testing_cls_chi2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am using pyccl version 3.0.2\n"
     ]
    }
   ],
   "source": [
    "import pyccl as ccl\n",
    "print(f\"I am using pyccl version {ccl.__version__}\")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scripts.presets import Presets\n",
    "from scripts.data_vectors import DataVectors\n",
    "from scripts.data_vector_metrics import DataVectorMetrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This analysis will be done for a cosmology with the following parameters\n",
    "# if you want to change the cosmology, you can do it here or just run without it.\n",
    "# For now, we will keep it as is.\n",
    "#cosmo = ccl.Cosmology(Omega_c=0.27, Omega_b=0.045, h=0.67, sigma8=0.83, n_s=0.96)\n",
    "\n",
    "# The class has default values for the maximal redshift and the resolution of redshifts.\n",
    "# You can change them if you want to or run the default values.\n",
    "# Since we are investigating the impact of the resolution of redshifts,\n",
    "# we will keep the maximal redshift as is (for now) and only change the redshift resolution.\n",
    "redshift_max = 3.5\n",
    "redshift_resolution_1 = 300  # this is the first resolution of redshifts\n",
    "redshift_resolution_2 = 5000  # this is the second resolution of redshifts\n",
    "# Please note that the redshift resolution is the number of redshifts between 0 and redshift_max.\n",
    "# Additionally, a lot of parametric sweeping is already coded into helper functions \n",
    "# so we will not need to worry about that for now.\n",
    "\n",
    "# We will also investigate ell binning but for this particular exercise,\n",
    "# we will keep the ell binning at default values.\n",
    "#ell_min = 20\n",
    "#ell_max = 2000\n",
    "#n_ell = 50\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved to data_output/redshift_distributions/lens_bins_y10.npy\n",
      "Data saved to data_output/redshift_distributions/source_bins_y10.npy\n",
      "3.0 500\n",
      "Data saved to data_output/redshift_distributions/lens_bins_y10.npy\n",
      "Data saved to data_output/redshift_distributions/source_bins_y10.npy\n",
      "6 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  20.           25.48549971   32.47553478   41.38276162   52.73301797\n",
      "   67.19636573   85.62664797  109.11189562  139.03855924  177.17335808\n",
      "  225.76757834  287.68997766  366.59614217  467.14429382  595.27028833\n",
      "  758.53803815  966.58604771 1231.69642213 1569.5199407  2000.        ]\n",
      "3.25 10000\n",
      "Data saved to data_output/redshift_distributions/lens_bins_y10.npy\n",
      "Data saved to data_output/redshift_distributions/source_bins_y10.npy\n",
      "6 2\n"
     ]
    },
    {
     "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[6], line 48\u001b[0m\n\u001b[1;32m     41\u001b[0m tracer2 \u001b[38;5;241m=\u001b[39m ccl\u001b[38;5;241m.\u001b[39mWeakLensingTracer(\n\u001b[1;32m     42\u001b[0m     data_1\u001b[38;5;241m.\u001b[39mcosmology,\n\u001b[1;32m     43\u001b[0m     dndz\u001b[38;5;241m=\u001b[39m(data_1\u001b[38;5;241m.\u001b[39mredshift_range, data_1\u001b[38;5;241m.\u001b[39msource_bins[idx_2]),\n\u001b[1;32m     44\u001b[0m     ia_bias\u001b[38;5;241m=\u001b[39mia_bias\n\u001b[1;32m     45\u001b[0m )\n\u001b[1;32m     46\u001b[0m \u001b[38;5;66;03m#plt.plot(data_1.redshift_range,data_1.get_nc_kernel()[idx_1,0,:]/np.max(data_1.get_nc_kernel()[idx_1,0,:]))\u001b[39;00m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;66;03m#plt.plot(data_1.redshift_range,data_1.get_wl_kernel()[idx_2,:]/np.max(data_1.get_wl_kernel()[idx_2,:]))\u001b[39;00m\n\u001b[0;32m---> 48\u001b[0m cl_values \u001b[38;5;241m=\u001b[39m \u001b[43mccl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mangular_cl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcosmology\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracer1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracer2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mells\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m plt\u001b[38;5;241m.\u001b[39msemilogx(data_1\u001b[38;5;241m.\u001b[39mells,cl_values)\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mprint\u001b[39m(data_1\u001b[38;5;241m.\u001b[39mells)\n",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/cells.py:96\u001b[0m, in \u001b[0;36mangular_cl\u001b[0;34m(cosmo, tracer1, tracer2, ell, p_of_k_a, l_limber, limber_max_error, limber_integration_method, non_limber_integration_method, p_of_k_a_lin, return_meta)\u001b[0m\n\u001b[1;32m     92\u001b[0m cosmo\u001b[38;5;241m.\u001b[39mcompute_distances()\n\u001b[1;32m     94\u001b[0m \u001b[38;5;66;03m# Access ccl_cosmology object\u001b[39;00m\n\u001b[0;32m---> 96\u001b[0m psp \u001b[38;5;241m=\u001b[39m \u001b[43mcosmo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_pk2d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp_of_k_a\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_linear\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     98\u001b[0m \u001b[38;5;66;03m# Create tracer colections\u001b[39;00m\n\u001b[1;32m     99\u001b[0m status \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/pk2d.py:538\u001b[0m, in \u001b[0;36mparse_pk2d\u001b[0;34m(cosmo, p_of_k_a, is_linear)\u001b[0m\n\u001b[1;32m    536\u001b[0m     pk \u001b[38;5;241m=\u001b[39m cosmo\u001b[38;5;241m.\u001b[39mget_linear_power(name)\n\u001b[1;32m    537\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 538\u001b[0m     \u001b[43mcosmo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_nonlin_power\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    539\u001b[0m     pk \u001b[38;5;241m=\u001b[39m cosmo\u001b[38;5;241m.\u001b[39mget_nonlin_power(name)\n\u001b[1;32m    540\u001b[0m psp \u001b[38;5;241m=\u001b[39m pk\u001b[38;5;241m.\u001b[39mpsp\n",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/_core/schema.py:128\u001b[0m, in \u001b[0;36mUnlockInstance.unlock_instance.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    126\u001b[0m bound \u001b[38;5;241m=\u001b[39m func\u001b[38;5;241m.\u001b[39m__signature__\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m UnlockInstance(bound\u001b[38;5;241m.\u001b[39marguments[name], mutate\u001b[38;5;241m=\u001b[39mmutate):\n\u001b[0;32m--> 128\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",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/cosmology.py:640\u001b[0m, in \u001b[0;36mCosmology.compute_nonlin_power\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    638\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_nonlin_power:\n\u001b[1;32m    639\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 640\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pk_nl[DEFAULT_POWER_SPECTRUM] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_nonlin_power\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/cosmology.py:624\u001b[0m, in \u001b[0;36mCosmology._compute_nonlin_power\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mps \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalofit\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    623\u001b[0m     pkl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pk_lin[DEFAULT_POWER_SPECTRUM]\n\u001b[0;32m--> 624\u001b[0m     pk \u001b[38;5;241m=\u001b[39m \u001b[43mpkl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_halofit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    625\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mps \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    626\u001b[0m     pk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pk_lin[DEFAULT_POWER_SPECTRUM]\n",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/pk2d.py:237\u001b[0m, in \u001b[0;36mPk2D.apply_halofit\u001b[0;34m(self, cosmo)\u001b[0m\n\u001b[1;32m    235\u001b[0m pk2d \u001b[38;5;241m=\u001b[39m Pk2D\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(Pk2D)\n\u001b[1;32m    236\u001b[0m status \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 237\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_halofit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcosmo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcosmo\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[43mpsp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(ret) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    239\u001b[0m     status \u001b[38;5;241m=\u001b[39m ret\n",
      "File \u001b[0;32m/opt/anaconda3/envs/levinpower_env/lib/python3.12/site-packages/pyccl/ccllib.py:828\u001b[0m, in \u001b[0;36mapply_halofit\u001b[0;34m(cosmo, plin, status)\u001b[0m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_halofit\u001b[39m(cosmo, plin, status):\n\u001b[1;32m    825\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"apply_halofit(cosmology cosmo, f2d_t plin, int * status) -> f2d_t\"\"\"\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ccllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_halofit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcosmo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is the fiducial\n",
    "forecast_year = \"10\"\n",
    "presets = Presets(forecast_year=forecast_year, should_save_data=False)\n",
    "data = DataVectors(presets)\n",
    "cl_gc = np.array(data.galaxy_clustering_cls(include_all_correlations=True))\n",
    "cl_ggl = np.array(data.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "cl_cs = np.array(data.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "\n",
    "\n",
    "N = 2\n",
    "zet_max = np.linspace(3.,5,N)\n",
    "zet_res = np.geomspace(500,10000,N).astype(int)\n",
    "chi2 = np.zeros((N,N))\n",
    "\n",
    "\n",
    "for i_max, val_max in enumerate(zet_max):\n",
    "    for i_res, val_res in enumerate(zet_res):\n",
    "        print(val_max, val_res)\n",
    "        val_max = 3.25\n",
    "        val_res= 1057\n",
    "\n",
    "        \n",
    "\n",
    "        presets_1 = Presets(redshift_max=val_max,\n",
    "                        redshift_resolution=val_res,\n",
    "                        forecast_year=forecast_year,should_save_data=False)\n",
    "\n",
    "        data_1 = DataVectors(presets_1)\n",
    "        correlation_pairs = (data_1.get_correlation_pairs_all()[\"galaxy_galaxy_lensing\"])\n",
    "        for idx_1, idx_2 in correlation_pairs[32:33]:\n",
    "            print(idx_1,idx_2)\n",
    "            ia_bias = data_1.get_ia_bias() \n",
    "            gbias = data_1.get_gbias() \n",
    "            tracer1 = ccl.NumberCountsTracer(\n",
    "                    data_1.cosmology,\n",
    "                    has_rsd=False,\n",
    "                    dndz=(data_1.redshift_range, data_1.lens_bins[idx_1]),\n",
    "                    bias=gbias\n",
    "                )\n",
    "            \n",
    "            tracer2 = ccl.WeakLensingTracer(\n",
    "                data_1.cosmology,\n",
    "                dndz=(data_1.redshift_range, data_1.source_bins[idx_2]),\n",
    "                ia_bias=ia_bias\n",
    "            )\n",
    "            #plt.plot(data_1.redshift_range,data_1.get_nc_kernel()[idx_1,0,:]/np.max(data_1.get_nc_kernel()[idx_1,0,:]))\n",
    "            #plt.plot(data_1.redshift_range,data_1.get_wl_kernel()[idx_2,:]/np.max(data_1.get_wl_kernel()[idx_2,:]))\n",
    "            cl_values = ccl.angular_cl(data_1.cosmology, tracer1, tracer2, data_1.ells)\n",
    "            plt.semilogx(data_1.ells,cl_values)\n",
    "            np.save(\"n_source\", [data_1.redshift_range,data_1.source_bins[idx_2]])\n",
    "            np.save(\"n_lens\", [data_1.redshift_range,data_1.lens_bins_bins[idx_1]])\n",
    "            \n",
    "        #cl_gc_1 = np.array(data_1.galaxy_clustering_cls(include_all_correlations=True))\n",
    "        cl_ggl_1 = np.array(data_1.galaxy_galaxy_lensing_cls(include_all_correlations=False))\n",
    "        #cl_cs_1 = np.array(data_1.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "#        metric = DataVectorMetrics(presets)\n",
    "        #chi2[i_max,i_res] = metric.get_delta_chi2(cl_gc, cl_ggl, cl_cs,cl_gc_1, cl_ggl_1, cl_cs_1)\n",
    "fname = \"./data_output/chi2/chi2_sweep_zmax_zres_y\" + forecast_year \n",
    "np.savez(fname, zet_res=zet_res, zet_max=zet_max, chi2=chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm, ticker\n",
    "\n",
    "fontsi = 20\n",
    "fontsi2 = 20\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "CS = plt.contourf(zet_max,zet_res,chi2.T,cmap = \"cividis\")#,  locator=ticker.LogLocator())\n",
    "cb = plt.colorbar(CS)\n",
    "plt.xlabel(r\"$z_\\mathrm{max}$\", fontsize = fontsi)\n",
    "plt.ylabel(r\"$N_z$\", fontsize = fontsi)\n",
    "cb.set_label(r'$\\Delta\\chi^2$', fontsize=fontsi)\n",
    "plt.tick_params(labelsize=fontsi2)\n",
    "\n",
    "#plt.xscale(\"log\")\n",
    "#plt.yscale(\"log\")\n",
    "\n",
    "for t in cb.ax.get_yticklabels():\n",
    "    t.set_fontsize(fontsi2)\n",
    "plt.tight_layout()\n",
    "fname = \"./plots_output/chi2_zet_params_y\" + forecast_year + \".pdf\"\n",
    "plt.savefig(fname)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n"
     ]
    }
   ],
   "source": [
    "forecast_year = \"10\"\n",
    "presets = Presets(forecast_year=forecast_year, should_save_data=False)\n",
    "data = DataVectors(presets)\n",
    "cl_gc = np.array(data.galaxy_clustering_cls(include_all_correlations=True))\n",
    "cl_ggl = np.array(data.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "cl_cs = np.array(data.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "\n",
    "N = 25\n",
    "zet_max = np.linspace(3.,5,N)\n",
    "zet_res = np.geomspace(500,10000,N).astype(int)\n",
    "chi2 = np.zeros((N,N))\n",
    "\n",
    "for i_max, val_max in enumerate(zet_max):\n",
    "    for i_res, val_res in enumerate(zet_res):\n",
    "        presets_1 = Presets(redshift_max=val_max,\n",
    "                        redshift_resolution=val_res,\n",
    "                        forecast_year=forecast_year,should_save_data=False)\n",
    "\n",
    "        data_1 = DataVectors(presets_1)\n",
    "        cl_gc_1 = np.array(data_1.galaxy_clustering_cls(include_all_correlations=True))\n",
    "        cl_ggl_1 = np.array(data_1.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "        cl_cs_1 = np.array(data_1.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "        metric = DataVectorMetrics(presets)\n",
    "        chi2[i_max,i_res] = metric.get_delta_chi2(cl_gc, cl_ggl, cl_cs,cl_gc_1, cl_ggl_1, cl_cs_1)\n",
    "\n",
    "file = \"./data_output/chi2/chi2_sweep_zmax_zres_y\" + forecast_year \n",
    "np.savez(file, zet_res=zet_res, zet_max=zet_max, chi2=chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsi = 20\n",
    "fontsi2 = 20\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "CS = plt.contourf(zet_max,zet_res,chi2.T,cmap = \"cividis\")#,  locator=ticker.LogLocator())\n",
    "cb = plt.colorbar(CS)\n",
    "plt.xlabel(r\"$z_\\mathrm{max}$\", fontsize = fontsi)\n",
    "plt.ylabel(r\"$N_z$\", fontsize = fontsi)\n",
    "cb.set_label(r'$\\Delta\\chi^2$', fontsize=fontsi)\n",
    "plt.tick_params(labelsize=fontsi2)\n",
    "\n",
    "plt.xscale(\"linear\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "for t in cb.ax.get_yticklabels():\n",
    "    t.set_fontsize(fontsi2)\n",
    "plt.tight_layout()\n",
    "fname = \"./plots_output/chi2_zet_params_y\" + forecast_year + \".pdf\"\n",
    "plt.savefig(\"./plots_output/chi2_zet_params.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n",
      "WARNING 18: ccl_cls.c: integ_cls_limber_qag_quad(): Default GSL integration failure, attempting backup method.: GSL ERROR: roundoff error\n"
     ]
    }
   ],
   "source": [
    "forecast_year = \"1\"\n",
    "presets = Presets(forecast_year=forecast_year, should_save_data=False)\n",
    "data = DataVectors(presets)\n",
    "cl_gc = np.array(data.galaxy_clustering_cls(include_all_correlations=True))\n",
    "cl_ggl = np.array(data.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "cl_cs = np.array(data.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "metric = DataVectorMetrics(presets)\n",
    "chi2_fid_y1 = metric.get_delta_chi2(cl_gc, \n",
    "                                 cl_ggl,\n",
    "                                 cl_cs,\n",
    "                                 np.zeros_like(cl_gc),\n",
    "                                 np.zeros_like(cl_ggl),\n",
    "                                 np.zeros_like(cl_cs))\n",
    "\n",
    "\n",
    "N = 50\n",
    "number_ell = np.geomspace(20.,presets.ell_max,N).astype(int)\n",
    "chi2_y1 = np.zeros(N)\n",
    "\n",
    "for i_ell, val_ell in enumerate(number_ell):\n",
    "    presets_1 = Presets(ell_num=val_ell,should_save_data=False)\n",
    "\n",
    "    data_1 = DataVectors(presets_1)\n",
    "    cl_gc_1 = np.array(data_1.galaxy_clustering_cls(include_all_correlations=True))\n",
    "    cl_ggl_1 = np.array(data_1.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "    cl_cs_1 = np.array(data_1.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "    metric = DataVectorMetrics(presets_1)\n",
    "    chi2_y1[i_ell] = np.abs((metric.get_delta_chi2(cl_gc_1, \n",
    "                                 cl_ggl_1,\n",
    "                                 cl_cs_1,\n",
    "                                 np.zeros_like(cl_gc_1),\n",
    "                                 np.zeros_like(cl_ggl_1),\n",
    "                                 np.zeros_like(cl_cs_1)) - chi2_fid_y1)/chi2_fid_y1)\n",
    "\n",
    "file = \"./data_output/chi2/chi2_number_ell_y\" + forecast_year \n",
    "np.savez(file, zet_res=zet_res, zet_max=zet_max, chi2=chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_year = \"10\"\n",
    "presets = Presets(forecast_year=forecast_year, should_save_data=False)\n",
    "data = DataVectors(presets)\n",
    "cl_gc = np.array(data.galaxy_clustering_cls(include_all_correlations=True))\n",
    "cl_ggl = np.array(data.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "cl_cs = np.array(data.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "metric = DataVectorMetrics(presets)\n",
    "chi2_fid_y10 = metric.get_delta_chi2(cl_gc, \n",
    "                                 cl_ggl,\n",
    "                                 cl_cs,\n",
    "                                 np.zeros_like(cl_gc),\n",
    "                                 np.zeros_like(cl_ggl),\n",
    "                                 np.zeros_like(cl_cs))\n",
    "\n",
    "\n",
    "N = 50\n",
    "number_ell = np.geomspace(20.,presets.ell_max,N).astype(int)\n",
    "chi2_y10 = np.zeros(N)\n",
    "\n",
    "for i_ell, val_ell in enumerate(number_ell):\n",
    "    presets_1 = Presets(forecast_year=forecast_year,ell_num=val_ell,should_save_data=False)\n",
    "\n",
    "    data_1 = DataVectors(presets_1)\n",
    "    cl_gc_1 = np.array(data_1.galaxy_clustering_cls(include_all_correlations=True))\n",
    "    cl_ggl_1 = np.array(data_1.galaxy_galaxy_lensing_cls(include_all_correlations=True))\n",
    "    cl_cs_1 = np.array(data_1.cosmic_shear_cls(include_all_correlations=True))\n",
    "\n",
    "    metric = DataVectorMetrics(presets_1)\n",
    "    chi2_y10[i_ell] = np.abs((metric.get_delta_chi2(cl_gc_1, \n",
    "                                 cl_ggl_1,\n",
    "                                 cl_cs_1,\n",
    "                                 np.zeros_like(cl_gc_1),\n",
    "                                 np.zeros_like(cl_ggl_1),\n",
    "                                 np.zeros_like(cl_cs_1)) - chi2_fid_y10)/chi2_fid_y10)\n",
    "\n",
    "file = \"./data_output/chi2/chi2_number_ell_y\" + forecast_year \n",
    "np.savez(file, zet_res=zet_res, zet_max=zet_max, chi2=chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:19: SyntaxWarning: invalid escape sequence '\\e'\n",
      "<>:19: SyntaxWarning: invalid escape sequence '\\e'\n",
      "/var/folders/4k/46233y0d3tx9bcbm7l78j0mr0000gn/T/ipykernel_79627/3522897430.py:19: SyntaxWarning: invalid escape sequence '\\e'\n",
      "  plt.xlabel(\"$\\ell$\", fontsize = fontsi)\n"
     ]
    },
    {
     "data": {
      "image/png": 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MNLl79+64rj148GDE9vDwcNzf6+jfdw4cOJD097p3796U/2wOHTo0ZjvZsREHBwfjunbv3r1j9sX7sxn9d1yW5bjLOzxq2oN4/x5Gs2/fvriu3b9//5h9AwMDUX9mo43+ezg0NITu7n68844Ic9u3i/8joikqErVwRiNQWwvs3TuIl18Ol3ffvuhzsfIzgp8Ro2X1Z4Qsh4PS4bB1oLwc2L0b2LMH2LMH8q5d6G9vD4exGMvwww+L9cP/QA7u3o3+OP6tAgAeeihicx+AeP6Uxn5CAAMA4nnqwVHbQ1GeGWfpAeRYsAMAt9sNg8GAurq6iFBls9nQ0NAQNbi5XC40NzfD5XKht7c34pgkSbBYLDAajbDZbGOuDQQC8Hq9Y65LphzxmDNnzoQ1dkdEefP5iCOOmDDFA9E/VOK5DgAKR419MH369LiuLS4uHrNvxowZcV07c+bMMftmxzkf0fRRVRnTpk2L+3stGDVhZXFxcVzXRivbrFmzUv6zKRo1uFhRUVHc10YrRzzXzpo1a8y+2bNnxxV2Rv+dKCgoiLu800Z134z372G0n83MmTPjujbav8k5c+bE1bN9+vTpGBwUnR1+9ztg/fpCSNL4z5w3T0x6f9JJwMqVwMi+Wq++Gt/Php8R/IwYTdXPiOJilM6cGVE7Fe3rrLfeEvPQDQyIkLZ7N2Zv2YLB3btFQNu/P9zkOOoXttF/IwoAxFva0R28p8d5bbS/NTPjvHYGIKZamTFDjORdUoI5+/ahpLBQ/IZWVCS6pCvrI5bpJ54oRvkuLgaKi1E4OIjSt94Kn19YiP2yDLjdcZQkx5piFcFgEHa7HRqNBpWVlfD7/TCZTFGHJgFEjVxtbS2WL18Op9MZcUyn00049ZdWq4U/StezRMsRi9IUG081KxFlv/ffB154QSxer/h/LZpPfQo45xxRI7dkCXDkkektJ00xsixqwYJBoLdXfO3ri73094fXBwbE9njVzNmgpESMsD1zplhGrkfbnjlTBLKJvo5eSkrC69Oni95LKZJIRsjJYJePGOyIcpssixkeWlvFsmlT9PMKC4H//E/g3HOBr30N+OxnU/r/AeUjWRbNjIHA+Etvbzi4jQxxwaB45yuTCgqAOXPEyNhHHDH268hl5L5ZsyLXlWXk9syZeTmydiIZIeeaYomIsoUsi3lWlTD3739HP+/II4GvflWEuaVLAQ5xSSGyLGrAdu0SS09PeH3k0tMjFiW4xfvOmFpmzABKS0W36zlzxHqsr8oye3Z4UbZnzuRvMynEYEdElIChITFdlxLm3n03+nkGgwhy554L1NRwZocpZe9e4KOPgI8/Fssnn4TXR+/75BPxon8qFRSI3yY0GqC8PLw+cikrCwe3aEuU9yApOzHYERFNYHhYzPLw5JPAb38r/j8eraBAjCW3bBlw4YXi3TnKI4cOiR/8hx9OvEw0OW+yZs4EKitFj5poy8hjI0PcnDn8zWIKYbAjIopCloF//AN44gkR6N57b+w506eLTg8XXQRccAFw9NHpLydNkiyLJs6dO8Xy/vuRX5X1jz6KPQlvooqKRE/II48Uy9y5YqmsDK+P3FdZKd4hI5oAgx0R0Qjd3SLM/eY3wFtvjT0+c6Z4X+6ii0QzK9+Xy2KyLDoL7NgRXt59N3L7/ffVe1+trAw45hixHHWUSPpHHRUOcMr6UUeJc/meGaUAgx0RTXm7dokhon7zG+CVV8YeLyoCvvIV4FvfAs4/X3TCoywwPCxq0rq7xy5KgNuzZ3LPmDZNBLV588Ry7LHh8DZyOfpokfqJMozBjoimpAMHgPXrgV/9CvjjH6OPAHHGGSLMWSyiJYwyoLcX8PvFIkmR4W379snVtpWViXnZjj8+HNyOOy5y/aij8nL4DMpfDHZENKW8/Tbw8MPAo4+KmrrRPvtZEeYuvhgYMbUmpcrwsHiPTQlvo5dRs/7EbdYsEdpiLSPmeiXKFwx2RJT39uwRTa1r1kRvaj3+eOCSS0SgO+00vvqUEsEg8M47YrA/ZVEmzI024e1EjjgCqKoS6XvBAuCEE8LrCxaIKlb+IGkKYrAjorwky4DPJ8LcE0+IMWBHKi4WHSCuukpM5cXRIFQwPCyaR998U1SNjgxy0caIiaWgQNSqnXgioNOFFyXMVVQwuBFFwWBHRHll717g8ceBhx4S84+P9tnPAlYrcOmlfG8uaSMD3Jtviu7DSpjbuzf++xQVibC2cGFkeNPpRHiLMuk9EcXGYEdEeWH7duDBB4GWlrGvZR1xBPDNbwJXXw184Qus6EnIRx+JAf3+8Q/gjTeSC3DHHAOcfDJw0kniq7IsWCAGAyQi1TDYEVHOkmXgpZeAX/wCeOYZUZE00uLFonbum9/ke/ITOnRINJkqIe4f/xBVnh99FN/106aJmrZTThHVoqecIsLbwoWi9ykRpQWDHRHlnP37xWwQv/jF2ObW4mIR5G64QczXSlHs2wf8859AZyfQ1SVC3Jtvxjd0yOgANzLEcRw3ooxjsCOinLFzp2hudTrHDlVyzDHAihWAzcapvSIMDooQ19Ulglxnpwhx0QbuG62yEviP/wgvn/sc8OlPM8ARZTEGOyLKev/6F7BypZgZYnQeWbwYuPFGMYhwcXFmypc1hofF+2+vvgq89poIc6+/Dhw8GPu6adNEk+miRZFBbt48vpBIlGMY7Igoa23aBNx9N/D005HzrxcViSB3ww3AF7+YufJlXDAI/P3vIsi9+qpY7+uLfc20aaL5tKZGtFUbDKImjhPME+UFBjsiyjovvywC3e9/H7m/vBy49lrR5HrccZkpW8YMD4vODUqIe/VVMczIyMQ7WkEB8JnPiBCnBLlFixjiiPIYgx0RZQVZBjZsEIHuL3+JPHb00cDNNwPXXDOFercOD4t34/78Z7H85S9AT0/sa44+Gjj9dLF88YuAXg/Mnp2e8hJRVmCwI6KMGh4GfvtbEei6uiKPnXAC0NAAfPvbU+B9/UOHRBdfJci99JJoah1PYaGofVOC3Omni3Hh+E4c0ZTGYEdEGTE8LN6d+9GPRIviSCefDDQ2ivlb83b82uFh8RKh1yuC3MsvAwMD45+v0QBnnAH853+KEFdTI0ZeJiIaIaFg193dDa/XCwAwGo1YsGBB6FhfXx+8Xi90Oh0WLVqkZhmJKI/IssgyjY1ja+iqq4Hbbwe+8Q1RIZV33n0XaGsTi9cbu2m1shI480zgrLPEctppefqHQkRqijvYrVixAi6XC/LhF3ULCgrQ3NyMm2++GQBQVlYGo9EIu92ONWvW4FA8YyQR0ZTy2msi0G3cGLn/S18C7rgD+MpX8qwlcWAAePFFEeQ2bBCdH8Zz9NEiwClh7pRTRA9WIqIExBXsWlpa0NbWhtWrV6OmpgYA0NHRgebmZvj9fjz44IMAwuGupaUldSUmopzz9tvA978vpv0aadEioKkJOOecPAl0siw6PDz/PPDHP4qeq+P9kltaCixZAphMQG2tmEc1L/4QiCiT4gp2Ho8HXV1dKBsx3191dTXq6+uxfPlyrFixAg899BAAUZNHRASIlscf/Qh49NHIeVx1OuAnPwGWL8+DSqn9+4E//Ql47jkR6HbsiH5eYaHoqWoyieXznxcD8hERqSiuT5WqqqqIUDfSunXr0NLSglWrVuGWW25RtXBElJt27RK9XB94ADhwILz/mGOAO+8ErroqxztFfPAB8LvfiSDX1gbs3Rv9vIULRYhbuhQ4+2xgnM9RIiK1xBXsNBpNzONWqxVPP/001qxZg/LycjXKRUQ5aHAQuO8+4Kc/jezgWVYG3HYb8J3v5GhHTlkWXXefflrUzHV2Rj+vuBj4r/8Czj8f+NrXgKqq9JaTiKa8uIKdyWQK1cg1NjZCp9Ph6quvjjhn2bJl2LRpE5qamlJSUCLKbr//vZizdcuW8L4ZM8S+hgagoiJzZUuKLIt5Vt1uwOMRE9ZGc/TRwLnnAuedJ2rnOCAwEWVQgSzHmo8mrL29HU6nEx6PBxaLBWvXro16niRJqKmpQSAQULWg+a6/vx9lZWXo6+tDaWlppotDFDe/H7jpJlGRpZg2TTS33nlnjk39JctikGCPRwS6kSl1pEWLRJA7/3wxnlzOvyhIRNkskYwQd7Cj1GKwo1yzZ4/o0bpqlWiCVZxxBvDLX4rskxNkGfD5wjVzfv/YcwoKxDdmsQAXXgjMn5/2YhLR1JVIRmCXLCJKiCyLDHTLLZEdQI89FrjnHjFbRE50jn/7beCxx4CnngK2bRt7fNo0Maac2QxcdJH4BomIspzqwe6aa67BbbfdFjErBRHlhzfeAG64QYzuoZg+XTTF3nEHMGdO5soWl48/FkHu178eO+0FIMLc2WeLmrlvfEO8P0dElENUD3arV6/GNddcg+XLl2PJkiUJXz+Za4koNfr7gR/8QAxfMjQU3v+Vr4hesCefnLGiTWzfPvEC4K9/DfzhD5HfACDGl1uyRNTMfeMbwJFHZqacREQqSNk7dvfccw90Oh0uuuiihK8dOeDxVMF37ChbvfACYLMB770X3qfVAj/7meg7kJXNrsPDwMsvi6bWdetEMh1Nrwcuuwz45jdZM0dEWS2RjJCyrly33norysrKsGrVqoSu6+/vR+d4Y0QRUdr09Ijcc+654VA3cyZw113Am28CF1yQhaHuk0/Ei34nnyzmW12zJjLUHX+8GFDvzTdFU+yNNzLUEVFeSWkf/draWixbtgyNjY0Tntvd3Y3GxkaUl5ejIucGvCLKL08/Leagf+yx8D6TSYzRe8cdYny6rCHLwIsvAhdfLIJbQwOwdWv4+OzZwOWXA+3tQHe36Mp7yimZKi0RUUqlbbiT2267DbfffntEFWJ3dzc8Hg/Wrl0Ln88HWZah0Wjg8/mmXOcLNsVSNvjwQ+D660WwU5SViWbXK67Ishq6XbvEJLQuF/DOO2OP19YC3/62GJ4kJ6e7ICISsnYcu1WrVkGr1aKtrQ1erxeSJAEAZFmGVquFzWZDfX39uPPS5jMGO8okWQYef1y0TPb2hvdfcAHw0EPAvHmZK1sEWQZeeglwOsWYcyMnogWAuXNFmLNaxTytRER5IOvGsWttbcXatWvh9XoRDAahZEmtVguz2Yy6ujpUV1enoyhENMqOHaJzxO9/H943d64YZLiuLktq6fbsEb1af/lLMf7caGefLb6Jb3wDKClJe/GIiLJFyoJda2srnE4nvF4vAITCnNFohF6vh8/nw4YNG1L1eCKagCwDLS1ioOGBgfD+b34T+MUvsmTUj507gfvvB1avjqxKBMTks1dcAdTXZ/l4K0RE6aN6sOvr60NNTQ0kSQq9M7d8+XJYLBbU1taGztu2bRvq6uqwZs0azMn6UU2J8ktPD3DllcD69eF9xx4r8tMFF2SuXCGbNokX+556Cjh4MPLYl78MXHONmA0iq3pxEBFlnurBzmq1wu/3Q6/Xo7GxEcuWLYt6XlVVFZxOJ6666io0NzdPuc4SRJny4ovApZcC778f3nfllcD//i+g0WSqVBBjz73wAnDvvZFTWwBieouLLxZTXOTMJLREROmnerDz+Xzo6uqK6505jUaDdevWcbYJojQ4eBD40Y/EaB9Kl6nKSuD//k8MNJwxe/eK3q333Te2d2tFhaidu+66LOrBQUSUvVQPdkajMeGOEKtXr8Ztt90GSZJw9dVXq10koilv2zbgkkuAv/0tvK+2VvRHyFheCgbFy3w//zkQCEQeW7hQ1M5ddhmHKiEiSoDqAxQbDIaEZ5sAgJUrV0KWZaxYsULtIhFNaU89JVovlVBXVASsXAls2JChUBcIAHfeCSxYIL6ODHVnny1e/PvXv4AVKxjqiIgSlJJx7LZt24aqqqqkrm1vb4fH4+FcsUSTtHs38J3vAL/6VXifVgs8+STw+c9noEA9PaJDxC9+EdkNt7BQdMX93vfE/K1ERBQhawcopvEx2JGafD6RlbZsCe+79FLggQeAtP/1+uQT0SHi/vtF2lQUFYmpvhobAZ0uzYUiIsodWTdAMRGlj9MpauqUUUJmzwYefBD47/9Oc0E+/hhYtUo8fM+e8P7p08XsELfdBiRZs09ERNGp/o5dPPr7+3HbbbehqIi5kkgtg4Ni8oVrrgmHusWLxZBwaQ11H38M3HyzeIfunnvCoa64WLw3t3WrSJ8MdUREqktrsurv74fdbofL5YIsyyjIirmKiHLfBx8AZjPw17+G9910k+gkUVycpkLs3SveoXM4It+hKykRc7fa7cDxx6epMEREU1Nagl13dzccDkco0Gk0Gmi1WmzatCkdjyfKa3//u5iEYedOsT1jhpgq7NJL01SAoSExDt0PfhAuhFIQmw1oaOAYdEREaZLSptjNmzejrq4OOp0OTqczNNtEIBCA0WhM5aOJpoRHHgHOPDOcp+bPB15+OU2hTpbFTBGLFgFXXRUuRGGhCHSSJAYdZqgjIkqblNTYbd68GXa7HV6vF7IsQ6vVwuFwjDu9GBEl5uBB0dT6wAPhfWeeCbjdwFFHpaEAXV2iJm7jxsj9F1wg2n8/85k0FIKIiEZTNdht3LgRdrsdPp8PsixPOF8sESXu448BiwX4y1/C+66/XowoMn16ih/e3Q18//vAE09E7v/850VHiTPPTHEBiIgoFlWCXWtrK5qamiICncPhQG1trRq3J6LDurqAb3wD2LFDbBcXA6tXi9FDUqqvD7jrLuCXvwQOHAjv12rF5LMWC8DOUEREGTepYLdmzRo4HA5IkgRZlmE0GuFwOBKeK5aIJvb446Jz6f79YnvePKC1FfjCF1L4UFkWtXM33wx89FF4f2Ul8MMfirFV0tbtloiIJpJUsFuzZg3sdjuCwSBkWYbZbEZjYyMDHVEKDA8Dt98uRhFRfOlLwNNPA8cck8IHv/UWcN11wIsvhvfNmAF897ti6BKNJoUPJyKiZCQc7FpaWmCz2QAAFosFK1euTHpeWCKK7eBB0eH0scfC++rrRYtoyirK9uwRza7/+7/AoUPh/d/4hhin7oQTUvRgIiKarISHO7Farejs7ITZbMbixYsZ6ohSZGAAOO+8cKibNk1Mt+p0pijUyTLwzDOiR6vDEQ51VVXA88+Ldl+GOiKirJZUU6xer8e6deuwadMmXHPNNTjxxBNRX1/PyeuJVPLRR8C554rOEoBoAX3ySeDCC1P0QEkSE8y+8EJ4X3GxmM/1ttuAmTNT9GAiIlLTpDpPVFdXY/Xq1di2bRsaGhpQXl6OxsZGBjyiSdi6FfjKVwC/X2xrNMBzzwFnnJGCh+3fDzQ3A3ffLSabVSxdKqoHFy5MwUOJiChVVBnupKqqKhTw7r77bhQUFDDgESWhsxP42teATz4R2/PnA3/4A3DKKSl42CuviHFStmwJ7zvuODFbxLJlHL6EiCgHqTpAcVVVFVauXIm+vr5QwLPZbFiwYIGajyHKS3/8o8hTe/aI7VNPBX7/e+D441V+0L59Yl7Xe+8V79UBYhqw734XuPNOYM4clR9IRETpkpIpxcrKykIBz+VyQZIk2O12BjyicTz2GHDlleH+CmeeCTz7bApGFPn734HLLwf+/e/wvi9+EXC5gNNOU/lhRESUbgn3ik1EWVkZbr31Vjz00ENwu91YsWIFNm/ePOn7BoNB2O122O12NDc3w2azwePxTL7Ah9lsNni93pjnuFwumEwmeDweBINBAIAkSfB4PLBYLPD5fKqVh/KXLIsOqJddFg51y5aJ2jtVQ93+/aITxJe+FA51JSXi/bqXX2aoIyLKEympsYvm1ltvBSDGwVu9ejUkSUrqPsFgEAaDAW63G3q9PrTfZrOho6MDjpGjuCZAkiR4vV44nU74fD5YLJYJy+H1escEQI1GM6ZsRNEMDwM33QT84hfhfdddB/z856JlVDWdnaKW7q23wvsWLwZ+9asUvbxHRESZkrZgp7BarbBarfB4PEmFO4vFArPZPCY4OZ1OlJeXw2QywWg0JnRPl8uFtrY2mEwmOBwOmEymuK5zOp3w+/2QJAkVFRUwGAyor69P6Nk0NQ0Pi+nBHnkkvO/uu0Wlmmp9Fg4cEAMNNzUBQ0Ni3/TpwI9+BDQ0AEVp/+dPREQpViDLytvT2U+SJOh0Ovj9fmi12jHHbTYbJElCW1tb0s/w+XwwGAxoa2uLGRCbm5tRX18PjUrtZf39/SgrK0NfXx97E+e5oSHg6qtFhRkgaufWrAGuuELFh2zeLGrp/vnP8L7qauDRR9nsSkSUYxLJCCl9x05tTqcTAKKGOgDQ6XTwer2hd96Iss3QkJgibGSoe+opFUPd8DCwapVoalVCXVER8OMfi44TDHVERHktp4Kdz+eLWUOmBL7Ozs40lYgofkNDYti4Rx8V20VFwNq1gNms0gN6eoCvfx249dZwT4zTTgNeew344Q9FMywREeW1nAp2yrts41FCX7IdM5Ll8/ngcrnYE5bGNTQkWkaVeV+LioB160QPWFX89a+iqfX558P77HbRcaK6WqWHEBFRtsupYBcIBGLW2CmhL11NsV6vF83NzQAQ6jRhMpkmHCoFAAYHB9Hf3x+xUH46dEgMZ/Kb34jtoiLA7Qa+8Q0Vbj48LIYsOfNMYMcOsW/uXDGy8cqVYr5XIiKaMnKqW1y8ga2npye1BUG42behoSG0T6/Xw+12o7y8HF1dXTGHPGlqasKPf/zjlJeTMuvQIeC//1u8RweI1lC3W7SYTtquXSIx/v734X1nngk88YSYGoyIiKacnKqxyyZmsxnmKC9HaTQamM3mCcfBa2xsRF9fX2jZodS2UN44dAj41rciQ93TT6sU6l5+GVi0KBzqCgqAO+4A2tsZ6oiIprCcCnYajSauWrvKysrUFyaGxYsXQ5KkmO/6lZSUoLS0NGKh/HHwIHDJJeI9OkC0iLa2AuefP8kbDw+LcenOPht4/32x78gjxVQVd93FsemIiKa4nAp2sTpOAOIdPACqjS2XLOX57EwxNQ0NiZo6t1tsFxcDzzwDnHfeJG/8ySfA174G3H57eMDhs88G/vEPIM5BtYmIKL+lNNi1traisbExYn7Y9vb2pOeL1Wq1ofAWjVKbN944d2qx2WzQ6XQpfQblJlkGrr02HOpKSoBnnxV5bFJeeUU0vf7xj2K7oAC4807A6wWOPXaSNycionyRkmDX2tqKiooKmM1mOByOiHHlamtr4ff7sWbNmoTvq9frYzbFKk2fiU4plqjOzs64Aibni5167rwTcLnEelGRqKn7ylcmedNf/Qr4r/8Cdu4U20cfDbS1ianBVJ1UloiIcp3qwW7Tpk1oaGiAw+FAb28vhoeHMXrWsmXLlsFgMGDjxo0J3buurg7A+E2cHR0dKQ91gAiOvb294x7v6OiARqNJec0hZZcHHhCvuSkefRT46lcnccOhITHY8Le/LV7aA4AlS8R0YbW1kykqERHlKdWDncvlQldXF6xWK8rKygAABVFmNa+urk54IGG9Xg+j0Yi1a9dGPe7xeGC328fsDwaDsNvtcY0vF4+6ujq4lGqZUSRJgsfjQUtLiyrPotywbh3wne+Et++7T3SeSFp/P3DhhWJ6MMX114um2GOOmcSNiYgon6ke7LRabSjQTSSZgYTdbjc8Hs+YWjubzYaGhoaoNXYulwvNzc0TDkEChJtzY5VNaRJWBiceea3BYEBDQ0PUoVAoP7W3A5deKt6vA4DGRuDGGydxw23bgC99KTyLRGEh8NBDwC9/yV6vREQUk+r/S5SXl8d9rt/vT/j+Go0GXV1dsNvt0Gg0qKyshN/vh8lkGjdMGY1GaDQaLF++POpxj8cDp9MJIDzPrNVqDe2zWCyhmSUUDQ0N8Hq9sNlsCAQCCAaD0Gg0aG9v57t1U0hXl6hYU1pKr7wS+OlPJ3HDl14CLrpIDD4MAOXlgMcjmmCJiIgmoHqw27p165h9o9+xA4DNmzdH3R8PjUYTCl3x0Ov1Md+JG2+w4YkYjca0vNNH2WnLFvEO3e7dYvuCCwCnU3RYTcojjwDXXBNOiSefDDz3HLBwoSrlJSKi/Kd6U2xdXR0WL16M7du3h/aNfseuvb0dtbW1Y5oyiXLFBx8AS5eKoeUA4IwzxAwTSbWUDg0BN98MXHVVONQtXQr87W8MdURElBDVa+yqq6thtVpRVVUFk8kErVYLSZLg9/shSRJ8Ph8kScKGDRs42wLlpL4+MYRJd7fYPvVUYP16YObMJG7W3w9cfDHwwgvhfTfcAPzv//J9OiIiSliBnGx76AR8Ph+sVis2bdoUsd9sNqOlpSXuDhZTRX9/P8rKytDX18fAm8X27xeh7s9/FtsnnAD89a/AvHlJ3Ozdd8XIxW++KbaLioD77wdsNtXKS0REuS+RjJCyKgG9Xo+uri4AYmw7jUaDqqqqVD2OKOWGhsQQJkqomztXjD6SVKh7+23R3Pree2K7vBx4+mkxEDEREVGSUjalWHd3N/r7+wGI5lkl1G3cuBHdShsWUQ656SYxkwQAHHGEaD09+eQkbvTaa8CXvxwOdQsXin0MdURENEkpCXb33HMPtFpt1Bq6JUuWoK2tLakpxYgy5ZFHxDByADB9OtDaCixenMSNvF4xdElPj9iurgZefhk48UTVykpERFOX6k2xa9asQX19PWRZjjrjBCDGiNu2bRtaW1tx0UUXqV0EIlW9+iqwYkV4+8EHRStqwjwe0Zar9Hw9+2zg2WcBvlNJREQqUT3Y+f1+lJWVoaGhIeZ5VVVVYzpWEGWb998X4wUfOCC2r7sOuPrqJG7kdIp0qPRVuvBC4MkngRkz1CoqERGR+k2xiXSyTXSuWKJ02r9fhLoPPxTbZ50F/OxnCd5ElsVUFNdcEw51V14JuN0MdUREpDrVg914za/RJDOlGFE6yLLIYq+9JrZPOEFksenTE7jJ8DDwve8Bd9wR3tfQAKxZwzHqiIgoJVQPdr29vRGzToyntbU16SnFiFLt5z8HHn1UrM+cCfz2t8CRRyZwg4MHgcsvB+67L7yvuRlwOCYx5xgREVFsqge7hoYGGI1GPKOMCzFKf38/VqxYAavVyinFKCt5vcAtt4S3/+//gEWLErjB4CCwbBnw+ONie9o04OGHgVtvVbOYREREY6jeHqTVatHU1IRly5ahvLwcNTU10Gg0kCQJkiQhGAxCo9HA6/VyhgXKOpIE1NWJwYgBoLFRbMftwAFg+XLguefEdkmJmET2wgvVLioREdEYKXnRx2w2Y+vWrbDb7di0aVOok4Rer4fRaMTKlStT8ViiSdm9G/j614FAQGyfey5w110J3ODgQTHv6/r1YnvmTOD558W4dURERGmQsje4tVot3G53qm5PpKrhYfFK3BtviO2TTwZ+8xugsDDOGxw6BFx6qRi5GBA9XhnqiIgozVI2pVg8OLUYZYuf/jScyUpLxbjBZWVxXjw0JFLhunViu6RE3IChjoiI0iyjwc5ms2Xy8UQARAb74Q/FekGBGDc47jlgh4eBq64CnnhCbCvzjSU1NQUREdHkpKwpduPGjQgGgwgoLyxF0dnZmarHE8XlzTdFC6ri7ruBr30tzouHh4H6+vC4KEVFYtqwuG9ARESkLtWDXV9fHwwGQ1yzSiQymDGR2oJB0Vl1926xXVcH2O1xXizLYn6xhx8W24WFovfrBRekoKRERETxUb0p1mKxwGw2w+/3o7e3F8PDw1GXQCCAqqoqtR9PFBdZBmw2YOtWsb1oEfDII3GOHSzLwA03AKtXi+1p00RPi2XLUlVcIiKiuKRkHLt4hjPRaDTQ6/VqP54oLv/3f+G+DhoN8MwzwKxZcVwoy8DNNwP33y+2CwqAX/86wcHuiIiIUkP1GrsTTzwx7nPXKf+zEqXRv/4FfOc74e2WFmDBgjgulGXgttuAn/1MbBcUiIT4rW+lophEREQJUz3YJTL/a39/v9qPJ4ppcFCMIbx3r9i2WgGzOc6Lf/pTMd+roqVFDHNCRESUJVQPdmazGatWrYrrXIvFovbjiWJqbAQ2bxbrn/50uPJtQo8+CvzgB+Hthx4Sw5wQERFlEdXfsSsoKEB1dTXq6uqwdOlSVFVVoaKiYsx5gUAgrp6zRGr5wx/CQa64WHRiPeKIOC5sbweuvjq83dwMXHNNSspIREQ0GaoHO71ej76+PsiyHJpSLNqwJrIsc7gTSpsPP4xsNW1uBv7jP+K48PXXgYsuElOGAWKIk1tuSUkZiYiIJkv1YFdRUYH6+nrUTdBLsKenBytWrFD78URjKPPAfvyx2D73XDFayYTef18MNqy8C3r++cDPfx7nmChERETpl7HhTgBwHDtKi5/9DNiwQawfc4zoyDphNuvvFwnwvffE9uLFYq6xwsKUlpWIiGgyCuREurHGoa+vD2Vxzp6eyLn5rr+/H2VlZejr60NpaWmmi5M3urqA008HDh4U2xs2ACbTBBcdPChCXVub2K6qAl59FTj66JSWlYiIKJpEMoLqvWJHBrXW1lY0NjZis9INEUB7e3tom6GOUmn3bjG0iRLqbr01jlCnTEmhhLqKCuD3v2eoIyKinKB6sANEoKuoqIDZbIbD4UBnZ2foWG1tLfx+P9asWZOKRxOF3HADsGWLWK+pAX7ykzguuusu0VYLACUlwLPPAiefnLIyEhERqUn1YLdp0yY0NDTA4XCE5ood3dq7bNkyGAwGbNy4Ue3HEwEQQ5ko+Wz2bPF6XHHxBBc9+ihw553h7V//GjjjjJSVkYiISG2qBzuXy4Wuri5YrdZQU2u0YU2qq6s5jh2lRHe3aE1VPPAAMOFMd15v5Fh199wDLF+eiuIRERGljOrBTqvVxv3uXDAYVPvxNMXJMnDlleERSi65BPjv/57gon/+c+xYdTffnNJyEhERpYLqwa68vDzuc/1+v9qPpynu4YeBP/1JrJ9wgpj5K+bQJu+9J8aqGxgQ2xyrjoiIcpjqwW7r1q1j9kUbUWXz5s1R9xMla+fOyEkhXC4gZq/wgQExrMn774ttjlVHREQ5TvVgV1dXh8WLF2P79u2hfaPfsWtvb0dtbS2am5vVfjxNYddfD/T1ifXLLgOWLo1xsiwDV10lmmEBMVbdc8/FOXksERFRdlJ95onq6mpYrVZUVVXBZDJBq9VCkiT4/X5IkgSfzwdJkrBhwwYOxEuqefpp4JlnxPqRRwL33jvBBT/7GXB4LmOUlgIvvMCx6oiIKOepPvOEwufzwWq1YtOmTRH7zWYzWlpaODjxKJx5InmBAHDKKcBHH4ntp54CYk5V/Oc/A7W1wNCQ2P7tb4Gvfz3VxSQiIkpKIhlB9Ro7hV6vR1dXFwAxtp1Go+HcsJQSt9wSDnUXXDDBKCXvvy9OUELd7bcz1BERUd5IWbAbqbq6Oh2PoSnI6w0PRFxaCjz4YIwOrQcOiFD38cdi22gE/ud/0lJOIiKidFC988Q111yDwsJCzipBKbdnD1BfH95ubgaOOy7GBbfcAvz1r2L9U59iD1giIso7KZkr1mq1oqamJhW3Jgr54Q+BbdvE+plnAlZrjJN/8xvgl78U68XFgMcDzJ2b8jISERGlk+rBTqfTYfXq1XF1AGCtHiWrowO47z6xXlICtLQA08b72/zPf0amvvvvF2PWERER5RnVg51er0dra2tc5zocDrUfT1PAgQNiCLrhYbH9ox8BJ500zsnBILBsGbBvn9i+8srIOWGJiIjyiOqdJ2pra7Fx40Y0NjZCp9OhpqYGGo0GFRUVEecFAgFIkqT242kKcDiA118X64sWxZjWdXhYjFSszIai14vaOk4XRkREeUr1YFd4+GV0ZXi80bNOKGRZHvcY0Xjefhv4yU/EemGhmBt2+vRxTl65UswmAQAVFWIU45kz01JOIiKiTFA92FVVVcFsNsNkMsU8r7e3FzabTe3HUx4bHhatqAcOiO1bbhGVcFFt2ADccYdYLygQnScWLEhHMYmIiDJG9WCn1WqxcuXKuM51uVxqP57y2IMPhkcrWbgQuPPOcU58913gkkvEfLAA8OMfA1/5SlrKSERElEmqTynW19cX93RhiZyb7zilWGzvvgt89rPA7t1i+8UXgbPOinLi0BBw9tnAyy+L7XPPBdavj9FlloiIKLslkhFU/99uZFBrbW1FY2MjNm/eHNrX3t4e2maoo3jIMnDtteFQV18/TqgDgHvuCYe6BQuAxx5jqCMioikjJf/jtba2oqKiAmazGQ6HA52dnaFjtbW18Pv9WLNmTSoeTXnohReA3/1OrM+bJ2aYiGrTJjFqMSDeq3vsMaC8PC1lJCIiygaqB7tNmzahoaEBDocDvb29GB4exujW3mXLlsFgMHCAYprQgQPATTeFt++9F4ha0btvH3DppcDBg2LbbgfOOCMtZSQiIsoWqgc7l8uFrq4uWK3WUFNrtGFNqqurOY4dTeiXvwS2bBHrZ5wBLF8+zom33w689ZZYX7RIdJggIiKaYlQPdlqtNu5354LBoNqPpzzy8cfA//yPWC8oAH7+83HGFvZ6I+cXe/xxMR8sERHRFKN6sCtP4J0mv9+v9uMpj9xxB9DfL9avumqcMet6e4Errghvr1wpus8SERFNQaoHu63K9E0jRBtRZfPmzVH3EwGiH4TSv6a0NDzbxBjXXgu8/75Yr60FbrghLeUjIiLKRqoHu7q6OixevBjbt28P7Rv9jl17eztqa2vRPG73RprKZBm48cbw+MI/+AFw9NFRTnzySeCpp8S6RgP86lcc2oSIiKY01WeeqK6uhtVqRVVVFUwmE7RaLSRJgt/vhyRJ8Pl8kCQJGzZs4EC8FJXbDbz0klhfuHCcSrgdO4AVK8LbDz0EHH98WspHRESUrVSfeULh8/lgtVqxadOmiP1msxktLS0cnHgUzjwh7N0LfOYzYqYJAHjuOeC880adNDwMmEyAMlzOxRcDTzyR1nISERGlSyIZIakau+7ubgSDQSxatGjcc/R6Pbq6ugCIse00Gg2qqqqSeRxNIatWhUPdOeeIGcHG+PnPw6Hu+OOBBx5IW/mIiIiyWcI1dn19faiqqkJfXx96e3vHJMf+/v4pXeOULNbYidbVk08WYw0XFgKvvy5q7yK88QZQUwMMDoptr1d0miAiIspTKZ0rtr29He3t7Vi9enXUm1sslrjv1d3dnejjKY/ddpsIdQBw3XVRQt3goJhdQgl1N93EUEdERDRCwk2xZWVl6O3thdVqjXo8kQpAm82GP/7xj4kWAcFgEE1NTQCAyspK+P1+mEwmmM3mhO81XrksFguMRmNGyzGVvPJK+DW5ykrgRz+KctKddwL/+IdY/+xngbvvTlfxiIiIckLCwa62thY1NTVYsWIFLBbLmFq7aNOHjSeZKcWCwSAMBgPcbjf0I0astdls6OjogMPhSPieSlm8Xi+cTid8Pt+ENY+pKsdUNDwshjdR3HUXMGac65deApThcaZPF7NLzJiRtjISERHlgqQ6T9TV1aG+vh719fXQaDQRx4LBIAoLC9UoW1QWiwVmszkiTAGA0+lEeXk5TCbThDVto7lcLrS1tcFkMsHhcMBkMmWkHFPVo48Ch/vZ4LTTgDGVwXv3ApdfHh7Y7q67xHywREREFCHhYNfX14eenh6UlZWhvr4eOp0udEyWZTQ3N8Nut094n97e3oRrtUbWqkWzfPlyOByOhAOVElIBMUxLpsoxFQ0MAI2N4e377gOKRv+tvOsuYNs2sX7GGcAtt6SreERERDkl4WDX3t6OxsZGrFy5Mupxj8cz7vt3o3m93oSerQQprVYb9bhOp4PL5UIwGBxTk6imbClHPvjFL4CPPhLrF10ELFky6oTXXxdjoABAcTHQ0iK6zBIREdEYCfeK7enpifkeXSLv2CVaY+fz+WIGJSVodXZ2JnTfRGVLOXJdX184s02bBhzuhxI2PAzYbMChQ2K7sRH49KfTWkYiIqJcknCwq6mpUS2wVFdXJ3S+JEmoqKgY97gStpLplJGL5ch1990HBINi/bLLgJNOGnVCSwvw6qti/aSTxHgoRERENK6Eg111dXXMmrZEhjs555xzEnp2IBCIWVOmhK2gkhZSJFvKkcsCAeDee8V6YSHwgx+MOuHDD4GR72quXs1esERERBNIqlfssmXLsHDhQjQ3N6O6uhoLFiwIHSsoKMDAwMCEAS8QCCRcoxVvUOrp6UnovolSoxyDg4MYVAbahRhVeiq5915A+Za//W1gzOuKN90k2moB0SP2v/4rreUjIiLKRUkFO6UH6bJly6K+UxdPhwFZlhN6Hy/fNDU14cc//nGmi5ERu3aJ6V4BMSTdHXeMOuEPfwCeekqsV1aGX8QjIiKimJIKdoAId3V1dZAkCYFAAIAIa7fddltcnSJ6e3ths9kSeqZGo4mrtqyysjKh+yZKjXI0Njbie9/7Xmi7v78f8+fPV6N4WW/VKmD3brF+9dXACSeMOLh3L3DttZEnz52b1vIRERHlqqSDHSCmFxvdAaKiogK1cc7f6XK5EnperA4LAEIBM9VDjKhRjpKSEpSUlKhZrJzw8cfAL38p1ouLgdtvH3XCyDHrzjpLNMMSERFRXBLuPDGRRGrh3G53QvfWarWh0BSNUos23vhyasmWcuSi5mZRKQeIkUyOP37EwdFj1q1eDUzh5noiIqJEqR7sli1bFve5ZWVlCd1br9fHbAJVOmOkesaHbClHrvngA+CBB8T6jBmRM05wzDoiIqLJUz3YJWLFihUJnV9XVwdg/Gm/Ojo60hKmsqUcuWblSmD/frF+7bXAsceOOLhmTXjMuoULOWYdERFREib1jl1/fz9KS0sj9m3evDnu6xOdUkyv18NoNGLt2rXQ6/Vjjns8HrS1tY3ZHwwG0dTUBJPJpErgSrYcU9l77wHK1LqzZgENDSMOcsw6IiIiVSQd7E488UR0d3cjEAhEhLslS5agTxl/LIZkhztxu90wGAyoq6uLCFU2mw0NDQ1Rg5vL5UJzczNcLhd6e3tj3l9pRp2o12sy5ZjKmpoAZdi+668Hjj56xMHvfS9yCooxE8YSERFRPJIOdkpv2NE1dhUVFWhsbJww2PT09CTcFAuInqZdXV2w2+3QaDSorKyE3++HyWSC2WyOeo3RaIRGo8Hy5cujHvd4PHAerk5SpkuzWq2hfRaLJTR232TKMVW9+66YHQwAZs8Gbr11xME//Ql48kmxXlHBMeuIiIgmoUBOZA6wOCxduhQbNmxQ/dx819/fj7KyMvT19Y0Jy7nOZgOUkW1uvx346U8PHxgeBhYvBpR3FdesAa66KiNlJCIiylaJZATVgx0lJ1+D3bZtwEknic6upaViOzQM4BNPAN/6llj/3OdEwCsszFhZiYiIslEiGSGjvWKn2vyoU9FPfhIeweSmm0aEuv37I0cnvucehjoiIqJJymiwi3eGCspNW7cCjz4q1jUa4LvfHXHw/vuB7dvF+tKlYiEiIqJJSarzxMaNGyf9YJ/PF+qBSvnpf/4HGBoS6zffLMIdACAQCL9oV1AgauuIiIho0pIKdmazGX19fRjv9TxlGJNYx5Md7oRyw7/+BfzmN2K9ogK44YYRB3/yk/DwJpdfLt6vIyIioklLKthVVFTA4XCMO3yI3W6HyWSKOnhvMBhEW1sbAoFAQvPKUm75n/8RnV4BMbxJ6F1PSRLNsAAwcyZw110ZKR8REVE+SirYabVaWK3WqMdaW1ths9lC49xFU11djfb2dmzbtg1VVVXJFIGy2JtvAk89JdaPPFIMSBxy++3AwYNi/aabgOOPT3v5iIiI8lVSnSdijT3X0dERM9Qpamtrx51rlXLbj38MKK3wdrsYlBgA8NprwNq1Yv3IIyOnESMiIqJJU71XbEVoPIuJlZWVqf14yrB//hNwu8X60UcDoclFZBm45ZbwiXfeOaJ9loiIiNSgerBLpENEPHPKUm65887wemMjMGvW4Y3164GXXhLrJ50EjJqijYiIiCZP9WC3a9cu/OMf/5jwvO7ubmzdulXtx1MGdXUBv/2tWJ83T0wlBkC8Uzey2XXlSmD69HQXj4iIKO+pHuxWrlyJZcuW4eGHHx73nNbWVphMJjQ3N6v9eMqgH/0ovP797wMzZhzeWLMG+Pe/xfp//idw4YVpLhkREdHUkJK5Yn0+H4xGI/r6+qDX60Pv3QUCAfh8Pmg0Gqxbt44zT4yQ63PFvvEGcNppYn3+fGDLFqCkBMDAAHDiicDHH4uDr74KfPGLGSsnERFRrsn4XLF6vR6BQAC33HILZFlGW1sb2tra0NvbC6vVCkmSGOryzP/+b3j9llsOhzoAaG4OhzqLhaGOiIgohVJSY0eJy+Uau507gQULxKt05eXAu+8eHuLk/feBhQuBffvEO3Vvvw3odJkuLhERUU7JeI0dTS333x8ec/iaa0aMW/fDH4pQBwDXXstQR0RElGKsscsSuVpjt3u3eKcuGBSVctu3A8ceC+D114H/+A8xfl1ZGeD3A5WVmS4uERFRzmGNHaXNI4+IUAcA3/rW4VAHAA0N4eknbr+doY6IiCgNGOwoaYcOAffdF96++ebDK14v8Ic/iPVPfQq44YZ0F42IiGhKYrCjpD3zDLBtm1j/yleAU08FMDwM3Hpr+KSf/nTEgHZERESUSgx2lBRZBu65J7wdqq17/HFg82axrtcDl1yS7qIRERFNWQx2lJSXXwY6OsT6f/wHUFsL0QP2jjvCJ91zDzCNf8WIiIjShf/rUlJGD0hcUADg5z8HduwQO7/2NWDJkoyUjYiIaKpKabBrbW1FY2MjNitNcwDa29sjtin3/PvfwPr1Yv2444C6OoiusStXip3TpokZJ4iIiCitUhLsWltbUVFRAbPZDIfDgc7OztCx2tpa+P1+rFmzJhWPpjT42c/CI5nceKMYvw6//CXQ1yd2XnEF8NnPZqp4REREU5bqwW7Tpk1oaGiAw+FAb28vhoeHMXoM5GXLlsFgMGDjxo1qP55S7JNPgEcfFetz5gD19QAGBsLjnhQWAt//fqaKR0RENKWpHuxcLhe6urpgtVpRVlYGACgoKBhzXnV1NSRJUvvxlGIPPgjs3y/Wr75aTCqBBx8EAgGx81vfArTajJWPiIhoKlM92Gm12lCgm0hQmbKAcsK+fWJeWEBUzN14I4A9e4BVq8TOadPELBNERESUEaoHu/Ly8rjP9fv9aj+eUujxx4Fdu8S6xQKccAIApzO8s64OOPnkjJWPiIhoqlM92G3dunXMvtHv2AHA5s2bo+6n7CTLosVVcfPNEFV4I0cp5rt1REREGaV6sKurq8PixYuxffv20L7R79i1t7ejtrYWzRwSI2d0dIQnlFi8GKipAbBmDfDhh2Kn2cyesERERBlWpPYNq6urYbVaUVVVBZPJBK1WC0mS4Pf7IUkSfD4fJEnChg0bUFpaqvbjKUWczvC6zQZgcBBwOMI7R844QURERBlRIKeoPdTn88FqtWLTpk0R+81mM1paWuLuYDFV9Pf3o6ysDH19fVkXePv6gHnzgL17gdJSYOdO4IjHncA114gTLrgAePbZzBaSiIgoTyWSEVSvsVu8eDE6Ojqg1+vR1dUFQIxtp9FoUFVVpfbjKA0ef1yEOgC49FLgiOKDQFNT+IQf/CAzBSMiIqIIqr9j19XVhWeeeSZiX3V1NUNdjpLlKM2wjz0GKO9QfvWrh1+4IyIiokxLyZRid999N1asWIHW1tZU3J7S6G9/A15/Xax/8YvA5045BNx9d/gE1tYRERFlDdWbYs1mM9atWwdANMHedtttmDt3Lurr67Pu3TGa2JjauiefBJTxB41G4PTTM1IuIiIiGitlnSdG6uvrw7p16yBJEkwmE5YsWZLqR+acbOw80dsrOk3s3y+mDtu5YwizFn8W+Pe/xQl//jNw5pmZLSQREVGey2jniWjKyspgtVoBiFq8xsZGzJ07F1arNWtCDI312GPheWEvuwyY9funw6HuzDMZ6oiIiLJMWoKdor+/H+3t7XA6nQgGg3jqqafQ0dGRziJQnMZ0mqiXgStXhXfw3ToiIqKsk5LOE6Nt3LgR55xzDsrLy9HQ0IDly5ejq6uLoS6LvfIK8NZbYv0//xP47MDfxPQTALBoEVBbm7GyERERUXSqB7vFixcDELVzq1atQmVlJYxGI/x+P1avXo3h4WGsXr0a1dXVaj+aVDSm08R994V3fPe7wKhp4oiIiCjzVO88UVFRAZ1OB5/PB1mWUV9fD5vNxiA3gWzqPNHTAxx3nJg1rLwc2Pn3HZjxmSpgaAg46ijg3XeBkpKMlpGIiGiqyGjniWAwiN7eXqxevTrUYYJyy69/LUIdAFx+OTDj4QdEqAOAa69lqCMiIspSqgc7rVaLrVu3qn1bSpMxnSYu3QOYXGKjuDg8PywRERFlHdXfsTObzWrfktLoL38Jj2hy1lnApzseEwPaAcDFFwNHH525whEREVFMqge7lStXqn1LSqOI2jrrMPDzn4d33Hhj+gtEREREcUvLcCfjWbFiRSYfT6Ps2gU8/bRYnzsXWFa6AfjXv8SOs84C2AGGiIgoq03qHbv+/v4xvTM2b94c9/Ver3cyjyeV/epXwIEDYv2KK4DiB+4LH/zud9NfICIiIkpI0sOdnHjiieju7kYgEIgIdxUVFejr65vwelmWUVBQgCGlt+UUl+nhTmQZOPlkYMsWsb3thbex4GuniI2qKnGgsDDt5SIiIprq0jLciTIu3egHVFRUoLGxEUajMeb1PT09bIrNIn/6UzjULVkCLFj/i/DBG25gqCMiIsoBSQc7t9sddb9Wq8Wtt94a1z2qqqqSfTypbGSnie98KwBc/6jYmDMHuPLKzBSKiIiIEqJ654kNGzak5FxKnY8/Bp55RqwfdRRw3octwL59YseVVwIZngmDiIiI4pPRXrGUHf7v/4CDB8X6VZcdRNFD94uNggLgO9/JXMGIiIgoIarPPDGevr4+rFu3DgUFBdBqtViyZEm6Hk0xDA8DLld4+4b5rcB774mN888HdLrMFIyIiIgSpnqN3TnnnBN1f1lZGaxWKywWC8rLy7Fq1aqEhkah1GhvByRJrJtMwDFP3hc+yCFOiIiIcorqwW6i0VPKyspQXV2NW265hePYZYGRnSZu+6+/A3/7m9j43OeAs8/OSJmIiIgoOaoHu4KCgrjP9fv9aj+eEvDhh8Czz4r1Y44Bzv7HiOnDvvtd8Y4dERER5YxJvWPX3d09Zl8gEMD27dtj1txJkgS32w1JaQOkjHjkEeDQIbH+XfN7mLb68BA2Rx4JXHxx5gpGRERESZlUsGtra4Pf74fX64XP5wvV1mm12pjXybIMg8HAptgMkmVgzRqxXlAA2IYfDKe8FSuAGTMyVzgiIiJKStJTio0mSRJMJhMqKiqwcuXKmOdqtVoOTjxKuqcUe+UV4IwzxPp5S/biuc3zgUAAmD4dePdd0TZLREREGZeWKcVG02q16OrqwvLly1FbW6vWbSlFfvOb8Prtn3oc2BgQG9/8JkMdERFRjlK184RGo4HFYlHzlpQCBw8C69aJ9ZkzZHz+byM6Tdx4Y2YKRURERJOm+gDFVqs15vF77rknNEjxRRddpPbjKQ5//CPQ0yPWv/8FLwr//JbY+PKXAYMhcwUjIiKiSUnbzBOKW2+9FYCYiWLVqlW45ZZbEr5HMBhEU1MTAKCyshJ+vx8mkwlmsznpciV6T5fLBbfbDZvNBqPRCI1GA0mS4PP5sHbtWjQ2NkKv1yddnlQa2Qx79Z77whsckJiIiCinpT3YKQoKCtDW1pZwsAsGgzAYDHC73RHByWazoaOjAw6HI+GyJHPPYDAIr9c7pmevRqMZc59sMjAQHrvu82X/xtGdL4iNBQuAr389Y+UiIiKiyUtJsNu4cSMcDgckSUIgEBhzPBgMAkBSIcxiscBsNo8JTk6nE+Xl5TCZTDAajWm5p9PphN/vhyRJqKiogMFgQH19fcLfUzr99rfAvn1ivfn4XwB9hw985ztAYWGmikVEREQqUG24E8W2bdug0+mg1+tRU1MDAOjs7Ayt9/T0YNOmTfB4PFi0aFFC95YkCTqdDn6/P+pYeTabDZIkoa2tLeX3bG5uRn19PTQaTULfw3jSNdzJV74i3rE7ArsRnHksivbtBmbPBt57DygrS9lziYiIKDkZGe5E0dzcjLa2toghT1paWsZ0qmhpaYFGo8GCBQvivrfz8MSm4w2ArNPp4HK5EAwG4w5cqbhntvroI0DJp9dWrkNRz26xccklDHVERER5QPW5YsvKysaMY9fX1zfmPKvVCo/Hk9C9fT5fzHClhLPOzs6M3jNbrV0LDA+L9etmPhw+cNVVmSkQERERqUr1YFdZWTlm39atW9Hf3z9mf1mCtUTKu2zjUQJaInPQqnFPn88Hl8sFn88X93MzQekNezL+hRPe+6vYOPVUYPHizBWKiIiIVKN6sFPmix3JYrGEhhIZKZEABgCBQCBm7ZoS0JTOGam+p9frRXNzMwCEOk2YTKa45sAdHBxEf39/xJJKW7YAr70m1m876pHwgauuEpPFEhERUc5LSVNsf38/Ghsbce211wIAamtr4XQ68fDD4ea/zZs3J1zDFW9g61FG303hPZUm2oaGhlBvWr1eD7fbDZPJNOH31tTUhLKystAyf/78uMucjCeeEF+LcBCWvY+KjenTgUsvTelziYiIKH1UD3ZWqxVOpxMOhyOiJ+m6detgtVpRWFiIhQsXwmAw5PT0Y2azOergxRqNBmazecLvrbGxEX19faFlx44dqSoqZDncDHsefocjdn8sNi68EJg7N2XPJSIiovRSPdgBYnaJ4eFhbNmyJbTPaDRiw4YNWLJkCWRZxsqVK3H11VcndF+NRhNXDVu09/zSec/FixdDkqSYTc0lJSUoLS2NWFKls1M0xQJAQyU7TRAREeWrtM48YTQaEx48eKRYnRwAhAZDTmRYklTcUznX5/ONO4xKOim1dcdiJ74QODzTxPz5wCR+FkRERJR9UlJjlyparTbqTBYKpeYtkTCVzD1tNht0Ol3cz8ikQ4eAp54S61cWPopp8uHxTr79bc40QURElGcyGuzOOeechM7X6/Uxm02Vps9EagWTuWdnZ2dcYTAb5ovduFEMTAzIuG7GiN6wV1yRoRIRERFRqiTVFLt582ZVHp7ocCd1dXVobm6Gz+eLGpo6OjoSbupN5p5GozHmPLcdHR3QaDRZ0Qz7+OPi65n4C47ds1Vs1NYCVVWZKxQRERGlRFLBbsmSJVFnk0iELMtRx7yLRa/Xw2g0Yu3atVFDmMfjiTpPbDAYRFNTE0wm05iQlsw96+rq4HK5QmPXjSRJEjweD9xud0LfWyrs3Qs884xYXzH9YeDg4QPsNEFERJSXCmRZlhO96MQTT4TNZptUR4ienh6sWLEioudsPILBIAwGA9xud0QQs9ls0Gg0UWvSmpubYbfbodFo0Nvbq9o9ATGOnUKSJBgMBtTX18es0YsmkQl+4/XUU8DFFwOl6MPHhceiZGgfUF4O7NwJzJihyjOIiIgotRLJCEnV2Gm1Wtx6661JFW6kqiSaAzUaDbq6ukJBrbKyEn6/HyaTKeq4coBoOtVoNFi+fLlq92xoaIDX64XNZkMgEEAwGIRGo0F7e3tWvFsHhHvDXownRagDgG99i6GOiIgoTyVVY0fqU7vGbtcu4NhjRa/YTdMXY9HBTnFg0yZg0aJJ35+IiIjSI5GMkNJesa2trWhsbIzobNHe3q5a5wsan9stQt1p+Gc41On1DHVERER5LCXBrrW1FRUVFTCbzXA4HOjs7Awdq62thd/vx5o1a1LxaDpMaYa9CpxpgoiIIlksFhgMBhQUFIQWk8k04XUulyt0fnl5eVzXUHqpHuw2bdqEhoYGOBwO9Pb2Ynh4GKNbe5ctWwaDwYCNGzeq/XgC8MEHwCuvAMUYxOWFh8c7mTEDuOSSzBaMiIiygtvtRldXV2h0B6PRGHVUidHq6+tRX18Po9GIbdu2xXVNNrLZbPB6vZkuRkqoHuxcLhe6urpgtVpRVlYGAFGHNamurk54HDuKz/PPi69fx7PQDB0eSHnZMiCBadGIiCj/OZ1OaLVaeL1e+Hy+uK4JBAJoa2tLaKrNbCBJElwuFwwGA1wuV6aLkzKqBzutVhsKdBOJNeMDJe/ZZ8XXiGbYK6/MTGGIiCirOZ1OAKJ5diIulws2my3VRVKdy+WC3W4HgISHI8s1qge78vLyuM/1+/1qP37K27MH8HqBT2E7TDhcRV5VBZx9dkbLRURE2cloNMJsNkOSpNAYrdEEg0F0dXVNagzbTKmvr4fb7UZ9fT0qKioyXZyUUj3Ybd26dcy+aCOqbN68Oep+mpwNG4DBQeAK/ArTcPjP98orgWkZnRaYiIiyWEtLCwDAbreP25pmt9vzvrYrH6j+v31dXR0WL16M7du3h/aNfseuvb0dtbW1MX8zoOSsXw8AMq7Ar8SOadOAK67IXIGIiCjraTSaUJOs1Wodc9zr9cJgMOTce3VTUVIzT8RSXV0Nq9WKqqoqmEwmaLVaSJIEv98PSZLg8/kgSRI2bNig2tRZJAwNiY4TX8DfUYVusdNkAo4/PqPlIiKi7FdfXw+n0wmPxwOv1xvR5Op0OmPOge7z+bB27VrodDoEg0H4/X7YbLaoMzG5XK5QrWBPTw+CwSDsdju0Wm3EecFgEBaLBZIkQZIkyLIc6uShvMqlhFEaQU6Rrq4uWa/XywUFBRGLxWKRg8Fgqh6bs/r6+mQAcl9fX9L3eOklWQZkeRW+J1YAWX7kERVLSURE+ayrq0sGIGu12tA+h8Mhd3V1jXuN0+mU9Xp9xL7e3l5Zq9XKbrc7Yr/D4ZB7e3sj9rW1tckA5La2tqj3b2hoCB1X7ldfXy8nG2GU73G852WjRDKC6jV2Cr1ej66uLgBibDuNRpPU3LAUP9EbVoYFh3+rKioCvv71TBaJiChlamqADz/MdClS75hjgBHj/KeUXq9HfX19qBepzWZDT0/PuHOg+3w+2Gy2MZ0hNRoN7HY7rFZrxJzrdrsdPT09Ee/qGY1G6PV6OByOqB0zTCYTmpub0dbWFrrO4XDE1Yt3KkpZsBupuro6HY+Z0mRZBLsv4O/4FHaInSYTkOe9f4ho6vrwQ+D99zNdivzjdDqxbt06NDc3Q5KkmE2wVqsVRqNxTDMqACxfvhw2mw0ejycU7rRabdTOGTU1NRMOGKzT6ULrGo0mJ3vnpoPqwe6aa65BS0sL2trasGTJErVvT+P497+BLVuAVRjxD5C/zRBRHjvmmEyXID0y8X06HA7YbLYJpwzz+Xyh2StGUzpadHR0hIJdrGHOAoFAzGfV1NTEPE5CSmrsrFYrfwBp9uyzQAGGw82w06cDF16Y0TIREaVSuponpyJlrLdYY74pM1UoMzpE43Q6o+YBr9cLt9sNnU4HjUYTMaf8eNgjNz6qBzudTodbb701rnM3btzIWj2VrF8PfB6vhZthjUYggcGiiYiIEqEELeW9vHh4vV5YLBbU19fD4XCE7tHV1TXhNKP5PrCwWlQfx06v16O1tTWucznQoTo++gh49VWEa+sANsMSEVFKKe/VxTvvu9frhclkgsPhiAh1pC7Vg11tbS00Gg0aGxuxZs0abN68Gd3d3ejv749Yuru74/7LQLH97ncAZDbDEhFRepnN5lCT7HiUThFKZU602r3R79dxAoPkqd4UW1hYCCA8jdjoWScUsiyPe4wS8+yzbIYlIiL1TdShoaWlBeXl5WMGNFZ4PJ6Imr1ovWcB8b7eeFOZUWJUD3ZVVVUwm80T9qTp7e2FzWZT+/FTzt69QFsb8BOsC+9cvjxzBSIiopyntKhNFLY0Gg3a2tpgsVjQ3t4eMd6dz+dDIBAI9Yi12Wyw2+3w+XwR57lcLrjdbhgMhtDzRgZAZV8gEFCl+Tbe7y1XFchK1ZpKli5dig0bNqh+br7r7+9HWVkZ+vr6Eppqbf164MKvD6MbC0SN3fTp4qU71tgREVGClJDV2dmJYDAIjUaDmpqaUIeH8SjTggEI9XTVarVjavGU++v1+tD0Y2azGVqtNjQIsclkQkNDQ2hKMaUsWq0WWq0Wdrs94THsPB5PaPqx0d8bgAm/v0xLJCOoHuz6+vpQVlam+rn5Ltlgd/XVwJsPv4pX8SWx46tfBV54IUWlJCIionRLJCOo3nkikaDGUDc5Q0PAc8+N6g3LZlgiIqIpS/VgR+nz2mvAJx8PwwyP2DF9OueGJSIimsIY7HJY1Llh+W4dERHRlMVgl8PWr+egxERERBTGYJejtmwB/vX2qEGJ2QxLREQ0pTHY5aj160Uz7Hy8J3awGZaIiGjKY7DLUc8+y2ZYIiIiisRgl4N27QL++jKbYYmIiCgSg10O+t3vgMUym2GJiIgoEoNdDlq/Hlg+cm5YNsMSERERGOxyzv79wIY/hAclltkMS0RERIcx2OWYjRuBU/eGm2EL2AxLREREhzHY5Zhnnx3VDMu5YYmIiOgwBrscMjwMPPcsm2GJiIgoOga7HNLZCSz46G+RzbAaTWYLRURERFmDwS6HjBmUmM2wRERENEJRpgtA8Xvu2WG8cDjYydOno4DNsERERDQCa+xyhCQBs9/8G47H+wCAgqVL2QxLREREERjscsT69ZwbloiIiGJjsMsR638bnhtWLmJvWCIiSp7FYoHBYEBBQUFoMZlME17ncrlC55eXl8d1DaUX37HLAYEAcPClcDMszmEzLBERJc/tFhUFNpsNLpcLRqMRbW1tE15XX1+Prq4uSJIEt9sNTY7+X2Sz2WCxWGA0GmOeFwwG0dTUBACorKyE3++HyWSC2WxORzGTwmCXA154AVg2HB6UuIDNsEREpAKn0wmv1wuv1wufzwe9Xj/hNYFAIK4QmG0kSYLX64XT6YTP54Nlgv9Lg8EgDAYD3G53xJ+LzWZDR0cHHA5HqoucFDbF5oCRgxIPsxmWiIhU5HQ6AWDCoAOIplibzZbqIqnO5XLBbrcDQNyBzGKxwGw2jwm7TqcTLpcLXq9X9XKqgcEuyw0OAj2/G9EMy96wRESkIqPRCLPZDEmS0NzcPO55wWAQXV1dEzZfZqP6+nq43W7U19ejoqJiwvOV2r3xQuzy5ctZY0fJefFF4Lx94WbYacvZDEtEROpqaWkBANjtdgSDwajn2O32rA0zalNqMbVabdTjOp0OXq933D+rTGKwy3Lrf8tmWCIiSi2NRhMKM1ardcxxr9cLg8GQs50lEuXz+WJ+r0rg6+zsTFOJ4sfOE1lMloH3Pa+GmmGHa5di2hT5R0VEROlVX18Pp9MJj8cDr9cb0eTqdDpDPWmj8fl8WLt2LXQ6HYLBIPx+P2w2W9TOGC6XK1TT1dPTg2AwCLvdPqZ2LBgMwmKxQJIkSJIEWZZDnTz8fn+oXKkgSVLMJlsl9EmSlJLnTwaDXRbz+YCzd4X/IRVdwrlhiYhCamqADz/MdClS75hjgDTVDLW0tMBgMMBms4XCU3NzMxobG8e9xuVywel0oqurK7RP6VHqcDgihgZpbm5GfX19RG2Y1+uFTqdDW1tbRJjUaDRoa2uD3W5Hc3NzqOmzoaEhNExLqoJdIBAYtxkWQCj0ZWNTLGTKCn19fTIAua+vL7Tvzh8Mye9hniwD8qHC6bLc25u5AhIRZZvjjpNl0biR38txx6X1j7W+vl4GIDc0NMh+v19uaGgY99yuri4ZgOz3+8ccczqdskajidin3Hc0vV4vG43GqM9oa2sbc11vb6/c1tYW77cUtcyxrgcg6/X6Ce8R689GTdEywnhYY5fFup98FcdhJwDg4NlLUchmWCKisGOOyXQJ0iPN36fT6cS6devQ3NwcGoh4PFarFUajMWrt1vLly2Gz2eDxeEK1dlqtNmotV01NzYTDh+h0utC6RqPJyd656cBgl6Xeew9YtDX8j2nGZWyGJSKKkIUvrucLh8MBm8024ZRhPp8P9fX1UY8pza0dHR2hYKc070YTCARiPqumpibmcTVpNJq4mlkrKytTX5gEMdhlqbY/hnvDDk2bjsILLshwiYiIaKpQ3iGL1YHA5/MBEB0IXC5X1HOcTmfUQOb1euF2u6HT6aDRaOLqXZrOHrkTjXWnhNBs7CXMYJel/Ou68O3DvWH7vrgUFVn4l4eIiKYuJdTo9fpxa+1G83q9sFgsqK+vh8PhCN1DmX82lngGFlaLVquNGTaV2rxYHSwyhePYZaHhYaD0pd+FtksvZW0dERFlFyXUxDvkh9frhclkgsPhiAh12Uiv18dsilW+52x8z4/BLgv94x/Akn3Ph7aLLjg3g6UhIiKKzmw2h5pkx6N0ilBmrYhWuzf6/bpYU5ulQ11dHQCM+711dHRkZagDGOyy0l+f/gA1EOMBfXL8IuC44zJbICIimpIm6tDQ0tISmlc1Go/HE2pClSRp3KZLn8+XVWPC6fV6GI1GrF27Nupxj8cDu92e5lLFh8EuC+1vfSG0Pu388zJYEiIimoqUpsaJwpYyiLDFYhlTu+Xz+RAIBEKzT9hsNkiSNOY8l8sVGlIlGAwiGAxGBEClDBOFzHjF+7253W54PJ4x5bXZbGhoaMjaGrsCWZblTBeCgP7+fpSVleHDD/vw6rwrcOHwMwAA+a+vouD0L2a4dERENBUoIauzsxPBYBAajQY1NTWhDg/jUaYFAxDq6arVaseEH+X+er0+NP2Y2WyGVqtFc3Mz2traYDKZ0NDQEJpSTCmLVquFVquF3W5POFR5PJ7QLBWjvzcA435/yvel0WhQWVkJv98Pk8kUMZtGOigZoa+vD6WlpTHPZbDLEsoP7Zm1H8NYV4XZ2IP+GUeidPcHQGFhpotHREREGZJIsGNTbJbZ/sQrmI09AIDA57/KUEdERERxY7DLMke89IfQeuXlfL+OiIiI4sdgl2VqAn8EABxCEeYsW5rh0hAREVEuYbDLMlp0AwC2n/BloKwss4UhIiKinJKTU4oFg0E0NTUBgGq9VJK5ZyrKoZC/xmZYIiIiSkzO9YoNBoMwGAyh7tIKm80GjUYTGtk61fdUuxyhHi8ASgEc+Oe/UHzayQl/L0RERJRf8nq4E5PJBL1eHzU4lZeXw+12Jzy+TTL3VLscI4PdwKwTcdzud4CCgoS+DyIiIso/eRvsJEmCTqeD3++POi2JMqp1W1tbSu+ZinKMDHZbv3wj9H+5L+5riYiIKH/l7Th2yqjR4801p9Pp4PV6E5pvLpl7pqIcI5X/N9+vIyIiosTlVLDz+XzQaDTjHleCVmdnZ0rvmYpyKHbjCCy47MyEryMiIiLKqWAnSRIqKirGPa6ELWWC31TdMxXlUPz7+CUoKClO+DoiIiKinAp2gUAgZk2ZErYSaQJN5p5qlGNwcBD9/f0RCwAMLTkn7rITERERjZRTwS7ewNbT05PSe6pRjqamJpSVlYWW+fPnAwCqbJxtgoiIiJKTU8EunzQ2NqKvry+07NixAwBw5KlHZ7hkRERElKtyauYJjUYTV21ZZWVlSu+pRjlKSkpQUlISTxGJiIiI4pJTNXaxOiwA4t03ADHff1PjnqkoBxEREdFk5VSw02q1odAUjVKLNt74cmrdMxXlICIiIpqsnAp2er0+ZhOoMrxIIlN5JXPPVJSDiIiIaLJyKtjV1dUBEAMER9PR0ZFwmErmnqkoBxEREdFk5VSw0+v1MBqNWLt2bdTjHo8Hdrt9zP5gMAi73Q6v16vKPZMtBxEREVEqFciyLGe6EIkIBoMwGAxwu93Q6/Wh/TabDRqNBg6HY8w1zc3NsNvt0Gg06O3tVeWeyVwTSyIT/BIREdHUkUhGyKnhTgDR07SrqysU1CorK+H3+2EymWA2m6NeYzQaodFosHz5ctXumcw1RERERKmUczV2+Yo1dkRERBRNIhkhp96xIyIiIqLxMdgRERER5QkGOyIiIqI8wWBHRERElCcY7IiIiIjyBIMdERERUZ7IuXHs8pUy6kx/f3+GS0JERETZRMkG8YxQx2CXJQYGBgAA8+fPz3BJiIiIKBsNDAygrKws5jkcoDhLDA8P46STTkJXVxcKCgoyXZyELF68GB0dHXn9/FQ8Q617TuY+yVybyDX9/f2YP38+duzYwYG3E5Tpf1fJyoZyT+XPhHR/HiR6HT8TkiPLMgwGA9555x1Mmxb7LTrW2GWJadOmobi4eMIkno0KCwsz+g80Hc9PxTPUuudk7pPMtclcU1payg/xBGX631WysqHcU/kzId2fB8lex8+ExBUXF08Y6gB2nsgq1113XaaLkJRMlzsdz0/FM9S652Tuk8y1mf55TxW5+uecDeWeyp8J6f48mOwzKX7x/jmzKZaIUoZzIBPRSPxMSD02xRJRypSUlODOO+9ESUlJzPO8Xi/a2toQDAYhSRIsFgvq6+vTVEoiSpd4PhO8Xi98Ph8AwO/3Q6fToaGhIV1FzHmssSOijFI+xJUP7mAwCIPBAKPRCKfTmeHSEVE6+Xw+SJIEs9kc2qfT6fh5kAAGOyLKKIvFArfbHbHP5XLBZrPB7/dDq9VmqGRElG42mw1erxd+vz9in8vlimsMN2LnCSLKMI/HA7vdHrGvpqYGgKjNI6Kpw2QywWg0ZroYOY3v2BFRRpnNZuh0ukwXg4iygNlsjmiGBYB169bB4XBkqES5h8GOiOJms9lgsVgm/I06GAyiqakJAFBZWQm/3w+TyTTmAxvAmGZYAOjs7AQA/uZOlMVS8Xmg8Hg86OjogM/ng9vt5mdBAviOHRHFJEkSvF4vnE4nfD4f2traYn7IKp0f3G439Hp9aL/NZoNGo4nrN2+dTgebzcaecERZJt2fBx6PB01NTXC73XzfNk58x46IxuVyuULvv8XbFGKxWGA2myM+xAHA6XTC5XJN+N6cUgPAUEeUXTLxeWA2m2E0GmEwGBAMBpMq91TDGjsiiovP54PBYIj5G7okSdDpdOP2ZrXZbJAkCW1tbVGvd7lc6Orq4rAGRFkuHZ8HCq/XC5PJBIfDwV/44sAaOyJSjRLIxmsy0el08Hq9UX/z9ng8CAaDEaGOv6ET5a5kPg/Ky8vR3NwccV5FRQUARAyBQuNjsCMi1fh8Pmg0mnGPKx/wSueIkdcFAoGI38aDwSCHOyHKYYl+HigBb3QQlCQJAGAwGNQvZB5ir1giUo0kSaHfrqNRPuSVD2plvampCXV1dfB4PKH9bW1tsNlsKSsrEaVWop8HGo0G9fX1Ud/H0+v1nGYwTgx2RKSaQCAQs+ea8iE/sulFeSl6ZKhT8F07otyVzOeBw+EIvWur0WggSRL0ej3HsUsAgx0RqSbed+J6enpC6729vSkqDRFlUjKfBwBYMzdJfMeOiIiIKE8w2BGRajQaTVy/pVdWVqa+MESUUfw8yAwGOyJSTawXpQHxzg2AmD3liCg/8PMgMxjsiEg1Wq029GEdzXjDGRBR/uHnQWYw2BGRavR6fcymF2VYA07oTZT/+HmQGQx2RKSauro6AGJg0mg6Ojr4IU40RfDzIDMY7IhINXq9HkajEWvXro163OPxhCYRJ6L8xs+DzGCwI6K4KM0mE/Vyc7vd8Hg8Y35Lt9lsaGho4G/oRHmAnwfZq0CWZTnThSCi7OTxeEKzP3R2diIYDEKj0aCmpgYAYLFYog4mGgwGYbfbodFoUFlZCb/fD5PJBLPZnNbyE5F6+HmQGxjsiIiIiPIEm2KJiIiI8gSDHREREVGeYLAjIiIiyhMMdkRERER5gsGOiIiIKE8w2BERERHlCQY7IiIiojzBYEdERESUJxjsiIiIiPIEgx0RERFRnmCwIyIiIsoTDHZERFnM5/PBYrHAZrPB4/FkujhElOUY7IiIspQkSbBYLGhpaYHT6URHR0emi0REWY7BjogoS5lMJtjtdmg0GgBAMBjMaHmIKPsx2BERZaHm5mYEAgHU19dnuihElEOKMl0AIiKKFAwGYbfb4XA4Qvt8Ph9MJlMGS0VEuYA1dkREWaapqQkAImrrnE4nzGZzpopERDmCwY6IKMu4XC4YjcbQu3WSJMFgMGS2UESUExjsiIiyiNfrRTAYhMViCe1zOBx8146I4sJgR0SURdxuNwBg+fLlAACPxxMR8oiIYmGwIyLKIuvWrYNWq4VGo4EkSejo6IDRaMx0sYgoRzDYERFliWAwiGAwCKPRiGAwCIfDEdEzlohoIhzuhIgoS3R2dgIAdDodrFYrWlpaMlwiIso1DHZERFlCmVli7dq1aG9vD/WKJSKKF5tiiYiyRCAQACA6UDDUEVEyCmRZljNdCCIiAgwGA3w+H/ixTETJYo0dEVGGKePW2Wy2TBeFiHIca+yIiDLM6/WipqYGAFBeXo7e3t6IplhJkuD1ejlIMRFNiMGOiCiLWCwWmEymUIjzer1wu91wOp0ZLhkR5QIGOyKiLGOz2UI1djqdjjV1RBQ3BjsiIiKiPMHOE0RERER5gsGOiIiIKE8w2BERERHlCQY7IiIiojzBYEdERESUJxjsiIiIiPIEgx0RERFRnmCwIyIiIsoTDHZEREREeYLBjoiIiChPMNgRERER5QkGOyIiIqI8wWBHRERElCcY7IiIiIjyxP8DPlGNcZ9SVmMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsi = 20\n",
    "fontsi2 = 20\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(number_ell, chi2_y1, color = \"blue\", lw = 2, label = r\"$\\mathrm{Year\\; 1}$\")\n",
    "plt.plot(number_ell, chi2_y10, color = \"red\", lw = 2, label = r\"$\\mathrm{Year\\; 10}$\")\n",
    "plt.plot(number_ell, np.ones_like(number_ell)*chi2_y1[-1], ls = \"--\", color = \"black\", alpha =.5, lw = 2)\n",
    "plt.plot(number_ell, np.ones_like(number_ell)*chi2_y10[-1], ls = \"--\", color = \"black\", alpha =.5, lw = 2)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "\n",
    "plt.xlabel(\"$\\ell$\", fontsize = fontsi)\n",
    "plt.ylabel(r\"$\\mathrm{relative\\;difference\\;}\\Delta\\chi^2$\", fontsize = fontsi)\n",
    "plt.tick_params(labelsize=fontsi2)\n",
    "plt.xlim(number_ell[0],number_ell[-1])\n",
    "ax.legend( frameon = False, fontsize =fontsi, loc = \"best\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./plots_output/chi2_ell_num.pdf\")\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "levinpower_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}