqakbnTzwXAfdRddImtAzF3BNTBc4ukDWSzXrhdqA5IKtC9eiEHBqewm9pQJ+etMb8eaV/cZnMxVgS0PRdYyBn6Y4F2mFSO9nPs/025uS3/J+rPjJbBz8GBI40ZqSfQ9+3kRAzZF5ySWXYMWKFYmfLVu21HWjgYEBXH311bj55psxbVrE4rnxxhuxc+dObN++Hf39/fjIRz6Sev7111+Pbdu2Ydu2bZg7d25d986C7hSuRRowsdS4wOnr8gykAT/TNKLQog1mJe5MV2rQaAvL/Gnd+NUnL8eqhTMyfDhJQaYvanrp6jzIYqnRv2nxBSZBwRejwXI1NbMyr5Kaa2HR8nURTD4MIGnakoJAbcfBgVHM7islSSgxaaDsB+lxOIaFsVY0uwnCNFnn++stFcwmNdc8NxzHwaXLT8EH33Bmou3UH0KTE8Xx0k2tS+ZFfhyKO/rs21Zj/cpTjO3QrzGjt5QeblCHqfXcBdOwbP7UxOd0piluLOvdyES6SdIHaR9cC+HXLLPknBz0nFVW3E/vd3mPiUkaqMlSu/fee1O/mz9/Pvbu3Yv+/n7s3bsX8+bNMx5XqVRw9dVX493vfjeuuuoq5XzCBz7wAbzlLW+pp+0tAX+pPLhNh8mHQ5Rj/nKndhcFy4bTorN2quouMc4q7MkUGP3Tu/Hs/gGltC8AY8oTYZJLMakpGk6qSa3VPpxYwwmzadE8g390TvTBwEg18Rzc1JE3eWd0XfNike7DiZ9Jo5CbbPt6PMjiOX3wvDjwM8iIwzG0X2pw9Ws49Qqc2X0lzJ1aSnyuaxYUDNxb8nDm3ClYcSor860JnrevXYi/vfsZUV+n4LoGMkn0Tv/vH1yAr/38BSyaFZnGrj5/Ia4+XyaHNaVByoN6NiJfv+HV5i80jTb6O/qdLXC81HubAj9FWx1HMZlxUD+UDeUJksk7J6aG0xQtesOGDbj11ltx00034dZbb8UVV1yROCYMQ7zvfe/DOeecgw9/+MPKdySsAOBb3/oWVqyoXWSq1VAKQHnJktMEMy06qeF8eN1ZGCqrdTBGDU5vDu6MBZskUV6lEKsWTseDOw5iz9HhTB+O0t54cusxCqY0/PplWu7Dib+q1ki7zksS8zYMjCYFDg/8rMt0opk7CbVIA7pz1hThzTW7X33ycniug5vvfSYiDVRDhRZdMJhGm9dwGhM4//z7awUjjIOTVRzHwSXnzsfXb3gV+qcnfSa66enG1y/BnqMjKHkObn34t/FuPOnDAYCFM3vx55efXfO5+Dl5IMZFEzt808aChmnWxkyGKCSPMflX+RigTAJpIRtqtmgk2geYNeiJgKZ8ODfddBPuueceLF26FPfccw9uuukmAMCLL74oGGcPPfQQbrvtNvzwhz9M0J8/9rGPYeXKlVi1ahXuv/9+fO5zn2vyceqHkg6k4CUmBcGo4RiSd644dTouPGMWAJWdlfXS+cS45Jz5uPH1Z6KnKCtxrlo4A0AkuNT0LOnXpMUvoeGUDAInYVKrf1gokydhm4++o6qNaSw1XuKBP9toNUiYBrkZUt9dZ0Fm/dYc1KmBn+q19YVVse2zf3pKHkoFV5IGggAFz8XLZvfi7FOmYtl8FhxqYKTRX/WkJcmz8zbh9Dl9mD2lK/G5Xp+o6Lm44PRZxmtQUCw/59NXrcTlceZmPWgZyL+x0bNR5wXflDQKR/sNQOQDzGo/WRJMVghT4CfvN8lCU88TKX5MqW2gHlxPMb7xRFMazuzZs3HfffclPl+wYIEQKq997WtFWWEdt912WzO3bwn4u3jLqn6sXjTdeFxJLFTyhOk9RZQ8NzW1Rlp9+MRxwjYOnDV/qtjtuS4AH4pt2eTbMUGY1LRdmCnbdC1nbh50ZWg4PFu0H4apOzdexE5vU5IWHf12nfo0nILBEQ1EpaBNSGN+mUwZpg2v50aJJ4naPa27iLv+5CL1WhkaTl1xOHX0Q67rGQRh1rEm4ch37mk+nFrg/VqXSa0BLVGHKTkr7YuyrktrgonAYdRw2Psup5nUhIbDs0WrG6CsZ5gImPSZBly22H9iw/LU40wmtWsuWIRXnDErNbUG1xSyFoE0thgNwi6Do58ivNNw6oweFD0H86d1K593G3w4+oBsxIeTZTOm+wRBmMiHxe/P82xl2aT599yHk2dhjGKUkn2XxjjSHc+6hsMvY9KO6fzRapC6QBnLE5C2Uofwb8QMl4V66Oae6xj7n2tdyaSu+cZZLaGehnP6p2HFqdNSzaV5QLfme2bSxLPYgJw0oKNk2AxmmVXlMdHvspItGuLvNEykbNFW4ORcrEy2/76ugnCcnjKtGy8dH1GvHQ+IwLDIcqQxTXim2h/8yUXwgxAvHR9WzknD+afNxBOfuCyR9qPH4MPRr9UIS81E4yYoGo7Jh0MCh0kcvaBdmsBRdnl5NBwDPTcLutag+3CUxdBwWRK2IxU/tUaNEPx8sRHtraOtKdpYo6hHy/Jcx5hJg+e5S4yzvCY1ReDklzivXToH31v6utzHmyAEDtNxKDN4VvuzSAO0QS0ZWGoqXd68HuQpwGY6byJg0gucvAwnWvDSjvvBn1yEY8PJjMEFLyrxnHX9NOc9D4RcdkpkVqMAwzwLkSnHlMn01ZLAT7Zb05tGEyegXGraAX908RI8+sIRvPxlM8Rnerp/PQkp96HowYdZiOJJ8i9aukkt6cOR9zRdV2g4lSBVkBt9OOK++dvaSBxOFtI2QuZ7m4/jdN+8MW9p7QCaIwA0AlrQ+XAMhA8nB2nAcIwIsfD4hjT6Tg0IVs9LkgbyrV+24ucEgvAF1Jijpno4HNN7i5jeW0x8XnQdlJE9udKcmzzuIdGOBsxe+j2kZqC1uYFrO8piaX6Oqh+Z1PTJcf5ps/DEJy5TPqv6avGutEJyfOeclzRQj0BNpLbRBA2/pWloUJvKcXkCE2T70++br63q9ZqFSaimoaBV/jS1KUEayNlOfliDw75h0L25H5oETlY/p+V3A3hGkKhERlRiOn7fysattoZDYy6rJycSaWCcX9/EQ970F10GH04eSCd1xg4kxZdiomQWDeSFRpFmMmlE4GSBLi/r4dQ+J6HhZPlw6jGpsYj5PNAXXQf6/zU0HHavtE2CMbWNOCd/W/OkXKkHJqGaeqzrGM06XOvSHdyN+XDGd8mi98t9OHlMaiRwKtrGCVDXEl0751pwmkbIU9/kWb8mkklt0gucvOaYtBxctWDKMp1sg2tsA09ZrrejlbuWtGzRrYJMXx8XlsoxAWr7cKLfPIFhnnW26KVnkzDB1RZx3Wbu1GBQqYHC5vsaU9s0pOHk1/TquV6+XGTm+/I2CcJLSixU+rX5IpzrlJZBkgaYD0ew1GqTBigrAAcnICkxeMjWcHSWWlaJaQ5LGphAoBdVa06Jhb7Ol5fH3OOltME0mGQBsdbNvEQczhjM6kjgILfA0TWcLs+s4TgOd+zXbvcrzpilsAf/9f2vSKW1R9dXNyRJkxpbDA2PlUvDMYwR3XeUB3Ro61hq+TUcHvVuukaBCXoyM+c3qTnGv8cDdDc+Gkn45Mk0UA2SGo7Iy+g4CcKIEkqRShqQ2aLTkneazpsImPQCJy8TR+xK6nx5kmyQfgwtlFksNXG9MdBwkrTo1g9QzyENJ1/b62Kpib9rt+MdF7wM77jgZeL/Vy+Zk3m87hfJCvzMokWbnkE/xkiLboA00KoFRjxjjvfFMz6YrsF388WCC5T93GM4K5faWKNR0gBtYioGDYfCHEzl0flmKI00UGXlC/L42awPZ4LBFJSmo9sQrJUHvcVk7iwdafRkQRpgtyw0aNrLQt4idM2gEGs4eX04OpKkgfi3I0kAYzGxdGFAd6gn8JOgZ0vQj1EzDdT/TEJItcqH4yafMQ2e4xjfK9cQhbbDsp/ngaf08fguniLwk5vUYqUlq5+7Y8qzTn4BJOOSM/cES81QB0v/v8xYamkVPzkmEkvNChxEL6tWhcTzT5uJ//nWFVh72sy6rk31ZzKderSYpexoVB9OUvVuFjTvSXuqtzxxvnswH04DEyAtW7SpCmcroZu7dBMGv2Ut0kDaZsXEUmzEPCa1vtZM63p8Qq5rDvzkJmVpDq5vDPPHGX9adPSbkwbymNSEhhOk+3Bc5tcystR0k1r8VcWQvDOrW5pJ7dNqWIGD2KldYyAXPBe/98rT6qYj92YEgIlrp2g4wr/EPpOkgda9OmFbHwNhRii4TpwtOp8PR0dqPRwnn5+sUSR8KZopjZs7a5EG0jJSZEWZN0JwaNXrS6smaUKhhkmN+3BkgcKcAqeNGg7dzRT4mY80kM5SUzQcA0utFmkgYqnF7cxiwVqBM7GQZg5oBagQVTOZBkwstVZqISLnlOfGGbNbPywiDSdEEDS2S02WJ0iaasZEw9G0Bt1mrhA6agqcbJNas7Ro6S9pzfsTwjYXS81MGuD9R+NqVm9UCmGGIW4t7dqmv8cDMpO5/Ixy/uXJNGBQcDA3Lus9s7eUyVLT97Y8DseJrTJZ2r1OcJkImPSkASCaqGOldvbkMKmlLZh6OhUALfFX/L/3vQIvHB4S/wt13nXQP70HC2Z0p53aMAqxwPENJabzIFFimpm26im0VS/EIq5pNHoBNv1vvZ1ABmnApKEJk1p+4UFrW+tZajlMao7ZD8rHFl1n2SlT8Zm3rcbSuPBanmvrbRovmE1q0e9M0kBG4Oc5/dPw4MfegEWzehMxZPzdZZUn0DO9m7qlGAeVTiTSgBU4UP0ArUYeDSc1tY3BpFFqAWngtUtVZpZIj+E6uOtPXpdZuycLC6Z348VjI8bvXCdK0x+GYWPJQdMqfropC3aLoGs49C5MPpyGSQNOcrFpJNMAmXrqpe6nIS0Dhgmemxb4Kb/nAc5nGaprprcj2abxAm0wgtBkUqut4aRh0axeAJwwEn2ehzQAJAVNKiXdn1gsNStwkL47awV64zor+UpMm3c0ar4uWohaH4dTcB3R3kbwwz97fSJ+hlDwolLLfhCiy1A7vhb0xZqbC8bWhyMFG90v+i2/dxzEWbCzSQNpPhwTG4z+queZ8iyE9SAPA0ocm+IH5XniTKma8qCdJjVTU0U/58g0UAt6IDef1/qzKpsbzZRmaudE8t0QrA8HxFIbm2vTTidLPqQtmKbdi+54bQVatWB3F73UVPBerOEEYX19TRpirlxqY/ASxc5T03BMprRaGk56HE7y2EYozhS71LpMA8l2pWH+tC7Mm5ZRxM1zUjX52u1IbrjGC3Q7Uy61bNJAPoGjm9S4X0gfzg7zNaexJjne9Yoo3ixNs24HrIYDKMGDrQYtmCkbf3F/wJBpgD5nnzmOg5JXX4r9WhhLH4i4h+sgCCOTWj33KRVcDJX9DJaaU5fpp14kyxMktZHYclGTFl0zDseQvLM+DSdiL7XMh5OxmOn4y7ecq5idCNQUzsiq1yym9PV4azjCpCY/C3LQovMu8nrgp0IaSDGTBX7Ixr/6m+PPLz8bf7rurNzCbzwwcURfG0G5vsYCJHAo4Z4JqYGfKepyocVMsgT1dwzguQ6qfkQayLPgnBIXjiOTg+7D4fnTxlLD0enJjvicHYP0hZS3KW0RygqwrOc9Sw2nVSw12ce10F30jOZYJQ6nQU1aMamNsw9HajjyszyBn3kFY0LD4X48EwlDG49ZY8d1nQklbAArcACQSW1sBjKx1EYzBE7aoKEMvKbqlC3NNGBgyLQapOEEQb4d7vf/6LUKgSGNFq36cFrcaCTNSiYzZ5b5K1ccjkGbaSQOp+U+HMOz1guehFQnXuQFf6/j7cN51ytOwzn90/COCxaJz0JBi6494N6yqj/zez1TfFbyTiBp/q4no/dEgDWpgRyaY3NtqeH4qcfotVZ4u0zjqOg5LWMi0X2A1u2MjfdwyYeTL7XN7CldmD2ly1j/HWCLoVtfeYJ6IYMpdZNa8pisBQJQS4VznDqzBz1FDzN6Suya0e+6fDh+a304rehXuUC60GNO8oJvuMZb4JwyvRt3/rFaNTSPSQ0Adv6v9TXHusgUb0ptYzg5bRyO1Ya51bACB2oAVavRE+dSy9Jw0soTuK55chY9t6Ey0GmQfoqWXTIBz40DP+v04ZATNTW1jaMyoVoNoeF46gTXfThADtJASge/dskcbP/4OkUDElpTWzWc5vvV7MOp7xrtzKVmgp+TnJGnrboQzkreCbC5moOlNhFhTWpIX9hbgd4cJjXa1JjSkZvatXzBNCytI44hDzxm8hgLRNmiqQBb/r4uFVyUPDexg+PO/HGhRYv7qztM5ZhapIEUDcdxnIS5TSbvbMCH0+rknU0MCyWrd4MaE+/DiRA1TwSCVmjU+thVyhOkbDb58WOZR3AsYDUcYExT2wgfTiXdpOalVAX1XMdYO/ZL117QugbSvZxkCeCWXj/WcMKwvola9FwjnZibEsZS4CSyRQsNJ/1Y5bMcGo4Jec02HO+4YBF+9PR+vO81Z+Q+Jw+aWcyMAqduk5r8eyLElsyf1oWBA9WWtCVRnqCGwOkSiT+h/m5/t+SCFTgY28BPCgDL1nDMpoaxFIQ6XHdsGUCe66BcDWKWWv7zSqkCR05UU8XMVsHVrm0yqcn6KNkCp54MC7SLrueZZvWV8G9/+Krcx9dsQ9yI5gRO9LvgNh6Ho2g4E2Bl/dr7X4mfP3eoqSBpQjK1TXrgJyC15KRJrf39kgfWpAaMS2qbLIGTmi16DNulwxtDoQvEGk6cLboewUYmNR2KDyelgF0roKeYcYRJTR5DAqcWLboeBC32xzTUhrB5EgLXPhvVcCaaD+eU6d24Ys2pLblWMnknf9bk8WR61ZmtnWJSswIH0aI1dgKn9i4obSKmWNTGBK7rjElZAoIgDQT17cbSNBxh2nLHNnmnYANp/gz+DCKZo4lV1GCb/BYs9s3CFxpO49dwHAfdRRd9XYUEBTgvuFYzEUxqrYTOUuNUa9M8IZOaXuW2U3qlKYFz+PBhrFu3DkuXLsW6detw5MgR43Gnn346Vq5ciTVr1mDt2rV1nz/WGFOTWql24FVapgFvPDUcd2w1HMoWnZcWTejtKhjT5XAhPbW7gKLnYGZvKXFcs+AmIYAHecpjwgwncqN9OjE0nOh3s2Nw8/WvwntecVrDmmitjNydjISGw963STsWAkesGepGaKKjqWZu3LgRF198MXbs2IGLL74YGzduTD32/vvvx/bt27Ft27aGzh9LuI45020r0FuHwEnmUhu7dulIS77Yyus3Qov+s0vPwmf+v1XG6wFRn83sK+H+P3s9Lj13fsvaa7oPYM6lRsW5xkLDaSSzdquQZSqsB2sWzcD03mLjPpwJZlJrJYhRKFlq2YGfXUW13MmpM3rwO2fNxeqFM8a4pa1BU6N5y5YtuPbaawEA1157Lb797W+P6/mtguuO3UDOE428ZtEMXHTWXCya2at87rnOuDkDx1LLAyLbdESLrs+kdtrsPqw4dXric8lSi34vnNk7RoGf0e+swM8smmzDAmdCaDgTI5CU795PMnmTzVIz+nBUDae76OHW6y7E4rn5agu1G00JnH379qG/P0rd0N/fj/379xuPcxwHl156Kc4//3xs2rSp7vMBYNOmTVi7di3Wrl2LAwcONNPsBK58+UK8eWV2CopmkaXpLJrVi69ed2HCdOSOI0vNc8emtDSBNJwoeWfz10vGx4wNeLZjwBzZTalOatGi60HYAEut1SCh16oubjTTANcyO4WNlRd1s9QELboz+6GmR/uSSy7BSy+9lPj8r//6r3Pf5KGHHsKCBQuwf/9+rFu3DmeffTYuuuiiuhp6/fXX4/rrrwcAxQ/UCrzvtWe09Ho6bv/AK/Gy2b21D9SQFvg5FvDG+F6FmKWWN3lnHszuK2FWX+v9Nhx6FgNquUqLjn4bTWoNPmuemitjjVYLvYYzDbjjs7loB3SGau04nLigY4d2RU2Bc++996Z+N3/+fOzduxf9/f3Yu3cv5s2bZzxuwYIFAIB58+bhyiuvxNatW3HRRRflPr/T8aozZzd0nueOX8qKgufmMv81CteNskUHdWYayMJdf3IRpvWMbSiZHodjMqnJY8eCpdY+H86qhZEp8y2rFrTkes2WJ+gUx3g9kESK6H+uwZl9OKpJrdPQ1CvcsGEDbr31VgDArbfeiiuuuCJxzODgIE6cOCH+vvvuu7FixYrc509mTOkqphY0azX+f285d0w1vYIbZ4sOW8c0mju1KzUDc6uQKE9gIA0QWkkamAgstcVzp+D5jW/GuhaRMdLYmLUgmYInn8TJMjOaXj2Z1DrVtNjUanbTTTfh7W9/O7785S/jZS97Gb7+9a8DAF588UW8//3vxx133IF9+/bhyiuvBABUq1W8613vwuWXX555vkWE//rGJXj3K182Lve6ZAwYXhw8W3QbiVd1I0/gpzj2JIvDaTUKbmO780ZNcZ2ArLRMZh8OmdQ6szOaEjizZ8/Gfffdl/h8wYIFuOOOOwAAixcvxmOPPVbX+RYRZvaVMHOMfRTjBc91EDSQvLPdoDnvaoveWGs4E4Gl1mo0nkstCg84mYQvoZgRDGvawOi51DoNHdpsi06D50QaTr3JO9uNhIYjBE76sRxNp7bpJHWwBhqNw4nOGVvafrvgZWh9WT4cqn3UaTh5RrPFhIbnupGGU2emgXZD99lIB3Y+baZhH04G861ToRMv6jr3JBU4WWW3zZkGIpMalaLoNFiBYzEu8Fywip+ds3DQpCd6MjXdtGiahEOjzt2T0ofjpS+utTDW2czbhSz/lOlxyaRW9dOTAU9kWIFjMS7wXFfWw+mghUPPylsvLZowb2pXXfedCCy1VqNZk1onmWLzIiuhaVbgZ6VDTWq2Ho7FuMBzgXK8K+sogaNRch3xeT4NBwC+et2FWHZKfRVaW51WZiKA/BWNmtROJuFLyGSpZeRSqwadqeFYgWMxLvCUlB1tbEidSI/DSR6bJkgvOmtu3feVPpwO6qwamNZdQMF1MKuBrN6ue5JqOBnMvWyTmtVwLCxSwXennRS0pmeLzqqwOBZpaE4mDWf2lC488LE34JRp3XWf6zonpw/njWfPx4fXjWLhzJ7Ed1n1cCpWw7GwSAdfODtpET1jTh9es2Q2li+YBiDbpDYWpsJ25lIbCyyYkVxY82Cs6zW1C3OnduGPLl6a+3jBUrMajoVFOpS69B20bkzvKeJr73+l+D+LNDAWC+LJ6LdoBCdrHE69oDicTiUNnDwGYosJDW4O6STSgI56c6k1i5PJh9MMTlYNp14IH06HmtTsaLYYF6gaTucuHNKHk/5dK+GdZCa1RjGepTomMsikVqlagWNhkYpO9eHoyKRFj4FwsCa1CK5r+wLgpAFrUrOwSMXJInD0QFDlu7HQcDq4r1oJ7yQN/KwXMpea1XAsLFLBF87u4tjWsBlLSA0n+d3Y+HDsIgvEpAFrUhMmtQ5VcKzAsRgfqAKng4ddRi61sdBGrIYTwbWkAQDSpNap6OzWW3QM+O60e4yrdI4l3Axa9FiYfCxLLcLJmi26XnS6wLFxOBbjAu5QPzlMauNEi7YsNQA4aQuwmfDtD74GO/cPGL8jzfrVZ84ezya1DFbgWIwL+ALd1cEmNVkPJ/27VsL6LSK8ftk8zJlyclS/rYU1i2ZgzaIZqd//9KY3YmYD+egmAqzAsRgX8N1/J5vU6q2H0ywsMyvCTW86u91NmDBoND3QREDnbjUtOgruSUIakKltDLRoKxwsLDLRuTPfoqNQsLRoC4tJDytwLMYFfPd/UvhwxokWbWFxMqFzZ75FR+Gk0XAc9TeHzfVlYZENK3AsxgUnSxzOeNOiLSxOJliBYzEu4OamYgfHlmTmUmuhwPmjNy7BsvlTW3Y9C4uJAEuLthgXcIHTSSWm0zDWj/DhS5fhw5cuG9ubWFiMM5rScA4fPox169Zh6dKlWLduHY4cOZI45umnn8aaNWvEz7Rp03DzzTcDAD7xiU/g1FNPFd/dcccdzTTHYgLjZHGoZ6W2sbCwyEZTAmfjxo24+OKLsWPHDlx88cXYuHFj4phly5Zh+/bt2L59Ox555BH09vbiyiuvFN//6Z/+qfh+/fr1zTTHYgLjZBE4WYGfFhYW2WhK4GzZsgXXXnstAODaa6/Ft7/97czj77vvPpx55pk47bTTmrmtRQfiZBE49BiWkWZhUT+aEjj79u1Df38/AKC/vx/79+/PPH7z5s145zvfqXx2yy23YNWqVbjuuuuMJjnCpk2bsHbtWqxduxYHDhxoptkWbcDJInAcWJOahUWjqClwLrnkEqxYsSLxs2XLlrpuVC6X8Z3vfAdve9vbxGc33ngjdu7cie3bt6O/vx8f+chHUs+//vrrsW3bNmzbtg1z586t694W7cfJQhl2rIZjYdEwarLU7r333tTv5s+fj71796K/vx979+7FvHnzUo+98847cd5552H+/PnK+YQPfOADeMtb3pK33RYdhpNlgSbfzUnyOBYW44qmTGobNmzArbfeCgC49dZbccUVV6Qee/vttyfMaXv37hV/f+tb38KKFSuaaY7FBMbJUkjMFPj5llX97WmMhUWHoalV4KabbsI999yDpUuX4p577sFNN90EAHjxxRcVxtnQ0BDuueceXHXVVcr5H/vYx7By5UqsWrUK999/Pz73uc810xyLCYyTRN4YSQO3vOs8PL/xzW1qkYVF56CpwM/Zs2fjvvvuS3y+YMECJaamt7cXhw4dShx32223NXN7iw7CSaPh2DgcC4uGcXKsAhYTHieJvBGCxsbhWFjUj5NkGbCY6CANp+PXaavhWFg0DCtwLMYFlC266HX2kLOBnxYWjaOzZ79Fx8CJR1qxw1UDGfjZ2c9hYdEOWIFjMS4oxZrN5Ss6m0KcVYDNwsIiG7Y8gcW4oLvo4Wd/cTFmTym1uylNoavgKr8tLCzywwoci3HDKdO7292EpnH5ilMwrbuIedM6/1ksLMYbdptmYVEHeksFXHLu/NoHWlhYJGAFjoWFhYXFuMAKHAsLCwuLcYEVOBYWFhYW4wIrcCwsLCwsxgVW4FhYWFhYjAuswLGwsLCwGBdYgWNhYWFhMS6wAsfCwsLCYlxgBY6FhYWFxbjAChwLCwsLi3GBE4Zh2O5G1Is5c+bg9NNPb/j8AwcOYO7cua1r0DjDtr996OS2A7b97UYntz+r7c8//zwOHjxY8xodKXCaxdq1a7Ft27Z2N6Nh2Pa3D53cdsC2v93o5Pa3ou3WpGZhYWFhMS6wAsfCwsLCYlwwKQXO9ddf3+4mNAXb/vahk9sO2Pa3G53c/la0fVL6cCwsLCwsxh+TUsOxsLCwsBh/TDqBc9ddd2HZsmVYsmQJNm7c2O7m1MTpp5+OlStXYs2aNVi7di0A4PDhw1i3bh2WLl2KdevW4ciRI21upcR1112HefPmYcWKFeKzrPZ++tOfxpIlS7Bs2TL84Ac/aEeTFZja/4lPfAKnnnoq1qxZgzVr1uCOO+4Q302k9u/atQtveMMbcM4552D58uX4/Oc/D6Bz+j+t/Z3S/yMjI7jwwguxevVqLF++HB//+McBdE7/p7W/pf0fTiJUq9Vw8eLF4c6dO8PR0dFw1apV4ZNPPtnuZmXitNNOCw8cOKB89tGPfjT89Kc/HYZhGH76058OP/axj7WjaUb8+Mc/Dh955JFw+fLl4rO09j755JPhqlWrwpGRkfA3v/lNuHjx4rBarbal3QRT+z/+8Y+Hn/nMZxLHTrT2v/jii+EjjzwShmEYHj9+PFy6dGn45JNPdkz/p7W/U/o/CILwxIkTYRiGYblcDi+88MLw4Ycf7pj+T2t/K/t/Umk4W7duxZIlS7B48WKUSiVcc8012LJlS7ubVTe2bNmCa6+9FgBw7bXX4tvf/nZ7G8Rw0UUXYdasWcpnae3dsmULrrnmGnR1deGMM87AkiVLsHXr1vFusgJT+9Mw0drf39+P8847DwAwdepUnHPOOdizZ0/H9H9a+9Mw0drvOA6mTJkCAKhUKqhUKnAcp2P6P639aWik/ZNK4OzZsweLFi0S/y9cuDBzQE8EOI6DSy+9FOeffz42bdoEANi3bx/6+/sBRJN0//797WxiTaS1t5Pexy233IJVq1bhuuuuEyaRidz+559/Hr/4xS/wile8oiP7n7cf6Jz+930fa9aswbx587Bu3bqO639T+4HW9f+kEjihgZCXJcEnAh566CE8+uijuPPOO/GFL3wBDzzwQLub1DJ0yvu48cYbsXPnTmzfvh39/f34yEc+AmDitn9gYABXX301br75ZkybNi31uE5pfyf1v+d52L59O3bv3o2tW7fil7/8ZeqxndL+Vvb/pBI4CxcuxK5du8T/u3fvxoIFC9rYotqg9s2bNw9XXnkltm7divnz52Pv3r0AgL1792LevHntbGJNpLW3U97H/Pnz4XkeXNfFBz7wAWE2mIjtr1QquPrqq/Hud78bV111FYDO6v+09ndK/xNmzJiB17/+9bjrrrs6qv8Jevtb1f+TSuBccMEF2LFjB5577jmUy2Vs3rwZGzZsaHezUjE4OIgTJ06Iv++++26sWLECGzZswK233goAuPXWW3HFFVe0s5k1kdbeDRs2YPPmzRgdHcVzzz2HHTt24MILL2xnU42gxQIAvvWtbwkG20RrfxiGeN/73odzzjkHH/7wh8XnndL/ae3vlP4/cOAAjh49CgAYHh7Gvffei7PPPrtj+j+t/S3t/1ayHDoB3//+98OlS5eGixcvDj/1qU+1uzmZ2LlzZ7hq1apw1apV4bnnnivae/DgwfCNb3xjuGTJkvCNb3xjeOjQoTa3VOKaa64JTznllLBQKISnnnpq+KUvfSmzvZ/61KfCxYsXh2eddVZ4xx13tLHlEUztf8973hOuWLEiXLlyZfi7v/u74YsvviiOn0jtf/DBB0MA4cqVK8PVq1eHq1evDr///e93TP+ntb9T+v+xxx4L16xZE65cuTJcvnx5+Fd/9VdhGGbP105ofyv732YasLCwsLAYF0wqk5qFhYWFRftgBY6FhYWFxbjAChwLCwsLi3GBFTgWFhYWFuMCK3AsLCwsLMYFVuBYWFhYWIwLrMCxsLCwsBgXWIFjYWFhYTEu+P8Dko1A84aKTS4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd3ca26770f743e4908dc1349d87d5a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a2cde7f53504954991b39cc7c4c14ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 90.0 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ecd2b386ff74f4b8508c214703af770",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75b7907c025947c1ae9de42f9e101947",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d7b5797f61441ceadb896d9e84fd8c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe38a913aee14ebf9b4130c767dd7df3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "235f857fcf264d33a244d2f0f7be9a26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c08129581dc41198a474b3c622bddca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.05\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4a36ddcf3664daeb06c185414c49c9e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "016b3dd75f6e4880b08b95ebc138ac3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.2\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ccf035f14ee4138b842ac1619936818",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.3\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d602a053f09b4353b80d07eaa8054d89",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89ce64ae761c46c6ba746a05f764326e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7882ea0ae5fb4150b6aa7f49ce05d157",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, num_reference_simulations=10, num_null_samples=5, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa0eec04-b85a-4901-bd74-065671c0391f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(shape[0], 1, shape[1])\n",
+    "no_data_reshaped = no_data.reshape(shape[0], 1, shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe263409",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "95254512",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "60ffa351",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "a2f19479",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aade365820164f8cb5ed466fbae2765e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f894f39f50dc4eab8983a648dab7c933",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, num_reference_simulations=10, num_null_samples=5, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fb5e49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/bf/v5_Aug24_bf_model_misspecification_1000_data.ipynb b/bf/v5_Aug24_bf_model_misspecification_1000_data.ipynb
new file mode 100644
index 0000000..0ba30e2
--- /dev/null
+++ b/bf/v5_Aug24_bf_model_misspecification_1000_data.ipynb
@@ -0,0 +1,2548 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "f9852932-d80d-4f94-b9a1-e6616c2cda14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import pickle\n",
+    "\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"../../..\")))\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import torch\n",
+    "\n",
+    "import bayesflow.diagnostics as diag\n",
+    "from bayesflow.amortizers import AmortizedPosterior\n",
+    "from bayesflow.networks import InvariantNetwork, InvertibleNetwork\n",
+    "from bayesflow.simulation import GenerativeModel, Prior, Simulator\n",
+    "from bayesflow.trainers import Trainer\n",
+    "\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import bayesflow as bf\n",
+    "from bayesflow import computational_utilities as utils\n",
+    "\n",
+    "matplotlib.rcParams[\"figure.dpi\"] = 72\n",
+    "\n",
+    "from hnn_core.utils import smooth_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "5b6c156e-f1f5-4b25-afcc-e5c1c624eb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RNG = np.random.default_rng(2022)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "c6b9639b-3c48-46eb-91c1-75d97604b1e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prior():\n",
+    "    random_array1 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(1,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(1,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "e22e01ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "a7ac1be5-905d-4246-8ec1-5177b1c62c70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(params, n_obs=1):\n",
+    "    \n",
+    "    res = np.empty((n_obs, 341))\n",
+    "    \n",
+    "    for i in range(n_obs):\n",
+    "        if n_obs==1:\n",
+    "            param0 = params[0]\n",
+    "            param1 = params[1]\n",
+    "            param2 = params[2]\n",
+    "        else:    \n",
+    "            param0 = params[0][i]\n",
+    "            param1 = params[1][i]\n",
+    "            param2 = params[2][i]\n",
+    "        \n",
+    "        net_sim = jones_2009_model()\n",
+    "    \n",
+    "    \n",
+    "        # add a distal evoked drive\n",
+    "        weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': .000007,\n",
+    "                           'L5_pyramidal': param0}\n",
+    "        weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,\n",
+    "                           'L5_pyramidal': 0.080074}\n",
+    "        synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                              'L5_pyramidal': 0.1}\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=weights_ampa_d1,\n",
+    "            weights_nmda=weights_nmda_d1, location='distal',\n",
+    "            synaptic_delays=synaptic_delays_d1, event_seed=274)\n",
+    "\n",
+    "\n",
+    "        # add two proximal drives\n",
+    "        weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,\n",
+    "                           'L5_basket': 0.19934, 'L5_pyramidal': param1}\n",
+    "        synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                                'L5_basket': 1., 'L5_pyramidal': 1.}\n",
+    "        # all NMDA weights are zero; pass None explicitly\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox1', mu=26.61, sigma=2.47, numspikes=1, weights_ampa=weights_ampa_p1,\n",
+    "            weights_nmda=None, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=544)\n",
+    "\n",
+    "        # Second proximal evoked drive. NB: only AMPA weights differ from first\n",
+    "        weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,\n",
+    "                           'L5_basket': 0.008958, 'L5_pyramidal': param2}\n",
+    "        # all NMDA weights are zero; omit weights_nmda (defaults to None)\n",
+    "        net_sim.add_evoked_drive(\n",
+    "            'evprox2', mu=137.12, sigma=8.33, numspikes=1,\n",
+    "            weights_ampa=weights_ampa_p2, location='proximal',\n",
+    "            synaptic_delays=synaptic_delays_prox, event_seed=814)\n",
+    "\n",
+    "        dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "        \n",
+    "        \n",
+    "#         res[i] = dpl[0].data['agg']\n",
+    "#         res[i] = dpl[0].copy().scale(3000).data['agg']\n",
+    "#         res[i] = dpl[0].copy().smooth(30).scale(3000).data['agg']\n",
+    "\n",
+    "        # v5: smoothing\n",
+    "        temp = dpl[0].data['agg']\n",
+    "        temp = smooth_waveform(temp, window_len=30, sfreq=1000.0 / 0.5)\n",
+    "        res[i] = temp\n",
+    "        \n",
+    "\n",
+    "    # print(res.shape)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "c9de03d3-bdae-4638-a114-933e98280aec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing 2 pilot runs with the Generative Model: Training model...\n",
+      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 3)\n",
+      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 1, 341)\n",
+      "INFO:root:No optional prior non-batchable context provided.\n",
+      "INFO:root:No optional prior batchable context provided.\n",
+      "INFO:root:No optional simulation non-batchable context provided.\n",
+      "INFO:root:No optional simulation batchable context provided.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "generative_model = bf.simulation.GenerativeModel(\n",
+    "    prior=prior, simulator=simulator, name=\"Generative Model: Training\", simulator_is_batched=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "c261add4-d914-47f6-bb8c-65ecd9a79449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Performing a consistency check with provided components...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "summary_net = bf.networks.DeepSet(summary_dim=3)\n",
+    "inference_net = bf.networks.InvertibleNetwork(num_params=3, num_coupling_layers=3)\n",
+    "amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun=\"MMD\")\n",
+    "trainer = bf.trainers.Trainer(generative_model=generative_model, amortizer=amortizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "fcb0e515-fd13-4a39-875a-1774fc559a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "a0fe4b3d-eded-491f-af11-c894a05f3be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)\n",
+    "theta = np.array(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "aa8f2ae3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "641c9d5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Smoothing\n",
+    "for i in range(len(x)):\n",
+    "    x[i] = smooth_waveform(x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "00e1bfdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "68c984d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "cbb39dfe-5309-413b-8b79-5213f56e044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create dictionary that will be saved as a pickle file\n",
+    "presim_dict = dict.fromkeys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws',\n",
+    "                             'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n",
+    "\n",
+    "# Your parallel simulated data here\n",
+    "x_shape = x.shape\n",
+    "presim_dict['sim_data'] = x.reshape(x_shape[0], 1, x_shape[1])\n",
+    "presim_dict['prior_draws'] = theta\n",
+    "presim_dict = [presim_dict] # for some reason, the pickled file needs to be a list\n",
+    "\n",
+    "\n",
+    "# Save the dictionary as a pickle file, need to create folder called 'parallel_sim' first\n",
+    "with open('parallel_sim/presim_file_1.pkl', 'wb') as f:\n",
+    "    pickle.dump(presim_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "77343d53-8c54-403a-b03e-bb4130e5ea6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a71240363c164e87a1757e297d146201",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training epoch 1:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "losses = trainer.train_from_presimulation(presimulation_path='parallel_sim/', optimizer=tf.keras.optimizers.Adam())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "caa1054e-5f4b-4bc2-ae01-bb0ed3a6eafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Loss</th>\n",
+       "      <th>W.Decay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.762184</td>\n",
+       "      <td>0.820034</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Loss   W.Decay\n",
+       "0  9.762184  0.820034"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fad86fa",
+   "metadata": {},
+   "source": [
+    "## set reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "1753771b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 1, 341)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = x.shape\n",
+    "x = x.reshape(shape[0], 1, shape[1])\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "70bcf019",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data = {}\n",
+    "reference_data['summary_conditions'] = x\n",
+    "reference_data['direct_conditions'] = None\n",
+    "reference_data['parameters'] = theta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fab3eca-c3ef-47ce-8373-0cd47d41c83b",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulation output (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "e4ce657e-dab9-4a3c-abcf-f57b47c0e485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_HNN = simulator(params=(0.142300 , 0.00865, 0.684013))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "2b0a740d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_HNN.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "c7b5bd74-45b1-46e2-bc09-77ceb754e5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1, 341)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = data_HNN.shape\n",
+    "data_HNN = data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "45a004f2-0666-44e3-927c-75739686a1e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_data_HNN = {}\n",
+    "observed_data_HNN['summary_conditions'] = data_HNN\n",
+    "observed_data_HNN['direct_conditions'] = None\n",
+    "observed_data_HNN['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "04ad3657-9721-407d-b956-afe6ce9d362c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "252448446a4448769f4e7df7bd08b400",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40b715c9",
+   "metadata": {},
+   "source": [
+    "## Five Actual HNN simulation outputs (this should not be mispecified)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "b08a5ed2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def five_priors(seed=666):\n",
+    "    if seed is not None:\n",
+    "        np.random.seed(seed)\n",
+    "        \n",
+    "    random_array1 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array2 = np.random.uniform(0, 1, size=(5,))\n",
+    "    random_array3 = np.random.uniform(0, 1, size=(5,))\n",
+    "    combined_array = np.vstack((random_array1, random_array2, random_array3))\n",
+    "    combined_array = np.squeeze(combined_array)\n",
+    "    return combined_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "8752bed4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 5)"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_params = five_priors()\n",
+    "five_params.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "97d2e389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "five_data_HNN = simulator(params=five_params, n_obs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "ad5aafe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 341)"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "968c776f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x28fefd7e0>]"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(five_data_HNN.T, color='gray', alpha=0.4)\n",
+    "plt.plot(data_HNN.reshape(341,).T, color='black', alpha=0.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "b59dda7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1, 341)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape = five_data_HNN.shape\n",
+    "five_data_HNN = five_data_HNN.reshape(shape[0], 1, shape[1])\n",
+    "five_data_HNN.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "a9cb5d72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_five_data_HNN = {}\n",
+    "observed_five_data_HNN['summary_conditions'] = five_data_HNN\n",
+    "observed_five_data_HNN['direct_conditions'] = None\n",
+    "observed_five_data_HNN['parameters'] = five_params.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "19b5ddb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc6e00f6a2f34a5988d92636cbc56dd5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pgg+Yw7xWTDIsmRfR6Wpfd8NeMdvXRzBTeh5gfMSqxzaRbGRWf3aByc0pqDgADdA4VVVXr15lJkyYwHh4eDBSqZSxsLBgfHx8mI8//ph5/vx5qccUjVMN3PA1c+TIEaZt27aMubk5Y2NjwwwZMoR58OCByv5RUVHM/PnzmZYtWzKOjo6MWCxm3N3dmaFDhzIXL14s9XucP3+eGT58OOPi4sKIRCLG0dGR8fX1ZebPn89cv35dZV93d3e1sZtlqVevHgOAsbe3Z/Lz88vcLz8/n9mwYQPTrl07xtramhGLxUzt2rWZgIAA5ocfflAb85qfn88sXbqU8fT0ZF/fF198weTl5VVpnCrDMIxCoWA2bNjAeHt7M2KxmHFxcWE++OADJi0trczXGhgYyPj6+jLm5uaMg4MD8+677zK3b99mFi9ezABggoODVfZPSEhgRo8ezTg7OzN8Pp8BwCxevJh9/uLFi0y3bt0YW1tbxtLSkunQoQNz4MABJjg4WG1ffaOrv3Eew1S+Ta1Vq1YICwvTTXYnRuXAjntY9mEIZPnF92M8G9jis+87olMfT7YsPrr0KdWKJMZnYe/mSBzd8xCKwuJfVa9G9lj/Vx94NrTTfvAA2/uTaCC5xHuFQyvu4iCkFLr6G6d7qkTrtq2LwFdTz7AJVSDk4aNv2+HXowPg1ci+gqNVOdeyxOzFbfD7yUFo3r542MGTeykY2f4vnD2sPh8sIYRwhZIq0arNq8Kw+tOL7LadoxSbjw9En+H1KzWJQFncPKyxalsPzF3WFmKJsoNTdmYB5gw/jl2bbmscNyGEaAMlVaI1+36PxLpFxT0uXd2tsC1oMGp72pRzVOXxeDz0H90A6//qgxquymnmGAb4dt45bFhylXoHE0I4R0mVaMWFf2OwfFYIu13HywY/H+4Pc0v12WM0Va+xPTYd6IdGfsXzmP7y7XX8uPiq1r8XIYRUBSVVorHoh6n4aPRJFP7Xkcihhhk27u8LM3PtJ9Qi1nZSrNreA60712LLflsZhl++LXvKOEII0TVKqkQj+XlyfDz2FLIzlROIS82F2Ph3X5hZ6C6hFjEzF2Hpz93QpmvxdGwbllzFX1vu6vx7E0JIaSipEo18//lF3L+VBEC54tjqHT3hWFN9WS1dEYkF+HpjV7RoXzzYffnsUJw/FVPOUYQQohuUVMlbO38qBrs33WG3J8z1hbev+nqNuiaWCLDk566o30S5MoeikMFHo07i0d3SJ3InhBBdoaRK3kp2pgxLPwxht719HTH2g9LXqawOZuYiLP+1G5xrKWvJOdnK4TYZaaa39BQhhDuUVMlbWf/1FbyMzQQAiMR8LP81QKNxqNrg4GyO/20OgNRcOaV17JN0LJwcVOqcw4QQoguUVEmV3QlLwO4SEy68/0Vr2DpIOYyomGcDO3yyoj27HXIsGoFrIziMiBBiSiipkiphGAYrPzqPonkW6nrbof+YBtwG9YYufT0w7L3G7Pb6ry/jTlgChxERQkwFJVVSJcf3PsLNK68AKHv7fr2xC+fNvqV57+MWbKepQjmDBeNOITtTVsFRhBCiGUqqpNJycwqw9stL7PY779aFq7s1hxGVTSji44u1nWD+33jZ508zsPqzixUcRUzVpEmTwOPxSl0knSseHh7sQuDEcNAi5aTSdm+6g1fPswAoh7F8uMif44jK51LHCnOWtsHKTy4AAPZtiUSPwV7o0KOO5ie/9TWQE6v5eXTFvA7gu0zj03h4eCAmpvQxvzVq1MCrV69UymQyGZYtW4Zdu3YhJfk1WjTzxppl89AiQH3pt6CgIPTs2RNHjx5Fv379NI7V0HTt2hWhoaE0Z7WRoaRKKiUrQ4bfv7/Bbk+Y4wsLKzGHEVVOwEBPXPg3Fhf+VSbARdPP4tDN0bCykWh24pxYwMJD8wB1JTtaa6eysbHBvHnz1MotLS3Vyj7//HP88MMPGDp0KNwcRfjjrxPo9u5M3H/QHS4uxRN0ZGVlYdq0aRg3bpxeJNQVK1bg888/h6ura8U7V5MzZ85wHQJ5C5RUSaX8seEW0lOUYz4trEQYMskwFvDm8XiYs7QNbl9PQEZqPhJeZGH911fw1fouXIdmMGxtbbFkyZIK92MYBr/++ismT56MrVu3AslhGNy3K7oOmok//vgDn376KbvvZ599hry8PKxfv16HkVeei4uLStLXB15eXlyHQN4C3VMlFUpPzcP2dcXDUibNbw6RWMBhRFVj52iG2YuLm6r//PUOoiISOYzIOL1+/Ro5OTnw9y++1v4tfABApQn53Llz+Pnnn/HTTz/B3r5qi9YXKbrfmJGRgY8++ggeHh4QiUQqyf/+/fuYNGkSateuDYlEgho1amDMmDF48OCB2vnKu6d69epVDBs2DDVr1oRYLEbt2rUxY8YMxMfHlxpbSkoKvvzySzRp0gTm5uawsbGBr68vPv/8c2RnZyM6Oho8Hg+hoaEAlB/8ir66du2q9hrflJ+fj5UrV6JZs2YwNzeHtbU1OnXqhL/++ktt36LvNWnSJERHR2PUqFFwdHSEVCpFq1atcPTo0fIv9BsOHjyIcePGoUGDBrCwsIClpSVatmyJH3/8EQqFQm3/ouv69OlTbNiwAc2aNYOZmZnK60xJScHChQvRqFEjmJmZwcbGBt27d8e///6rdr709HSsXr0aAQEBcHNzg1gshpOTEwYOHIgrV66o7c8FqqmSCu3edAeZ6cqes9a2EgwYrV9DaCqjS18PnPz7MW5ceAlGASybFYLd54eDz9e/nsv6Jj8/Hzt37kRsbCwsLCzQrFkzdO7cGQKB6gcrJycnmJmZ4caN4tsEYTfvAQDc3d0BALm5uZg6dSqGDh2KoUOHahSXTCZDQEAAUlJS0LNnT1hbW8PT0xMAcPLkSQwZMgQFBQUYMGAA6tWrh7i4OPzzzz84duwYgoOD0aJFiwq/R2BgIKZNmwaJRIKBAweidu3aePToEbZs2YIjR47gypUrqFOn+B79s2fP0K1bN8TExKBly5Z4//33oVAo8PDhQ/zwww+YOXMmbG1tsXjxYmzbtg0xMTFYvHgxe3xFHZNkMhl69eqF0NBQeHt748MPP0ROTg7+/vtvjBw5Ejdv3sS3336rdlxMTAz8/f1Rt25djB8/HikpKdi7dy8GDRqE06dPo1u3bpW65p9//jn4fD7atGkDV1dXpKen4+zZs5g7dy6uX7+OP/74o9Tj5s6di/Pnz6Nfv37o27cv+7sTExODrl27Ijo6Gp06dULv3r2RnZ2No0ePonfv3vj1118xbdo09jz37t3Dl19+ic6dO6Nfv36ws7NDbGwsDh8+jBMnTuDIkSPo3bt3pV6LrlBSJeXKzSnArp9usduTP/KDQGh4DRw8Hg+zFrfB9L6HUVCgwJ3ridgfGIXh7/lwHZree/XqFcaPH69S5unpicDAQHTpUtyMzuPxMH36dPz4449IT0+Hq70Qf+w7DitLC4wdOxYA8NVXXyElJQU//fSTxnG9fPkSjRs3RmhoKCwsihdxSE1NxejRo2Fubo5z586hcePiMcuRkZFo06YNpk6divDw8HLP//DhQ8yYMQMeHh4IDQ1Vud969uxZ9OjRA3PnzsWBAwfY8nHjxiEmJgbffvstFi5cqHK+pKQkWFpaQiqVYsmSJQgJCUFMTEylmtaLrFmzBqGhoejTpw8OHz4MoVD5Fr548WL4+/tjxYoV6N+/P9q3b69yXEhICJYsWaKSwMeMGYPevXtj9erVlU6qx44dU2uWVigUmDx5Mnbs2IFZs2ahTZs2aseFh4cjIiKC/dBTZOLEiYiJicGePXswatQotjwtLQ1du3bFnDlzMHDgQNSoUQMA0KhRI8THx8PRUXWO8bi4OPj7+2P+/PmcJ1XDe3ck1eqfbfeQmpQHADC3FKH3sPocR/T23DysMWJacRJds/AiUpNyOYxI/02ePBlnzpzBq1evkJ2djTt37mDGjBmIjo5Gnz59cOvWLZX9V61ahc8++wzXrl3D77sOw6dhXZz55ye4urri6tWrWLduHdavXw9HR0csXboULi4uEAqFaNmyJS5erPqQpzVr1qgkVADYsWMH0tLSsHTpUpWECgA+Pj6YNm0aIiIiEBUVVe65f/75ZxQUFGD9+vVqHZgCAgIwcOBAHDlyBJmZyuk6b9y4gUuXLsHPzw+fffaZ2vmKml01sXXrVvB4PKxdu5ZNqADg7OyMRYsWAQC2bNmidpy7uzu++uorlbJevXqhTp06uHbtWqW/f2n3efl8PubOnQsAOHXqVKnHffrpp2oJ9datWwgNDcXQoUNVEiqgvI+/dOlS5OXlYf/+/Wy5jY2NWkIFADc3NwwbNgz3799HbCy3vfKppkrKJJcrVO6ljpzuA6HIsD+HjX6/Kc4cfoZXcVnITJPhhy8vY9mvAVyHpbdK1mwAoEmTJvjll19gaWmJNWvWYMmSJSo1NYlEghUrVmDFihVAchhbLpPJMGXKFPTt2xdjx47FunXr2JpThw4d8M0336B37954/PgxWyupiFQqRbNm6os4XL58GYDyTbu0WuDDhw8BKJsS30y6pZ0nNDQU169fV3s+MTERhYWFePjwIVq2bMne0+vVqxf4fO3/nWRmZuLx48dwdXWFt7e32vMBAcrf44gI9Wk5/fz81JrrAaB27drs66yM5ORkrF69GsePH8fTp0+RnZ2t8vyLFy9KPa7kffYiRd83PT291J/T69evASh/TiVdvHgR69evx+XLl5GYmAiZTHVSlxcvXqg0yVc3SqqkTP/uf4wX0cWT5g+eaBg9fssjkQrx4SJ/LJpxFgCwPzAKQyY3gl9b/er5qe9mzpyJNWvW4Ny5c5Xaf+nSpXjx4gXb+WT16tXo3r07+2basGFDeHh44KeffsKyZZUbX+vs7FzqbF7Jycol/zZv3lzu8VlZWeU+X3Se1atXV+o8aWlpAKCzYTnp6ekAUGYv5aLyojhKsrW1LfUYoVBYagej0qSlpaF169Z49uwZ/P39MWHCBNjb20MoFCItLQ3r169Hfn7pq0LVrFlTrazo+gYFBSEoKKjM71vy53TgwAEMGzYMUqkUPXr0gJeXFywsLMDn8xESEoLQ0NAyY6gulFRJmXZuLG7a6zuyPszMRRxGoz1tA9zQrrsbLp+JAwCs+uQCdp8fppfTLeorZ2dnAFCrqZQm4vYDfPfdd/jll1/g6uqKjIwMxMfHs/dZAaBOnTpwdHREZGRkpWMo6+dlY2MDQFlTLa0mW1lF50lPT4e1dcUzhxUlrrJqa5oqiufNCTeKvHz5UmU/bduyZQuePXuGxYsXq9UsL1++XO7wqNJ+VkVxrl+/HnPmzKlUDIsWLYJYLEZYWBgaNVL9kD9jxgy2RzWXDLstj+jM3RsJuHVVOQk9jwdO10rVhfe/bA3Rf03Zt68l4PTBpxxHZFiKmu7q1q1b7n5yuRxT5i5Dt27d8N5776k892aNIi8vTyuxtW3bFgBw/vz5aj1P0f6nTp2qVO2vqDm2sLCwUue3srKCl5cXXrx4gUePHqk9HxwcDACV6tX8Nh4/fgwApfbafptk9jY/p8ePH6Nx48ZqCVWhUODChQtVjkEXKKmSUu36qXhpt+btXGDnaMZhNNrnUtsKA8c1ZLfXfnEJBQWVe3MzFZGRkUhJSVErj4mJwaxZswAoe7uWZ8W6bXj8NE6lKdba2hqurq44efIk5HI5AOWbcmZmJnx8NO+NPXnyZLajS2mdcBQKBUJCQio8z6xZsyASiTB//nz2PmxJMplMJSG0bNkS7du3x82bN7Fq1Sq1/ZOTk1U+ODg4OABAlTrWTJkyBQzDYMGCBSrJOCkpCcuXL2f30YWi4T5vXruIiAjlPfQqatWqFTp16oR//vlHOVlIKe7cuYPExOIx5R4eHnj06JHKGGGGYbB06dIKO55VF2r+JWqSE3Nw4q/iT8KTP/LjLhgdGvN+U5z8+zGyMwsQ+yQd+7dGYdSMplyHpTf27duHlStXolu3bvD09ISVlRWePHmCY8eOIS8vD3379sUnn3xS5vGR95/gf2u3Yu3yeew41SKffvop5s6di06dOqFNmzbYtWsXLC0t8eGHH2oct4ODA/7++28MHjwYbdu2Rffu3eHj4wM+n4/Y2FhcvnxZLcGVxtvbG1u3bsWUKVPg4+OD3r17o0GDBigoKEBsbCzOnz8PJycn3L9/nz1m586d6Nq1K7744gvs378fXbt2BcMwePToEf7991/cv3+fTU7du3fHvn37MGTIEPTt2xdmZmZwd3dXG75U0ieffIITJ07g0KFD8PX1Rd++fZGTk4N9+/YhMTERn376KTp27KjxNSzNhAkTsHr1asybNw/BwcGoX78+Hj16hKNHj2LIkCHYu3dvlc+5e/duBAQE4L333sOPP/6INm3awNbWFnFxcbh9+zbu3r2Ly5cvs7cb5s+fj5kzZ6J58+YYOnQoRCIRLl68iKioKAwYMABHjhzR9suuMkqqRM3+rVEokCmbr2q4WsDb14njiHTD2k6KUTOa4vfvleMVNy69igFjG8LCUv/nNK4O3bp1w4MHDxAREYHLly8jOzsbtra26NixI8aPH4/x48eXeV+zsLAQU+YsR5sWTfDBlOFqz8+ePRsZGRn45ZdfEB4eDl9fX/zwww+V7vlbke7du+P27dv4/vvvcerUKZw/fx5isRi1atVCQEBApSeeGDduHHx9fbFmzRoEBwfj33//hYWFBWrVqoVhw4Zh5MiRKvt7enoiPDwc3333HQ4ePIiNGzdCKpXCw8MDH3/8MZscAGDq1KmIiYnBn3/+ie+++w5yuRxdunQpN6mKxWIEBQVh7dq12L17NzZs2AChUAhfX1+sW7cOo0ePfrsLVgm1atXC+fPn8fnnn+PChQs4deoUvL29sWnTJrzzzjtvlVTd3Nxw48YNbNiwAfv378euXbtQWFiImjVronHjxpg9ezaaNi3+oDtjxgxIJBKsW7cO27dvh5mZGTp16oTAwEDs379fL5Iqj6nCEgmtWrVCWFhYxTsSg6VQMOjT6A/EPcsAAMz/X1v0HandGZRqeRR3pIiPTtfquasqP0+OyT0O4vWrHADAh4v88cEif9y7d0/tvo0KE1mlRiMlhtTAQX2VGn0yatQo7N27F/Hx8Xo3BzDRjQr/xt8S1VSJiitnn7MJVSTmo/ug8juiGDqJVIgJc/2wZqFyndjfvw/HiOlNKj6Q64RFtOrhw4eQSCRwcjLOVhlSfaijElHx9+/FN/s79qwDidT4P3f1GFwXHg1sAQB5uXL8ukJ9oD8xTuvXr8fQoUMRERGBQYMGqcxSRMjboKRKWCmvc3HmcPHQktHvm0anHYGAj6kLioch/LU5EoXyyg2IJ4Zt3bp1uHLlCqZMmYJff/2V63CIEaCPZYR1eOd9yAuUyaRmbUt4NrDjOKLq49/FFY38HHHvZhLkBQpkZcgqPogYvGfPnnEdAjEyVFMlrMO7iteZHDa57DlRjRGPx8P4Ob7sdnZWAQpkNG6VEFI1lFQJAODhnSQ8uJ0EAODzeXjnXePuoFSaVh1roZHffytgMEDSfz2CCSGksiipEgDA0T3FM8Y0aeUMCyvTG6vJ4/EwfnZxbTUlKZdqq4SQKqGkSqBQMDj2Z3FSHTrZ8FejeVutOtWCt6+ytsowQFIC1VYJIZVHSZUg7PwLvIpTLq8kkQrQurNulq4yBDweDxNK3FtNeU21VUJI5VFSJTi6u7iW2r5HbYjE6osZm5JWnWpBJFb+aVBtlRBSFZRUTVx+nhyn9j9mt98db7pNv0V4PJ7KPeXU17nsUCNCCCkPJVUTF3Ismh2TaW0rKe79auLEUgGkZsoau4JRrtxDCCEVoaRq4o7uLh6b2mNw3TJXHTE1PEBlDdmU17koLKTaKiGkfJRUTVhaci7OnYxht/uPbljO3qbHykYM8X/3VgsLGaQmlb/+JiGEUFI1Yaf2PymeltDNEm6e1hxHpG94sHMqrq0mvcqGQlHplRIJISaIkqoJO7HvEfu436j6HEaiv6xtJRAKlU3icjmD9BTTrK326NEDPB4PoaGhZe4zc+ZM8Hg8bN68uRojI0S/UFI1UcmJObhxPp7d7jHYi8No9BePx1O5t/r6VQ4YxvRqq+Hh4RAIBGjZsmWZ+1y9ehUA0KqVfi9IToguUVI1UWcOP2WbMt08reHgbM5xRPrLxl4CgUBZW5XlFyIjLZ/jiKrXkydPkJKSAh8fH1haWpa6T05ODu7evQuJRIImTSqxyDshRoqSqokKOvCEfdxrKNVSy8Pn82FjL2W3k0ysthoWFgYAaNOmTZn73LhxA3K5HL6+vhCJRNUVGiF6h5KqCUpLycO14BfsdsBA01uRpqrsHKQoGm2UmyNHTnYBtwFVo8ok1StXrgCgpl9CaJFyExR89BnkcmWvX5falnB2seA4Iv0nEPJhbStBeqqy6Tc5IQcWlqaxkk9RUt27dy+Cg4NL3YfupxKiREnVBAX9U9z023MINf1Wlp2jFOmp+RjZ7i+uQ6m0SNksjY5nGAYREREAgKCgoAr3p6RKTB01/5qYrAwZLp2OZbe7U9NvpYklQlhYmtb9wocPHyI9PR3t2rUDwzClfqWkpAAAzM3N0bhxY5XjN23dB88WgyCVStGyZUucP3+ei5dBSLWhpGpiQo49Q4FM2fTrXMsCLnWsOI7IsNg5SiveyYgUNf02b968zH3Cw8MBAH5+fhAIilc42nvgX8z9Yg2+mDcJERERaN++Pfr06YPY2NiyTkWIwaPmXxNTsum3+0BPDiMxTOaWIhyMGI38POUaqzVcLeBU03jvSRcl1RYtWpS5T1FSfbPpd+3PuzFpVH9MmzAYcGiEDRs24OTJk/j555+xYsUK3QVNCIeopmpCsrNkOH+qeK5fmvDhbfBg51BcW01OzDXqqQsrk1SL7rmWTKoymQw3bt1Hz25tVfbt2bMnLl26pINICdEPlFRNyIWTsWwNy97JDLXr2nAckWGyKjl1YYECGanGORmEQqHAzZs3IRaL4ePjU+Z+pdVUk5KSUFhYiBpO9ir71qhRA69evdJNwIToAUqqJiToYHHTb9d+HtwFYuB4PB5sS04GkWCck0Hcu3cPWVlZ8PHxgVhc+vChrKwsPHr0CJaWlmjYUH2VozeXEmQYhpYXJEaNkqqJkMkKcaFE0y8NpdGMjb0U/P9yQ16uHDlZxjcZxI0bNwCU3/R769YtKBQKtGjRAnx+8duJo6MjBAIBXiUmq+yfmJiIGjVq6CZgQvQAJVUTEX4hHpnpMgCApbUYdb3tOI7IsAmEfFjbSdjt5IQcDqPRjQkTJoBhGGzZsqXMfTp06ACGYdRWrxGLxWjp642gkKsq5UFBQWjfvr1O4iVEH1DvXxMRfCyafdy6iys1wWmBrYMZ0lKU91Mz02XIz5NDIqU/qSIfvT8G4z9YDP8WPujQywK//PIL4uPjMXPmTK5DI0Rn6B3ABDAMg5Cjz9jtnkNowgdtEEsEsLQSISuzAAyUPYFr0bhf1sjBPZGcmo7/rd2Kl5+tRpMmTXD8+HG4u7tzHRohOkNJ1QQ8iUpB3LMMAIBQxIevf02OIzIeto5myMpU3k9NS8qFcy0LCIV0V6XIB1OG44MpwwEHmr6QmAb66zcBwSVqqT4tnCASC8rZm1SFuYUQEqnyeioYIDUpl+OICCFcoqRqAkom1R7U61fLVCeDSDHyySAIIeWjpGrkkhJycPtaArvdrpsbh9EYp5KTQRQUKJCZZpyTQRBCKkZJ1cidOxGNonkJ6tSzgbWdaU0IXx14PB5sSk4GkWh8w2sIIZVDSdXIhRyNZh93H0AT6OuKrb0URaOUcrONczIIQkjFKKkasbxcucraqZ37eHAXjAGqytSDAiEf1jYlJoOg2iohekuX04pSUjViV4PjkJsjBwDYOkjh5mnNcUSGg8cTQC6XV+kY2xJrrWak5kMmK9R2WIQQLSgoKFBZ+1ebKKkasZBjxb1+O/SozWEkhkcklCIzM6NKx0ikQphbKId+M1D2BCaE6J+MjAxYWelmohZKqkaKYRiElJiaMGAAzaJUFVKxFZJTUpCSkoyCgoJKNxfZOpixj1OTclFYqNBViISQKmAYBjKZDElJSUhNTYW9vX3FB70FmlHJSEVFvEZifDYA5XR6Pi2cOI7IsAgFIliZOSEjPRPJySlgmMo15TIAklNzUChXJuFsWTwsrEpfNs3oZScVP068x10chPxHIBDAysoKderUgUQiqfiAt0BJ1UgFHylu+vVtUxMCmjqvyoQCESzNqv5p9tyBe9j0v+sAAI/6tjhyZyz4fBNcwGB34+LHY2hCDGIa6J3WSJVs+qW1U6tXr6H1YG4pAgBEP0rDxX9jKziCEGIsKKkaoZfPM3Hv5msAAI8HtO5ci+OITIu5pQh9htdjt3f8eJO7YAgh1YqSqhEqWUv1bGhnuvf0ODRovDfb5Hvp9HM8jkzmOCJCSHWgpGqESg6leWcQ9frlgkttK7R/p3gY086fbnMYDSGkulBSNTLZWTJcDY5jtzv1pgWhuTJkUiP28aE/7tOycISYAEqqRubq2TgUyJRjI+2dzVDTzZLjiExXk1bOqO+j7D0syy/Evi2RHEdECNE1SqpGJuR4NPu4fXeaRYlLPB4PQyYXDyvZtek2TV1IiJGjpGpEFAoG505Es9vvvEv3U7nWpY877J2UsywlvcrBv/sfcxwRIUSXKKkakXs3X+P1S+XqKGKJAN6+jhxHRERiAQaObchub19/U6crZBBCuEVJ1YiElhhK07S1MwQC+vHqg36jGkAkVv4sosJfI+LyK44jIoToCr3rGpGS91N7Dq5X9o6kWtk6SFWa4v+gySAIMVqUVI3E65fZiLyRCEA5i5J/V1eOIyIlDZ5YPLwm6OATxMdUbVk5QohhoKRqJM6djGEf165rA0trmkVJn3g2sEOL9i4AAEah7AlMCDE+lFSNRMn7qV37eXAWBynbkMnFtdV9WyKRnSnjMBpCiC5QUjUC+XlyXDrznN3u1t+Tw2hIWVp3doWbpzUAIDuzAAf/uM9xRIQQbaOkagSun3uB3OwCAIC1rYR94yb6hc/nYfBEb3Z7x/qbUChoeA0hxoSSqhEILdHrl5Z502893vVi73fHPctQWUyeEGL4KKkaOIZhVJJqz6G0ILk+M7MQof/oBuz25u9u0GQQhBgRSqoG7klUCl5EZwIAhCI+mraqwXFEpCLvTvCGSKT807tzPQHhl15yHBEhRFsoqRq4khM+NGzqAJFYwF0wpFIcnM3xzuDiySB+Xx3OYTSEEG2ipGrgVJp+h1DTr6EY/p4PeDzl49Dj0XgcmcxtQIQQraCkasDSknNxs8Q8su3fqcNhNKQqate1Qft3ipfmC/whgsNoCCHaQknVgF04FcsOyajpZglbBynHEZGqGDGtCfv4yO4HeBWXxWE0hBBtoKRqwEreT+3cm2qphqZxcyc0aeUMACiUM9i58RbHERFCNEVJ1UAVFBTi4r+x7Hb3QXQ/1RCNmOrDPv7z1zvISMvnMBpCiKYoqRqom5dfsW/A5pYieDa05TYg8lbadHODez0bAEButhy7fqKJ9gkxZJRUDVTJXr/N27uAV9SVlBgUPp+HkTOK761uXxdBE+0TYsAoqRqokkm19zBakNyQBfT3hEttSwBAZroMe365w3FEhJC3RUnVAMU+ScfT+6kAAL6Ah+btanIcEdGEQMjHqJlN2e3AtRHI+W+BBEKIYaGkaoDOnYhmH9drbA+JVMhdMEQrerxbF861LAAAacl52LclkuOICCFvg5KqASrZ9PvOoLpl70gMhkgswMjpxT2Bf//+BvLz5BxGRAh5G5RUDUxmej6uhb5gtzv1cucwGqJNvYfVh72zGQAgOSEX/2y7x3FEhJCqoqRqYM6fjIG8QAEAcHaxgGNNc44jItoilggwssS41d9WhkEmK+QwIkJIVVFSNTBnDxcvat2lnwd3gRCd6DuqATvdZGJ8Nv4JjOI4IkJIVVBSNSCy/EKcOxnNbvcYTPdTjY3UTKgyy9Km/11DXi7dWyXEUFBSNSBXQ+KQnakcamFlI4ZHfVtuAyI6MWBsQ5V7q3t+plmWCDEUlFQNyNnDT9nHbbq50SxKRkpqJsS4D5qx27+tvIGsDJpliRBDQEnVQCgUDIKPFt9P7TOcZlEyZr2H10NNN+UsSxlp+di+jtZbJcQQUFI1EHfDEvD6ZQ4AZS9Rn5bOHEdEdEkkFmDCXF92O3BtBFKTcjmMiBBSGZRUDcSZQ8VNv35ta0IgoB+dsQsY4MneN8/NkWPL6hvcBkQIqRC9MxuIkkNp+o6sz2EkpLoIBHxMmu/Hbu/66TZexWVxFxAhpEKUVA3A0/upePpAOYG+QMBDq061OI6IVJf279RGw2YOAIACmQLrv77McUSEkPJQUjUAZ48UN/02bOZIE+ibEB6Ph6kLWrLbh3c+wJ2wBA4jIoSUh5KqASg5lIbWTjU9fm1rokOP2uz2yo/Og2EYDiMihJSFkqqeS3iRhVtX/6uZ8KDy5kpMx7TPWkIoUv653rzyCif+esRxRISQ0lBS1XP//vOEfexRzwbWdlIOoyFccXW3xuAJ3uz2959foukLCdFDlFT13Mm/i2skfUZQr19TNvbDZrC1V36oSniRhW0/0IQQhOgbSqp67FVcFm5efqXc4AEBA2gCfVNmYSXGxHl+7PZvq8KQ8IKG2BCiTyip6rF//3nMPvaoZ8suCUZMV5/h9eDZ0BYAkJ9biFWfXOA2IEKICkqqeuzU38VJlSZ8IAAgEPLxwVf+7Pap/Y8RcuxZOUcQQqoTJVU9FR+biZtXSjb9enIbENEbfm1rosdgL3Z7+exQZGfRKjaE6ANKqnqqZNOvez1b2NhT0y8pNuPzlrC2kwBQ3nvfuPQqxxERQgBKqnrr3/3FSbX/KGr6Japs7KWY+UUrdnvnhtuIDE/kMCJCCEBJVS/Fx2SwEz7wqOmXlOGdQXXRvH1NAMr1dr+eeRZyuYLjqAgxbZRU9ZDKhA8NbGnCB1IqHo+HucvaQiwRAADu30xC4Foau0oIlyip6qGTJXr99h/dgMNIiL5zdbfG+NnN2O2NS64iKoKagQnhCiVVPfP8aTruXC9u+u3aj5p+SfmGTfGBdzNHAIBcrsCnE/5Fbk4Bx1ERYpooqeqZI7sesI+9GtvD2lbCYTTEEAhFfHy2piOk5solAZ89SMOahZc4jooQ00RJVY8wDIPDu+6z28MmN+YwGmJI3Dys8f6XrdntPT/fwbkT0dwFRIiJoqSqR8IvvsTzpxkAlLWPTr3dOY6IGJI+w+upLA345bQzSHmdy2FEhJgeSqp65NDO4lpqq0612F6dhFQGj8fD/P+1g72TGQAgJTEXn038F4WFNMyGkOpCSVVP5OXKVeb6HTHNh8NoiKGysZfik5Xt2e1Lp5/jx6+vcBgRIaaFkqqeOHPoKbIylPO3WtmI0aSlM8cREUPVurMrxn7QlN3esjpcZdpLQojuUFLVEyU7KHUfWBc8Ho/DaIihGz/HF627uLLbX753Bo+jUjiMiBDTQElVDyTGZ+FS0HN2e+gU6vVLNCMQ8LFwTUfUqmMFAMjJLsCcYceQmZ7PcWSEGDdKqnrg6J6HUCgYAICbpzVqullyHBExBlY2EizZ1BUSM2WHt5jH6fho9EnIZIUcR0aI8aKkyjGGYXDoj+Km38ETvTmMhhgbz4Z2+Phb1Y5Li6adYT/EEUK0i5IqxyLDE9l7XXwBD+8M8qrgCEKqplt/T0yY48tuH93zEGu/oBmXCNEFSqoc++u3SPZx83Y1YW4p4jAaYqzGzWqGfiOL1+UNXBuB7etvchcQIUaKkiqHMtPzcXzvQ3Z7/GzfcvYm5O3xeDzMXtpGZcal7xZcUJlrmhCiOUqqHDqy+wFyc+QAAHsnMzRu7sRxRMSYCQR8LFzbCT4ti3/PvnjvtMo9fUKIZiipcoRhGPz12112e+jkRjQ2leicRCrEsl8C4NHAFgCgUDD4cupp/LMtitvACDESlFQ5En7xJR5FKjsoCQQ89BtFi5GT6mFtK8HqHT1R19sOAMAwwKLpZ/HX5rsVHEkIqQglVY78seEW+7hNNzdYWIk5jIaYGlsHKVbv6IF6je3ZsqUfhlDnJUI0REmVAy+iM3Dm0FN2e9J8P+6CISbL2k6K73b0QMNmDmzZdwsu4Jt5oZDLaWUbQt4GJVUO7PnlDjv43qOBLTwb2HEcETFVVjYSrNrWA41bFHde2r3pDuYMO4bsLBmHkRFimCipVrPsLBn+/r14bOqkeX7cBUMIAAsrMb7b3gOd+7izZaHHYzCh2z9IeJHFYWSEGB5KqtXsn8B7yExX1gCsbSVo1712BUcQonsSqRBfruuMUTObsGX3byVheNu9uHL2eTlHEkJKoqRajWSyQmz7IYLdHjHNB3w+DaMh+oHP5+G9j1tg/jft2N/L5IRcTO1zCD8tu4rCQrrPSkhFKKlWo2N/PsSrOGVzmkQqwOCJjTiOiBB1fUfUx8pt78DGXgJAOeRm0/+uY1rfw3j9Kpvj6AjRb5RUq4lCwWDr6nB2e9B4b4glAg4jIqRszdu54NcjA+DbtgZbdjU4Du8234Njfz4Ew9AqN4SUhpJqNQk68ARPH6QCAIRCPsa835TjiAgpn4OzOVZt64Fxs5qhaLKvtOQ8fDrhX8wZdhyJ8dSJiZA3UVKtBoWFCvy07Bq73WNwXZrsgRgEgYCPiXP9sHJbDzjWNGfLzx55hkF+e/D31ki610pICZRUq8GJvx7hyb3/piQU8jB1QQuOIyKkalq0d8GW4wMxYEzxdJoZaflYPDMYI9vtQ9j5FxxGR4j+oKSqY3K5ApuWF9dS+46oD2s7KYcREfJ2LKzEmLO0Lb7f2RM13SzZ8ns3X2Ni9wOYN/IEnj9N5zBCQrhHSVXHDu64h5jHyjcaoYiPKR9TLZUYNt82NbH5+ECMn91MpbNd0IEn6N9kF76ecZaSKzFZlFR1KDtLhg1LrrLbA8c2hKU13Uslhk9qJsSEOX4IDHoXAQM92XK5XIH9gVHo57MTX/0+BjEJjhxGSUj1o6SqQ1u/j0DSqxwAgNRciMkfNec4IkK0y9nFAgvXdML6v/qgaWtntrywkMGB8+3Q7/NF+HDddFw5+5yG4RCTQElVR17FZanMnjT1kxaQmgk5jIgQ3Wnc3Alrd/fG9zt7opl/8dhWhuEj5GZTvNf7EAb57cGeX+4gPTWPw0gJ0S0eU4WPj61atUJYWJgu4zEa80aeQNCBJwAAxxrm2HVuKE1J+J9aHjbs4/houvdmjO5cT8CBlWtx/raP2nNiiQDdB9bFuxO90a57bQgE9NmeGA9Kqjpw7kQ03h90lN1ete0dtOhQi8OI9AslVdNQ66INnr10xu7TnXHgYgfk5qm31DjWNEePwV7oOcQLLTvWogRLDB4lVS3LzSnAu833IO5ZBgCgVadaWLH1HY6j0i+UVE1DrYvFP+cEi4k4GloPR09I8fBJ6UPKHJzNEDCwLjr3dkebADdYWFKnPmJ4KKlq2aoF57Fj/S0AyiE0e84Pg60DjUstiZKqaSiZVLOdx0EhcQMAPHoixPF/zXEiSIq0dFGpx4rEfLTsWAuderujUy931PW2A49Ht0+I/qOkqkXXz73A5B4HUHRFZy5shaFTGnMblB6ipGoaykqqReSFwK3bYpwJlSL4nBSpZSRYAKjlbgX/Lq7w7+IG/66ucKltpbO4CdEEJVUtyc6UYXDLPXgRnQkAqOtth18O96dP16WgpGoaKkqqJRUWAncixbh0TYoLl0R4GlN+607tutbKBNvFFa27uKKGq2W5+xNSXWiMhxYwDIPF7wezCVUo4uObzQGUUAmpJIEA8Gsmg18zGT6YCiS+5uPyNSkuXRHjWrhErZPT86cZeP40CvsDowAA7vVs0LqLK1p3doV/F1c416IkS7hBSVUL/vz1Lk789YjdnrXIH441LTiMiBDD5uykwKB+ORjULwdyORD1QIzwm2JcvyHEnSgpZAWqaxHHPE5HzON0/P27Msl61LdVSbJOLvT3SKoHNf9q6Pa1V5gQ8A8KZMrlr9p2c8Py3wI4jkq/UfOvaahK829VyGRFSVaC6zeEiLwvUUuyb/JsUJxkW3emJEt0h5KqBl5EZ2B0x31ITswFoBwSsOPsEJVJxok6SqqmQVdJ9U35MiDq/n9JNlyIyHtSFMjLH+9aMsn6d3WDYw3zcvcnpLIoqb6ljLR8jOuyn10nVSjiY/PxgXDzsOY4Mv1HSdU0VFdSfVO+DIi8J0b4LQnCKplk6/vYo01AbbTt5obWnV1p4Qvy1iipvoXsTBmm9T2EW1cTAAA8HrAi8B20pFmTKoWSqmngKqm+qSjJ3rgpwY1wEe7el0BeTpIVCHho2roG2nRzQ9sAN/i1daHWJ1JplFSrKCe7ADMHHMGNC/Fs2ewl/hg41pvDqAwLJVXToC9J9U15+cU12ethQkQ9kEJeWHaSlZoJ0aKDC9p0c0O77rXh7etI0ymSMlFSrYK05Fx88O5RtoYKAONnN8OEOX7cBWWAKKmaBn1Nqm/KzeXh5h0xroeLcS1MhCfRUjBM2cPhrO0k8O/iirb/NRd7NLCl4XOERUNqKulFdAZmDjiCpw9S2bKR030ooRJi4MzMGLTzz0c7/3wAQFo6HzduinEtTIxrN0R4maA6EUVGaj5OH3yK0wefAgBqulkqm4r/ay6mMbKmjWqqlXAxKBYLxp9Ceko+WzZuVjNMnOvHXVAGjGqqpsFQaqoVeZUgQFiEBFeuCRAWIUVaRvmdmOo2tEObADe0C6iN1l1cYW0rqaZIiT6gpFoOmawQm5Zdw5bVN9j5fMED5i1ri36jGnAamyGjpGoajCWplsQwwLMYIa7fEOPKNQFu3jVDbl7Zcxbz+Tw0buGEtt3c4N/VDU1b16Aka+QoqZbhTlgCFs8MxoPbSWyZxEyAb7e8g2b+NTiMzPBRUjUNxphU3yQvBO4/EOFamBhXrvER9dCy3E5PgHKMbNPWNdCkdQ00bV0D3s0cqXexEaGk+oaEF1nYuPQq/tl2T6W8jpcNvt/ZE3aOZhxFZjwoqZoGU0iqb8rLB27fleBamAhXrwvxONq83E5PACAU8uHZ0BYNmjqifhMHNGjigAZNHVDTzZI6QBkg6qj0n/iYDGxbdxP7tkRCll/IlvP4wNgPmmH8bF/w+fQLTggpm1QC+LfMh3/LfMyaAWRkpiuH7twQ49YdAZ7FSFGoUK3JyuUKPIpMwaPIFJVya1sJ6jdxQP0mDvCobwuP+raoU88Grh7WEAppSI++MumkWlBQiIv/xuKfwHsIPvoMCoVqpb1+Ewd88UNHuJWoWRFCSGVZWzHo2jEPXTvmAVDWZB89FiPqvgh3owS4GyXCy8TSl7nLSMvHjQvxKmPiAWXN1s3TGu71beBe3xbu9Wzh6mGNWnWsUMvdCmbmZd/jJbpnckn19atsXD79HJdOP8f5UzFIS85T28e5lgVmL26DtgHG31xFCKk+UgnQ1EeGpj4yjByqLMvO5uFJtAhPngrx6IkADx/x8SxGguzc0pOjXK5A9KM0RD9KAxCj9rydoxS13IuTbK06yi+XOlao6WYJWwcpNSvrkFEn1ewsGR7eScb9W0m4f+s1bl9LwMM7yWXu79HAFpPn+6Fd99r0S0cIqRYWFgya+cjQzEfGljEMkPhagCfPhHgaLULMcz5iYniIixchJa383sOpSXlITcpD5I3EUp8XivhwrGkOxxrmcKppAScX5f+OLsXb9k7msHWQwsxcSO+FVWRQSbWgoBC52XLkZhcgN7sAWZkypCXnIfV1HlKTc5H0KgfxsZmIj8lAfGwmXr/MqfCc5pYidOnrjqGTG8O9nq3uXwQhhFSAxwNqOBeihnMh2rfJV3kuN5eHuHgBYuOEeB4nREwsH/EvgVeJQiSlSFBYQe9jeYECr55n4dXzrArjEIn5sHWQwsZOqvK/tZ0E5hYimFuJYGElhoWl8n9zSxEsrEQwtxBBLBFAJBFAXOLLFKZ35CSp7g+MwtE9D1AoZ1AoV0AuV6g8Vm4ry+RyBWT5hcjNLmDXLNUEjwe4elijXXc3dOnrgfo+DtQBiRBiMMzMGNT3kqO+l1ztucJCIDmFj5cJQrxKEOBVggDx8Ty8eMlDQqIAyaki5JTRrFyaApkCr1/mVKqCUhkCAU8t2QoEPPD4PAj4//0v4IEv4IPP54Ev4IH/Xxnvzf//q0HzeAD+ewvn8Xjg8YD3v2wNv7YuWom5qjhJqnHPMnAt5EW1fC9LazFc6liigY8D6jayh0cDW0jNil/246iUco4mulByLZ+Hd8tujieGreTPOSM1F3Je6c2RRLvEANydlF9oov58voyHlFQRUtKKvoRISRHhdQofKSkCpKSLkJklQlaOGAVy7Y6fLSxkkJsjR26O+gcCbRo5rZQXXk2qNE7V0dERHh4eWvvmD+4kQV6gee0TAAQCPoRCPngati7kyzMhEVppJSZTQNer6kztmvF5DKzNMsDjVfqtRkVmbj6szGgWosqqvuvFKL+Y/75QuZ8vuxfDK/MIhv2n6q2IAokl7F2rVkt9/fo1nJycSn3O0dERJ0+erPS5qpRUTYEpTHChTXS9qo6uWdXQ9aoaul5Vp81rZvx3jQkhhJBqQkmVEEII0RJKqm+YPn061yEYFLpeVUfXrGroelUNXa+q0+Y1o3uqhBBCiJZQTZUQQgjREpNIqs+fP8ewYcNgY2MDa2trDBkyBLGxsZU6Ni8vDwsWLICLiwvMzMzQrl07nDt3TscRc+9tr1lYWBimT58Ob29vmJubo06dOhg7diyePXtWDVFzR5PfsZJWrFgBHo+Hjh076iBK/aHp9bp37x6GDx8OR0dHmJmZoWHDhli/fr0OI+aeJtcsNjYWEydORJ06dWBubo4GDRrgq6++QnZ2to6j5k5cXBxmz56Ndu3awdzcHDweD9HR0ZU6VqP3fcbIZWdnM/Xq1WN8fHyYAwcOMAcPHmSaNGnC1K1bl8nKyqrw+DFjxjA2NjbMb7/9xpw+fZoZPHgwI5VKmYiICN0HzxFNrtnHH3/MtG/fnvnpp5+YkJAQZteuXYy3tzdjb2/PxMbGVtMrqF6a/o4VefLkCWNhYcE4OzszHTp00GHE3NL0el2/fp2xsrJiBgwYwBw4cIA5e/Ys8+uvvzJr1qyphui5ock1y8rKYurXr894eHgw27ZtY86ePcusWrWKkUqlzIgRI6rpFVS/4OBgxtnZmenTpw/Ts2dPBgDz7NmzSh2ryfu+0SfVdevWMXw+n3n06BFb9vTpU0YgEFT4R3jz5k0GALN161a2rKCggGnQoAEzYMAAncXMNU2uWWJiolpZdHQ0w+PxmEWLFmk9Vn2gyfUqqWfPnsz06dOZLl26GHVS1eR6FRYWMo0bN2beffddXYepVzS5ZqdOnWIAMKdOnVIp/+yzzxiBQMBkZ2frJGauFRYWso83b95c6aSq6fu+0Tf/Hj58GG3btkW9evXYMk9PT3To0AGHDh2q8FiRSISRI0eyZUKhEKNGjcKpU6eQn59fztGGS5NrVtqsJO7u7nBycsKLF9UzNWV10+R6Fdm9ezfCw8OxYsUKXYWpNzS5XiEhIYiKisJHH32k6zD1iibXTCZTrn5jbW2tUm5rawuFQgHGSPuq8vlvl940fd83+qQaGRmJJk3U54H08fFBVFRUhcd6enrC3Nxc7ViZTIbHjx9rNVZ9ock1K829e/eQmJiIRo0aaSM8vaPp9UpNTcX8+fPx3Xffwd7eXhch6hVNrteFCxcAKO95tW3bFiKRCM7OzpgzZw5yc3N1Eq8+0OSavfPOO6hfvz4+++wzREVFISsrC2fPnsX69esxc+ZMWFhY6Cpsg6Tp+77RJ9WUlBTY2dmpldvb2yM1NfWtjy163hhpcs3eJJfLMXPmTDg5OeG9997TVoh6RdPrtWDBAjRo0ACTJk3SQXT6R5PrFR8fDwAYOXIkevbsiaCgIHz66afYsmULxowZo5N49YEm10wqleLChQtQKBTw8fGBlZUVunfvjv79+2Pjxo26Ctlgafq+b1Drqb6t0hbZrUyTB8Mwb32sodPW6541axYuXbqEY8eOlfqLaize9nqdP38eO3bsQHh4uEktBv2210uhUC7AMW7cOCxbtgwA0LVrVxQWFuLzzz9HVFQUGjdurN1g9cTbXrO8vDyMHDkSiYmJ+OOPP1CnTh1cu3YNy5Ytg1AoxM8//6yLcA2Wpu/7Rp9U7ezsSv1kkZqaWuGbvL29fald1os+GRprU50m16ykhQsX4rfffsP27dvRs2dPbYaoVzS5XjNmzMB7770HNzc3pKWlAVDW7gsLC5GWlgYzMzNIJMa1Qosm18vBwQEA0KNHD5Xynj174vPPP8fNmzeNMqlqcs1+//13hISE4PHjx/Dy8gIAdO7cGTY2Npg+fTpmzpwJX19fncRtiDR93zf65l8fHx9ERkaqlVfmE62Pjw+ePXuGnBzVBXqjoqIgFotVOg0YE02uWZFvvvkGK1euxPr16zF+/Hhth6hXNLle9+7dwy+//AI7Ozv26+LFi7hy5Qrs7OyMshah6d8koF5rK6pFvG3nFH2nyTW7c+cO7Ozs2IRaxN/fH4Dyd5AU0/R93zh/A0sYOHAgrly5gqdPn7Jl0dHRuHjxIgYOHFjhsQUFBdi3bx9bJpfLsXfvXvTs2dPoahBFNLlmAPDjjz/iq6++wjfffIPZs2frMlS9oMn1Cg4OVvvy9fVFkyZNEBwcjGHDhuk6/GqnyfXq06cPJBKJ2vqWp06dAqBcwssYaXLNatasidTUVLUONlevXgUAuLq6aj9gA6bx+37lR/0YpqysLMbLy4tp0qQJc/DgQebQoUNMs2bNGE9PTyYzM5PdLzo6mhEIBMzSpUtVjh85ciRja2vLbN68mTl9+jQzdOhQRiKRMDdu3Kjul1JtNLlme/bsYXg8HtO7d2/m8uXLKl+RkZFcvByd0/R37E3GPk5V0+u1ZMkSRiAQMAsXLmSCgoKYFStWMFKplJk4cWI1v5Lqo8k1e/bsGWNlZcXUr1+fnfzhu+++Y6ysrJiWLVuqjOc0Nvv27WP27dvHzJw5kwHAbNq0idm3bx8TEhLCMIxu3veNPqkyDMPExMQwQ4YMYaysrBhLS0tm0KBBaoOAnz17xgBgFi9erFKek5PDzJ8/n6lRowYjkUgYf39/Jjg4uNpi58rbXrOJEycyAEr96tKlS7W+huqkye/Ym4w9qTKMZtdLoVAwa9asYby8vBiRSMTUqVOHWbRoESOTyarvBXBAk2sWGRnJDB8+nHFzc2OkUilTv3595uOPP2ZSUlKq7wVwoKL3Il2879MqNYQQQoiWGP09VUIIIaS6UFIlhBBCtISSKiGEEKIllFQJIYQQLaGkSgghhGgJJVVCCCFESyipEkIIIVpCSZUQLeHxeODxeODz+Xjy5EmZ+3Xr1o3dd9u2bSrPTZo0iX1u8eLFZZ5j+/bt7H5du3ZVeS4kJIR9rujL3NwcLi4u6Ny5MxYsWICIiAhNXiohpAyUVAnRIqFQCIZh8Pvvv5f6/KNHjxAaGgqhsPwFooRCIbZu3YrCwsJSn9+8eXOF53B3d8fixYuxePFizJs3D/3790d2dja+//57tGjRAmPHjkVWVlblXhghpFIoqRKiRTVq1ECrVq0QGBgIuVyu9vyWLVvAMAz69+9f7nn69++PuLg4tYnjAeWqIhcvXsSAAQPKPYeHhweWLFmCJUuW4Ntvv8XmzZtx48YNREREoGnTpti9ezeGDx9etRdICCkXJVVCtGzatGl49eoVjh49qlJeUFCA7du3o3379uwSZmUZO3YszMzMsHnzZrXnisqmTp36VvH5+fnh9OnTcHJywsmTJ3Hw4MG3Og8hRB0lVUK0bPTo0bCwsMCWLVtUyg8fPoyEhARMmzatwnPY2tpi+PDhOHbsGF6+fMmW5+fnY8eOHejSpQsaNGjw1jE6OztjxowZAIBdu3a99XkIIaooqRKiZVZWVhg1ahROnjyJuLg4tnzz5s2wtrbGiBEjKnWeadOmQS6Xq3RmOnDgAJKTkyuVmCtS1MHp2rVrGp+LEKJESZUQHZg2bRoKCwuxdetWAEBMTAyCgoIwduxYmJubV+ocHTt2RKNGjdj7sIAyMdvZ2WHo0KEax1i0OPXr1681PhchRImSKiE60KZNGzRt2hRbt26FQqHAli1boFAoqlzDnDp1Kp4+fYqzZ8/i8ePHCA4Oxvjx4yGVSjWOsShR83g8jc9FCFGipEqIjkybNg0xMTE4efIkAgMD0bJlSzRv3rxK55gwYQIkEgm2bNnC1li10fQLAPHx8QAAJycnrZyPEEJJlRCdGT9+PMzMzDBjxgy8ePEC06dPr/I5HB0dMXjwYBw4cABbt25Fu3bt0KRJE63EFxwcDEBZqyaEaAclVUJ0xNbWFsOGDUNcXBwsLCwwevTotzrPtGnTkJ+fj9evX2utlpqYmIhff/0VgHL4DiFEO8qfkoUQopH//e9/GDJkCJycnGBlZfVW5+jWrRsOHToEhUKBXr16aRzTrVu3MGHCBCQlJaFv374YOHCgxuckhChRUiVEh+rUqYM6depodA4ej/dWiS86OhpLliwBoJx4IikpCTdu3MCNGzcAKGuoRbVVQoh2UFIlxEjFxMRg6dKlAACpVApbW1vUr18fn3zyCcaOHQs/Pz9uAyTECPGYon71hBBCCNEIdVQihBBCtISSKiGEEKIllFQJIYQQLaGkSgghhGgJJVVCCCFESyipEkIIIVpCSZUQQgjREkqqhBBCiJZQUiWEEEK0hJIqIYQQoiX/BxgvbsEXLevZAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1000 simulated data sets from the well-specified model from training (for reference)\n",
+    "\n",
+    "# Actual HNN simulation output (this should not be mispecified)\n",
+    "\n",
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_five_data_HNN, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18bd4ab5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4803a069",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4c5838-25c0-4cb6-ba6d-388a15682aaf",
+   "metadata": {},
+   "source": [
+    "## Flat line/constant values (should be mispecificied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "eb4acafb-09b4-4d4f-977f-f53c6342b4fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0\n",
+    "\n",
+    "data_flat0 = np.zeros(shape=(1,1,341))\n",
+    "observed_data_flat0 = {}\n",
+    "observed_data_flat0['summary_conditions'] = data_flat0\n",
+    "observed_data_flat0['direct_conditions'] = None\n",
+    "observed_data_flat0['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "c83bd074-a7d5-45a6-adde-2536758899e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEpCAYAAACnRpT/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAAsTAAALEwEAmpwYAAATbUlEQVR4nO3cX2ib593G8Uut8CCsyRiNUzny5onHdV3FjpfKaY7GmtQ1M6tCGyiGlBjcTSR4hK2jI2dZoZm9boMGXAhiLDgb2GeL8ja2V2eDbYRlQnW70WgHIrNBUtzEWWKabmudavd7UOK9fm3HXh/vJyn5fo4q6ZZ05UnjL/6DA845JwAA/svuK/cAAMC9geAAAEwQHACACYIDADBBcAAAJggOAMBEsNwDPo0HH3xQDQ0N5Z4BAJA0PT2ta9eurXquKoPT0NCgTCZT7hkAAEmxWGxN5/iSGgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmCA4AwATBAQCYIDgAABMEBwBgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAICJdQnO+Pi4mpqa5HmeBgYGljzunNPhw4fleZ5aW1s1OTm56PFSqaQvf/nL+vrXv74ecwAAFch3cEqlkvr6+jQ2NqZsNqvh4WFls9lFZ8bGxpTL5ZTL5ZRMJnXo0KFFjx8/flzNzc1+pwAAKpjv4KTTaXmep0gkopqaGnV3dyuVSi06k0qldODAAQUCAe3atUtzc3OamZmRJBUKBZ09e1bf+MY3/E4BAFQw38EpFouqr69fuB0Oh1UsFtd85tvf/rZeffVV3Xcf304CgLuZ74/yzrkl9wUCgTWdeeONN1RbW6vHHnts1fdJJpOKxWKKxWKanZ399IMBAGXhOzjhcFj5fH7hdqFQUF1d3ZrOnD9/XmfOnFFDQ4O6u7v1m9/8Rs8///yy75NIJJTJZJTJZLR582a/swEAxnwHp729XblcTlNTU5qfn9fIyIji8fiiM/F4XKdOnZJzThcuXNCmTZsUCoXU39+vQqGg6elpjYyMaPfu3frFL37hdxIAoAIFfb9AMKjBwUF1dnaqVCqpt7dX0WhUJ06ckCQdPHhQXV1dGh0dled52rBhg06ePOl7OACgugTcct9gqXCxWEyZTKbcMwAAWvvHZH40DABgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmCA4AwATBAQCYIDgAABMEBwBgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE+sSnPHxcTU1NcnzPA0MDCx53Dmnw4cPy/M8tba2anJyUpKUz+f1xBNPqLm5WdFoVMePH1+POQCACuQ7OKVSSX19fRobG1M2m9Xw8LCy2eyiM2NjY8rlcsrlckomkzp06JAkKRgM6ic/+Yn+8pe/6MKFC3r99deXPBcAcHfwHZx0Oi3P8xSJRFRTU6Pu7m6lUqlFZ1KplA4cOKBAIKBdu3Zpbm5OMzMzCoVC2rFjhyTpgQceUHNzs4rFot9JAIAK5Ds4xWJR9fX1C7fD4fCSaKzlzPT0tN5++209/vjjy75PMplULBZTLBbT7Oys39kAAGO+g+OcW3JfIBD4j8588MEH2rdvn1577TVt3Lhx2fdJJBLKZDLKZDLavHmzz9UAAGu+gxMOh5XP5xduFwoF1dXVrfnMrVu3tG/fPu3fv1/PPvus3zkAgArlOzjt7e3K5XKamprS/Py8RkZGFI/HF52Jx+M6deqUnHO6cOGCNm3apFAoJOecXnjhBTU3N+vFF1/0OwUAUMGCvl8gGNTg4KA6OztVKpXU29uraDSqEydOSJIOHjyorq4ujY6OyvM8bdiwQSdPnpQknT9/Xj//+c/V0tKitrY2SdIPfvADdXV1+Z0FAKgwAbfcN1gqXCwWUyaTKfcMAIDW/jGZ3zQAADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmCA4AwATBAQCYIDgAABMEBwBgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATKxLcMbHx9XU1CTP8zQwMLDkceecDh8+LM/z1NraqsnJyTU/FwBwd/AdnFKppL6+Po2NjSmbzWp4eFjZbHbRmbGxMeVyOeVyOSWTSR06dGjNzwUA3B18ByedTsvzPEUiEdXU1Ki7u1upVGrRmVQqpQMHDigQCGjXrl2am5vTzMzMmp4LALg7BP2+QLFYVH19/cLtcDisP/7xj6ueKRaLa3ruenv5fy4qe/n9/+p7AEA1ebRuo44+Hf2vv4/v4DjnltwXCATWdGYtz70tmUwqmUxKkmZnZz/NVABAGfkOTjgcVj6fX7hdKBRUV1e3pjPz8/OrPve2RCKhRCIhSYrFYp96r0XFAQBL+f4eTnt7u3K5nKampjQ/P6+RkRHF4/FFZ+LxuE6dOiXnnC5cuKBNmzYpFAqt6bkAgLuD789wgsGgBgcH1dnZqVKppN7eXkWjUZ04cUKSdPDgQXV1dWl0dFSe52nDhg06efLkHZ8LALj7BNxy30ipcLFYTJlMptwzAABa+8dkftMAAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmCA4AwATBAQCYIDgAABMEBwBgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmfAXn+vXr6ujoUGNjozo6OnTjxo1lz42Pj6upqUme52lgYGDh/pdeekmPPPKIWltb9cwzz2hubs7PHABABfMVnIGBAe3Zs0e5XE579uxZFJPbSqWS+vr6NDY2pmw2q+HhYWWzWUlSR0eH3n33Xf35z3/Www8/rP7+fj9zAAAVzFdwUqmUenp6JEk9PT06ffr0kjPpdFqe5ykSiaimpkbd3d1KpVKSpKeeekrBYFCStGvXLhUKBT9zAAAVzFdwrly5olAoJEkKhUK6evXqkjPFYlH19fULt8PhsIrF4pJzP/vZz/S1r31txfdKJpOKxWKKxWKanZ31MxsAUAbB1Q48+eSTeu+995bcf+zYsTW9gXNuyX2BQGDJawWDQe3fv3/F10kkEkokEpKkWCy2pvcGAFSOVYNz7ty5FR/bsmWLZmZmFAqFNDMzo9ra2iVnwuGw8vn8wu1CoaC6urqF20NDQ3rjjTf061//ekmIAAB3D19fUovH4xoaGpL0STj27t275Ex7e7tyuZympqY0Pz+vkZERxeNxSZ/89NoPf/hDnTlzRhs2bPAzBQBQ4XwF58iRI5qYmFBjY6MmJiZ05MgRSdLly5fV1dUlSQoGgxocHFRnZ6eam5v13HPPKRqNSpK+9a1v6ebNm+ro6FBbW5sOHjzo848DAKhUAbfcN1kqXCwWUyaTKfcMAIDW/jGZ3zQAADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmCA4AwATBAQCYIDgAABMEBwBgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATPgKzvXr19XR0aHGxkZ1dHToxo0by54bHx9XU1OTPM/TwMDAksd//OMfKxAI6Nq1a37mAAAqmK/gDAwMaM+ePcrlctqzZ8+yMSmVSurr69PY2Jiy2ayGh4eVzWYXHs/n85qYmNAXvvAFP1MAABXOV3BSqZR6enokST09PTp9+vSSM+l0Wp7nKRKJqKamRt3d3UqlUguPf+c739Grr76qQCDgZwoAoML5Cs6VK1cUCoUkSaFQSFevXl1yplgsqr6+fuF2OBxWsViUJJ05c0Zbt27V9u3b/cwAAFSB4GoHnnzySb333ntL7j927Nia3sA5t+S+QCCgf/zjHzp27JjefPPNNb1OMplUMpmUJM3Ozq7pOQCAyrFqcM6dO7fiY1u2bNHMzIxCoZBmZmZUW1u75Ew4HFY+n1+4XSgUVFdXp0uXLmlqamrhs5tCoaAdO3YonU7roYceWvI6iURCiURCkhSLxVb/kwEAKoqvL6nF43ENDQ1JkoaGhrR3794lZ9rb25XL5TQ1NaX5+XmNjIwoHo+rpaVFV69e1fT0tKanpxUOhzU5OblsbAAA1c9XcI4cOaKJiQk1NjZqYmJCR44ckSRdvnxZXV1dkqRgMKjBwUF1dnaqublZzz33nKLRqP/lAICqEnDLfZOlwsViMWUymXLPAABo7R+T+U0DAAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAEwQHAGCC4AAATBAcAIAJggMAMEFwAAAmCA4AwATBAQCYIDgAABMEBwBgguAAAEwQHACACYIDADBBcAAAJggOAMAEwQEAmCA4AAATBAcAYILgAABMEBwAgAmCAwAwQXAAACYIDgDARMA558o94j/14IMPqqGh4VM/f3Z2Vps3b16/QcbYXz7VvF1if7lV8/47bZ+enta1a9dWfY2qDI5fsVhMmUym3DM+NfaXTzVvl9hfbtW8fz228yU1AIAJggMAMHFPBieRSJR7gi/sL59q3i6xv9yqef96bL8nv4cDALB3T36GAwCwd88FZ3x8XE1NTfI8TwMDA+Wes6qGhga1tLSora1NsVhMknT9+nV1dHSosbFRHR0dunHjRplX/ltvb69qa2u1bdu2hfvutLe/v1+e56mpqUm/+tWvyjF5keX2f//739fWrVvV1tamtrY2jY6OLjxWSfvz+byeeOIJNTc3KxqN6vjx45Kq5/qvtL9arv+HH36onTt3avv27YpGozp69Kik6rn+K+1f1+vv7iEff/yxi0Qi7tKlS+6jjz5yra2t7uLFi+WedUdf/OIX3ezs7KL7XnrpJdff3++cc66/v99973vfK8e0Zf32t791b731lotGowv3rbT34sWLrrW11X344Yfur3/9q4tEIu7jjz8uy+7bltt/9OhR96Mf/WjJ2Urbf/nyZffWW28555x7//33XWNjo7t48WLVXP+V9lfL9f/Xv/7lbt686Zxzbn5+3u3cudP94Q9/qJrrv9L+9bz+99RnOOl0Wp7nKRKJqKamRt3d3UqlUuWe9R9LpVLq6emRJPX09Oj06dPlHfR/fOUrX9HnP//5RfettDeVSqm7u1uf+cxn9KUvfUme5ymdTltPXmS5/SuptP2hUEg7duyQJD3wwANqbm5WsVismuu/0v6VVNr+QCCgz372s5KkW7du6datWwoEAlVz/Vfav5JPs/+eCk6xWFR9ff3C7XA4fMf/oStBIBDQU089pccee0zJZFKSdOXKFYVCIUmf/CO9evVqOSeuaqW91fT3MTg4qNbWVvX29i58SaSS909PT+vtt9/W448/XpXX///ul6rn+pdKJbW1tam2tlYdHR1Vd/2X2y+t3/W/p4LjlvmBvDsVvBKcP39ek5OTGhsb0+uvv67f/e535Z60bqrl7+PQoUO6dOmS3nnnHYVCIX33u9+VVLn7P/jgA+3bt0+vvfaaNm7cuOK5atlfTdf//vvv1zvvvKNCoaB0Oq133313xbPVsn89r/89FZxwOKx8Pr9wu1AoqK6uroyLVnd7X21trZ555hml02lt2bJFMzMzkqSZmRnV1taWc+KqVtpbLX8fW7Zs0f3336/77rtP3/zmNxe+bFCJ+2/duqV9+/Zp//79evbZZyVV1/VfaX+1XP/bPve5z+mrX/2qxsfHq+r63/b/96/X9b+ngtPe3q5cLqepqSnNz89rZGRE8Xi83LNW9Pe//103b95c+O8333xT27ZtUzwe19DQkCRpaGhIe/fuLefMVa20Nx6Pa2RkRB999JGmpqaUy+W0c+fOck5d1u0PFpL0y1/+cuEn2Cptv3NOL7zwgpqbm/Xiiy8u3F8t13+l/dVy/WdnZzU3NydJ+uc//6lz587pkUceqZrrv9L+db3+6/lTDtXg7NmzrrGx0UUiEffKK6+Ue84dXbp0ybW2trrW1lb36KOPLuy9du2a2717t/M8z+3evdv97W9/K/PSf+vu7nYPPfSQCwaDbuvWre6nP/3pHfe+8sorLhKJuIcfftiNjo6Wcfknltv//PPPu23btrmWlhb39NNPu8uXLy+cr6T9v//9750k19LS4rZv3+62b9/uzp49WzXXf6X91XL9//SnP7m2tjbX0tLiotGoe/nll51zd/73Wg371/P685sGAAAm7qkvqQEAyofgAABMEBwAgAmCAwAwQXAAACYIDgDABMEBAJggOAAAE/8LaDBMDsHNza4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat0.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "d7ffe4eb-d861-4b9d-94bf-ae7c957d8f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b89f404dc6ed4b81be84edac2cd66854",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat0, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61ec5f7d-1b4e-4279-8463-05f174c9e2da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "ebc65e13-6ba9-46dc-a74f-c660edb62712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 0.015\n",
+    "\n",
+    "data_flat = np.ones(shape=(1,1,341))*0.015\n",
+    "observed_data_flat = {}\n",
+    "observed_data_flat['summary_conditions'] = data_flat\n",
+    "observed_data_flat['direct_conditions'] = None\n",
+    "observed_data_flat['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "f0e51b1e-a46c-443c-83bb-9bea5929ce6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "7be0f6b0-d442-4ecc-b066-ea301c41a138",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83e9cc9678c640048979890a781a93df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403effc0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "f35e5221-152c-44d7-959d-df42fb3df7c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all 1\n",
+    "\n",
+    "data_flat_2 = np.ones(shape=(1,1,341))\n",
+    "observed_data_flat_2 = {}\n",
+    "observed_data_flat_2['summary_conditions'] = data_flat_2\n",
+    "observed_data_flat_2['direct_conditions'] = None\n",
+    "observed_data_flat_2['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "72ee0003-a0ef-4da8-a56b-6a1b8e376af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_flat_2.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "f6165eb0-0344-41a6-a1e7-b6003b88a172",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c80778dfa044b049cfcd965522015fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_flat_2, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df5a8553-f2a1-4efd-b8e2-d9ddb58e2b37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a7a47f2-9485-4324-93c6-de6dfa2dbb68",
+   "metadata": {},
+   "source": [
+    "## Flat line with gaussian noise (try different values of increasing noise amplitude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "108b52dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "np.random.seed(666)\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,)) #\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,)) #\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,)) #\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "1062a565-4950-4f29-96fd-718598607c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    plt.plot(data_flat_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "012e923a-6e8c-4ffa-adbe-0aa20664d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfbe1e77acc042d094f240d994000e1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03d1bece1c344fadade2f69913998be4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86c36fa232824c45935382f7c17adc57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9ef1da60c10400aafe3d652ea2919a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b2e7536f5f94653bdd88c13d4d27dc3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b8071ae335a4a43af4484841aa8ab16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bfa0f5b851f49f193848eff776da22f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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deo1D/XTl3b59mxk7dizj6OjI6OrqMgYGBoyLiwvzww8/MG/fvlW6TVF/2cDAQObEiRNM+/btGX19fcbExIQZPHgw8/z5c7nyT548YWbMmMF4eHgwlpaWjFAoZBwcHBhfX1/m+vXrSl/j6tWrzNChQxkbGxtGW1ubsbS0ZNzc3JgZM2Ywdz/pq+ng4KDQd7U4jRo1YgAw5ubmTF5eXrHl8vLymLVr1zIdOnRgjI2NGaFQyNSvX5/x9vZmVq5cqdDnNy8vj1m4cCHj5OTEvr+5c+cyubm5ZeqnyzAMIxaLmbVr1zJNmzZlhEIhY2Njw3zzzTdMampqse81MDCQcXNzY/T19RkLCwvmf//7H/Pw4UMmICCAAcAEBwfLlf/48SMzcuRIxsrKiuHz+QwAJiAggH3++vXrTPfu3RlTU1PG0NCQ6dSpE3PkyBEmODhYoaxGVMF+ujyG+f+2/WXUpk0b3Lt3r/SCpNyWfH8Fu9Y9ZJfnreoCr35OCuXev83A/K8uIfqlpAWjUEeA7ZcGo2Vb5a1ESdkVtV4tkxrWkIqQakc2R7Up+TNYrs94OdA93Soq7MZ77F4vTbjjprsrTbgAYFPfCEu390Q9e8lgAaK8QswYfgbJCTlKyxNCCOEGJd0qqLBQjF+/DUbRNYiGzczg903xY90CgIWVPpZs/QyGxpLxVT/EZmLexAso54UMQgghakBJtwo6vS8SkRGSFoh8AQ8L/+muUl/ceg5GmP2XdHSbK2eicXjb0xK2IIQQokmUdKsYkagQ6xfdZpf7j2gC63qGKm/v2d0Og8ZJ70ss+f4KPsRmlrAFIYQQTaGkW8Wc2vcCb1+nA5B0DZrwg+JAAaWZ8EMr2DlJpjDLySrA8jnXKzVGQggh5UNJtwphGAa71j5gl/83thkMjIQlbKGcrp4Wpi9qzy6f3h+J0GtVZxJnQgiprSjpViGh197h2YNEAACfz8OISa7l3pebZ1106+vALi/6LgSFheIKx0gIIaT8KOlWIbJ9ctt728HYVKdC+/tqdhvo6EqmQIt8nIT/NlXuuLSEEELKhpJuFZGSmIPgk9JxWcdNdyuhtGqsbAwwcrK0q9Hq+TeRnkpTaxFCCFco6VYRp/e/QEG+5PKvTX1DNHA2r5T9Dp3oApv6ktbPGWkiBK4Ir5T9EkIIKTtKulXEsZ3P2McDxzSttP0KdQQYP0PaAnrH6vtIis+utP0TQghRHSXdKuBlRBIiwhIASBpQ9RrcsFL379XPEU7OpgCA3JwCbF4WWqn7J4QQohpKulXAuUMv2cfNW9WBkUnFGlB9is/nYfx0d3Z579+PaMAMQgjhACXdKuD8Yem8mT5+zmp5jQ496qNpS0sAQL5IjI1L7qrldQipCcaPHw8ej4eoqCiuQ2E5OjqyE8WT6osmsefY62cpePlEOs5ye287tbwOj8fD+BnumP3FBQDAoa1P4D/LA/UcjNXyerXOg1+A7Bj5dXmJ0sc6lpqN51P69oDbrxXejaOjI6Kjo5U+Z21tjQ8fPsitE4lE+PXXX7F7924kJyejdevWWL58OVq3bq2wfVBQEHr16oWTJ0+iX79+FY61uvHy8kJISAhNUlLDUdLlWNARaS23aUtL6Bloq+21WneyQYu21nh09yMKCxlsWR6O+Wu6qe31apXsGMDAUX4dX2Y0Mb16Gg1HQVZUpe3KxMQE06dPV1hvaKg4Rvjs2bOxcuVK+Pr6ws7ODjt37kT37t3x7Nkz2NjYsOUyMzPh7++P0aNHV4mEu2TJEsyePRu2trZch8K6ePEi1yGQSkBJl2MXj79mH/cb0Vitr8Xj8eD3TQvM/uIjAODQlghMntMGdWwM1Pq6pGYxNTXFggULSi3HMAw2btyIL774Alu3bgUADBo0CF5eXti5cydmzZrFlv3pp5+Qm5uL1atXqyvsMrGxsZH7UVAVNGxYuQ0sCTfoni6HEj5kISI0HgDA4wEdP7NX+2u27mQD55YWAID8fDG2r76v9tcktVNCQgKys7PRrl07dl3RY9lL1FeuXMHff/+N9evXw9y8fP3Ti+53pqen4/vvv4ejoyO0tbXlfhw8e/YM48ePR/369aGjowNra2uMGjUKz58/V9hfSfd0b9++jSFDhqBu3boQCoWoX78+Jk2ahHfvlI9vnpycjHnz5sHV1RX6+vowMTGBm5sbZs+ejaysLERFRYHH4yEkJASA5Mdx0Z+Xl5fCe/xUXl4e/vjjD7Rs2RL6+vowNjZGly5d8N9//ymULXqt8ePHIyoqCiNGjIClpSV0dXXRpk0bnDx5suQD/YmjR49i9OjRaNKkCQwMDGBoaAgPDw+sWbMGYrHisLNFx/X169dYu3YtWrZsCT09Pbn3mZycjDlz5qBZs2bQ09ODiYkJevTogfPnzyvsLy0tDcuWLYO3tzfs7OwgFApRp04d+Pj44NatW2V6L5pCNV0OXTsn/eKp38CYnYBenXg8HkZ93QIBX18GAOz5+yEmzmwNUws9tb82qRny8vKwa9cuxMTEwMDAAC1btkTXrl0hEAjkytWpUwd6enoIDZV2Ubt37x4AwMFBMi54Tk4OJk6cCF9fX/j6+lYoLpFIBG9vbyQnJ6NXr14wNjaGk5MTAODs2bMYPHgw8vPzMWDAADRq1AixsbE4fPgwTp06heDgYKX3mT8VGBgIf39/6OjowMfHB/Xr10dkZCQ2b96MEydO4NatW7C3l/54fvPmDbp3747o6Gh4eHjg66+/hlgsxosXL7By5UpMnjwZpqamCAgIwLZt2xAdHY2AgAB2+9IaTolEIvTu3RshISFo2rQpvv32W2RnZ+PgwYMYPnw47t+/j8WLFytsFx0djXbt2qFBgwYYM2YMkpOTsX//fgwcOBAXLlxA9+7dVTrms2fPBp/Ph6enJ2xtbZGWloZLly5h2rRpuHv3Lnbu3Kl0u2nTpuHq1avo168f+vbty5470dHR8PLyQlRUFLp06YI+ffogKysLJ0+eRJ8+fbBx40b4+/uz+3n69CnmzZuHrl27ol+/fjAzM0NMTAyOHz+OM2fO4MTy5ejTsaNK70VTKOlyKOS0NOl26+uosddt710fjk1MEfUiFXk5hdi17iGmBHhq7PVJ9fbhwweMGTNGbp2TkxMCAwPRrZu0jQCPx8NXX32FNWvWIC0tDba2tti5cyeMjIzg5+cHAPj555+RnJyM9evXVziu9+/fo3nz5ggJCYGBgfSWSUpKCkaOHAl9fX1cuXIFzZs3Z5+LiIiAp6cnJk6ciLCwsBL3/+LFC0yaNAmOjo4ICQmRu9976dIl9OzZE9OmTcORI0fY9aNHj0Z0dDQWL16MOXPmyO0vMTERhoaG0NXVxYIFC3D58mVER0erdOm+yPLlyxESEoLPP/8cx48fh5aW5Cs9ICAA7dq1w5IlS9C/f390/CTxXL58GQsWLJBL8KNGjUKfPn2wbNkylZPuqVOnFC57i8VifPHFF9ixYwemTJkCT0/F75awsDCEh4ezP4qKjBs3DtHR0di7dy9GjBjBrk9NTYWXlxe+++47+Pj4wNraGgDQrFkzvHv3DpaW8g0VY2Nj0a5dO8xYubLKJV26vMwRkagQNy5IW7t2+9xRY6/N5/PkxmTeseY+MtNFGnt9Un198cUXuHjxIj58+ICsrCw8evQIkyZNQlRUFD7//HM8ePBArvzSpUvx008/4c6dO9iyZQtcXFxw8eJF2Nra4vbt21i1ahVWr14NS0tLLFy4EDY2NtDS0oKHhweuXy/7PNDLly+XS7gAsGPHDqSmpmLhwoVyCRcAXFxc4O/vj/DwcDx58qTEff/999/Iz8/H6tWrFRpYeXt7w8fHBydOnEBGRgYAIDQ0FDdu3IC7uzt++uknhf0VXdatiK1bt4LH42HFihVswgUAKysrzJ8/HwCwefNmhe0cHBzw888/y63r3bs37O3tcefOHZVfX9l9Zj6fj2nTpgEAzp07p3S7WbNmKSTcBw8eICQkBL6+vnIJF5C0I1i4cCFyc3Nx6NAhdr2JiYlCwgUAOzs7DBkyBM+iohDzSYt6rlFNlyMPbn1AVkY+AMDQWAj7RiYaff1ufR2wffV9vIvOQFZGPvZtfISJMz00GgOpfmRrRgDg6uqKf/75B4aGhli+fDkWLFggV9PT0dHBkiVLsGTJErntRCIRJkyYgL59+8LPzw+rVq1ia16dOnXC77//jj59+uDly5dsraY0urq6aNmypcL6mzdvApB8qSurRb548QKA5FLlp0lZ2X5CQkJw965iP/f4+HgUFhbixYsX8PDwYO8p9u7dG3x+5ddvMjIy8PLlS9ja2qJpU8WhY729vQEA4eGK4627u7sr3A4AgPr167PvUxVJSUlYtmwZTp8+jdevXyMrK0vu+bi4OKXbyd7nL1L0umlpaUr/nRISJKP2PX36VG799evXsXr1aty8eRPx8fEQieQrEHHx8bCvW1fl96RulHQ5cvPiW/Zxqw51wePxNPr6AgEfIye5YvlcyYkeuCIco6e4QVePTglSdpMnT8by5ctx5coVlcovXLgQcXFxbOOYZcuWoUePHuyXrbOzMxwdHbF+/Xr8+qtq/YutrKyUfo6SkpIAAJs2bSpx+8zMkkdpK9rPsmXLVNpPamoqAKit21FaWhoAFNvKumh9URyyTE1NlW6jpaWltAGUMqmpqWjbti3evHmDdu3aYezYsTA3N4eWlhZSU1OxevVq5OUpn9WsrpIkWHR8g4KCEBQUVOzryv47HTlyBEOGDIGuri569uyJhg0bwsDAAHw+H5cvX0ZISAjy8vNVej+aQt+wHJFNut36OXISQ4+BDbBj7QMkvM9GalIuDm2NgN+3FZ9SkNQ+VlZWAKBQ01EmPDwcf/75J/755x/Y2toiPT0d7969Y+/zAoC9vT0sLS0REaH6HNDF/XA1MZFcRXrw4IHSmrCqivaTlpYGY+PSB5UpSmzF1fYqqiieTwckKfL+/Xu5cpVt8+bNePPmDQICAhRqpjdv3iyx+5eyf6uiOFevXo3vvvtOpRjmz58PoVCIe/fuoVmzZnLPTZo0iW0RXpXQPV0OpKfm4fG9eHa5VQdu+gNqCwUY5u/KLm/5Kwz5+YWcxEKqt6JLgw0aNCixXEFBASZMmIDu3bvjyy+/lHvu01pRbm5upcTWvn17AMDVq1c1up+i8ufOnVOp9lh0ubewULXPoJGRERo2bIi4uDhERkYqPB8cHAwAKrXKLo+XLyVjxitrdV6eZFeef6eXL1+iefPmCglXLBbj2rVrZY5BEyjpcuBuSBzEYslQb9a2BjA2rdwJDsqiz5BGMDWXNOb4GJeF0/sUP7yEAJKWvsnJyQrro6OjMWXKFACS1rolWbJkCV6+fCl3qdfY2Bi2trY4e/YsCgoKAEi+tDMyMuDi4lLhuL/44gu2IY6yRkJisRiXL18udT9TpkyBtrY2ZsyYwd4HliUSieQShoeHBzp27Ij79+9j6dKlCuWTkpLkflhYWEj6z8fExCiULc6ECRPAMAxmzpwpl6wTExOxaNEitow6FHVn+vTYhYeHK9zDV0WbNm3QpUsXHD58mB1M5VOPHj1CfLy0wuLo6IjIyEi5PtIMw2DhwoWlNozjCl1e5sDNS9JLy+26cTvMnK6eFv43rim2rbwPAPh36T0M8HMGn6/Ze8yk6jtw4AD++OMPdO/eHU5OTjAyMsKrV69w6tQp5Obmom/fvvjxxx+L3T4iIgK//fYbVqxYwfbTLTJr1ixMmzYNXbp0gaenJ3bv3g1DQ0N8++23FY7bwsICBw8exKBBg9C+fXv06NEDLi4u4PP5iImJwc2bNxUSoDJNmzbF1q1bMWHCBLi4uKBPnz5o0qQJ8vPzERMTg6tXr6JOnTp49kw6N/auXbvg5eWFuXPn4tChQ/Dy8gLDMIiMjMT58+fx7NkzNnn16NEDBw4cwODBg9G3b1/o6enBwcFBoXuWrB9//BFnzpzBsWPH4Obmhr59+yI7OxsHDhxAfHw8Zs2ahc6dO1f4GCozduxYLFu2DNOnT0dwcDAaN26MyMhInDx5EoMHD8b+/fvLvM89e/bA29sbX375JdasWQNPT0+YmpoiNjYWDx8+xOPHj3Hz5k32dsaMGTMwefJktGrVCr6+vtDW1sb169fx5MkTDBgwACdOnKjst11hlHQ5cO+K9B5P517qH4WqNANHN8V//0YgOysfUS9ScfnkG3j7lHyZkNQ+3bt3x/PnzxEeHo6bN28iKysLpqam6Ny5M8aMGYMxY8YUe1+1sLAQEyZMgKenJ7755huF56dOnYr09HT8888/CAsLg5ubG1auXKlyy+XS9OjRAw8fPsRff/2Fc+fO4erVqxAKhahXrx68vb1VHphj9OjRcHNzw/LlyxEcHIzz58/DwMAA9erVw5AhQzB8+HC58k5OTggLC8Off/6Jo0ePYt26ddDV1YWjoyN++OEHNnkAwMSJExEdHY19+/bhzz//REFBAbp161Zi0hUKhQgKCsKKFSuwZ88erF27FlpaWnBzc8OqVaswcuTI8h0wFdSrVw9Xr17F7Nmzce3aNZw7dw5NmzbFhg0b8Nlnn5Ur6drZ2SE0NBRr167FoUOHsHv3bhQWFqJu3bpo3rw5pk6dihYtpN0dJ02aBB0dHaxatQrbt2+Hnp4eunTpgsDAQBw6dKhKJl0eU84pLdq0acOOLkNUl5qci051Jf3meDzg2P2R0NNX3yQHqtr0Zyj+2yRptNKijRX2Xh+q8RbVVdnTp08V7hvJqSWzDNUWI0aMwP79+/Hu3bsqNwYzKQPZHNWmTYlFS/2MVxKq6WpY+I337OO6doZVIuECwODxzXBk+1Pki8R4dC8ed0Li4OmlnmkGayRlCS1J5gNvUfIHnlQtL168gI6ODurUqcN1KKSGoYZUGhZ2XZp0W3WsOr+gLaz00WtwI3Z585+hJZQmpGZavXo1fH19ER4ejoEDB8qN8kRIZaCkq2Fh16Wt7Dy9qs5cnQAwbKIL24DqxoW3iAiLL2ULQmqWVatW4datW5gwYQI2btzIdTikBqKkq0G5OQV4HCpNZC6trUoorXn1HIzQ9XNpq1Kq7ZLa5s2bN4iLi8OWLVuKHbWJkIqgpKtBD+98QEG+pJO8eR09mJhXbLBzdRgxSTpYRtCRV4h6kcJhNIQQUrNQ0tWg0GvS+7kt2lZOV4jK1rCZOdr+f99hhgG2LlccLJ0QQkj5UNLVILn7ud2r1v1cWSNlarvHdj7Dh9iSB4InhBCiGkq6GlJQIMb9W9KByVtW0ZouIKmFu3hIukoUFIixY819bgMihJAagpKuhjx7kIDsTMkUUwZG2rC2NeQ4opKN+Eo66sv+jY+RmpTDYTSEEFIzUNLVENn+uc4tOR6dSAWe3W3h5GwKQNLqes+GR9wGRAghNQAlXQ2RHYmqffeqP9ITj8fDiK+k93Z3rn2A7KyqNRk0IYRUN5R0NeSRzPy5LdpUrf65xenW1xF17SSXwdNT83Bwi+oTihNCCFFESVcDEj5k4X1MBgBAIODBydmM44hUI9DiY9hE6XymgSvCIRLRJPeEEFJelHQ14NFdaS23noMRBFrV57D3HtIIZpaSQTzi32Xh1N7nHEdECCHVV/X59q/GHt2RdhVyrSaXlosIdQQYPF463dWmP8NQWCjmMCJSFfTs2RM8Hg8hISHFlpk8eTJ4PB42bdqkwcgIqdoo6WrAw7sf2cetq9DMQqoaMMoZ+oaSKQijI1Nx8dhrjiMiXAsLC4NAIICHh0exZW7fvg1AMvc2IUSCkq6aicUMHss0omrqVv3m5zQwEsJntDO7vOnPUDAMw2FEhEuvXr1CcnIyXFxcYGiovL95dnY2Hj9+DB0dHbi6uiotQ0htRElXzd48T0FmuggAoKuvBWtbA44jKp/B45pBqCMAADwJS8CtS7EcR0S4cu/ePQCAp6dnsWVCQ0NRUFAANzc3aGtrayo0Qqo8SrpqJntp2amJKXg8HofRlJ+ZpR56D5FOcv/v0nscRkO4pErSvXXrFgC6tEzIp7S4DqCmeySTdFtVw/u5soZ+2Ryn9r2AuJDBnctxuH/rPdzbV+/3RMquKOnu378fwcHBSsvQ/VxClKOkq2aP7kiTblWe5EAVNvWN0L2/E9uQ6u/f72LjCR+OoyKaxDAMwsMl0z0GBQWVWp6SLiHyKOmqUW5OAV48SmKXq8OYy6UZ9XULXDr+GgwDXDsXg4d3P1b7HxMV5SJcp0KpW2qPQxURoikV2v7FixdIS0tDhw4dcOPGDaVlUlJSYG5uDn19fTRv3lzuuQ0bNmDZsmV4//49XFxcsGrVKnTp0qVCMRFSndA9XTV6ej8BBQWSPq2mFrowNBZyHFHF2Tc0gVc/R3b5n9/vchcM0biiS8utWrUqtkxYWBgAwN3dHQKBgF2/f/9+TJs2DXPnzkV4eDg6duyIzz//HDExMeoNmpAqhJKuGj2UubTc2MWCw0gql983LVHUHizkdBQiwuJL3oDUGEVJt3Xr1sWWKUq6n15aXrFiBcaPHw9/f380a9YMa9euhY2NDf7++2/1BUxIFUOXl9XoiUwy8uhccxocOTQ2RdfPHRByOhqApLa79lA/jqPiTrGXbJNkWnhb1Ix7m6ok3aJ7vrJJVyQSITQ0FD/++KNc2V69ehV7mZqQmohqumr0JDyBfdzMvfoNilESv29aso8vnXiDp/cTSihNagKxWIz79+9DKBTCxcWl2HLKarqJiYkoLCyEtbX8/X9ra2t8+PABhNQWlHTVJDsrH2+ep0gWeGAnhK8pnJzN0KW3Pbv8z2K6t1vTPX36FJmZmXBxcYFQqLx9QmZmJiIjI2FoaAhnZ2eF5z/tp84wTLXtu05IeVDSVZPnDxNRNFKieR096OnXvFF5/L6V1nYvHH2N5w8TOYyGqFtoaCiAki8tP3jwAGKxGK1btwafL/16sbS0hEAgUKjVxsfHK9R+CanJKOmqieyl5UbNzDmMRH0aNjNHp5712eWNS6i2W5ONHTsWDMNg8+bNxZbp1KkTGIZRmH1IKBTCw8NDoW9vUFAQOnbsqJZ4CamKKOmqiWwjKjfPmvtLXra2e/7wK7yMSCqhNKnNvv/+e2zbtg2bN2/G06dPMW3aNLx79w6TJ0/mOjRCNIaSrprI1nSr48xCqmrsYoH23nYAAIYB/llCYzIT5YYPH45Vq1bht99+g7u7O65du4bTp0/DwcGB69AI0RhKumqQl1uAV0+S2eWGzcw4jEb9Rk+R1nbPHojEq6fJJZQmtdk333yDqKgo5OXlITQ0FF27duU6JEI0ipKuGrx4nITCQkkrKlMLXRgYVf+RqEri3MIS7brZApDUdjf+QbVdQghRhpKuGjyVubRc02u5RWRru6f3v5B2lyKEEMKipKsGsvdzW7aruY2oZDVzr4M2XeoBABgx8Df12yWEEAWUdNVArhFVDZhZSFVjprqxj0/ve0EtmQkh5BOUdCtZfn4hXjySDhLRqHnN7KOrTPNWdeDpJb23u+7XOxxHRAghVQsl3Ur2MiIZ+SLJdH7GpjowNtPlOCLNGjfdnX0cdOQVnoTTDESEEFKEkm4lkx34v6aNt6yKxi4W6NxLOibz2gW3OYymcjFF43oSQmoUTX62KelWMvlGVHU5jIQ7Y6e5sfPtXjkTjQe3q/8sMgKBAPn5+VyHQQhRg/z8fAgEAo28FiXdSvZUbiSq2tOISpZTEzN07+/ELq8JuMVhNJXDyMgI6enpXIdBCFGD9PR0GBkZaeS1KOlWosJCMZ49kDaiauxSexpRfWrMVDfwBZLq7q1LsbgTEstxRBVjbm6OlJQUJCYmQiQS0aVmQqo5hmEgEomQmJiIlJQUmJtr5vtaSyOvUku8eZ6K3JwCAICBkTbMLPU4jog7dk7G6DWoIc4efAkAWBNwGzuDbavt3Kk6Ojqwt7dHcnIyoqKiUFhYWPpGWTJTHcY/VV9whBDlEmU+g08VP4MCgQBGRkawt7eHjo6ORkKipFuJZC8tOzQ25S6QKsLv25a4cOw1CvLFCL/xHteDYtC5V/Ud3F5HRwc2NjawsbFRbYM9zaWPR1HNmBCNay7zGawiV6fo8nIlenJf2j2mZdvaMRJVSeraGeLzYY3Z5TUBt+myLCGkVqOkW4lka7rNWtXc6fzKYtTkFtAWSk6ziNB4BJ+M4jYgQgjhECXdSiIWM3LdhRrXopGoSmJZVx8+fs7s8pqAWxCLqbZLCKmdKOlWkrev0pCVIenHqauvBcu6+hxHVHUMn+QKXT1J84HIx0k4f+glxxERQgg3KOlWEtlarn0Dk2rbSlcdzCz08L9xTdnl1b/cQkGBmMOICCGEG5R0K4ls0nVtQ/dzPzX0SxcYGGkDAGJepeHwticcR0QIIZpHSbeSyI657NKaWi5/ythUByMmubLL6xbeQXYWDatICKldKOlWAoZh5GbTaexKjaiU+d/YZrCwlgwYkvQxG7vWPuA4IkII0SxKupXgfUwG0pLzAABCHQHq2hlyHFHVpKunhbEyE91v+jMUqUk5HEZECCGaRUm3Esjez7V1NKJGVCXo7dsI9RsYAwCyM/Px79JQjiMihBDNoaRbCWSTbnMaFKNEAi0+JvzQml3evf4h3kXT7D2EkNqBkm4lkJ9DlxpRlaZTz/po5i6Z9rAgX4y1C2vORPeEEFISSroVxDAMnoTJNKJyseAwmuqBx+Nh4kwPdvnE7udyrb8JIaSmoqRbQQnvs5AUL2kMpKXNh62jMccRVQ8t21nD08sWgGTyj6U/XqPJEAghNR4l3QqSvbRsY28IPp8aUanK/ycPdqL7u1ficOn4G44jIoQQ9aKkW0GySbeZGzWiKguHRqYYMKoJu7zsp2sQiVSYHJ4QQqopSroVJDudX0tPakRVVmOnusHQWAgAePs6HXs2POQ4IkIIUR9KuhUkW9NtQo2oyszYTBdjprRklzcsuoPkBBowgxBSM1HSrYCk+Gx8iM0EAAgEPNg3NOE4ouppgJ8z7JwkDdCyMvKxftEdjiMihBD1oKRbAU/CpLVcaztDCLTocJaHtlCAr36SdiH6b9NjvIxI4jAiQghRD8oSFfA4VNo/t2lLSw4jqf7ae9uhdUcbAIC4kMHv069QFyJCSI1DSbcCZAfFcO9Ql8NIqj8ej4fJc9uwXYjuhMTh1N4XHEdFCCGVi5JuBcg2oqKabsU5OZth0Lhm7PLSmdeQnprHYUSEEFK5KOmWEzWiUo+xU91gaa0PAEhOyMGagFscR0QIIZWHkm45yTaisrKlRlSVRd9QG1/Pa8su7/vnESJkLuMTQkh1RpminGQbUTm3oP65lalLH3u07VoPgGRc5oXfXkZhoZjjqAghpOIo6ZaTbCOqola3pHLweDx8+0s7aAslp2dEaDwObI7gOCpCCKk4SrrlJNeIyo0aUVU2WwdjjJzcgl1eMfcGPsZlchgRIYRUHCXdckj8KG1ExadGVGoz/CtX2DoaAZCMVLXw22Dqu0sIqdYo6ZaD7KVla1sDakSlJkIdAX5Y3JFdDjkdjRO7n3MYESGEVAxli3KIkGm57NyCLi2rU4u21hg4xpldXvz9FSS8z+IwIkIIKT9KuuVAjag068sfWqOunSEAICNVhF+nXKbLzISQaomSbjlQIyrN0jPQxozfO7DLl068weFtTzmMiBBCyoeSbhlRIyputO5oA5/R0svMS2ZcQXRkKncBEUJIOVDSLSP5RlQ0EpUm+c/yQP0Gkh85OdkF+Gn8eeTnF3IcFSGEqI4yRhnJNqJq2pJGotIkXT0tzFnRGQItyUxEj+7G45/f73EcFSGEqI6SbhnJ1nRbUSMqjWvsYoHx093Z5Y1L7uLWpbfcBUQIIWVASbeMaDo/7g2d6AL39pL5ixkGmDnmPHUjIoRUC5R0y0C2ERVN58cdgYCP2cs7w8xSF4BkCsCZY8/TpAiEkCqPkm4ZRMjMLGRtR42ouGRhpY/Zy7uAJ7m9i7shcVi34Da3QRFCSCkoa5TBg9sf2McuretwGAkBJN2Ixkx1Y5f/XRqKcwdfchgRIYSUjJJuGTy6+5F93KZLPQ4jIUVGfdNC7t9i7pcX8PR+QglbEEIIdyjpqkgsZvDwjjTpNnOnmm5VIBDwMXdlF3Y2otycAkz1PYWk+GyOIyOEEEWUdFX05nkKMtNFACT9RYvGAibcMzLRwa//eEPfUBsA8P5tJmaMOAuRiAbOIIRULZR0VfTwjvR+rkNjU/CKWvCQKsG+oQnmrpQ2rAq99g5LZlyhiREIIVUKJV0VyV5abtWxLoeRkOJ4etlhwg+t2eX/NkVg28pwDiMihBB5lHRV9OC2TNLtQCNRVVXDv3KB9wAndvmv2Tdwev8LDiMihBApSroqyMoUIfJxErvs3ILGXK6qeDwefljSES3aWrPr5n55AXevxHEYFSGESFDSVcGTsASIxZJ7g+Z19GBgJOQ4IlISoY4ACzZ4sSOG5YvEmDrkFF4+SeY4MkJIbUdJVwWyg2I408xC1YKxqQ4Wb+kB8zp6AICMVBEmDziOj3GZHEdGCKnNKOmq4JFMI6q2XW05jISUhbWtIX7f5A09fS0Akq5E/n2PITUph+PICCG1FSXdUjAMgwcy3YVcPaw4jIaUVSMXC8xf142dg/fV0xRMGnACWRkijiMjhNRGlHRL8f5tJhLeS0Y30tLmw74RzSxU3bTtYotZf3Zm+/A+vhePKb6nkJdbwG1ghJBah5JuKcKuv2Mf2zkZQyCgQ1YdeQ9wwncL27PLdy7H4YdR55CfT6NWEUI0hzJIKUKvSZMuTXJQvfUf2QRf/igdPCP45BvM97/EtkwnhBB1o6RbitBr79nHnl52HEZCKsOISa4YMcmVXT6x5zl+n07DRRJCNIOSbglSEnPw6qmkbyePBzR1s+Q4IlIZJvzQCv1HNmGX9/3zCMvn3KDESwhRO0q6JQi7Lq3l1rUzhK6eFofRkMrC4/EwJaAduvd3ZNcFrgjH2gW3uQuKEFIrUNItQahMIyo3T5rkoCYRCPiY9WdndOpZn123cck9/P37XQ6jIoTUdJR0S3DvqjTpdpT5ciY1g5Y2H3NXdkW7btIBT9YtvI0tf4VxGBUhpCajpFuMrAwRnt1PYJdbtLEuoTSproQ6AgSs90LrTtKZo1bMvYEda+5zFxQhpMaipFuM+7c+oLBQ0rDGwkoPhsY0yUFNJdQRYOHf3eHeXnoLYemP17D3n0ccRkUIqYko6RZDtn+u7DRxpGbS1dPCr/90h4tHHXbdb9+F4ODWCA6jIoTUNJR0iyGbdOl+bu2gZ6CN3zf1kOsatuDrYBzb+YzDqAghNQklXSVEeYV4KDOzkFs7arlcWxgYCbFk62do7CqZwpFhgJ/9L+LUvhccR0YIqQko6SoRduM9RHmSMXlNzHXYOVlJ7WBoLMQfgZ+hQVMzAIBYzGDOF0E4vZ8SLyGkYijpKnEjKIZ9LNu4htQexqY6WLqtJxwbmwIACgsZ/DQuCCf3POc2MEJItUZJV4kbF96yj7v3d+IwEsIlUwtd/LmjJxwaS6ZzFIsZzP4iCEd3POU4MkJIdUVJ9xNJ8dl4+v/9c3k8qunWdmaWeli2sxecnE0BSO/xHt72hNvACCHVEiXdT9y8KK3l1rM3goER9c+t7cws9LBsRy/2Hi/DAPO/uoQDW6g7ESGkbCjpfuK6zP3c9t40lR+RMDHXxbIdPdGouTm7bsHXwdj/72MOoyKEVDeUdGUwDCN3P9erH93PJVLGZpJ7vE1aWLDrfp1yGbs3POQwKkJIdUJJV0bk4yQkfsgGAGgL+Wjsal7KFqS2MTKRtGpu2lI6gMbi6VewdTlNkkAIKR0lXRmytVznlpYQCOjwEEWGxkL8se0zNHOXJt7lc25g5bwbYBiGw8gIIVUdZRUZ1y9I7+d69XPkLhBS5RWNXNWynXRc7s3LwrBoaggKC8UcRkYIqcoo6f6/nOx8hMrMn9uBGlGRUhgYCbF4Sw+07y49V/b/+xg/jQuCSFTIYWSEkKqKku7/uxH0Fnm5ki9KMwtdWNUz5DgiUh3o6GohYL0XevhIG92d+S8SUwafQlaODoeREUKqIkq6/+/Sidfs40697DmMhFQ3Wtp8zFrWGT6jndl118/HYOySafiYYsJhZISQqoaSLoDCQjFCTkexy70GN+QuGFIt8fk8TPmlHUZPacmuexZTH6MW/YAXb204jIwQUpVQ0gVw/+YHpCTmAgD09LXgLNMdhBBV8Xg8jJvmjh8Wd4BAwAMAfEg2w5jFM+RGOiOE1F6UdAFcOPaKfdy6kw34fB6H0ZDqrs/QxvhtUw8Y6OYAADJz9DBpwHHs3vCQuhQRUsvV+qQrFjM4f0iadPsOb8JhNKSmaNOlHnbOWwVrsxQAQGEBg8XTr+Bn/4vIyy3gODpCCFdqfdJ9cPsDPsRmAgCEOgK07kj330jlcK7/Dnvmr4CLUzS77uiOZxjrfZg95wghtUutT7pnD75kH3t0rgct7Vp/SEglqmueip1zVqFvryx23eN78Rjafj/uhMRyGBkhhAu1OsMUFopx/pA06Q6U6fJBSGXRERZg/qxUfD8lFXy+5J5ucnwOJvQ6ijUBt1BQQCNYEVJb1Oqke+tSLOLfSWogOroCmrCeqA2PBwwblIW1yxJhaiIZhIVhgI1L7mH8Z0fwLiaD4wgJIZpQq5PusZ3P2Mede9tDoFWrDwfRAA93EXb+Gw8P91x2XfiN9/Btsw/nZK66EEJqplqbZTLTRbh4TDoK1ZAJLhxGQ2qTOpZirPkzCZMnpLGXm9NT8/D9yLP4YdRZJCfkcBwhIURdam3SPXswErk5kq4bFlZ6aNSc5s4lmiMQAOP9MvHPqkRYW0m7EJ09+BI+brtx9kAk9eklpAaqlUmXYRjs++cxu9xvJPXNJdxo6SLCrk3x6N9H2ro5JTEXP/idw/ThZ/AxjroWEVKT1Mqk++juRzy9nwBAMmbuwNFNOY6I1GZGhgx+npmKlUsSUcdSWuu9cPQ1+rfYjW0rw5GfT1MFElIT1Mqku2+jtJbbupMNjE1pCjbCvQ7t8rB3SzwG9pPWerMz87Hsp+sY2m4/7l2N4zA6QkhlqHVJN+F9Fk7vf8Euj/3OjcNoCJFnaMhgzvepWL88AY72InZ9ZEQyxvU4gh9Hn0Psm3QOIySEVEStS7q71j1AvkgyGEFdO0M0daMZhUjV4+Euwq5NCZg6KQ26utJLy2f+i0T/Frvw1+zrSEvJLWEPhJCqqFYl3awMEfb/K720POHHVuDxaEYhUjVpaQF+wzLx37Z49OiWza7PF4kRuCIcnzfbiZ1rH0Akovu9hFQXtSrp7l7/EBlpkkt2xqY66NrHgeOICCmdVR0xfv8lBf+uSYBLszx2fVpyHv744Sp83Pbg1L4XEIupixEhVV2tSbrpqXkIXBHOLvt92wICQa15+6QGaOkiwua1ifhtfjJsrPPZ9W9fpWHW2PMY7LEXF46+ov69hFRhtSbrbFsZjvRUSS3ByEQIHz/qJkSqHx4P+MwrB/u3xeO7yWkwNJBeWo6MSMa0YWcwvMN/uHo2mpIvIVVQrUi672IysH3VfXZ54qzWNIUfqdaEQmDU0Ewc3v0R4/3SoasjTb4RYQmY7HMCo70O4fZlmj6QkKqkVmSev366LjfkY2/fRhxHREjlMDZiMHlCBo7siceooRkQakunCbx/8wMm9DqKcT0O43pQDNV8CakCanzSvXY+Wm72ltl/daZ7uaTGMTMV47vJ6Ti8+yOG/C8TWgJp8r139R2+6nccwzsewIWjr6jBFSEcqtHZJyMtDwGTg9nllp7WcO9gw2FEhKiXpYUYP05Nw8Gd8fDpmwWBTPKNCI3HtGFnMNB9D47veoaCAnEJeyKEqEONTboMw+D3aVfwIVYyYLy2kI+fV3flOCpCNKOudSHm/pCKgzvjMeR/mXKXnV8/S8GcCRfQz2UX9v7zCNlZ+SXsiRBSmWps0j2wOQIn9jxnl6f92h5mFnocRkSI5tlYF+LHqWk4sucjxozIgJ7M6Faxb9Lx23ch6OG0Dct+ukbDSxKiATUy6d69EofFM66wy2271qPGU6RWszAX41v/dBzb9xFfjU+HkaF0NqP01DxsW3kffZruwFTfU7gVHEuNrghREx5Tzk9XmzZtcO/evcqOp8KeP0zEuB6H2ZGnzOvoYcelQdDR1eI4MlLb1Ltuwj7OqD+bw0gUZefwcOKMPv47rI+490KF5xs4m2Hg2KYYMMoZ1raGHERISCWQHea3ivyQrFFJNyIsHv59jyEtWTIIhlBXgC1nfFDXzojjyEhtVJWTbhGxGLh1Vwf7DhngTqji7Rc+n4eOPetj4Oim6NbPEQaGigmakCqrCibdGlP9u3T8NX4aH4TsTEmjEIGAhxW7e1PCJaQEfD7Q0TMPHT3zEBWjhQNH9HH6vD5ycgUAALGYwbVzMbh2LgY6ugJ06mmPz/7XEF79HWFipstx9IRUP9W+ppuXW4C1C27LjauspcXHX7t6wcXDisPISG1XHWq6yuTk8HD5mi5OnNFD2APljQ+1tPho1bEuOvSwR4fP6sOldR3q/06qnipY0622SZdhGFw9G40/Z17Dmxep7Hp9Q22s2N0bDZubcxYbIUD1Tbqy3n0Q4GyQPi4EC/E6uviarbGpDtp0qQe39nXh5lkXLh5W0DfQ1mCkhChBSbfiRKJCBJ94g+2rwvHg9ke555yamGLZzl4wMafLXoR7NSHpyoqJFeDyVT1cChHiWWTJ3e8EAh4au1qgSQsLNHaR/DVyMUddO0Oaw5poDiXd8klNysHNi7EIOR2FK2ej2IZSRQRaPIyd6oaRX7egDzSpMmpa0pWVmMTH3TAd3L4nxJ17QiSnqtbAytBYiAZNzWDraAw7R2PUczSCnaMxbB2MUbe+IfUyIJWLkq5yDMMgOSEHH+My8SE2Cx9iM/AxNhNvnqfiyf0EvI/JULodjwd07GmPab960sAXpMqpyUlXFsMAUdFaePREiIePtfDosRZi3umCYcr+A9jIRAgLK31YWOvDwkoPlnX1YWquC0MTHRiZCGFgJISRiVCybCyEobEQQl0BhDoC6Ohqgc+nH91ERhVMulXiZ+VQz//w9H6CyuX1DbXRa1BDDJ/kCktrfTVGRggpDY8HODkWwMmxAD59JesyM3l48Uobr6O08fI1Hy9f8vEmWgdZOSXf581IEyEjTYSoyNRyxaKlzf//BCxJwto6Amhr8cEX8MDn89j/8/g8CEpYx+Pz0LqjDSbPbVuuOAgpTpVIuhbWJddSeTzA1EIXbp514dndDvUbGIPH4yE5IQfJCTkaipKQsqkn8zj1YzxncXClQV3J32ftJcsMAySlaOPdRx18iBfiY4IQ7z8K8P6jNuITdZGaroNCccVaQBfki1GQL2a7DlaEvkGV+HokNUy5Ly9bWlrC0dFRYX1CQgLq1KlT5v3li8R48TiRXdYWCiAQ0KUiWXkFGdDRon7H6qKO46unnQtdYW6l7rM6ysjJg5GeTgX2IJZkbabkmZHYLzOGB2VfbAz7n9K/W8ztG0CgJShTlFwp7/cuKd2nx9bS0hJnz54t9/7KnXSLw3VXopqMjq160fFVHzq26kXHV30q+9hSb3ZCCCFEQyjpEkIIIRpS6Un3q6++quxdkv9Hx1a96PiqDx1b9aLjqz6VfWwr/Z4uIYQQQpSjy8uEEEKIhqiUdN++fYshQ4bAxMQExsbGGDx4MGJiYlR6gdzcXMycORM2NjbQ09NDhw4dcOXKlQoFXdNU5PjyeDylf/fv31dv0NVEbGwspk6dig4dOkBfXx88Hg9RUVEqbUvnbskqcmzpvC3ZwYMH4evrCwcHB+jp6cHZ2Rlz5sxBRoby0flk0Xlbuooc34qeu6VeXs7Ozoabmxt0dHTw22+/gcfj4eeff0Z2djYePnwIAwODEl/Az88Pp06dwrJly9CgQQOsX78eZ86cwc2bN+Hu7q5SkDVZRY8vj8fD+PHjMWnSJLn1LVu2hL4+jdZ1+fJlDB8+HB4eHigsLMT58+fx5s0bpX3MP0XnbskqcmzpvC1Z+/btYW9vj4EDB8LOzg7h4eFYsGABmjZtihs3boDPL76+ROdt6SpyfCt87jKlWLVqFcPn85nIyEh23evXrxmBQMAsX768xG3v37/PAGC2bt3KrsvPz2eaNGnCDBgwoLSXrhUqcnwZhmEAMPPmzVNniNVaYWEh+3jTpk0MAObNmzelbkfnbunKe2wZhs7b0sTHxyus2759OwOAuXjxYrHb0XmrmvIeX4ap+Llb6uXl48ePo3379mjUqBG7zsnJCZ06dcKxY8dK3VZbWxvDhw9n12lpaWHEiBE4d+4c8vLySti6dqjI8SWlK+kXa0no3C1deY8tKZ2y0aXatpWMAx0XF1fsdnTeqqa8x7cylPqpiYiIgKurq8J6FxcXPHnypNRtnZycFKrcLi4uEIlEePnyZRnDrXkqcnyL/P3339DR0YG+vj68vb1x9erVyg6z1qFzV/3ovC2bkJAQAECzZs2KLUPnbfmpcnyLVOTcLTXpJicnw8zMTGG9ubk5UlJSyr1t0fO1XUWOLwCMHj0aGzZswIULF/Dvv/8iKSkJ3t7euHz5shqirT3o3FUvOm/LJi4uDr/88gs+++wztGnTpthydN6Wj6rHF6j4uavSNBrKJoZnVOjeyzBMubetTSpyjHbu3Mk+7tKlCwYOHAhXV1f8/PPPuHbtWqXFWNvQuatedN6qLjMzEwMHDoSWlhYCAwNLLEvnbdmV5fgCFT93S63pmpmZKf11lJKSovQXlSxzc/Nity16vraryPFVxsjICP369cPdu3crI7xai85dzaLzVrnc3Fz4+Pjg9evXOHfuHOzs7EosT+dt2ZT1+CpT1nO31KTr4uKCiIgIhfVPnjxB8+bNS932zZs3yM7OVthWKBTKNR6qrSpyfItT3K9dojo6dzWPzlt5+fn58PX1xZ07d3D69Gm0aNGi1G3ovFVdeY5vccpy7paadH18fHDr1i28fv2aXRcVFYXr16/Dx8en1G3z8/Nx4MABdl1BQQH279+PXr16QUenIvNr1gwVOb7KpKen49SpU/D09KzMMGsdOnc1i85beWKxGH5+frh48SKOHTuG9u3bq7QdnbeqKe/xVabM525pfYoyMzOZhg0bMq6urszRo0eZY8eOMS1btmScnJyYjIwMtlxUVBQjEAiYhQsXym0/fPhwxtTUlNm0aRNz4cIFxtfXl9HR0WFCQ0PL3c+pJqnI8V22bBkzceJEZvfu3UxwcDCzbds2xtXVldHW1mauXLnCxdupkg4cOMAcOHCAmTx5MgOA2bBhA3PgwAHm8uXLDMPQuVsR5Tm2dN6Wruh4zps3j7l586bc39u3bxmGofO2Isp7fCvj3C016TIMw0RHRzODBw9mjIyMGENDQ2bgwIEKneDfvHnDAGACAgLk1mdnZzMzZsxgrK2tGR0dHaZdu3ZMcHCwSsHVFuU9vsePH2c6duzIWFhYMFpaWoy5uTkzYMAA5vbt25p9A1UcAKV/3bp1YxiGzt2KKM+xpfO2dA4ODsUe26JjSedt+ZX3+FbGuUuzDBFCCCEaQkPKEEIIIRpCSZcQQgjREEq6hBBCiIZQ0iWEEEI0hJIuIYQQoiGUdAkhhBANoaRLCCGEaAglXULUhMfjgcfjgc/n49WrV8WW6969O1t227Ztcs+NHz+efS4gIKDYfWzfvp0t5+XlJffc5cuX2eeK/vT19WFjY4OuXbti5syZCA8Pr8hbJYSoiJIuIWqkpaUFhmGwZcsWpc9HRkYiJCQEWlolz7KppaWFrVu3orCwUOnzmzZtKnUfDg4OCAgIQEBAAKZPn47+/fsjKysLf/31F1q3bg0/Pz9kZmaq9sYIIeVCSZcQNbK2tkabNm0QGBiIgoIChec3b94MhmHQv3//EvfTv39/xMbG4uzZswrPPX36FNevX8eAAQNK3IejoyMWLFiABQsWYPHixdi0aRNCQ0MRHh6OFi1aYM+ePRg6dGjZ3iAhpEwo6RKiZv7+/vjw4QNOnjwptz4/Px/bt29Hx44d4eLiUuI+/Pz8oKenh02bNik8V7Ru4sSJ5YrP3d0dFy5cQJ06dXD27FkcPXq0XPshhJSOki4hajZy5EgYGBhg8+bNcuuPHz+Ojx8/wt/fv9R9mJqaYujQoTh16hTev3/Prs/Ly8OOHTvQrVs3NGnSpNwxWllZYdKkSQCA3bt3l3s/hJCSUdIlRM2MjIwwYsQInD17FrGxsez6TZs2wdjYGMOGDVNpP/7+/igoKJBrbHXkyBEkJSWplLhLU9QA686dOxXeFyFEOUq6hGiAv78/CgsLsXXrVgBAdHQ0goKC4OfnB319fZX20blzZzRr1oy9DwxIEreZmRl8fX0rHKOtrS0AICEhocL7IoQoR0mXEA3w9PREixYtsHXrVojFYmzevBlisbjMNdSJEyfi9evXuHTpEl6+fIng4GCMGTMGurq6FY6xKJHzeLwK74sQohwlXUI0xN/fH9HR0Th79iwCAwPh4eGBVq1alWkfY8eOhY6ODjZv3szWeCvj0jIAvHv3DgBQp06dStkfIUQRJV1CNGTMmDHQ09PDpEmTEBcXh6+++qrM+7C0tMSgQYNw5MgRbN26FR06dICrq2ulxBccHAxAUisnhKgHJV1CNMTU1BRDhgxBbGwsDAwMMHLkyHLtx9/fH3l5eUhISKi0Wm58fDw2btwIQNI9iRCiHiUPYUMIqVS//fYbBg8ejDp16sDIyKhc++jevTuOHTsGsViM3r17VzimBw8eYOzYsUhMTETfvn3h4+NT4X0SQpSjpEuIBtnb28Pe3r5C++DxeOVKjFFRUViwYAEAycAciYmJCA0NRWhoKABJDbeotksIUQ9KuoTUEtHR0Vi4cCEAQFdXF6ampmjcuDF+/PFH+Pn5wd3dndsACakFeExRPwFCCCGEqBU1pCKEEEI0hJIuIYQQoiGUdAkhhBANoaRLCCGEaAglXUIIIURDKOkSQgghGkJJlxBCCNEQSrqEEEKIhlDSJYQQQjSEki4hhBCiIf8HKmTJdzNlu0oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    # print(noise_names)\n",
+    "    \n",
+    "    data_flat_noise = data_flat0 + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_flat_noise = {}\n",
+    "    observed_data_flat_noise['summary_conditions'] = data_flat_noise\n",
+    "    observed_data_flat_noise['direct_conditions'] = None\n",
+    "    observed_data_flat_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_flat_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/flat_zeros_data_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51073f5-8e53-40bc-b9eb-947bdaf67424",
+   "metadata": {},
+   "source": [
+    "## One Actual HNN simulations with increasing noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "d4079813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_HNN_noise = data_HNN\n",
+    "plt.plot(data_HNN_noise.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "cec7ab8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Gaussian noise\n",
+    "\n",
+    "# np.random.seed(666)\n",
+    "\n",
+    "# noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "# noise2 = np.random.normal(loc=0, scale=0.05, size=(341,))\n",
+    "# noise3 = np.random.normal(loc=0, scale=0.1, size=(341,)) #\n",
+    "# noise4 = np.random.normal(loc=0, scale=0.2, size=(341,)) #\n",
+    "# noise5 = np.random.normal(loc=0, scale=0.3, size=(341,)) #\n",
+    "# noise6 = np.random.normal(loc=0, scale=0.5, size=(341,)) #\n",
+    "# noise7 = np.random.normal(loc=0, scale=1.0, size=(341,)) #\n",
+    "\n",
+    "# noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "# noise_names = ['noise_0', 'noise_0.05', 'noise_0.1', 'noise_0.2', 'noise_0.3', 'noise_0.5', 'noise_1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "afa897f5-9b3b-4240-b728-b7b8b6bc2a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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JLsU1xWm/Eb1/kiRzdoMno8j2FF4lYx1Ns6mMIcbWKHusRXp/Rai/8jDU8+qvBg7MAVMJq3X57hzXxZZXD2Pn4UHv9zHBVKLf1YL1d+D/vf13yr+xg1GCsfOHKP76Gqf+0i7FLUCzDPV8XAdvsLcdsU50I3r/JEW60iaprNun0371v50Gnm0SNLKRf//hV/FuyUkjDzSq2qM5GdhU4tsLgh/rqL/YBh7+m1jWNv24HSZU6jAVB/jzHz2Ojb96xet3DDAVla2Hx/9+RF0eOo2hnhfU/L3ajotykQz12qW46aBHn9d84z0watzPAJ9xlOu/Ab1/EvWXIwmvrHDc9otTcRp4tonabWBdvtI7gG1NKB3Q6CshNQipv+qVqE6bUFKdpiWb+ivepiKOYWDExpCfADVvbUQjIO+vtEhz6LQVh1X6uYMM9Vr91Xzknaalyk5qLjOKsdMGEy7hXDwNqb9ivlPLmVnw3mztgkaebRyODlYwVLEbS7fRYPBb3Xazen9VEqq//I283rOMO/CITCXdePnvjyiyZcs2leGqHVp/o21TaeRdpUnTwrfP7zE1zVRai/xtKt5Lq9gOWzhVaVLz77URR4GkOcVUf/6/T+1pqICS47aj91f86Tstln9hM675zkMNR0Y34/llTdMyKKm/6gmVet3FGuq5Q0kj7DGOqfCqn6qvJahKh6/RtqnwSOqFleZgJKa4F4WsjlNpIep5vaQFTQI+q6vNeYQBIsNoJOUKf6p87dAA7n1uv3Ic8kR8pbcff/6jJ/DJ/3gqcV98nzXHxdp/3YJv37ct9fdHA/VqhKQBvZ+X9vc3tDk16v1TD2lv7dhQFe/b+Ah2HPKM2P0jUkR9RHtkqHcR32FcDjExTUu6cfNt17OpkEqPNu6xkFBSfh5JK2kGAjFdH2JdeieIU2mxtuGEdCmmRZKfod5rZ5jzUJFtKrYiXUUjwY+2A1z+tV/CdYEdG94hXKMSKtTXtv19ifvi2zMM4KX9fZiUo6tyMxHYVLK3dXAgqLfDHwpc14VhxHtEAd67Ggvqr1cPDuA3rxzCpA5vuQ9WbF/fX0/9lYxRx3p/5WZTiVd/DfkqvSAQ2b8m4bM6MlBB33ANp0zvSj3GKMi3O1Jz0F2u/71GXIpVP2um0kLwtP+Hj76OBevvyFThkBYO70svlwRVMpUG1F+eMV59jedWKH/qXTxYSXd/ZGS0Hc9NWqXTHovI07Nv95GgSiK/OSU9cTZLfZj23qqSLaXmuKjYDsuCW4+p1LepRJ+ssyaUDGwq8YZ6MtBXpXWX1J7wpr9/AJd99YHU44uDzPCS1qdJpf4SvL84TYnL2VR06vvmg89k+8X/8oro3PXMXnzix0821B4tHIGpSF5ge44OYXuv5wnUWJoW7/94Q70TapNOm0MphQq/YEeqTmS98m37+8ZWWvgcDfV7jnrFrGZPKgvPXbXBqWD7B4A8no8jbM7pvluVjNcAMDhiJ0jTInpSRY4txvtLCH5sKE7FP7CpbCqC+ouYii8IE9ggeZBKME/I95s0hVKaiHp+nkd6f2mm0nzQI7Ydl51wPvHjp/DzJ3Y3pK6oKaJ+bUdcyO/8x4dw5T886PXfgIomyQncccJtVtgpNd2i4SfocM1WbqSvHRrAqm/8Cg+/fChV281Enuqv3Uc9G8TsSR3CBpF4c2iAkUYhy4lfFVHdP1KLNbADaby/AhYtQzYepwVjKkr1V/BzSP2VwqbSrENR+ICnFowbf7Ude48FrDiV+ou7hn/PgveXdiluPuJOs0MNqHnopfGnKdmmwqMRpqJyM5QXgyqivspUGIm7Co2tartKpkIpyXv7h9M13kTk6VJMTKWrZAntDSdkKnF1RtJCPPGna0+l/his2EHFxszqL+9/1X1mDX4MhEo9Qz2pv8T5nuRZHYpJrZ8FKpuKjMMDFfyvO1/Anb/bxz5LkjyWXauwqZCKvGiZMAztUtwS0GtQzfGBBmgwMyZWw+ov1UJrKE2LYoNyXODlA32sbrrKUJ9UjytDHrfqpBgE040de0sjZQWisMu3qcgVMNOqMfIQcFWFo0fy74bnwEClVnceVmvJNre4Q1rVaXzcAL+2woMQ1HnM+0s8fCVhKjsODqQfWAKEvb/C84YEzXDVxtfveRFHBytc5cck6q/g55rkaVowDRRNs+XqrxPS+yvOW6V/pIZZKdtjhnrBpTh6UvOG+qSeRLSwbWkSXfX1XwEAXvlfq0N/BxBpC6kHeUKrhBNtVmntNc1Eoy7Fg5Ua+oZrmD2pg312oM9jKnL+tqRMJc+CYVlO/Cr11wCn/opS/4xI7rlRiFOj2UI54XzVX3xz5GhTldhVEpb4SsuESnje0GcPvHAAW187glcODqSyC6q8v+h/0zRgmYY21LcCNLl54xxlYm3k1E2nMZrYJcvkjKPxXitJ15kqyRzftB2xQVRrjS1qWW2gWhAkaMaSUGnUpvJPD2zHe/75N8JndDqWK2AmZSqNZKOOQhbXXJ6pTPTdigdG7LrxWoFNJb79OCeSrHEqSVPfD3KebUC6iHqeqeRpX5FbGqmFhTTNJXp2h/orqRJKympqILj3gmmgYBnaUN8K0HvYdyywBXQUPU+JRrxAZKZSLpqRk9p13YZOnSoPmySpX3imorIXHRmo4P4X1IGUQjsqoUIR2mPI3bhR76/Dg5VQ2VqWcdoW31lSppJGN14PWdRIvKGWSiMMJlB/9Y94WRjqBj/GGeoz2lSSBz9K3l8pNuajXAGwPNPEy12rMhTQZ5avrRis2pyzQZI+wkyFnrlpGCiYhjbUtwK0SPYKQsV7FJlsKv6po6NoRdpU5PxcSTcIlYukwFQccQNzHBdv/caD+Nnju9k1RwfDqVp+8tgufPiWraE4HfnU2T5MpTGhIgt7IDD6ysXKUruG5qH+4m0qKaUKf7CY0uUJlYGKHQhgRXuu66K3zwv+rMtU2NwM/y234Edl7q/gZxanQjYVV/x+HHg2nzSJZhLIrIcxFcVnJBSHEgh7HqrcX/Q+LNNAwTLb01B/9913Y/Hixejp6cGGDRtCf3/hhRdw8cUXo1wu42tf+1oeXWYCvSvejY9SGgw0sEHSSYBOsOWCGVR+tMMTS5UHrB5UlFgOduLbG6k5eGl/P57ZfYxdoxIqnsE2vJjkedg2NhVH3FTSfC9U55tLBso/j9RMJQ9DfYZiV/zGTkzFs6lQe+Hv9I/U2H3Wt6lEjytrOeE47y8xTsU31DsOqrYT6ygjg5/7eZ7qQzYVlkE5+JzWFbn8D1ZsLl9g/Fi+++tXWKp/IHyIsczRYSqZDfW2beNjH/sYNm/ejHnz5mHlypVYs2YNzj77bHbNtGnT8O1vfxu333571u5yAb2r48MBKylnYiqBBwfgMRWqqa3yxuI/S7rQaE8RnAF4xuPfFH1Ek7WPu8ejQ2HXSVVgnNy2129YcFAfY0n9RcNOqxu3nbDrpVAnJwNTySNVSy2T91fwhYnlIgzDSyoZJ/QO+CxlYkchgU0lup2scSrxaVqCnwe5OJVFf3MXqFhlklM6/3wadWxRIXQwU7j3033RvsMf0Oo9r3ue3Y8tOw6z32XPN9NoU5vKli1b0NPTg4ULF6JUKuG6667Dpk2bhGtmzZqFlStXolgcG/mjSP3Fn1Ao+jSpUDk2VA3cFqU4lY4Ym4osVJKqRug7vM1H6flBTMX26TS34R9TMBUSDPI45Y2waofTuI9FptJIChwgSEvD32OVizVqJE4lzoCdFir7mYxv37cN9zy7L/Q5z3JKBRNdRQsDFZtLVxRui1Rfsyd1JA5+VJcTblwYAsltKjQHab6zZ5/gVfGCpJ4QGqnZWPuvW/D83uN12w0zFVJ/uaHPqBrnIFdmod7Y5XkluyJbo+RSnFmo7N69G/Pnz2e/z5s3D7t37475Rjw2btyIFStWYMWKFejt7c06PCXoXfCnqFKBmEr9DXKoYuONG+7Hf/1ur9eO5P1VLkTbVEZqtmRTSaj+8tsZqCNUmPpLsfHF2UVkgaEal3yKo/aGqvmluLAdF9954OWG02YkcSlWHRxUEdh8bQ7++STNE5c0VchIzcab/v5+pcNEMBae3arb++Gjr+PuZwKhMlSxcflXH8DDLx9knxUsA13lgmCoV7UXCJVyXaYVuLurmEp9YRgHVbYK1i8vVKrBpsxD5X0pI43665ndx/DgS734zM/UpYDjwLI+u+HPaPxDVZvZz+rtDSHtghT4aRlt6lKsmpBJ4i6isG7dOmzduhVbt27FzJkzswwtEjRmfjLRXSRJZzJUtdE/UsOuI4NCOzxTCWwq4gutSDaVpHW7aYLx4xOECtsg/H4UE0nlxVOJiPxXbQDywqaTfJ5M5aX9ffjqL17EQ9saO1Aw/X7EBvbkzqM492/vwe6jQ8LnqnxLPFMRgx8TxqlI7yQKB46PYOfhIXz29mcjrxEi6iPas10v88FbvvZL3PrIa9h2oA87Dg0KaXSKpokJ5QL6R+zYHFMHOKZSb/xJvb+aGacSFQqgOqXvPz6Mm+7fptwH6qm/ukqU6bn+PhHJVNzwZzySxlrJz1O2xRgGfEN9mzGVefPmYefOnez3Xbt2Ye7cuVmbbSpUniEk5ZOckOll02ZKE5/ZVAoWU6WEmYqjzFicdMy8jUSuSc23pzKsqw5tdJ08TtW4SKVGYEWfchQqWSv2ybYlGXuODqHmBJ5NBMZencA4TWqjmi2pLBN60yRlKkXLW4ZxGxp/go5Sp1FG6VcPDuCztz/D2DePgmWgq2T5NhV/nBHqr6JlYGpXKYH6i/pXj0m+Lg2Sx6mo163q2f/JrY/ha/e8hO29XnwKZQ4A6jMVSiefZM7LXZNgFNRfiocWpO+vw1RkoSJ9LzDUtxlTWblyJbZt24ZXX30VlUoFt912G9asWZPH2JoGehcq2juYQqgMSkns+DgVwHvp8uY4UnOEE0baOBVedSMY9RzxunqLkBBpqFfMQ/lUxVyKczTUp/HaUaFu6pEIBimf2inDMI2JvzypwEtqU1HZ+GQkOfHXbEew95Ss8PIuWia6SwUMVGqxZbV7+0Ywc0IZppEk+DG6nVoGrzUgeB/KQxLXXBRbVs0jSr9DYQS8MK/nUkytJRMqYt+vHR7Ewf4RiakoHGAUajIV5KGqbCqjYajP7P1VKBRw00034eqrr4Zt27j++uuxZMkS3HzzzQCAG2+8Efv27cOKFStw/PhxmKaJb37zm3juuecwadKkzDfQCOhlVxX63v4ENhWaWXISO0IHV3FNFUTIT4ak7zuwqQTj4yd24HIc9BMatqKvKEO9ajHKJ+lmGOqp30Zt2/VciqMSfdLz2310CEcGq5gzOUjXInt/JfXmSur9RddVY9Rq/FyN2pwdVzyxW2ZYDV0wDXSVLRweqLB2trx6GK67DX92xSJ23YG+YcycWIZpGImDH+k+jg1W8crBfrzhlKk5ZilOZqiP+j6PY74XJH+4LJje5ltXqLADZf3Dp3y7dzy9F/c9vx9b/99V7LM4VWq9g5U8r2wpTmW0gh9zyf21evVqrF69WvjsxhtvZD+fdNJJ2LVrVx5d5QJ6xIJKQWEIjwK9yyF/Yskvv6PkCZWq4yiYSmOGeocJvWB8on2FjHTRJzvV5hDFVJTqL/8UfGSggildxcBQn6NQyZraJPBoijjNM5WWdL9+f+/49kMAgN99/q0AgKJ/0hOywaZkl/VuhQmVmAtFpwz1NTXHEQ4dqmEWCya6ywW8fniQtfngS7347Y7DglA5NlTF1K4SDMNIwFT8cfkd3vrIDnz7/pfx4hfflmOcisqmwqm/IthyzXFD+fXIVsYLlc6Shb7hWiii/thQFeWCyTJu0O0k8QBUMcrhqqhKjUv4Wm+eRXl/MUO9aaBgtmnwY9tB5enjJD+BMJuKFMVLmFD2ZLXKCCe7FKe1qfBCZZDbQOTkhRU7vMhUmwOxD3niRTGVkZqNN3xxMz70/d8GNpWq3ZARVgW5HkZa1DOO07uS2WW4ZID3e0fBCmUpTqqiTlpsieZe3Cm5muDE7zji/FVdVzQNdJcsv0hXeKyESs1BqWDCNJInlCTBe3y4hkrNO1BlTdPC21TkcfBDjlNHxTk2AN6z7fYN8LJa9L3//Bt8675tXFvJ7yGqX0H9FSNU6j73CO0CSyhpoD3jVNoRqmccqL8S0Fr/fznfEIGEisr1dCQkVJKMOFgAAxFMpSa5E/ICjQy2Sjdh5lKs7k8Ye9XGcMW78MGXevHoq17glWcgzmfipsnZFPv9GLsDIKY9ARDKs0QZijtKVlj9ldQNPKEXTz3nAkAU+pE2FcdhGSGMCFtIwTLR5dtUVHmjCBWbhEoCpiLYoVxmJ6jaTqaKlfy4XDd8eOPbjjvxR53UeXtNF9MuiH0c6BvBfi6dUxoGrXrvnUVLyNMSy1RSGuqrkiuyzv3VQqjUQDTxksSp0GRm1eakl0+ZYFUG7JBNJanRV+EVxTsVBJSemErQSZkJlXC7UUxFNS5ZIL58oJ/9nJcKLGu+LDmh5MBIDW/9xoN4cudRAMHzq3e/rx3y3MXJmKuyv9UD3UJSphKHekyJgjdpTliGoRQ+BcvAhHJBCLKjMfDXV20HJcv0DfXhdhzHxS3/vQNDlUCdu/n5/Vj+hc044gfZVmtynrv07zSujk3S/T1K+8O7FHeSUJE2+WrNETy00tyC6tpTp3cJzyEuO0M9WRAVpyJ4f7WjS3E7Io6ppDHABem2xYk4saMo/J1HxW4wTYviQj5PmVzhkafVlNdM1Vm095ea1URt9oM5BUCmSfutAn2N/j/QN4KX9vfjpX19AMTYE/F74u+vk1Dxnx1/okwqVJIW6UqWnyp+c2Y2QX9OmKaaYRRNE11lj33JdgH++krNEyqGYcB1w+zo+X3H8bn/fBYPvtTLvnd0sIpjQ1WW/btiO8K9ZYlTAcKqoqTt8etTCMbkhApTf0lruWI7wrtPp/4KX2sYhjKhJBB2rEir/pK9v8x2dSluR6jeFb2QJLl/iOkQE5Hp5YSytxGpBNSIZH9IG1HPg29frifOT1Y6bSuZSkScilL9VXMiN/usTKVqO/jQ97fgidePRo41CUSB7QZBqMRQIhJ9yvf/+mFPqNAJtpGNJalQScZU4tVfchuWYSj7LVgG20DDbfCutS6KBQOmb+CWm6L5NVKzQ3OC8t5VQ/E9ym5jUXMclsdLFiqJVcfchUe4VEV0u1XbDd6zNC+qIaGSdOTie583tROAN/9UCSUBYGqXmMYqrfrLlrQVluExlTzq+aTBCSpUFCc9LtYkaVZW2tT5U2SpYDIbhmqj9U5vfFsJ1V+Ky3gmRDSaFjg/WeNsKnJhH9afKlDStqOZCjeWV3r78e37tqU6mR4ZqOCXL/bisdcCO00jkHX4VcnwTzrzUFZmaayvHVYzFa/md9J3Rs82/rq0TEV1vfxZwTSUh6eCZbINNK4Nj6lYbEMPOTKQUJG8mQBRqNiOy07gjaq/KIpdjumQ52JZEewJiO+Lr5kTOLU46C5TGIDISB1XVFHJh5Y48H9+/4WnYs25c31BG3zOty2/l3rTQp5XNLfpc7KpyE4pzcaJKVQUn9GDd936m0Y4ol60X5Qsb3KobCryIkwb/MiD9/6qUD1xl34Pq79UXUXGqSgN9U7kROedEn7x7H58ffNL2OGrkJJAHnej6eL58TmuG9TtloIewy7UYjuvH/KirctSgFzJMpPbwZhQSa7+WrD+Dtzl55TjIdYlUbQhPS9P/RW+sGQZKFrqNEqCULEdFAsGDCZUxGur7BBmh/5G2bCrtgPbDaqqNqr+IiN6mKmI7UUJSydGqLiuF5vSWSywMRPoZ34t8fdQL+iXfy6W6QWeVm1XjKjn2i5Kwap1c67JTEWyqZim9+xlp5Rm44QUKqqXFaW7/dT/eQpfv+dF4Vp6lyr1V7lgMWaQxKbSCIUn8N5fQVrtYLETAvWXWqUFhDcl1TOq2GH1F0Vt8wuMJvdTvnE8Caj/KHVc2nYA734DpuJ9xtx369iQmPqrKDKVUgp1Au1P9TZTWY9/5zPhTMP1XHPljcOKYiqmiYKpXvaBp5W30ZZ9mwoQdm6hQ5jsvAEEMRyVmhffQ5tlI6+05rjoJhf9OjaVrqJaqEQxFXIVd10wpqJKg1+JEOj9w/F2RH58puEJ86rtRHp/lSyTzTegvrOK/NyDOBVe/WXExj81AyekUFEyFX4ycS/6/zy2C9++/2Xx+8zA5y2+qsxU4tRfNdnNMgNT4Q310glfZCqmP+5wu8xQX8fGAHhMRf68rLDX0OR+atdR5b2oIKc4b9j7S3KCCJiJ939Umhb5+dL9dEqn5GLBTB38WHdzkLQTncXwsqwXRCgzTTPGphLFVALPOG+jLVpmpE2Fqb8UQiVoz5svBSub+os2Wln9JT+3jgimQu9815FBwSWfV4/Se+bnBd2jYFPhnnNfnfADJyRUPE8s/imMSGrqLu4eUmcpllyKWfCjNtQ3H/Xmdr0iTPy7HKzYwsstF012elcxFS+hJNdWwvetOmzwC0QuAKRSf8XFqdTzhqI+5A2SIo35Uxk9jzRMhddve+0l/qrYjiSwg1xi3mdRyfqiFjCzqfgNFC0jdZqW+i7F4iToVJy4VV5LPOTxW6b6nopWfaZCmzAFP6raZ+qvqh05h6v+fKH+GmcqydRfXRFC5c1f/SW+fs+LuPTvHhAOOo7rsvdKzgv84ZJ+jjLU18u+wV9LgYjVWnREfUmyd9UvOaBmKoH6y099r5lK81HvBBBVY4TAnzWGKrYwET31l+H/TeH9Vcvu/UULXTDUS6VKVXEqaoamtmGo07SEPX1ULIjG+uye44lrfsuJAxtPKCn+XJWYSo0xFpmZqduTvb8Kplk3fiDon9RJ8deF0vwohMqIsLGFG1R7fwW/00Gn4CcZVKEmvQOeqYRtKvWZSqD+ympTUau/5DGphDHhh1teBxCoNQFvfZOw7uJSKxHkkhZen0Gn9dRf/LWWaaBkmajYjhRRH6zhohUwlSSJPPnDhedAIq5lUrm1Ze6v8QaaRH0Rk4Y/mfWPiNUUS3UM9T/aslP4Pck6kzfyTr9yH+9SHETT+vfAxSCUi2qm4jiuoPLgIW+ylmn4G0jwu+24XGBlmKmM1By8tL8PS+ZOrn+PEsNq2KYiMxXGTLzPAm83aYOK6K8sBT+WCikM9YmZilpQ8xipBUkPVXNGHpNpisGP3WULlUEnnqnQyZy712FWSVFmKvWFCmMqDaq/XNezeQRMxQ79nYdKGBNIOB4Z5GwqnM0tCH7kWITCUM/fA6m/XNfF/uMjOIlLQur9IfjR4Er7xqm/On0BWkxgu+OnMG/ro7muXYpbiKRMhZ+AUd8/LgkewaaSICV8Ev182MuFCgVx3l+SV5OSqUhd8dfIm6w8rq6S5anu/PYpa4DKs4zfJJ/aeSzu1hhk9Vej3l/8RuM6wemNMRU7gqnUU38xpmLUXaRP7jyKz97+TFAWtq5NRa3G4DFStdlJnPrf/Nx+/PyJXcrvWKYYZDep04uBKBfMSKYiO0uUeJuK7L4a4/0VXOPZD4uk/lKwwX9/9DW8enCA/V6pOfjWvdswXA3aJc8sOZee3G+U+gsIAguPDIhxKiQcOwqe+3RNwVSi1F8HjntBnr98sRdv/Lv7sf94kM7Fu1ZkKiQoePsl7zVJpZ7p57o2Fe7vpYIZCg8g7y/tUtwCJLWpHB0K13SXIbMZXqg0UnNBBXlT6ixR6eNw8CPgbawqQ728eQm1uWPyKhV86s4Hs5FQUXmW2Y6LiR0FTO0q4umExvq81F/R3l/e/+QJE0rTEvEeeEN90kp6v3zxAG595DXmBVWP2ahq7sgYqQWpRGisH/nBVnzix08BCB8KLEO0/Vx42jR84ZolWHnatBiXYlHgesGPEPokMKaiiFPhr4ljKsNVG3/z82dwm6+aAoCndx3FN+59Cb955RB7R8RU/mPrTnz4lt+ya0OHrRimwoTKoOhSzGxlBRMFXz3Fxu+zFj5NC98npfLZ3tsP23FxqF88hMo2FVUxtsGKzdZniVN/FSJcwnnwa6SzaLG1w4IffUO969afg3nihFR/Rb0s0nmOpGEqvuAJ1EEWW7RJosyT6Jnlg0ZXMZqpeOMTVQWMTUjtVmP09PyELVgGi3tgTKVcBDDEBVYG3605nppl6cmTWc6tuvfoBiozIIv3F3cPbhCnEoqolw31khClv3f491epOX7N7/ReOXV14yGhonbwkNOvi22Iv1tSmpaOooUPXrwAACLVX7JNpWRZMM2a32ec+kvZHCq2V9wsMNSLF1KQJH/CrzBhFTjAkE2FEpgSohi8CiRU5BpETK1peQenmsql2A+INqR8ahSHRVVEh0O5yYJryb4BiLEwQxXbKyPRX0GpYLK4oKrtQlFjLWhbVomXLDZvWJEuI7CfVR0HZTNa6OaJE5OpRHxOp27a2I4NqpkK/z6PD3vX0OZTLqZlKurPf7frGBasvwO/3XE4MsiL3xhlve9IzUG3f11UnIrAVGSbCndt0TRhGYYfYex9PsFnKsFlPFPxFtGiWRPYaa4eSHBmLdIle38xWxMTKsH//ObN3y+xMCDQ01dqjudNY9RXfyVVrRFCTEVRMmGk5p1oTUN96pSZlyXZVPjAukhDPZUF4Dzd6MqwoZ5Tf0U8j6rvPl+MSGgaCJWgtDNvj6OfuyPUWvJjjWMq6qJ1LmMjRctTCwouxcLPNI+83+dM7sDrhz21HQkV+b3x4yOXYnksfBxO0S/1DHgsLu7AGdJecEyFnpvhR9Tzn7UCJ6ZQqaPqkG0qlMpe9X1Sf9Hm40XUR8epyOAX5H9s3ckm6JYd3qnsjqf3Ku0bMmShUqk5TI+uYhOAaJSkSffwywfxoy2viyd3y/CZWCCYJvrPhH4XGILjGZVLhRSBgjFMKQ3CcSqk7hLVX5uf24/zv3gvK3XA78mUEBTg1F+2x1SiItV5hGJgUrqGKtVfVQfloukzkHB78vOSU9YLQqWOSzFvqI8MfkxgqK85nvqrGJGm5ah/aNvfFzAVel/DHFORI+WjyiDH2VRU7r+2w7uKmyhappD7ixcqLA2S3+dpM7rx2qFBOI6L3n41U+GfGWUMBsL5BWl/KRWC4Mea48YeRlQegzRv+DgVYmh5laZIghNUqAQ/8/plEgz0cmjSy5NVYCr+aYsXKobhUekkhnpq69hQFZ/+ydP4z6f2AACm+ALh2FBV6f0lQ7SpePcwyd8ci5ZHq2Vhyhfyokn6/u8+is/87HfChC5YJkxf7UProUNiS/wCrzmur881UgcKyuNJC9GlOFB/BUzF+/31w4PoH6nhqH9w4PvnmUqZM9RbCZlKktLMcderTtUjNQflghVZiTEkVCQ1HT/Po5jKz5/YjW/e+5LaUB9iKrxNRX1fFdtjtoFNRfw7PfsDHFOhZzFcDaqmlgsmO3Hz7cjtRaVpAcSM3nwusiovVMxophKkD/J+XzCjGyM1Bwf6RjimEp2a3zA8FRvfFiFgKia6uANs3LxR2ZMCpuLfJ8eOWhkAecILFf7UJqfkCDYc+fvBB6T+ImMbvcRSwQxlKf7+H6/E2otPFT6jyVHj9LdA4K1zbKiaaPHI9T4qNQdT/KynnUXLqzUutVNRMBUCz7KK3GZK15FXVJCJN/guRVGTnSmZ3Ui8JokDw5GBCkuzrmrHccOp7mWHBJVjgKj+IpWFDdPwXHXTBjPWt8GI16ttKpz6KwFTcRxZqATzvBjBVO56Zi9+8tiuRMGPzPaRRP1lkfehxFT8A1n/SI0xxhrXblAXxBTcrGuOg83P7ccPH31NaC/OpZh/PnRIdBzOplIwUCyYkiAJvhME5XqfLZzRDQDYcWggECoxwZlecsew+gsIWH+pYOJPLluIdy6bgz84f16sCliV2UJmKqYZCFCt/moyHOEUHpyAyoypiN5f8ibBvx5Sf9F36WRXtIyQ+qtomqGJLycdpEVFozo6WAlNCBVTkT1UKraD6RNKuPkD5+NdbzhZuRnF2VR2Hx1iP3tMxWMd1AbFb9Am7UYwFf7e4pCknouML9/5PD72w8eFz4S8ak4Q3Eafy+6VcnwPIKm/ikFOKGIq9dVfyQXki/v6GCMmRHl/lQtmyKuL9akQyvwnhQRMZbjqpXlPEvxYYzaVBN5fxAykRnibJbnn8kyF5kDBNNj6Ary58R9bd+Jxv0wCIU79xYOi571DR3CvnvutWv3FDh/+vVJMyv7jwzjsHz5592DXFeOJLNNgtiX5/RI7KVkmpnSVcNMfnocpXcU6TEVsu1wwUak5WLD+Dnzxv54L+mSG+tYJlRPS+4t/vPwJrrMoniSo9kKcuy0TKv6EoZNBqWBiUKLDBctg9g3Wlt9U2OXV+//oUDVWd1zyJ5PsS++dbC28belJAAADYbWJqvAUsYtdR3ihYrDNjAkVKeiRH6LjuL6nlMnupVBnvctjS7IGqCgUD/5VuW6YocjvUpVRINJQbxh+EGj84JJG67uui6u/+avQ55E2lYLFbCWheu0KocxfU0pgqB/iygADELyR5PZ5m4qpbs6PUwGzJUQZ6gHPWL9w5gR2gONtKqa/aRJqHMPgEWeo59HluyjbrsvYSMH0bCq8R6QsVDbc9QLzyJrWVQIAbO8dYHOff2+ynDUNMNuSPHaqv8S/I7OO6ph/H5ZhoFSw2PujPYlnR63MVKyZihltUxli9VJkWhv8zLsU8+156i9JqPjxHqqxyEyFPj+uECq866SssgOCOBW+L5VNRVUid8YEb7GQUDEM3/vLlNRfxWQ2Fb7tOKii/V89OIB3/dPDrF686jtxObx4nTlf5Y/HiEr9xem16RBQsR2WS6ne+pSZbdTmMCDNj1996i244sxZGKnZGKrYuPN3e7nN20a5aPq1592QsVflvccPg5/nUYZ6OpyMKJhKtE3FjlTTeC7FQZqWkKF+KHDZp3fMMyC6p4IkVGw7QqhITEVeawQ6lLmupP6SYpD4PvYeG8bND27Hdx7YDgCY4guVlw/0sWt4piLfa5T3FxAwpyJ3j2ZEOWiCnKKlZJnoGxYPWKbkUtwqnJBChacqglCRoqflBG3B14PfyUOMhApjKpYZmjyWGWYqrixUZKYyWI1Vf3WVwkLFcf0iS/IkhQjZtREApneXAXgZXek+CpbBst7SYlk8eyIA4PIzZvr3EbRLNhXTfxZpa7AD3qL58W934onXj+IH//2a8js1RyFUHFGoyAkk5bGomUqg/qL5EcSpGCEbiGpcPKI2h+MSy5o1qYxywcRQxcbHb3sCf/rvj+Mf/QzZTP3le38NV+IFF88qAXHDimIqXj9BLrtywXPQABSqUwoMrEWXmCb1FzFW+bKjg1V2iCFjfaD+stlz9tQ7wZz3mEq4T5mpRN0nxb3YjmioL1BqerpHro9+yXusq2ShXDDx8oF+9hnvUiyPjt/g5QNBN6f+ItRzCBGYimmgXDRD2T0o+JHutVU4IYWKaFPhDJj+omVBRNxGJKT/4N4PGeloQtMmJBfc8f5mhtVfUnxGOAGieOIERAMqy1kk21RCQiWswlClaSGhSBS65EcaB0zFu/70mRPwxGdXYe0lC1ifBI+pmJmZCjkaPL/3uPo7KqEiMJXghCYzQQITKhGGeosXKqYnXOs50sh9RN3/celkSbrx7b0D2Pzcfsyf1onvPPAyDhwfZt5fJNxlz8JQ6QLXFV2KOXYSZagHvGdGbYs2FbF9YmOxLsUsTYvYxpGBCgYrNRwbqmLe1C5YpsEOZyrvL9o02b1FqL/KnLpONWYCxb3IQqVoiYZ6XhUmCxXTMDC5syikmBmOqBBJ91CKYiqk/iqI6q+4ZcMLcsv3NpXnGR2CgDBDbyZOSKHCP3r+NGMaYAYvQNS/RxVJotMBCRU6lfF0vcCxmLBNRTxJB0IluEZWp5QKQVJApv5SGOr5vlSuqKJNBUL/rC/f1dL01T68Z8nU7hJbxCJT8eJULMZU6k/okEux6+KIX1DpyZ1HIwurhV2Rg59dBVORT7iyegyQmQqv/vJTytfz/kpQmwYIB9dahngiv/a8ebAdLw5ipGozd3XbCeeVC2cIEBlSEkM9oZ87ULD2/KaOD1exYP0d2PSk5/rueX+p26narpSmxfv8DV/cjLd989c4NlTF1K4ipnQWA6cYpfeXbFNxlJl3C5YpCEyVbQoIDOOuG7ART6iIGX35tSBnJDYMYEpXMUhpYxkiU5GGRyl+5HYB4KTJnZjUUcCC6d3sM+Z1x73Xz/zsaSxYfwcAcU4ZBgShy9owA1dyzVSajKgTnGl4k1elZ+cnG00Y3kBJjIEWEL8g6eeiZYQYDBMqzLWYNsFg4snpYni1hFL95XgLusgN0DCiA9j4/mRqXip4JzjLZzo0yekEqzrJ2g15f8m/uzjkC5VDAxXsPBKOzPfSsESrmhw3XD5YFnBBmYPgM4GpcBswU3/V8f6SPW2iTsy8uoLclfnNYfYkz8NoxLdzeMGP3j3KnoWqOB8axpVnzsJlvpoSEFW+KlDm7aKv9vTgNbaH8woEvHkWdWio2I5vUwlndHj98CCODlYxpauEyV1FJmDpPYW8vwqi95fq5F2yTEzuCg4E550yFe+74BTMmFAWrmNMxXUZGympmAr3s8wqTdNjKgAwqaOAiR3FukyFNnhZ2E3rKuHpz1+NSxfNCK73nzs/1/gM5/wjt0wDZYVmxDR08GPLEHWCM4zAmwqQMpZyP9OE6S6FvYRoEaqEinzi8try/pdP0vxmSSo2vj06QXcoDPW8SyhBHacStqnIi5V0zUz95QanR/5++aZt5v3lM5UEE1pehK4rln6V3W4BUcixvmVDvWQXC8WpSNXygCAFDSBuwKav/qrHVEJp9SMu572f6Fnx82PWRG8zHByxUXPcWPWX0lDv39P6t58pbKyGES9UBka8tj01q8gyOhRufFGZI+Q4Ffm5HR2sYHInMRWqa0+2GltUfyXw/ipYBv7jTy7GRy8/3Ru/ZeIr7z6HxZQQukpBNgim/ip49gexnHDws+xlaBrA5E7PHjRzYhkdBZMlEPXahnR9cKCUhYqsvQDA7JFRBxJB/aXQgNDnOvixRRDVXzJTsRIzFXJNBILNgHl/ce0GfzPre3854b4P9iuYir8vdJVEjzUgiJQX7y08QQU7DAkVzl3WNLz7Id26zXlb0WbDXE4lm0rBCgyTiWwq8qboMxUKPlTVtrHdcCoLvhnPUB/v/UUnVb4dfuO0zAaYSkL113GlUAlsc+RhxAfYkk1nWLapyB5nTsBL6wkRGfSsi2Y4+NFSsJxhRa6yzqIVjlORHgOxryldJXZooPvgS1erbSrhZ1q0TJw2o5tlo6CxFgvimMmGwQc/Fv2qi7zthJ8rYaESMJWZE8soc2lSvHt1Q9cXI9RfqqzRTJgrZIHrirZE2rdUbejcXy2CkGiPP4mGmAovVHjPDp+p+LpZPuEdm8iWgqnExalIJ2l+EsjU2/PI8trpYmVQg/EFLqE8CwsH7Y1w1F/FVCh3ENlHeI8ius9AqATtkvqL7EtJUrWEcn+5Lg4PjDA9s/wMAN9QL20uskNFPe8vrxKfGKgmpDThhUrSNC0Jc3/xG1VBssVN6CgwgUrCh9SerusqhAqk3wPnkjrarhD6R6qe6ss0mECizU11cpZVpjT+qu1KcSphNmoZhsdUfKFCwmI4ZFPhvL9sV3nypvcmz035INfFBT8SGymYBk6b3oVdRwaZow5vqD8+pLapAMDMiR0ocwXN6N54mEagFZEN9UqmEpHJAAjbEk0jmqkELsVaqDQV/HsSDfVkUxG9vwDxpdACo0RwEzoKgf5XYVOhBVFM4FJctcNMRT4l8W10KAz1avWXKtbA+6CzZLH++HZMw8CfvPl0vO/CU9gJne6ddL5M584zFds7nabz/pJ+d1wc7q/gNF91IfvgA+qke3xfSdRfXo0YsV3+ufEnc4MSStYTKiGbivo6XlBSN3Qi7yhYbN4wplJUq7+8k2tYkDGnipRMpX+kxp6BvLnVe5cXnjYNV545C1O7ip5NxfVSuKvipGzX9U78XUU2xwObSiBUClKaFttxBdUUgYQX7xgDhD0xuyTvr5LlOUD0zJ4IxwXz6IqzqVg8U5lQFhI6AuF7jfP+UsXTsPQqCqFStUWmolKrA6Rp0OqvliAqJ5LhS/wRjqnQy+JfCn2fJueEcgEXnz4dALBs3hQAkk3FCmwq9dVf4RO1HM/AZ/9ltbVrYaZSCDEV8TnQ5O4sWiGhRj///vnz8JbFs5gtgSY57VOqNB6O6zKWAyS0qUiDG6zYGKjYOJWYypAqy2wCl2KJdcpBYBUuxTpB8JQyeQEj1lqJQsj7KzJOJbgn2hBJkHQUTY6pBFkbPDWkaMewnbDDAn8ASC1UhnmhIgY/1jsfXNozA9/70Ep0kPrLt6/JWZNp3KZpYEpnCf0jNVRth70fPvhR5f2lsqkwpiKNvShtuN28TaXmsO/1zJwAAHj5QD92Hh7E7qNDIbZIMA2DMRWKLxKDH8WxGTHBj2qmQgxRzQwF9ZcZbsMwvD7bNvfX3XffjcWLF6OnpwcbNmwI/d11XXz84x9HT08Pli1bhscffzyPbhuGqOoQT/Oy91cHl/uJfd//nybnhI4iVp8zB0/9z7di+fwpAESDK50+VXEqtDZknX8cUykXLDbZVC7FjKmY4r2pIuoLPkXmHQXI+4mfhwFTkdRf/t9VEfXMgyWR95d4zUE/nfgp07pgGGqmwgdjss8c8e98lmJPJSS2UbHDcRZRTIVS3x8ZrGDB+jtw73P7lfcSSiiZQP0lG+o7igqm4pe8dST1F/9e2GccU0kpU9A3UmPzlKbQDbf8Fnc8vTcyGwWBNnDek8pzcFBnQLC4zfnYUJWpM0PBj1LuL6X6yxSZCm3MsmcU2ULJUE9jXjizG6YBbNvfjzf9/QO49/kDTBsh2/TMOkxF5f0VFfyoimkLGGLoT6jZTqhWi7yvyJqEJAla80JmoWLbNj72sY/hrrvuwnPPPYcf/ehHeO6554Rr7rrrLmzbtg3btm3Dxo0b8dGPfjRrt5nAvyjBu8fPoRPEqTjspMJvFLQ5k02F0nrw7oyCTYWYitKm0pj6i05xQfBjcL2SqSBsU6nYDkukR9HptuOyxcKDDMSR3l8hm4rJ3HEbiVMhoTJ9QgkTy4VQtDD1o0qkSPDScATMT3W6rdbCgklMaSJ6f1mGwdrc+OtXlPeSmKlwgpI2Aab+KlrsZ8Gm4qfv4NVfjhORUNINxp0GAyM1NmfJpnKgbwRP7jwizMvucjh1YBD8azADvmUYjCnL78DkbBNHB6vsPrwiXUGbYe+v8DMl4RDMTf/zCPUX2VTo7x1FC/OndQlR8mR/kdegYQapWmZO9JjKSGyaFqRiKnQPT+06ir+47QlRFW+Lal85vgkI3nnAVEJdNA2ZhcqWLVvQ09ODhQsXolQq4brrrsOmTZuEazZt2oQPfvCDMAwDF110EY4ePYq9e/dm7ToD1OovotkqpsJvFPR+yYtELuIFiCovPi+YrPsMpWmRXIonlgthoWKZbFIpMxbXgohoAm9TefClXixYfwf2HhtGqRBEy9OCn9IVFioU9CfHqRgmPROeqTgN2FTEayiZ59SuEiZ2FNWGete7J56BeXr64O+MqShiWgAwFQ2PomXiqrNm4e1LT/KN1f4z4NQJ9LsKsootMk5FwVRKVqD+orlynGXCJu8vF0NcmhZbwQAEpqLsPRr9wwFT4b9rO5LrtWrec0yF2BQxFVfxDkzOy+3YUEVKKBkwlWuWz8Vbz54NwFsjqlxWBW6d0feAwPuLPpfTtPAq6VOmdWEXF4tTLnhBkaqI+osWTsNfrz4TFy2cjo6ixe53pGYLNWLo+mIKpkLC/I+//1vc/uQeIT5InrOmwlZLc5OabrQ8dyPInKV49+7dmD9/Pvt93rx5ePTRR+tes3v3bsyZMyfU3saNG7Fx40YAQG9vb9bhKSEwFWWcijc5ao7L3Ev5Exa9n0D9Fb24TprUIQgV2jTksQSp2UnIeBvzxA6FUFHYVHhUIr2/vJ//+me/AwBs29/nBTZKQkXFVJj6i06/ftPUg8BUbMmmwj3wvuEqDMMIbUhRJ6lSwcSkzqLapZgzwvNR2wXT9NRa3Ik2ymOo4mfS5VG0THx37crgd789ilMhRGU7kT3SotRfvFCh8dP/nUWLbXYBU7FY+g6eqfC2JXqXjgvuXaVkKhWbzR3+u44rpgxSCRWyQfFChdLb8C7eBMs0mAswn+duuOqwd1cwDfTMmog/efPpuOe5/ajY6iSWRUtiKpKgXj5/CvpHalg407PTkUsxv07KBTFTMXmeVW1ZqHjvY91lXkxMB1fP5MO3bMWvtx2Uro+2qaiM7LLrNl9j54mdR/HSviCRpWmEjf1hTUIbqb9Ug5X94pNcQ1i3bh22bt2KrVu3YubMmcprskIIfqwTUR+ov3imIqq/VIvrlV7Pg+QjbzpNSDYZpf4KUrNT3971EzoKoXQeZU6oqAp20aTl7433vqFaKRWbkhSagkohSv3lSJsXfc7fBwAWm6BKZvcXtz2J9T99OtR+1GmeBKtsKOXbFd6NJGD4rM8qlUlFkbtKTmPC3p9hgF+7UZu17L4ZJTB5QcnnGAM8VYzhz0cxTiXsUuxwasDLFs3A/Gmdfr++TSMtVUFwKOLvUXZl7S6H5x49O1n9RepT+R3w6q8jg1Xh70Mc0wECpqGKiwH4TOGyod77/4yTJuLuv7gMM/ykqaSOEwqYSVH1/SO1yPXAo1wImIosUGhsBekd832Grpfa5+fKP963DTc98LIwFjlNi+xI88K+Pnz29mew83A4M0XeyCxU5s2bh507g/QBu3btwty5c1Nf00rw07oouBR7p1Ly8a85LjMQygkbgWBRTVQwlT+6+FS8adEMXHfBKSzOw+AocNCW2CafpsUyDV/1I56SxBowKvUXRQmLAtNxXezgEuB5Xj5+2V/Oo4YihXkETMVXf8knIe5a8upRMZX9fcMh1QB//zKKlolJHYVYpiKnu6d+eXVLzQmM9h3cAqQAPblPHrw6hbdPqAIBAUWcSsS98YZd2kRoY6L3Wi6YgfcXp/7ihUqNU0v+ywdX4LqVp/j3Rob69FJFdimmfmxBqITnPd1H0Qpc83lDvWxfMw0Dsyd1oGgZ2La/TziAUF15MsDT846K4GdjYIcA73c6xQd2Iu9zqqfCv++ClP7+2GBVqQ6WHynPVFQwDbD1L6u/VPNIbp9Xv8lrQeVVKmdNf+3QAG595DWW+qiZyCxUVq5ciW3btuHVV19FpVLBbbfdhjVr1gjXrFmzBj/4wQ/gui4eeeQRTJ48Wan6ahWi1F+m791TcwI1D29Tuen+bXj0lUPsetLNqpjKG3tm4NYbLkSHH1vAF+8SxiKdtklXTB5UShUDN2ZVCdXA+0sUmC6A7b2BEbLP151TjRD6nlL9ZYj1VGjziIyoF2wqomeaqlxulIqoYBmY1FHEc3uP4/p/+62w6dAGJ3zmiBHcVTu4hgQ2/8yqvpqMhyz4yeGADPX8M1EhSeVHxxFrotD8OGVaFwBgxYJpALzYlGN11F+8vYhUTXRvQGNMhdSqvEByHDFbt0r1SuS4ZJkcU/Geneu6QsYGGm9H0cIbTpmKh7cfFGNDhsTEljTvhxXzh4fs/cWECtmJjMDGw3t/Ad6755lE30hNKVSimEqUmilgW6bSYUSGLGj6hmvsPQ4o7DsdElNh3l+MHQWqxGYjs02lUCjgpptuwtVXXw3btnH99ddjyZIluPnmmwEAN954I1avXo0777wTPT096Orqwve///3MA0+CT/z4SfyPN5yMN58hqtH4F89vDIaf1sDhT7UUp+I4+No9LwEAvvHecwGIwY9x4KlvPe8v2pAcf3NUtc27Cnt5wMTYCdq05RQ0svfNUNXmbCpOrKGegv6oGzlqOdamwudT8isoyogKZSmaJmOC979wAMeHqpja7TEpEgb84dd1xQhuPqEk3V9n0cJRVNl4ZKYiF7EKNqmwN5gKshFZ5aggn1Zpw7xw4XTc+8nLcLofM1EumOj1BQhfo57PmkvCnk7D9NppTqS1qQDBwYK/Rc8hIPhd7QobZiqBTSX8bOj6S3tm4Bv3viQcokjtx4RKUqZiiRtq4OYsHiADQ33wedE0Q2yKHAmEPqRn2lE04bjAnmPqgnJ8mXFZ/RV3PaF/uOZltbBd9FckoaKw1TLvr9ABow2ECgCsXr0aq1evFj678cYb2c+GYeA73/lOHl0lhuu6+PkTu/HzJ3Zjx4Z3SH8Lfg6oubeALDNwrwW4uvV8sjj/R9p8pykmHQ9BqISCH+l/n6nYIlOZxAmVr7/nXOw6MoRJnWJlQksSKipDPfzNSJXMzjK9lN9xNhXLENVfss5WrlFP8S/8vfFjkxHHVLq4jUaoAUMqQynZZ8BUghM8737cKTAVse4IEGYqJGQoToUQdeiLC8gk8GquoaotbFI9syayn3kjLiV4dFwXfSOSUHHdkC0hy0ZCKlD+fuXCXyq1Gu9xRfNJUH/JNhX/+jf2TMfXNwOPvHKY/Y13pfba9v6XU9TIKEgbKgk/wRvSDA5Zgk2FGzdhagL1F7n0vrjvuHJM/FiSCBWZefSN1Pz3GI61sozwYTVgKt7vNBeiVLZ5YtzWqI/zYuVTwAfRvi5TU/GGT2IqvAcWffv0mRNw6w0X4KKF02PH4gU+iX7/rC3JlsKra2T11xmzJ+Ld580Tvl/ymQYPvhwswTQ8/ZfKSFgwvYp3cd5fpunHqUjqL1VEPVX7o8UtsignVBXP+360UHmFU9nxwp15f8lOAhYJOjHuh37mA+mODlXw7O5jQX+mEXpHkzuL2Hd8OKz+UixQPjZGHicPekcTOgqeUIlY7Hz8wYRyAZZhwHFEvToFFdIGEoqibmAfiWIqvPBXNWtKGzl9FhWnQs9zzuTOUFu05mSmEmWoZ22a4oZK3w8VrXO9dC9dJc6mYoreXwAwRbIxGkZ4HZMQeHFfP1Sgyz0GV1+oXLpI1K70DVcj5wg5GImfef/LTCWiwnKuaEEXo4O42Ai5FkFQG4RjKpL+/SgnVPgN8E2LZirVADx4VVBoLK5obK5xNpWCaWJCOdjgVSdOKjHLQxX8SItI3vDKHNMhFqDSlVumGBEdF1FPdg0ary2xKNWiivKjL5omPnjxAu7eRK8n7//getcNmAV/Mq5xNpVO7hT48MuHsO7Wx9jvqgJWU7v9rLeGbKgPv3fVvFNNRRKOFDgbVTe+gwVDmr5HmK/+GqkGBmf/3gqSQZsxhQaECrFwfvPkSx94fwt/TyVULCMmTsWg+wzPuePD3j3KcSdy2n8ZMmMj9ZZcrtdxKE2LyAZ5m03PrAkhdbBqHU73Sws89LI6DIJ5pikM9SpMKBeEGjj9w7VIG54qTkUOfqywudB8pjJuhUpcWgKeqfDBbZQrh1eV0KLmT00s+2vC1Tq9u4Tp3WoVmZfJlfNS4mwqpil6lqn2nZJCqNCkld2lvTLD4oIU4lS4rMUyyFDvuK5wUgsM9d7/Ljs1By7FvMpjxHaEyGNCnPrrjT0z8C8fXAFAPKXWVEzFCby/HFcOfgzcdaOgKrU73XdBlQ8HqgKKqgBL1b2RcCSbWVTMCzEVOi0z9ddwjcV3ULoa2c27FqP+qjd1ianwlzluWFUY1a5gvzADbUAoot7/An/SpuH2DddYtUuAZyrJvL9kASezJ9sfT4lLje+xdu8mb3zz6fi/f3YppkrqbdWzO2vOJADAf28/FP6jNJYk6i8A2PhH5+OW6y/ArIll9I/UIvcby1AEPzK25guVmlZ/ZUYcU+HlDam/AO/Fk+3AZkLFZyp+rEhHwVRWfozDJ1adgRvffLryb1/9xYt4cV8fVi6YCkC0qRRMUzDUq04qZKjnQadglf0mVNnRMll5VlYaNaJoEOnU+U3KMHyhLDkcFEyDGUzpM9d1vbgQxYOLel20EdCmM6KorscHG8o2lSD4MQimU7lhs/4U905MRVZ/qRa5SqioWBhTf9VhKhR/MJmrETJS8wz186d14Yif2qTmu6DTNfxYVEKFjL5RCNRfIlPhBaTqXlWZgS1T7SgCBHOaF/TdpQL6R7wa9gK7SMxUxOegVH+ZBlOPynEqhBkTSugsWez9E1S2pFOndaGrZGEwwomAXq9pRB+gZHQULbz5jJmY4LvURwkEstmSbRjgIupHwVA/bplKXFoClaEeCNRfvP6dJvsxvzJdR9FiL85IqKzuKhUYPVbhP5/aEwrkoxM3lZSlscooWWZoojCmInm7uCqbis90hCp4UUzF9/6RhZuBYDLzrq2yTaUqqKLqe0gBwQYRCJV4mwof/Ohy3l+Oi5BLsSyMS1ZYQAPANJ+pGBDfgUrIq6L2VfdGp21iotE2FVGoGAYwVPUy+E7yP/O8v8QgTSDYSFT7SL3YlSlKoSJqAGTbA3992KYSZaj3/rfMIIaL4r+OD1VR4mxKJHhHktpUYplKUOxLFCoiawHCcVuqV2WaBs48yXOwOOfkyeG/cwfXtClTJnYUOUO9CC9tf5BhnR8PwKtCW8dUxq1QiTsNqAz1AHyVjfgSaFEzplK0ggDAHN8PL0xIhVQwDcZgvP6SMhVyKeZP1T5TIDbC6Zllm4qqEl2Q/sMNqWpItQEEmw4FfHr35LXLsyTZrhKlrqQ2SBAQU3E5VYxYQ4W3qQTR7TXHCam/5KDVkyZ3CEKcMM3XqfcNVwWVl+p9qKL2VbcWMBWv7aj4AVJ/UbJS0zCYVxRTfzlBWh+AYyoxevQo/TxhEifECHKW4fnTukJrIEiNIjIMysigCn4kUEokYm/9IzVBLUbMtx5TkRNKBvnIDOEa23VZUlWC6Anm/Sx7f0Wd9s+e66nAzp0fFiq8Y0vaNPQTywX0D1eVRnbepkpqWq8f738aKs2FVsSpjFuhIr+4/7N1J0ufzv+JCggB3v+0KGjRh9RfRbPhMq1x4L15qrZnYzENgwVYAtHqL1psBWnsJem06Pjqp5JlsgVcKngVGnl2prKpmD6bofoYwt8Mgz2TOKbCs6QkQqVoBZ5YpAaiU6rgbSapZIqcKzNjKpznGrUlxwB96urF+PePXBgaxzSfZR4dqrJaHUCU+isZUyHhOJHZVJIxFcs0mH2PPrPdYL7QNUB88GO9E+uUrjBT4YOCP3X1Ynz6bYtDajsKuBU2cN9Wuf/4CPZL2RT4cbD3Ug5KL/BCJalNJaT+klSodF8U/CjEqfBCxSShIjMV9bO76qzZWHrypFimYhjpa5tMKPvqL0W/N7zpNJZB4UcfuQh/dNGpQn/0HTrQpc1Y3QjGr02F26R2HRnEp37yNAzDwO+fP0+IqTDNwKOHaDog5mACAkN9R9Fi329UpvzdtedgQrmIj/3wcfbZtgNBgjgvC6nDmMZlZ8zEr17qVRvqLZNNnILlMw7m/cUZP+HbVGoOSgUT5aKJvhFS+TiCUClaJm5bd5HA9nhDvTwxSbUBBPYN3qhNi4i3h8hR9SqHGH7DohP7sKIqJ2+zOT5UZcFqNrcJekzFV3/5bXWXxOnfWbQwqSPsTk1xSMcGq5JLcXjMsnqnaKnVHSQcA5tKhFDxN9opnIsvpeygjZ9sHTRf6P0QS1NtgvJHxEQJzKbC3SMfp3LlWbNQLli+NxPw8St6YLsuc68vSqoYwwB+88oh/OYV0ZDNj43eMZ/+hVfpJLWpBIG5ceqvwDklUv3l/zypU2Yq6n4vXzwLly+ehf98ak/ob4FNpRH1l2djUh0E/mDFfJw8xXPHPmV6F2NVsn2Nqb+0TaVx8AdGPiU1IDEVI+xS7F0r5oliQoUrkNWo0eu9K0/BO5aJaWpe5LKO1mwXthuM5Z8/cD5u/sB5mDe1K9QWX92NZUFl3l+GcJ0L+N4uZlDi2DK5BRYY6i9aOB2X9Mxg36dgMcdxQ/dtGIGKhzZuPqGkiqnIsQYqpsKr75hNhTEV0TgPeJtNxXaYp10Qr2MINpVTp3ehXDDZYiREnd7JUHt0SFRB0HPYfXQIH/juozg2VA0ZrwumqVTFDteS2VSoj8CmElw3mfP+omBZINg4SO2omqZyf3KcAwm7qISSQWp17/+ZkzrwqavPDKLY+YqZhhG5VgT1l7/WeKEiqL/8a+tF1Aeu1d7vi2ZNwAULpjH1FI3bcxxwpTQt4Z8t08Cmj70RS/zv1zvtqw4IgYpdrQ6Nw4SOAouolyELCUtyKydHGlp7rWAq41ao8KcB2sBoU+HfqSUY6oMNml4Cbb50OixYgaonz/ezvTdI9Pi+f3kE2w/0swnTWbLwtqXRudJCQiWyRr3HYopWECwVpHlxYm0qdK9VbvMK/mbg0VcPY8H6O/DaoQF/TGaIqYjqL3FjUG28/PhlQ31NwVRYDRZfqDC2WaDqmF6fb+yZgac+99ZQkGfUGYF01ceGqsImSBvs73YdxUMvH8T23v6Qob5ghYujAVycSod4spRBByJiJZZCqFCaFrmGRlxCyXDuKnErMLiDFoH3ipRZgDz8Iuemy68xGfx9k1agm4uTko3PphFWnYbapLLC/hindpfwHzdeLARYGkaQf01OKMnugVsH586fgmndgVt3bP+qzV9yHkiDieUC+iu1iGBT8XfGVnlGbRg6oj4P8JsUbZb0YAX1l2FwC8jgmEpg7OZza/EqlaTeX2nx3N7jAIB5U8NRxirQ6WRiRwGHByoYqdkC6wI4m4rPVPhiSpZFacnj41QAz+NHXhimYeCpnUcBePm5AGIqsvqLEyoSU1GpBPhFLRvqZTsKABwd9Dz0pktChVR9QUkAL4mh7P5Zj6nYkkAl+UHOD8NVmwkwgldQLdwmH1Ef1zcdiFTxLCGhIm1cNduJ3MxVCRGBcHkBOaEkYyp+w3LyRoKcEiUK/J/oHRct069r5ITiLwqmGWIqP7nxYoHd0JjibJ6WaWCEzffgOtH7q76gVUHFVAK7bfo9o1y0fPfn8ESSn63spEDXsDiVFqi/xq1Q4Tcd2sBo8xHiVMywSzHARaWbBrNVULvMptJknqeK7ia8Z8U83P7kHn+s3iJbMncSXjs0iJGaI5y4ADqZgRnqZabCJ5RUJgrkdLPyn/lRUuYBi7NVDfgxB7HeXwqmItpUJPWX4PFFQiWoFsn3QWyTfqd3PCjp5qMWHH3/wtOmCZu/IzEwrwSubFNRq7+Yob6OTUVOhc9vSrztiBcqfJxK1MlYfodku/mTyxaKgYjcNV6aFu9nGq6cZ4sgR9RHx2+EVZwFy0CHL1TkMrmWaYSyFFNGZ/4a/n9lv4bBvCSjvL9CdXUSCgaVELUSCiQVAoeXMEOTn7vspCBfExVkmydOCPUXLWBSTcgR9bxLMfOW4DYgXj9cc7ja3zlJ/Xr6dBX+/vfPxUtfejuAoCDYUt/rpFJzhLT3ANlUiKlYbLFSnAtfpEsZp8KEitqmQiC2wJ9k//lXr+Dcv71HsqlI6i/FaV40mprChiJH0Xt9e0Jl2gRvsx30s7mS3SJwYCDdfDjbaxQe+cyV+P4frxRtDFIS0JGqHcrCG6X+khlIJFNhbMt7X0r1l29TYayBOwAksWUAwYa+9pIF+ORbFyuvc5wgTQvzLrKIFYjtC56HJpRF1uT7IaZimQa7X5k1F0wjgU2l/gbOq9GihEp0NcXY7uNtKg3sGXLaHaFdqS8Vc+TnVlSQbZ4Yt0KFPx2Su26VpUAJruONiLzRm6m/TFM4sVBKDCA/m4rsskhI61O+dK4nVEZqTigy3DQ47y8rqBQXMBWXs8VEn7RUGxU/sWljV50W41yKVeovmW2VC6aSqQQ2FU+gkbcW2cHI6DwiqQAGRsTNKW7BnzS5A12lgshUZKFScxTeX2aIvXjX2iiYBmMgUUKFmAx5pdFlfA17UkvJhvqa7UbaidTqr/DpXBairqT+ogNXnPrLMgwhqzIP0abiMxXTZD+H0o9YRn2biiRcVTANgx1s+LVSkA4y8nf4/+v1L3w3oUCKa0+V3kXui1zeRdU3lD83C+NX/cVtUlSXgegjv8RNg0/jHn6BlmkIk6tm529T6S5bOKhIbqpKWBiHub43U6XmhPJbGTDguk7gUhyyqQT1upWGXU6lIk9kA2BlmEmokEAsmAYoMkGwqSQw1MtCVSj1rGAq5KFHBtXBETG/lqz+kl1Tk2RwjVN/DVdtxoY7il6RqiLHVEh1WrA84VgumGzzjTpBful/LMXKBVNx3ilTAAQb2oRyUXCEqNmuwA4Bz6kikqnI6i9ShyrUpgQvSzWEcQQbuNieHGgY5fHEt0/2qIJpsJ9lB4IkB62OooXZk8qs4JkKXrqbsE2lFKP+SixUQjbH4Gd5banq08tgZYgVfvchtaNK/cUJtDxj66IwfoUKt0nRZsO8v+Q4FaWhPrCp8Kok23GZ+iyv9xOV5ThpmupLTp+OA30jgkCUo8VNE3Bsb2J6p1wKfvTiXEZqDv7pl9tDJ0M2Fv9ePZtK9GlWVn9FMpUELsXyc+koWpwqM7j+2/dtw/N7j+PIQAWdRYsJ1H6m/vJO+Tz7BIBVZ8/Gxl+9oryPKFjCyd2/L3+xHzg+godePsTG6gkVj6m80tuPd/7jQ7jyrNn4x/e9ASM1B+WixTbfqL6ndpfwoTeeFozRf55zp3Sw7ziu65cbEE/oNduJnKPyZkTMNXQ6F+a+w5WT9tuJMtQrUobUGwc/hnIUU0kgVIqWiUf/+qrYawyOqfACnRdaUeqvetMkZIuJYA03XHoa3nfBKfGNIThcqpiKLMzlGB2vz/BabCbGsfor+JnKklaZUAn+Rmm5Ae+Fs/xC3KmWrw9dc5zcbSpRQiWp/vOHH7kI937yzew+ZDdJAEIEMXnXeH0bOHPOJJbuPiqDKi+w5LlpGEHgHBnqaWHxmxS59ALe8/3liwew8/AggIjgR2lx8kyFF0JbXzuCX77Yi6NDVUztKrLxDUjqL8Y+/Xb/6m1n4o6PXyo8o3pQMRWK7/mHzS/h//qBb+zUbXkVAb/6ixcxWLHx0DYvNfpw1RaZSoxTBg/qfv60Lo6pULkBUfVRU9i/CF9+1zk4Y/YE9nsQtxR9yubjVOS8WiGhIsWpREGIqFcwFZX3Vx6wzMCuJcSpRKjCgEA41tucZQ2DaoMHgPddMB89syagHuLYWchQz9ypg8/MCMHfLIxbocKrRxhTccIbEuUlAsRSrHxNEgqaKvpqItpM8npHkewg5cmCnzTygqAiSbL3V7lgYs25c/HM56+ObzvGo8g0As868vKRg7AAUWB9ffOL+ND3f4uv3fMigIjgx5D6y2KnS9lOYTsujg5WMLmrxMZHQmWipP7iPWRO4nJ9JXne4sndV3/ZYcNxpy+ki6bh1z/xxkK5xUZqnvqL3n3Sd02P6dRpXWyusjQt0uZRdaKZyht7ZuCeT7yZBYCSMJFT/4txOYEg5bM4yNcB4TiVKBhKpmIwtqny/vL6i2wyETx27r03uZwwQRZgdFla9ZcVIVSSqqJimZ5sU1GsOzlQtdk4IdRf5HlSVQQ/8vVUPPUXMRWixgZuveFCuC6w/qdP47evHeaCH/N5SaWIU2paQz0/+eTNwUAQ/CjbVOi7937yzdh5ZFDZNm+oD9lUFMNkm06EUDnYXxGuF8vUeptnSP9cVDMVwDswHB0kpkJCxbepyEyFf05SPqh6ENVfZKgPC0Q+5mKoajN1HZ/doaNocTaVZO96f59noTplWhebq7bjeGlapJN0HFMh/OxPL8Gze47hJ4/tgmmENzDRpuIylR9jKlL0OiFUdTQClmLOmoYYnMuD2epS1CVRwVN/Kby/OGEYZm3J1F9h9TDfL/dzwrHGzQ15jRSlgwU/nlbEqADjmKk4CqZiq5iKIadp8T4PNiCTqYss04Bt5+/9FaX+SptSgb88bGT0hCkFPzKXTW7R9syagLcsnhU7lkpE8KMMlU1FVQaWpa/nDgFRUdq895esLqs5Lo4OVTG5s8jihwYiXIr5xSW7vtaDnAuLb5cHeS8VC6ZQMZGeATGVejYVGbt8oX/K9C4uHYvIIAPvr+jgR8LsSR244szZbJ6H7pcXolyMlmxTkTe3pM9VVZ655rgcU1HbNVQBumngGerDcSo8OwmpkBOqkQQPMlOsFtoIU0nDMFTrjuUd0zaVbBCYyrBoqOepChUQAmSmEs6fRUGQzPur6TaVxtVfKpsKn6U4ytsnCnFMRbXImE2Fu7Zf4VZaUTCPssItEhAN9bL6q2a7GK7a6CxaIfUXz1Tk07hcYrYeVExF5ZVDNoGi7/lE46U4G8+mkp6p7DoyBMBjKrRZOI4rpL6nz+O8v2QUTEM5D/lv86nvqd1I9VfC58r/KcjA4DBVWJRQSWqDioJpQMlU4ry/kgYwWtKewT8b/rtJl3eafSAqTUvadrJg3AoVR6X+chTqL4MvJxy8AJWqhNVCcKP9/9PgJzdejF9/+i0x3l/pOuHHJFN3wzC8iHpK02IFJ+kkEIMfk1/Pb+ADklCxzKBeN+9YEbVRRRnqgSCq3FvE3mckxLpZnIod0pMLp8gENyamaSFDfViokE2l4GcpJpvTUMVGpeZgYMRG2a87/8VrluCa5SfX7RvwKo8CwJzJnYGh3nUxXA024kD95aQ6Datzvon3S4JUdl+WvymXE77qrNnKTU3Q/VsBUylHGerJ9pORqfD3JZQTjrCvAMmZCi9EC6YZ2kMISUMS0jAMmt+qed0qpjJ+bSoqQ70db6iPcikmUA4wx83HnkLpJWhSky2BH1samNJk5mEYEJlKMR1T4d1U5cmpUm+oNpCBio2iFdQA75k5gQlv/n3RhhH26Q8M9XI24JrjlQu2zKAS5nDVQWfREvz866XuqAdV8KOSqXDuscQkAG9eXfb3D2Df8WFcddZsAMAfXbygbr+En/3pJXhhXx8s0+DUXy6OD1eDmiuk/nJcSHbuSHSXLCF/FkFOoCl7PjK7jiTk+cOKZRj47toVeH7vcbz9W78WrlPZVGzbDYIfQ269wedPfHZV6jTyqvuqV6Qr+I73fz1BLceIiDYVXv2VbKypmAqzn3DjkdSizcb4FSqC+ivapdirp+L/bPBZismLiZ8gJotezvP10ESW10cWoRK1IKq2F20fdRKMQlDsR12kK2osfDzJYKWGkmWi6ntLTZ9QYkKFjx2iMSltKiSEFN5fpALih9NVsoKDQtWJXaCJ1F8qpqIQKlTmtWD6TIV7DvuODwMAKxqXBj2zJqJnlle2loS77Xh1ZCZJGY9rtgtTnawhhI9d0YP3rgzHTPD57YgNAsG7IUYSyiTAuxT7F3cWwxKOfx1Km0pRbagvF02WjboRCMIsqp5KyLMqmforZFOJUH81Mtak15qSYEvbThaMW/UXv0EH9d/VhnpeVxpmKqKOteZ4oY95+nzTpG4kepgHvynKub/4/F4N2VRIT69gKqpR0sLiN9yBkZogxEoRQoLGFFJ/xXp/eRu3rMPu5IRKxXaYikWFRIZ6/uROWYol9Vd3ycK07jKbW3xqHx5P7Tpav8MY0PvuG67CcYFJnWIesbjcXzJmTewQ6o0QZPVXKEux/67kZIey+gsIVIJR7ReY+svhmIrapXh6BoECyKriCKYSkaalbpyK5HwgCpXwc6mHJN6WhCBOJdxnK5JJAicIUyHUVMGPZpCWhM/9JQfK0bW0sPJkkjR5F82egI9fsQg/+M1reOjlg6l1oPypUpVigvnl+0W4rlk+l6V2qQcW+2A7iZhKgdvYCAMjNkoFE2fMnoC5UzpRLpic+gtMNRbt/WWxzLIqpkJJFQWhUkzBVNLaVJj6KxjLlWfOwvc+tBIv7uvDqdO78PSuY3CcsLoOAG5633l1+4sdi/+ODw+I5YUZS0xhqI8C/0jEGC1fqHCsSBibzxhdN9jg5NRBdJ38s80xlSiX4qh8eUnB96syzvMHTPk7adRfYQ+y4Oekb0YeR7lgKj0pAc6mol2K84dK1xpVT4XemWUGNUBUNhXLMFhEfZ7vh1iDZZp465KTMNkvyJSn9xeMIDVKuWBiwYxufOu6NyRXf/GnX7lpxTBJ983HcAxUPKZyzyfejH/74wtQKljMHuG6gTChWIFYphKyqZCh3hQ2wi6ZqTTBpsIb6uk0vvikiVh7yQJWAE0Wgp9+22Jcdfbsuv3FjsUfLyXSlNVfQPZ5KieUdNxoQcDDMAJvMpovavUXz6591mO7QnAuD8ZUJmQTKqKqODwGOV2N9x3x/ygUJNUf/w5El+JkY5Xto3HaBZX6i41bq7+yQZWgkNVT4T4TDfW8+ktlU4kuqZsFRUZZvd8Dl9p0r4efMyrqrkr1nbhtJlRUEfXJmEr/SE1YECUugM12XKFwmKrdrmIBIzUH/739YOjQYDsuqsymIqm/DGIqYe+vevcRdw2LU+HuUa57T8k6a7bLUuEAgZtzFtDcZEKlMyxUss5T/uuO4wkWcZ7571lR64PeNT1/pXcZ1xix5gUzuiOZCt1bVqYSdQAzTU9bIauP+bHWe6b8FCuYRuT7SPpu5INQKcb7gqm/uDHQuLVLcUaomIra+0uOU5HUX4JHVX5qBR58LWzh95R9mIqxBn8LormTshMeqiA1gmqYgbcYZ6gfsYUFIdtUGFORTriEa88/GQtndONPbn0sZMeo1DwGGWw63gbbVSqwhVaPqTSq/uIFp2w3KJoGY1G8d1VXKbtQofdNZZSD9PjhU2rWPgBiKuLctxTvmcAOSzFqI35eXbpoBn744Qtx45tPj0x9T2mApmW0qfCbrnzIKlqGkqkkjVORmUrU+2hE/XXeKVPwwYtPrdu3sF65/a0VGL9CxT9FTuKy9Sq9vwwpToVTf5mG5EXBbU55vh75ZE79pA3w4udMqPIjApVeI9HIqlxCBD59BZ3A6fJKjKHes6l4m4TrAlM6vWSQZISVN6F5U7tw4+Wno2+4hh2HxHQyw1IVv4UzvUR9fDBkPZtKkg1YCH5UGOq7SrJh2YRte/EdPDuZUE7o6xs3Fn/AlBl6cjOYCvcz2VT4NicqVG4EPgVQFOQ/XdIzA5ZpYELZa1dmdMel8gaNQohTkYVKRHaBRmwqskux8D4Sq7+CC1edfRI+fuWiuteq4lS091dGEBvhJ1/NcTAwUoMLvoaKFKdCrrM1J6QqCYyS0Yn6GgFttCTsVCVBk0BIIx5yKebofgNMJS69hME9v0+/zasaqFLvDFRqLHgPkJiK62L2pA7c84nLsMq3Nag2xMWzPXfa5/ceFz4n1SY9s9NndgPwmAPLPF3PppLgeYvqIIX6S7rvomWg6ttU+GeiiglJCxru4QFSf/neXw1sXNF9iA3I9XT+/IoefPzKRXjPivmh7yZh3FHP/PLFM3HzB87HIimLLwmVLO7Ecr+yWq7Ipc/h0UiN+rctPUkIbI3KWBwHUUglu1YVUd8WQuXw4cNYtWoVFi1ahFWrVuHIkSPK666//nrMmjULS5cuzdJdKtA6n8LpXl/a348ln/sFevtGhI1bFacyUotOR1K13VyNXjSB5WjlLOovuZ6KEXMyS4K4iU1/MQ3ggxcvwKtfWc1OsDwcNyiLC/hMxXbg+uk/DMOLw6ChqogapQqXhQqB3isxFdtx2futKN6pcI8pn7dS/VUMu8B6NhUH3Rw7yUP9ZfhOJlTZlJ45fxbK06YCkJty8HtXqYBPrjpDqVKV1V8qRD3zomXibUtPCh1gKOZsWk42FZWqq2AaSi1BUjUSvzdce948gVlkVX8lzTumcqZoC/XXhg0bcOWVV2Lbtm248sorsWHDBuV1H/rQh3D33Xdn6So16BQZRZP5B60y1HtMRXwJvPG5GeovUtlRP6rYhjjwc0ZmCvytJKk2J4NfdyFhywllbxzRT0dgKpYJ1/VOvy5XZIpsvqpF0F0uYP60Trywr0/ZPhMqMzymsvfYUCjWKApJFh3/TlQJJWX1V8EyUbU9d2dR/ZWPNz/d28RyUOqYv9+sZx/5XdZSHKhUDhcPr78C699+Jje+dAOk1DuZbSp+tyrhXrSi1F/e/2nGLF+aNaFkXaGicCluK/XXpk2bsHbtWgDA2rVrcfvttyuvu+yyyzBt2rQsXaUGnSKj678HxnE+TiVwKbZDgXJU/zlJSvE0CCLqialQQFlaocIzlaL0t3B/aRDntWImPMEBYqwCnW4rNcerXGiIwjRq0Z0xa6IyDgkI3tFpvlDZc3RYOEXHedQlcbbj8zWpUt93ScKCP4jwG1h3DjYVIBgzeX7xnwH5n04rijilKMgOKABw8pROzJxQDsbX4A6Ul01FPgQAHnuR834BnPdXis1ZnsONxKkUFKyj3rXCIZAx/zYQKvv378ecOXMAAHPmzMGBAwdyGVQesBlTCathgOAE53l/BZ/xrrOxTCXHF0TqKNqkGFNJKVR4TJDLCfOeYQ1keFXRaYIR8bkKZcmmAgAf/NcteGb3cfZMg/xS6jbmTY0O2CRX0FOnd+PkKZ3469VnCX1mTdMyf1on/nr1mbjk9OlBkS6eqRRlphLMJ/6dyK7HjYLGzKs7rQZOw0lRtd3EbZLtTn7kcXMpKVTCIA1onauFiqlcI7xGI3E/0rVZbSr1BJrFwhMU6q+xElF/1VVXYd++faHPv/zlLzdlQBs3bsTGjRsBAL29vQ23Q6fdKRFM5Q2nTIUL4OQpXVwaAyP2VMAH0TXDUE+qeRZomEGoxNlUGvFXj2MqAdOr346KqTz2mmeLo9OVXLJWRmfMhkzPrlQw8fD6KwAAB/tHQn9XIckiNwwD6y47HS/t78eOgwMAvPnQVbIwWLHD6i+uv7wN9UCwyfBMRVSX5NINQ7Xm1DUWE0p+xuiwY0fwc1om9dl3no3/enpPZmFJz0Wl/ipYZh2X4uR9h1k990vCZtK8T2JYKuGVVynmeqg7s++9997Iv82ePRt79+7FnDlzsHfvXsyapS7wlAbr1q3DunXrAAArVqxouB27jk3ltBnd+PyaJQDUTAVQlRP1/lZLmP49KWT1F19XolFMCjGV4OdGTodxet3g+SVRf4k2FVUfxNii1kDcKVWl2islZCpp1BqW4SWKdF0XVdvB1K6yJ1RC6q+gbxp3yTIbihVSjsMf82ROqHjR7IYyUDUrak7yfGJFy1TOtSwuzzdcehpuuPS0VN9Rgcagmksly1BWYw2CH5P3E29TSdaGyp3/8c+uUiYyDWLdws+4LSLq16xZg1tuuQUAcMstt+Caa67JZVB5gITKlM4o9VdYRcDbVIDw5ssnvGtGRD2N2WK/N94m+fkT+OE2cmKJ8/5KmmgPEGuOlyVVEb0Hh6m/1O3FCRXVGMqCTSWf92aaBqu46LqB7U5miHLG2nLBRFdO9hQg2GQmSTa0IKYit64AeHnOks59L2WOQqgoVDOtBs011VzqLFlKNkyvMg1Liss+kfg58o4X/vOa1l3C7EkdoWtLBRNf+4Nz8a7z5gXjZt6kiYedCZmEyvr167F582YsWrQImzdvxvr16wEAe/bswerVq9l173vf+3DxxRfjxRdfxLx58/C9730v26gTgFQosm2BIFJw+l9MqRCV+rpi55z63t/0HImp1DJIFXlzU0VBp0Hc9w32/MLf++lHL8FfXBW4VMYyFWZTqaf+iklToRiEkDAwp03MMr33RafFty+dg29dt5x5nQX9iU4CnSUrN3sKwKu/pPgYhRokD9QUud+iUJIyRhNE1UxeI0sHmmsyswSAv12zFJ99x1nh7zTCVOTfjei/RUGVcTgOv3/+PJzMJYpttfdXptk9ffp03HfffaHP586dizvvvJP9/qMf/ShLNw2B9uOoeABTECrBZIk7RYnBj/m9INr06ITeqPcXj2Ya6qNsKqoJf/6pU7FwRje+ee82AKJNJSpZIDkoRK0BVWJCgkoXbhiGl2fMdpTeXzd/4Hz851O7I9tUwTSC8syAJ8RV1RvlIm8dBSs3d2IgmK+TJUbOZ9vNE6os1VGop/4yjPyFXlIw9ZdiLi0+aaLyO3HzPApxTCWx+kvIlJ646+A7KdTTeWDcpr6nU3/PzAmY2FHAwhndeGrXMfZ3lSFLZiphQ733RlWZerMgKk4lym02CSaUZEN98HNDNpVYl2L15wReiKm8v+Qx0m1HbThx6q8oJlLyAy1Vf3/b0pPwtqUnRbapgml4QY0UTR+VpaBgifOpo5iz+ouYSocsVMLxCnkgjfqrKGWMJjDPy1ESKEAw19I4TDQSRBhnqE9aTrgQc6BLNIZ2ilMZy6ANubts4XefvxpvWzpH+Dv/eOk90cmJnr18oqdTQs12E0+IJJBtKrMmloX/G0G4kFa0Wi9Ze8HPYZfi+FMxbzwvK7y/WLtSnEoj3l9RLIyVF8hJsWyZBhy/6BkQZJZWXcePraOYM1NReH8BXA6onPcRVTnpKEQxlVbXTFeB5lqcKjXqO2n2dSPC/pimnay53JjXWjuov8YymNFb4Q0BRDMVwNODe6daWT3jMxUnuQogCYqWaFN529KTcNMfvgFXL0l3eo5DU72/KNI4ol1eqAguxRGV9d53wSl4ds9x/PkVPcr20hrqgUCA5WdT8by/SP3F1+Tgwd+7ZRq44dLTlClssowDCHv7kXDNQ710/qlTUbUdPL3rWCr1V0dR7eWWNNtvM0Gq5e4UQqWRE3+Uqtj7OVkbWR0bgkwLWqhkAlVnpJcYl66d0XF2ggJgK1JuUPxIzUWxlB/JK0mGesMw8M5lc3NrH4h3lU4CcWJLbdfRNVOmVseNV3/RGLvLBXzjvcsjxxJrU4m4N+orN+8vw4DjBHm/5LK3BNkd9A8UiRezjcP7X7apkKE+j9v96UcvwX89vQd/9sMnULVdlCMEqIyPvGmhUq3IvAVHUf1F9ZLS5GBL4zovf0f1e1Jth2kGVTQzqb+0TSUbbMcVHqJswI0y1AO0MTmKNOa+ULEdwMhPL84i6jO4ENdDVpuKWEkuWnhEoeAX5Io31CcbS6z3Vx31V57eXwJTiei3KNlU8kak+ouYSk5qWj57d9Jo9gUzurFA8oYD6jPbVoDK8aaJzG/ETVt+/o3WuimYFHeU/DuEVqu/xq9NxRUT38mV3PiXzcepAMHLlnX3fJqWXONUJKbSDGS1qQiGelO9UOIeCT1/OfW9qp16aNRQD8Tn/koDyzfUv+9fHgEQbajn+2vE664e6JnJQoX6zcuhhBVdyyFGq9VFo1Sg+jsql+IopMlxR5AvFZhKinZYrEkG9Vdb5P4ay3BSMRX63xCuld0NxZK6+Y2VTrPNFCqCTaWBzU0oVpbS+wsINt04Q33SUTWk/rLyZSr0PCjtfFQ5Af4wk5dA46GKqAeCOZXXxk3vPI+yD632RlKBCZWYuSSjkTiVWJtK8mYysQ0WR6ZtKtlgO+KkDaknlIZ68XdZzVLgTmt5en+VJJfiLHjvivlYevKk0OdCnEpWQ30EU4k7CdFmzwc/liU7RCWh/q8x9Zf3nbw2Mv5ep3QVcWZEbENcMG0eKPj2KtngTH3lFQfCq36z3kYjJ/68QeqvjhRCJR+bCq8hSdxMppooQZGu1F9tCONWqHi1tIPf5ROswFRIx8u8v9RCJVhYbq7pL6jdPIjK3/3+MuXnecapyLYQ3iU7CiTUOwrRTGWkmkyoxBUZa5X3Fy9Yf3D9BZg+Qe3+zTPkZmyipmlgUmcxJDyCOJX8+gHSBT9GtmWI/48Ghn1DPX/IqYck9YKivhP8Hvycph16n408e63+ygm2VPZUPsGqbSre76SGCqUxN5uzQdCGd2nPjNzalCFmKU7/2vmvnDq9S9l2nLAi19oyn6ZFFiq1ZEIlbjGq6mDwfeUZp0Lg85nJkCPq84ZlGKHAR76vvNVfedQSymIfyAuNMJWG0rTITKUBY7/XJ6m/0n2P71OrvzLCdt1Y9ZfKpZheHKlhwkxF/f2sKBcs/PL/uRwnTQ4niMsLKhtSGvCnnNMkj54kaoGCgqnImwq5eWZBlNDI3fsrhrnxECLqm2GoN42QPcXrN2+m4v1f48ozN9zWGFB/jVQbYCoNCOqotP9p7zzLIYHmqo5TyQjHEU9UseovaZKPMHdD8fFYTWIqAJSul3mikTKmPHgBsGC6ONZ6EfVAwCBUJ8PJnUUcG6omZipxkL38CHl7f/GnvnLMxiSUMm7Cor60Z4YyR1yRnYjzF6JZGUaWU3deoPeXzqaSnWE1KlCzsDvNVHKCrP4KBz8Gv8uGemIqcQWXRlMf3AhouI2Om39ecr6kJHEHFHEun+pf+tLbsfW1w/jDf3mUeeRkQdSio3eXm02Fa6YjTv3V5DiVj1+5SPl5EKeSD7KmClG1NZrBj//ywRW4/YndQjbfemgkTYsMeoxp2yhk8OZjhnod/JgNtisyFbmgDf98A0Oz9wN5YUUZ6kMNtAGa6caZJHtrwTRRssyQ4CkVTJYKPg+mospSDAQMJTfvr8RMJZstq1HknVAyT6HC1KWjeDI7bUY3PrHqjFTfkR16GgEz9qcU91lS29CS0AklM8KRmIrsrsu/1Kja0+OKqUiCM9e2/f/jTkIly4zcfEk1ldT7i8f1bzwN5586lf0exUTyXlj8xhJrqBdyf+XSdSIwHXxOfeZZorgR28RYQB45y9KU3hb6zkP9pZlKNtiu+AIomyxBZbiWH7osVJKkKhmriBKcafCFa5YIG7jcdtwjKVhG5OZLKrHhBgz1//P3zsbOw4N4098/ACB60eWtAohTrfIQK4m2kKnkXKQra5E3Hq1Wx+SFPDbnRtVf+cSpaKGSCZ6hPvhd9vIQvL8iXlhnUZ2mBchPV90qBDaVxkf+wYsXKD9P5v1lRnrakLG0EaYCyEGu9dOl5IGkG2KzXYqjkHtEPT/3M6u/iEW11ypqJE4lso206q8MQiUPB4NU/bWkl1GAbKh/8xkz8aX/sRTTu71a4qpa0XLtg6iEkvL32wHNzAybZNKW/FoiKsycWMa8qZ34/Jolifv86UcvwbeuWw4gmVqSDJ12Tqlwkm6IYjnh1s2ZvCs/ytmWsyCwTWRqpuVoJE5FRsOG+hzUX5qpZIRsqDcMAx+46FT886+249CAeG1S9Re/QbSZTEkU9d544/XbfvPiWVh8bEj5t6Jl4qG/uiJVl+efOpWp4viaOVGnSHq3eaTC8fpMdh3v/dVKpkJzNa/Dj3AIyylOZTSDHxtBHokwGylJDPA2lfR90hTU3l8ZIRvqCaqJEXWKj/P+ajeh0kyVQ5JN4o8uOjX3fgkshUUC20ZeQiXpphCXM62ZYLm/cmqvGS7F7cb2abjZmEpj7yWLjSyoE6XVX5kgR9QTVEblKI8MORtuO6u/ggXRDKHSvLaTIIhBiZ7OZs5CJWk1Pd7G01KmYuVrqG9G8GPbMZUUm/NVZ81Wfs6+mvLWaWo3wjaCIl2pv9oQxi1TsR11jiJTcYKL2hSTFPZqFzQzNUaSiPpmgm3wMauGFmNe5QVY6os6K5V/Jq3cRIs521R4eZ1bnEp7LaFUDOuf/+h85QGm0XVYyBBn1Wrvr3HLVJxIpuL/rzA81nvPhmFwKcXzGWer0MyFnEdQWBYkiZanzV+V0qQR0PyJSmBJMAyDbfCtDH7MewPR6q90bvmWaYQSpgKN2zYbqTpJ0GlacoJcTpig0mn+3rlzMamzGOmOyqNUMFGr2E0JImwqmslUmth2EgSqqBj1FzGVvNRffntRFR+Fa02vFGxrmYpfoydnZgbkF/zYbuqvwHOr8XE3alPJVPmxxXFB41aoOI7aS0Xlax5VS1sFb7HabUfdm8lUqMnR2iQMw4BlGrH903khb5tKVG16HkXTxDCcFnt/5SxEEwZ7JsFYyFLcCPJgWLQnpRVMWfKltdpQP26Fiu26StVEEhr5m89cgYGRmvJvdAJsr+XQGu+v0dwjLNOItW9Qeng5GWajoHtNotKilPct9f7y52lOMiXX4McspXFHE3lkpWi0jUIGwUDf0anvM6KeoT7utDFncnTmUkop0m6nrGZ6aI229xfgLZi4RfP7589H/4iN9194Si79pWEqeWdITgLmQt2EYM+sOcxomrTKGykv5MFUAoHcGFNpLKJebKPZGLdCpZ6hvtHHS5tIu9lUmumhlaTyY7NhmUYsa7BMAzdcelp+/ZFNJcEOm8Vzp1GwDAJ2M2wqJ7ahPsuwG42oz6T+arG6cdx6f0UZ6rM+YKb+aq/1EMSpNGFja2q0fkIULbOue2+eYCqFBEIlaUxLniDVb26G+jy9v9pV/ZVj6vu0tx7YRRros8WOEeNaqKgmLZsQDT7fYs5lWluFZhpHm5lXLCmsOuqvZvQHJDTUW61nciRg3bzUXzkylUD91V6LKA/GGWhK0rWRRzlhzVQywnEjXIrJ+6LBdkvtalNpYhK/sWJTGQ31WxL1l2UaMI3WqkzpWeTl7VYwjUAYZLapeM9jNMsJN4KpXUV8ctUZuPKsWQ230Xg9lQzBjy1mKuPWpiJnKSZkPbGX2lX9headVlicyihuEp73V+sGQJVEk7CjomW2NPCR+gS8ukJ5wDQNTOsq4dBAJRe1lWkYbXcwMwwjsnxzUjQepyJ+P1WfTKik/mpDyNTN4cOHsWrVKixatAirVq3CkSNHQtfs3LkTb3nLW3DWWWdhyZIl+Na3vpWly8RwXLXONktkKhDUWm87Q30T2UQz206Ket5feaPm79ZJmUqrWVTecSqAV6IAyOc9m6PELEcbjQZQBlmn0/cZpGlpjVTJ1MuGDRtw5ZVXYtu2bbjyyiuxYcOG0DWFQgH/8A//gOeffx6PPPIIvvOd7+C5557L0m0ieIb68OdZo78Dm0p7LQiziWxiLASzjRZTSeRSbJktFXgAx1SaIFTysIVYbchU8kCjHmTZygn7bbSDTWXTpk1Yu3YtAGDt2rW4/fbbQ9fMmTMH5513HgBg4sSJOOuss7B79+4s3SZClKHeYieFxtottXvwYzOYiv//aJ48C2ZrN24qT51EkBVMgwVAtgrMppKToR4AZk3sAJCPXc402u9glgey5P4yGrTLnTq9G3Mmd+DU6V2pv9sIMtlU9u/fjzlz5gDwhMeBAwdir9+xYweeeOIJXHjhhZHXbNy4ERs3bgQA9Pb2Njy2SEO9Ik1LGhQL7en9FUzm5nl/jeYe8YZTpmD2pI6W9bds3mQAwB9eUD+YsmAaLfd0Iu+vZqi/8njRnvorczNth0YPd0WrcfXuyVM68ZvPXNnQdxtBXaFy1VVXYd++faHPv/zlL6fqqL+/H9deey2++c1vYtKkSZHXrVu3DuvWrQMArFixIlUfPKIM9cz7osF2S22r/vL+b8aBeSx4f224dllL+5s7pRM7Nrwj0bUFq/X2g7wTSgLAjAleKe6+4WrmtjyPuPZaQ3mgUUP9e1ecgjNmT8x/QE1AXaFy7733Rv5t9uzZ2Lt3L+bMmYO9e/di1iy1q121WsW1116L97///Xj3u9/d+GhTwHEj1F8ZA5gC76/2WhDNzCQ8FiLqxzJarZoDgneRJ1OZ7guVg/2VzG1ZhtF2wY95oFFD/SnTu3BKi9RXWZGJgK5Zswa33HILAOCWW27BNddcE7rGdV3ccMMNOOuss/DJT34yS3epUC/1faMIvL8yNdNyNNWmktFONd4xKjYV/2XkKFMwY4Kn/jrUP5K5rb9625l474r5mdtpN2TVlLQDMgmV9evXY/PmzVi0aBE2b96M9evXAwD27NmD1atXAwAefvhh3Hrrrbj//vuxfPlyLF++HHfeeWf2kddBZJwKneAaVAu0a0Q9DbcZG/9YiKgfyyhY8XnJmoG8gx8BYHq3J1QOD2RnKu9ZOR/nzp+SuZ12Q6O5v9oJmQz106dPx3333Rf6fO7cuUxwXHrppbmlikgDx1WfylmxpgbH1LY2lSZWZxwLNpWxjNmTOnBkMLsdIg2yznMVSP3ye+fOza3NEw2snPk4XivjNqL+9o+9EZM6wrdHWohGT3CUpqXd5kQzo94De03+bY8H/PXqs3IrY5wUzWAqE8oFvPzlt2vbWQZkzZLeDhi3QqVn1gTl52ZGXTOpv0aBfGUCTeKmRtTrzUaJjqLV8j5ZpcucJ2orA0zHI0a79HYrcMLNEDOjVwwJlVafPLOC9pZmZikezwul3cAOT202T8c7xkJMV7NxwgkVK6tNxVd/1fw0He0CF979NqVIl/+/VouMHTQjol4jO06EJXLCCRUzo1qg5Btlqm12AnR8GdhMpjKeT1/tBlK5UWoVjbGBE4HVj1ubShTysqnkVaa1VSAh2gyvk7GQpVhDxOxJHfjGe8/FmxbNHO2haHA4EWK6Tlyhktmm0mbqL1+oNMPOquNUxibe9YZ5oz0EDQknAqs/4dRfVsbgR7KpVNuMqThNNNSfCKcvDY08EOT+Gr+L5YQTKrTxNeq/34w6Fa0ACdGm1qg/EayQGhoZEAQKj+44mokTTqhk9/7yDfVt5v1FQrA5aVro/3G8UjQ0coBxAtD6E06oBLm/Gvt+yfK8atqNqZAMbQqb0BH1GhqJcCJE1J9wQoUOCI0nlGxTl+Kmqr/8/7VU0dCIhXkCHMBOOKEyvdurCTGx3JjjW7FNgx9JBjYzS7FWf2loxCNr5dl2wAnnUnz9G09DV6mA9yUoA6tCqV0N9U7zmAqLqB/HC0VDIw8YWv01/lCwTHzgolMbti2wNC1tJlSuOns2Fs7sxo1vPj33tk8E33sNjTwQpL4f5YE0ESccU8kKFvzYZuqvad0l3P+XlzelbR1Rr6GRDI2WE24nnHBMJSuo1ni7BT82EzpORUMjGYLgx/ELLVRSomDlX/yo3XECuN5raOSCE2GtaKGSElRrvN1yfzUTmqloaCTDieApqYVKSrA4Fa3+YtA2FQ2NZDgRnFq0UEmJbj++5cOXnjbKIxk70DXqNTSSIYioH7+LRXt/pUTRMrFjwztGexhjCjr3l4ZGMhiaqWho1MeJoCfW0MgD2qVYQyMBdI16DY1k0C7FGhoJcCJQeg2NPKAN9RoaCaBtKhoayXAieEpqoaKRGTpORUMjGbT6S0MjAU6EKGENjTxgngBrRQsVjcxgTGU8rxQNjRxwItRT0UJFIzOYnlirvzQ0YqHrqWhoJMD07jIAYGpXaZRHoqExtmEYBgxDq78icfjwYaxatQqLFi3CqlWrcOTIkdA1w8PDuOCCC3DuuediyZIl+NznPpelS40xiHPmTcZvPnMFemZNGO2haGiMeZiGMa7TtGQSKhs2bMCVV16Jbdu24corr8SGDRtC15TLZdx///146qmn8OSTT+Luu+/GI488kqVbjTGIOZM7R3sIGhptAdMAzHGsI8p0a5s2bcLatWsBAGvXrsXtt98eusYwDEyY4J1gq9UqqtXquDZSaWhoaMTB0EwlGvv378ecOXMAAHPmzMGBAweU19m2jeXLl2PWrFlYtWoVLrzwwsg2N27ciBUrVmDFihXo7e3NMjwNDQ2NMQfTwLi21NfNUnzVVVdh3759oc+//OUvJ+7Esiw8+eSTOHr0KN71rnfhmWeewdKlS5XXrlu3DuvWrQMArFixInEfGhoaGu0A0zDGdUR9XaFy7733Rv5t9uzZ2Lt3L+bMmYO9e/di1qxZsW1NmTIFl19+Oe6+++5IoaKhoaExnuEZ6scvMqm/1qxZg1tuuQUAcMstt+Caa64JXdPb24ujR48CAIaGhnDvvffizDPPzNKthoaGRttCuxTHYP369di8eTMWLVqEzZs3Y/369QCAPXv2YPXq1QCAvXv34i1veQuWLVuGlStXYtWqVXjnO9+ZfeQaGhoabYjxzlQyVX6cPn067rvvvtDnc+fOxZ133gkAWLZsGZ544oks3WhoaGiMG5iGzlKsoaGhoZETTMMY195fWqhoaGhotBA6TkVDQ0NDIzd46q/RHkXzoIWKhoaGRgtRLpooFcbv1pvJUK+hoaGhkQ7fuu4NmDWxPNrDaBq0UNHQ0NBoIc47ZepoD6GpGL8cTENDQ0Oj5dBCRUNDQ0MjN2ihoqGhoaGRG7RQ0dDQ0NDIDVqoaGhoaGjkBi1UNDQ0NDRygxYqGhoaGhq5QQsVDQ0NDY3coIWKhoaGhkZu0EJFQ0NDQyM3GK7ruqM9iCjMmDEDCxYsaPj7vb29mDlzZn4DajHaefztPHZAj3+00c7jb+exA/Hj37FjBw4ePBj7/TEtVLJixYoV2Lp162gPo2G08/jbeeyAHv9oo53H385jB7KPX6u/NDQ0NDRygxYqGhoaGhq5YVwLlXXr1o32EDKhncffzmMH9PhHG+08/nYeO5B9/OPapqKhoaGh0VqMa6aioaGhodFajEuhcvfdd2Px4sXo6enBhg0bRns4ibBgwQKcc845WL58OVasWAEAOHz4MFatWoVFixZh1apVOHLkyCiPMsD111+PWbNmYenSpeyzuPF+5StfQU9PDxYvXoxf/OIXozFkAarxf/7zn8fJJ5+M5cuXY/ny5bjzzjvZ38bS+Hfu3Im3vOUtOOuss7BkyRJ861vfAtA+zz9q/O3w/IeHh3HBBRfg3HPPxZIlS/C5z30OQPs8+6jx5/rs3XGGWq3mLly40N2+fbs7MjLiLlu2zH322WdHe1h1ceqpp7q9vb3CZ5/61Kfcr3zlK67ruu5XvvIV99Of/vRoDE2JBx980H3sscfcJUuWsM+ixvvss8+6y5Ytc4eHh91XXnnFXbhwoVur1UZl3ATV+D/3uc+5X/3qV0PXjrXx79mzx33sscdc13Xd48ePu4sWLXKfffbZtnn+UeNvh+fvOI7b19fnuq7rVioV94ILLnB/85vftM2zjxp/ns9+3DGVLVu2oKenBwsXLkSpVMJ1112HTZs2jfawGsKmTZuwdu1aAMDatWtx++23j+6AOFx22WWYNm2a8FnUeDdt2oTrrrsO5XIZp512Gnp6erBly5ZWD1mAavxRGGvjnzNnDs477zwAwMSJE3HWWWdh9+7dbfP8o8YfhbE0fsMwMGHCBABAtVpFtVqFYRht8+yjxh+FRsY/7oTK7t27MX/+fPb7vHnzYifsWIFhGHjrW9+K888/Hxs3bgQA7N+/H3PmzAHgLcQDBw6M5hDrImq87fRObrrpJixbtgzXX389U2GM5fHv2LEDTzzxBC688MK2fP78+IH2eP62bWP58uWYNWsWVq1a1XbPXjV+IL9nP+6EiqtwZouTxGMFDz/8MB5//HHcdddd+M53voNf/epXoz2k3NAu7+SjH/0otm/fjieffBJz5szBX/7lXwIYu+Pv7+/Htddei29+85uYNGlS5HXtMv52ef6WZeHJJ5/Erl27sGXLFjzzzDOR1461sQPq8ef57MedUJk3bx527tzJft+1axfmzp07iiNKBhrjrFmz8K53vQtbtmzB7NmzsXfvXgDA3r17MWvWrNEcYl1Ejbdd3sns2bNhWRZM08RHPvIRRvPH4vir1SquvfZavP/978e73/1uAO31/KPG3y7PHwCmTJmCyy+/HHfffXdbPXuCPP68nv24EyorV67Etm3b8Oqrr6JSqeC2227DmjVrRntYsRgYGEBfXx/7+Z577sHSpUuxZs0a3HLLLQCAW265Bddcc81oDrMuosa7Zs0a3HbbbRgZGcGrr76Kbdu24YILLhjNoSpBmwIA/PznP2eeYWNt/K7r4oYbbsBZZ52FT37yk+zzdnn+UeNvh+ff29uLo0ePAgCGhoZw77334swzz2ybZx81/lyffZ6eBWMFd9xxh7to0SJ34cKF7pe+9KXRHk5dbN++3V22bJm7bNky9+yzz2ZjPnjwoHvFFVe4PT097hVXXOEeOnRolEca4LrrrnNPOukkt1AouCeffLL73e9+N3a8X/rSl9yFCxe6Z5xxhnvnnXeO4sg9qMb/gQ98wF26dKl7zjnnuL/3e7/n7tmzh10/lsb/61//2gXgnnPOOe65557rnnvuue4dd9zRNs8/avzt8Pyfeuopd/ny5e4555zjLlmyxP3bv/1b13Xj1+pYGbvrRo8/z2evI+o1NDQ0NHLDuFN/aWhoaGiMHrRQ0dDQ0NDIDVqoaGhoaGjkBi1UNDQ0NDRygxYqGhoaGhq5QQsVDQ0NDY3coIWKhoaGhkZu0EJFQ0NDQyM3/P+p/WLuJRmwLQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    plt.plot(data_HNN_noise.flatten().T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "39c800ef-4492-46e8-b846-539729ec8949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8dbfc04d73674338ba85be697e5e970d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4a0c3fb41474fb3ae31e31a1fcf412e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cf696205a87487d83e915252f7d1b60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b31ef7f3d491415fa570a87a76a910ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_0.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "654d0c618b7b40cd88b4c2bef8655353",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "679a0aac01784ec69d924acb0222c6f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise_1.5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f2ffb98364543dcbafff600d60c008b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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8hz158kTlPqX9MUNCQtjhw4dZ165dmYmJCbO0tGSjRo1iDx48UCh/9+5dNnfuXObr68tsbW2ZWCxmLi4uLDAwkIWHh6t8j/Pnz7MxY8YwBwcHZmhoyGxtbZmPjw+bO3cuu1quL6CLi4tS38iKtGjRggFgNjY2rKCgoMJyBQUFbN26daxbt27MwsKCicVi1qxZMxYQEMBWrVql1Ke0oKCALVy4kLm5uXGf78svv2T5+fnV6gfKGGNSqZStW7eOtW7dmonFYubg4MDeffddlpGRUeFnDQkJYT4+PszExIQ1atSIvfHGG+zWrVtswYIFDAALDQ1VKP/ixQs2YcIEZmdnx4RCIQPAFixYwL0eHh7O/P39mZWVFTMzM2M9evRg+/fvZ6GhoUpl60U99wMVMPZv+3M1dOrUCdeuXaubTN6AMcYwxf9PRP4j6zfn0sISvx0dDqGwrMHC4R0PsDb4Mre+4Ke+GBvkXe+xNhg75Rp7TFT7Eq01pa0wCSENiHx+6tSpyuI1/T2nZ6C14MLfCVzyFAiAb371V0ieADBskgeGTmjFrS/7LBwvEnNACCGkYaIEWgu2rL7BLfcY4IymzhYqy70zvzOaNZe9lpdThO/nnquP8AghhNQBSqA1FB+TgYuny1qpvfNlxbcNxBIR5nzTlVs/dSAWZw4pzzBBCCFE+1ECraE9G8v6obX0toFdU7NKy/v4NcGg0S249W/nhKEgX7kJPCGEEO1GCbQGpFKGv/5XNqfhpHdVj7FZXtBnvrC0lgAAXiTmYsdPt+okPkIIIXWHEmgN3LryHM+fyhoCiSUi+PV1Ums/CysJpnxYNq7mr4uv0QALhBDSwFACrYG//yzr+NyxhwMMDNU/nUPGtYKjq2x6ppysQmxYSt2DCCGkIaEEqiHGGE4dKEugwyZ6VGt/A0MhZnzckVvf/uMtJMZl1Vp8hBBC6hYlUA3FPcxAYlw2AFky7NCtetMRAUCvQc5o7SOb4La4SIq1wdUfu5MQQgg/KIFq6MLfZXMktvRuBEOxqJLSqgkEArz9mS+3fmTnQ9yNTK6V+AghhNQtSqAaCj9ZlkD9h7pqfJy2ne3RrV9Z46MVX/xTk7AIIYTUE0qgGigsKMHVsERuvVtAsxod7615Hbmh/y6deaqQnAkhhGgnSqAauH3tBfJfyQY/sLCWoIlT5YMnVMWlhRVeH1M2uMLyz8NRUiKt0TEJIYTULUqgGog4XzYhrVfHxrVyzCkf+sDIWDY968PbqTiy82GtHJcQUrXp06dDIBConNCbL66urtyk1UQ70YTaGrh2oSyB9njNuVaO2cjOBKPf8sT2H2WjEq3570UMGtMCEiP6L9JKN/8L5GnxrXYTZ8DnmxofxtXVFfHx8Spfs7e3x/PnzxW2FRYW4ptvvsGOHTuQlpaGjh07YsWKFejYsaPS/idPnsSAAQNw5MgRDBkypMaxNjR9+/ZFWFgYqjGjJNEy9Ne5moqLpdzUZQDg07X63VcqMuYtLxzZ9RAZqfncEH8z/qP8h4dogbwEwNSV7ygqlhtXa4eytLTERx99pLTdzEz50cXnn3+OVatWITAwEE5OTti2bRv8/f1x//59ODg4cOVycnIQFBSEyZMna0XyXLx4MT7//HM4OjryHQrn9OnTfIdAqkAJtJoe3k5BXk4RAMDEzBD2jqa1dmwTM0NM+aAd1gVfAQD8svgaRk1vA6tGxrX2HoRUl5WVFYKDg6ssxxjDr7/+ijfffBObNm0CAIwcORJ9+/bFtm3b8Omnn3JlP/vsM+Tn52PNmjV1FXa1ODg4KCR4beDu7s53CKQK9Ay0mu5cK+un2cLTBgKBoJLS1Td4bNkQf7lZhfh1CQ3xRxqGly9fIi8vD126dOG2lS7L3wY+d+4cfv75Z/z000+wsbHR6L1Knw9mZWXh448/hqurKwwNDRUS/f379zF9+nQ0a9YMEokE9vb2mDhxIh48eKB0vMqegV6+fBmjR49GkyZNIBaL0axZM8yaNQtJSUlKZQEgLS0N8+fPh7e3N0xMTGBpaQkfHx98/vnnyM3NRVxcHAQCAcLCwgDI+oOX/vTt21fpM5ZXUFCAJUuWoF27djAxMYGFhQV69eqF//3vf0plS99r+vTpiIuLw/jx42FrawsjIyN06tQJR44cqfxEl3PgwAFMnjwZrVq1gqmpKczMzODr64u1a9dCKlVu+Fh6XmNjY7Fu3Tq0a9cOxsbGCp8zLS0NX3zxBdq0aQNjY2NYWlqiX79++Pvvv5WOl5mZiWXLliEgIABOTk4Qi8Vo3Lgxhg8fjkuX6n8gGqqBVtOdiLIE2rFH7X9jNTAU4q15HfHN+7Jfrh0/3kLgm15o4anZHxpCaqqgoADbt29HQkICTE1N0a5dO/Tu3RsikeLgIY0bN4axsTEiIiK4bdeuyb4Auri4AABevXqFmTNnIjAwEIGBgTWKq7CwEAEBAUhLS8OAAQNgYWEBNzc3AMDx48cxatQoFBUVYdiwYWjRogWePn2KP//8E0ePHkVoaKjK57LlhYSEICgoCBKJBMOHD0ezZs0QHR2NjRs34vDhw7h06RKcncvaQTx+/Bj+/v6Ij4+Hr68v3nnnHUilUjx8+BCrVq3C7NmzYWVlhQULFmDz5s2Ij4/HggULuP2rajRUWFiIgQMHIiwsDK1bt8Z7772HvLw87N27F+PGjcONGzfw/fffK+0XHx+PLl26oHnz5pgyZQrS0tKwe/dujBgxAqdOnYK/v79a5/zzzz+HUCiEn58fHB0dkZmZiTNnzmDOnDm4evUqtm3bpnK/OXPm4Pz58xgyZAgGDx7MXTvx8fHo27cv4uLi0KtXLwwaNAi5ubk4cuQIBg0ahF9//RVBQUHcce7du4f58+ejd+/eGDJkCKytrZGQkIBDhw7hr7/+wuEVKzCoe3e1PkttoARaTfI1UK+OdnXyHj0HOMO7kx3uXEtGSQnDt3PCEPL3G7Ve2yVEHc+fP8eUKVMUtrm5uSEkJAR9+vThtgkEArz99ttYu3YtMjMz4ejoiG3btsHc3ByTJk0CAHz11VdIS0vDTz/9VOO4nj17Bk9PT4SFhcHUtOxRSnp6OiZMmAATExOcO3cOnp6e3GtRUVHw8/PDzJkzcf369UqP//DhQ8yaNQuurq4ICwtTeD565swZ9O/fH3PmzMH+/fu57ZMnT0Z8fDy+//57fPHFFwrHS0lJgZmZGYyMjBAcHIyzZ88iPj5erdvjpVasWIGwsDC8/vrrOHToEAwMZH/CFyxYgC5dumDx4sUYOnQoupdLImfPnkVwcLBCsp44cSIGDRqEZcuWqZ1Ajx49qnRrWSqV4s0338TWrVvx/vvvw8/PT2m/69evIzIykvuCU2ratGmIj4/Hrl27MH78eG57RkYG+vbtiw8//BDDhw+Hvb09AKBNmzZISkqCra2twnGePn2KLl26YO6qVfWaQOkWbjXkvypGTFQqt97Sq25qhQKBAB8G+0EokiXMq2GJOLqLurWQ+vfmm2/i9OnTeP78OXJzc3H79m3MmjULcXFxeP3113Hz5k2F8kuXLsVnn32GK1eu4Pfff4eXlxdOnz4NR0dHXL58GatXr8aaNWtga2uLhQsXwsHBAQYGBvD19UV4eHi141uxYoVC8gSArVu3IiMjAwsXLlRIngDg5eWFoKAgREZG4u7du5Ue++eff0ZRURHWrFmj1LgoICAAw4cPx+HDh5GdLRsTOyIiAv/88w/at2+Pzz77TOl4pbdOa2LTpk0QCARYuXIllzwBwM7ODl9//TUAYOPGjUr7ubi44KuvvlLYNnDgQDg7O+PKlStqv7+q57JCoRBz5swBAJw4cULlfp9++qlS8rx58ybCwsIQGBiokDwB2XP3hQsXIj8/H/v27eO2W1paKiVPAHBycsLo0aNxPy4OCeVahtclqoFWw4NbKSgpkTU5t25sDFNzcZ29l5uHNUZNb4O9v8t+yZd+egG9B7vCwkpSZ+9JSHnyNRYA8Pb2xi+//AIzMzOsWLECwcHBCjUwiUSCxYsXY/HixQr7FRYWYsaMGRg8eDAmTZqE1atXczWiHj164LvvvsOgQYMQExPD1TaqYmRkhHbtlCexv3jxIgDZH2hVtbuHD2VfRu/du6eUYFUdJywsDFevXlV6PTk5GSUlJXj48CF8fX25Z3ADBw6EUFj7dZPs7GzExMTA0dERrVu3Vno9ICAAABAZGan0Wvv27ZVuuQNAs2bNuM+pjtTUVCxbtgzHjh1DbGwscnNzFV5PTExUuZ/8c/FSpe+bmZmp8v/p5cuXAGT/T/LCw8OxZs0aXLx4EcnJySgsLFSMITkZzk1qr3dEZSiBVsOday+45Vbedf9Mcsr7Pjh7JA4pL/KQlvwK64IvYf7qPlXvSEgdmz17NlasWIFz586pVX7hwoVITEzkGoYsW7YM/fr14/5wenh4wNXVFT/99BO++Ua9/qt2dnYqH2ukpsruEm3YsKHS/XNycip9vfQ4y5YtU+s4GRkZAFBnXWEyMzMBoMLWwqXbS+OQZ2VlpXIfAwMDlY1/VMnIyEDnzp3x+PFjdOnSBVOnToWNjQ0MDAyQkZGBNWvWoKCgQOW+TVQktNLze/LkSZw8ebLC95X/f9q/fz9Gjx4NIyMj9O/fH+7u7jA1NYVQKMTZs2cRFhaGgqIitT5PbaAEWg3yDYja12L/z4qYmBninfmdsehDWYOiXb/cxrBJrdGus3rf0AmpK3Z2suf/5WsgqkRGRuKHH37AL7/8AkdHR2RlZSEpKYl7LgoAzs7OsLW1RVRUlNoxVNQmwNLSEoCsBqqqhqqu0uNkZmbCwsKiyvKlSaqiWlhNlcZTfvCKUs+ePVMoV9s2btyIx48fY8GCBUo1xosXL1baJUnV/1VpnGvWrMGHH36oVgxff/01xGIxrl27hjZt2ii8NmvWLK5lc32hZ6DVEBXxkltu7VM7Q/hVpdcgZ3Tq1RQAwKTA/LdOoSC/uF7em5CKlN5+a968eaXliouLMWPGDPj7++Ott95SeK18bSU/P79WYuvatSsA4Pz58/V6nNLyJ06cUKtWV3pLtaSkRK3jm5ubw93dHYmJiYiOjlZ6PTQ0FADUal2siZiYGABQ2Xpak8Slyf9TTEwMPD09lZKnVCrFhQsXqh1DTVECVVNudiFi76fJVgSAexvrenlfgUCADxf6wchEdrMg9n461gVfrpf3JvotKioKaWlpStvj4+Px/vvvA5C1Oq3M4sWLERMTo3A71cLCAo6Ojjh+/DiKi2VfBsPCwpCdnQ0vL68ax/3mm29yjVBUNZCRSqU4e/Zslcd5//33YWhoiLlz53LPTeUVFhYq/PH39fVF9+7dcePGDSxdulSpfGpqqsKXhEaNGgEAEhLUHxJyxowZYIzhk08+UUi8KSkpWLRoEVemLpR2sSl/7iIjI5WeeaujU6dO6NWrF/78809u4I3ybt++jeTksjt/rq6uiI6OVuiDyxjDwoULq2wUVhfoFq6a7t14idIhK23tTWBsYlhv7+3QzBxvf+aLtQtkiXPzqkgEDG+Ojt21a+QUolv27NmDJUuWwN/fH25ubjA3N8ejR49w9OhR5OfnY/DgwZg3b16F+0dFReHbb7/FypUruX6gpT799FPMmTMHvXr1gp+fH3bs2AEzMzO89957NY67UaNG2Lt3L0aOHImuXbuiX79+8PLyglAoREJCAi5evKiUzFRp3bo1Nm3ahBkzZsDLywuDBg1Cq1atUFRUhISEBJw/fx6NGzfG/fv3uX22b9+Ovn374ssvv8S+ffvQt29fMMYQHR2Nv//+G/fv3+cSUb9+/bBnzx6MGjUKgwcPhrGxMVxcXJS6DMmbN28e/vrrLxw8eBA+Pj4YPHgw8vLysGfPHiQnJ+PTTz9Fz549a3wOVZk6dSqWLVuGjz76CKGhoWjZsiWio6Nx5MgRjBo1Crt37672MXfu3ImAgAC89dZbWLt2Lfz8/GBlZYWnT5/i1q1buHPnDi5evMg9Mpg7dy5mz56NDh06IDAwEIaGhggPD8fdu3cxbNgwHD58uLY/dqUogaop6nrZ7duWXo3q/f2HTmiFC38n4Hr4MzAGfPnWKfx5bTxMTOsvkRP94u/vjwcPHiAyMhIXL15Ebm4urKys0LNnT0yZMgVTpkyp8DlkSUkJZsyYAT8/P7z77rtKr3/wwQfIysrCL7/8guvXr8PHxwerVq1SuwVuVfr164dbt25h+fLlOHHiBM6fPw+xWIymTZsiICBA7UEcJk+eDB8fH6xYsQKhoaH4+++/YWpqiqZNm2L06NEYN26cQnk3Nzdcv34dP/zwAw4cOIAff/wRRkZGcHV1xX/+8x8uEQDAzJkzER8fjz/++AM//PADiouL0adPn0oTqFgsxsmTJ7Fy5Urs3LkT69atg4GBAXx8fLB69WpMmDBBsxOmhqZNm+L8+fP4/PPPceHCBZw4cQKtW7fG+vXr8dprr2mUQJ2cnBAREYF169Zh37592LFjB0pKStCkSRN4enrigw8+QNu2bbnys2bNgkQiwerVq7FlyxYYGxujV69eCAkJwb59++o9gQpYNaYC6NSpEzeyiL6ZP/MUDmyVfdOc9YUvRs+o+a2m6kp+lougwYe4sXgnvttWd1vl7pT7wzyx/meruHfvntJzFgV6MhuLvhg/fjx2796NpKQkrRsTl1SDfH7q1KnK4lX+nleBaqBqio4qexbUwrP+a6AAYOdginfmd8aKL/4BAOxcfxv9hjdH14BmvMSj1yg56ZSHDx9CIpGgceP6aRxIdAM1IlJDSYkUj+6WJVDXlla8xTIw0B1d/Z249S9mnEJWhuq+V4SQyq1ZswaBgYGIjIzEiBEjFEb3IaQqlEDV8PRxFvJfyVoLGpsawKpRzYbjqgmBQIC533WDhbVsRKLkpFws/li9zuyEEEWrV6/GpUuXMGPGDPz66698h0MaGEqgaoiRu33b1Nmcx0hkbBob46NvunLrh7Y/wKkDj3iMiJCG6fHjx0hMTMTvv/9e4Wg9hFSEEqgaHt4pG0C+VVt+nn+W12uQC/oNLxucecE7oUhNzuMxIkII0S+UQNUgPwNLax/lmQD48t5/u8DW3gQAkJGaj+B3QlGNRtWEEEJqgBKoGuRroG6t6mcEInWYW0owb0nZ3HdnDj/GwW33K9mDEEJIbaEEWoXCghLER2dw63y2wFXFt2dTDJvkwa1/P/cckhKyeYyIEEL0AyXQKjx+kM7NAWphJYGxFo78E/RpRzR1kTVuys0uwndz6ndGAkII0UeUQKsQLXf7tlnzqqc04oOxiSE+/aEHSkdVO3s0DmcOxfIbFCGE6DhKoFWIjpJvgas9DYjK8+poh8FjW3Lr384JQ25OYSV7EEIIqQlKoFWQH8JPm1rgqjJjXkdY2cgGeXiRmItfvtPPcYsJIaQ+UAKtQuyDdG5Z2xoQlWdhJUHQZ77c+ta1N/D0cRaPERFCiO6iBFqJgvxiJMolIEdX/kchqkr/kc3Rpr2splxcJMWqr/7hOSJCCNFNlEArkRCTCam0rAWuxEj7B5oWCASY9UXZND7H98Tg5uXnPEZEdEH//v0hEAgQFlZxC+/Zs2dDIBBgw4YN9RgZIfyhBFqJxw/Lbt86NDPjMZLq8epoh96vu3DrSz+5QCMUkRq5fv06RCIRfH19Kyxz+fJlALJ5gwnRB5RAK/H4QQa33Ly19oxApI63/tMRBoay/96bl57jwgktnvyZaLVHjx4hLS0NXl5eMDNT/UUyLy8Pd+7cgUQigbe3dz1HSAg/KIFW4rFcA6KWWjKIvLqauphjyPiybi3rFl6mWijRyLVrstbcfn5+FZaJiIhAcXExfHx8YGiofYONEFIXKIFWQv4WrnNzSx4j0cz4t9vCUCz7L46KSKZaKNGIOgn00qVLAOj2LdEv2t8qhieMMYUaaLMGmEBtm5hgyLhWOPDvAPPrFl5Gz4HOEJQOWUSIGkoT6O7duxEaGqqyDD3/JPqIEmgFXj7LRW52EQDAUCKEta0RzxFpZtzb3ji6+yGKCqWyWujfCeg10KXqHQmB7ItkZGQkAODkyZNVlqcESvQJJdAKxMo1ILJrYtpga23la6Gbll+nBFpDXuIf+Q5BbVGF79do/4cPHyIzMxPdunXDP/+o7lOcnp4OGxsbmJiYwNPTU+G19evXY9myZXj27Bm8vLywevVq9OrVq0YxEaIt6BloBeLknn82c294t2/ljX7LE0KR7AvAlbBE3I1M5jki0lCU3r7t0KFDhWWuX78OAGjfvj1EIhG3fffu3ZgzZw6+/PJLREZGonv37nj99deRkEDP4oluoARaAfnnn60aWAvc8uwdzdBHrl/ophWRPEZDGpLSBNqxY8cKy5Qm0PK3b1euXInp06cjKCgIbdq0wbp16+Dg4ICff/657gImpB7RLdwKyI+B69aqYfUBVWXMTC+EHokDAJzYG4OPv+uGpi7aOT2btqvpbdGGRJ0EWvqMVD6BFhYWIiIiAvPmzVMoO2DAgApvBRPS0FANtAJxDzO4ZW2dB7Q6Wno1QvtuTQAAUinDth9v8hwR0XZSqRQ3btyAWCyGl5dXheVU1UBTUlJQUlICe3t7hbL29vZ4/pyGliS6gRKoCq/yipAUnw0AEAiAps7aP4i8Osa8VfZHcN+mu3iVV8RjNETb3bt3Dzk5OfDy8oJYLFZZJicnB9HR0TAzM4OHh4fS6+Ub3zHGGmyDPELKowSqwpNHmdyyuZUEhmJRJaUbjk69mqKpi+zLQG52Ef76XzTPERFtFhERAaDy27c3b96EVCpFx44dIRSW/TmxtbWFSCRSqm0mJycr1UoJaagogaoQH1OWQJs4NpxB5KsiFAowbGJZLWH7ups0vB+p0NSpU8EYw8aNGyss06NHDzDGlGZpEYvF8PX1Veo7evLkSXTv3r1O4iWkvlECVSE+JoNbdtHySbSra8Aod4glshr1g9upuH31Bc8REV318ccfY/Pmzdi4cSPu3buHOXPmICkpCbNnz+Y7NEJqBbXCVSFBrgbawrPht8CVZ2Elgf9QV5zY9wgAsOvn22jXpQnPURFdNG7cOKSmpuLbb7/Fs2fP4O3tjWPHjsHFhQbyILqBaqAqyNdAG+IYuFUZNqnsNu6x/0UjPeUVj9EQXfbuu+8iLi4OBQUFiIiIQO/evfkOiZBaQwlUBfkEWtroRpd4tLWFRzvZ4BDFRVLs33KP54gIIaThoQRaTm5OIV4+ywMg68KiS42I5Mk3Jtq5/hakUmpMRAgh1UEJtBz555+WNkYQGejmKeo7xBXmlrK+fc+e5ODK2ac8R0QIIQ2LbmaHGkiQ6wPq0Ew3a58AIDEyQL8Rzbn1fSF3eYyGEEIaHkqg5STIPf901bEuLOUNGt2CW/77z0fITM/nMRpCCGlYKIGWIz+IgnsbGx4jqXvubWzQ0kv2GYuLpDi2m0YmIoQQdVECLUe+Ba6ja8MfRL4qg8aU1UL3bLzDYyTah0ZpIkR31cbvNyXQcuRroI6uuteFpTz/oW4wFMsugwe3UnH/ZgrPEWkHkUiEoiIabJ8QXVVUVKQwAbwmKIHKyckqROoLWRcWoVAAu6amPEdU98wtJeg5wJlb/3MzNSYCAHNzc2RlZfEdBiGkjmRlZcHcvGaVJEqgchIeZXDLVo2MIBLpx+mRb0x0cNt9FBaU8BiNdrCxsUF6ejpSUlJQWFhIt3MJ0QGMMRQWFiIlJQXp6emwsalZOxcaC1dOfLR+dGEpr303B9g7muJFYi5ysgoRevgxBsolVX0kkUjg7OyMtLQ0xMXFoaSEvlQQovVS5B5B3VM9wppIJIK5uTmcnZ0hkUhq9HaUQOXE61EXFnlCoQADA1tg69qbAIA9v0fpfQIFZEnUwcEBDg4OfIdCCFGHp2fZcj3cNdKPe5Rqkh9EoXkb3ZqFpSoDRrlDIJAtXzrzBM+eZPMbECGEaDlKoHLka6BOetCFRZ69oxk6dJPVtBgDDmy9z3NEhBCi3SiByklQ6MKiXwkUUOwTum9TFA0wTwghlaAE+q/szAKkvZTNiykUCdDYQfe7sJTXo78zzCzKBpi/dj6R54gIIUR7UQL9l/ztW+tGRhAKBfwFwxOxRAT/YW7cOs0TSgghFaME+i/FLiy6PwJRReT7hB7fE4OcrEIeoyGEEO1FCfRfCl1YWlnxFgffWnrZwM3DCgBQWFCC43tpgHlCCFGFEui/EhRmYdGvLizyBAIBBgWW1UL3/k5D+xFCiCqUQP+l0IXFzZK/QLRAwPDmEBnIngHfvvoCsffTeY6IEEK0DyXQf8kPotDURX+fgQKycYC7BTTj1qkxESGEKKMECiAzPR8ZqfkAAJFIAFt7E54j4t9Audu4+7feQ3GxlMdoCCFE+1ACheIcoNa2xnrZhaW8zr2bwqaxMQAg/eUrXDgRz3NEhBCiXSiBAkiIzuCWHZz1ZxaWyogMhOg/sjm3/mcINSYihBB5lECh2IDIrZX+tsAtT/42bujROG6kJkIIIZRAASjewtXnLizlNWtuCc+OjQEA0hKGwzsf8BwRIYRoD0qgAOLkbuHqexeW8gYq9AmNAquHOfYIIaQh0PsEyhhDgkIfUP2bhaUyfV53gZGxbN712PvpuBORzHNEhBCiHfQ+gaa9fIXsTNl4rwaGQljbGvEckXYxNRej1yAXbn3PxigeoyGEEO2h9wlU/vmnTWNjCATUhaW8weNacstHdj2gAeYJIQSUQBEfXTZMnaOej0BUEa+OjbkB9gteleDILmpMRAghep9A4x5mcMvNW1MLXFUEAgGGjG/Fre/65TY1JiKE6D29T6AKXVg8bXiMRLu9NqI5JEYiAEBMVBpuXXnBc0SEEMIvSqDyXVhcqQVuRcwsxOg7xJVb/9+GO/wFQwghWkCvE6hUyhRGIXKkBFqpoRM8uOWjfzxEZno+j9EQQgi/9DqBvkjMQUF+CQBAYiSChZWE54i0m0e7RtxITUWFUhzeQY2JCCH6S68TqPzt20Y0hVmVyjcm2rn+FjUmIoToLb1OoHH0/LPa+g1vDhNTQwCyBlgXTz/hOSJCCOGHXidQ+eefLbyoBa46TMwMMXB02fi42368yWM0hBDCH/1OoHI1UOoDqr4RUzxQOmDT+b/iFb6IEEKIvtDrBCo/iALdwlWfo4sFuvRxBAAwBuz8+TbPERFCSP3T2wRaVFSCxLhsbr0pDeNXLW9MbcMt79sUhdxsGh+XEKJf9DaBJsVlo7hYCkD2XM/YxJDniBoW354OaNZcNnfqq9xiHNh2n+eICCGkfultApV/bmfnYMpfIA2UQCDAG1PLBlbYtu4mpFLq0kII0R96m0Dlu7A4u1vyF0gD1v8Nd5iay2ruTx5l4txfcfwGRAgh9YgSKIAW3tSFRRPGpoZ4fWzZXKG/L7/OYzSEEFK/9DaBJsjNwuLawoq/QBq4UdPaQGQg69NyPfwZbl15znNEhBBSP/Q2gcrXQGkQec01djBFwDA3bn3TikgeoyGEkPqjlwk0/1UxniX824VFADg0M+M3oAZuzFte3PKpA48UBqgghBBdpZcJ9PGDdG7Z0loCQ7GIx2gaPjcPa3SWG1hh82qqhRJCdJ9eJtDY+2UJtKkzDaBQG8YFldVC92+5h5QXeTxGQwghdU/vE2iLNtQCtza062IPj3aNAMjmCt1Bg8wTQnScnibQNG7Zw8eWx0h0h0AgwJiZZbXQ7T/dQlZGAY8REUJI3dLTBFpWA6VBFGpPzwHOaNZc1qI5L6cI26kWSgjRYXqXQIuLpQrD+FECrT0ikRAT323HrW9dcwM5WTTIPCFEN+ldAk18nIWiQtkg8samBjA1F/MckW7xH+LKNczKzizErp9v8RwRIYTUDb1LoI/kbt/aO1L/z9omMhBiwmxvbj1kVSTycot4jIgQQuqG3iVQ+QZEbq2s+AtEh732hjvsHWUz3GSmFeB/v93hOSJCCKl9ephAy2qgrakFbp0wMBRi/Ky23PrG5deR/6qYx4gIIaT26XUCdfOw5jES3TZglDsaNzEBAKS/fIW9v0fxHBEhhNQuvUqgjDGFW7jUArfuiCUijH277FnohqURKMinWighRHfoVQJNTspFbrasQYuhWAibxsY8R6TbXh/TgjvHKS/ysGcj1UIJIbpDrxKo/O3bxk1MIRAIeIxG90mMDDBOrhb6y/dXqUUuIURn6FUCjY5K5ZZdWlrxF4geGTqhVdmz0JR87FxP/UIJIbpBvxLonbIE6t3JjsdI9IdYIsKk98pGJ9r4QwSyM2mMXEJIw6dXCfTB7bIE2tKLZmGpLwMDWyiMTrRl9Q1+AyKEkFqgNwm0pESKR3flB1GgLiz1xcBQiCkf+nDrW1bfQHrKKx4jIoSQmtObBPrkUSbXmd/YxABWjYx4jki/+A91hUsLWbehvNwi/L78Os8REUJIzehNAn0o9/zT4d/biaT+iERCTJvTnlvfuf4WXj7L5S8gQgipIb1JoPINiFp6N+IxEv3Vc6AzWnjKnj0X5Jfgt6XXeI6IEEI0pzcJ9KFcAyJv38Y8RqK/BAIBps9tz63/77coJMVn8RcQIYTUgP4kULk+oC3aUAtcvnTp4wjPjrIvMMXFUvz83VWeIyKEEM3oRQLNyy3Ck0eZAACBAHBuYcVvQHpMIBBgxscduPX9W+8hRq51NCGENBR6kUBj7qaBMdmyVSMjiCUifgPScz5+TeDb0wEAwKTAqvn/8BwRIYRUn14k0Ie3U7hlF6p9aoWZn/iidCjis0fjcPVcIr8BEUJINelJAi17/unZgRoQaYMWnjZ47Y3m3Pqyz8LBSm8TEEJIA6AXCTTqejK33KY9JVBtMf2j9jAUyy7BqIhknNgbw3NEhBCiPp1PoMXFUty/UXYL16Md9QHVFnZNzTByWhtufeX8iygsLOExIkIIUZ/OJ9BHd9O4IfxMzQ1hbUuTaGuTCbPbwtxKDABIjMvC/367w3NEhBCiHp1PoPK3b6kBkfYxsxBj0rtl0539tOgKTXdGCGkQdD6B3okoS6DtutjzGAmpyLCJHmjiZAYAyEovwK+LaYg/Qoj20/0Eeq0sgbbv2oTHSEhFxBIR3pQbXGHb2puIf06NvQgh2k2nE2hhQQke3CprQESDyGsv/6GuCkP8/fDHSJ4jIoSQyul0An14JwXFRVIAgLmlGBZWEp4jIhURCAR47+suZYMr3GiLC7db8xsUIYRUQqcTaFTES27ZzcOax0iIOlp5N8LAwBbc+tKdgSgq1ulLlBDSgOn0X6couQZEHbo58BgJUdeM/3SAiZkhACD2WRPsOt2b54gIIUQ1nU6gt6+94JbbdrbjMRKiLmtbY0x+X65by4HBSE7K4TEiQghRTWcTaHZmAaLvyM0B6klzgDYUb0xpDbcmsi8/Oa+M8f3c8zxHRAghynQ2gV4Pf8ZNYWbX1BSm5mJ+AyJqMxSLsGD6H9z6yf2PEHrkMY8REUKIMp1NoNfOJ3HLNIBCw9O5dQxG9rrIrS/64Cxycwp5jIgQQhTpbAKNuFA2v2SP/s14jIRoat64A7A2zwYAvEjMxbrgyzxHRAghZXQygeblFil0YWnbiWqgDZGVWR4+m/Ant77jx5u4deU5jxERQkgZnUygty4/R3GxbAAFq0ZGsLQx4jkioqmh3a6hi28+AEAqBT6ffhKv8op4jooQQnQ0gco///Type4rDZlAAHz+cQZMjGVfiOJjMrFq/sUq9iKEkLqnmwn0QlkC7TnAmcdISG1o2qQEc9/L5NZ3/HQL/5xK4DEiQgjRwQRaWFCCW5fLnpPRDCy6YeigPPTs9opb/2za33j5LJfHiAgh+k7nEujNy89RkF8CALCwksDW3oTniEhtEAiALz7OgI217P827WU+Ppn6N/esmxBC6pvOJdDzx+O5ZZ+u1PpWlzSykWLhl+kQCGQjZFwNS8RPC6lrCyGEHzqXQC/8XfZs7LURzXmMhNSFzh0L8NbUbG79t6UROLb7IY8REUL0lU4l0OSkHG4CbYEAaN+VZmDRRW9OyoZfp3xufX7Qady6+qKSPQghpPbpVAI9J3f7tpm7JTctFtEtIhGw6Ks0uDST9QctzC/BB6OOIOFRZhV7EkJI7dGpBHr6YCy3HDDMjcdISF2zMGdY9m0qzM1ljYpSXrzCzEEH8CKRpj4jhNQPnUmgudmFuHj6CbdOCVT3OTuV4Idv0iAWy1riJsZn461BB5DyIo/nyAgh+kBnEuj54/EoKpT9IbW1N4FDM3OeIyL1oUO7QiwJToNIJGuZ+/hBBqb470NiXBbPkRFCdJ3OJNBju6O55T6DXXiMhNS37n4F+GZ+GoRCWRJNiMnE5L778PB2Cs+REUJ0mU4k0Mz0fJw7HsetD53gwV8whBf9+uRjSXAaDA1ldyGSk3IxsfdenNgXw3NkhBBdpRMJ9O8/H3G3b+2amsLJzYLniAgfevfIx+rFqdzA869yi/HxhONY/nk4CgtKeI6OEKJrdCKB7t98l1t+fWwLHiMhfPPtUIgN617CsWnZlGchKyMxtutu3It8WcmehBBSPQ0+gT64lYKbl2Wd6AUCYMj4VjxHRPjm7laMkPUv0d2vbLCF6Kg0jOv+P3z3URgy0vIr2ZsQQtTT4BPo7g13uGXvznawbmTMYzREW1iYMyz/NhX/+SADkn+7uZSUMOxcfxuDPbfhtyXXkJNVyHOUhJCGrEEn0PSUVzi49T63Pvk9Hx6jIdpGKATGvJGLbRuS4du+rNaZmVaANf+9hP4ttmD55+GIj8ngL0hCSIPVoBPorl9uI/9VMQCgkZ0xOnSjuT+JMmenEvy4PBVLF6aiaZOyZ6NZGQUIWRmJwZ7b8dagAziy6wHVSgkhajPgOwBNZWcWYPuPN7n1qXPaQyAQ8BgR0WYCAdCnZz56dM3H8VMmCNluisRnYu71S2ee4tKZpzAUC9H9NWf0H9kcPfo7w66pGY9RE0K0WYNNoJtX3UBmWgEAwNTcEANGuvMcEWkIDAyAoYPy8Hr/PFy8YoT9h41x8YoxpEz25auoUIqwY3EIOxYHAHBvY4Nu/ZzQrV8zdO7tCFNzcSVHJ4TokwaZQJMSsrFldSS3PvNTXxgYNui70aSeiURAz2756NktHy+Ss3D0bxOcOStGzGMjhXKP7qXh0b00bP/xFkQiAbx87dCljyO69HVCh+4OMDGlGX8I0VcCxhhTt3CnTp1w7dq1uoxHLXPGHMOpf2desWlsjJ3nAyESUQLVJU3DLbnl7Gaf19v7Pk0S4ex5Y4RfMsTtu0YoLq74ujIwFKJtZ3suobbv2gRGxg3yOykhukH+MZ76qU3zt2toCfTIzgf4bPpJbn3Z1v5o340mztY1fCVQefn5Aty8I8aVCDEuXTFEbLwRGKv4ObtYIoKPnz269HFCl76OaNelCcQSUT1GTIieowRasbiH6RjXfQ/XUrJLH0d8t7Efb/GQuqMNCbS8zCwBIm9JEBEpxtXrhohLMKq0vJGxAfz8ndB3qCv6DnalBkmE1DVKoKqlp7zCpN57ER+TCQAwsxBjR1ggTMzoGZQu0sYEWl5auhCRt8SIuCHBtQhDJCRKKi3v5WsH/6Fu6DvEFa19bKnVOCG1jRKostTkPMx8/SAe3k4FAAiEwOo/BsGzg129x0LqR0NIoOWlpApx/WZZDTXpecUJtUkzM/QZ7Ar/IW7o0tcREiN6dkpIjVECVXQ3MhkfjfsLiXHZ3LZ5S7pjYCANGq/LGmICLe9pkggXLhrhXLgEN29LUCJV3SDJ2MQA3fo1Q58hrujzuisaO5jWc6SE6AhKoDIZqa+wcdl1bFt7E8XFUm77u193xsipbeolBsIfXUig8rJzBLh0xQjn/5Eg/LIEuXkV1zg9OzRGl76O6NTLEb49m8LCqvJbw4SQf+lzAmWM4f6NFBzd/RB7NkYpDKsmFAnwxYqe6DvErc7en2gPXUug8oqLgVtRYpz/R4Lz4WI8fVZxYySBAGjt0xjtutjDo50tPNo1QkvvRjA1owEdCFGiLwm0sKAETx9n4enjTMRFZ+DOtWREXnyGpPhspbJNnMyweFM/OLlZqjgS0UW6nEDLS3higAuXjHA+3BA3o4whlVbeuEggAJzcLODkZommzuZwcDZHU2dzNHYwgaWNESytjWBpI4GZhZgaKhH9Us8JlJeWC/HRGRjivb3Kz2dhLcHbn3ZE/1EtIBTSHwKim5ybFWNisxxMHANk52Tgxi0Jrt80RMR1A0Q/Nlbqe8oY8CQ2C09isyo9rkAg60pjKBFBYiSCxMgAYonsX0OxEEKRACJR6b8CCEUCvP1ZJ3Tu7ViXH5cQncFLAm3SrOL+cEKhAK6trNBvRHO062wPoUiAmLtp9Rgd0QZN5ZYzXiTzFgcf2rrLfqaNAnLzhLgXbYrYeGPEPJYgNk6CpJemkFbQIEkeY8CrvGK8yitW+70Dp3vWJHRC9Eq1buHa2trC1dW10jIvX75E48aN1TpeZlo+nsZlQSAQQCwRQdfuNhUUZ0NiYM53GFqjuudDYlAAE8mrOoyIP9mvCmBuXFuNgxjApLIf5VdkL0O5Foty24RiUzRyago+VOfvhq6jc6FIG86Hra0tjh8/rrS9WglUHXyPVqRN6FwoovNRhs6FIjofZehcKNLm80EjsBNCCCEaoARKCCGEaKDWE+jbb79d24dssOhcKKLzUYbOhSI6H2XoXCjS5vNR689ACSGEEH1At3AJIYQQDaiVQJ88eYLRo0fD0tISFhYWGDVqFBISEtR6g/z8fHzyySdwcHCAsbExunXrhnPnztUoaL7V5HwIBAKVPzdu3KjboOvI06dP8cEHH6Bbt24wMTGBQCBAXFycWvvq2rVRk3Oha9fF3r17ERgYCBcXFxgbG8PDwwNffPEFsrOVRxorT9euC6Bm50PXro0TJ04gICAATZo0gUQigZOTE8aOHYu7d+9Wua/WXRusCrm5uaxFixbMy8uL7d+/nx04cIB5e3uz5s2bs5ycnKp2ZxMnTmSWlpbst99+Y6dOnWIjR45kRkZGLDIyssp9tVFNzwcANn36dHbx4kWFn9zc3HqIvvaFhoYyOzs79vrrr7MBAwYwAOzx48dq7atr10ZNzoWuXRd+fn5szJgxbPv27ezs2bNs1apVzNLSkvn5+bGSkpJK99W164Kxmp0PXbs2du7cyebNm8f27NnDzp49y7Zu3co8PT2Zubk5i4uLq3Rfbbs2qkygq1evZkKhkEVHR3PbYmNjmUgkYitWrKh03xs3bjAAbNOmTdy2oqIi1qpVKzZs2LAahM2fmpwPxmS/DPPnz6/LEOuV/C//hg0b1E4aunhtaHouGNO96yI5OVlp25YtWxgAdvr06Qr308XrgjHNzwdjundtqHL//n0GgC1fvrzCMtp4bVR5C/fQoUPo2rUrWrQom3/Tzc0NPXr0wMGDB6vc19DQEOPGjeO2GRgYYPz48Thx4gQKCgrUrypriZqcD10kFGr2GF0Xrw1Nz4UuUjVyTOfOnQEAiYmJFe6ni9cFoPn50BeNGjUCABgaGlZYRhuvjSp/46OiouDt7a203cvLq8p71lFRUXBzc4OJiYnSvoWFhYiJialmuPyryfko9fPPP0MikcDExAQBAQE4f/58bYep9XTx2qgpXb8uwsLCAABt2lQ8n68+XRfqnI9SunhtlJSUoLCwENHR0Zg1axaaNGmC8ePHV1heG6+NKhNoWloarK2tlbbb2NggPT1d431LX29oanI+AGDy5MlYv349Tp06hd9++w2pqakICAjA2bNn6yBa7aWL10ZN6Pp1kZiYiP/+97947bXX0KlTpwrL6ct1oe75AHT32vDz84NEIkGrVq1w69YtnDlzBnZ2dhWW18ZrQ63ZWFTNKcjU6D7KGNN4X21Wk8+0bds2brlXr14YMWIEvL298dVXX+HChQu1FqO209VrQ1O6fF3k5ORgxIgRMDAwQEhISKVl9eG6qM75AHT32ti2bRuysrIQGxuL5cuXo3///rhw4UKFE5Zo47VRZQ3U2tpaZWZPT09X+W1Ano2NTYX7lr7e0NTkfKhibm6OIUOG4OrVq7URXoOhi9dGbdKV6yI/Px/Dhw9HbGwsTpw4AScnp0rL6/p1Ud3zoYquXBtt2rSBn58fJkyYgNOnTyMnJwdLliypsLw2XhtVJlAvLy9ERUUpbb979y48PSufO9DLywuPHz9GXl6e0r5isVihIU5DUZPzUZGKvlnpMl28NmpbQ78uioqKEBgYiCtXruDYsWNo27Ztlfvo8nWhyfmoSEO/NsqzsrJCixYtKn2OqY3XRpUJdPjw4bh06RJiY2O5bXFxcQgPD8fw4cOr3LeoqAh79uzhthUXF2P37t0YMGAAJJLamg+x/tTkfKiSlZWFo0ePws/PrzbD1Hq6eG3UpoZ+XUilUkyaNAmnT5/GwYMH0bVrV7X209XrQtPzoUpDvzZUefHiBe7fvw93d/cKy2jltVFVP5ecnBzm7u7OvL292YEDB9jBgwdZu3btmJubG8vOzubKxcXFMZFIxBYuXKiw/7hx45iVlRXbsGEDO3XqFAsMDGQSiYRFRETUSj+c+laT87Fs2TI2c+ZMtmPHDhYaGso2b97MvL29maGhITt37hwfH6dW7Nmzh+3Zs4fNnj2bAWDr16/nOkkzpj/XBmOanQtdvC5KP//8+fOVBgB48uQJY0y/rgtNz4cuXhtvvPEG++abb9iBAwfYmTNn2C+//MI8PDyYpaUle/DgAWOs4VwbVSZQxhiLj49no0aNYubm5szMzIyNGDFCqYP448ePGQC2YMEChe15eXls7ty5zN7enkkkEtalSxcWGhpaS+HzQ9PzcejQIda9e3fWqFEjZmBgwGxsbNiwYcPY5cuX6/cD1DIAKn/69OnDGNOva0OTc6GL14WLi0uF56L0s+vTdaHp+dDFa2PJkiWsY8eOzNLSkhkbG7NWrVqxt99+W+FvaEO5Nmg2FkIIIUQDNHQKIYQQogFKoIQQQogGKIESQgghGqAESgghhGiAEighhBCiAUqghBBCiAYogRJCCCEaoARKSC0RCAQQCAQQCoV49OhRheX8/f25sps3b1Z4bfr06dxrCxYsqPAYW7Zs4cr17dtX4bWzZ89yr5X+mJiYwMHBAb1798Ynn3yCyMjImnxUQggogRJSqwwMDMAYw++//67y9ejoaISFhcHAoPKZBA0MDLBp0yaUlJSofH3Dhg1VHsPFxQULFizAggUL8NFHH2Ho0KHIzc3F8uXL0bFjR0yaNAk5OTnqfTBCiBJKoITUInt7e3Tq1AkhISEoLi5Wen3jxo1gjGHo0KGVHmfo0KF4+vQpjh8/rvTavXv3EB4ejmHDhlV6DFdXVwQHByM4OBjff/89NmzYgIiICERGRqJt27bYuXMnxowZU70PSAjhUAIlpJYFBQXh+fPnOHLkiML2oqIibNmyBd27d4eXl1elx5g0aRKMjY2xYcMGpddKt82cOVOj+Nq3b49Tp06hcePGOH78OA4cOKDRcQjRd5RACallEyZMgKmpKTZu3Kiw/dChQ3jx4gWCgoKqPIaVlRXGjBmDo0eP4tmzZ9z2goICbN26FX369EGrVq00jtHOzg6zZs0CAOzYsUPj4xCizyiBElLLzM3NMX78eBw/fhxPnz7ltm/YsAEWFhYYO3asWscJCgpCcXGxQkOj/fv3IzU1Va0kXJXSxkdXrlyp8bEI0UeUQAmpA0FBQSgpKcGmTZsAAPHx8Th58iQmTZoEExMTtY7Rs2dPtGnThntuCsiSsLW1NQIDA2sco6OjIwDg5cuXNT4WIfqIEighdcDPzw9t27bFpk2bIJVKsXHjRkil0mrXHGfOnInY2FicOXMGMTExCA0NxZQpU2BkZFTjGEuTskAgqPGxCNFHlEAJqSNBQUGIj4/H8ePHERISAl9fX3To0KFax5g6dSokEgk2btzI1URr4/YtACQlJQEAGjduXCvHI0TfUAIlpI5MmTIFxsbGmDVrFhITE/H2229X+xi2trYYOXIk9u/fj02bNqFbt27w9vaulfhCQ0MByGrLhJDqowRKSB2xsrLC6NGj8fTpU5iammLChAkaHScoKAgFBQV4+fJlrdU+k5OT8euvvwKQdZkhhFRf5UOZEEJq5Ntvv8WoUaPQuHFjmJuba3QMf39/HDx4EFKpFAMHDqxxTDdv3sTUqVORkpKCwYMHY/jw4TU+JiH6iBIoIXXI2dkZzs7ONTqGQCDQKMnFxcUhODgYgGwQh5SUFERERCAiIgKArOZZWgslhFQfJVBCdFR8fDwWLlwIADAyMoKVlRVatmyJefPmYdKkSWjfvj2/ARLSwAlYaVt2QgghhKiNGhERQgghGqAESgghhGiAEighhBCiAUqghBBCiAYogRJCCCEaoARKCCGEaIASKCGEEKIBSqCEEEKIBiiBEkIIIRqgBEoIIYRo4P/1fAofEI5llAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    print(noise_name)\n",
+    "    \n",
+    "    data_HNN_noise = data_HNN + noise\n",
+    "    # plt.plot(data_HNN_noise.flatten().T)\n",
+    "    # plt.show()\n",
+    "    \n",
+    "    observed_data_HNN_noise = {}\n",
+    "    observed_data_HNN_noise['summary_conditions'] = data_HNN_noise\n",
+    "    observed_data_HNN_noise['direct_conditions'] = None\n",
+    "    observed_data_HNN_noise['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "    \n",
+    "    MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data_HNN_noise, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True)\n",
+    "    _ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)\n",
+    "    \n",
+    "\n",
+    "#     plt.savefig(f'hypothesis_test_figures/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52b68025",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b448cb9",
+   "metadata": {},
+   "source": [
+    "## Real ERP data from HNN-GUI example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "929f0aa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1000 simulated data\n",
+    "\n",
+    "ori_x = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/x.pt')\n",
+    "ori_theta = torch.load('Aug15_data1000_3ERPDrives_no_preprocessing/theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "20a6f47c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_x = np.array(ori_x)\n",
+    "ori_theta = np.array(ori_theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "b50030c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 341)"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ori_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "ffa6518a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No preprocessing for the 1000 simulated data\n",
+    "\n",
+    "for i in range(len(ori_x)):\n",
+    "    ori_x[i] = smooth_waveform(ori_x[i], window_len=30, sfreq=1000.0 / 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "bf2e21db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_x = np.mean(ori_x, axis=0)\n",
+    "avg_x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cd06657",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "1b21e998",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Real ERP data from HNN-GUI example\n",
+    "\n",
+    "yes_data = torch.load('Real_ERP_data/np_yes_data.pt')\n",
+    "no_data = torch.load('Real_ERP_data/np_no_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "277bd5b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess the real ERP data from HNN-GUI example\n",
+    "\n",
+    "# Divide the experimental data by the scaling factor\n",
+    "yes_data = yes_data / 3000\n",
+    "no_data = no_data / 3000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "97b0b5a2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yes_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "877837e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yes_data_reshaped = yes_data.reshape(1, 1, 341)\n",
+    "no_data_reshaped = no_data.reshape(1, 1, 341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3804c1b0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "134e3ac0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "1bbb50ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "999f7265",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "1ddd1fbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_yes = yes_data_reshaped\n",
+    "observed_data_ERP_yes = {}\n",
+    "observed_data_ERP_yes['summary_conditions'] = data_ERP_yes\n",
+    "observed_data_ERP_yes['direct_conditions'] = None\n",
+    "# observed_data_ERP_yes['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_yes['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "4785ed24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_yes.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "7862ab84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c71a35870d464835b484604932af29a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_yes, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00137721",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "d4e77ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# no_trial_S1_ERP_all_avg.txt\n",
+    "data_ERP_no = no_data_reshaped\n",
+    "observed_data_ERP_no = {}\n",
+    "observed_data_ERP_no['summary_conditions'] = data_ERP_no\n",
+    "observed_data_ERP_no['direct_conditions'] = None\n",
+    "# observed_data_ERP_no['parameters'] = np.array([[0.142300 , 0.00865, 0.684013]])\n",
+    "# observed_data_ERP_no['parameters'] = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "3a114db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ERP_no.flatten().T)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "e749bf4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2c3595134ea43578efcf3511122f12b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/500 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(\n",
+    "    observed_data_ERP_no, reference_data=reference_data, num_reference_simulations=1000, num_null_samples=500, bootstrap=True\n",
+    ")\n",
+    "_ = bf.diagnostics.plot_mmd_hypothesis_test(MMD_sampling_distribution, MMD_observed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77de88fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dea55462",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "aeb34c99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 460.8x345.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(ori_x.T, color='gray', alpha=0.2)\n",
+    "plt.plot([], color='gray', alpha=0.5, label='1000 simulations')  #just for legend setting purpose\n",
+    "plt.plot(avg_x.T, color='black', label=\"avg simulation\")\n",
+    "# plt.plot(yes_data.T, color='red', label=\"yes_trial_S1_ERP_all_avg\")\n",
+    "# plt.plot(no_data.T, color='blue', label=\"no_trial_S1_ERP_all_avg\")\n",
+    "plt.plot(data_HNN.flatten().T, color='green', label=\"data_HNN\")\n",
+    "plt.plot(five_data_HNN.reshape(5,341).T, color='yellow', alpha=0.4)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b442c0b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/figures2/.ipynb_checkpoints/real_obv_withnoise_0-checkpoint.png b/figures2/.ipynb_checkpoints/real_obv_withnoise_0-checkpoint.png
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diff --git a/hnn_sbi.ipynb b/hnn_sbi.ipynb
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+++ b/hnn_sbi.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "81cdeac6-f5dc-44d6-8c6c-09e89f04a0af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e742fb7e-9a04-4c32-82a5-f17f1aea6ba3",
+   "metadata": {},
+   "source": [
+    "Write function that accepts a net object and parameters theta as input, and returns the simulated current dipole dpl[0].data['agg'] (agg stands for “aggregate”)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "ddacc068-7e4e-4d1c-8180-06aff01e1ceb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(net, theta):\n",
+    "    # \"theta\" is just a float number\n",
+    "    \n",
+    "    net_sim = net.copy()\n",
+    "    \n",
+    "    # your code here that updates \"mu\" or mean time of drives using parameter theta\n",
+    "    weights_ampa = {'L2_basket': 0.09, 'L2_pyramidal': 0.02, \n",
+    "                'L5_basket': 0.2, 'L5_pyramidal': 8e-3}\n",
+    "    synaptic_delays = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                   'L5_basket': 1.0, 'L5_pyramidal': 1.0}\n",
+    "\n",
+    "    net_sim.add_evoked_drive(name='evprox1', mu=100, sigma=theta, numspikes=1,\n",
+    "                     weights_ampa=weights_ampa, location='proximal',\n",
+    "                     synaptic_delays=synaptic_delays)  # mu=theta\n",
+    "    \n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170) # , n_trials=2\n",
+    "\n",
+    "    return net_sim, dpl[0].data['agg']   #net_sim, "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e97dabb-4b1d-4d2a-ad3a-2af80a0140c7",
+   "metadata": {},
+   "source": [
+    "Using a random number generator, create a vector of random “start times” called theta_samples that are uniformly distributed between [20, 160]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "e62fa419-593f-4820-b0fd-6e576bfbc926",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_theta_samples(size):\n",
+    "    return np.random.uniform(low=20, high=160, size=size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "b87c906c-a49e-4a3e-8a25-f06a47dfb0b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# theta_samples = generate_theta_samples(128)\n",
+    "theta_samples = np.linspace(0, 30, 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd57c236-4a0b-4e8c-8d42-12fdb9b43adf",
+   "metadata": {},
+   "source": [
+    "Look into parallelizing these simulations using joblib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "5a6190aa-6a14-4bad-8657-0177e74a2db3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = jones_2009_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "f117f165-842d-417a-a5d8-ddb56ab53e39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.plot_cells()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d418cbb8-4efc-4771-9099-e4be7cd04a14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net.clear_connectivity()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "a50179a3-35bf-4ab7-96dc-eb67842e4fe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Execution time: 4.362928867340088 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/1p/sf57j0k93z103tg3g01ch4k80000gn/T/ipykernel_64167/1598601830.py:4: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  res = np.array(Parallel(n_jobs=8)(delayed(run_simulation)(net, theta) for theta in theta_samples))\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = time.time()\n",
+    "\n",
+    "from joblib import Parallel, delayed\n",
+    "res = np.array(Parallel(n_jobs=8)(delayed(run_simulation)(net, theta) for theta in theta_samples))\n",
+    "\n",
+    "end_time = time.time()\n",
+    "execution_time = end_time - start_time\n",
+    "print(f\"Execution time: {execution_time} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "a1304d35-f415-40d2-a699-4f18d278f19e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Network | 10 x 10 Pyramidal cells (L2, L5)\n",
+       "35 L2 basket cells\n",
+       "35 L5 basket cells>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res[0,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "d87d3b5a-a077-4132-b1c2-d7f31bdc94d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(341,)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res[0,1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "015cb43f-f9a0-4186-ad24-2678f152036b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "497"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(res[0,0].cell_response.spike_types[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "8717310c-f046-49ef-a897-c9d370a67485",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "270"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = np.array(res[0,0].cell_response.spike_types[0]) == 'evprox1'\n",
+    "np.sum(mask)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d18eb820-e0f7-4afd-917a-8abde5f820d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pYx1zoYyMDLlcLv+WmJhY36cDAADQLDSZWJwyZYr27dunN998s9oxxwVPGxhjqu270MXGzJ07V16v178dP3687hMHAABoxppELE6dOlWbNm3Stm3b1LFjR/9+t9stSdWuEBYWFvqvNrrdbpWXl6uoqMg65kJOp1MxMTEBGwAAAKoLaiwaYzRlyhRt2LBBW7duVXJycsDx5ORkud1uZWZm+veVl5crKytLffr0kSSlpqYqIiIiYEx+fr4OHDjgHwMAAIC6CQ/mF588ebLWrl2rd999V9HR0f4riC6XS1FRUXI4HEpPT9fChQvVpUsXdenSRQsXLlSrVq00duxY/9hJkyZp5syZatu2rWJjYzVr1ix1795dgwYNCubpAQAAXPGCGosrVqyQJPXv3z9g/8qVKzVx4kRJ0hNPPKHS0lI9+uijKioqUu/evfXRRx8pOjraP37p0qUKDw/XqFGjVFpaqoEDB2rVqlUKCwtrrFMBAABolprU5ywGC5+zCAAhgs9ZBPyuyM9ZBAAAQNNCLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsKpTLObm5mr//v3+2++++65GjBihJ598UuXl5fU2OQAAAARXnWLxoYce0uHDhyVJX3/9tcaMGaNWrVrprbfe0hNPPFGvEwQAAEDw1CkWDx8+rBtvvFGS9NZbb+n222/X2rVrtWrVKv3nf/5nfc4PAAAAQVSnWDTGqKqqSpK0ZcsW3XXXXZKkxMREff/99/U3OwAAAARVnWKxZ8+e+uMf/6jVq1crKytLd999tyQpLy9P8fHxtX6cTz/9VPfcc488Ho8cDofeeeedgOMTJ06Uw+EI2G655ZaAMWVlZZo6dari4uLUunVrDR8+XCdOnKjLaQEAAOACdYrFpUuXKjc3V1OmTNG8efN0zTXXSJLefvtt9enTp9aPc/r0afXo0UPLli2zjrnzzjuVn5/v3zZv3hxwPD09XRs3btS6deu0Y8cOlZSUaNiwYaqsrKzLqQEAAOAnHMYYU18PdvbsWYWHhys8PPzyJ+JwaOPGjRoxYoR/38SJE3Xq1KlqVxzP83q9ateunVavXq3Ro0dLkk6ePKnExERt3rxZQ4YMqdXX9vl8crlc8nq9iomJuey5AwCuEA5H7cbV369GoMmqbf/U6cpi586d9cMPP1Tbf/bsWV177bV1eUirTz75RO3bt9e1116rBx98UIWFhf5jOTk5qqioUFpamn+fx+NRSkqKsrOzrY9ZVlYmn88XsAEAAKC6OsXi0aNHa3yat6ysrF5fLzh06FCtWbNGW7du1eLFi7V7927dcccdKisrkyQVFBQoMjJSbdq0CbhffHy8CgoKrI+bkZEhl8vl3xITE+ttzgAAAM3JZT1fvGnTJv9/f/jhh3K5XP7blZWV+vjjj5WcnFxvkzv/1LIkpaSkqGfPnkpKStJ7772nkSNHWu9njJHjIk81zJ07VzNmzPDf9vl8BCMAAEANLisWz7+e0OFwaMKECQHHIiIidPXVV2vx4sX1NrkLJSQkKCkpSUeOHJEkud1ulZeXq6ioKODqYmFh4UXfaON0OuV0OhtsngAAAM3FZT0NXVVVpaqqKnXq1EmFhYX+21VVVSorK9OXX36pYcOGNdRc9cMPP+j48eNKSEiQJKWmpioiIkKZmZn+Mfn5+Tpw4MBlvSsbAAAANbv8ty3rx89TrA8lJSX66quvAh537969io2NVWxsrObPn697771XCQkJOnr0qJ588knFxcXpt7/9rSTJ5XJp0qRJmjlzptq2bavY2FjNmjVL3bt316BBg+pljgAAAKGsTrEoSR9//LE+/vhj/xXGn/rrX/9aq8f47LPPNGDAAP/t868jnDBhglasWKH9+/fr9ddf16lTp5SQkKABAwZo/fr1io6O9t9n6dKlCg8P16hRo1RaWqqBAwdq1apVCgsLq+upAQAA4P+r0+csPvPMM1qwYIF69uyphISEam8m2bhxY71NsDHwOYsAECL4nEXAr7b9U6criy+//LJWrVql8ePH13mCAAAAaPrq9DmL5eXlvIEEAAAgBNQpFv/1X/9Va9eure+5AAAAoImp09PQZ8+e1SuvvKItW7boV7/6lSIiIgKOL1mypF4mBwAAgOCqUyzu27dPN954oyTpwIEDAccu9pdTAAAAcGWpUyxu27atvucBAACAJqhOr1kEAABAaKjTlcUBAwZc9OnmrVu31nlCAAAAaDrqFIvnX694XkVFhfbu3asDBw5owoQJ9TEvAAAANAF1isWlS5fWuH/+/PkqKSn5WRMCAABA01Gvr1kcN25crf8uNAAAAJq+eo3Fv//972rZsmV9PiQAAACCqE5PQ48cOTLgtjFG+fn5+uyzz/TUU0/Vy8QAAAAQfHWKRZfLFXC7RYsWuu6667RgwQKlpaXVy8QAAAAQfHWKxZUrV9b3PAAAANAE1SkWz8vJydGhQ4fkcDjUrVs33XTTTfU1LwAAADQBdYrFwsJCjRkzRp988omuuuoqGWPk9Xo1YMAArVu3Tu3atavveQIAACAI6vRu6KlTp8rn8+ngwYP65z//qaKiIh04cEA+n0/Tpk2r7zkCAAAgSBzGGHO5d3K5XNqyZYt69eoVsH/Xrl1KS0vTqVOn6mt+jcLn88nlcsnr9SomJibY0wEANJSL/KnaAJf/qxG44tS2f+p0ZbGqqkoRERHV9kdERKiqqqouDwkAAIAmqE6xeMcdd2j69Ok6efKkf9+3336rxx57TAMHDqy3yQEAACC46hSLy5YtU3Fxsa6++mr98pe/1DXXXKPk5GQVFxfrz3/+c33PEQAAAEFSp3dDJyYmKjc3V5mZmfrf//1fGWPUrVs3DRo0qL7nBwAAgCC6rCuLW7duVbdu3eTz+SRJgwcP1tSpUzVt2jT16tVLN9xwg7Zv394gEwUAAEDju6xYfP755/Xggw/W+I4Zl8ulhx56SEuWLKm3yQEAACC4LisWP//8c915553W42lpacrJyfnZkwIAAEDTcFmx+I9//KPGj8w5Lzw8XN99993PnhQAAACahsuKxQ4dOmj//v3W4/v27VNCQsLPnhQAAACahsuKxbvuukv/9m//prNnz1Y7VlpaqqefflrDhg2rt8kBAAAguC7rz/394x//0M0336ywsDBNmTJF1113nRwOhw4dOqTly5ersrJSubm5io+Pb8g51zv+3B8AhAj+3B/gV9v+uazPWYyPj1d2drYeeeQRzZ07V+c70+FwaMiQIXrppZeuuFAEAACA3WV/KHdSUpI2b96soqIiffXVVzLGqEuXLmrTpk1DzA8AAABBVKe/4CJJbdq0Ua9evepzLgAAAGhi6vS3oQEAABAaiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsApqLH766ae655575PF45HA49M477wQcN8Zo/vz58ng8ioqKUv/+/XXw4MGAMWVlZZo6dari4uLUunVrDR8+XCdOnGjEswAAAGi+ghqLp0+fVo8ePbRs2bIajy9atEhLlizRsmXLtHv3brndbg0ePFjFxcX+Menp6dq4caPWrVunHTt2qKSkRMOGDVNlZWVjnQYAAECz5TDGmGBPQpIcDoc2btyoESNGSPrxqqLH41F6erpmz54t6ceriPHx8Xruuef00EMPyev1ql27dlq9erVGjx4tSTp58qQSExO1efNmDRkypFZf2+fzyeVyyev1KiYmpkHODwDQBDgctRvXNH41Ag2qtv3TZF+zmJeXp4KCAqWlpfn3OZ1O9evXT9nZ2ZKknJwcVVRUBIzxeDxKSUnxj6lJWVmZfD5fwAYAAIDqmmwsFhQUSJLi4+MD9sfHx/uPFRQUKDIyUm3atLGOqUlGRoZcLpd/S0xMrOfZAwAANA9NNhbPc1zwlIExptq+C11qzNy5c+X1ev3b8ePH62WuAAAAzU2TjUW32y1J1a4QFhYW+q82ut1ulZeXq6ioyDqmJk6nUzExMQEbAAAAqmuysZicnCy3263MzEz/vvLycmVlZalPnz6SpNTUVEVERASMyc/P14EDB/xjAAAAUHfhwfziJSUl+uqrr/y38/LytHfvXsXGxqpTp05KT0/XwoUL1aVLF3Xp0kULFy5Uq1atNHbsWEmSy+XSpEmTNHPmTLVt21axsbGaNWuWunfvrkGDBgXrtAAAAJqNoMbiZ599pgEDBvhvz5gxQ5I0YcIErVq1Sk888YRKS0v16KOPqqioSL1799ZHH32k6Oho/32WLl2q8PBwjRo1SqWlpRo4cKBWrVqlsLCwRj8fAACA5qbJfM5iMPE5iwAQIvicRcDviv+cRQAAAAQfsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyadCzOnz9fDocjYHO73f7jxhjNnz9fHo9HUVFR6t+/vw4ePBjEGQMAADQvTToWJemGG25Qfn6+f9u/f7//2KJFi7RkyRItW7ZMu3fvltvt1uDBg1VcXBzEGQMAADQfTT4Ww8PD5Xa7/Vu7du0k/XhV8fnnn9e8efM0cuRIpaSk6LXXXtOZM2e0du3aIM8aAACgeWjysXjkyBF5PB4lJydrzJgx+vrrryVJeXl5KigoUFpamn+s0+lUv379lJ2dfdHHLCsrk8/nC9gAAABQXZOOxd69e+v111/Xhx9+qL/85S8qKChQnz599MMPP6igoECSFB8fH3Cf+Ph4/zGbjIwMuVwu/5aYmNhg5wAAAHAla9KxOHToUN17773q3r27Bg0apPfee0+S9Nprr/nHOByOgPsYY6rtu9DcuXPl9Xr92/Hjx+t/8gAAAM1Ak47FC7Vu3Vrdu3fXkSNH/O+KvvAqYmFhYbWrjRdyOp2KiYkJ2AAAAFDdFRWLZWVlOnTokBISEpScnCy3263MzEz/8fLycmVlZalPnz5BnCUAAEDzER7sCVzMrFmzdM8996hTp04qLCzUH//4R/l8Pk2YMEEOh0Pp6elauHChunTpoi5dumjhwoVq1aqVxo4dG+ypAwAANAtNOhZPnDih++67T99//73atWunW265RTt37lRSUpIk6YknnlBpaakeffRRFRUVqXfv3vroo48UHR0d5JkDAAA0Dw5jjAn2JILN5/PJ5XLJ6/Xy+kUAaM4u8QZIP341IgTUtn+uqNcsAgAAoHERiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwIpYBAAAgBWxCAAAACtiEQAAAFbEIgAAAKyIRQAAAFgRiwAAALAiFgEAAGBFLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAK2IRAAAAVsQiAAAArIhFAAAAWBGLAAAAsCIWAQAAYEUsAgAAwKrZxOJLL72k5ORktWzZUqmpqdq+fXuwpwQAAHDFaxaxuH79eqWnp2vevHnas2ePfvOb32jo0KE6duxYsKcGAABwRXMYY0ywJ/Fz9e7dWzfffLNWrFjh33f99ddrxIgRysjIuOT9fT6fXC6XvF6vYmJiGnKqAIBgcjhqN+7K/9UIXFJt+ye8EefUIMrLy5WTk6M5c+YE7E9LS1N2dnaN9ykrK1NZWZn/ttfrlfTjogEAIH4fIASc755LXTe84mPx+++/V2VlpeLj4wP2x8fHq6CgoMb7ZGRk6Jlnnqm2PzExsUHmCAC4wrhcwZ4B0GiKi4vlusj3/BUfi+c5LnhqwRhTbd95c+fO1YwZM/y3q6qq9M9//lNt27a13qc++Hw+JSYm6vjx4zzdfQHW5uJYHzvWxo61sWNt7Fibi2tO62OMUXFxsTwez0XHXfGxGBcXp7CwsGpXEQsLC6tdbTzP6XTK6XQG7LvqqqsaaorVxMTEXPHfYA2Ftbk41seOtbFjbexYGzvW5uKay/pc7IrieVf8u6EjIyOVmpqqzMzMgP2ZmZnq06dPkGYFAADQPFzxVxYlacaMGRo/frx69uypW2+9Va+88oqOHTumhx9+ONhTAwAAuKI1i1gcPXq0fvjhBy1YsED5+flKSUnR5s2blZSUFOypBXA6nXr66aerPQUO1uZSWB871saOtbFjbexYm4sLxfVpFp+zCAAAgIZxxb9mEQAAAA2HWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiMVG9NJLLyk5OVktW7ZUamqqtm/fHuwpNaqMjAz16tVL0dHRat++vUaMGKEvv/wyYIwxRvPnz5fH41FUVJT69++vgwcPBmnGwZORkSGHw6H09HT/vlBfm2+//Vbjxo1T27Zt1apVK914443KycnxHw/V9Tl37pz+8Ic/KDk5WVFRUercubMWLFigqqoq/5hQWZtPP/1U99xzjzwejxwOh955552A47VZh7KyMk2dOlVxcXFq3bq1hg8frhMnTjTiWTSci61PRUWFZs+ere7du6t169byeDx64IEHdPLkyYDHaK7rc6nvnZ966KGH5HA49Pzzzwfsb65rIxGLjWb9+vVKT0/XvHnztGfPHv3mN7/R0KFDdezYsWBPrdFkZWVp8uTJ2rlzpzIzM3Xu3DmlpaXp9OnT/jGLFi3SkiVLtGzZMu3evVtut1uDBw9WcXFxEGfeuHbv3q1XXnlFv/rVrwL2h/LaFBUV6bbbblNERITef/99ffHFF1q8eHHAn+kM1fV57rnn9PLLL2vZsmU6dOiQFi1apH//93/Xn//8Z/+YUFmb06dPq0ePHlq2bFmNx2uzDunp6dq4caPWrVunHTt2qKSkRMOGDVNlZWVjnUaDudj6nDlzRrm5uXrqqaeUm5urDRs26PDhwxo+fHjAuOa6Ppf63jnvnXfe0f/8z//U+LeUm+vaSJIMGsWvf/1r8/DDDwfs69q1q5kzZ06QZhR8hYWFRpLJysoyxhhTVVVl3G63+dOf/uQfc/bsWeNyuczLL78crGk2quLiYtOlSxeTmZlp+vXrZ6ZPn26MYW1mz55t+vbtaz0eyutz9913m9///vcB+0aOHGnGjRtnjAndtZFkNm7c6L9dm3U4deqUiYiIMOvWrfOP+fbbb02LFi3MBx980GhzbwwXrk9Ndu3aZSSZb775xhgTOutjW5sTJ06YDh06mAMHDpikpCSzdOlS/7HmvjZcWWwE5eXlysnJUVpaWsD+tLQ0ZWdnB2lWwef1eiVJsbGxkqS8vDwVFBQErJPT6VS/fv1CZp0mT56su+++W4MGDQrYH+prs2nTJvXs2VO/+93v1L59e9100036y1/+4j8eyuvTt29fffzxxzp8+LAk6fPPP9eOHTt01113SQrttfmp2qxDTk6OKioqAsZ4PB6lpKSE1Fqd5/V65XA4/FfwQ3l9qqqqNH78eD3++OO64YYbqh1v7mvTLP7cX1P3/fffq7KyUvHx8QH74+PjVVBQEKRZBZcxRjNmzFDfvn2VkpIiSf61qGmdvvnmm0afY2Nbt26dcnNztXv37mrHQn1tvv76a61YsUIzZszQk08+qV27dmnatGlyOp164IEHQnp9Zs+eLa/Xq65duyosLEyVlZV69tlndd9990nie+e82qxDQUGBIiMj1aZNm2pjQu1n9dmzZzVnzhyNHTtWMTExkkJ7fZ577jmFh4dr2rRpNR5v7mtDLDYih8MRcNsYU21fqJgyZYr27dunHTt2VDsWiut0/PhxTZ8+XR999JFatmxpHReKayP9+H/1PXv21MKFCyVJN910kw4ePKgVK1bogQce8I8LxfVZv3693njjDa1du1Y33HCD9u7dq/T0dHk8Hk2YMME/LhTXpiZ1WYdQW6uKigqNGTNGVVVVeumlly45vrmvT05Ojl544QXl5uZe9nk2l7XhaehGEBcXp7CwsGr/d1FYWFjt/3JDwdSpU7Vp0yZt27ZNHTt29O93u92SFJLrlJOTo8LCQqWmpio8PFzh4eHKysrSiy++qPDwcP/5h+LaSFJCQoK6desWsO/666/3v0EslL93Hn/8cc2ZM0djxoxR9+7dNX78eD322GPKyMiQFNpr81O1WQe3263y8nIVFRVZxzR3FRUVGjVqlPLy8pSZmem/qiiF7vps375dhYWF6tSpk//n8zfffKOZM2fq6quvltT814ZYbASRkZFKTU1VZmZmwP7MzEz16dMnSLNqfMYYTZkyRRs2bNDWrVuVnJwccDw5OVlutztgncrLy5WVldXs12ngwIHav3+/9u7d69969uyp+++/X3v37lXnzp1Ddm0k6bbbbqv2MUuHDx9WUlKSpND+3jlz5oxatAj8UR4WFub/6JxQXpufqs06pKamKiIiImBMfn6+Dhw4EBJrdT4Ujxw5oi1btqht27YBx0N1fcaPH699+/YF/Hz2eDx6/PHH9eGHH0oKgbUJ0htrQs66detMRESEefXVV80XX3xh0tPTTevWrc3Ro0eDPbVG88gjjxiXy2U++eQTk5+f79/OnDnjH/OnP/3JuFwus2HDBrN//35z3333mYSEBOPz+YI48+D46buhjQnttdm1a5cJDw83zz77rDly5IhZs2aNadWqlXnjjTf8Y0J1fSZMmGA6dOhg/vu//9vk5eWZDRs2mLi4OPPEE0/4x4TK2hQXF5s9e/aYPXv2GElmyZIlZs+ePf5389ZmHR5++GHTsWNHs2XLFpObm2vuuOMO06NHD3Pu3LlgnVa9udj6VFRUmOHDh5uOHTuavXv3BvyMLisr8z9Gc12fS33vXOjCd0Mb03zXxhhjiMVGtHz5cpOUlGQiIyPNzTff7P/ImFAhqcZt5cqV/jFVVVXm6aefNm632zidTnP77beb/fv3B2/SQXRhLIb62vzXf/2XSUlJMU6n03Tt2tW88sorAcdDdX18Pp+ZPn266dSpk2nZsqXp3LmzmTdvXsAv+FBZm23bttX4M2bChAnGmNqtQ2lpqZkyZYqJjY01UVFRZtiwYebYsWNBOJv6d7H1ycvLs/6M3rZtm/8xmuv6XOp750I1xWJzXRtjjHEYY0xjXMEEAADAlYfXLAIAAMCKWAQAAIAVsQgAAAArYhEAAABWxCIAAACsiEUAAABYEYsAAACwIhYBAABgRSwCAADAilgEAACAFbEIAAAAq/8Hdw0yTcm5ooEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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XY+TIkfF3f/d3ZZwaAGDgKGss/va3v40jjzyyeH7WrFkRETF9+vS49tprY+nSpXHTTTfFq6++Gg0NDXHkkUfGLbfcEsOHDy/XyAAAA0rFvBu6v5T6zh+Aknk3dO9sS3+GvBuabVipTbRNvWYRAICtSywCAJAlFgEAyBKLAABkiUUAALLEIgAAWWIRAIAssQgAQJZYBAAgSywCAJAlFgEAyBKLAABkDS73AADQI4VCz9dNqf/mgAHGkUUAALLEIgAAWWIRAIAssQgAQJZYBAAgSywCAJAlFgEAyBKLAABkiUUAALLEIgAAWWIRAIAssQgAQJZYBAAgSywCAJAlFgEAyBKLAABkiUUAALLEIgAAWYPLPQDANqFQKPcEAGXhyCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgqKRaXLFkSS5cuLZ7/yU9+ElOnTo0vf/nLsXbt2j4bDgCA8iopFk8//fT4wx/+EBERTz31VEybNi2GDRsWt956a5x//vl9OiAAAOVTUiz+4Q9/iAMOOCAiIm699dY47LDDYt68efG9730v/uu//qsv5wMAoIxKisWUUnR2dkZExIIFC+LDH/5wREQ0NTXFSy+91HfTAQBQViXF4sSJE+MrX/lK/Od//mcsXLgwPvKRj0REREtLS9TV1fXpgAAAlE9Jsfj1r389lixZEjNmzIiLL7443vWud0VExI9//OOYNGlSnw4IAED5FFJKqa9ubM2aNTF48OAYPHhwX93kFmtvb4/a2tpoa2uLmpqaco8DbKsKhf653b77Ffz/+mvW/tLTbdCb+9Uf27U3M/TXz4ctUGoTlXRk8Z3vfGe8/PLL3ZavWbMm9t5771JuEgCAClRSLD799NOxfv36bss7Ojriueee2+KhAACoDL16vvinP/1p8d+/+MUvora2tnh+/fr18atf/Sqam5v7bjoAAMqqV7E4derUiIgoFAoxffr0LpdVVVXFnnvuGV/72tf6bDgAAMqrV7G44bMVm5ub46GHHoqRI0f2y1AAAFSGkt623NLS0tdzAABQgUr+jJtf/epX8atf/SpaW1uLRxw3uP7667d4MIABoRI+DgZgE0qKxcsuuywuv/zymDhxYjQ0NERhW/tMLwAAeqSkWPz2t78d3/ve9+LTn/50X88DAEAFKelzFteuXetr/QAABoCSYvHzn/98zJs3r69nAQCgwpT0NPSaNWviu9/9bixYsCD+5m/+JqqqqrpcftVVV/XJcAAAlFdJsfjoo4/GAQccEBERjz32WJfLvNkFAGD7UVIs3nPPPX09BwAAFaik1ywCADAwlHRk8cgjj9zk08133313yQMBAFA5SorFDa9X3GDdunXxyCOPxGOPPRbTp0/vi7kAAKgAJcXi17/+9Y0unz17drz22mtbNBAAAJWjT1+z+KlPfcr3QgMAbEf6NBZ/85vfxJAhQ/ryJgEAKKOSnoY+4YQTupxPKcWKFSvit7/9bVxyySV9MhgAAOVXUizW1tZ2Ob/DDjvEu9/97rj88stj8uTJfTIYAADlV1Is3nDDDX09BwAAFaikWNxg8eLF8fjjj0ehUIh999033vve9/bVXAAAVICSYrG1tTWmTZsW9957b+yyyy6RUoq2trY48sgj4+abb47ddtutr+cEAKAMSno39FlnnRXt7e2xbNmy+POf/xyvvPJKPPbYY9He3h5nn312X88IQEREodCz07Zme71fsJ0opJRSb69UW1sbCxYsiIMOOqjL8gcffDAmT54cr776al/Nt8Xa29ujtrY22traoqamptzjANsqsbJt6f2ftp7p6X7QXz8ftkCpTVTSkcXOzs6oqqrqtryqqio6OztLuUkAACpQSbF41FFHxTnnnBPPP/98cdmf/vSn+NKXvhR/+7d/22fDAQBQXiXF4ty5c2PVqlWx5557xl577RXvete7orm5OVatWhX//u//3tczAgBQJiW9G7qpqSmWLFkS8+fPj//5n/+JlFLsu+++8aEPfaiv5wMAoIx6dWTx7rvvjn333Tfa29sjIuLoo4+Os846K84+++w46KCDYr/99ov77ruvXwYFAGDr61UsfuMb34jTTjtto++gqa2tjdNPPz2uuuqqPhsOAIDy6lUs/u53v4tjjz02e/nkyZNj8eLFWzwUAACVoVex+MILL2z0I3M2GDx4cLz44otbPBQAAJWhV7G4++67x9KlS7OXP/roo9HQ0LDFQwEAUBl6FYsf/vCH45/+6Z9izZo13S5bvXp1XHrppXHcccf12XAAAJRXr77u74UXXogDDzwwBg0aFDNmzIh3v/vdUSgU4vHHH4+rr7461q9fH0uWLIm6urr+nLlXfN0f0Cd83d+2xdf9QTelNlGvPmexrq4uFi1aFF/4whfioosuig2dWSgU4phjjolrrrmmokIRAIAt0+sP5R4zZkzcdddd8corr8STTz4ZKaUYO3Zs7Lrrrv0xHwAAZVTSN7hEROy6665x0EEH9eUsAABUmJK+G7qv/PrXv46PfvSj0djYGIVCIe64444ul6eUYvbs2dHY2BhDhw6NI444IpYtW1aeYQEABqCyxuLrr78e48ePj7lz52708iuvvDKuuuqqmDt3bjz00ENRX18fRx99dKxatWorTwoAMDCV/DR0X5gyZUpMmTJlo5ellOIb3/hGXHzxxXHCCSdERMSNN94YdXV1MW/evDj99NM3er2Ojo7o6Ogont/wPdYAAPReWY8sbkpLS0usXLkyJk+eXFxWXV0dhx9+eCxatCh7vTlz5kRtbW3x1NTUtDXGBaCSFAo9PwGbVLGxuHLlyoiIbh/FU1dXV7xsYy666KJoa2srnp599tl+nRMAYHtW1qehe6Lwtv/rSyl1W/ZW1dXVUV1d3d9jAQAMCBV7ZLG+vj4iottRxNbWVh/8DQCwlVRsLDY3N0d9fX3Mnz+/uGzt2rWxcOHCmDRpUhknAwAYOMr6NPRrr70WTz75ZPF8S0tLPPLIIzFixIjYY489YubMmXHFFVfE2LFjY+zYsXHFFVfEsGHD4uSTTy7j1AAAA0dZY/G3v/1tHHnkkcXzs2bNioiI6dOnx/e+9704//zzY/Xq1fHFL34xXnnllTj44IPjl7/8ZQwfPrxcIwMADCiFlFIq9xD9qb29PWpra6OtrS1qamrKPQ6wrfIRK9uv3vwZ7Ol+sH3/aWUbVWoTVexrFgEAKD+xCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkDW43AMAAP2gUOj5uin13xxs8xxZBAAgSywCAJAlFgEAyBKLAABkiUUAALLEIgAAWWIRAIAssQgAQJZYBAAgSywCAJAlFgEAyBKLAABkiUUAALLEIgAAWWIRAIAssQgAQJZYBAAgSywCAJA1uNwDAPS5QqHn66bUf3MAbAccWQQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgaXO4BAMqqUCj3BJSbfQA2yZFFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFmDyz0AMIAVCj1fN6X+mwO2Fb15zJT753vMbjccWQQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFkVHYuzZ8+OQqHQ5VRfX1/usQAABozB5R5gc/bbb79YsGBB8fygQYPKOA0AwMBS8bE4ePBgRxMBAMqkop+GjohYvnx5NDY2RnNzc0ybNi2eeuqpTa7f0dER7e3tXU4AAJSmomPx4IMPjptuuil+8YtfxH/8x3/EypUrY9KkSfHyyy9nrzNnzpyora0tnpqamrbixLCNKRR6fiq3bWlW6A37NhWukFJK5R6ip15//fXYa6+94vzzz49Zs2ZtdJ2Ojo7o6Ogonm9vb4+mpqZoa2uLmpqarTUqbBt688enP35V+OPH9qo3j5dKeBz0dN5y/85gi7S3t0dtbW2vm6jiX7P4VjvttFPsv//+sXz58uw61dXVUV1dvRWnAgDYflX009Bv19HREY8//ng0NDSUexQAgAGhomPxvPPOi4ULF0ZLS0v893//d/z93/99tLe3x/Tp08s9GgDAgFDRT0M/99xzcdJJJ8VLL70Uu+22W7z//e+PBx54IMaMGVPu0QAABoSKjsWbb7653CMAAAxoFf00NAAA5SUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZg8s9ANDHCoWB/fOB3iv347Y3Pz+l/puDjXJkEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAEDW4HIPAGwjCoVyTwDbDo8XtiOOLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDIGlzuAYAeKBTKPQHA9qs3v2NT6r85KpQjiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFliEQCALLEIAECWWAQAIEssAgCQJRYBAMgSiwAAZIlFAACyxCIAAFmDyz0AZVQo9Gy9lPp3jp4o96w9/fm9naE3twuwLemv32/b0u/N/vrbsZU5sggAQJZYBAAgSywCAJAlFgEAyBKLAABkiUUAALLEIgAAWWIRAIAssQgAQJZYBAAgSywCAJAlFgEAyBKLAABkiUUAALK2iVi85pprorm5OYYMGRITJkyI++67r9wjAQAMCBUfi7fcckvMnDkzLr744nj44Yfjgx/8YEyZMiWeeeaZco8GALDdK6SUUrmH2JSDDz44DjzwwLj22muLy97znvfE1KlTY86cOZu9fnt7e9TW1kZbW1vU1NT056jbnkKhZ+tVwi5S7ll7+vN7O0NvbheA8uuv3/Fb4W9tqU00uB9n2mJr166NxYsXx4UXXthl+eTJk2PRokUbvU5HR0d0dHQUz7e1tUXEXzYQJdqWtl0lzFoJMwDQP/rrd/xW+NuxoYV6e5ywomPxpZdeivXr10ddXV2X5XV1dbFy5cqNXmfOnDlx2WWXdVve1NTULzMOCLW15Z6g5yph1kqYAYD+0V+/47fi345Vq1ZFbS9+XkXH4gaFtx3GTSl1W7bBRRddFLNmzSqe7+zsjD//+c/xjne8I3udvtDe3h5NTU3x7LPPerq7F2y30thupbHdSmO7lcZ26z3brDQ93W4ppVi1alU0Njb26vYrOhZHjhwZgwYN6nYUsbW1tdvRxg2qq6ujurq6y7Jddtmlv0bspqamxg5eAtutNLZbaWy30thupbHdes82K01PtltvjihuUNHvht5xxx1jwoQJMX/+/C7L58+fH5MmTSrTVAAAA0dFH1mMiJg1a1Z8+tOfjokTJ8YhhxwS3/3ud+OZZ56JM844o9yjAQBs9yo+Fk888cR4+eWX4/LLL48VK1bEuHHj4q677ooxY8aUe7Quqqur49JLL+32FDibZruVxnYrje1WGtutNLZb79lmpenv7Vbxn7MIAED5VPRrFgEAKC+xCABAllgEACBLLAIAkCUWAQDIEot95Jprronm5uYYMmRITJgwIe67775yj1Qx5syZEwcddFAMHz48Ro0aFVOnTo0nnniiyzqnnnpqFAqFLqf3v//9ZZq4MsyePbvbNqmvry9enlKK2bNnR2NjYwwdOjSOOOKIWLZsWRknrgx77rlnt+1WKBTizDPPjAj72ga//vWv46Mf/Wg0NjZGoVCIO+64o8vlPdm/Ojo64qyzzoqRI0fGTjvtFMcff3w899xzW/FebH2b2m7r1q2LCy64IPbff//YaaedorGxMT7zmc/E888/3+U2jjjiiG774LRp07byPdm6Nre/9eRxaX/rvt029ruuUCjEv/7rvxbX6Yv9TSz2gVtuuSVmzpwZF198cTz88MPxwQ9+MKZMmRLPPPNMuUerCAsXLowzzzwzHnjggZg/f368+eabMXny5Hj99de7rHfsscfGihUriqe77rqrTBNXjv3226/LNlm6dGnxsiuvvDKuuuqqmDt3bjz00ENRX18fRx99dKxataqME5ffQw891GWbbfgGqE984hPFdexrEa+//nqMHz8+5s6du9HLe7J/zZw5M26//fa4+eab4/7774/XXnstjjvuuFi/fv3Wuhtb3aa22xtvvBFLliyJSy65JJYsWRK33XZb/OEPf4jjjz++27qnnXZal33wO9/5ztYYv2w2t79FbP5xaX/r7q3ba8WKFXH99ddHoVCIj3/8413W2+L9LbHF3ve+96Uzzjijy7J99tknXXjhhWWaqLK1tramiEgLFy4sLps+fXr62Mc+Vr6hKtCll16axo8fv9HLOjs7U319ffrqV79aXLZmzZpUW1ubvv3tb2+lCbcN55xzTtprr71SZ2dnSsm+tjERkW6//fbi+Z7sX6+++mqqqqpKN998c3GdP/3pT2mHHXZIP//5z7fa7OX09u22MQ8++GCKiPTHP/6xuOzwww9P55xzTv8OV8E2tt0297i0v/Vsf/vYxz6WjjrqqC7L+mJ/c2RxC61duzYWL14ckydP7rJ88uTJsWjRojJNVdna2toiImLEiBFdlt97770xatSo2HvvveO0006L1tbWcoxXUZYvXx6NjY3R3Nwc06ZNi6eeeioiIlpaWmLlypVd9rvq6uo4/PDD7XdvsXbt2vj+978fn/3sZ6NQKBSX29c2rSf71+LFi2PdunVd1mlsbIxx48bZB9+ira0tCoVC7LLLLl2W/+AHP4iRI0fGfvvtF+edd96Af0YgYtOPS/vb5r3wwgtx5513xuc+97lul23p/lbxX/dX6V566aVYv3591NXVdVleV1cXK1euLNNUlSulFLNmzYpDDz00xo0bV1w+ZcqU+MQnPhFjxoyJlpaWuOSSS+Koo46KxYsXD9ivfTr44IPjpptuir333jteeOGF+MpXvhKTJk2KZcuWFfetje13f/zjH8sxbkW644474tVXX41TTz21uMy+tnk92b9WrlwZO+64Y+y6667d1vG77y/WrFkTF154YZx88slRU1NTXH7KKadEc3Nz1NfXx2OPPRYXXXRR/O53vyu+ZGIg2tzj0v62eTfeeGMMHz48TjjhhC7L+2J/E4t95K1HLSL+EkVvX0bEjBkz4tFHH43777+/y/ITTzyx+O9x48bFxIkTY8yYMXHnnXd22/EHiilTphT/vf/++8chhxwSe+21V9x4443FF37b7zbtuuuuiylTpkRjY2NxmX2t50rZv+yDf7Fu3bqYNm1adHZ2xjXXXNPlstNOO63473HjxsXYsWNj4sSJsWTJkjjwwAO39qgVodTHpf3t/11//fVxyimnxJAhQ7os74v9zdPQW2jkyJExaNCgbv9n09ra2u3/yge6s846K37605/GPffcE6NHj97kug0NDTFmzJhYvnz5Vpqu8u20006x//77x/Lly4vvirbf5f3xj3+MBQsWxOc///lNrmdf664n+1d9fX2sXbs2Xnnllew6A9W6devik5/8ZLS0tMT8+fO7HFXcmAMPPDCqqqrsg2/x9sel/W3T7rvvvnjiiSc2+/suorT9TSxuoR133DEmTJjQ7XDu/PnzY9KkSWWaqrKklGLGjBlx2223xd133x3Nzc2bvc7LL78czz77bDQ0NGyFCbcNHR0d8fjjj0dDQ0PxKYW37ndr166NhQsX2u/+6oYbbohRo0bFRz7ykU2uZ1/rrif714QJE6KqqqrLOitWrIjHHntsQO+DG0Jx+fLlsWDBgnjHO96x2essW7Ys1q1bZx98i7c/Lu1vm3bdddfFhAkTYvz48Ztdt6T9bYveHkNKKaWbb745VVVVpeuuuy79/ve/TzNnzkw77bRTevrpp8s9WkX4whe+kGpra9O9996bVqxYUTy98cYbKaWUVq1alc4999y0aNGi1NLSku655550yCGHpN133z21t7eXefryOffcc9O9996bnnrqqfTAAw+k4447Lg0fPry4X331q19NtbW16bbbbktLly5NJ510UmpoaBjQ22yD9evXpz322CNdcMEFXZbb1/7fqlWr0sMPP5wefvjhFBHpqquuSg8//HDxXbs92b/OOOOMNHr06LRgwYK0ZMmSdNRRR6Xx48enN998s1x3q99tarutW7cuHX/88Wn06NHpkUce6fL7rqOjI6WU0pNPPpkuu+yy9NBDD6WWlpZ05513pn322Se9973vHbDbraePS/tb98dpSim1tbWlYcOGpWuvvbbb9ftqfxOLfeTqq69OY8aMSTvuuGM68MADu3wszEAXERs93XDDDSmllN544400efLktNtuu6Wqqqq0xx57pOnTp6dnnnmmvIOX2YknnpgaGhpSVVVVamxsTCeccEJatmxZ8fLOzs506aWXpvr6+lRdXZ0OO+ywtHTp0jJOXDl+8YtfpIhITzzxRJfl9rX/d88992z0cTl9+vSUUs/2r9WrV6cZM2akESNGpKFDh6bjjjtuu9+Wm9puLS0t2d9399xzT0oppWeeeSYddthhacSIEWnHHXdMe+21Vzr77LPTyy+/XN471s82td16+ri0v3V/nKaU0ne+8500dOjQ9Oqrr3a7fl/tb4WUUur5cUgAAAYSr1kEACBLLAIAkCUWAQDIEosAAGSJRQAAssQiAABZYhEAgCyxCABAllgEACBLLAIAkCUWAQDI+j+jS4EbK+zYzQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(len(res)):\n",
+    "    res[i,0].cell_response.plot_spikes_hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "84e64849-327b-4934-a828-264cfd30f69e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(len(res)):\n",
+    "    plt.plot(res[i,1].T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "302746a5-9e9d-40be-bdec-63caa4ecbc6b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sbi.ipynb b/sbi.ipynb
new file mode 100644
index 0000000..abfba8f
--- /dev/null
+++ b/sbi.ipynb
@@ -0,0 +1,1031 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "62e9600c-0f25-444e-9025-73812098fcbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "44b81639-8a81-4e98-9976-1c1dcf49a456",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulator(parameter_set):\n",
+    "    \n",
+    "    # net_sim = net.copy()\n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "    parameter_set1 = parameter_set[0]\n",
+    "    parameter_set2 = parameter_set[1]\n",
+    "    \n",
+    "    \n",
+    "    # your code here that updates \"mu\" or mean time of drives using parameter theta\n",
+    "    weights_ampa = {'L2_basket': 0.09, 'L2_pyramidal': 0.02, \n",
+    "                'L5_basket': 0.2, 'L5_pyramidal': 8e-3}\n",
+    "    synaptic_delays = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                   'L5_basket': 1.0, 'L5_pyramidal': 1.0}\n",
+    "\n",
+    "    net_sim.add_evoked_drive(name='evprox1', mu=parameter_set1, sigma=parameter_set2, numspikes=1,\n",
+    "                     weights_ampa=weights_ampa, location='proximal',\n",
+    "                     synaptic_delays=synaptic_delays)\n",
+    "    \n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170) # , n_trials=2\n",
+    "\n",
+    "    return dpl[0].data['agg']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87f0f999-a71b-4b7d-a3bb-19993365a601",
+   "metadata": {},
+   "source": [
+    "### Step 1: sampling parameters from the prior ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "997ca652-0544-4bb3-bdd0-5b7b3edb0f70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_dim = 1\n",
+    "prior1 = utils.BoxUniform(low=20 * torch.ones(num_dim), high=160 * torch.ones(num_dim))\n",
+    "prior2 = utils.BoxUniform(low=0 * torch.ones(num_dim), high=30 * torch.ones(num_dim))\n",
+    "\n",
+    "# prior = util.BoxUniform(low=[20, 0], high=[160, 30])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf3ae33d-06e9-4dd6-9cc9-f4058c950498",
+   "metadata": {},
+   "source": [
+    "### Step 1: ... simulating synthetic datasets from these parameters\n",
+    "\n",
+    "### Step 2: using a deep density estimation neural network to learn the (probabilistic) association between data (or data features) and underlying parameters, that is to learn statistical inference from simulated data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d88b30d3-62b9-4b99-8409-397af2b29bff",
+   "metadata": {},
+   "source": [
+    "SNPE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f31be658-c824-47e9-957e-7ce3ee7151fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "848a6c197b734d34a1219bb2b8a3b3db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 10 simulations.:   0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      " Neural network successfully converged after 67 epochs."
+     ]
+    }
+   ],
+   "source": [
+    "posterior = infer(simulator, (prior1, prior2), method=\"SNPE\", num_simulations=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f199837-2e3a-46bf-969a-493b206056fc",
+   "metadata": {},
+   "source": [
+    "### Step 3: This density estimation network is then applied to empirical data to derive the full space of parameters consistent with the data and the prior, that is, the posterior distribution. High posterior probability is assigned to parameters which are consistent with both the data and the prior, low probability to inconsistent parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "dc9b7468-a0a3-4a81-a771-d8292894d01b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prior1 = utils.BoxUniform(low=20 * torch.ones(num_dim), high=160 * torch.ones(num_dim))\n",
+    "# prior2 = utils.BoxUniform(low=0 * torch.ones(num_dim), high=30 * torch.ones(num_dim))\n",
+    "observation = simulator((40, 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "75a69ede-7b4d-4acf-b666-079764ba13bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# observation = torch.zeros(341)\n",
+    "# # observation = torch.ones(341)\n",
+    "observation = np.random.random(341)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "74d49f49-c9fa-4bfe-8611-688cc8671134",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6398c66e450d4e5fa2a2fbd205d93961",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "samples = posterior.sample((10000,), x=observation)\n",
+    "log_probability = posterior.log_prob(samples, x=observation)\n",
+    "_ = analysis.pairplot(samples, limits=[[20, 160], [0, 30]], figsize=(6, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c00177ad-f4b2-4867-8733-e7c148bc1c6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta = (40, 15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "5a9e5de2-79c1-4385-9e29-e8cb1b77cb6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = analysis.pairplot(samples, limits=[[20, 160], [0, 30]], figsize=(6, 6), points=theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "9e7cb436-4fa4-4d70-9d03-5b2f3772ef51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x29e518670>]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(observation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4afcb7a-dd98-4add-b276-95ac1422d56d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "072a1a3c-cb7d-4d99-ba14-6b9ea6f7fabf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "0dd1deea-47f5-4f32-8644-f778a926c10c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Posterior conditional density p(θ|x) of type DirectPosterior. It samples the posterior network and rejects samples that\n",
+      "            lie outside of the prior bounds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(posterior)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6c0edf5-78f0-4785-b4ef-81940ef2a00d",
+   "metadata": {},
+   "source": [
+    "SNLE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5d9c3ea8-5378-4c45-8470-dafb9445255f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c17bf591036c4c979be32896ecca789b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 5 simulations.:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      " Neural network successfully converged after 21 epochs."
+     ]
+    }
+   ],
+   "source": [
+    "posterior = infer(simulator, (prior1, prior2), method=\"SNLE\", num_simulations=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "796253a7-c9cb-41b9-abbb-c2362cb62c12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d69628b25b4c453aa5bf098599d8eaaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/50 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[43mposterior\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobservation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m log_probability \u001b[38;5;241m=\u001b[39m posterior\u001b[38;5;241m.\u001b[39mlog_prob(samples, x\u001b[38;5;241m=\u001b[39mobservation)\n\u001b[1;32m      3\u001b[0m _ \u001b[38;5;241m=\u001b[39m analysis\u001b[38;5;241m.\u001b[39mpairplot(samples, limits\u001b[38;5;241m=\u001b[39m[[\u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m160\u001b[39m], [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m30\u001b[39m]], figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/posteriors/mcmc_posterior.py:287\u001b[0m, in \u001b[0;36mMCMCPosterior.sample\u001b[0;34m(self, sample_shape, x, method, thin, warmup_steps, num_chains, init_strategy, init_strategy_parameters, init_strategy_num_candidates, mcmc_parameters, mcmc_method, sample_with, num_workers, show_progress_bars)\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(track_gradients):\n\u001b[1;32m    286\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_np\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_np_vectorized\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 287\u001b[0m         transformed_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_slice_np_mcmc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpotential_function\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpotential_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m            \u001b[49m\u001b[43minitial_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m            \u001b[49m\u001b[43mthin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    292\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwarmup_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwarmup_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    293\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvectorized\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslice_np_vectorized\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m            \u001b[49m\u001b[43mshow_progress_bars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    297\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhmc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnuts\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    298\u001b[0m         transformed_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pyro_mcmc(\n\u001b[1;32m    299\u001b[0m             num_samples\u001b[38;5;241m=\u001b[39mnum_samples,\n\u001b[1;32m    300\u001b[0m             potential_function\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpotential_,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    306\u001b[0m             show_progress_bars\u001b[38;5;241m=\u001b[39mshow_progress_bars,\n\u001b[1;32m    307\u001b[0m         )\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/posteriors/mcmc_posterior.py:477\u001b[0m, in \u001b[0;36mMCMCPosterior._slice_np_mcmc\u001b[0;34m(self, num_samples, potential_function, initial_params, thin, warmup_steps, vectorized, num_workers, init_width, show_progress_bars)\u001b[0m\n\u001b[1;32m    475\u001b[0m num_samples_ \u001b[38;5;241m=\u001b[39m ceil((num_samples \u001b[38;5;241m*\u001b[39m thin) \u001b[38;5;241m/\u001b[39m num_chains)\n\u001b[1;32m    476\u001b[0m \u001b[38;5;66;03m# Run mcmc including warmup\u001b[39;00m\n\u001b[0;32m--> 477\u001b[0m samples \u001b[38;5;241m=\u001b[39m \u001b[43mposterior_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwarmup_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnum_samples_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    478\u001b[0m samples \u001b[38;5;241m=\u001b[39m samples[:, warmup_steps:, :]  \u001b[38;5;66;03m# discard warmup steps\u001b[39;00m\n\u001b[1;32m    479\u001b[0m samples \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(samples)  \u001b[38;5;66;03m# chains x samples x dim\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/samplers/mcmc/slice_numpy.py:288\u001b[0m, in \u001b[0;36mSliceSamplerSerial.run\u001b[0;34m(self, num_samples)\u001b[0m\n\u001b[1;32m    277\u001b[0m seeds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(high\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m31\u001b[39m, size\u001b[38;5;241m=\u001b[39m(num_chains,))\n\u001b[1;32m    279\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tqdm_joblib(\n\u001b[1;32m    280\u001b[0m     tqdm(\n\u001b[1;32m    281\u001b[0m         \u001b[38;5;28mrange\u001b[39m(num_chains),  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    286\u001b[0m     )\n\u001b[1;32m    287\u001b[0m ):\n\u001b[0;32m--> 288\u001b[0m     all_samples \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_workers\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    289\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_fun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_params_batch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    290\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minitial_params_batch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseeds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    293\u001b[0m samples \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstack(all_samples)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m    294\u001b[0m samples \u001b[38;5;241m=\u001b[39m samples\u001b[38;5;241m.\u001b[39mreshape(num_chains, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, dim_samples)  \u001b[38;5;66;03m# chains, samples, dim\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/parallel.py:1085\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1076\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1077\u001b[0m     \u001b[38;5;66;03m# Only set self._iterating to True if at least a batch\u001b[39;00m\n\u001b[1;32m   1078\u001b[0m     \u001b[38;5;66;03m# was dispatched. In particular this covers the edge\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1082\u001b[0m     \u001b[38;5;66;03m# was very quick and its callback already dispatched all the\u001b[39;00m\n\u001b[1;32m   1083\u001b[0m     \u001b[38;5;66;03m# remaining jobs.\u001b[39;00m\n\u001b[1;32m   1084\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m-> 1085\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1086\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_iterator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1088\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch_one_batch(iterator):\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/parallel.py:901\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    899\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    900\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 901\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    902\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/parallel.py:819\u001b[0m, in \u001b[0;36mParallel._dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    817\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    818\u001b[0m     job_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs)\n\u001b[0;32m--> 819\u001b[0m     job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    820\u001b[0m     \u001b[38;5;66;03m# A job can complete so quickly than its callback is\u001b[39;00m\n\u001b[1;32m    821\u001b[0m     \u001b[38;5;66;03m# called before we get here, causing self._jobs to\u001b[39;00m\n\u001b[1;32m    822\u001b[0m     \u001b[38;5;66;03m# grow. To ensure correct results ordering, .insert is\u001b[39;00m\n\u001b[1;32m    823\u001b[0m     \u001b[38;5;66;03m# used (rather than .append) in the following line\u001b[39;00m\n\u001b[1;32m    824\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs\u001b[38;5;241m.\u001b[39minsert(job_idx, job)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/_parallel_backends.py:208\u001b[0m, in \u001b[0;36mSequentialBackend.apply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_async\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, callback\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    207\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Schedule a func to be run\"\"\"\u001b[39;00m\n\u001b[0;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mImmediateResult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m callback:\n\u001b[1;32m    210\u001b[0m         callback(result)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/_parallel_backends.py:597\u001b[0m, in \u001b[0;36mImmediateResult.__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    594\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch):\n\u001b[1;32m    595\u001b[0m     \u001b[38;5;66;03m# Don't delay the application, to avoid keeping the input\u001b[39;00m\n\u001b[1;32m    596\u001b[0m     \u001b[38;5;66;03m# arguments in memory\u001b[39;00m\n\u001b[0;32m--> 597\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults \u001b[38;5;241m=\u001b[39m \u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/parallel.py:288\u001b[0m, in \u001b[0;36mBatchedCalls.__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    285\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[1;32m    286\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[1;32m    287\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[0;32m--> 288\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    289\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/joblib/parallel.py:288\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    285\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[1;32m    286\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[1;32m    287\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[0;32m--> 288\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    289\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/samplers/mcmc/slice_numpy.py:315\u001b[0m, in \u001b[0;36mSliceSamplerSerial.run_fun\u001b[0;34m(self, num_samples, inits, seed)\u001b[0m\n\u001b[1;32m    304\u001b[0m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mseed(seed)\n\u001b[1;32m    305\u001b[0m posterior_sampler \u001b[38;5;241m=\u001b[39m SliceSampler(\n\u001b[1;32m    306\u001b[0m     inits,\n\u001b[1;32m    307\u001b[0m     lp_f\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_prob_fn,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    313\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_workers \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m    314\u001b[0m )\n\u001b[0;32m--> 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mposterior_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/samplers/mcmc/slice_numpy.py:116\u001b[0m, in \u001b[0;36mSliceSampler.gen\u001b[0;34m(self, n_samples, logger, show_info, rng)\u001b[0m\n\u001b[1;32m    112\u001b[0m logger \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mdevnull, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m logger \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m logger\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;66;03m# logger.write('tuning bracket width...\\n')\u001b[39;00m\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tune_bracket_width\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m tbar \u001b[38;5;241m=\u001b[39m trange(\u001b[38;5;28mint\u001b[39m(n_samples), miniters\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n\u001b[1;32m    119\u001b[0m tbar\u001b[38;5;241m.\u001b[39mset_description(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGenerating samples\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/samplers/mcmc/slice_numpy.py:167\u001b[0m, in \u001b[0;36mSliceSampler._tune_bracket_width\u001b[0;34m(self, rng)\u001b[0m\n\u001b[1;32m    164\u001b[0m rng\u001b[38;5;241m.\u001b[39mshuffle(order)\n\u001b[1;32m    166\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dims):\n\u001b[0;32m--> 167\u001b[0m     x[i], wi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sample_from_conditional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrng\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    168\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth[i] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (wi \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth[i]) \u001b[38;5;241m/\u001b[39m (n \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/samplers/mcmc/slice_numpy.py:200\u001b[0m, in \u001b[0;36mSliceSampler._sample_from_conditional\u001b[0;34m(self, i, cxi, rng)\u001b[0m\n\u001b[1;32m    197\u001b[0m     lx \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m wi\n\u001b[1;32m    199\u001b[0m \u001b[38;5;66;03m# find upper bracket end\u001b[39;00m\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[43mLi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mux\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m logu \u001b[38;5;129;01mand\u001b[39;00m ux \u001b[38;5;241m-\u001b[39m cxi \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_width:\n\u001b[1;32m    201\u001b[0m     ux \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m wi\n\u001b[1;32m    203\u001b[0m \u001b[38;5;66;03m# sample uniformly from bracket\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/samplers/mcmc/slice_numpy.py:185\u001b[0m, in \u001b[0;36mSliceSampler._sample_from_conditional.<locals>.<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChain not initialized.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# conditional log prob\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m Li \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlp_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mt\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    186\u001b[0m wi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwidth[i]\n\u001b[1;32m    188\u001b[0m \u001b[38;5;66;03m# sample a slice uniformly\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/utils/potentialutils.py:41\u001b[0m, in \u001b[0;36mtransformed_potential\u001b[0;34m(theta, potential_fn, theta_transform, device, track_gradients)\u001b[0m\n\u001b[1;32m     38\u001b[0m theta \u001b[38;5;241m=\u001b[39m theta_transform\u001b[38;5;241m.\u001b[39minv(transformed_theta)  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     39\u001b[0m log_abs_det \u001b[38;5;241m=\u001b[39m theta_transform\u001b[38;5;241m.\u001b[39mlog_abs_det_jacobian(theta, transformed_theta)\n\u001b[0;32m---> 41\u001b[0m posterior_potential \u001b[38;5;241m=\u001b[39m \u001b[43mpotential_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrack_gradients\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrack_gradients\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m posterior_potential_transformed \u001b[38;5;241m=\u001b[39m posterior_potential \u001b[38;5;241m-\u001b[39m log_abs_det\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m posterior_potential_transformed\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/potentials/likelihood_based_potential.py:92\u001b[0m, in \u001b[0;36mLikelihoodBasedPotential.__call__\u001b[0;34m(self, theta, track_gradients)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Returns the potential $\\log(p(x_o|\\theta)p(\\theta))$.\u001b[39;00m\n\u001b[1;32m     82\u001b[0m \n\u001b[1;32m     83\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;124;03m    The potential $\\log(p(x_o|\\theta)p(\\theta))$.\u001b[39;00m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     91\u001b[0m \u001b[38;5;66;03m# Calculate likelihood over trials and in one batch.\u001b[39;00m\n\u001b[0;32m---> 92\u001b[0m log_likelihood_trial_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_log_likelihoods_over_trials\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx_o\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlikelihood_estimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrack_gradients\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrack_gradients\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log_likelihood_trial_sum \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprior\u001b[38;5;241m.\u001b[39mlog_prob(theta)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/potentials/likelihood_based_potential.py:140\u001b[0m, in \u001b[0;36m_log_likelihoods_over_trials\u001b[0;34m(x, theta, net, track_gradients)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;66;03m# Calculate likelihood in one batch.\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(track_gradients):\n\u001b[0;32m--> 140\u001b[0m     log_likelihood_trial_batch \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_repeated\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta_repeated\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;66;03m# Reshape to (x-trials x parameters), sum over trial-log likelihoods.\u001b[39;00m\n\u001b[1;32m    142\u001b[0m     log_likelihood_trial_sum \u001b[38;5;241m=\u001b[39m log_likelihood_trial_batch\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m    143\u001b[0m         x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    144\u001b[0m     )\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;241m0\u001b[39m)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/distributions/base.py:40\u001b[0m, in \u001b[0;36mDistribution.log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inputs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m context\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     38\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of input items must be equal to number of context items.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     39\u001b[0m         )\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/flows/base.py:39\u001b[0m, in \u001b[0;36mFlow._log_prob\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_log_prob\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs, context):\n\u001b[1;32m     38\u001b[0m     embedded_context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_embedding_net(context)\n\u001b[0;32m---> 39\u001b[0m     noise, logabsdet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membedded_context\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m     log_prob \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distribution\u001b[38;5;241m.\u001b[39mlog_prob(noise, context\u001b[38;5;241m=\u001b[39membedded_context)\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m log_prob \u001b[38;5;241m+\u001b[39m logabsdet\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/transforms/base.py:56\u001b[0m, in \u001b[0;36mCompositeTransform.forward\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs, context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     55\u001b[0m     funcs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transforms\n\u001b[0;32m---> 56\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cascade\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfuncs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/transforms/base.py:50\u001b[0m, in \u001b[0;36mCompositeTransform._cascade\u001b[0;34m(inputs, funcs, context)\u001b[0m\n\u001b[1;32m     48\u001b[0m total_logabsdet \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mnew_zeros(batch_size)\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m funcs:\n\u001b[0;32m---> 50\u001b[0m     outputs, logabsdet \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m     total_logabsdet \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m logabsdet\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs, total_logabsdet\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/transforms/autoregressive.py:38\u001b[0m, in \u001b[0;36mAutoregressiveTransform.forward\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs, context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m---> 38\u001b[0m     autoregressive_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautoregressive_net\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m     outputs, logabsdet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_elementwise_forward(inputs, autoregressive_params)\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m outputs, logabsdet\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/transforms/made.py:282\u001b[0m, in \u001b[0;36mMADE.forward\u001b[0;34m(self, inputs, context)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m block \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[1;32m    281\u001b[0m     temps \u001b[38;5;241m=\u001b[39m block(temps, context)\n\u001b[0;32m--> 282\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinal_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtemps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/nflows/transforms/made.py:72\u001b[0m, in \u001b[0;36mMaskedLinear.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "samples = posterior.sample((100,), x=observation)\n",
+    "log_probability = posterior.log_prob(samples, x=observation)\n",
+    "_ = analysis.pairplot(samples, limits=[[20, 160], [0, 30]], figsize=(6, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12b35098-a611-4ae5-bcb1-cd799b5dfec2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6421e12d-1dac-4510-a4f6-68edd3753c8c",
+   "metadata": {},
+   "source": [
+    "SNRE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ddacc405-5578-4368-977f-2daff6ea2450",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77f64f78495f42ef91167edfe682cac7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 5 simulations.:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:num_atoms=10 was clamped to 2; must be in [2,1] range\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "probability tensor contains either `inf`, `nan` or element < 0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m posterior \u001b[38;5;241m=\u001b[39m \u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[43msimulator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mprior1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprior2\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSNRE\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_simulations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/base.py:78\u001b[0m, in \u001b[0;36minfer\u001b[0;34m(simulator, prior, method, num_simulations, num_workers)\u001b[0m\n\u001b[1;32m     71\u001b[0m inference \u001b[38;5;241m=\u001b[39m method_fun(prior\u001b[38;5;241m=\u001b[39mprior)\n\u001b[1;32m     72\u001b[0m theta, x \u001b[38;5;241m=\u001b[39m simulate_for_sbi(\n\u001b[1;32m     73\u001b[0m     simulator\u001b[38;5;241m=\u001b[39msimulator,\n\u001b[1;32m     74\u001b[0m     proposal\u001b[38;5;241m=\u001b[39mprior,\n\u001b[1;32m     75\u001b[0m     num_simulations\u001b[38;5;241m=\u001b[39mnum_simulations,\n\u001b[1;32m     76\u001b[0m     num_workers\u001b[38;5;241m=\u001b[39mnum_workers,\n\u001b[1;32m     77\u001b[0m )\n\u001b[0;32m---> 78\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43minference\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend_simulations\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     79\u001b[0m posterior \u001b[38;5;241m=\u001b[39m inference\u001b[38;5;241m.\u001b[39mbuild_posterior()\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m posterior\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/snre/snre_b.py:97\u001b[0m, in \u001b[0;36mSNRE_B.train\u001b[0;34m(self, num_atoms, training_batch_size, learning_rate, validation_fraction, stop_after_epochs, max_num_epochs, clip_max_norm, resume_training, discard_prior_samples, retrain_from_scratch, show_train_summary, dataloader_kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Return classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\u001b[39;00m\n\u001b[1;32m     66\u001b[0m \n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;124;03m    Classifier that approximates the ratio $p(\\theta,x)/p(\\theta)p(x)$.\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     96\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m del_entries(\u001b[38;5;28mlocals\u001b[39m(), entries\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__class__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/snre/snre_base.py:273\u001b[0m, in \u001b[0;36mRatioEstimator.train\u001b[0;34m(self, num_atoms, training_batch_size, learning_rate, validation_fraction, stop_after_epochs, max_num_epochs, clip_max_norm, resume_training, discard_prior_samples, retrain_from_scratch, show_train_summary, dataloader_kwargs, loss_kwargs)\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m val_loader:\n\u001b[1;32m    269\u001b[0m     theta_batch, x_batch \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    270\u001b[0m         batch[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_device),\n\u001b[1;32m    271\u001b[0m         batch[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_device),\n\u001b[1;32m    272\u001b[0m     )\n\u001b[0;32m--> 273\u001b[0m     val_losses \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtheta_batch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_batch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_atoms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mloss_kwargs\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    276\u001b[0m     val_log_prob_sum \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m val_losses\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m    277\u001b[0m \u001b[38;5;66;03m# Take mean over all validation samples.\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/snre/snre_b.py:111\u001b[0m, in \u001b[0;36mSNRE_B._loss\u001b[0;34m(self, theta, x, num_atoms)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m theta\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes for theta and x must match.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    110\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m theta\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 111\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_classifier_logits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_atoms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;66;03m# For 1-out-of-`num_atoms` classification each datapoint consists\u001b[39;00m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;66;03m# of `num_atoms` points, with one of them being the correct one.\u001b[39;00m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;66;03m# We have a batch of `batch_size` such datapoints.\u001b[39;00m\n\u001b[1;32m    116\u001b[0m logits \u001b[38;5;241m=\u001b[39m logits\u001b[38;5;241m.\u001b[39mreshape(batch_size, num_atoms)\n",
+      "File \u001b[0;32m~/opt/anaconda3/envs/2060/lib/python3.10/site-packages/sbi/inference/snre/snre_base.py:316\u001b[0m, in \u001b[0;36mRatioEstimator._classifier_logits\u001b[0;34m(self, theta, x, num_atoms)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;66;03m# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.\u001b[39;00m\n\u001b[1;32m    314\u001b[0m probs \u001b[38;5;241m=\u001b[39m ones(batch_size, batch_size) \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m eye(batch_size)) \u001b[38;5;241m/\u001b[39m (batch_size \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 316\u001b[0m choices \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultinomial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_atoms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplacement\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m contrasting_theta \u001b[38;5;241m=\u001b[39m theta[choices]\n\u001b[1;32m    320\u001b[0m atomic_theta \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((theta[:, \u001b[38;5;28;01mNone\u001b[39;00m, :], contrasting_theta), dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m    321\u001b[0m     batch_size \u001b[38;5;241m*\u001b[39m num_atoms, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    322\u001b[0m )\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: probability tensor contains either `inf`, `nan` or element < 0"
+     ]
+    }
+   ],
+   "source": [
+    "posterior = infer(simulator, (prior1, prior2), method=\"SNRE\", num_simulations=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3ed6c70-1560-4c7c-a716-35f1195cc7e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = posterior.sample((100,), x=observation)\n",
+    "log_probability = posterior.log_prob(samples, x=observation)\n",
+    "_ = analysis.pairplot(samples, limits=[[20, 160], [0, 30]], figsize=(6, 6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5568aca4-598b-433d-a30c-c676ba35cf8f",
+   "metadata": {},
+   "source": [
+    "--------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7992cc9-a630-43a1-b86c-4acf4f1932e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sbi_2.ipynb b/sbi_2.ipynb
new file mode 100644
index 0000000..c7253d0
--- /dev/null
+++ b/sbi_2.ipynb
@@ -0,0 +1,14257 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a053f557-3ad3-4cbd-86e8-cb58d38a9671",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model\n",
+    "from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f1374ae7-e4e7-4dcc-a8e8-4561e9331bf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hnn_simulator(parameter_set):\n",
+    "    \n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "    parameter_set1 = parameter_set[0]\n",
+    "    parameter_set2 = parameter_set[1]\n",
+    "    \n",
+    "    \n",
+    "    # your code here that updates \"mu\" or mean time of drives using parameter theta\n",
+    "    weights_ampa = {'L2_basket': 0.09, 'L2_pyramidal': 0.02, \n",
+    "                'L5_basket': 0.2, 'L5_pyramidal': 8e-3}\n",
+    "    synaptic_delays = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                   'L5_basket': 1.0, 'L5_pyramidal': 1.0}\n",
+    "\n",
+    "    net_sim.add_evoked_drive(name='evprox1', mu=parameter_set1, sigma=parameter_set2, numspikes=1,\n",
+    "                     weights_ampa=weights_ampa, location='proximal',\n",
+    "                     synaptic_delays=synaptic_delays)\n",
+    "    \n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "\n",
+    "    return dpl[0].data['agg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6d2133c6-0c38-4bdb-9230-e1bf8524bef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prior = utils.BoxUniform(low=[20, 0], high=[160, 30])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "20ec0d56-93c5-4493-ac50-0cece59ae889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b6ddb15b57f47a4bd0223c64b847a00",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Running 1000 simulations in 1000 batches.:   0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n",
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 100.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
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+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
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+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
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+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 50.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
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+      "Trial 1: 120.0 ms...\n",
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+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n",
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "simulator, prior = prepare_for_sbi(hnn_simulator, prior)\n",
+    "inference = SNPE(prior)\n",
+    "\n",
+    "theta, x = simulate_for_sbi(simulator, proposal=prior, num_simulations=1000, num_workers=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "304dd035-351d-4b4d-9e4f-f50585e1fe92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 341])"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "292862bf-c5dd-467a-9d6d-f5d7f038a5e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1000, 2])"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "theta.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "ed58300e-ea99-4951-a19b-56e1e7f365d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # save x, theta\n",
+    "# torch.save(x, 'x.pt')\n",
+    "# torch.save(theta, 'theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c87d2f49-e4d2-4820-8314-fd186019b5b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load x, theta\n",
+    "x = torch.load('x.pt')\n",
+    "theta = torch.load('theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "96cfafaf-39c7-44fd-9955-4166a9925efd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Loading custom mechanism files from /Users/tianqicheng/opt/anaconda3/envs/2060/lib/python3.10/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "obv_random = np.random.random(341)\n",
+    "obv_zeros = torch.zeros(341)\n",
+    "obv_real = hnn_simulator((90, 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c0c36c09-cf65-4430-923d-4ef33bc2fca1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observation_list = [obv_random, obv_zeros, obv_real]\n",
+    "obvName = ['random_obv', 'zeros_obv', 'real_obv']\n",
+    "num_sims_list = [10, 100, 1000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd3f0870-8a58-42ca-85cb-71b21eb1c964",
+   "metadata": {},
+   "source": [
+    "## Generate 9 plots for different observations (random, zeros, real) and different num_simulations (10, 100, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "25b7e716-b1c3-42d7-8944-71145cb63efd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 43 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccfffa4144f5489bb7dec467234bf1c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 48 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5ecf3c801664e8c8815e52c90b9d993",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 40 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d45779a80a142a08dc3684b169ef6e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 62 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9f8d58c29424926a2ba05771133e843",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 57 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "095f9e9415944c6fbc4bb41dd2a685ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 58 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e01252c8b1443398bbfc8a9ab238137",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 30 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0461fcc59a334054a8803625e001df38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 27 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51eac2f2a52043c399adc197ed4075c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 28 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ba6e1d150874b378f54ade5c2d8e938",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for num_sims in num_sims_list:\n",
+    "    for obv, obv_name in zip(observation_list, obvName):\n",
+    "\n",
+    "        # train posterior\n",
+    "        inference = SNPE(prior)\n",
+    "        density_estimator = inference.append_simulations(theta[:num_sims, :], x[:num_sims, :]).train()\n",
+    "        posterior = inference.build_posterior(density_estimator)\n",
+    "\n",
+    "        samples = posterior.sample((10000,), x=obv)\n",
+    "        log_probability = posterior.log_prob(samples, x=obv)\n",
+    "        _ = analysis.pairplot(samples, limits=[[20, 160], [0, 30]], figsize=(6, 6))\n",
+    "        \n",
+    "        plt.savefig(f'figures/num_sims_{num_sims}_obvtype_{obv_name}.png')\n",
+    "        # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "138178b2-203d-48f4-b225-3198caa59801",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sbi_3.ipynb b/sbi_3.ipynb
new file mode 100644
index 0000000..661cc16
--- /dev/null
+++ b/sbi_3.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a053f557-3ad3-4cbd-86e8-cb58d38a9671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning: no DISPLAY environment variable.\n",
+      "--No graphics will be displayed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from sbi import utils as utils\n",
+    "from sbi import analysis as analysis\n",
+    "from sbi.inference.base import infer\n",
+    "\n",
+    "import hnn_core\n",
+    "import os.path as opx\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "import numpy as np\n",
+    "from hnn_core import jones_2009_model, simulate_dipole, read_params\n",
+    "from hnn_core.network_models import add_erp_drives_to_jones_model\n",
+    "from sbi.inference import SNPE, prepare_for_sbi, simulate_for_sbi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f1374ae7-e4e7-4dcc-a8e8-4561e9331bf9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hnn_simulator(parameter_set):\n",
+    "    \n",
+    "    net_sim = jones_2009_model()\n",
+    "    \n",
+    "    parameter_set1 = parameter_set[0]\n",
+    "    parameter_set2 = parameter_set[1]\n",
+    "    \n",
+    "    \n",
+    "    # your code here that updates \"mu\" or mean time of drives using parameter theta\n",
+    "    weights_ampa = {'L2_basket': 0.09, 'L2_pyramidal': 0.02, \n",
+    "                'L5_basket': 0.2, 'L5_pyramidal': 8e-3}\n",
+    "    synaptic_delays = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,\n",
+    "                   'L5_basket': 1.0, 'L5_pyramidal': 1.0}\n",
+    "\n",
+    "    net_sim.add_evoked_drive(name='evprox1', mu=parameter_set1, sigma=parameter_set2, numspikes=1,\n",
+    "                     weights_ampa=weights_ampa, location='proximal',\n",
+    "                     synaptic_delays=synaptic_delays)\n",
+    "    \n",
+    "    dpl = simulate_dipole(net_sim, dt=0.5, tstop=170)\n",
+    "\n",
+    "    return dpl[0].data['agg']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6d2133c6-0c38-4bdb-9230-e1bf8524bef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prior = utils.BoxUniform(low=[20, 0], high=[160, 30])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6dee69ab-dc53-4e15-9b4e-497253f7cf33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BoxUniform(Uniform(low: torch.Size([2]), high: torch.Size([2])), 1)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c87d2f49-e4d2-4820-8314-fd186019b5b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load x, theta\n",
+    "x = torch.load('x.pt')\n",
+    "theta = torch.load('theta.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "066eebeb-635c-42b5-aedd-66094f0fac2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Joblib will run 1 trial(s) in parallel by distributing trials over 1 jobs.\n",
+      "Building the NEURON model\n",
+      "[Done]\n",
+      "Trial 1: 0.5 ms...\n",
+      "Trial 1: 10.0 ms...\n",
+      "Trial 1: 20.0 ms...\n",
+      "Trial 1: 30.0 ms...\n",
+      "Trial 1: 40.0 ms...\n",
+      "Trial 1: 50.0 ms...\n",
+      "Trial 1: 60.0 ms...\n",
+      "Trial 1: 70.0 ms...\n",
+      "Trial 1: 80.0 ms...\n",
+      "Trial 1: 90.0 ms...\n",
+      "Trial 1: 100.0 ms...\n",
+      "Trial 1: 110.0 ms...\n",
+      "Trial 1: 120.0 ms...\n",
+      "Trial 1: 130.0 ms...\n",
+      "Trial 1: 140.0 ms...\n",
+      "Trial 1: 150.0 ms...\n",
+      "Trial 1: 160.0 ms...\n"
+     ]
+    }
+   ],
+   "source": [
+    "obv_real = hnn_simulator((90, 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b893dca3-57dc-4858-9105-3f6fea0d56f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta_real = (90, 15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a76e21fe-395a-4f73-b8e6-b90ee779621b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gaussian noise\n",
+    "\n",
+    "noise1 = np.random.normal(loc=0, scale=0, size=(341,))\n",
+    "noise2 = np.random.normal(loc=0, scale=0.001, size=(341,))\n",
+    "noise3 = np.random.normal(loc=0, scale=0.01, size=(341,))\n",
+    "noise4 = np.random.normal(loc=0, scale=0.1, size=(341,))\n",
+    "noise5 = np.random.normal(loc=0, scale=0.5, size=(341,))\n",
+    "noise6 = np.random.normal(loc=0, scale=1, size=(341,))\n",
+    "noise7 = np.random.normal(loc=0, scale=1.5, size=(341,))\n",
+    "\n",
+    "noises = [noise1, noise2, noise3, noise4, noise5, noise6, noise7]\n",
+    "noise_names = ['noise_0', 'noise_0.001', 'noise_0.01', 'noise_0.1', 'noise_0.5', 'noise_1', 'noise_1.5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "473a5796-b608-495c-9b47-bdd1482b75ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Q8Gf3TcKGTXvhn695CKZq+bMnkmHC8cIkZoP4tYaUy8yYMDT0dYK1/JNE7j9Qg637W0vDwYxJDyFPxiTvqJz7nt0Pr/7cLXDLozut29nChcXAn1ebdMOk+V+pAlprBPDp6x6BL934eKxHyRPMmNDAxvVsEL9iw6TdPEKM/kO3WOhOQNaY5HN+b/7KbXDOp26C8el65t96bJj0Dhotakx2jk3DVfdulQbWOI9JTubvu//9Lrh/yyhc8PU7rNuZmh2EieGV1+StPkAqY1JHxtmBps6kCL0Ja0xoTCJ2ajYYJjPo+em9YZHRbcR5TLq4+AiCEG57fBeMTtXy3W8BUTlP7z4Ak7UG7B6fzvzbXlwXDKxhUmsxXPifr3kI3vu938B1D2yPP4sZk5xcC2NTblav6aENwzBuS16Tt7obNcFa7M5qhFDPma2R2sGGCYkDyAislHpwpFGAC172Gl3/1K4JWLf+F/CNXz7Z7aYMLHqBMbnh4R3w+//6a1h/9UO57reIIn7tXK8SMya9g1bDhXc3k9HsRklpGgpr0C5cNQKmozXCMG5LXmO++gAlCdai9+J4tSCIjb4iBhV8jbNc73ojgAe2jPaE2KwIHJhBxuwsOEW8MOi1RHmfvPpB2Lp/Cj72350tyslIkKSk715n3j4asQ87x/JN2V5ESvp25ATsyukh1Fs0TMSNxyv3oI1OQWHI0TAxJ1jLX/di0pjE0TmYMWm6tIrQgDSI6+6CL9zwGLzqc7fAT+/ZknubXHDjwzvg767cWIggGEBmTGaD9oZyhfYK+tV4nVWIq5d3rwlBQW3A+8ujr4VhmBhyLeyPXTk9hHoDa0zcf0dRZnnnMXE1TIwakyB/40CdPBKNSfQ+ZkwaQRzxVESUEtbxZNn/M82ooWc6XAFa4PPXPwbf/fUm+NUTuwvZP2ZMZoOLq5ddORUu3tN1tDPR5gU1JUJeyFtjgvfRyrPPUTk9hFYZEyrjad7+0CHHgdHEGARhGLclr9WfaS/quTeCEGpN46GIrI2Y9s903+L2JZ9NTNfhL35wj6QXKgpCCDxdEGMyMZ3st9cmegpY/Fp0ds+9EzPwhi/+Er79q6ecti+zYdJ19ILGJGaFc99va3OPCTKLnP33vRgVN7BPYKu1cigXSd61ctxdOebP60Q724G6m9hAU3K41IMwvrZFDCpyEb8MLjgik+TlNz4OV254Ft7973fl10ADhOuiVlDuFcmVMwsYkxpiTIpOR3P303vhns374D/ufsZpe86r0n10q3o5RmdcOe3vD1+jVuagHiRMBtgwCVqjkuO8Heg3jbwZE2SYPL5zHK7euJVkPkwx8I0ijIMUV454X2t0TvwahO6MEKUN6qRbp56z8aoCu3JcLvvtT+yG/7l/WyFtcQE20IrWdGR9PmdDVFO/Qx1futMGuS357Rf1/Rz4mHbrvrErp4eAb2aWgTEe5IjMsUW4cs79l5vgz777G7jl0V3adrbMr4JZyOuZ0hkT8b854TaS/0WKX9UHz/WaU7U3imIvKAhNRb2gY04gxsTFNfKe79wN7/nO3bDvQHdKnmPxa9EMT9bnc5BcOWNTta71ARd0k/0rymDGu81jnYLHxFYi3HqRIBycJ1BB264cqTMk1n0e/nKq1smDW0fh3mf2SWmHjbVyULhwUQnWVGGYMITqQVCoK0ed2F0FsBRjMtPBONV6fH2KGewmsfjV4Z7vn6xBEEJLmSLzgKQxKXjuybryrfTiSF0Aao0ATv3E/8I5/3KTJEbuJn6zaS9s2TeppG3vjnFSlCsnb40JNm76Rfxa7nYDuoW2xa9YY6K4dfw2c1lS4tfRqRq8/ou/hGNWLoSr3v8SqS1aG4MOhAs3HwY9KgcxJoVoTGiXUvrv9N/XOmiY1GJGqZhjYvFr2nWXwgu7NOh3MionqytnUBiTDZv2wXQ9gOn6DIxO1WDZ/GpX2/Psvkl445dug+NXj8iRJjmMqa2gEY9x+fbPvDUmjRYMHbyo5TwmPQRZY+L+O4oxyTsbKZVgbXyqDmEIsH00SfZjFr/mn2DNJH7VmZOw4HDh1hgTypVTlFuFgjCCCmNMau5RObgJnS48KdANV47rYQYlXBjX4uqFSK6dY9Px/zBnVqEVzBbGBD8/WRlkAHbl9BTqLYrvEiYCfZbzQE9F5YjPXJgeHC6cmytHE7/K/8X1rDWCuI1FULBaPpU2XDmdZUwK1phMu+cxoSLKOo1OJljLGnqKxa/dciN0AjejIpsFabIzIXGJh7nrMFqBGPPyKrQngLtUHntuxe1VlwyT3rNMBtYwaVXJTOYxwQN9DhMPFVcu6GVp/0bGBHLXeaiXSDys6qA/jSj6Voy0bfunYMxSNKtdjYksfu2gxkQYboVF5WDGxL5tLxgmM4R4vChkdWvi56+TOqROYs/EDNz77P74fS8wJslYogg6u8aYyP/zAjZ08mZMXPu4xJj0oBXQg03qDPAEkWWusOUxAcgnHJTqrGq+ENN2oj2tVk82QRe/Jv9D5DrCyPrQ7RidgtPX/wJO/Ph1xm00jUlWxqQLbgx8ffIq9KhCChdOY0wI4XanUat3TvyaNScGduX0q2Fy99N7pcVGL+S+Ef02DOXRpltti/tL7q4c+nWrkOYixz7OjEmPQgr3zcSYNH9vEr/m0NOo5lC5QWzhwvUWrGgbTBoT8R31QGR1W/xm014AsLdXPY4zY0KIlouMRNg1Pg23ProLwjCUwpJrBQ2yWWrltOKTzht4wi96RZxZ/IoYk1qPRKvkDZWV7AHCJHlGw1DO9dFlxiRvV07e1YVbcuWg568H7ZLBNUzwgJylb1CMSZDzQE8ZSnUUjmvbTrStFSs6S5tkqz8kmYCsjInLpVMnF+c8JsR9K9KVc86nboR3fP3XcNXGrYrQswOunJRrIou1uzPx1joZlYO0C1kx2xiTB7aMSqJWE6YVg6sXCj/iSL92Q2DzQCLsz3e/QYtzjwntil97EQNsmLQ2MJJ5THJmTKh9iMktCJEoy3CoIGzd8DLCypjQrpysbi2Xa6fu09mnSkXlFPhwjk5FrpUbHtopMUcdEb+mMSZSfy2kOamodbBWjnhe3JPxJa97Jb+HK97973fBhf92B+wan7Zup9Zs6rTGZKYewG2P7ZKqbVP6PYBuakwS11K++9WP0d7+srP/agbtXsMAGyat3Rgyj0nOjAnVt2bq+vFMD0wYKoxJHm0ijpG8plfeWSc9l4dKPYzroBWHjGLGpAOTTqXkSavutGyztUYAF3z91/Cp/3k403HkWjn2bWWGrzsT70yLCQ5bQVbxK6buZ5thsvfADAQhwL4DZgE5gM6YdDr66Ad3boLf/9qv4V9vfiL+rIEWXLJ7oqNNS44bu3LyBd6fetnDMITv3P403P30Xuf9NVpgl1rNfN4pDK5h0qLGhHTlSCvQ9p8iqnPhCSQOxzU0uxGGsvugCFeOUuWXmm+zXguXZrbPmCSfzXQgj0nJ96Q2p12TGx/eCbc8ugu+cMNjmY6DDZO0gaYXxK94wi88j0ko/08DvnzqBN7rcC0oqhkmHe4GW/ZH+Zi24bxMyOUmCXO7zJjkHpVj0Zg8sn0c/u9P7oMP/3ij8/6yLEIPzNThk1c9AL9+co/zb7qBgc382qrFSCXqwmNAYRqTht75zHlMlPPLuR6D+j7oIGPSqsYkzuLYYbagUvKhhtiuNPFrq6sX2fhJMUx6QPzay3lMulW2IA+otatMmK7LrpxOT06CraQmVVX82q1cMnETcnflyGwzxvh0xHSNWlIm2PaXdh9vfmQn/OstT8KqkWH0e+dDdQyDy5i0mPk1Xn0ZxKVpA8L+AzX4zaa91gmIGqjxAJnGmASBEpWTR7iwsgt8bJztVd4m23Fd7oM6kbYlfu2QKweHpqelpMfJ9bIMyJLhmiEqp6jw5TR0xTBxPA7eara5chLGJMUwqamMSYcNEyITMma28tZhtII4fDnv/VrOTXyXpcBolpwvonSF0MBRbegFDK5h0m7mV9y5MlBpr/jMzfDGL90GNzy8w7gNtQsqhbep3Tjzq0ubXKAfSr5+tPspq2FSIGNCTE5Fhe5ilEu+dO/Srkm1nBRwnFJWtTZIhnYGxqRbNLkULlzw3E+xZTbgftjJJHztQq6BlNWV09l+IO4/5RKP7lO+41crSAyl4hgTUybrLG5wU4SobVuc96gH7ZIBNkxaFb8SIsoskR7Cp3rt/dvNxyB6ijy5Ra/do3Ly73my1U9PcFkpWJd2aoZJO+LXDkw6Zd+TXDlpjBpmTLBuJA1ZjI0s1G9RqLW4MGgFWRmT2RqVg08vbcWtunI63Q2EmJ/KtxQgA0u87waKqpWD92diorNE72V5ngV72wvX14bBNUxapJLF4GZaobpXMDVntaHDhekH2PR7bHEXk8dEtvrTBLtux0hemyYrMZCJCbwdxqSo5xH3h7Lvy66c1GuS/HbS0TBRk7ilHQLbY11LsFZ3r4bcLpKMoo5GENpmNhkmWVzKWh6TTmtMYsZEH4cjV05rC8c8kZaWod39ApijHbOUrjDVbaO3JRaQvWeXDLBhErQ2QdFF/DBj4tahbBVMqfZgQ0oMOqYOFYYhKZZtB2kJ1qiBsB3xq5ENah64mtUwITQmreKHd26GH9652fg9dlOUS56kZUmbMHDzpmpuhol6StmK+HUpwVonw4UzMkQSYzKLXDlZSmOoGpNOh4xSRS2N9cf6zpWDX9MMcBbGJMvznIcWsBMYWMMkzyJ+eOxyHeexYVJrBHDLoztjv58rY2IaTBoKg5FHv1P3oecxoQyT1hkT6p7gVPtZDRMcitgOpmoNuOTKjXDJlRuNhgNejVZKnmQEpzEU+HwmHQ0TdRJKO0dT3+0kOit+TV67sIe4PbMpXBi3u+ejcgjxqykfR9dr5RS4X9OCrx6EzsZilueZMlg5j0kPodUVW3pKereBDNfjuHLDs3DB1++Az1//WLM9lGFCReXQDVc1JkUkWNNcOURbsgZ8hKH9nuDPhkotunLavBbT9QBEyn+TRgUP+r6nJliz9w98XV1dOVphwyxROd1KsNbBlPSB9Kymb49bM6tcORkM4G7nMRHHrxvG0YZl8u4URHPyPry0AAvU79zvoUCWRTZZbLUHu/jAGiaUbzMNWPVuCtFynfgwY7J1XySI3d5MOpRmmFCVcjGCIP9aOapVrdayyIUxSXnAsNGXWWMSR2ZkapIGF4oZ0+Sqmyutvbh9BxwZE1XomHaMLAxOUaD6c1HIEk6pbr9t/xRce/+21DDvXoCcT6m3o3JsGpPo89YWjnkiqZWTcwMs+wtbmEuyzD/syulxtBK1IlHCRsbErdNVkPhVrLDFyprahTyZNKNyjO0MpYEpnwqW+jGS/dMDYdYJRxa/Et+jQ4iwWvf6J/kMMi6TuhoKWyP0QSbg9k21yJikGj89EJUz00FXTtaFA27OF254DP7k23fD9+/YVETTrNgzMZOJsWnHldN5wyQ6nin/Tt6MbyuIo3Jy369+jPg9ut2uUYNZ9DgkY9J7dskAGyYtuHJMfk/XgU8WRSaXXgw+iaiVYEyIFN6umV/zWOypxg1+F4QhyUTgNmzdPwnv+/4Gaw2ItGJUFGOSle5M6nG09jS6+L5VxoQK9TbuH7tyWtaY2LfvtP8+DEP452segu/c/nT8GQ6hLppKlqu5uhgm+jY3PJxesTdP7BidgpP/4To4/7M3O/8mS9oCVfzaqX5w2c8fgh/etRkxJvQCD9+C7rty8j2+XWPiblwKZHmeKRa7FxkTTkkP7jfG1KFcfbuyKBIZJrEQrMmYEPvAdV3EMYwJ1oKQFMu2A3UXqh4kjTH56/+8F255dBf892+3wFOXvZo8RtpghPcnDBPneyeMueb/Vl0YUoI2B42Jei/Sjhu2YphkdOV0OuJhw+Z98KUbHwcAgHecvhYAFFdOJ8WvDudLbTKv2tmh8tbHdgEAwOM7J5x/IzG3KasR1ZXTibnpV4/vhi/fFPWD561YAABuzEjXGZOcDy8zJubvXEOGs7gq2ZXT46hJvk2335gMEHmgN3cmvErB4lfx+YwlDJjUmBgOpeYVyaPjaRoTxYigNSbJZ5v2HEg9RlruAskwKWVkTIT4tfkfU+T4XqTux8HgkwrUhaE0SaQlvsL3tDDxawsCu3bwzN5J7TPMHhYdFZBVb0Vdv/kdNkyWzq/Gryem65YtE2RiTJrGs+j7nZj8cT+gM7/Sv+vWxCkOm/fRbUX8WmNM0O/ZlTO7IYfTZpvcAMwKcltnwuGl+PgxY0I8rMl+qTwmhhVGKNfKycUwUd4HysNFdXh8XWx5W5J9ouNZGBPfSxLUuWaXVfPP4BWjLdmdCqrKswq874gxwUZlBleOo2GitiNbVE7xo9L+AzPaZ52sLiyxey5ROURz5ldL+ocFYhhlAN4xNu30G2nh5MiYzBmKzqsTk/84MrBmqKgcIwMsv5+qNWDPhN6n8kbCmOR7bWz5mlrRf2X5DfU9hwv3EEyJfWwwGSOm+HsV04YQSbF6sWpMiMnEdKRIY5LvwK8/QOrxKGMqq2FiXz2JQazs++B7nvSZ677FPcQTo9hXlv0AWDQmCmMiuXJSVkGtuHJUY6fXxK/7DuiVUuU8JsUeX3pWW9SYzK9Wcm1TGvB92dEsY5EG3GxXjcmcSucME8z8CKPbZZxS79k7vvZrOPOfrof9RL/KE2nRj63C5rKWXDmu4tcMTBm1T3bl9BBaqS5sEpS6doxpQxpuMUkmUTmEYULWyqGPpTIYeTxYKkOihrXRqY6Tz4YcWIm0cGFxjJLvQclvkTEhXDlZHkwpKsdgZMj3Wb53aX5j3K9cDRPVPRSE9lVQp1PS7yUNk3wZPRuyUN0AJo1JZxkTfF9aYkws5xmGYdxH58aMSSutzIZxVDhurGmkuIxT6j17fOc4HJhpwNZR3UWYJxJXTt6MCf06Oqa7kSGQxW1PjtM9GAk/wIZJ9oHR6MpxpManavJKWiBJNiSHC19w+lp40aFLAIC2sk2HUg2FfDQm+Pi6EJh6iPBnLoxJ2j0R51RGhonrCljsOmZMGkigmuHyuOQkwUaPaiQ2soQL1xpO+TNEO6qI/redk7xKLX5U2jeZ0O5i4O1kuHDW3BDURJRFh5QHcL/e7siYyC5l832tB8nzMGco0s50gjkbm9JdOS7jlCkcvlP5b/IXv5rPGb93ZUyyuGYpjdvAMSY333wzvPa1r4VVq1aB53nwk5/8pMjDZUIrKdulpGIGI8WVMcG/mTaEC7/2hFVwyqGLtf2kaUyC0G0CzQI8WKvhwWFossTNhsn20Sn46//8Ldy/ZT/ZTurUxLX1fQ9KGVw5eBNx36SohAyXR2bHTFE5Mj2NDZVaSnvx/n+yYQu84NL/gf/67Rbrb9TChuK4JnSaMcGUexiKooOd05hkF7+6fVYksAG705ExcR+Hkms/p5Ituq0d7BnXdSEu45TaNvF2thomEpup7FtKkucofk1Ls4BBLUR60C4p1jCZmJiAE044Ab7whS8UeZiWIGlMUjr4TD2AL1z/KNz3bDKJmvUmlqgcacLSPxeDtdi370E8AWNkzfyax6CDjxUqjIkx8ysWv5blrvan374bfnjXM3Dhv90Zf5aJMckgfqVWFCa9Txpcwhunscg5lJPdpTEgUkr6WgOm6wG8//sb7G1q7lNmTCyGCb53WesGtIC9SPwq2DWJgSs6j4l0LDeGTd9HZ0fvllw5jiJ83D87KX6lBKt1B42J2ra8ykukQew+f/Erfk2fG0AWVw5+bf8NHZXTe5ZJoTFw559/Ppx//vlFHqJlZNGYfPYXj8AXb3hc+T09QVlXKsqEJTATGyayweH7HlAMcmoeE1VjknOCNbUjm84ZT3oV5UTu2bwPAAB2jSeDbiPlnkgak0yMiW6EtqoxcbnXelSO+2DTSjK8mDEpuRkmJqO6KOybTBiTIMxedLBdZE1JT21i+9nYVA2+ddtT8OrjV8Fhy+a10kQNuM1Xb9wKm/YcgI+85hg4Yc0i829w1lCHBdJQyYeSL0o7tNdeF+ye0A0sl+hItW1pCSbzgmhP3keR2Wf6mADpixiBLMYM5zFpAdPT0zA6Oir9FYUsUTlXb9ymfWZiJGwr0GlFeyAwE0flyH5X3/PAJyyTOCrHcKiounC+Cawk9lFhTEy+UJdwYREVAGDODRPvDxkm5QziV4rKl3QgqXug92Valap5TFpNSS+waK49IkS0yd2Vk8765AkclROEoZT1FaB44yhrFBJ1D2xjxD/87AH41LWPwCs+456lNQ34WZiuB3D303vhH69+0PobahxqBKGWfl6MQ9WyD0KT3j3GJN1oVD8Xb11dHa0i1qV1kDHB3zlHHKaMmxicx6QFrF+/HkZGRuK/NWvWFHasLLVyntylZ19sKfNrjaYtY1eOwoSUPI905aRVFw7D1sS9NuA9BEhMCmAxTLDGBE2aeCVwyJK55PZUk8UqsFxKDDaXh5cK7ZYFqqm7INvoojEJQjqiygRqYFkyd8j6G7F/bJjYDtPpBGv7DmDxqyx8BSh+YJTTnqdvn1VjcseTewAg30rEVD8QbhcTqHHobV+9Hc7+fzdKOZSEoVKtJGH3rtFtraLeCCTmTEBOWEj/Vh2f1WSJKq64YxN8+rpHWmxpgk5oTNRdty1+TWkrNf5wHpMUXHLJJbB///74b/PmzYUdyyXjoMvvQ2WStkbl1NNcOYH0neeBgTExhxUDEBqTHAadQHmYnBgTbJig83gaZYE9aEGS4TLNmBL1giolP2FMXMSMxH71lNxu18hJY6KInOsZXDnU14vn2Q0TkjFxdeUUvOqcqQfaClHtL0VPinm4cmz9rIjWU/3k0KV2N5FscEbX+J7N+2Dr/ilJQCsWSNVyKR5fijYO9x6oWQXtADZXjsoqNBdmhnn7E1c9CJ/7xaOwdX974cSxxqStvRD7lQIHLIxJK+LXVFex/n0n3LlZ0VO1cqrVKlSr1fQNc0DNMVxxypBLwhSy68qY2KNyos9Lvkcm/0oYE/o4URr09Ak0CySxYiiLF2fq9P5N4cKPbBuLX0vRGSnuNbG/iu8njInDw0u53XC4cPQ5gEsCWJfIBzUqBz/4YRh9VjKEn1LnvSTFMKE0JrZ77kKf54W9StbXIAw1ZqFwjUkGcSAAPUHaDFfTV7vGp2G4UmopnT0los9SA0k8F0nRymQ77MoR3bDofmDK1OriVlR1KLErx2CZiPw/aqHCrIg1Jrm7csyL4nbzmKSKXymNCecx6R24MiaP7Ri3/j5LnRI18ZaAypiIzul7HlDSDMzWUFAFhnmvhsLAjTGRwoXLyUR8/5ZEOyS7Oez3RGxbKScaE5cBlar+3Ork6MKY6LVy5O1s7hyqHViHQyGOViolE41tMO1k5ld1QqLEr4WHC1uE2xRojUm27cen63DqJ/4XTvr4tW6NVEA9UvUghGvv3wZfuP5RQ+RQ8rqmpBSQ8yZF49BQ2Y8N5KLpfEr4CqBE5RjHM3pcoK57GCZscbtuysJcOcQx4vcOrmIVmcSvs6S6cKGGyfj4ONxzzz1wzz33AADAk08+Cffccw9s2rSpyMOmQk16ZXso0wwTLULFUfxKpaRXS4H7Hp0uXRzDKH4NdPo8T0Qak3TDxMSY4NwlchVkWhysbotT0n/uF4/C12990t5eIvOnapi4XiLcxlZq5QDY+wgp+k1pnNi/a+I5bAjUGgFce/8257TnWbFXMUyoukpFj4vZE6zpsLpyiK82N92VtUYIY1PZU6eLfnbimkXwquNWxJ997L8joS1VdVheOQcSuyCNN8KVUymB53WmiJ+JMQnCpM+b3BAmVzk17OSZUVg86nlfGVutHDw0uLpyVDbbBmrM6kG7pFjD5K677oKTTjoJTjrpJAAA+OAHPwgnnXQSfOQjHynysKnIwnI8umOM/FzsQs9KaLZyqSJ+9UZiRIiHVLz30URDtd/UbjW7X7uDjjpghCBP9qYHCLcPG1iPbE+MPWwgpDEmQjRbKXlSzo5v3faUtf1UrRRVY+Kc/VdyC5jEr3JWWfV+2AYc6itXvzF2/blG5dzy6C74k2/fDS/6x19Yj9EqdlOMido/Cx4Zs+qtqE2yPkLDiOXauj+70SeehdWL58CJzRDhehDGbgqqwKO0WFB0ZqG0EMJROZ3RmNiK7on7b3RNG7Rn1PMn5wzKhzFp1cB5Yuc4/PE374S7n94rfa7uzlRtuIjMrxwuDABnn312XFcF/33zm98s8rCp0Kuxmrd9evcB8nPxAKiDqmvGRdMEWQsClGCtNY1J3j78tOq1apQF9Tv8G5y7xJQBlGrzTGyY+PCmU1bD2qVRRI9JBxTvS5qYov/qdXeFy8A3rZQe0BgTi/FKMUXp9KxgkjwUZWHeHl/b/ShS4iki+qxdUBqTrNWQ24W04m7RlePqGouPgw767L7sIky5/IIffyb6EtWHpAmqoYryk9dxVA7SmNgMttse2wUf+uFvpb6SFbaCe6oeRoUps2nRjIn4eau7+dNv3w3XP7QDfvfy26TPbSHCcmZp18WS+znPlgRrA6kxyTIwmiYwMS6oD7TNYqXEr6oRUWskbpKSMY+JrEVRoYdj5rNywO9dXDlhmJwnJb5T25oWlSMGsHLJh+cevAAuf/sp0b5T2p+Wx8R0vNR9mTQmDfk+q5OIbcBJS+1PAV8XN1cO/fnP7rWnvm8FuD4KQHSd1ZVuR6NyXI5FMibm31Ff4eNsacMw8VHOnjpiQdKiK+pBYJzQE8YER+WYz+/3v/Zr+NFvnoH1KXlUbLCt/uMaYQ7i1zQjs24YT1pBojFpbT+P7aRlAGqzTBqarJXTAdJZIk5J38NQQyRtNyYteZg6QNg60xRRXVg1IuqNQAoXtucxMbRZmXTbzeqonmMYuolfATBNa5jElQgWfAwVYgATlYorJTf/OCVYbTWXhqTPMGlMlHw1akIx2/Widpm2yheDTRllCrZdE9O9SKvJ0wqmFTYrDAlXYyddOQWIX6mif20bJiFmTJr9vJGwTbRWAD+Todkwqel5TFzuwUZUkiMrbDWi0lzTZo0J5crJaIRa0K741fQ7PUSYvk/OmV+JhZcJ7MrpYWRJiW2yQOOVi/JbZ8Yk1D8DEANK9DrSmJiPbSrHnTdjQmVexKdJVayMf5uqh3FnTGaQ+BUA4gE7zRdLKd918WsLjInhuLLGJNTSg2c1GlwNrxKaxGznY9rfI9vHNbFqu5gimClN41VwuCK+ptfctw1e/blb4NHttHZM3d72WfKd/plsmGTXmIjFU8n3JcZEzQ4tH1M+vimxHKUxcen++yzumDSoiyUM0X9NjzFmUmQhs76taaHTCsTP8562dY1JpJP702/fJc0HrhoZU0FZCrMl82tP5THpFLJoTKyr2yDUBlXn6sLCpaDk06g1Ainzq2djTAxNyzuBlfr7oKkVErBlvEwME/p7U1QONaiIQbkcMya+cVuqDfH7UE/T7XqJ8P11isoJ9fthM+RsqfhNEN9XHF05tj5q0gsJjE7V4PO/eBRed8Jz4LjVI9ZtAXT9TxBmc6XmAfyc/OSeLTBTD+CmR3bCcw9eQG9PNCfRG4QwMdOQcpOQrhz04bN7szMmibEpG+CibdQ9VKOtTNoF7MoR067LJL7vQOtGq4v70tQPqHB/APpZyZMx6UQek2j/AF+79QnYvGdSYWTdLPYsta8oFoYZkx6BHq7Y2iDeCEOCMbFF5egT75TCmNQbyUonvbqwgTFRXTkFiF9lxiTdlWPUw6C2ysI1fds49XpJZkzS/ao6q9UqY+KiV1BXbdnymOifpQ2wtXh17RaVYzNU0/za/991j8C/3vIkvPYLt1q3E9AME7Tqz5K9tx0EhBFtO0+x+QlrFsGC4cgAEdfssmsegpM+fq0U8k6tqfE40Ir4VbS57PuxIS73K/sE0whCMuMxgCp+db8HE0QkkCvEs0tFGSaMicEwaVFjkhdjkjejQGn2hLsXL2qcGRMiHYIJHC7cw1CtRtuNsfn51Ic/2t6NMYlFmOpqGuUgMYULp+UxmWmoHd/YJCdo4cKhu8YkyVFAfz+DGCJTeGNynOZg3RyoE4o7oysnDDWD0PXhdEkvr+ar0XVEdmMXAODlxxwMf/ziw6LPUhkTrDFJj8pxTVdP4Qkif4YN1HWOM9WW869su3X/JHz5pselKBDqfG3XVPS933/RGrjg9LUAkDxDG5/ZD7VGCA+j7MW0MZm83jY6lXmSFNcoSrIYXSesUaP6kKp/MmtMmoxJR2vlRPufR9T7EW4ro8bEYGBRz59cybu9jhVrTHJ25qjNxpFqWcpX4N+7/oZ2AfaeZTKYhkkGKtlKuxP+cqvGREm8BaBrTPCqyFRdOC2+XvXntjvokBoTiTZOX4GrDzcmgihBH9Vk8dAKF065+T8I7edI3aNRJelVnlE504rIWWdM0ifF5yyaA6cfviTaRxo9G9P+bgnW2mFMFs6xVzpWobtykmdGGCZ5UuUXfP0OuOznD8Ff/+i3yTHJCczGWkXbeyhcX3xGre7pEG955b5jLNKZ/HzjVrjkxxtTQ9yTbL5JVI6pCKjabgBZRB99l2yHXTmlmLWyNqdtiOtNpeePo3IcxK+SsUW6cgJy21Ygdp83o6BpTCBZXMjpCFoQv6ayq+zK6VlkSbBms7rrge7KwQP75j0HYBtKrkQlWFNX09h4KXlJlAXVJqMrR9lnuxYxdb3woZ0YE6UJi9AEJ4yxRsqgEqekV1w5APYJVRe/gpaTwfUKuaR/1qJyiMgr4/4DMSkmzJBruHCl5ENzcW295/bv7IPhyJxssjQqkZ3qksszKkdkar72ge3omPp2Vsak+d9Hz5/K6kmGCbEP9TJu2TcJ9UYA7/nub+D7d2yCGx/eaT0PnDRP9HNs8Kblo4jYXH1/AMk4hF05tnuwoIVaPypEVM48Yl+2EGi1bWmTsMSYtJlgLdGYtLUbUL3xqiEbBsm5uGjYVGQpuUAbtE6H6SgG0jDRxKGWG5MmVDTlMZmqNeBVn7sFXv/FW+OOSKWkV7UO+L3n2zUmppblnWBNHWSDUOY/7DkKaHZn0dykMF2cNColOVINuSwAknBhgLTJVnkfhpph0hpjYojKQQcMQyLzq9WIiv7j5HquCdZKvoeiLCzXo0VNFQDAguHEoFQFxBQo8avKmBThRsCnSAqpHe6BBxCLz8VnMWOSYeUOALB7fAZ+/eSe+P1QmVhxIMSGiZcwJpRGTf5N8joSv9LPE1XEz9b/F81L7vmBmbpxOxuEMT5/mGJM6MWLgByVk3xuE8hH+2uXMRHjbHv7GVJCKynXcmyYoLHCNfOrJH5NY0xIjUnvWSYDaZjoeTnMN8Yq7CQZk2j7/ZM1GJuqw/bR6XhiUqvORp/JAzd+b3LlpGpMcs/8qugElH26hAurTVg4XI4HRcHwpOUxEQKxSllnTGwKdorxGVUZE8dLlOYDDsNQK+KXMD3pYt04uR52y2TRmKDfPLFzHP7x6gelkvcAKa6clFXmXJRq3SV8VA0XxrVyYsOk4HExawh2XETTB82VI661NFk6HHOqHsBVG7fG76loO6p9MmOCaH5q5atMUCq7cM1922DLvsm4f1ZRET9bnxguJ/d893hrkTlijKBcObGYPytjQrrQzM/ntv1T8Ir/7+bUEhYCeblydMNEPU7yTEjpE5zFr9gAdd8WH7/XMJCGiTqR2m5mmlDRpDHBdL7obJIrp/kz1YjAvyt5XktROaox1a64UA9vk4V1thBTU1vnDJVil0ziyrEzJsJAqgjGxE+6r5o0z9b+INAZk/HpOvzysV2pfl3JlUMcU3VdRBqT6DNRP8UalYNcOSXHiAmKMWmEIXzjl0/BV29+An6y4Vlye/L4KcfCP7XVPxFQE6wFYXLdhhzDvduFLYswBbE5duXEjEnzd9I1JHal7v/AdB3+575t6Bhu97Tse3HeHlm7pPch3DdrDdndeuWGZ+Gi79wNZ1x2ffy8DpV9jRGigPfrcs8piD4/b0g3TOLipUaNCT0u0K4cM6v0pRsfg4e3j8Gl/3W/U5uDlHHWFUNlO2MSQnuuHLxZWrkLduX0MPLSmASB7ua479lR+ML1j0riyjrBmFDuHQB5kvc8e3idq8ak7QRrmitH7sy25EmmAl3zhsrxxFQjBnvqYakp4lff92L/bRbGZHy6rhmn7/zmnfD2r/0avnLzE8b9RPuy0+nUtRfHmiMME4eonJKXrJRTXTlESvogADjQDO8cn9bTwhv3leo2Ss7PZZKixa/Jij2tPXmA1piki18BIGagbBoTl4RsD28fkwoapgWMiPb5BsaEYinl6AxZ/Hrro7vQbxOtVslBk4SZjFYNE3HMVjQmpqgc2pVj/z4LEldOduBjV6gsmfg4AY7KyS5+lQXB5u1MzzYzJj0CPfOreVurmyLUXTmTtQZ86tpH4Is3PBZ/JkJiqayEGmOCVkUlNPFKx42pT7pdmiunzQeUFr/i1Zms/aB+q3b+udVyvJKoEa4cm/i1jB70soO7Q71He5suiJLvxW14qlms8d9/9ZRxPwDqika/AepE3AiSzK9zhtIZE7xad6HZcZvKfpKQr4EMAPV4rQpj8bEAXA0Ts6E21CnDJLPGJPrO95LnL7AYJtSe1P2r1ypdpBj9L/ueIY8JYZgorht8DLzAEQbOEBK/umqS1GrRrkhcOXq4cBy5Z2iClKcjxJ9T99Xs7lpA6FtsaMeVM4nGgTTGBLezRriKR6dqzs9sK892D9olA2qYaLVyLCvIlIgT082+95kkAVM9CEiKH4DQmKCBHE9OUptSlluaK6dtxkS9Xgpj0ryeuNS7+lv1Gs8jXDn4vMgQTEWrAZCkp7fqNpT2i7TrI3MqWtTTgZQkUmlCs4lpKpNv9DpmTFwmRawXSZ3EoutSQiUMpFWY5m407yvNr42fB7VyMIUppX+HISF+LXhgzJ7HJPqPBcixK0cYJmif1O1R97/3QDaxtXxPzayp6Zi1hizMx3vAjIlLVA4ebvZMTBu3s8Emfk1lTAwsCb14weyB/P3CjMLtQLrH8r4e3zkO379jk3F+wCLhckm+f+rwbXI/1RoBbB+dgpM+fh28/Wu3G9spaXBa0Ev2ImMykCnpyyUPVo4Mw9ZmKG+reUwaQWg0anBnrNVDLV+JKSoHGzB5ZX5td+BXO3sQ0tWFq2UfxpVxy8TuzB0qQ6UZmUCJXynbS3XlAIBUR8TYfnX12pxQFw6XNYZjMsUwkRgTom9MKG4TfD+HXVw5gVitIzYo1VgQ18WTdCmxHkLTVHWSMdFdObH4tYsaE9tzLTb3PNCiViihpou4Vq1BlHbKWDdEM5GUxkQ+Pj4GFtvOEIyJVWOCvmxV/CrOh3LlUMYehkljQj3zNsZk7pAs3D54ob6QwsDNCUM57Pfcf7kJACJj59XHr9R+i8cRqqQHhikpXL0RwrX3b4NGEMLtT+wBE1yjclwMv17BQDImZxyxDH51ybnw+bedBAB2v5xNu4DLkKvAk2ctCLSVY8KY0K4cz2smeGpBY6KJe3N25YSh/NBiw0RArfyrhtzNHSolGhMiTbjdlYMYk/g4bpoBAJUxka9vlsyJNGOiGCbIIBWMiUt14RJarbsnWPOlqBzKb21qd7KvFPEv+m1awb8wTDLsVhE7UkcrdrwtxlSt0Xa/FfukTsmlv/geIFeH+J3OQlGtbNeVk6Skd2dM5KSHgXSvkE48eV6RxsSaoDBHVw6VE0XcC9Miz7RgIRPnoUre6jXG+3ExqiXGBH2+C62+TJWjsStHL4GitBk9n6orZ8XInPi9KtgXcHXlmDUmxp90DQNpmAio/mMVESMSvaYSnQWExkQAr3JqjUBzEaRF5YgBUZ04Rbui45OH1hOstWkR2x5wfDzsyhEhhqYcBXOryJUjGJOUWjliX3hCE+m67cyW/D5mTOZUgLitVuBzpx70CSXPAzZIhcbERWiIRY9pGjixv7IvMyZiwFdzF1hp+5S+gq/znpRwYdwPxblLjAkyZPE12T9Zg9PX/wL+5Nt3W/dPAYdmCqOfjvCyMCbN/x4qoqlmfpUmRWJXmvvwQEbGpJH0g7KvD9MUi6ayCZKIl2BMKlJUTnqfBDBPjmmwiV/Fubqs6FOjchTGQTpOBqPadtybH0mS440YMiHj8T4t2MLkyqkHgfSMbN5zILWd0/UGfPrah6U2xvszjJGcx6THoK6GVOAOY9JPmBZeOBKi3gi1xERxSnpDVI6YYChBd5LHxK1DtR+VY5/YxCqliq5RtSILG3WNiS5+lVahFsYEa0xUZoZsv7KvfRPR4Doyp0KKi21IW52MNzUmYoJMDM3kMyrJkUCSDh0Le+2WSR3pEXxkzJgYE5c8Jo9uH4MHtoxq3+O2pA3uWPgq8p/gkEU86OImPb17AvYdqMFvn9ln3T8FzC6IZ5BORuZwDwC0cGFx/mnZNlXDR12YpD27rTAmar4PiTGhXDm4GrWli5nyiGRBrDGxReUYdm3KXUK6cixJGiU3pIM+SrI90eub0KQ/bbhw2JWjMSbKtjJjIhtW+Fl9Zi9tmODrM1UL4HPXPwZ/8G93wDd++aS0nYkNLdqV2goG3DCJ/psmbtyh5hgME9PqEyfwmqEYE6MrJ3ovxhEbY+Jqb+TtylHfi1XKcCXpTtWyrKfQGBPkypmp6ysmqslJ9A9mTDzpOwrq+YtBKTJMslkmaVE5wpWzsJm6fSZ2PyVVYq0p6Zs3teTJydKsbUIaE9GncY6dLLV6ImM7hDd/5Vfw5i/fpmlE8G/TaH2Rw8TzEqM1CJP+UpUMk2S/tZQVtA24H4xN1eNjqsiafVdlTNJcOa6uGhOwe84W7SbtU30uceoB9PlM3F/cMr+6ugpssCVYI1koBNlAsBuE0sSu7E8SbjswJvhYwhUdBCHcgkKv1Tw9Ani81xYGFo2JaqTIJU5ot5HJoPvkVQ/CPmSAmRiTHrRLBtswSaMx6ymMSRAmFq06eIwhxqRW1w2TIIySfKkZSEVHFxOuLSrHPY2602aW36sPOD0A4olGzVGhtnVetay5ctIKcFHi14qDgNIkRFxIROWkAQ+eNvGrSN0ujl1BIkbXiJA4WVqaYYImMRxiHGeTVKlkYn+CvagHUTXkfQdqMDHT0MTA+Jz3TszAnokZuOTHG2HDpr3aPgVjMlwuJRNgEMZuCOx2UaMRomNl67h1ZSAfjw2TbIyJuAlR5lfxkWwsyWnS7St3Cul5TMQ9tecykn8jv5fKW6BdiDFGFr9aDDVpwdAiYyISrNkyv5rEr5JhpP9OPo7ZiMpiVEftSV6Lpu1u9nkBU3JJzJCL496/ZT/87uW3wYZN++R2GVzYqoZxs4ExMRl09SCUsjO7FA/tFQxkVI5AmiIdd7pqmfDzBjItXUeDeKh0sAOKKHJipg5n/b8btLTe4pjtaExUtO3KSdGYCFfO0vlVOGblQpg/XI5XqyaNyZyhUpxavlYPorpDaBs6W6fuykkYE4thouwLMybU9bUhLTOjCBdWcyZEjImLHkZMim6VgvFvyr68wq8bJnhqf3OHSjBTjwSTks/bJiA8MAPX3LcNvn/HJhidqsEXf3+xtK3Q1wxX5OgPSmMi6SOo7KoOUCeJsema1ub4GA6MiQdIYxLIv0tLBijOp1LyUpOhUUgME18LN42+p6Jy5H1iNwPu50IHNYTDhR1doa27cqLfDVeiisaUVsu07yxF/HBftzG9WTUmqvhZQI22FJgkNCZ/9I07YceYHm5tYnvrSr2jZ/YaGBPLPZmqm7UuGEEIQHSzrmGgGRN1NaQCU+TU4IDFr2oSHQzsyhHuji37pshaI1iTEP3PHpVDtbMdaIUKDeKtasmHn73vTPjBn5weswPit6TGpJRoTNR9Uk22hQu7CEoFcFRO3hoTMejjnAminRUHVw6OCMFZXG3A0Uq4vo5onzoxUu3GOVZsuSDwIDpTD2B3M0JBNbwBklDh4UpJSuQlVs+SYYLOUbh6shom6iRhZ0zS74HngcYoUIwJBVt4LEC6GzZmwYy5jPQdqM+YmhNJQPx0CNXKsbXHVKU4C2ZQH1UXeeJ5sAUhCIQpRhKVoIz6Lk24DaCwYk1XjjpOmRkTXWOyS82lkLKPOmI9AXTxa70RwINbR+2GCVEaxRTI0UtgxgTMzEOc6tv34+gPjEaQdF61UJO6H0HtLRiuwFRtWutMc4dKcGCmEWtMfIsrJ6vGpF1xk56gS36Q4g6PxJeqPkLL/DpUiiusziihjdT2+DhUuLAtzNWU+dWkMQnD0Kg9UVXzKsYVjQlup0uKeXHeOLGWawivGpWTrO7tPm6AxDAJglBedaawZeJaUoNr7MqplFAEXLKPoVLiHqUYk6z9VtVriXtBhpW2UCsHC3ddWax5Q2VyAeLOmBiickhXjnnSpLrzEKou7MqYtOzKQe67atknJ26XqJy0ZGKSO9gybrlpTHAb6H0aGZOarjEp+R4ERL8zMyaq+HVSGpu+dOPj8OnrHrGeg1SfDfXJsQxlKrqBgWZM0sKFxcqtXPIk94EArkdhY0xqjQAmmg8iFccPkKysRB4TYTSRUTmZGRP5/Y6xKfjO7U9rNVSMv1d9tQ16AMQ6GzXxmTq3ShqTeqBN1tQYZQsXzpL5VWAEhQvjgVtNo061AYAeSAVzoDMmfpKlNqX+UtQelJI+TMlOjGh/HJVjEr+SjMkQYkwc/fQAEIvr1LB3gGRQrCpahpj5KicXXS5AF8THzuL7VrN5xoYJsQunqBwpj4lerdcG8T0l9jS1ifp9q1E5API9oSa/SilZSBQtfq2jsVQI44UxnLbQknKXYGOBdOU4MiaZXTnCyFfHPpP4NRlbgzBqq8ltbBq7aoE8Lk7WGrALJbh7avdEyhnIhom4B3OGdL1kj9klg22YuDImFRRWhxGgwT/NMBErBColM0DSgWLGpNk2auUepDzIpu0Ffu/Lv4L/+5P74LKfP+j0eyrzK4Z44PE1UivjauJXHJXTCLS8DNRAGedfwOLXFjK/CmCNCWa8bAYbXnVRxxw3aEwqJc8pS23MmCiVpW3zQTzoK9WFqVLqALRrKJkkAokxUbdVjSqRn2OGGFwlV45I5IUm+Irvk4sDW4VYG1TGROic6JW1G2OC25dmlGLEq1OiNoypTdTvjZlfKd2KOmmm1NZxEb/qblxrs0mEyBgt+36cSgAbw9G+6Z1LjI3EWNqZMFv+JZdyCnj34qWrxkTPWxWScwiAxZWjMCYAALtRSQBqMaACPxNiPptXLcOy+VVpsc2MSQ8hrYAVzptRoejUMEliZHPl1BqJ+NVUSOp5KxYAADZMos+plPQqY0INXGo7MUTBupsf2UVtrv9eo0Tp73E7VLeFeoVl8WuoTXhkpAOagPXj2Fw59OcLhstIZ5R8rmZvNe2L1JgoUTm4nUL8amV3kMYEZ/21nV9SXdhLDACkMdHCJol9SYwJrtCq3Ae17bErhxgkRV+uSsXikvaUcQp99HPbqtcGTWNiceXYNCZCTyBrTFTmwN6WxDAxaUzcDZPWo3KSyZFyXVV8Hy0gDO1QFyUtMCa4rZWSBysWDgMAwKpFw812JwwZBVNUEJ0/Rp+Iqe/2TMw455IBAAgD+pgmo0KNZmsEZsPEJn7VQ56T9y6GCeXKGSr58NOLXwxXv/8l8Xdtevtzx4AbJtH/tDwmZd/EmITxYEBF7cT7aQRwoNlBVGp3qOzDN/7ohXDGEcsAIBnMbeHCKvVJpa2X2mk4v8Vz6ayFab83TZIlIr+ISfw6VxW/urhylKq0AG7hwqbBdE6lFDNS+JycGRMqXDgWv6qMie8ofo3++568UrbJTPAkJkXlCBGpFpWj7wPT6vhapOWwEZlAZ4iiaJgxwaH5deT6o1bstnonNmiuHIv41W4cRv898CSmR2ZMUnQ/zWOqzzsOm7b+Hhn7rrVy1POctrhyhBsnLVdO2v13Ab7W5ZIPn3nrifC9d50Gx6xcGH2fwgBnicqxMSa4HdP1AB7ePgZn/tP1WiIyAfzzWPzaImNStxkmBgOjHuh5siTxuUM4PW5fDY0Tz1k0B9YunRd/x4xJD8FLWS1gxoQO2UuotoqVMQkQYyIbA2sWz4GXHb08nmxnFFcOXStHVrFTrAqGaRBcPG/I+rv4eA4aBQBZD6MyJvgnq0aGoeR7yTkTKwPSlUNoWVoJFxYYKvuSKFPAxpjga2FjTBYqqapdxa9UuLDtHPBvsAHdCJIifi61k+aidPm27JlqTpTElUNoTOpC/OpLIlJZEyOfQ7QvdI0z+A4o8WsYhuTz7aIxUWvlSJNiSrNMjIl4nza/i2dc7QfJ9/oOtNW8xZUjFgVpizP181YmMJwmvlLyYOXIHDjjyGXxQkbcY7P4lX6dFi6sLqDUa/Y3P9oIz+ydhI/99wOG4yLGJKSP6RKVI9plGqdN40E90F05eFsnxkQKF5aDB3C3CtN31VEMtGGS9lAmUSCG7IsoXHjR3CG4+GVHwitecDCxnzDRmFT1lTRAMtnG4ldfbqN03MyunOQ1Zi6WzHUzTNJq5QjYGBOxj7/4naPgij9ZBwBJPhKRP0M+pr5/rPkRcEnbbmpvtVwiBWk2xkTSQpCZX6P7p4pfS74ft9vZlYPaZpugRTtKjlE56vXwPDnBmk3joV7nhDEhXDlEuDDWmGBXzln/7wZ43/c3RG2wTC42qIzJ2FTdvAq3WQaIifQIBir6vVuklPq8zxsShol9ghfNKzfboBonWaNyNMakeb8TgbUbY6Jut/7qB+HyGx83nQYAyP0du8RVzZWxurDBlUMzJua+qxoAuw3hu9Sx1HBxAbXPCUzW5DGkHoRGZttk3KiZX8Vn8e8yunISnY8wTNwWPt3AYBsmKTH8dXQjqXBh7Mcv+QB/+Yqj4a9ecbS2HRa/qhS/GBjEpKUW8bNpTESzs7hysCXvypiYNCUqbBoT8ZMXrFoIhyydCwDJOVMPoL1WDhrcSvJxKJgYI8yYYNgMkzQB5IRBS1TxvaR+jmEwA0AsmELhWxkTnJLeISpHnVwqKPlb5Moxn6O6L7ErirGKXTko82skhKRcOQD//dstsHNsOpWVMkHXmNSM181FgOyBvHhpJSpnrhIBMbcqagYBfOf2p+GWR/Via1H7EsYEQHfpZmVM1Psj+qKaQE7bp+XZ3zk2DV+5+Qn41LUP26PGUP4Mnxgj0nLDNEyGCbG5NaJMTcA3ZY9KxD8XL22RTxhUCRLTAhJXRMagxK+SxsTiyokXujXdUBPRgfhe9JorZ8DzmET/zRqTZCKkwoWx+FU8ZCIUTt5PksdEjcoRE4LYvxqVQxkdjUYURimaberwQyUfZhqB1Ll3o3AzXNvGBk0AaWRM9Kgc8VsxcGH7Druv1BWo+kCGiAGQ8pg4hAtTk5MQFVKMiWA9yH2hdumTdBhrTFSXXbnkxZVIbRVacbgwvve2iTBxjSiMSXPgUpkddV9VxAiqjIl66XDWVjwo0+HCiSvHI0SkuOigwC8f2yVXiM2kMZHb8NiOCeO1dtH5eJ5sOMkGk70tceRRyY+fQ4CEQfntM/vgZ/duBQCApy57tf57ZXVb9j3AcSQujMm0xZUjjHv1OdX2qblyktdC4CmMWWqMBJDrRWFoKQUc2iAbh/pNkNk2+7g1OmV+DlVDS7zV8pgYDBM15UA9MIcLG8Wvgb5gk1yeFsZkZE4Fdk/MSK4cavz0vWY4c48ZJgPNmKTVyomprxLt520gxkR0uiox2c+gWjnqhCXCXcVDOxPnMYm+NzEmuMkmUZWY+HFnxuFmrn2xoYonXQwTUfVXyeqIw5+HLIyJeghZ2d8+YyLEytSVUzUm/3Ltw/CPV0eh1bZV82StEbdbTbBWKfkwMjfdMMFF/AD0VSUFLNLGmg1jHhOVMSljbUpgjcoR+1SZP1sek8iVE30WGUzJpK123Zsf3SmtILOFC0fHO+OIpXDIkrmwa3waLv3p/eS21nBhEM+0nB06bVKk9l/yPWkBIBiU7aNT1t83lAWPG2Miv5dcOUp7Rf/H50e2wzIxTklRP9H+v3/HJvif+7fJbSVqIwGgMSIlKkcqpoc2oTUmiF1JYUxs45+6a7VWkoDJOFBZ0UbDIn419KV6EGpzk6srR2jcsIGEE70JYA1VL2GgDRM/hcbEA6ipwide/QHQxf6iBGtNxqSqZwQVxwAgGBNDrRzcYdMME/wsYcbEdcxXyQgnwyReiTWP1bzG+HwSV06Y6m7ADyRVK8elYi+GuDZprpypWgM+f/1j8NWbn4DRqZoi0gzglkd3xoaGYFo8T7/PJT9hTPZZDJOYWRKGqTAYHKjycilZ4WOXTJr4daiUhI3WAyXzq8HHrYo6ZxqBNrmJiasqaUwSJpIKhb310V3WCrE2iGdn0dwK/MvvnQCeB3DVxq3kto0ghBse3gFX3bsVtu6fhPM/ewt85/anozYi1sqT2o0NNntbAskwScYEoTHB2Vwpo0AdV9Txh4zKsUya6iHEs5cWlWPTl0kRH/UQntg5Dpf8eCP86bfvln5TV0SXAuKcRP803WrTYoAat7ErJ40xsUE9b/HO1TBRP68FgXGcNrG9YajvB5+Dia0BgHiskROsySwcgF5yoVcw4IZJ9N8lj4lKQwLIBoIY2Kmw4XoQxrSn6sqpGFw5ohMT0haoB4H0EJsoQrFCwZ1OZkzcOqM64Jk6MZX5VQyggTLhAiTGwTQhflUPgSdXPKhX4myq5nOhbJZkxUi5chLDRDVS8HH2TMzABV+/A97+tdul380bKmuDUNn3YZFgTCx1OsTuY21BbDzbzi8ZcJLaJziPiT06oVL24tVrECi1cgwTE5XRVPV5i9WanMcklNqrJhDcMTYND20bTdqaocLwdHy8Erzw0CWaABmj1gjgj75xJ7z3e7+BH975DDy4dRT+467NcRsBZAGyqjFxrZVT8j0p06Yw6PAkTU0wam4gVeNGTWau+geA5NlLSzJpE79iVqAWBLCTKFAHgEWXCmPiy4yu2ZVDH58SRkvJAS3hwmnQDJNQtEX+3GQcqNe+lTwm1P5dw4WFYUIlWMNzGRWV2AsYcMOkOVgavk9EejRjEg1WzX01vx8q6YLKmXqAojUUxiReEcm3wlOofPm48oNDhTIDJEI7PLjskhgTt97YisYEizAxaMaEDhfGg7+RMYlzg5jPhTpPG2MygdJJH0B6k+laQE5I9z0bTaTCiJlXLWn3rVLyYNGcSGy8b7KWSp37yv23GV54EowZExyV48CYYH+/LY9J3WKYqMyMGBTlWjkoCyhiajC27EvcHK3kMRFGpy2EHw/Y9z6zDwAAto9GE6s4olorR86vYjeYcMTccBkbJqX4cwG8qk32L+6pr20P4KoxMWulhuKoHLm9afuUXDlKgTij0BiJszHUrM1ZE6xRj7yUnM8xzQEF9VTUqBzxXBoNE2XgqzdCY7iwzTBR+4ZrgjWxCJITrCWicwHfYeHTDQy0YZJWKyepzWLKY6LnEvE8vXrmTCOIizrNr+phpOIY8ufRfxfBlMkSp0ITW3LlWAYnDBfGBLdUnDOVYO2mR3bCCR+/Fn527xYAkCOk8Cq71XBhIVKmri9W62Mjhcq3goEZE3W/5VLCmDSC0Bj5Y3TlOGpMxPa2OinqBDJULkmrVxc/PZXBWB0oE42JL7lNKYYHA1/zbBqThKEBABiy1HHHE8rTzaqtO8enNTepqVZOWhSzuCe+58FwkzGplpN6SfisJgnDJJ4ADQuUrFE5KsSzl6azs+UxkRiTunx9sOGNKwtjqBoTlzbg0yaLM1qMavFdShAj+VvxTrRlbtM9ZzL+KMbEFD1py8GkGj6NIHKZjU/XU8WvAIrGRDx3yGBPvAbGXXUFA22YpNfKwYwJnZIeJ8QSUHUmeKKjaqhE/+X92zQmAHJnNlniYnWGH+w9yJXjzJg4GiY2xkT8BBsVVRSVo65Ab3t8N4xN1eHi720AADpUGABF5Ti4OjBsJQSwKwe/pvKtYAiB87wqYZg0tQbinIUupdYI4E2X3wYf+el9UVtDuT+l5ZmQXCNIpI0HLV3ZL+9jSKnjI+WCMLBlpCvHZJiUFfErWrmRjNW07hd3QWyYNJ+/iiUbM8ampmHSCELYPT6duNOUWjlSRFYaY4LuyXA5qQ0jrgM2/qiikbFh0hwf1Emd6odqk2xU/1DTMI+1YIZNXRmTmYbsXsav68Zn140xMWlMqL4hRU4Z3JBqAAIF3ZUj2hi9F8ZmmsYkOceALMgKkObKkQ2fR3eMwTn/chNc/L3fOLpycJXjZEEgwBqTHkR6rRx9wMdQ85gIqIyJmIQ8L2ExBIT1qg48NldO1DZ3xkSOykkYE+eoHEcRWYlInmTTmGBXTtrKuGZYdTlF5VhcOWnhwhMoHwGlhcGwuXLE9YgFsE2dyVO7JuCup/fCj3/zbNRWRSQs/ptcVbg9eKLHg1YjkKv0qtdjCEflNOx5TGKNiQNjMh2HC5fQsyaHLVJ994DEmGTRmLi7ckzt3j46jUo9AMn0AKSLX+M8JF4ifjWVQFDrqgBQGpN0xiSTxqQk9y/TGGgVvypROSaXS8w8p2pM6LZSVX4BDIyJVbhtNqr1Y8rvY41J876J6Krpui76DoLkGcI1qFSdUCL+ddeYPLFzIv5vG4ts4lfcl9K8Bt3CgBsm0X8jY4LymBhT0iuuHACdMdnfTNs9p1KKV0ACcbiw+tB6chtV4EHHZJjMJdJft6Ix0Qcn+kGiLPEkj0nzc7SNmDhmiKgcFYlbzbDqsvyeGsRj8SvxBIxNY42JzJi4uHLmV8vafRMG6CIlZFgMlmJwUvtT2cKYbN0/Cf/vfx6O3+M8JrpoTjc2cHI/HP3jIiBUXZIAegl4EZUj5zFJ7nXJ90nDECenyiJYVF05roYJxvbRqXii8UCtPWSfFDHEJSz7XlyHKGJM9P46RbgDsCtI7Ef6npjMbFE5KoaU/m9mK5RjoM1UYaXJoDUuKhBj4iLu1l4Tz4TdqDa7IVWY8piI6zEHjfGqKwYvCoQBE2lM5GOIsXDGkGANIDG2BcSzYcvBAmASvybBHGobekxiMtiGiWseE1yyHkPKY4K+NzEmc4fKmttFPKxDZflzMSB5SIAnty3dMJkvXDmYMRlv35XjIn5NGBP5WFRUzky9kUrZi0FW1eJgqtQEatCLxa9EJpMx9NBjxoRKBIfx6I5xAABYMTKs3TfRzlgA22RM4kJ78apRvk42jcnXbnkSvnLzE/F7bGCogyUVURJrMco+ul9KdWHldOuWwV0dYOk8JjIbkObvV/vFHU/ugdd+/lbYsGmvtm1imET93qYxMWHb6JR0D7APPs2NgCH6ie8neUzmoOuAfz+lMCa4v5qickjxq2PECAAKF04ZA+2uHPRsNAIyhTsASGJnDMzS2cLhTS4iOo+J2YVZb4cxEUX8hMYERVqp7hZ83eci1lodp8VckIUxEcVgRy0pBwDcGRN25fQg4sHSMMjgWjlUSnpcK8fKmDQ70bxqSVuhJ6p7RWNCdB4M/NDFocXKZvFDgTod1ru4Wsmupc/xiggn7IqO1VyFGvKYuCasUq+TGOyyakwS8au+PRamYrfCdL1hpU/vejqaLE9du6S5b2SoNa+NSLK2b3JGanejuWpUDV1xutT5Pb37gPQeR+Wo1X4lUWDzXgwhZgFH/5jo8CBICuLNG9Lz9ZjChSXxK9aYlOjMuxjqef/eV34FG5/dD2/56u3atnFUTqV1xmTH6FQsdJTzmGRkTJpfl1G48JxKKb6v2JBWGRN8HLG9xphQhonKmNg0JiLzq7JivvOpPXD7E7vj7TS2VBK/ygJr2ZWD2ipW6gb3JpVIDLdR6oM4Kod05ZjvUayPcmBMdNGvvE88xqvMFH4vmJV6EJC6M/GdCWqZhckZUTHb3n5K/NogxlCs/eolDLhhkvi9KcRhbr6nPVQAyWQCIFuhw2XaMJlTKWmMScUgbsOHo9TcuPPHIcfKQCwmD9yJ5XTjjoyJtvqmt8OCUk38qmgn8PZUuLAKMbgNlelVV9YEa3HmV0NUjrg2WG9iE78emKnD/c/uBwCAUw9dDAC020pNS48p/VoQJC6vmMLXc9EIbN0/Kb0vebT4VT1OnFa+RDEm8r3AfQR/Pp8QEJrEr9VyYpBLpQUMUTkYJoOVLhqYhytnOn6mPU8euHFbxGRiDvuOvi/5XmwEzxlKwqbx/Zickc9FSgVg0JhQ/VBtijqpYSQaq+SYM/UA3vzlX8Fbv3p77CpQS27gyV4uECf3G8mVY3DD4sULdZuFgYnvdZYifjpjItg+ve/WGwE8vnM8vp8m8SuObElqXymGCXKZCAF2Iwi1tBTCrW+qlRPtWzZaqQguCiJfjnSPrOHCTrvtGNgwAYsrJ17Z+ZI2RBgT0Qq3uS/sylHS0serzCqdeAuASNeMtsPGTELVJz1JTK6q8RSXWBdugkBd9YETdHU7/UNsNKjiVzUMFm+fFu0CgEIOLasuE6yuHGJebARhvNI44BgufM/mfVAPQlg5MgzPWTQHAOT7lrhy5CRr2KCqIUo7CRdP2qRiy77EMPG9qA+a8itIuhFhmDSvQRUxgvWGOSoHtyFTVI5SKyeJDvBJw1But/tKTnXluEblYGwfm5KMQ1zoUwqjDuX/KrCOB4tfY42JYYJXvzNlfqVW2apg1saYqK6cRhBK24u0Ao3YqGhuZ2NMDMJUnJkYAwvXqcWDYBswo4RPiRq3pfEtQ0TZJ656EM79l5vg7E/dCLc/sVsz8sTbeCHqyeMXhniP8wPViIJ8Ynyw3Sf1OVaNWBOGK4k4V0AsMPFcNtCunC996Utw2GGHwfDwMJxyyilwyy23dOKwqTBlvds5Ng2v/fyt8K3bngKA6AHCivKYYgxp8StVyA8g8kuqA3HCmNDhwgCykTKkuC7wqk41eoT4VQwu6gPgLH5V/cyG3+EVkSZ+VT6Ptk9W97YJaLreILMW4vf2In76Z7bMrwCJzmRCTbBmOPe7ntoLAACnrF1MRlTFhslcOSoHp9Cu1ZP9q/tQDZPJmQbsRRlk1SgePSU2MjBIVw7ExzFV98XuIEpj8t1fPw2/e/ltsKupY8KGAuXKiVLSa7uRkC2PiRyV05LGZP8U6qsqY6Jfl7QQ15Knil9B+526CsYMpalWjk1jYpowMdT+HwShdFwRKST6o3i2wzBZZGBGZkbJYyJF5TTkfSTnljy71PkIHQeOWqKifTAkxkR58MX2VN99fGekD3t69wH415ufMIcLxwsHP76GU/UG7J2YiV3AsWEi1aDSjS9qkalCNVonZ+wVkQWEronSmFQIV06P2SXFGyY/+MEP4AMf+AD83d/9HWzYsAFe8pKXwPnnnw+bNm0q+tCpSBTJ8l259bGdsPHZ/fGquYISVwEkKzFKEwBAF/IDSFYA0oQVi1/l32ADhmIZaqimjhhc1Ac/ceVEbVQfgFY1JqbBmGZMZGoU2wHC1zsxU7euGsam6nHb1clGPQ4Fu/iVxmhTiyNpTCyMyW837wMAgFPXLo4/k8SvwpUzV2R/FStStOpElHaS+VX3swPobpwkiiN6r2eeTJgrcTuxKyc+ThhKBohEnaOBnlp1/vy+bXD303vhsp8/BGEYSplfSfFryTPm4FHPywVJHpPsrhzxTO4Ym0bJAC21cpR+rQIzJsesWggAAMeuGiEFj+rkg5+3ODpL6fdkgjWFCbNNeBVCYyKFMNdE5eDoPWZ0xbnJlWvlxQU+hzS2Uw1nF5jT1MjhkFxJx0JcA5NRHX0XtYPqu9jImqo3tLExicpJGE1xnV/5mVvgpH+4Dk7+h+vg6d0TkmGCdSRqexI3tG1RJrsnDzi6cjBjkrihkgWBQFoASLdQuGHy6U9/Gt75znfCu971Lnj+858Pn/nMZ2DNmjVw+eWXF33oVJisxV1jM9L7spL5tYIYE1L8amBMxAMhu2YMrhyDxiTOloomIt8weMWunNgwoVcBadAf8HTDRC0OJnaBo2CWNCfpIATYZaizARAp0E3K/oQOtmhMiAEsjTERqx8pj0mtYVxZCFHxQQuG4898wgBdpOQxwfek1tAZuJjJUA68dT9dndakMRHHwdcCMyZ4kpAH92QfmKUTyfsoPLZjXKKQ5cyvKCW94sqhJoy0RGYYuFaOOC9XHHHQPACI6h8JQ8HzZKrbhTFRq9CWfA9efszBsOHvXw7vfunh8fmaolsAcA4UnGhPicqx1MoRY4lLuLC4/LiEAUAS+p64cpBhQjAmtUYgGcP4tsXiV2XxZZu0AdTIFyGiT76nw4VpN2T0nZkxkSsl624X8Q5rbrQM3/UAHto2FofNa4yJwTAxVRcG0A0TV/sBt03sQ3XLASTi+oEyTGZmZuDuu++G8847T/r8vPPOg9tuu03bfnp6GkZHR6W/ImHyr+0clyfJaODWXTn3bxmF+7dEbZQSrBkYE8EQ4DFG6EJUxkRy5SBaP+7Mzc7moeyUasSKmmBNHajSOuMTO8dhqqZHohgZE3QRVO1HrDFBTSyXfFjcdG1sHzOXgR+bqkvZQjFc8pioFDd+rdolYjAUrpwDUoQOvVqpln0p0kQAG6CCPrWJX+uNQGOW4sycyvk9u09mTARiV47KmIjS8uiey4xJch0pPQreh8hia8LuiWlpssW1cvDkp4pfScMkk8aktQRrAACHLp0XvxZN9FE4c6gyJgq1r/5WDc1cPC8ywv2YMcGGiXyv1DwzAG5ROSpjYmObVMYkDGVtkTC0Y1cOSmcg5tEpJSW9Kf9N4kKgNSaUmwNAzhWyY3Qa/vPuZ6T8HeozEZ2DhTGxaEywu6jWCDQDQJyP6I++52ljNkA0xmIXJh4H1fY4JVgTAnLL80YBP5/iWRTXpiS5chLGrJeQHjfVBnbt2gWNRgMOPvhg6fODDz4Ytm3bpm2/fv16+NjHPlZkkySYst6pVTLVPCbC4rzx4Z3xZ1JK+gyMiWAAVOtbChcWqybPi40P0Zl9D4lfFYGueC9OT6+ZQjYTAABueXQnXPD1O+DENYvgiIPmK7+jf4jPQVXxx4O9YgksnV+FvQdqcQE1CpIrR1t1pQ/C8YBd8pEwLbpHqmEyMqcCB2YaMD4lGBNXw4RajWBmTNaYxIZJIK86xS1StQUaY7IvG2MiBlS8OBMGNE5J3whCUo+C91H2fathsmtsJp5sS74HlZKPJuSkAaWSnMdkwXAZtilrEXUwr5Q8Y20RTfyaQWOydH5V+8wDlTHRQy+1lXUYAoBHGhcACUtLuU3UfZeI/qNugyE+oiZMFXERP8Tg4H2OT9ek46QxJjONwGgUmPOYJM8uNaSIcbcehPCH37wjznqqtoM6JvXeFpWD70G9oYcvi7cyY6I/A7VGIItfkQZO3acYH+xROTJj4gqRAqARhBpjQkXluLLnnUJHxK+q4DMMQ1KNf8kll8D+/fvjv82bNxfaLpO1qBomZd8nXTkYkvjVwJgI1wrlmlFdOXgcEvv2fTTxxIZJMrhLIcuVkjapqQpvG2Pyn3c/AwBRtIlJ3a5iiDBMTInDBJbNj1aSO0ZtjAly5RhWXVZXTizg0++RaigtbA5aYsWIxa8m4Znve+RqRGJMRB6TOXTmV/E6iV6SDRN1AlQ1JrgtAJQrJ3qPrxNmTHB+DbzypdwXOJsphclaA9XJkcNScbsqSuZXKr+E2teomlUC03XZeM3CmCydN6T1TbVWDqUxUSc/8dZomBAMn6YxQcJZgSxRObY6UAIJY5iMgbgviv6vuofwcdSU9DMmxoTIOIrPiXJzAMjp/FWjBLcjPo7FMME5eEhXjuKWMhkm+L5SBqBkmChh+CbGxDZ2iXOyLQRMEM9ewpjorK4pAKTbKNQwWbZsGZRKJY0d2bFjh8aiAABUq1VYuHCh9Fck8KCILUaKMZHEr5RhQjAmc5UkVOKBoCha35dZGTk7X/MzL2FBxGrFg0TAids1jJI5mcSv+JxrjQD+7dYn4cGt0ZIVW+jaSsSwYsXHFw9t7BuO9Qk6YwIAzoyJsYifzZUTiy11dxzFmAAkaeldGJMGShBXMdzDhBmTi381VMZECxeWDTwBkyvHlJI+NhDRxxVkmMQDaKiGxequnFLJ7soBSDQwYjuSMfHlBGvUSlYbzG0Vg1uslQMAcNCCKsluJOG0tJGmDujielHaMwBALi0zY0Jl6FTbFoS6sSqO7bK6FiJybPji84tdOQRjIj5TNSZUOLX4DoAou2FxcwBE12rYsMgD0PuGxghLbsjkNeXKUaNXXO4rdZ1nGknYteQiJfYpFjGUFkg1erIyJgDJsyeMrriwIBE9OVAak6GhITjllFPguuuukz6/7rrr4Iwzzijy0E7AzzruNLsUjUlZ1ZgQnQQPsOJhUh8AUvxKTOYASlSOMF68hD4UgxmOysGD9lBJLjUPQETloLf/dc8W+PjPHoDzPxuFcmOaUjNMDJ0Yt1+cqxDR4dwQGMuavnebxmR0qmYJOaQnboyAWPUZGRNhmMQak2TAMini60gwih96vOukLg0aqIJQYUwCzYAT91QdOMTE/6LDlkifU8yE2DeAfO9WNfOtHLxwWEp2pRYAxOcJEE0wJd+zrswfbvpkxOAozgfvW9WYLCDFr6orx3xMse84Kkcp80DhkCVz4W0vWgOvOX6lLoRGhkkY0mHUaVE5euJEnTFRE6EllYl1zZZ0DIMrw8UgoxKs4fFhXBG/4vNIGFhZMGrKGGxKsIYZE+o6eh7tLlHbIWAKD1bbQzFzkzVVY0LfV5xQk2RM6gljUlVKPejiV3E8/VhV5Vq1wphUFcZkb7Nmmwg6AOjdzK+FakwAAD74wQ/CBRdcAKeeeiqsW7cOvvrVr8KmTZvgoosuKvrQqcCTfxCGUAIPao0A9hxQonJ8T5v0VeABVnSI+dUy7EDsC+nKQa+Hyn68KqfymPi+Fw+6OHIgnsSQ8VQt+5L/GMAufhWl38X2NsbElI7bZpiYXDmCMbE9F6NTdVjYHExshcBMoNToCWMi708wJpTGZDK+N0p0QJCE2JpWueLYODKhprhNoqgcaB7Dk/6r90C4vj78qufDpT+9D859fsRAxn5rLVxYdz186Lyj4GXPWw4vPmIp3P7Enng7U8ilWja9WvGNYd4Pb4/yQlQNrhwRcYKfBYpiVzP6UhM0gBztE7uoHCboUw9dDOvfeHy8b7wkwW5SLSon1K8nAMBD28Zg4zP7YnG6Gk2TaEzMrhy1gB+1H3FsPF9RIm8TtARriqtqXBG/lprXAjM1qvujltGVkywq6Kgc30uq81IIm22J+3ygMiaofSk5eNR+rjZHnE58b4ioHIDIOMYaE6yj0QwTgkUUGCr7gDujS19WkTAmUf8SleWXzMOGibx47RUUbpi85S1vgd27d8PHP/5x2Lp1Kxx77LFw9dVXw9q1a4s+dCpkxiTqNHsmZrRJEodTAtArMTzAnrBmEVTLPqw7Yik8sSvxjS6wiF8B5M6Hn2EclSMehpgxQZEDZcXIUUPB9ARryesVI0mY66Y9BySdjB7hYTBMSrphIlwi4he6+HVIer926VytBszYVC3WNGirrljZb36yxFeUq0m9kwvnNNvdTEuP3Tcip0nJ9yBQUsknjA6aTCSNgD5ZqtWKsW8b33MA+ZrP1IM4z8qhS+fCTy8+Uzumlo5bqVlU8j1YMFyBs446SDpOIwglNwN+FlQXw5xKSaq9hCEYk6rBlSOuhyp+VeHKmOA+mkVjQpVRiN976XlM1MnmDV/8pfRedeVQ4ekm8St+nimDTL02cX4aJ1dO8/r7yfnhZyhhTCDeTvR7mjGRxa90ET91URG1YfOeSTjnX27S2uiBZ3XlAEQGlQ86CxW1Hd0v9N0CojI2BqUxEe+TzK8eDCE2Z9n8Idg1PhMxJsiVEzOkxD5tbK/mykm5DhSqSvbXPU3DBI+5A+nKEfizP/szeOqpp2B6ehruvvtueOlLX9qJw6YCr5bFfVH1JQBykTPxXgVmI046ZDFs/Ogr4OJzjpS2iRkT9HxKq3icBwS1Tbw0uXLicGEs7iz7Wqez5THBVvvD20alyCJRkVgMEi7hwvMMjIk6Li9ToiGeu3yBtt+xqbpFQOegMYnFr5hRoov4xYzJdB2mlVT5gjFR/f1hSPvR00TOEf2trtTk6yQmJMxSiQGm7HuxWFfAVHtGzWNiqnLdUNxLckp6+R4kbhr9eI82GZPhimyAJH5uT2uHi8YE33/cf1s2TAjBtoCH2m2MykkZ0LV9EhfLKH7F7BuhrdFrWOkuSxPUqBzVrSjCchsSYyKzd3JKerMrJwkzl9uV5mrzfXOEI3kci2EiGBOhWzE8Js32ElE5Yp+GPCYrRyK3qCp+telobEJu1TBJuw4UcPbXWiOIcyctxYyJsnjtFXTEMOlVUIyJmsMEoJmSHj3s1ID35K5x6X1kLcvbJXlM9JW0+E3cNsIlUPKR37A5SUaDp6e1S03uA2B35WA/90PbxqS2bGu6DcSkTanIKyWalk8o4eZ5qRoThTF57sFyaDKASLBmEL8izYYJsSunjA0T2pUjJvrRqZomdhXvqQElzhJsYEwEpYtFzlGNIDnkUrxNKkYnVLuA0EAtmTekr/INI67qylFPAYts68QEDIDzICSMCQCtDRFMmRhQvZgxkfeRnmBNGcwx44QmQtx/xcQshW4bJiKZpdTZDVzoU22LKhiloJe617eZVDQmlPiVZkxosWcmxsTgqhKMCdZUJELZaBtsUM3UFVcOapoIh1UXFUceNB9eddwKYxsjxsQ+IQdhCP949YNw4b/doRW8o+o8lf2IBbNFldXqeh6TJHFe9F7VmKxaFDHOM40kPBfXyqF0NLYClqpxmYUxEb8Vz95UPYj1JZ4HsGiuzpj0mF0y6IZJ0jHEM0kzJrJIj1qRqCvX6HfydrErx6BDkGhl1Gex3qCqKK09LH5VXTlKOLQtJT0eZB7aOiZtJ66JMEwor4mWDl8wJjMNCIIkDFZdMC6dlzAmKxYOk9dxbKoeC+hUoyBJsGZx5cTiV5lRAjAzJmNT9ZjtETiANCYqxPWT0z2jdhLh5ir9jRkTNVwYTxq7Y0pWz71hSvGuunJM11FlTDBTo4orh5v+fyEYpjCsiIzFoF1GxrYA7fs3a0zwih3T58LYwROHiT2pGBYDAACeb878CkCvrFWYwoUxphXGJLlH9DghoIcqZzBMyomhHP1WHh/EgiIxZJPyAVT6gXpgceUEpkWFD196+ynwokNlAbdAWlROdNwQvnrzE3DTIzvhlkd3Sd/R+qhm37UZJgHlyhH7TLRkNXT+ZsbEojGxGSZtMCZirMF6RMGyLp47xCnpex14DI8ZE8IwKfs+qMnLBP7qFUfD2160Bv7kpYdrv1NXCPMIjUmFmCwBaOPFRyFqIuui70EslCgrrop4NeTAmOAsjg9vH1OqdEb/hdFAMSbqg4RXvxMzdUuCtcR6X714Djnpj03XpEq1GHECIyfxK6ExMYlfp+saYyLcZ2qiKHx8fAzJ/efjSbLJmDQCPcGaMEx8eR+SYdJkTFS2Kdpe+6i571Bqp3qdJY2JobqwupKf2xzcRdI4AGJArcgus7huiiK+BDCUo1fuK95eXbEDyNEM0v02XBgbY+KBnIRRY0zCpLq4CS6uHC1cuJEYAwIuGhPRR1xCS1XxK4A8PsQak9iVk7RH1LbB179WN1elNhXgFBg2CFzxQswEbDgnuZ0gbmfcBkW3YzNMKPGrcObEmhvPk/RVwjjH+VyGykn+q3pDF/ia2E3xW4wsjEl8juVEYyKqRWPha3Qe0f8U4q/jGGjDRMpj0uxwwjDBfaZc8iRVPB7wXnXcSlj/xuMleozaDiDJayLXUKFdOXIRv2T1HItfUfQOyZggXYwYJFQRKzaSMRX+1O6JeGDCEA8fRV9r1CMSfo1P140aE2zALJo7RIoDx6bqWlZPAaeU9ITRIPajHm1kbhIuPDGjMibRe1N9HQAlJb2JGUO5THC7Z1AqbCtj0hxkls7T+xw18QEkjFKgGBdquyNXDs2YxOXrm799x+lr4SXPXQbnPC/JSbQSiagBkGEiIicUxgSDjsqhWQEAtbptMhkI4Pttyn9i05j4nicxCmo0min/BoZ6nja2TYASv1JROS/+p+vh4//9QPxeNKUVjQmAPD6oeUx8D7lymkYaPnUtKofQmJgy8Q4bDCnPA6vLJdq3fv1Fn8P3TDAdoh/Yon2o+6pnfgUpPb5gY2fqdII1Ko+JKcIMQJ87XIxNodd7SVPQLoyZ6VojYVk1w4QZk56D7MqJbozwxYkcDwB6VA5+AA9aoNPpAmrHi0vZY8ZEcb8kbUPt9JNJShe/ogRsRldOqLUbfw4gMyZhCLBnXA6ZBkjYBCqPCVUdWTBE41N1Yx4TPJEumlshhZTjU3UjY5IljwnFmJgyv45P1aUcJgCJ+8w2oMiJ8WhmTAxiNaVaMabGS0pfwQPlronIeM7iyhGuMCzew8AZKk3i13jV2byOrz5+JXz7nadJxsiKhaphIpipZjsaclg1PlZaGKf6nmJMZMMEX/90xoTS3YjLFBKMiSn/hrQPwthRoUXlEPeI6nNhCPBvv3wyvg5ZXDnienhoU5Ux+ev//C38+sk98Xlg8auawE9NSY8v1Uyddh8KmIyEKPOr/VxwO8StwGxIIx77xDWN9pdm8OjscnN/QbIfzJiI64nDhaultDwm5nFENURs+VwErvyzM+AvzzsK/vENxwEAKubYCGDPuBgzaMZkIFPS9yoo8atYhS1HBkelJKekF+nEAQDmWSxvPOni8cjImBhoZXFo30us4Fjv4Cf1evGgV0VpxoW3QDwwmJ4WUJM8TSlCsrLvxQMIRV9Tg6FgQ0bRA0wNzMc9ZwQAAN76wjXk91O1Rtw+lYIVk48tXDgRBepGoDpWCuNrYqYhrYgwbAMKdtlQmV8BEl1DxJhgAWdyzTXGBN2rmDEhXTl2xqSBVsAYWPwqR1fgfdBGDZ70VcYkiX5K3Ff4N/i+uVQXlg0TdO3qSUVXgSGDWwdDYkw0oxmvKOl8PvkwJnJCL6xjiNtm6XN3P723+Tt3w6RKMSbKZPzDu56BKzc8G28nLmEjCDWWR2VMqKgcE2NiMhI8L11bgYvvifEMMzBqWLdoQ6phYsiSjaOURGqBaL9CNxaaNSaq+NXCvKqGSZqBBgCwZslcuPic58as7xAaZ/YQOUwA5JIEvYSBNkzkBGvRf9EhMRNS9uV08dgwMVHnKmT9CKDX2JhIHha8X1zQTexnX5PZmVMpIXFsst8hnGBNYUzEPiTxqzIoqYbK/OEy8t0SGhNi4I9zmaAJnhpfv/vu0+Dnf/4SOPXQJeT3uPaK+sCWHMKFxXnKrpzmilFx5uDBRrj11AnTNkmUDZEgqpsNoLnKNDAmYiVL1cqJNSbzdMYkLSpHjfpJ2ufHxzFXiaUZIzwRrhiZI31XNYQLU0zXPMIwsdVDwcYzzrYpIEfS0delIjEmumEia0z06BlTFuR4nxaGEAPfe7WQo/paxa2PRaJPKn28CVTmY1OyPHF8MZ48vnMcvnbLk9L3tYZ8feQEa/Z2mfQeLlE5qoEEIFfiFf1HZepMuhYBVZAszqYRG+cAn3jDcfCiQ5fAN/7ohfFiQ81jEofhN0LNFUiFgAvoKemzhwtjkf2u2JUjjxk+6t+9hMITrPU6fC+auIRFLFZeOL9GRUlJr4b3uQB3LJP41eTv9mLDI3HliJj0uUOl5HvVldPcnZrHpFouaas09UFUGZMFw2VpVa3CxphgypMamBcOV2Dhyop2DgJBmIjx1IEqS+ZXKjRbbc5wOSp+2AjC2ABdMFyWNDdl34O3vnANXHGnXmRSymOCdk5H5chGAOXK8dH5bdi0F/7vT+6D+7eMAoCBMTG6cpqMiRL1E/8OaUxMVWKpmkMAslG6bP5QXBEWIFnxqowJVfKdMm5tuSnwpDRNVJ+uGIwUqe0WxkQLFyb0LqYsyAI6Y0Lfn4e2jcFxzxlp9j3dALQZJr9sGiYumV/Pfd5yOGL5fFi+IGK2MGNI1WyJ2+0neps/v+Ie7ftaI5Cq5MoJ1mR9hwqzKyedKZBq3MTjG2JMlAy9oh+MWCLJAMwFTxNxugeHLZsHP7xoHQAA/LA5FtQaAZTq0XkOlXwImuLFWhBkYkzaCReOf4MZEwPLaipk220MNGMCoN8Y8XBiw6SshAt/6OVHweK5FfibVz7P+Tj4YcGTszGPCeqzJckwaTImzUlzuJJE3+BBD2tMwqbhJR428bDbGBN1JbKgWkHpiwnDhGJMhoUrx86YYJhWlMIQ06NyxISa7soJIWm3KY+J7yc1YATTo2ofSr4H6994HNzzkZfHqfLV9ojt4s+J+6yKX3EeBlXQHAQhXPLjjbFRAkBrTEyTkprHxKwxMecxUVPSC+C+Pa9alq6XWitHnaTw+VNt1xmTpG1TlPjVwJKUDZ9XDPcLQM4RFLVdN5LSXDm6xoTe7g1f/CV867anAIBOSU8xbgIbn90P+w7MkDWhVFx09hHw4Vc9H7Un2a86GWOUPLtxpFYXNtVYomByq+DqwiZgfY7oG7gfCYZDzZ/z/nOOhPef+1x43ookoWOlJOcYktA8nbiOkerOLCeGd1LluiSJ81VSwuoSbkH8atqHzZVjG9O7CTZMFIFobJhgjYkva0yOOngB/ObvXw7vOfsI5+PgB4yqoQLgkmAtqZUjHvyIMRHnkhyvWi5JFnkDaQcE6xJYGBN1kFowXEaVUd0YE0HNj07aNSYYpmd13+SM1HaBMvLhmhCfJ9pELWKGX4trLFgSNYy15EdJmhbNHdLYA9Mql6rTo4lf0UQrxnCcYE1dcVJROWpFawFVY6IOrNi1Yq4uTP92SDNMkuulZn4Vq2rhgsOuLKoP6YLT5HWa+BVP0DiHDdYtVKXFAMWYJO+p6rVprhxXxgQA4FPXPhzvF8Bs5KqTVBgCPLN3MnbT2SYxjSmzaEyk3yFXDgWbKydJjmhgTGwakzTDBGlMVOMDQGdMxP147sEL4IMvPwoWo2jKMhrnNY2Jsj/1OmIjQHLlmAwd0CMCMfJw5cQLoEaYCOYVVw6lN+wFDLxhot4YMSEfhCivehAoqcbdtSUCJsrYJH7FHT+uLuzr1TbnDCUaE3XwwsZNECYPhxtjQhgmkKyqozbS5xf/htCYpF0208At3CoqpSke/JAQJwpQ6cOTzJfoOnue9J0Q7VKMCfVaZJWkzkUWvyaDFR7MsTGo3tN6EGoRL5Qrx2SYxFE54t4ZGBO1HfiaqSGX8flINZJKJGMi+nwsfo1Xksmxousnt1sXv2LGBLly0CqVapeU4wdNdtaU9J78nFOGSVrxM5cEawLPXT4/3i+AmTGhcnuMTtWcXDnq4fH1tjMmnrXtmisH7UqMO0aNiaHPeg6MCR63RF8qodDmWGNi6LtlhT3DBgZGoBg4JgF4lGAtEWKL6vGUfsc2h+j5gOhrZzNCKfGrOmYkIeDG3XQFA2+YqCl5RQfCeUlG5lSc/b0mSCszdNXLhpUQtZIveXpHnFMpxx0cd/SoVk6yHS5rLowbm8ZETe+8YLgS74+iZin6OGZMJFdOa4zJlCEqBwvITO4cMajgZy++ZtKxo3fC+BmbohkTU3E1PQ8G/RtJ/Gpw5YiJHLtyRpWCeXOHdIkY9RkAymOCogqktkqGSdIOPJ6qIZfx+WDGZEh15fjS/pOquwnFLeB5ntaPbKXs8aSUJn7F1x8P8nbxq50xcRG/2vqECpHGn2KmStiwIiajsak6ma8nrT34HG2MCRa/UlBT0uPrMmYw8AXsjEmKxmQGu3ISo1s1TBoNfcwCUFnNpIyIOv6J0zEZJgkLGkpuRRtjYusLrowJJRgXwOciXOE4GWLUBmGY9JZlwuLXZueIDRM0wP3kvS+Giek6LJ1flVb9tgfUBFNW17JJ/CqFCzcZE08vtT13qIToxeRzXCsHQHXlCMYET4r2qJwFw2UUfowezobedgFa/KptJiGNiVLPH4fnpjEm1LNHhXSnaUwoNxuAPiGY8lDg1ZVJ/Bq75+IBNqoZpH6vwiQkrGdgTNSU9GEYwsXf3wBX3bs1ar9hUAYgXDlxrZzo/YyiMVFL1Q+VfSVCxSx+nZZcOUS4MBEeDiAbt6aK3pRua6YF8atLdWGBHaMR3Z48X7RhRQkhcd+w0f7U8UXV4JlGdA3PP3YFHLNyIfzLdY8kv/MdGBPsykHXRSxMTKULTMaH7zmEC+Pssyjzq5r/xxRRVlbGYnUeEIhdOYZwe/xMh83lThWNwbRhYr6eVWUsMYlfbWHPos/j0hrq9sn811uGCTMmBo1JtVyCE9csghcfuQwAzFVjXYEHC5mipVkHySWAXTkVypUTvcaD4FDJl44TSOLXpsbE4K8H0FcM86vlmF2gEkBRjImY0PGgmc6Y2L/XGBPDhCqAq6ZSqwJPmozEgBIdI17pKasSE2Oi0sQmV44kfiU0Jj5yISQDbBC350/POhyuet9LtHMBsGlM5KgC9XaZWMBGGMKu8ZnYKKG2xZP+fIP4VY/K8aV2CaiTqintOoCiMRFGtyF3iZExMSwYcH0qgZoyuaSJXz0vXfz65lNWw9Xvj+5lVAahjlL/J9vhtuHJWkw0OIWB3ZWj32fR18TYt2TeEJz7/IOlbXAeEwr1IFSicqL/jSCM+60pEqYd8SvuA5jNUKP1qMKIAPL4ixkTkyuHqmMEID/TWO8kdDXqeAogM+cqXBkTyp0b76N5bLwwVPfDeUx6FCaNidoxJF1BC4wJHgxdxK/U8UoEY4LzmGBj5uS1i2XDJMDhwmbGRLRB15hU4v03CJGZVfyakmANQx241UHLFC4MQDMmNz2yE8an6zC/WpYirajjifMR5yIYkzlDJWNGV1t2TtN3kviVcOVQ+2+EYbzyfMupa+CYVQu1cwGIBlcy7FYU8RMDtGceoDGCIIRt+6fkbTWNSfJ+XrUsFWJU85iILmcqvqj2b3vmV8Q21YhwYYORMsfAmODrLl7iy5RV/EqNE1o16zkVeP7KBXGbtuybjCeSsoExwcaUqGorqHoAu2FCGaAlxTCplHyJbRK/s4pfDa6ccVxPhqiFBGBx5UC6K0dmTBI2A9f1ATDnUilJGhNsSNBROSbGEde/ohKs0fod8/XUDRP6OrzvnOfCMSsXwodfpUeIin0IEb/n6QJkcRrsyukx4FoYALSvGiC6yecfuwLGp+uan84FWP1tFL8aU9I3f+fTrpx9B8RvPLj1b14G20en4aiDF8g5KMIwXvFVK4lh8pWbHoet+6filcf8ahn21Gf0cOHhcjxRJwXr7IaJa4I1DNVwmVctS4OPev5Rquzo/lEVhr968xMAAPC2F60h3UTUZFSNDZPogR4qR5P9ZCCK+NGMlzq5yxl+5QEQwCx+pQyTeiOMBxhbNV+AyJCamUyoa5ybxCRENN2XRhjC1v2T0mfqeWKmSmVM1Myv6nnVFMND7Uf4+qhp4fEqdKahP7emWjnYuMXb421w7iABWvxqMUyIi6pH/kTHWr6wCk/vPgBv/vKvYG/TyKBE1mXfk1xpqxbNgcd3TsRRawDm6JdoP/pnscYEJSEbKqmUv92VM9OQMwaL6yKYnDmVktFgakf8SoUL+17CmDRSGRN58SDcMKohIVIN1A2GPXblBEFzgYM1JsTYZNWYKDfKZKAtX1CFq/+cZk/FPRTjWLXsa2Mg5zHpUSTi1+bATSRqErj8HafAt995WqaInE+84Vh4/sqF8DfnJxatqWqoKVw4ppV9Oionpv19gNWL58Ipaxc3f5dsF4RhfG7DcbgwwPqfPwTfvO2pOMX9vGr0nUqh4wRrrq4c1wRrGOrX86t2xgTAXGH4vmf3w6+e2A1l34M/evFhZHZYypUj7oO4JtVySTEaDYyJxZVTofKYNEJF/NocWAkKf9+BWsw2mESEAtidI66XMNom4vss78PzPHIibQQhbFUYE3W756C6UsMVnxS/qvddDOSqMWkTv6qDJ5nHxBAuHIYoHNwQuSNHaMn/AXSNSZr4lTZMlPfND0QJjL2I+aDy4JR8+T6tambaxb+zRWqQxnlzf4J1Kpe8OHIsaYs+GR+6dC78wxuOBQCRkh7fq+h1oi8x99l2XDlYCxdXZfYQ0xgzJnTIsqoRMxkSwj4OiLEPIOl3tUYozSGtakyGlHHe5MqxsWOV2JVTM+5Dnf96BcyYxFSW7DN2qdDpgnecvhbecfpa6TOJMTFM7tTk53u6CEpOSS93dM+Lwi/DUGgtdMZExTxDVAfOY2LLpIohEqwJw8RFmkMxJhhUJdKy78EMRLkcVo4Mx4Pvv94SsSWvOX4lrFo0B05YM6L91iMmI/UBHir70mB/kJJ8D7cDA9c48pUBEEDkMcHuCLMrJ8nj4qfmNJgjGSY+jE8nzISokDyvqu9DZLzFCELdMFHPc85QCe75yMuhXIpWZHIeE8GY6McC0DUmOmMSotdqSDsVLkwzII0ghKGyD1O1QDp3Y7ZlSGdMgtCuMXFjTIRhMqxtSzEmKtMlio3ux66ckrl/UO4YrY6R7+uMGiF+/cYfvSjen6lWjtCXmdw4AO1F5ch5TBLGRzNMTGG+ksbEgyCkDQlN/Kq5cprPdD2IPTRpeUysjInqylGuw6K5Fdh3oGZlT1VXDmWwxlKGHqNMBt4wScQ/odR5XAphtQpqRaweU9I+WDQmc4fKsQDKlKK8HkYrOyzsBaCjVKhCagCKxoR4yG2uHPFgpOlLqG2wYVLyPS2hGW7H733lV/Dp3zsB3njyanh23yT8rCnYfNdLDgcAgNcevwpm6gGcdMgidDz92HplT186v6NRtkhZ/Eq7clTXhynza1xHhojIEhqCNDcOgGxcinsdMybNislUWLEw8DAaQQjbVFcOcQ9weL1N/IqPBUBH5ajHF1CjwaVwYbFKLdFsSBCG8JfnHQ3bR6eM2pMSYaSmaUxsvnnKMFEfAXFMqko5tYAp+Z5kkK0UGhPkysmsMRGMCQrl1rQYnq4xKftekpBMEXKL6yJcObYU8PaU9FlcOdExMfsnGC3R/9W+izUm5ZKPSpMohokQvzY/1jK/EonZhkp+fDxKY2Jjj1VmR12MfPatJ8GzeycltlKFWOiK20L1i1515Qy8YYLFPx0zTDyx+pETcpmyUOJaOLorx4c/PONQOOKg+XDWUQfpx2oKMIIweWjEw06t9uYaDZOyZuFTIbAYqpHjZpjI73H1ZootAZBdRbc+tgveePJq+M7tT0MjCOHFRy6FY5vVi33fgzefukb6LS7i57VkmNACRQCUi0QZZIRaXsv8qlR/Fm0GgDhBUpobB0BnTAASZiJmTIjJgHblQCpjokIOF5bFr/E+SvSAqLlyXBkTUvyaHLQehLGB+ulmhlV1e8p9illHOioHjLCxE8n76P/yhbphgjcVE2jZ96TJeEnTIHSNyqEewVhjEuuP9Hwyqgsp2i7JlKq6UcXbtFBhALPxkTUlfcyYYMOk2WdMWYvlMQwzJrLGLo4yMmR+TdyzSYeopmV+tTxGeoZrL2Y0y75HjvUqXAS0vSp+ZY1J7GMDmG7G8Xte+uDbDhIhG72Sxu2Ktm/+9zzClVOGedUyvPLYFeTKI+54RB4Tzb9f9qXU3Rjzq2XtYbSlyQYgVkIOl1RdRWDGJG2QEu0EAPjFg9sBAOAtLzzEur3ETDVPIe2Bft6KJCLGJY+JvrrCjAkWvza0fYrfxoyJhRIXoDQmwpUTMyaEAUr1edKVYxFXAtCMiXpfRbTCv134QlhQLcNn33oiACTXXvxvGOr2ABjChdG9wsfEvy0ZQvQlQ4Jg0lSNSZr4lfrGlAmWcuVs2nMgfi3uTbnkxdongORa75uoadtSsLmXksrPPhG9obtyyiWdWREQ19uJMTE91555MSJAFfHzfd1VaKrXo4cLmwwJu0tI9KOoLlnzM6wxIcWv5vukV1H3pD7gApeQ415NsMaGCboxYtVFqZfzhJrVUwDT0JR/mYrKMdGg6rEaQcIIqZOVwHDZ1x5cAICFw1GorXpF0lw5ep0Qa1PJbTDrYhL1feYtJ8avJ2casGXfJDyyfRx8D+Clz11mPR6VQp6KyHpmT+LOOHghrTGhsmoC6AYLXl1J4tc4jwlqkzKwubhysGEyRxG/ZmdMiHDhlBu50MGVIyaAM5+7DH576Xnw+hOfAwDJtY/bLTEmcn+dlsSveoI19Tzi9juE6FNCWM2VE9rFr+r2APoqWRyHuh8Pbh3V2lb2fWkyFv1BZI0tIfcKBSeNSbNoqcTY+B6ou634dGg6ABK/NutkqcUupf0Y2ut7kevWFmU0ZYjKicc9Q60cAVUjVjG4XsRtTpLfqeegXwesMRHHH5lTgYvOOgI+85YTreMhtcgR+7Jl9rXtg0rSpkal9goG3pUTi39C7Kcu1l7zDZavMVw4ppX1tpkSaqnHilLSy3lMVMZkuFKSfK4AAP/0u8fBWUctj6oYq64Kg3BXPbZ6Htb2KttgLYSJMXnDSc+BvQdm4GP//QBM1hpw8yM7AQDgxDWLJO0DBVtUjkC1XJJWPNiYMeWkib7Tt4m2SxT8ZLgwNkqV62Eb4AWoayZWjiIqh9KYUIbJrvFpbbWnpqRXcdD8YaiUPFg4XJGE26Zj4fMV13644sP+SVkcqzEmOFzYkH+I+i3Wa5jagdsb3e9QN0wadvErFQVmcuW8YFUizD78oHnwxM4JOG71ItTmJCqHYkwEsBuDArXgUiNHRM2nSslHLIq+33LJbASJCdzFlWNaBIpPh8slqDXq5DY4MgvnMdFq5SgZhwVUxlNNGxGfj5rHxBBlhjFU8rVrVvI9+NtmhOYDyPDE8D2inZ7Q1zWc56fZ7MoZeMMEMybJ4Ja9kmMWJNoDiyuHWL2VmgNGFaXttqUkxr/F51dVdAcC1YqvpRtfvnAYVoxENLM6fphCnanvcVus7VV2g8OFqeJlAuI6TNUacOPDkWFy1lHL048naXmaxyGicgRUw0DOg6ALBgEsjEm9IU1swgDAl029hmrdHgpzCY2JEJkemDZH5VBs2bP7JuM2iaamMSYjcyvwnXeeJrnhTOJXFUMKYyLl4snoysHAA28S4aIO/slrKlqLYkxsAzpVu0kzTJo7P2TpXPjvi8+EZQuGwAMP/v1XT0nRfEevWAArR4bhrKMOgu/8+un4c7U/+L45WR6AXZAbGyHN/lpFhklUaZkwTAz3UQwtLq4cE2I2qVqOGSEVk6bMryXZMDEyJpphIqeNEBB5TJLMr/I1phKXlUs6A02NNypwHhaxne978TFcGRPVgLGHCzvtsmNgw6R5/0MpaqUzjImt5ggVMiomOskwSWFMcGpmNY+JGhExXC5pq+EyYSDF3xmSw6ntFnDxjqmrp7kOrhyA5DpM1hrwyPb9ABC5CVKPh16bGJOhkg+rF8+BZ/ZOwlteKItnrXlMTMwYYkyoNPqUK0fAlg9CAPeJqsaYRAO8K2MitC3PXb4AHt4+BgAgrdhNOO3wpdJ7zag1DK7VmDERrhyzxmSSEr8a9lsnGBN1WxNjIu5HLc4e6kGtyZbYGBP63qrvkw+OW52wJn/9SjmT58icCtz2t+eA53nw7duRYVItx+JcALGyNj9olCtH1UGIcalS9gGmxTZ0qK0ovKjn/XAPFzZBHO5D5x0FGzbvg+/9epO2DRUu7HmyCzv6rmlQlNTxDYnXbeHCKmOidLOIYfLi44gxRGdo8PnR90m4sOLfxK7/6DM1x4wJLoxJHJXaY74c1pigcClbcrU8IfqclTEhBkYxcGLmIM2VE6vmG3oeE3VQrVZ0f66c20E9D3m1oR3bslowwRYubMtpICayyZlGPBgeTEQ6aMcjJiMtKqfiw3ffdRpc+tpj4C9fcbT0nUtUjjagl5PBj8pWSwmfBZzEr5Xkmok6P2ISVxPpYdiYEMGaAcihqa5wZkzEap1gTFSNiRDyAiDGxNBH8MArGAGt7AQRCUe1XbQxzTChoImAM2jZTMnR5g/JzJTtPnrE5RHnJ/LoUIySKn71PbTAIgwhNVzYRRulNzba75tPXQOfbCZyExD3AGf/xVlZVVdOw1TEzzElfaBoVaj7hg3d+dUKeTz8O9NCSw2+KCkLnFYZEzpcOPrfY3YJGyaxxiSQy1UXCZP41RQujKNy1O3Sqm+KibMRJCnpY8ZEWdFFjIk6gdBtUttv6/Sm9xRkg0xW5duicgT1Pz5djwcVU7I4DIkxEStpgjFZu3Qe/NGLD9PoUJkxoVfgFTX6qilynmkEmgA5+h3ev/xbpzwmyOgQFLqoWSIqjboyJnGb0TXBdVlcoa4wTceKNSZCByW5cuTJYhzR+8mzS/cRLFI1MSYmI1ydg4TRFKS4ciiop52Hxh7rTHxCC4JBi1+j/4n41Zf+A8jiSwC5r1eIZ19cb1Eny4XpM7ULQM9MLAwIzJjUG4nbSdeYCMbEvPCqlLz4WaXCewHorNfx77HLt3m+pjEBIEmOp8L3PO3aR+1r3pcUjRfVHgCOyplVkBiTFAFdbsd0Er/qdL7ojwnd7adWOo7D5oIgdVUZMSaWwVo5lBQuTBhznrJ6y8qYlH1fdks4uHJEvg8AgLkEK0C1UT22+gDbtC1qHgQMsT9T0btaIyBX3FSCNQEX8Su+ZiPNuk7CIIkZk6yGCbq/ah0lF7iIBQEgLrS4fGHE0DQaOmOCc0MIFjBd/Jq8LhsMUErXRbVdXIt6w57HhIIt5N4V5x0TVf590ymrAUDWmUQGRDaNieqqEv11SJkccV+sSP1eP14+rhzzQkhMumQRP2Sc1RWNic1VXfaTvCymqBxT5lcA+TqI87Ux0KsX04aJGlmFozIBMrhyXKJymrvilPQ9BlwrQNCChWtMFJ+hgFTbg9henTipVa+KcikZdJKoHHqipRkT8wCdxpgAQJzgDSC9Tk60Df6tzAi5MCbCMCn7epIosn2e/loLF7bsRw7rVo266L/JZTddow2Ttl05yDBZNCeKShqfqUMQhAljYkhJb0Kl5MH/ffXz4cs3PQEfOu+o1Dao0POY0Mf6/dMOgSXzhuD5KxfCf/92i6SDwiGXu5v3eWK6DovmDhmrggtgV46JEqcyv6qvAZArtCXGhDZes+DTbzkRbn10ZyzuxmxEJFI1/5Y6nOmZV+sIyUUpEWNCHFBc7nbEr+pey74nJC/x2IkfnyRcODkncX/Ed2pbjeJXLcFaU/xqENECyOOEYDZt7sI1i+dq+wCwuHKI+2KDnj3WojHpLbuEDZPYlRPS9TaKgCkqAB8X09ZlxWIWA2NaRA7+LV5ZmLQaVPifK2NiumY4zbmbK0dmC7AxYnNbDVfkgWouKm5og2QIGcSvJoYJQPFRG/zJer4afbWH4VuMHSfxK9KYiErYYRgJX22MiU2bMFT24V0vORzeeeZhLeX4UXdtOtaC4Qq8+dQ1cUVjKipHZOKdqQfNat+JYWJaVGARrbim2qQh9fV0xiRoSWMiv09jPCnMr5bhlceujN9jxsT3EqZS1eQA0K4c9X5SIkuNMcH9nnLlBCFM1RrxfWlFY6IndPMBoJmvhtC1CIF3FNrsS5/F36njm1SewIvHDxNjIq4pZVDiayKYTZvAGuu2pG0M4lfKxWaDKkxmV84sAh0u3CHGxCJOwvqPc563HJ63YgG84gUrAAAloUoRvgIkgzD2xZoYk1oj1PyX5ZJ5gHYxTGyGDQU1GmnOULJfm4Ggsilq8T+X45lS0rsyJsaoHE38Gu1PJDvT24T3L393yJJ5xrYIYMZkXrUct3HvRC0eWLMzJlFDWk086Cp+VdtCJVgrlbw48Z4wtNISrOE52rTyNIVyqucsjlFvwTDRMr+2djklYI2JSvunHT/6THlPMEollTFRMqaqCMIwzmHieYkIOwu0SC7ClYNRwxqT5qZxrZzmfdLGN+X5NdW2EXfZVF0YQEkr4MCYVEq+sUQAXuSo4tcsGkh8fHseE+dddgRsmDSvQBCGHUuwZkq8hR8SHK1x0iGL4ZoPvBRefGQU/pq4ctINE0okZlpVTtUaKa4cUL6zD07679NHYXlSlmsDubhy4vcO1wZADReO/ruE2Qnga6CyG76nD/AASf86MG1gTAyr9UVzK7CMKNSoAotfK34yie8YSzK4ziWuJT4Xtc6R6yrNBLXvlFL2J9oiKmMDyFWtxTkKAWzas4sNiGNWLoT51TKcdvgSuU3op1TmVwHRH1oTv9KsWjtYjgoAJm5ier9kZI+q/xGMErognidfHzWaRUUQhnHF44XDlZaYIU9x5qQdE7MZ4llUE6xRCc/wPim3FAARlZOiMRGuK6pKM8bKhTprojEmwpAWTJZjSnp1W3vm196yTNgwiTUmnRO/JtSc3MHwoEHRsAJq2m7rsZod7wAKBTTlOZiuB4RYCw9OFsbEMCHg37sYJmpWVdmVky5+FXCJyKGOByAzSkMle3kCm/hVXekk26W4chQBsMCRB813YiywK6eEDJPto5GHvlr2NbYOQI6cOXHNIum7dp8J3V3gxpgAAHziqgfh2X2TElUv7u/EdN0pBxE2TA5dNg82fOTlcMn5z5e2wZMG5eITiBmTlMyvFNTTbkVjouLkQxbHr22MiYlFoRKnAejp+tXVvgDlVmkEADvHo/7mYkzT7ZLf20LzAVCtHC/5XtwfYcDOH1YNbvmcTPl1BGXiapgILViURTfZRt095c7xPFr8mjVcGEC+h2R2buEx6DHKZOANk0T800FXTkzxm49jNUyak7ULKyAe0KkmY1Ip6RkcBSLGxEx12qhV06Bn+z0FlTGhkoVRUPUnLmxSdDzcPn1ATusLUtl0A2OiaUyIUFjpdz79+rkHz7e2RQCfe7mEDZOIMTG5uXD7Tz5kkfRdllUahayuHPz9v/3ySXj/9zegrJtefA4T03WoN6tnA7ilpAcw5d2hmSq1qSIzdJBSK4eCaqDlES58yqGJYSLEzdQEa2JnXMSvJV915SSvKRF+EIawazxSl4lIKxtec3ykmVmzJIlU0cYbQ40jAaEjwunzxX03RQeVFGPHNOknmV+bvyOupSx+LTfPwdNcYhgrR/TzxYaVOB/8v1XDhBo/2ZXTo8A3plOZX02MCYZtJSba5zL5xozJjBCN6fUbBKZqDWt4m211Z2JhXH9PbVPyPIklsd0X35cLHLpqTOSVsX6ctL6g+qgx4qgcS/QVBVO48JHLF1h/J4D7he958Spxx9i09j3G2FSSn+QktAoHyN+Vk1YhVe2jdz+9V6Lqxf0dn25IOSdM+imboR8f01FjIvpEWnVhCppLKweRCa5MLKKVbKnn09okjBo1wRq+PtjwOUkxYgGiFfiuZn9btiDdMPnnNx0PX/j9k6SCnNYoQNJ9FP3HOU/SqhyrUTmmMVnsGxs/KrBYGBtAOJWC6srBjIm4vlGSPMKVQ9yXNODnltaYJAvzXgIbJujGJMr+YmvluDAmVGVSATVttw2iYx6oRSupoTItuAKICmJRhbridiu/wwN21ZDYSjZMUpsrGyYlxZWTcr6YXXFnTPRjV7MwJhZ62ZjHRMlDoEZJedIEkLx+7nJHxgQZZY0gjCfxmDExuLnu3zIavxbRPHGb2zZM1EnG3eADiCaUOHtnyYtrKE1M1yXDRL1fwt1JTZ5aG02MidJUWfyaulv5GDmEC1NYpbgEKK2EsyvHwJiYClaecYRe+qERhrCr6co5yIExmTtUhtccv0qKMDJFCwHY2eKSn7RdjOmmRG/qOZmYvKS6cPQ/NY8JMoCwUFdlTNYuTUKGxT5LviddXzW6LxNjkmqYRP97LY/JwBsm4vaHWPzaoXBhNXQNg6pMKpBF/CqOlbhyfONg+Ik3HGutJaEOFMsWDMEFp6+FP3rxoXEiLxVZxa+SP1YJF05jL7DmphWNScKYuB/TlA0TAODUQ5fAyJwKnHmkPHBTxbVkP3TyBi/IXV05+DrM1IM4IkIYJqbEc5hVUCexdgXhNjcgBfX4S+YNkRqT8el6/NxSFXB/9v4z4U/POhwue+NxqW2UGBP0udpvxQo4CHLIY5IDYwIAcIbSx6ixxeTKMWpMbHlM0OtT1srsGkCTMWlBY4Ivh9pabKQvtlQN9z0PlsyLxqM9E1EbRg2MCTYAyhaNiXDlxJlf01w5SMsyZGFMfu/UNXDq2sXwZ2cfEZ+758msj8aYZJif5Kgc/bnnPCY9CjLza8FROS7iVVvW0lWLotXRakOCHgwxgMSunLJumKwaGYb/+YuXwoLhClxxxybpO1utHA88+AelhoWKdsKFRQZHEYufypjgGkIOWV/VNrWkMcErLmXQOXHNIrjnIy/XDDp1YBG+7aSSa/IdLpi3glDwp7VpuhGgqJxokDYZbSesWQS/3bwPLly3lsjU2t4EmjXjqXrNlswbkirEYo2J7bk94qD5msjV2EajxkQxTFCCtbbzmORjl8CHX/V8eHDrKLzm+FUAQDNSpufPVIZCY0wMrhyKvQhCiDUmSx0Yk6SNZoYVG0NL5tkNE6Fr2TU+Y82nUlIiC0sefT/FbbaKX4lw4ehznf0QGK6U4D/fcwYAAHzrtqfi9gNE59sIwvbChTFjQmZ+7U1XzsAbJqKfYFdO0YzJq49fCY9sH4O3n7ZW++4f/89x8PP7tsKF6w41/v4P1h0KL1g1AievXZR6LDHIiKiciDGRt/F9L6ZQs2R+dRlUM2tM0KUXx65W3AyTLMUNqTZRtYjS3HqSSI2YvKkoGnVgKZc8qPg4ER2myZfCq45bAScfsjhTDpFl86uwa3waTly9CG59dBcAIMbEcG2+/I6T4fqHdsCbTlkNj++YkL7Lskqj4JqS3oRq2ZfqlMxHhsl0Sg4TV5iMaPW1uH83P7LTqdIyhrpiziNcGCCaqK96/0uS/WZw5ZjYLDnBmtx21VA966iD4KZHdsbvsSvHRfwqYKusjY2hNMbkoKauZefYtJRPZb5ilKtRdQ3T7QhlPRF1LfG+sMZETe1vbHfzO7GJyHSbZH7NrjGZrXlMBt4wEQ/Cxd/bEK+4izZMli8YhvVvPJ787vdPOwR+/7RDrL8fKvuw7oil1m0ExGSJXTnqBIcHA1utHHUR5mJoYPo1M2PSfD1cKcHYVN3BlZN875KuH0At4hf9b50xces36jUu+3408TfvkXo/vvT2U5z2i3Hr37wMpmoNWDR3KGYXxpp+dpMweOXInNhYVgfQ3POYZKQKpmoNabWKxa9pWV9d4VJduFLy41X2bzbts+6PYrhsOq08QbnKTM+rSVeGCyJq4lelr3/ubSfBV29+HLbum4Ifb3hWFr/m5cqRGBNzJtmSD4gxmZYicjTD0JfPyTMwJiEork7iWmKtE056V7G4cjDU0iMi062akt4Y0kwgVfza3CdrTHoM+PkSeSWKduV0EmpK+qGSXktDrvdiZkzUhEdOjImFEqeA9ykGSLE6npfinsGU8rwWGBNS/JqaCAzT226TDCUklAcvp91YMVwpwaLmqlLNupl2HaM2yu/b15iY+5ULJmsB0pj4sfj1wEw9tzB/2ZUD5OuK72nXRsVQyYf/++rnw4/+7AztO3VCyyMqhwJZrM9Z/Cq0DPK4gM9bXbWPzKnAX73ieXBEU6DdCLKFC1Nt0foM1phYXDme5MqZhv2T5grHch4Tj8zJAqDriahnVC77QevUbEOEuGfitEXbRJ8R4xuVHNGEdI1J9J9dOT0GarIsmjHpJMQgIyrClgnxqzTwZqiV47LaM9UfMUFKeNZ8/f5zj4TbHtsNx69eZP2trDHJHi7soZWK70X0pi0NPoCcwTTLZDtU8mEySJLeVaRJMd/JSk0q5SIMbtf1ou9Pfp9l1QcAMI0YE6wxGZ/OzzDBXd+kMamU0yt6Lxguw7tecjj5nc5WttBQB1B9sR3xqy0lvXSM5jb7J2uxKPkgh3DhuC2W8QYfc4nFlVPyPVi2IPp+z8QM7G2GUFOFBFWNiUkzFIKcwoEy/CYNbj1Vq2OCxpg02yZ+8/bTDoHJmQb8n5OfY9yHivSonGjf7MrpMZAagL4yTARjEsTvbULEkhI/bytm5uTKkTK/preX8jH/n5NWw/85aXXqb4dzisoBiFYXk7VGRsYkC8XqQZNhhkrJh3oZrcZyNkxU142Lm0uLysmRjaD2n4bJWiOplaOKX3MqJWHWmMiunDQD1E7Xu2/bDkjGxHAorZYTIX71NfGryciJ/m9vunHmV8tOaQ2oNtrymNgYE9+LDBevubh4anekl6Iqc6uMp+nRC0OQkulR13fKkMkZPzu2Z1vsUhW7ivfPPXgB/NObaAmACWmMSawx6THLpNAZ+JOf/CScccYZMHfuXFi0aFGRh2oZ1MNadIK1TkJoTKZriUBQjwygBxy94FgLrpys4lfsysk4aLcSlWMaCMUDbcs2C6D6qDMwJqiPlZX8CXnT+6orZ7HFPy+Qf1SO/D7rvZ2qNeKJoSyJXxsw3TS60+5VahsN7J7myknpx7ZTKyqPiQqK0TAZQSqTQBbx09yP9BgpzmdnU2idNR29LeMuPn5aVE655MPS5jaP74wME4oxUevvmM4rCENoNOwaE1OJCVfGJHHlCMbES/1NGmZrVE6hM/DMzAy8+c1vhve85z1FHqYtUANDPxkmqsYkjTEpWyZazaBxeGBsCdoo2MI00yBrTFoQv6LjiT5QhMZE3W/Z96VBM+9FtOrKecGqkdTf5J/HhHYXuGKy1oCGqB6runJEWfd2GRPDpJjVlWNfFSvPXkGGSZaoHDWZXpLIS34W5RpO9L7EMbaPZY/IAVCYKk3TJj+fJhZPbCeO/cTOcQBIZ0xUrZeKNMbEZJhIjImTK6fZtpLsymkFWCfErpwmPvaxjwEAwDe/+c0iD9MWqHveX66c6FwmJY2JvI2qTKc+B2htwsyqMfFbnOgBFMbEVfxqEDwmjIl7VE5aNlMMHH5bKXnNtOL7mu3Id7LCRlrJ9+CYlQtTf6NF5eQcLuxyrb56wSlw3QPb4T/ufgamakHsyokYk2bm14LEr7LBitttzg6abG/+3tMi27K00B3Us2Nql5ocUZyfVsQPT+IpRoHQY2Q1TGTxq/Kdj197MHeoJEXC4LYmxx5LGBMiCaScudkSlYOqXHsePZbNr1YAYFL7XNXqmCCaotbYasd4xZFVVlfOIDEmWTE9PQ2jo6PSX9Gg80wUm5K+kxAD1BSKOFIHKNmHbmNMstPQplWoCTYfcxqGWzBMTBoaV8YEr7Ay5RdQ6N2VKKV43oYJDl086uAFTsUfixe/pp/jeS9YAR957THx+4npRCxMJljLMVzYFB1SKTkwJpZm2J69PEGHC9PbLpoju0WoYnGq+JVKeQ+gMwJUJIwNNsZUfVZN0SmiCcKNJPKpLBzW26LVyjHcPFyw0WQofOYtJ8Kxz1kI//aHp0qfqwaeCUmtnKQ9ab9JA2ZMqOcjCRdu+RCFoKcMk/Xr18PIyEj8t2bNmsKPSd3yfmJM4pT0taTOiPpc4TnCJIQF6JTGBB+/dcPEuYgfPjY6XVFBtijGBBsHlZIvF/PKeRk9H12LF6xKZ0uoNnS6urAAvqcTM/X4t+L+1hohjDUr6uYpfjVpHYbKvuZiUOEicKSOmSfUZ9d2LOxGLPuJ4F1OpQ7GzK/SMdo0aHETbVq4kucZDWwx2arRQGrWVwBVY+IZDWYclWO6jkevWAA/e99L4JznHSx9rrrETBDtTqIDPevxXCDcm2VfL9eAjzXrGZOPfvSj4Hme9e+uu+5qqTGXXHIJ7N+/P/7bvHlzS/vJAipzYz8ZJurKRiRYw8+HSdSmMSbKvrMmWMtaKye7+DVpeyuZX6WihMKVk0FjYqt9pOK5qFJw2ffI8ud5ARtpRxzkVm9HnWDyrpXjOtjiKJjxpgFS8n3JPSXCQdOMyDRIq30Dc1f2Pdh3YAZssNL1LRj3rSBLgjWsMZEmas2VA+R28jHk91kNE3sUIN7OHF2makwE6HBh2dgytTcM0w0TE2TGxLydpjHJQ/wqxjGj6y3632uGSWaNycUXXwxvfetbrdsceuihLTWmWq1CtZrNJ9kuRJlwjH5KsKaunIT17nteTE2aWApNY6JcFpcJVDq+kysnmyGDIVcXduva+BTxhCIe6CIyvwIAnLBmBH70m2cAIBoQVxToysHncPzqdOErgH6v864unGV/cyolGJuuw8R0wpiUfA+GKz5M1QLY0zQU2mZMXMSvJT9O7W+C7fa1IiBvBSUluq4RhGaNCa7rgm48TjZWUsSvpr6unk/WRZ4zY+JbGJPYlaMwJqT4VXbFmm5HgOoiZdV8OEfleLIhouYxaQXi2KaItb4Rvy5btgyWLdPLXM9W7G76HzH6iTFRVzZxZ/c8aIC+ApCrbSqMSQsaE7nWTnp7TaHLLhC0/3DFd36YPWkCSl7HjElqrRw57NcVOFlc2ffiwoxF4R9e/wJ4dt8UnOFayqBg8WuWwbbaNEzGpwRjEv12frUMU7UkgVb7tXLo9uKmR4aJPmZg2J4LdVIrLlxYZnlwMTgVmDEpSa4NJY+JgzBdPb+srKct86v8rHrG7M5iH89XRN6UK0dli03iV4CEVchqTDrnMSnAlePKmAxUSvpNmzbBPffcA5s2bYJGowH33HMP3HPPPTA+Pl7kYTNhD8GY1Bq60nu2Qkue1BxsTK4cNcEaRqfzmGRmTJqGiWuoMICD+DUDY5JlEH7eisSVs310Cg5Cq7u9Ka6CVnDBukPhb89/nrPYspfymMwZiu5B4sqJfiuMRlGkre2oHIMxIjMmHvzBuqie0O88fzm5H5fsnvG2BRkmVL80il9RFtU6ytWhil+d8pi0ybTJ44Wyb4XRSnPlHLNqIbzuhFXx5yMpKenLvmcU9YZhCGJayGpsORfx8+T/sfi1naicFMMk0Zi0fIhCUKhh8pGPfAROOukkuPTSS2F8fBxOOukkOOmkk1rWoBQBXJjpdSesgpMPWQRHHbzA8ovZBZOI0eQyseYxUfbtsnLIWitHSkmfVWPSXEG5RJ3ExzMc+6yjDoJFcytwytrF1t+XJYbJ/XHCos4Hto5Kv92RsiLvBNR7lX8ekwzXqpyEBgMk/XK4qSkZbdZCadcwMemh8GRbKflw/nEr4X8/eBZ86e2nwHHPiVxjOImdzfhTvyrILomvkeclz6npecXMgzD+AHQXhEsek3YNWvxzW20uzyJ+xePGh1/1fACIJublRFHFKKla5MIZrpSMhtTP7t0Kb//ar6N2tGGYWNm02IAUrpz2GZPYlWNgfns1wVqheUy++c1v9nQOExWfe9tJ3W5C7lAHkHJsmCSf4ddy5ld7VI7L44KpYZdBWNJ8ZHwgj1w+H4bKPhzrkEAsOR69Qrtg3aHwjtPXpjIMrTImGCJiSmD7mF3D0AkUXV04G2PSNExQuDBAYtwJxiTN7ZbeRtqNoGpMAKK+BgDwwz9dB8/uOwCf+p9H4Jr7tzXbZz6GEJ6LeaDoqBzMdJhW3qY+PoRCTXVXjj2PiUD2qBw3xgTAXKgTb7ZiZBiu/9BZMFlrkBqTSsmHy954PNQaAcyrliEIa+Q+H9o2Fr/OrDHBWZ4d2LTYMMkjKiclH1Mifm35EIVg4GvlCKj+yH6BOoCIgcIkeJU0E5orR963i1tA1pikb6+GBGbBypE5cOeHf0fLdGo9nkFXAJD9/LJqYv7jonXw/u9vgI+85hjp830H6MGxk1DvdZ5uEoBsg61gTMamVMakaZhMCsMkv3BhE5OmMgBzhkpw5PIF1n6kwiQ8zxNltPpWJzxXaHlMLNch3q5Ng9auMZHfn7x2MXzrV0+ntuHwlEi03z0lqcM1b6gMlZIHtYZ5ps4clZPRlSNOMw/xq0gVMN+QPuH1Jz4HXnvCqsL6YasYeMPkR+85A75y0+Pw98rk0C9w0ZiYBhz1gWgpwVoHxa8AdHZHGzBd3EqERKt5TAAAXnjoEvjVJedmPmYnoLle2lzZt5rHBCBZ7YmoHD82TJqunKmc8pgYo3KS1yamAPejNIPW9wBEkoKiAgBjt4CPVuIZjyWnUnfr6+26cuxROfL7152wCsam6nDP5n3wn3c/Y2xDpuP7URbmZ/fpGVyTbbLtsyJdxwyunBwYkxcfuQz+7Owj4NznH0x+X/I9KDlx351F/4SftIhT1i6Gr/7BqbBmydxuN6UQqJ06XklJglcTe6IaJvK+XZ6XrIyJyWAqCqaoHFeo4Ybt4HvvOg2Wza/Cl99xclv7KQLtZihVxdZZ9idEzbjeE0DiuhFhnHmmpDdpsEwMgHR+KafmWViBvCAzJs12WTo4ZdRJOXq8Xshjoi+U3nH62ljnY9ouK3DoPoWsC5CqwjyZINodZ95F7rhWMVwpwV+/8nmpWrlew8AzJv0OdQChXDlSqmkL7Zg2UFDAOhWnWjltiF9bgW0gdIGL390VZxy5DO78u3MLm6y6CZMh7IJhJQeD6FPDit+8/XBhbDAkn+PmDhknZPd+1E7kmSviAnCOrpz5w2UtQlF9FjvhyomOG2kedI2JeXuX7VyxghDJtrP/iqLVMe9X3KfofcyYtLngmY0YeMak36Fa9xVC/FoyGAOqUaM+Uy6ZNuXcEKmbtyV+bQUmyt4VtiimVtCPRgmAfG1NIZkmzFEMk1hjoohd29WYmPQN+LXRlZPB2GhHR+UKHJWjuggozKvqQlK1AKeTK8cgts8C0U49gslgFCrHbHfcwIwJdcjsGpPk2trudym+Z03GBKWTHzQwY9Ln0KNyhMYk3X2jRuWoA4NLdlWZMUlvbycGbdPxWjEK8ojKGQSYjF8XqMxInMdEMVjyZExMBqsxf4fEQNqPY8qXkifiSBw/KT9hu+5UZVz1epQcGJM8wsyjfYTOmrZWmFwbMGNS8jyoK6G02UXEuO+bt4vDupubv/aEVfDA1lE4/9iVmY7XD2DDpM9holZNIjOxMmoEYarGxKUeTTsak05QmPgIrQxoUm2hPiplkDfUCr1ZMDykunKajInqyslR/CqJotHnJldONsYEHbMgY5aOyjFvP59gTPDCw/fk6sImxqTdIn4ATcOuQRkchu1VV06b1/RgpaBmXYmlbadWjt2VI/5HL05Zuxh++KfrMh2rX8CGSZ9DHRioBGtUGmkqhbU6ULgkMsua+RXneegEYyIzR9l/z4yJG9qZjFWXjZr5VaB98avhtYsrB3A/SjFMMj4TrUAY9Tj/iO1Ybzx5Ndz51F44fNm8+LMl84bgr15xNJT8qJqzS0p61V5py5Wj7duVMcl8SAkrUyp9t5X51ebKcdACDQrYMOlz6FE59jwm0TYeTEN6HpOsjInr8ybyPHRG/CofNyvayWMySHDJGmqCagCrmV8F2k2wpmoqktfJNmZXTvI6PVzY3e3TKnAaehdXzltOXQOrFs3RIlze+7Ij49cu0Un5uXKoKBz79gLtLmhUV46KGUuOEwpydWELY4J0QYMONkz6HHoeE73z64K1iEtNY0xcatJkZUyi7aI8D50Rv9KTkSsWza3AWUcdBHOHSm1PjP0MF9bBhGGFCVEzvwq0rTGRXDkJXKJRbBlL9W3p3+UJKfOrw0rc9z0466iDUvaZblzmUfxRNFNtrsngSMu3lBXLFyZ1q8ZQin4BkdDPFVIxRAfGpBPjXq+DDZM+h1PmV80vHL1PW9lmd+Wkbg4AYmAJOyR+pV+7wvM8+NYfvyi/BvUg8hgnPZwDo13GpNk/VYOl7agcgxHtIn6VtFEp/dazPHt5AecrShKstXeskoNx2U4iPXUf6qUxTeqaAdPmeaYtMEQJBFe4MybN/0yZcLhwv8MkfjUlVcPv02rlFCF+jbZr/p8FrpxBQB4ruHby06jMiNhXkYyJidUwGybuzFtnGJOEJYldOW0eSi5maGKO5Pet5jGJ/quMrWl7t+3ygiiN4AqJMbGKX9mVI8CGSZ/DVMTPNiELHYqmMVF6iypKpJA1wRpuTyfEpJ5hMmIkyMcwSV5nduVoeUzowmRtR+UYNSaYKUifkNNdOajPFawxKTmKX13gksdE7SutGIsm8atp/NBczjn01xPWLAKApFhjO8BMnkseE14gsSun76EzJnrnV59jU8ZB/Js5lZLTACAzJm5tVlMzFwlTBlxGgqwpuCm0I37VM78KV46SYM0h4Z8NvmSYoM/Ra5PxI7ly0qJypGevWMbE85CYNEdXjmsek1YWF56hvWkVck1taAXff/dp8OSuCbh641Z4bMd4W/uSs2mbt3MJ6x4UMGPS59DDhfU8JqZ6OloeE/TaxY2j7tt1vHCJIsgL+Ai8UqGRi8Ykw8Stwpj5VXXl5FrEj35tZkzcjY12roUrYpeth2rltNm/pTwmjhqT9lw58ufvOH0tHHXwfHj/OUcq28sb5qHbmTtUhhesGpHCwFsFZo1s+3MpHTAoYMakz+ESLqxF5RjKbWMqdS6RkIkCHsjdNSZdYkx4PCCRt8Yka7HDpfOH5PY0f6+5cnLMY+IZPncJk03r5p3M/IoTo7VLfEniV9eonDbChVVnzsLhClz7F2dZtm/+KsdrmseYgPt7IzSHGotL1a9lKbKAGZM+hzlcOPlcS7BmiMrBb+dW3GzaVsJxTeK3IoAPwQMCjW6LXw9bOg/mV5P+Jvpr3uJXk8sK9wsXVibdlYNfF9PnyKicHDUmJoND/biVituJUeW2vXrbcl3Q5HB/cL9sBGbDJFmQtX3IWQ++BH0Oc3Xh5DM6j4m9Vo5LqDBAexqTzohf9eMyZORxXVxCbo2/9T0p8ZfoF2p4cJ6uHDx9yMLd9l05UqXlwqNykva3O2HLrhx6X6px30oek8XzKgAQZZ51apcWlZPfNaX29A9vODbTPiq+o2HC4tcY7Mrpc6irwLJjSnr8X0BiTFrQmLg+cCbxWxFgV0468liBmopGuuKENYvgV0/sln6PGZOhst8244UfFcy4u2U8pbenty3elSPq3MyplPJjTPB1cK2V04L/6PNvOxme3j0Ba5fOS98YCI1Jjstt9ZLd8XfnwvIFw/TGBuBxzGaYcEr6BMyY9DlcivipDzIONcTAD4yrYSJrTJx+Em/XEcYEveaMizTyGijbua8nrkGMSUk3TKo5zEb4/ofIMnHRx5iieChgcWdR7sMXHbYELjrrCPjQeUfnZphgG8PEmJiiALPgsGXz4Oyjl7u3S9OY5HdNTakUWoXdlRP9Z7uEDZO+hzowCMNEyt9hyHViqy481yEdPUB7eUw6In415K5gJMjrPiQuuuzDzvGrF8WvZ+rR4I4zv7arLwGQJ6EAGSa4W7iIX10ZkyJXxkNlH/72/OfBuiOWooSJ7e1TzvyabqB5XqeeYfl9nu4xdU/t7tsmfmVXTgJ25fQ5tKic2JWTfGZaFah5TLxWGJMWXDkdFb8Sx2XIyNcwCbV+5QJc8XVkTqRBUF057UJiTNDnWTO/prkgO+mqjI4X/W/3eZo/XIYFw2WYUymZXTlYIOu3715zQaEaE2VXrfRdDDdXTluH6AuwYdLnUFenFSJcuBWNiav4tRUNh+fRxy8CXoaV7qAir9sgLm+rSbf+++Iz4Yld43DMqoUAIItf262TAyA/B4GkMUle5+3K6QTySrBWLZfg6ve/BMolz7gvlyRseUPP/JrfvlXDqm3GxGKYnHjIIqiUvDjr7CCDDZM+hx6Vo9OFJleOrVZOSxoTx4ExLmbVEfEr/ZqRII/MrwDtuXIAAI5bPQLHrcZaEx/Kvgf1IMzHleOkMclP/NqJIpUA2cNvbVizZK71ezl9f2eUAkVkfhVQd9Xuo1C3GCavOX4V/M7zD9bC4AcRrDHpc6iDH05VnWwj/0aE6S2eW5E+b01jgjQcTr/odLgwa0zSkJeBWISoWQzieRgmGHj68JwME8RAOuYx6RRDl1fmVxfI9XQ65aqix7hc9q2MWu0a6Scdssj6PRslEZgx6XNECZYiarpS8hL/tmUg/YvfOQpOWbsEXnXcSulz/JCqacJNwINTVvFrpxkTjsqhkdcEEzMFOVL8wxUfxqfbz2GiQmZMks+Nok/8OuX0Oq8x6dzxShYmthPHBCg282urp3TTX50ND20bg7OPOqj9Rg0A2DAZAJR9H2YagVJ+G9Br+WlbvnAY3nTKam0/7ecxcWuvGFg6scLjPCbpyGuCEZe6kuOFrpYLYkwQZYLZE3MRP7NmS0WnNSadzI/hocvTKVdVEbVyBFTtUKus6tql85zzsjDYlTMQoMJ/beJXE6Tqws5ROcgY6sFwYQx25cj4g3VrAQDgL887Kpf9CQNH1S61g+FmvZxqOV8KHBsmWLDoUrwuzZDrdHl7cbk78TyVLExsUVC7U76ZX7FriqfLToEZkwGAGCDwqrKV0ut4O9eJoBXG5HUnrIKf37dNEjoWhU6UoJ+t+NjrXgAfOu/oODy3XcTaoRxdOUUxJjiPSb2RvHaJyuk1V04nq9bKBlrhh9OOGR23IMaE7ZKOgQ2TAUAS/osNk+R75wcZbebKmEjiV8eB8f3nPhfef+5z3drUJrKEeQ4aPM/LzSgBKEr8GvXpIsWv2Egx5e+QtEo95spJDJMOHKsHXDl5nqcUZcSWScfAV3oAIOhnOT08plzd9oMf+GHHiaCVBGudBDMmnUMR+WlEFEMeKekxsCunHgTxa5MRnyUfTqfDhecPR+vPBcP5GZkmdEX8WmB1YbwnXrh0DsyYDADERICFe60kFmtFY9KKK6eTaIk5YrSEmDHJ0YgoLFwYWSaNwLJhE9kSrHnN33Smv/3Z2UfAoUvnwmtPWJm+cZtoRbvWLtTrmOcCg6P2ugM2TAYA4oEyFdRzfeDwA+8aby8xJj34YLMrp3MoQtSciF+Lc+U0gnTLJIv4NY4661CHW714LvzJS4/oyLHwuXfq/FQDKN8Ea/h82MHQKfCVHgCIMGFThIzryiZEw7VrHpNeX2VwSvrOoYjEecWFCyPxqyVbp0ArmV97/NFoG51LIFekxiR53aFEtgxgxmQgIIyDCo7KseQxMWG4XIKVI8NwYKYBK1BRNRuwMRRaKmt2C+xD7hziWjm5unKKEb9iWyRwMExwWKlzrZw+73CdWpSo9k+uGpMuuKYYbJgMBMQKFSe2aklj4ntw01+9LNqX4+SCDSCH8b3jwOfOeUyKRRGMyemHL4WfbNgCp65dkts+AWRXjgtjIoeVujIm/d3fOid+lY+T53OM95RnxmKGHWyYDABojUn2qByA7CtTzJgEPciYcFRO5xDXbMlxwnr9ic+BVx+3MveCcZjdc+m3WfqRmDj7fQXeqXk8S52irJBrivX3/eolsNdsACAGbSklfQafeDvAA0UP2iXsQ+4gRD8zJSlrFUVUsTUlWDMhywQmmtvv81w3Mr/mfcgsomZGfuCheAAQu3JKBvFrgQ9cWTJMes8ykTN28sBTJJJolN4fdkwp6U2QXYJu2/a6MLxddEP8mvcx8d46VS2ZwYbJQCB25fj04FkkRYlXGb2oMeGonM6hCI1JUZAMEydXDn7NGhOADjImRRomHWKWGTLYMBkACOq8YqqV06EBpDc1JvRrRv4oolZOUQikBGsO/TYDAyk27ff+1o08JvlrTDrDLDNksGEyABDUOY7K6UZGw15kTFj82jnE4cKzYICXonIcNCZZDNyYMZkF16EddOp58tAslvch2ZXTHbBhMgCIi/ghjUk3UsX3pMYEv+Zxp1Ak2opZMOxkduW4h50nRfz6u8P1G2PS74ZkL6GwEeKpp56Cd77znXDYYYfBnDlz4IgjjoBLL70UZmZmijokwwBK/NoNbUXvmSWcQKmTEPWV5jnWWeomsCvntcdHNWaOOGiecXsp34VjHpN+72/9IH7NUjWakR8Ky2Py0EMPQRAE8JWvfAWOPPJIuO++++Dd7343TExMwKc+9amiDssgIHz6lTZr5bQLJ199h8FF/DqHv3nl8+DWx3bBiw7LNxlaEcA99e2nrYXDD5oPx64aMW4vT472fQtjuN/nuU6F3xcpUPW6ME4yCjRMXvnKV8IrX/nK+P3hhx8ODz/8MFx++eVsmHQYscbEEC7cqZVNL4pfvQwTCqM9vOiwJbPCKAGQ3Y6+78GLj1xm3T7L5FhEorleRMdcOQW6pXGpgX6/X72EjmZ+3b9/PyxZYh6YpqenYXp6On4/OjraiWb1PRKNCW2MdOqB60G7RBrIOI8JQyAruZfFNToo4cLdcOUUmvmVDZOOoWMqtMcffxw+//nPw0UXXWTcZv369TAyMhL/rVmzplPN62sktXKwxiT5vnNROb1nmcjF13jgYUTI2lNll2DKtv5gROV0Lo8Jfs3hwv2AzIbJRz/6UfA8z/p31113Sb/ZsmULvPKVr4Q3v/nN8K53vcu470suuQT2798f/23evDn7GTE0iPwlQ6Y8JoPsyikwnTVjFiNjX83yPCVROZlbNavQKbFoNO9Er/MO+JJEzbxw6Rgyu3IuvvhieOtb32rd5tBDD41fb9myBV72spfBunXr4Ktf/ar1d9VqFarVatYmMVLwplNWw5Z9k/Cq41bGn3UjsVgP2iWcx4Qh4f3nHAmfu/4x+PvXHJPpd9k0JgMSldNBy8v3PGiEYQFRORwu3A1kNkyWLVsGy5bZhWACzz77LLzsZS+DU045Bb7xjW+APxvyF/QhTj5kMXzzj14kfdYNjUkPBuVIKyI2TBgfPO9oeO85R0K1nC2kOZvGRP9NP6KThlfJ86ABYe7HxLvjBGudQ2Hi1y1btsDZZ58NhxxyCHzqU5+CnTt3xt+tWLGiqMMyHNGVPCY9SJnIK6IuNoTRM8hqlACo4ff2bcWz1+/zXCcZBs+T/+e2X/SaGZPOoTDD5Nprr4XHHnsMHnvsMVi9erX0XS9OUIOG7qSk7737zkW6GHkAi6jTM782XTl9OtG9/sRV8NN7tsA7zzy0Y8cs6prie8mMSedQ2BrxD//wDyEMQ/KP0X10w5XTCDpymEyQDZPutYMxu5ElQ2i/p6T/zFtOhAc+/go4cvmCjh1TjGGFJljr0/vVi2DyekBRZIidCcesWtiR42RBlhonDIYJkoE74OHCnufB3KGOpshCFZtzNkzQ6369X72IzvYeRs+gk/H5V73/TLj10V3wB+vWFnqcVsDhgIw84GUwcJNJtMgWDRZixiTnpbbPrpyugA2TAUWW2h7t4gWrRuAFljoj3QSHCzPygOQa5XDhjqOoayozYXy/OgV25QwocOTAID9weODheYLRKrKEnYvJk12H+aGoEGzWmHQHbJgMKLwMK7x+hhQ2PcAGGqM9+BkyCCdF/Iprz6ChE1E5/RpF1YvgR2NA0Y2onF7FoKQIZxSHLAZukseEO1xeSAoj5rtfSYPGA0THwIbJgKIbUTm9ioVzKlDyOx9JwOgfZHHlVJpVvnG1b0Z7KC5cmBdw3QCPxAMKZkwSfPkdp8DoZA1G5lS63RTGLEUWMfmrjlsJGzbtg989ebV9Q4YzOhEuPOjjZCfBhsmAghOLJTj98KXdbgJjliNLsbfDD5oPX//DFxbdpIFCURqTLNFWjPzArpwBhR9HBnB0AIPRLri0QXchDJLca+VwuHBXwIbJgCKODOBBlMFoGxxW2l2IS557VA56zQnWOgc2TAYU/Z4Wm8HoJOTSBl1syICiVFCkE4tfuwM2TAYU4oHj1R2D0T6yROUw8odfmGGiH4NRPNgwGVD4BVGfDMYgAjOP/Ex1HkXVH2JXTnfAhsmAoqiERAzGIELOC9S9dgwqhDGYe1QO2h+7vTsHNkwGFMyYMBh5AmtM+JnqNApz5aDXnA+vc2DDZEDBabEZjPyA7Xs29juPosT8UrQVFzfqGPhKDyjiFQYPogxG28iS+ZWRP4qqd8XFTrsDNkwGFJzHhMHIDxy90V34BUUZyinpc901wwK+1AOKosRiDMYgIktKekb+EAZJ3voeOY8JT5edAl/pAUWsMeEewGC0Da491V0kmV/z3a+sHcp33wwz+FIPKNiVw2DkBw9Yi9BNCOY3/6gcZky6Ab7SAwoWvzIY+QHPWRwu3HkUNZ5xDaTugA2TAUWiYueHjcFoFxyV010UlvmVXTldAV/qAQXXymEw8oMcvcHPVKcRi/nZldMX4Cs9oGBXDoORH7D7hl05nYdfWFRO8poZk86BL/WAIklJ3912MBj9AK6V010UVWJDdtHxje0UeFoaUByyZC4AAKxdMq/LLWEwZj/kfBc8gXUaRRUlxbZImV05HUO52w1gdAfPPXgB3PLXL4PlC6vdbgqDMeshMyZsmHQahUXl4GOwXdIxsGEywFjTZE0YDEZ7YMq/uygsjwnXyukK2AZkMBiMHMGenM4jzvxaoPi1XOIb2ymwYcJgMBhtArsQmDHpPArTmBDHYBQPNkwYDAajTUgaE6ZMOo7YlVNgVA6LmjsHNkwYDAajTYhEXDx3dQdJ5tci85jwze0UWPzKYDAYbWLZ/CE46uD5sHzBcLebMpAQ2pK8jQc58ysbJp0CGyYMBoPRJsolH67585cCyxC6g8MOivIxHbo037xMXMSvO2DDhMFgMHIAa0u6h/ecdQS87oRVsHpxvikQ2JXTHbDGhMFgMBizGp7n5W6UiP0KsGHSObBhwmAwGAwGAc7o2x2wYcJgMBgMBgEsfuUEa50DGyYMBoPBYBBg8Wt3wIYJg8FgMBgpYHFz58CGCYPBYDAYBMIweV1mw6RjKNQwed3rXgeHHHIIDA8Pw8qVK+GCCy6ALVu2FHlIBoPBYDByQYAsE2ZMOodCDZOXvexl8MMf/hAefvhh+NGPfgSPP/44vOlNbyrykAwGg8Fg5ALJMGGNScdQaIK1v/iLv4hfr127Fv72b/8W3vCGN0CtVoNKpVLkoRkMBoPBaAvYlcOESefQscyve/bsge9+97twxhlnGI2S6elpmJ6ejt+Pjo52qnkMBoPBYEhYMJxMkZUSSzI7hcKv9N/8zd/AvHnzYOnSpbBp0yb46U9/atx2/fr1MDIyEv+tWbOm6OYxGAwGg0Fi0dwh+PqFp8K33/kiNkw6CC8MMVmVjo9+9KPwsY99zLrNnXfeCaeeeioAAOzatQv27NkDTz/9NHzsYx+DkZER+NnPfial+hWgGJM1a9bA/v37YeHChVmayWAwGAwGo0sYHR2FkZGRlubvzIbJrl27YNeuXdZtDj30UBge1st/P/PMM7BmzRq47bbbYN26danHaufEGAwGg8FgdAftzN+ZNSbLli2DZcuWZf0ZAAAIGwizIgwGg8FgMBgChYlf77jjDrjjjjvgzDPPhMWLF8MTTzwBH/nIR+CII45wYksYDAaDwWAMHgpT88yZMwd+/OMfw7nnngtHH300/PEf/zEce+yxcNNNN0G1Wi3qsAwGg8FgMGYxCmNMjjvuOLj++uuL2j2DwWAwGIw+BMc/MRgMBoPB6BmwYcJgMBgMBqNnwIYJg8FgMBiMngEbJgwGg8FgMHoGbJgwGAwGg8HoGbBhwmAwGAwGo2fAhgmDwWAwGIyeARsmDAaDwWAwegaFJVjLA6K2zujoaJdbwmAwGAwGwxVi3s5YJxgAetwwGRsbAwCANWvWdLklDAaDwWAwsmJsbAxGRkYy/cYLWzFnOoQgCGDLli2wYMEC8Dwv132Pjo7CmjVrYPPmzZlLMvcT+DpE4OuQgK9FBL4OEfg6JOBrEcHlOoRhCGNjY7Bq1Srw/WyqkZ5mTHzfh9WrVxd6jIULFw50BxPg6xCBr0MCvhYR+DpE4OuQgK9FhLTrkJUpEWDxK4PBYDAYjJ4BGyYMBoPBYDB6BgNrmFSrVbj00kuhWq12uyldBV+HCHwdEvC1iMDXIQJfhwR8LSIUfR16WvzKYDAYDAZjsDCwjAmDwWAwGIzeAxsmDAaDwWAwegZsmDAYDAaDwegZsGHCYDAYDAajZzCQhsmXvvQlOOyww2B4eBhOOeUUuOWWW7rdpELx0Y9+FDzPk/5WrFgRfx+GIXz0ox+FVatWwZw5c+Dss8+G+++/v4stzg8333wzvPa1r4VVq1aB53nwk5/8RPre5dynp6fhfe97HyxbtgzmzZsHr3vd6+CZZ57p4Fm0j7Tr8Id/+IdaHzn99NOlbfrhOqxfvx5e+MIXwoIFC2D58uXwhje8AR5++GFpm0HoEy7XYRD6xOWXXw7HH398nChs3bp18POf/zz+fhD6gkDatehkfxg4w+QHP/gBfOADH4C/+7u/gw0bNsBLXvISOP/882HTpk3dblqheMELXgBbt26N/zZu3Bh/98///M/w6U9/Gr7whS/AnXfeCStWrICXv/zlca2i2YyJiQk44YQT4Atf+AL5vcu5f+ADH4Arr7wSrrjiCrj11lthfHwcXvOa10Cj0ejUabSNtOsAAPDKV75S6iNXX3219H0/XIebbroJ3vve98Ltt98O1113HdTrdTjvvPNgYmIi3mYQ+oTLdQDo/z6xevVquOyyy+Cuu+6Cu+66C8455xx4/etfHxsfg9AXBNKuBUAH+0M4YHjRi14UXnTRRdJnz3ve88K//du/7VKLisell14annDCCeR3QRCEK1asCC+77LL4s6mpqXBkZCT88pe/3KEWdgYAEF555ZXxe5dz37dvX1ipVMIrrrgi3ubZZ58Nfd8Pr7nmmo61PU+o1yEMw/DCCy8MX//61xt/04/XIQzDcMeOHSEAhDfddFMYhoPbJ9TrEIaD2ycWL14cfu1rXxvYvoAhrkUYdrY/DBRjMjMzA3fffTecd9550ufnnXce3HbbbV1qVWfw6KOPwqpVq+Cwww6Dt771rfDEE08AAMCTTz4J27Ztk65JtVqFs846q++vicu533333VCr1aRtVq1aBccee2zfXZ8bb7wRli9fDkcddRS8+93vhh07dsTf9et12L9/PwAALFmyBAAGt0+o10FgkPpEo9GAK664AiYmJmDdunUD2xcA9Gsh0Kn+0NNF/PLGrl27oNFowMEHHyx9fvDBB8O2bdu61Kricdppp8G///u/w1FHHQXbt2+HT3ziE3DGGWfA/fffH583dU2efvrpbjS3Y3A5923btsHQ0BAsXrxY26af+sz5558Pb37zm2Ht2rXw5JNPwt///d/DOeecA3fffTdUq9W+vA5hGMIHP/hBOPPMM+HYY48FgMHsE9R1ABicPrFx40ZYt24dTE1Nwfz58+HKK6+EY445Jp5MB6kvmK4FQGf7w0AZJgKe50nvwzDUPusnnH/++fHr4447DtatWwdHHHEEfOtb34rFS4N2TTBaOfd+uz5vectb4tfHHnssnHrqqbB27Vq46qqr4I1vfKPxd7P5Olx88cVw7733wq233qp9N0h9wnQdBqVPHH300XDPPffAvn374Ec/+hFceOGFcNNNN8XfD1JfMF2LY445pqP9YaBcOcuWLYNSqaRZbzt27NCs4n7GvHnz4LjjjoNHH300js4ZxGvicu4rVqyAmZkZ2Lt3r3GbfsTKlSth7dq18OijjwJA/12H973vffBf//VfcMMNN8Dq1avjzwetT5iuA4V+7RNDQ0Nw5JFHwqmnngrr16+HE044AT772c8OXF8AMF8LCkX2h4EyTIaGhuCUU06B6667Tvr8uuuugzPOOKNLreo8pqen4cEHH4SVK1fCYYcdBitWrJCuyczMDNx00019f01czv2UU06BSqUibbN161a47777+vr67N69GzZv3gwrV64EgP65DmEYwsUXXww//vGP4frrr4fDDjtM+n5Q+kTadaDQr31CRRiGMD09PTB9wQZxLSgU2h8ySWX7AFdccUVYqVTCr3/96+EDDzwQfuADHwjnzZsXPvXUU91uWmH40Ic+FN54443hE088Ed5+++3ha17zmnDBggXxOV922WXhyMhI+OMf/zjcuHFj+La3vS1cuXJlODo62uWWt4+xsbFww4YN4YYNG0IACD/96U+HGzZsCJ9++ukwDN3O/aKLLgpXr14d/u///m/4m9/8JjznnHPCE044IazX6906rcywXYexsbHwQx/6UHjbbbeFTz75ZHjDDTeE69atC5/znOf03XV4z3veE46MjIQ33nhjuHXr1vjvwIED8TaD0CfSrsOg9IlLLrkkvPnmm8Mnn3wyvPfee8MPf/jDoe/74bXXXhuG4WD0BQHbteh0fxg4wyQMw/CLX/xiuHbt2nBoaCg8+eSTpRC5fsRb3vKWcOXKlWGlUglXrVoVvvGNbwzvv//++PsgCMJLL700XLFiRVitVsOXvvSl4caNG7vY4vxwww03hACg/V144YVhGLqd++TkZHjxxReHS5YsCefMmRO+5jWvCTdt2tSFs2kdtutw4MCB8LzzzgsPOuigsFKphIccckh44YUXaufYD9eBugYAEH7jG9+ItxmEPpF2HQalT/zxH/9xPBccdNBB4bnnnhsbJWE4GH1BwHYtOt0fvDAMw2wcC4PBYDAYDEYxGCiNCYPBYDAYjN4GGyYMBoPBYDB6BmyYMBgMBoPB6BmwYcJgMBgMBqNnwIYJg8FgMBiMngEbJgwGg8FgMHoGbJgwGAwGg8HoGbBhwmAwGAwGo2fAhgmDwWAwGIyeARsmDAaDwWAwegZsmDAYDAaDwegZsGHCYDAYDAajZ/D/A4luc2oDUxN3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise in noises:\n",
+    "    obv = obv_real + noise\n",
+    "    plt.plot(obv.T)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a637fd5-64d4-43f3-9ddd-27b75512a3cb",
+   "metadata": {},
+   "source": [
+    "## Generate plots for real observations with different noises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "c000bcc5-3e0b-45f3-83a5-d54cad9b244e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 28 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77e3a5a1ed214faabae4786ccf329964",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 25 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58559a103f774f66a4351862af17f205",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 27 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5ba9c48ce874309a7c7054ae3946167",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 26 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac37076b83294e2f9a9aeb79a95521fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 30 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d09c89f842ea4c87b94c7caaeafe8d1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 27 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "561151800844483f8bef1688a3caebe0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Neural network successfully converged after 27 epochs."
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf8badffeaf045e5b78551a2d41bb6ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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5qRsOOIwG88rWbqbS8IZZjz1xif+nzSwyf8LxJRFr21x4IjFsvj4biWmi0V+TzisS0UTDIg1e0aqT6/znc8m5ZsOW02fMovb/uWSPsf2RliO+fbz7BbOGPbjHbMjyzID5j42Vv/KPPUi89LqxPfmrN8wXFNCgJO8/TApZQMURXGkDQAXzCr0lPuP7+Wu+mvDbBIAKFvEWdoM74mXzvwjOILQBoIJFvIyi2bQ01/D2PEUzaUK7ylDTBjAje152JD77QgySFLUXvLDmaWetebaBVw72XG7r6exJs24e7Vgqm70gRrat2dheFDAv29ZywHzNa+82520vGjYDMeGfQqzkgBm2Qxebz59ZZtZXI2n/GVmUOKLxg+fkO1xTRFq5/udaft4rqo/5f0+rkmZN++JFZs36nwc3GNtf+acbfftoGTC3u39pLe7x/C/NFwR8XybnOqd6PvVoh2vYNq60a9zA4KieP3xSh4fH8r8YQCgSHW9I0awKGHL1a56isYyWrfKvqgW3caVdo9oaE0rGY+rb1S9JSsZjemr7RvW0+pffAxCuSF1GDRe9pNP/c5HOBvdsg9M8KeLpgit+orp4/g52cAuhXaN6WpN6avtGDaXSGhgcVd+ufg2l0oQ2UKHibSfVcMnPdPpnF0jZqZuk08P77FV4JJbRhVf8RK0db/j2AfcR2jWspzVJSAMOibed1OK3/acWjyY1eHC10qcbc8/VJcfUvOo1NS0/qtaWoRCPEqVEaAMomD3wLLqo3v8aqyFLxBoQFq033xNptAauSfKy1ohne0CcNbgt8/qvfPuINjWa22nrVvGkeZzZBv/PMtFkNhxZ+ry5j/G2/L29R861hw5Zdek6czsSsPbJDec+Z2zXRyblveUHOpOu18REQl3JE1pUP57rcnpewjwfB9Kdvn2+uf41Y/tD/+9jxnb78+ZxrTrkH/cSO2UerPfzg8a2PW7dO3PGt4+8qmgQWTEQ2gDgoEhEWlR/RovqzygZz7/KGaoDo8cBAHAEoQ0AgCO4PQ5gZnkaXQQtKGLXLTN2HdPaR1BVOGu9J9raYmxHEtYCIXaDDvmbq0ReNRemsGvp0YC1p7262a9r2l40Fy4ZXeWvz4+3mvuIWqds+Py0sV2/dMK3j2+9st7YvnDpoLH9y5PtxvaxV1uN7UjGP0Ws5UXz5z3v6ePme0bMczr5qlkDl/w1a5QeV9oAADiCK+0KcHh4TEOps//aHhgczfPq0pn+2W2NCaaDAUCFIbRDdnh4TFft3Kuxid/cM0vGY2pr9K8PXCp2d7SpY6BDGgBUFkI7ZEOptMYmMrrv5rdoTcfZxQjKfZU7vTuaJDqk4Tes+nPQvGyb3R07Ys3Ltmve3qS/1aY9H9y3yIg1BzvW3ubbR+aE2WAk0mwu9uGlzVqyjvo7iDWcMD/XW2zuY3ylWWtfdMJfj64bM8/hZNKsSi7bbf43lon7/5ubXGTWpP/zrebiJx0/Np9fNWJWm2Nj/nOc/PlhY9s7bS6OMjkUsPoJQkdoV4g1HU1a29OS/4UlQnc0AKh8DEQDAMARhDYAAI4gtAEAcAQ1bQAFy1qDleyBakHsBUJ8i0gENGix2QPT7MYpmYBBU/agOe/U7NMp7UVIJP9gtqi1kMmi4RFzH3bTF0mxHrPxScMpcyBetsF8T2qFv0FL6/+cMraX7TPPWXTE+r1kzOPMDPoH2U1a55SFOdzAlTYAAI4gtAEAcAShDQCAI6hpY0ZTbU1paYqcApqtZMetBUNGRnyvmc5uvhL4mrj5V5X9Gfbzkr9WHonNvtiJF7DoiO89Vl3c17DFrvlLqvulVTuuN2vYkZjZGKX5P170H0fLYmPbt3hHg1kHt88P9erqQWjDx25rSktTAKgMhDZ8prc1paUpAFQOQhuBaGsKAJWH0AYwszy1UN+87QLMp4Ztz9O2BT5vHbuXZ055UF3crg3bNfx8C5lI0uTrR83XWPVnDZqbGbseLSliLbJin8P5/B7gJkaPAwDgCK60URBGkgNA+AhtzIqR5ABQOQhtzIqR5ABQOQht5MVI8hqWb0GQgIFq9iApzxpE5Rs0FjTYzf5c+zXW84HNVc5Y78mzj0IGs+Vr6pI5fsK/D+tz5tP4xPezoGYxEA0AAEdwpY05mxqUJjEwDQDKidBGwexBaRID0wCgnAhtFGz6oDRJDEyrBfnqrQE1b7uGnXefQXXzfJ9rN04JqvnmqYsH1cFt+eri3kTeXbBYB4qK0MacMCgNAMLDQDQAABxBaAMA4AhujwMorXxzrovxnnzzyQNeU9B88XyoV6PMuNIGAMARhDYAAI4gtAEAcAShDQCAIxiIBmBG+Rb/KGggVr5mKvMZzFWMfQAOIrRDcnh4LLfcpeumfgb6kANAaRHaITg8PKardu7V2MTZq4NkPKa2xkTIRzV3di9y+pADQGkR2iEYSqU1NpHRfTe/RWs6mpy9Qp3ei5w+5ABQeoR2iNZ0NGltT0vYh7Eg9CKvbnkX/5iPYtSfqWGjRhHaKCrq2wBQOoQ2ioL6NgCUHqGNoqC+DQClR2ijaKhvA0BpEdoomelz0KlxA8DCEdooOru+LVHjBoBiILRRdNPr25KocQNAkRDaKAnq2yiU3d9cCpgfTq9xQBKrfAEA4AxCGwAARxDaAAA4gtAGAMARDEQDEKqCFiVh4BkgiSttAACcQWgDAOAIbo+jbFi2EwAWhtBGybFsJwAUB6GNkgtatnPfwRMa6mjiqhsA5oDQRllMtTXlqhsA5o/QRlkFXXWzkAgAFIbQRtmxmAgAzA9TvgAAcAShDQCAIwhtAAAcQWgDAOAIBqKV0eHhsdyoaQAA5orQLpPDw2O6audejU2cXa0oGY+prTER8lFVhun/iKHZCgDMjNAuk6FUWmMTGd1381u0hk5gkvztTSWarQDAbAjtMlvT0aS1PS1hH0ZFmN5oRRLNVgAgD0IboaLRCgAUjtBGxWEJTwAIRmijYrCYCADMjtBGxWAJTwCYHaGNisISngAwM0IbFYklPAHAj9BGxWJkOQCYCG04g85pAGodoY2KR+c0ADiL0C6hqQVCJLFIyALQOQ0AziK0S8ReIERikZCFoL4NAIR2ydgLhEjUYQEAC0NolxgLhAAAioXQhrPoUQ6g1hDacA7d0gDUKkK7yKZGjDNavHRm61Fu4yocQDUhtIvIHjHOaPHSmalHuY2rcADVhNAuInvEOFd5pWfP4Z6O+dwAqg2hXQKMGC8v5nADqBWENqoeo8wBVAtCuwgYfFaZgkaZP/DBDVrSmCDAATiJ0F4gBp9Vrun17uOptLZ+Y7+2fP0nkmYP8Ok94wl3AJWE0F4gBp9Vtun17kICfOq56f8IY/Q5gEpBaBdo+tXXdFO3xBl8VvkKCXDpbFA//KErNJRKM/ocQEWp+dCeKYyl39waDVqxazpuibsnKMCnTP3enz98UlJxl1XlTgyAhaja0B4cGdfgqTOzvsa+FWqbum0atGLXdPxF7LaZpozla9wyH8l4TC9+/pqi7Q9AbYl4nueFfRAAACC/aNgHAAAACkNoAwDgCEIbAABHENoAADiC0AYAwBEFTfnyPE+nTp0q9bEANaO5uVmRSCTswwDgmIJC+9SpU2ppodsXUCwnT57U4sWLwz4MAI4paJ62i1faIyMjWrlypV599VX+ciwA56twxThXXGkDmI+CrrQjkYizf5EvXrzY2WMPA+ercJwrAOXGQDQAABxBaAMA4IiqDe36+nrt2LFD9fX1YR+KEzhfheNcAQgLC4YAAOCIqr3SBgCg2hDaAAA4gtAGAMARhDYAAI5wOrTvvvtuRSIR439dXV255z3P0913363u7m4lk0lt2rRJL7zwQohHXF7f//73df3116u7u1uRSETf/va3jecLOT9nzpzRxz/+cS1dulSNjY264YYb9Nprr5XxpyiffOfrD/7gD3zft3e84x3Ga2rpfAEoP6dDW5Le/OY36/XXX8/977nnnss998UvflFf+cpXdP/992vfvn3q6urS1Vdf7VxL1vlKpVJav3697r///sDnCzk/fX192r17tx599FH98Ic/1OjoqK677jplMply/Rhlk+98SdI111xjfN/+/d//3Xi+ls4XgBB4DtuxY4e3fv36wOey2azX1dXl3XvvvbnHxsfHvZaWFu+BBx4o0xFWDkne7t27c9uFnJ/h4WEvHo97jz76aO41hw8f9qLRqPfd7363bMceBvt8eZ7nbdmyxbvxxhtnfE8tny8A5eH8lfbLL7+s7u5urV69Wr//+7+vV155RZJ08OBBHT16VJs3b869tr6+Xhs3btSPfvSjsA63YhRyfvbv36+JiQnjNd3d3Vq7dm3NnsOnn35aHR0duuCCC/ThD39Yg4ODuec4XwBKzenQfvvb365HHnlEjz/+uP7u7/5OR48e1ZVXXqnjx4/r6NGjkqTOzk7jPZ2dnbnnalkh5+fo0aNKJBJqa2ub8TW15Nprr9U//MM/6Hvf+5527typffv26T3veY/OnDkjifMFoPQKWuWrUl177bW5P69bt069vb0677zz9PDDD+cGCNnLH3qex5KI08zn/NTqObz55ptzf167dq0uv/xynXPOOXrsscf0/ve/f8b31er5AlB8Tl9p2xobG7Vu3Tq9/PLLuVHk9hXO4OCg7+qyFhVyfrq6upROpzU0NDTja2rZ8uXLdc455+jll1+WxPkCUHpVFdpnzpzRiy++qOXLl2v16tXq6urSk08+mXs+nU5r7969uvLKK0M8yspQyPnZsGGD4vG48ZrXX39dzz//POdQ0vHjx/Xqq69q+fLlkjhfAErP6dvjd9xxh66//nqtWrVKg4OD+vM//3ONjIxoy5YtikQi6uvr0z333KPzzz9f559/vu655x41NDTolltuCfvQy2J0dFQDAwO57YMHD6q/v1/t7e1atWpV3vPT0tKi22+/Xdu3b9eSJUvU3t6uO+64Q+vWrdNVV10V1o9VMrOdr/b2dt199936vd/7PS1fvly/+MUv9JnPfEZLly7V7/7u70qqvfMFIAThDl5fmJtvvtlbvny5F4/Hve7ubu/973+/98ILL+Sez2az3o4dO7yuri6vvr7ee9e73uU999xzIR5xee3Zs8eT5Pvfli1bPM8r7PyMjY1527Zt89rb271kMuldd9113qFDh0L4aUpvtvN1+vRpb/Pmzd6yZcu8eDzurVq1ytuyZYvvXNTS+QJQfizNCQCAI6qqpg0AQDUjtAEAcAShDQCAIwhtAAAcQWgDAOAIQhsAAEcQ2gAAOILQBgDAEYR2hdq0aZP6+vpy2+eee67uu+++0I4HABA+p3uP15J9+/apsbGx6Pv9i7/4Cz322GPq7+9XIpHQ8PBw0T8DAFAcXGk7YtmyZWpoaCj6ftPptG666Sb90R/9UdH3DQAoLkK7AqRSKd16661qamrS8uXLtXPnTt9r7NvjkUhEDz74oK677jo1NDTo4osv1o9//GMNDAxo06ZNamxsVG9vrw4cODDrZ//Zn/2Z/uRP/kTr1q0r9o8FACgyQrsC3HnnndqzZ492796tJ554Qk8//bT279+f932f//zndeutt6q/v18XXXSRbrnlFn30ox/Vpz/9aT377LOSpG3btpX68AEAZUJNO2Sjo6P62te+pkceeURXX321JOnhhx/WihUr8r73tttu0wc+8AFJ0qc+9Sn19vbqs5/9rN773vdKkj7xiU/otttuK93BAwDKiivtkB04cEDpdFq9vb25x9rb23XhhRfmfe+ll16a+3NnZ6ckGbe5Ozs7NT4+rpGRkSIeMQAgLIR2yBaynHk8Hs/9ORKJzPhYNpud92cAACoHoR2yNWvWKB6P65lnnsk9NjQ0pJdeeinEowIAVCJq2iFramrS7bffrjvvvFNLlixRZ2en7rrrLkWj5fn31KFDh3TixAkdOnRImUxG/f39ks7+Y6KpqaksxwAAKAyhXQG+9KUvaXR0VDfccIOam5u1fft2nTx5siyf/bnPfU4PP/xwbvutb32rJGnPnj3atGlTWY4BAFCYiLeQoioAACgbatoAADiC0AYAwBGENgAAjmAgGgA4yPM8jU1kJEnJeCzXlwHVjSttAHDQ2ERGl3zucV3yucdz4Y3qR2gDAOAIQhsAAEcQ2gAAOILQBgDAEYQ2AACOILQBAHAEoQ0AgCMIbQAAHEFHNABwyOHhMQ2l0hqnoUpNIrQBwBGHh8d01c69vg5oR4bHtaajKaSjQjlxexwAHDGUSmtsIqP7bn6L/nlrb+7x4dPpEI8K5cSVNgA4Zk1Hk960rDHsw0AIuNIGAMARhDYAAI4gtAEAcAShDQCAIwhtAAAcQWgDAOAIQhsAAEcQ2gAAOILQBgDAEYQ2AACOoI0pAFSwqVW9JGlgcDTko0HYCG0AqFBBq3ol4zG1NSZCPCqEidAGgAo1fVWvqaU32xoT6mlN6nR6MuSjQxgIbQCocGs6mrS2p2Xe759+i136TfDDPYQ2AFSxmW6xP/DBDVry69vshLg7CG0AqGL2LfbjqbS2fmO/tnz9J7nXJOMxPbV9I8HtAEIbAGrA9FvsT23faIxI79vVr6FUmtB2AKENADWmpzVJQDuK0AYAzBmD28JBaAMA5mSmwW3UxUuP0AYAxx14Y1SL4rHA50rRRc0e3EZdvHwIbQBw3Ke++dysz5eqi9pC549j7ghtAHDcP2/tnfFKW6LeXE0IbQBw3CXdi9WQ4K/zWsBvGQBCYo/AlrgqxuwIbQAIQdAIbIlR2JgdoQ0AIQhawYtR2MiH0AaAELkyAnv6rfxSTCNDYQhtAMCsZmqmUoppZJgdoQ0AmFXQrXwGzIWD0AaACjN1+7nSbkMv9FY+/coXjtAGgDLJVxdua0woGY+pb1d/7rFquQ1Nv/LiILQBoAwKqQv3tCaNta4ld69G7avqgcFR+pUXAaENAGVQaF24Gta6nm0O+ttWtzv/84WJ0AaAMnJlitdCBP0DRXL3rkElIbQBAIagW9vzUQv/QCk3QhsAClArfcJnu7VdDQPiXEdoA0Ae8+0T7mIXMW5tVzZCGwDymE+fcNe7iHFruzIR2gBQoLkEGV3EUAqENgCUUC1dsVZqJ7dqQmgDQJG4WMMuhmru5FZpCG0AKALXa9gLUU2d3CodoQ0ARVDrNexq6OTmAkIbAALM91Z3LdWwUX6ENgBYavlWNyoboQ0Allq/1Y3KRWgDwAxq6VY307XcQGgDwAK4HnZM13ILoQ0A81AtYcd0LbcQ2gAwD9UUdkzXcgehDQDzRNih3KJhHwAAACgMoQ0AgCMIbQAAHEFoAwDgCEIbAABHMHocQE2aviCI5O50LdQWQhtAzZlpQZCntm8kuFHRCG0ANcdeEGRgcFR9u/o1lEoT2hWEuyF+hDaAmlVLC4JUquk926eH8kx3Qx744AYtmdYqttaCnNAGAJTdTL3bp0oU9t2Q46m0tn5jv7Z8/SfGfmqtrEFoAwDKzu7dPlOJYvrdELvXey2WNQhtAEAo5tq7nV7vhDYA5Li+NjaqH6ENoOZVy9rYqH6ENoCaV01rY6O6EdoAIOqllYISxewIbQBA6ChRFIbQBgCEjhJFYQhtAEBFoESRH6ENoOrZPaypl8JVhDaAqhbUw1qiXgo3EdoAqprdw3oK9VK4iNAGUBNY0QvVIBr2AQAAgMIQ2gAAOILb4wAAp02fDVDtYxUIbQCAk2bqovbU9o1VG9yENgDASXYXtYHBUfXt6tdQKk1oAwBQaWqtixoD0QAAcAShDQCAIwhtAAAcQWgDAOAIQhsAAEcQ2gAAOIIpXwCqzvT1s1k7u7bZa6m73jGN0AZQVYLWz2bt7No003fB5Y5phDaAqhK0frbrV1eYH/u7UA0d0whtAM4Luh3O+tmYUk3fBUIbgNO4HY5aQmgDcBq3w1FLCG0AzuF2OGoVoQ3AKdwORy0jtAE4hdvhyGfq7ks1ztEntAE4idvhsLU1JpSMx9S3qz/3WLXdhSG0AQBVoac1qae2b6yqDmg2QhsAUDV6WpNVFdI2FgwBAMARhDYAAI7g9jgAoKa5tBIYoQ0AqFmurQRGaAMAasr0+dsDg6NOrQRGaAMAakLQPG7p7JX121a3GyE9Pdgr6XY5oQ0AqAlB87glM5RnatBSKbfLCW0AQM3IN4/bDvZKu11OaAMAME0lN2hhnjYAAI4gtAEAcAShDQCAIwhtAAAcQWgDAOAIRo8DqHjTe0NPb3oB1BpCG0Co7MUabMdTaW39xn5fb+i2xkQ5Dg+oKIQ2gNAELdYQJBmP6eEPXaElvw7qSmorCZQToQ0gNEOptLFYw0wIaeAsQhtA6NZ0NGltT0vYhwFUPEaPAwDgCEIbAABHENoAADiC0AYAwBGENgAAjiC0AQBwBKENAIAjCG0AABxBaAMA4Ag6ogEoKXtBEFqSAvNHaAMomaAFQZLxmJ7avpHgBuaB0AZQMvaCIAODo+rb1a+hVJrQBuaB0AZQciwIAhQHA9EAAHAEV9oAACxQuQZcEtoAACxAOQdcEtoAACxAOQdcEtoAym5gcNT4f8Al9q3wqe9xOQZcEtoAyqatMaFkPKa+Xf25x5LxmNoaE+EdFDAHQbfCpfJ9jwltAGXT05rUU9s30iENzrJvhU8p1/eY0AZQVj2tSUIazgur9wChDaBoZqr1ASgOQhtAUYRd6wNqAaENoCjCrvUBpVQpMx4IbQBFRZ9xVJNKm/FAaAMAMINKm/FAaAMAMItKmvHAKl8AADiC0AYAwBGENgAAjiC0AQBwBAPRAAAogelzuos1DZLQBgCgiILmdv/i3t8pyr4JbQAFs3uL0+0M8LPndhezixqhDaAgQb3Fk/GYHvjgBi1pTITe3hGoJKWa201oAyiI3Vv8eCqtrd/Yry1f/0nuNSwOApQWoQ1gTqb3Fq+k9o5ALSC0AcxbJbV3BGoB87QBAHAEoQ0AgCMIbQAAHEFoAwDgCAaiAZjR9GYqzMMGwkdoA5hRUDMV5mED4SG0AcxoejMViXnYQNgIbQCzmt5MBUC4GIgGAIAjCG0AABxBaAMA4AhCGwAARxDaAAA4gtAGAMARhDYAAI4gtAEAcAShDQCAIwhtAAAcQWgDAOAIQhsAAEcQ2gAAOILQBgDAEYQ2AACOILQBAHAEoQ0AgCMIbQAAHEFoAwDgCEIbAABHENoAADiC0AYAwBGENgAAjiC0AQBwBKENAIAjCG0AABxBaAMA4AhCGwAARxDaAAA4IuJ5nhf2QQAAgPy40gYAwBGENgAAjiC0AQBwBKENAIAjCG0AABxRF/YBAKhMnufp1KlTYR8GUDWam5sViUQWtA9CG0CgY8eOqaOjI+zDAKrG4OCgli1btqB9ENoAAiUSCUnSq6++qsWLF4d8NJVjZGREK1eu5LwE4NwEmzovU/9NLQShDSDQ1G28xYsX8xdwAM7LzDg3wRZ6a1xiIBoAAM4gtAEAcAShDSBQfX29duzYofr6+rAPpaJwXmbGuQlWzPPCgiEAADiCK20AABxBaAMA4AhCGwAARxDaAAA4gtAG4PM3f/M3Wr16tRYtWqQNGzboBz/4QdiHVHbf//73df3116u7u1uRSETf/va3jec9z9Pdd9+t7u5uJZNJbdq0SS+88EI4B1tGX/jCF/S2t71Nzc3N6ujo0Pve9z79/Oc/N15Ti+fmb//2b3XppZfmGsv09vbqO9/5Tu75Yp0TQhuAYdeuXerr69Ndd92ln/70p3rnO9+pa6+9VocOHQr70MoqlUpp/fr1uv/++wOf/+IXv6ivfOUruv/++7Vv3z51dXXp6quvrvpFVvbu3auPfexjeuaZZ/Tkk09qcnJSmzdvViqVyr2mFs/NihUrdO+99+rZZ5/Vs88+q/e85z268cYbc8FctHPiAcA0V1xxhbd161bjsYsuusj70z/905COKHySvN27d+e2s9ms19XV5d177725x8bHx72WlhbvgQceCOEIwzM4OOhJ8vbu3et5Hudmura2Nu/v//7vi3pOuNIGkJNOp7V//35t3rzZeHzz5s360Y9+FNJRVZ6DBw/q6NGjxnmqr6/Xxo0ba+48nTx5UpLU3t4uiXMjSZlMRo8++qhSqZR6e3uLek4IbQA5x44dUyaTUWdnp/F4Z2enjh49GtJRVZ6pc1Hr58nzPH3yk5/Ub/3Wb2nt2rWSavvcPPfcc2pqalJ9fb22bt2q3bt365JLLinqOWGVLwA+9mpEnucVZYWialPr52nbtm367//+b/3whz/0PVeL5+bCCy9Uf3+/hoeH9c1vflNbtmzR3r17c88X45xwpQ0gZ+nSpYrFYr5//Q8ODvquEmpZV1eXJNX0efr4xz+uf/3Xf9WePXu0YsWK3OO1fG4SiYTWrFmjyy+/XF/4whe0fv16/eVf/mVRzwmhDSAnkUhow4YNevLJJ43Hn3zySV155ZUhHVXlWb16tbq6uozzlE6ntXfv3qo/T57nadu2bfrWt76l733ve1q9erXxfC2fG5vneTpz5kxRzwm3xwEYPvnJT+qDH/ygLr/8cvX29uqrX/2qDh06pK1bt4Z9aGU1OjqqgYGB3PbBgwfV39+v9vZ2rVq1Sn19fbrnnnt0/vnn6/zzz9c999yjhoYG3XLLLSEedel97GMf0z/+4z/qX/7lX9Tc3Jy7emxpaVEymVQkEqnJc/OZz3xG1157rVauXKlTp07p0Ucf1dNPP63vfve7xT0nRRvbDqBq/PVf/7V3zjnneIlEwrvsssty03lqyZ49ezxJvv9t2bLF87yzU5t27NjhdXV1efX19d673vUu77nnngv3oMsg6JxI8h566KHca2rx3HzoQx/K/TezbNky77d/+7e9J554Ivd8sc4JS3MCAOAIatoAADiC0AYAwBGENgAAjiC0AQBwBKENAIAjCG0AABxBaAMA4AhCGwAqxKZNm9TX15fbPvfcc3XfffeFdjyoPIQ2AFSoffv26SMf+UhR9/mLX/xCt99+u1avXq1kMqnzzjtPO3bsUDqdLurnoDToPQ4AFWrZsmVF3+fPfvYzZbNZPfjgg1qzZo2ef/55ffjDH1YqldKXv/zlon8eiosrbQAIQSqV0q233qqmpiYtX75cO3fu9L3Gvj0eiUT04IMP6rrrrlNDQ4Muvvhi/fjHP9bAwIA2bdqkxsZG9fb26sCBAzN+7jXXXKOHHnpImzdv1pve9CbdcMMNuuOOO/Stb32rFD8miozQBoAQ3HnnndqzZ492796tJ554Qk8//bT279+f932f//zndeutt6q/v18XXXSRbrnlFn30ox/Vpz/9aT377LOSpG3bts3pWE6ePKn29vZ5/RwoL26PA0CZjY6O6mtf+5oeeeQRXX311ZKkhx9+WCtWrMj73ttuu00f+MAHJEmf+tSn1Nvbq89+9rN673vfK0n6xCc+odtuu63gYzlw4ID+6q/+KvBKH5WHK20AKLMDBw4onU6rt7c391h7e7suvPDCvO+99NJLc3/u7OyUJK1bt854bHx8XCMjI3n3deTIEV1zzTW66aab9Id/+Idz+REQEkIbAMpsISsix+Px3J8jkciMj2Wz2Vn3c+TIEb373e9Wb2+vvvrVr877eFBehDYAlNmaNWsUj8f1zDPP5B4bGhrSSy+9VJbPP3z4sDZt2qTLLrtMDz30kKJRosAV1LQBoMyampp0++23684779SSJUvU2dmpu+66qyzheeTIEW3atEmrVq3Sl7/8Zb3xxhu557q6ukr++VgYQhsAQvClL31Jo6OjuuGGG9Tc3Kzt27fr5MmTJf/cJ554QgMDAxoYGPANfFvIbXuUR8TjtwQAgBMoZAAA4AhCGwAARxDaAAA4gtAGAMARhDYAAI4gtAEAcAShDQCAIwhtAAAcQWgDAOAIQhsAAEcQ2gAAOILQBgDAEf8fgWXDkaqmFNgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for noise, noise_name in zip(noises, noise_names):\n",
+    "    \n",
+    "    obv = obv_real + noise\n",
+    "\n",
+    "    # train posterior\n",
+    "    inference = SNPE(prior)\n",
+    "    density_estimator = inference.append_simulations(theta, x).train()\n",
+    "    posterior = inference.build_posterior(density_estimator)\n",
+    "\n",
+    "    samples = posterior.sample((10000,), x=obv)\n",
+    "    log_probability = posterior.log_prob(samples, x=obv)\n",
+    "    _ = analysis.pairplot(samples, limits=[[20, 160], [0, 30]], figsize=(6, 6), points=theta_real)\n",
+    "\n",
+    "    plt.savefig(f'figures2/real_obv_with{noise_name}.png')\n",
+    "    # plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "846fe005-ba5a-431d-8515-947805665d8f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "2060",
+   "language": "python",
+   "name": "2060"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}