decodingCallTypesNaiveBayesPart2-SameBird.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies \n",
    "import os\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pickle as pk\n",
    "import multiprocessing as mp\n",
    "from scipy.stats import t\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from scipy.special import comb\n",
    "from scipy.stats import norm\n",
    "from decoders import naiveBayes\n",
    "import itertools\n",
    "import h5py\n",
    "\n",
    "import config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some paths\n",
    "rootPath = '/Users/frederictheunissen/Code/songephys/'\n",
    "dataPath = 'data/birds/'\n",
    "\n",
    "\n",
    "# Color code for call type\n",
    "callColor = {'Be': (0/255.0, 230/255.0, 255/255.0), 'LT': (0/255.0, 95/255.0, 255/255.0), 'Tu': (255/255.0, 200/255.0, 65/255.0), 'Th': (255/255.0, 150/255.0, 40/255.0), \n",
    "             'Di': (255/255.0, 105/255.0, 15/255.0), 'Ag': (255/255.0, 0/255.0, 0/255.0), 'Wh': (255/255.0, 180/255.0, 255/255.0), 'Ne': (255/255.0, 100/255.0, 255/255.0),\n",
    "             'Te': (140/255.0, 100/255.0, 185/255.0), 'DC': (100/255.0, 50/255.0, 200/255.0), 'So': (255/255.0, 255/255.0, 255/255.0)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/frederictheunissen/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator PCA from version 0.24.2 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Load the data base\n",
    "outPath = rootPath+dataPath+'HerminaDataBase.pkl'\n",
    "fileIn = open(outPath,\"rb\")\n",
    "dfDataBase = pk.load(fileIn)\n",
    "pcKDE = pk.load(fileIn)\n",
    "fileIn.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfDataBaseNW = dfDataBase[dfDataBase['call'] != 'Wh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dfDataBaseNW' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/zn/qkxxn59x2712k0l7j64f7ry40000gn/T/ipykernel_36054/1192288430.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Number of Units without Whines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0munitNames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfDataBaseNW\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'site'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mdfDataBaseNW\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'unit'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mnunits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munitNames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Number of Calls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dfDataBaseNW' is not defined"
     ]
    }
   ],
   "source": [
    "# Number of Units without Whines\n",
    "unitNames = np.unique(dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'])\n",
    "nunits = unitNames.shape[0]\n",
    "\n",
    "# Number of Calls\n",
    "callNames = np.unique(dfDataBaseNW['call'])\n",
    "ncalls = callNames.shape[0]\n",
    "\n",
    "# Stims per Units\n",
    "nstimsUnit = dict()\n",
    "unitNamesFull = []\n",
    "for unit in unitNames:\n",
    "    nstimsUnit[unit] = len(np.unique(dfDataBaseNW['stim'][dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'] == unit]))\n",
    "    # Select only Units with 110 stimuli (for now) or 97  if NW\n",
    "    if (nstimsUnit[unit] == 97):\n",
    "        unitNamesFull.append(unit)\n",
    "    \n",
    "nunitsFull = len(unitNamesFull)\n",
    "\n",
    "print(nunitsFull)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191\n"
     ]
    }
   ],
   "source": [
    "# Number of Units with Whines\n",
    "unitNames = np.unique(dfDataBase['site']+'_'+ dfDataBase['unit'])\n",
    "nunits = unitNames.shape[0]\n",
    "\n",
    "# Number of Calls\n",
    "callNames = np.unique(dfDataBase['call'])\n",
    "ncalls = callNames.shape[0]\n",
    "\n",
    "# Stims per Units\n",
    "nstimsUnit = dict()\n",
    "unitNamesFull = []\n",
    "for unit in unitNames:\n",
    "    nstimsUnit[unit] = len(np.unique(dfDataBase['stim'][dfDataBase['site']+'_'+ dfDataBase['unit'] == unit]))\n",
    "    # Select only Units with 110 stimuli (for now) or 97  if NW\n",
    "    if (nstimsUnit[unit] == 110):\n",
    "        unitNamesFull.append(unit)\n",
    "    \n",
    "nunitsFull = len(unitNamesFull)\n",
    "\n",
    "print(nunitsFull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the results of Part 1 Runs that includes all neurons\n",
    "\n",
    "# Read the PC and Confusion matrices data base\n",
    "# Use the following data if NW\n",
    "#inPath = rootPath+dataPath+'HerminaDataBase1UnitPCNW.pkl'\n",
    "\n",
    "inPath = rootPath+dataPath+'HerminaDataBase1UnitPC.pkl'\n",
    "fileIn = open(inPath,\"rb\")\n",
    "unitNamesFull = pk.load(fileIn)\n",
    "confMatTot = pk.load(fileIn)\n",
    "pcc = pk.load(fileIn)\n",
    "fileIn.close()\n",
    "\n",
    "# These are the neurons that we will be keeping  Use 13 if NW 12 otherwise.\n",
    "indGood = np.argwhere(pcc > 12).flatten()\n",
    "nGood = indGood.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZF5M_9_5t_190701_125653_goodPlayback-e10-c115.pkl 26.360434326725603\n",
      "ZF7F_7t_190806_115742_goodPlayback-e13-c0.pkl 19.43376352108709\n",
      "ZF5M_8t_190627_132428_goodPlayback-e12-c76.pkl 19.3922611150445\n",
      "ZF4F_2t_190612_134337_goodPlayback-e16-c98.pkl 18.669185016155314\n",
      "ZF4F_5t_190615_114154_goodPlayback-e20-c56.pkl 18.180298735672\n",
      "ZF7F_5t_190720_152938_goodPlayback-e19-c65.pkl 17.986097749648305\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e11-c62.pkl 17.741038436633367\n",
      "ZF4F_7t_190619_095313_goodPlayback-e19-c49.pkl 17.316159758960968\n",
      "ZF4F_6t_190616_142033_goodPlayback-e19-c38.pkl 17.230253782733975\n",
      "ZF4F_2t_190612_101201_goodPlayback-e17-c38.pkl 17.05126138896764\n",
      "ZF4F_3t_190613_150928_goodPlayback-e17-c97.pkl 16.907595803020097\n",
      "ZF7F_7t_190806_115742_goodPlayback-e10-c65.pkl 16.82358990700664\n",
      "ZF4F_2t_190612_101201_goodPlayback-e16-c66.pkl 16.576001372930396\n",
      "ZF7F_6t_190724_104215_goodPlayback-e19-c54.pkl 16.180409934563144\n",
      "ZF4F_2t_190612_134337_goodPlayback-e11-c27.pkl 16.03076077468061\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e19-c60.pkl 15.998596064371162\n",
      "ZF4F_2t_190612_101201_goodPlayback-e20-c54.pkl 15.923618339348605\n",
      "ZF4F_2t_190612_134337_goodPlayback-e11-c35.pkl 15.86967886606098\n",
      "ZF4F_2t_190612_134337_goodPlayback-e17-c57.pkl 15.816046289314079\n",
      "ZF4F_2t_190612_134337_goodPlayback-e17-c55.pkl 15.803797226449506\n",
      "ZF6M_10_5t_190808_102150_goodPlayback-e21-c72.pkl 15.58544544815578\n",
      "ZF4F_2t_190612_134337_goodPlayback-e20-c49.pkl 15.56642737614275\n",
      "ZF7F_7t_190806_115742_goodPlayback-e19-c32.pkl 15.448050397917367\n",
      "ZF5M_10_5t_190703_133641_goodPlayback-e18-c48.pkl 15.421267745794214\n",
      "ZF6M_7_5t_190723_141539_goodPlayback-e14-c23.pkl 15.38231195512827\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e16-c63.pkl 15.215760876073531\n",
      "ZF4F_5t_190615_114154_goodPlayback-e18-c106.pkl 15.111566534649256\n",
      "ZF6M_9_5t_190805_102553_goodPlayback-e15-c17.pkl 15.012735041474762\n",
      "ZF6M_2t_190716_085010_goodPlayback-e18-c27.pkl 14.887819748738206\n",
      "ZF4F_2t_190612_101201_goodPlayback-e11-c31.pkl 14.798885144015031\n",
      "ZF5M_11t_190704_100420_goodPlayback-e10-c51.pkl 14.70594525206345\n",
      "ZF7F_7t_190806_115742_goodPlayback-e17-c31.pkl 14.569581024692468\n",
      "ZF4F_3t_190613_150928_goodPlayback-e11-c6.pkl 14.545972094471695\n",
      "ZF4F_2t_190612_101201_goodPlayback-e8-c38.pkl 14.524052301282309\n",
      "ZF5M_11t_190704_121850_goodPlayback-e10-c72.pkl 14.350343732735201\n",
      "ZF7F_6t_190724_104215_goodPlayback-e19-c53.pkl 14.334006573548185\n",
      "ZF4F_2t_190612_134337_goodPlayback-e20-c50.pkl 14.285957952569763\n",
      "ZF4F_2t_190612_134337_goodPlayback-e18-c1.pkl 14.239122610848026\n",
      "ZF7F_3t_190716_130715_goodPlayback-e10-c14.pkl 14.206815517624042\n",
      "ZF4F_2t_190612_101201_goodPlayback-e17-c36.pkl 14.15759549528161\n",
      "ZF5M_8t_190627_132428_goodPlayback-e11-c50.pkl 14.093999193644654\n",
      "ZF7F_5t_190720_152938_goodPlayback-e19-c64.pkl 13.995060544163435\n",
      "ZF7F_5t_190720_152938_goodPlayback-e18-c12.pkl 13.971404859451479\n",
      "ZF4F_6t_190616_142033_goodPlayback-e16-c70.pkl 13.947174474834597\n",
      "ZF4F_3t_190613_150928_goodPlayback-e20-c99.pkl 13.903031128536922\n",
      "ZF7F_5t_190720_152938_goodPlayback-e18-c11.pkl 13.839977066084245\n",
      "ZF6M_2t_190716_085010_goodPlayback-e15-c32.pkl 13.792407283184646\n",
      "ZF4F_3t_190613_150928_goodPlayback-e17-c98.pkl 13.744732784742414\n",
      "ZF4F_5t_190615_114154_goodPlayback-e19-c104.pkl 13.620821880962769\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e8-c42.pkl 13.589663330602757\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e16-c52.pkl 13.58622182537735\n",
      "ZF5M_11t_190704_100420_goodPlayback-e18-c53.pkl 13.583982799995322\n",
      "ZF4F_2t_190612_101201_goodPlayback-e8-c22.pkl 13.518575229295008\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e11-c65.pkl 13.489246122948048\n",
      "ZF4F_2t_190612_134337_goodPlayback-e8-c59.pkl 13.449362183260249\n",
      "ZF6M_9_5t_190805_102553_goodPlayback-e19-c71.pkl 13.443832880790794\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e8-c43.pkl 13.420430889404797\n",
      "ZF7F_3t_190716_130715_goodPlayback-e19-c11.pkl 13.402337251878564\n",
      "ZF6M_8t_190725_105432_goodPlayback-e15-c70.pkl 13.278063503635815\n",
      "ZF5M_11t_190704_100420_goodPlayback-e13-c13.pkl 13.161757297714846\n",
      "ZF4F_2t_190612_101201_goodPlayback-e19-c2.pkl 13.1439829131898\n",
      "ZF5M_12t_190707_102304_goodPlayback-e19-c55.pkl 13.136293487635323\n",
      "ZF6M_7_5t_190723_100723_goodPlayback-e13-c131.pkl 13.102254571991079\n",
      "ZF7F_5_5t_190722_121506_goodPlayback-e19-c58.pkl 13.082575362424901\n",
      "ZF5M_8t_190627_132428_goodPlayback-e10-c15.pkl 13.075504428397187\n",
      "ZF6M_7_5t_190723_100723_goodPlayback-e14-c59.pkl 13.074215456381117\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e20-c136.pkl 13.062118746235765\n",
      "ZF4F_2t_190613_101015.old_goodPlayback-e20-c134.pkl 12.960004493482968\n",
      "ZF4F_2t_190612_134337_goodPlayback-e16-c94.pkl 12.913361269469148\n",
      "ZF5M_11t_190704_100420_goodPlayback-e20-c66.pkl 12.907279514661438\n",
      "ZF4F_2t_190612_101201_goodPlayback-e9-c1.pkl 12.896338530831141\n",
      "ZF4F_3t_190613_150928_goodPlayback-e9-c22.pkl 12.84368836888477\n",
      "ZF5M_11t_190704_121850_goodPlayback-e18-c49.pkl 12.828980667661686\n",
      "ZF5M_11t_190704_100420_goodPlayback-e13-c14.pkl 12.760528546382968\n",
      "ZF7F_4_5t_190718_091732_goodPlayback-e17-c1.pkl 12.756907898632825\n",
      "ZF5M_10_5t_190703_133641_goodPlayback-e15-c29.pkl 12.753767084464386\n",
      "ZF7F_5_5t_190722_121506_goodPlayback-e13-c15.pkl 12.670231700229023\n",
      "ZF4F_2t_190612_134337_goodPlayback-e16-c96.pkl 12.621254254552964\n",
      "ZF7F_7t_190806_115742_goodPlayback-e19-c34.pkl 12.614832354614439\n",
      "ZF6M_7_5t_190723_100723_goodPlayback-e21-c27.pkl 12.607127562337583\n",
      "ZF5M_12t_190707_102304_goodPlayback-e10-c1.pkl 12.602097954496056\n",
      "ZF7F_5_5t_190722_121506_goodPlayback-e11-c1.pkl 12.59531368084923\n",
      "ZF5M_10t_190702_095313_goodPlayback-e11-c57.pkl 12.578780830056727\n",
      "ZF5M_11t_190704_100420_goodPlayback-e19-c59.pkl 12.530623773571886\n",
      "ZF6M_8t_190725_105432_goodPlayback-e15-c71.pkl 12.518373254585361\n",
      "ZF6M_9_5t_190805_130046_goodPlayback-e19-c63.pkl 12.490556912928238\n",
      "ZF6M_7_5t_190723_141539_goodPlayback-e11-c7.pkl 12.404233973534765\n",
      "ZF5M_10_5t_190703_133641_goodPlayback-e20-c30.pkl 12.316732471342133\n",
      "ZF7F_7t_190806_115742_goodPlayback-e10-c68.pkl 12.2837147836912\n",
      "ZF7F_8_5t_190809_133540_goodPlayback-e17-c29.pkl 12.277729289952834\n",
      "ZF4F_3t_190613_150928_goodPlayback-e12-c35.pkl 12.253578453741783\n",
      "ZF6M_9_5t_190805_130046_goodPlayback-e19-c64.pkl 12.252012713210114\n",
      "ZF4F_2t_190612_134337_goodPlayback-e19-c2.pkl 12.241156467648198\n",
      "ZF4F_2t_190612_101201_goodPlayback-e20-c55.pkl 12.224729879149285\n",
      "ZF5M_11_5t_190705_102710_goodPlayback-e18-c53.pkl 12.222176743722656\n",
      "ZF7F_4_5t_190718_091732_goodPlayback-e9-c1.pkl 12.203871117811302\n",
      "ZF6M_9_5t_190805_130046_goodPlayback-e21-c27.pkl 12.180034182764063\n",
      "ZF7F_7_5t_190807_105305_goodPlayback-e19-c41.pkl 12.143825785603143\n",
      "ZF7F_8_5t_190809_133540_goodPlayback-e19-c2.pkl 12.095244590762533\n",
      "ZF6M_8t_190725_105432_goodPlayback-e15-c67.pkl 12.07198998458496\n",
      "ZF4F : 43\n",
      "ZF5M : 19\n",
      "ZF6M : 16\n",
      "ZF7F : 22\n"
     ]
    }
   ],
   "source": [
    "pccGood = pcc[indGood]\n",
    "unitNamesGood = [unitNamesFull[i] for i in indGood]\n",
    "\n",
    "indSort = np.flip(np.argsort(pccGood))\n",
    "\n",
    "# Printing the name of the most discriminating units\n",
    "birdName = []\n",
    "for ind in indSort:\n",
    "    print(unitNamesGood[ind], pccGood[ind])\n",
    "    birdName.append(unitNamesGood[ind][0:4])\n",
    "    \n",
    "birdUnique = np.unique(birdName)\n",
    "for bird in birdUnique:\n",
    "    print(bird, \":\", len([x for x in birdName if x == bird]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decoding for a variable number of units.  \n",
    "# For 1 and 2 and when combinations are smaller that maximum number of tests, all permutations are done. \n",
    "\n",
    "# FIrst make the list of arguments so that the multiprocessing can be called out.\n",
    "# We are doing 1,2,3,4,5,10,20\n",
    "\n",
    "numUnits = 15\n",
    "code = 'PC'\n",
    "maxTest = 9999\n",
    "mpflag = True\n",
    "nBirds = len(birdUnique)\n",
    "argListBirds = [[] for i in range(nBirds)]\n",
    "ntests = np.zeros((nBirds,), dtype=int)\n",
    "\n",
    "for ibird, bird in enumerate(birdUnique):\n",
    "    nGoodBird = len([x for x in birdName if x == bird])\n",
    "    \n",
    "    if nGoodBird < numUnits:\n",
    "        continue\n",
    "        \n",
    "    ntests[ibird] = int(comb(nGoodBird,numUnits))\n",
    "    if ntests[ibird] > maxTest:\n",
    "        ntests[ibird] = maxTest + 1\n",
    "    \n",
    "    # Generate the good index for this bird\n",
    "    indGoodBird = [ind for ind in indGood if unitNamesFull[ind][0:4] == bird]\n",
    "\n",
    "    # Generate the list of arguments to run naiveBayes in parallel\n",
    "    argList = argListBirds[ibird]\n",
    "    if numUnits == 1:\n",
    "        for i, ind in enumerate(indGoodBird):\n",
    "            if i == ntests[ibird]:\n",
    "                break\n",
    "            unitChoice = [unitNamesFull[ind]]\n",
    "            argList.append(unitChoice)\n",
    "    elif numUnits == 2:  \n",
    "        i = 0\n",
    "        for i1, ind1 in enumerate(indGoodBird):\n",
    "            if i1 == ntests[ibird]:\n",
    "                break\n",
    "            for i2, ind2 in enumerate(indGoodBird):\n",
    "                if i2 <= i1:\n",
    "                    continue\n",
    "                if i2 == ntests[ibird]:\n",
    "                    break\n",
    "                unitChoice = [unitNamesFull[ind1], unitNamesFull[ind2]]\n",
    "                argList.append(unitChoice)\n",
    "                i += 1\n",
    "    elif ntests[ibird] <= maxTest:\n",
    "        # Generate all possible combinations\n",
    "        unitNamesGood = np.array(unitNamesFull)[indGoodBird]\n",
    "        for unitChoice in itertools.combinations(unitNamesGood, numUnits):\n",
    "            argList.append(list(unitChoice))\n",
    "    else:\n",
    "        unitNamesGood = np.array(unitNamesFull)[indGoodBird]\n",
    "        for i in range(ntests[ibird]):\n",
    "            unitChoice = np.random.choice(unitNamesGood, numUnits, replace=False)\n",
    "            argList.append(unitChoice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with chunk 1/100 for bird 1/4\n",
      "Done with chunk 2/100 for bird 1/4\n",
      "Done with chunk 3/100 for bird 1/4\n",
      "Done with chunk 4/100 for bird 1/4\n",
      "Done with chunk 5/100 for bird 1/4\n",
      "Done with chunk 6/100 for bird 1/4\n",
      "Done with chunk 7/100 for bird 1/4\n",
      "Done with chunk 8/100 for bird 1/4\n",
      "Done with chunk 9/100 for bird 1/4\n",
      "Done with chunk 10/100 for bird 1/4\n",
      "Done with chunk 11/100 for bird 1/4\n",
      "Done with chunk 12/100 for bird 1/4\n",
      "Done with chunk 13/100 for bird 1/4\n",
      "Done with chunk 14/100 for bird 1/4\n",
      "Done with chunk 15/100 for bird 1/4\n",
      "Done with chunk 16/100 for bird 1/4\n",
      "Done with chunk 17/100 for bird 1/4\n",
      "Done with chunk 18/100 for bird 1/4\n",
      "Done with chunk 19/100 for bird 1/4\n",
      "Done with chunk 20/100 for bird 1/4\n",
      "Done with chunk 21/100 for bird 1/4\n",
      "Done with chunk 22/100 for bird 1/4\n",
      "Done with chunk 23/100 for bird 1/4\n",
      "Done with chunk 24/100 for bird 1/4\n",
      "Done with chunk 25/100 for bird 1/4\n",
      "Done with chunk 26/100 for bird 1/4\n",
      "Done with chunk 27/100 for bird 1/4\n",
      "Done with chunk 28/100 for bird 1/4\n",
      "Done with chunk 29/100 for bird 1/4\n",
      "Done with chunk 30/100 for bird 1/4\n",
      "Done with chunk 31/100 for bird 1/4\n",
      "Done with chunk 32/100 for bird 1/4\n",
      "Done with chunk 33/100 for bird 1/4\n",
      "Done with chunk 34/100 for bird 1/4\n",
      "Done with chunk 35/100 for bird 1/4\n",
      "Done with chunk 36/100 for bird 1/4\n",
      "Done with chunk 37/100 for bird 1/4\n",
      "Done with chunk 38/100 for bird 1/4\n",
      "Done with chunk 39/100 for bird 1/4\n",
      "Done with chunk 40/100 for bird 1/4\n",
      "Done with chunk 41/100 for bird 1/4\n",
      "Done with chunk 42/100 for bird 1/4\n",
      "Done with chunk 43/100 for bird 1/4\n",
      "Done with chunk 44/100 for bird 1/4\n",
      "Done with chunk 45/100 for bird 1/4\n",
      "Done with chunk 46/100 for bird 1/4\n",
      "Done with chunk 47/100 for bird 1/4\n",
      "Done with chunk 48/100 for bird 1/4\n",
      "Done with chunk 49/100 for bird 1/4\n",
      "Done with chunk 50/100 for bird 1/4\n",
      "Done with chunk 51/100 for bird 1/4\n",
      "Done with chunk 52/100 for bird 1/4\n",
      "Done with chunk 53/100 for bird 1/4\n",
      "Done with chunk 54/100 for bird 1/4\n",
      "Done with chunk 55/100 for bird 1/4\n",
      "Done with chunk 56/100 for bird 1/4\n",
      "Done with chunk 57/100 for bird 1/4\n",
      "Done with chunk 58/100 for bird 1/4\n",
      "Done with chunk 59/100 for bird 1/4\n",
      "Done with chunk 60/100 for bird 1/4\n",
      "Done with chunk 61/100 for bird 1/4\n",
      "Done with chunk 62/100 for bird 1/4\n",
      "Done with chunk 63/100 for bird 1/4\n",
      "Done with chunk 64/100 for bird 1/4\n",
      "Done with chunk 65/100 for bird 1/4\n",
      "Done with chunk 66/100 for bird 1/4\n",
      "Done with chunk 67/100 for bird 1/4\n",
      "Done with chunk 68/100 for bird 1/4\n",
      "Done with chunk 69/100 for bird 1/4\n",
      "Done with chunk 70/100 for bird 1/4\n",
      "Done with chunk 71/100 for bird 1/4\n",
      "Done with chunk 72/100 for bird 1/4\n",
      "Done with chunk 73/100 for bird 1/4\n",
      "Done with chunk 74/100 for bird 1/4\n",
      "Done with chunk 75/100 for bird 1/4\n",
      "Done with chunk 76/100 for bird 1/4\n",
      "Done with chunk 77/100 for bird 1/4\n",
      "Done with chunk 78/100 for bird 1/4\n",
      "Done with chunk 79/100 for bird 1/4\n",
      "Done with chunk 80/100 for bird 1/4\n",
      "Done with chunk 81/100 for bird 1/4\n",
      "Done with chunk 82/100 for bird 1/4\n",
      "Done with chunk 83/100 for bird 1/4\n",
      "Done with chunk 84/100 for bird 1/4\n",
      "Done with chunk 85/100 for bird 1/4\n",
      "Done with chunk 86/100 for bird 1/4\n",
      "Done with chunk 87/100 for bird 1/4\n",
      "Done with chunk 88/100 for bird 1/4\n",
      "Done with chunk 89/100 for bird 1/4\n",
      "Done with chunk 90/100 for bird 1/4\n",
      "Done with chunk 91/100 for bird 1/4\n",
      "Done with chunk 92/100 for bird 1/4\n",
      "Done with chunk 93/100 for bird 1/4\n",
      "Done with chunk 94/100 for bird 1/4\n",
      "Done with chunk 95/100 for bird 1/4\n",
      "Done with chunk 96/100 for bird 1/4\n",
      "Done with chunk 97/100 for bird 1/4\n",
      "Done with chunk 98/100 for bird 1/4\n",
      "Done with chunk 99/100 for bird 1/4\n",
      "Done with chunk 100/100 for bird 1/4\n",
      "Done with chunk 1/39 for bird 2/4\n",
      "Done with chunk 2/39 for bird 2/4\n",
      "Done with chunk 3/39 for bird 2/4\n",
      "Done with chunk 4/39 for bird 2/4\n",
      "Done with chunk 5/39 for bird 2/4\n",
      "Done with chunk 6/39 for bird 2/4\n",
      "Done with chunk 7/39 for bird 2/4\n",
      "Done with chunk 8/39 for bird 2/4\n",
      "Done with chunk 9/39 for bird 2/4\n",
      "Done with chunk 10/39 for bird 2/4\n",
      "Done with chunk 11/39 for bird 2/4\n",
      "Done with chunk 12/39 for bird 2/4\n",
      "Done with chunk 13/39 for bird 2/4\n",
      "Done with chunk 14/39 for bird 2/4\n",
      "Done with chunk 15/39 for bird 2/4\n",
      "Done with chunk 16/39 for bird 2/4\n",
      "Done with chunk 17/39 for bird 2/4\n",
      "Done with chunk 18/39 for bird 2/4\n",
      "Done with chunk 19/39 for bird 2/4\n",
      "Done with chunk 20/39 for bird 2/4\n",
      "Done with chunk 21/39 for bird 2/4\n",
      "Done with chunk 22/39 for bird 2/4\n",
      "Done with chunk 23/39 for bird 2/4\n",
      "Done with chunk 24/39 for bird 2/4\n",
      "Done with chunk 25/39 for bird 2/4\n",
      "Done with chunk 26/39 for bird 2/4\n",
      "Done with chunk 27/39 for bird 2/4\n",
      "Done with chunk 28/39 for bird 2/4\n",
      "Done with chunk 29/39 for bird 2/4\n",
      "Done with chunk 30/39 for bird 2/4\n",
      "Done with chunk 31/39 for bird 2/4\n",
      "Done with chunk 32/39 for bird 2/4\n",
      "Done with chunk 33/39 for bird 2/4\n",
      "Done with chunk 34/39 for bird 2/4\n",
      "Done with chunk 35/39 for bird 2/4\n",
      "Done with chunk 36/39 for bird 2/4\n",
      "Done with chunk 37/39 for bird 2/4\n",
      "Done with chunk 38/39 for bird 2/4\n",
      "Done with chunk 39/39 for bird 2/4\n",
      "Done with chunk 1/1 for bird 3/4\n",
      "Done with chunk 1/100 for bird 4/4\n",
      "Done with chunk 2/100 for bird 4/4\n",
      "Done with chunk 3/100 for bird 4/4\n",
      "Done with chunk 4/100 for bird 4/4\n",
      "Done with chunk 5/100 for bird 4/4\n",
      "Done with chunk 6/100 for bird 4/4\n",
      "Done with chunk 7/100 for bird 4/4\n",
      "Done with chunk 8/100 for bird 4/4\n",
      "Done with chunk 9/100 for bird 4/4\n",
      "Done with chunk 10/100 for bird 4/4\n",
      "Done with chunk 11/100 for bird 4/4\n",
      "Done with chunk 12/100 for bird 4/4\n",
      "Done with chunk 13/100 for bird 4/4\n",
      "Done with chunk 14/100 for bird 4/4\n",
      "Done with chunk 15/100 for bird 4/4\n",
      "Done with chunk 16/100 for bird 4/4\n",
      "Done with chunk 17/100 for bird 4/4\n",
      "Done with chunk 18/100 for bird 4/4\n",
      "Done with chunk 19/100 for bird 4/4\n",
      "Done with chunk 20/100 for bird 4/4\n",
      "Done with chunk 21/100 for bird 4/4\n",
      "Done with chunk 22/100 for bird 4/4\n",
      "Done with chunk 23/100 for bird 4/4\n",
      "Done with chunk 24/100 for bird 4/4\n",
      "Done with chunk 25/100 for bird 4/4\n",
      "Done with chunk 26/100 for bird 4/4\n",
      "Done with chunk 27/100 for bird 4/4\n",
      "Done with chunk 28/100 for bird 4/4\n",
      "Done with chunk 29/100 for bird 4/4\n",
      "Done with chunk 30/100 for bird 4/4\n",
      "Done with chunk 31/100 for bird 4/4\n",
      "Done with chunk 32/100 for bird 4/4\n",
      "Done with chunk 33/100 for bird 4/4\n",
      "Done with chunk 34/100 for bird 4/4\n",
      "Done with chunk 35/100 for bird 4/4\n",
      "Done with chunk 36/100 for bird 4/4\n",
      "Done with chunk 37/100 for bird 4/4\n",
      "Done with chunk 38/100 for bird 4/4\n",
      "Done with chunk 39/100 for bird 4/4\n",
      "Done with chunk 40/100 for bird 4/4\n",
      "Done with chunk 41/100 for bird 4/4\n",
      "Done with chunk 42/100 for bird 4/4\n",
      "Done with chunk 43/100 for bird 4/4\n",
      "Done with chunk 44/100 for bird 4/4\n",
      "Done with chunk 45/100 for bird 4/4\n",
      "Done with chunk 46/100 for bird 4/4\n",
      "Done with chunk 47/100 for bird 4/4\n",
      "Done with chunk 48/100 for bird 4/4\n",
      "Done with chunk 49/100 for bird 4/4\n",
      "Done with chunk 50/100 for bird 4/4\n",
      "Done with chunk 51/100 for bird 4/4\n",
      "Done with chunk 52/100 for bird 4/4\n",
      "Done with chunk 53/100 for bird 4/4\n",
      "Done with chunk 54/100 for bird 4/4\n",
      "Done with chunk 55/100 for bird 4/4\n",
      "Done with chunk 56/100 for bird 4/4\n",
      "Done with chunk 57/100 for bird 4/4\n",
      "Done with chunk 58/100 for bird 4/4\n",
      "Done with chunk 59/100 for bird 4/4\n",
      "Done with chunk 60/100 for bird 4/4\n",
      "Done with chunk 61/100 for bird 4/4\n",
      "Done with chunk 62/100 for bird 4/4\n",
      "Done with chunk 63/100 for bird 4/4\n",
      "Done with chunk 64/100 for bird 4/4\n",
      "Done with chunk 65/100 for bird 4/4\n",
      "Done with chunk 66/100 for bird 4/4\n",
      "Done with chunk 67/100 for bird 4/4\n",
      "Done with chunk 68/100 for bird 4/4\n",
      "Done with chunk 69/100 for bird 4/4\n",
      "Done with chunk 70/100 for bird 4/4\n",
      "Done with chunk 71/100 for bird 4/4\n",
      "Done with chunk 72/100 for bird 4/4\n",
      "Done with chunk 73/100 for bird 4/4\n",
      "Done with chunk 74/100 for bird 4/4\n",
      "Done with chunk 75/100 for bird 4/4\n",
      "Done with chunk 76/100 for bird 4/4\n",
      "Done with chunk 77/100 for bird 4/4\n",
      "Done with chunk 78/100 for bird 4/4\n",
      "Done with chunk 79/100 for bird 4/4\n",
      "Done with chunk 80/100 for bird 4/4\n",
      "Done with chunk 81/100 for bird 4/4\n",
      "Done with chunk 82/100 for bird 4/4\n",
      "Done with chunk 83/100 for bird 4/4\n",
      "Done with chunk 84/100 for bird 4/4\n",
      "Done with chunk 85/100 for bird 4/4\n",
      "Done with chunk 86/100 for bird 4/4\n",
      "Done with chunk 87/100 for bird 4/4\n",
      "Done with chunk 88/100 for bird 4/4\n",
      "Done with chunk 89/100 for bird 4/4\n",
      "Done with chunk 90/100 for bird 4/4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with chunk 91/100 for bird 4/4\n",
      "Done with chunk 92/100 for bird 4/4\n",
      "Done with chunk 93/100 for bird 4/4\n",
      "Done with chunk 94/100 for bird 4/4\n",
      "Done with chunk 95/100 for bird 4/4\n",
      "Done with chunk 96/100 for bird 4/4\n",
      "Done with chunk 97/100 for bird 4/4\n",
      "Done with chunk 98/100 for bird 4/4\n",
      "Done with chunk 99/100 for bird 4/4\n",
      "Done with chunk 100/100 for bird 4/4\n"
     ]
    }
   ],
   "source": [
    "# Run the decoders using multiprocessors if possible\n",
    "\n",
    "# Run Naive Bayes in chunks of 90 with a pool of 30 workers\n",
    "chunckSize = 100    # A chunk size of 100 is used for memory since it has to keep the results of all or 90 and 1800 tests.\n",
    "poolSize = 30\n",
    "\n",
    "# Allocate memory for results\n",
    "pccBirds = []\n",
    "selHBirds = []\n",
    "confMatTotBirds = []\n",
    "\n",
    "for ibird, bird in enumerate(birdUnique):\n",
    "    \n",
    "    # Allocate bird specific storage for the results\n",
    "    pcc = np.zeros((ntests[ibird],))\n",
    "    selH = np.zeros((ntests[ibird],))\n",
    "    confMatTot = np.zeros((ncalls, ncalls))\n",
    "    confMatCount = 0\n",
    "    \n",
    "    # Calculate the number of chunks\n",
    "    nchunks = ntests[ibird]//chunckSize\n",
    "    if ntests[ibird] % chunckSize:\n",
    "        nchunks += 1\n",
    "        \n",
    "    # Arglist for this bird\n",
    "    argList = argListBirds[ibird]\n",
    "    \n",
    "    \n",
    "    if mpflag == True:\n",
    "        if __name__ == '__main__':\n",
    "            for ichunk in range(nchunks):\n",
    "                istart = ichunk*chunckSize\n",
    "                iend = (ichunk+1)*chunckSize\n",
    "                if iend > ntests[ibird]:\n",
    "                    iend = ntests[ibird]         \n",
    "        \n",
    "                with mp.Pool(poolSize) as p:\n",
    "                    results = p.map(naiveBayes, argList[istart:iend])\n",
    "        \n",
    "            # I don't think I need these??\n",
    "            # p.close()\n",
    "            # p.join()\n",
    "        \n",
    "                for i, res in enumerate(results):\n",
    "                    confMat = res[1]\n",
    "                    if (confMat.shape == (ncalls,ncalls) ):\n",
    "                        pcc[istart + i] = res[0]\n",
    "                        pccDiag = np.diag(confMat)\n",
    "                        pccDiag = pccDiag/np.sum(pccDiag)\n",
    "                        pccDiag[pccDiag == 0] = 1. # Because 0log0 = 0\n",
    "                        selH[istart + i] = 1 - np.sum(-pccDiag*np.log2(pccDiag))/(np.log2(ncalls))\n",
    "                        testsPerClass = res[2]\n",
    "                        confMatTot = np.add(confMatTot, confMat)\n",
    "                        confMatCount += testsPerClass\n",
    "                \n",
    "                print('Done with chunk %d/%d for bird %d/%d' % (ichunk+1, nchunks, ibird+1, nBirds))\n",
    "    else:\n",
    "        for i in range(ntests[ibird]):\n",
    "            pccval, confMat, testsPerClass = naiveBayes(argList[i], code = code)\n",
    "            pcc[i] = pccval\n",
    "            pccDiag = np.diag(confMat)\n",
    "            pccDiag = pccDiag/np.sum(pccDiag)\n",
    "            pccDiag[pccDiag == 0] = 1. # Because 0log0 = 0\n",
    "            selH[i] = 1 - np.sum(-pccDiag*np.log2(pccDiag))/(np.log2(ncalls))\n",
    "            confMatTot = np.add(confMatTot, confMat)\n",
    "            confMatCount += testsPerClass\n",
    "\n",
    "    # Calculate the average confusion matrix. \n",
    "    for i in range(ncalls):\n",
    "        if confMatCount[i] > 0:\n",
    "            confMatTot[i,:] = confMatTot[i,:]/confMatCount[i]\n",
    "            \n",
    "    # Store the results in the bird lists\n",
    "    pccBirds.append(pcc)\n",
    "    selHBirds.append(selH)\n",
    "    confMatTotBirds.append(confMatTot)\n",
    "    \n",
    "        \n",
    "# save the PC and Confusion matrices data base\n",
    "outPath = rootPath + dataPath + 'HerminaDataBase%dGoodUnitPCPerBird.pkl' % numUnits\n",
    "fileOut = open(outPath,\"wb\")\n",
    "pk.dump(argListBirds, fileOut)\n",
    "pk.dump(confMatTotBirds, fileOut)\n",
    "pk.dump(pccBirds, fileOut)\n",
    "pk.dump(selHBirds, fileOut)\n",
    "pk.dump(birdUnique, fileOut)\n",
    "fileOut.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.88488113434178\n",
      "35.72121433293536\n",
      "24.3743652347753\n",
      "40.40394474902341\n"
     ]
    }
   ],
   "source": [
    "for ibird in range(nBirds):\n",
    "    print(np.mean(pccBirds[ibird]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "\n",
    "for ibird, bird in enumerate(birdUnique):\n",
    "    ax.plot(pccBirds[ibird], selHBirds[ibird], '+', label = bird)\n",
    "\n",
    "ax.set_xlabel('PCC')\n",
    "ax.set_ylabel('Selelectivy (Ent Z)')\n",
    "ax.set_title('%d Unit Good PC' % numUnits)\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/scatterPCCSelH%dUnitGoodPCPerBird.eps' % numUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "\n",
    "for ibird, bird in enumerate(birdUnique):\n",
    "   ax.hist(pccBirds[ibird], bins = 30, range = (0,60), label=bird)\n",
    "\n",
    "ax.set_xlabel('PCC')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('%d Unit Good PC' % numUnits)\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/pcchist%dUnitGoodPCPerBird.eps' % numUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG4AAAHECAYAAABocmxFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAC4jAAAuIwF4pT92AADCgklEQVR4nOzdd5gcV5X38e+ZpBnlLFmyLclyjnIC2yTbJJNtE5dowpLTkmFZbMK7y8ICu2QWMCYsGIwBY8DYGHC2cY5ykmTZVg6jUZg80+f9o3o01TXV06m6u3r0++jpR123q27d6ZnTt/rWDebuiIiIiIiIiIhI+jTVuwAiIiIiIiIiIhJPDTciIiIiIiIiIimlhhsRERERERERkZRSw42IiIiIiIiISEqp4UZEREREREREJKXUcCMiIiIiIiIiklJquBERERERERERSSk13IiIiIiIiIiIpJQabkREREREREREUkoNNyIiIiIiIiIiKaWGGxERERERERGRlFLDjYiIiIiIiIhISqnhRkREREREREQkpdRwIyIiIiIiIiKSUmq4ERERERERERFJKTXciIiIiIiIiIiklBpuRERERERERERSSg03IiIiIiIiIiIppYYbEREREREREZGUUsONiIiIiIiIiEhKqeFGRERERERERCSl1HAjIiIiIiIiIpJSargREREREREREUkpNdyIiIiIiIiIiKSUGm5ERERERERERFJKDTciIiIiIiIiIimlhhsRERERERERkZRSw42IiIiIiIiISEqp4UZEREREREREJKXUcCMiIiIiIiIiklJquBERERERERERSamWehdARKSRmdkc4FnA/sAUYBuwErjF3YcTOscHgFnZzQzwFXfvTiJv2beY2XTgYOAAYD9gKtAO9AA7gY3AXe6+sYZlmgEcny3XLGAS0E0QS6uBle7eVavyxDGzFuA04DBgLrAHeBK41t13JHSOZwFnhJKucvebksi7XsysDVgBHArMByYDfUAX8BjwkLuvr1f5GpWZGbAMOIogjmcS3IzdkX3cR/Deer3KmEaKY9mXmVkrcDJwBDAHaAV2EdSzt7n71joWbwxdX8dw94Z5EFT+nwH+AqwhqPiHgaE8+88EDsw+FtS7/FV6T5YCXuPHBQXKtLYK55yZ4HtmwDV5znNNGfmdXoWf93f1/tuK/IzPJvhAiyvr6Qnk3wacALwd+F/gTmAg5lzn1fu9CJX5COCy7GdQ3PuyHfgk0K54HXPOixI8158TjteK/56z53ljgfMsrcHfaBvwDOATwO9L/F0/CXwZOLJKZWsH/hm4ARgqUJYM8CDwbeBFNY7zycC/EXwZrnXspjqOC5TnRcBvCRrgCp1nPXBxNmamKI7zluUEgli+kqDBodD7uiP7vj6rxjEzH9hazt9xFctUKI6HgF8Dh4aOWVrD+E5lnFfpfCOPixKI83Iev6vH3+A4P2NVr6+z5zgM+CFBI02+9yUDXAe8PAXvScnX12We51WRfC+s989e6NEQPW7M7Bjga+S2YFsRh55B8EEM0G1mC929J+nyScN5H0ELrhTBzKYRfOAXE3PF5jkVeDVwUvZxLMGXzIZgZmcDPwc6xtltNvDvwDlm9mJ331KLsknF3kLQsJtEPvV2JnBFmcfuD3wE+LCZ/QD4hLt3JlEoM/sngkahxcUeAhyefbydGvUWNrOFwJ8IegNJEczsROA7BHd1i7WIoD54NcFNuRsSKMqEiGMzm0sQh68EDirx8Jlk31czuwF4i7s/mmwJY32HoDdLKhQZx83Ay4GzzOw17v6HmhRu3+b1LkC9VeP6OpJ/E0GD5b8S9K4Zd3eCGz3PMLNrgNd6DXve7i1Eja6vs+/910JJ24GPlZpPraV+jhszOw+4haARJvqHXSjoLwOeyB43heBDWfZhZnYw8B/1LkeD+QqwJOE8DwZ+ALyToOGmkRptTgMuIbdSeRL4HvCfBJ87A6HXTgb+mB0yIOn38uxworKZ2UHAMxMqT70ZQc+YO7I/V/kZmbWZ2U8JLsqKbbSpCzObRNDoFf6y10/Qi+Q7dSlUymW7nN9MaY021TJR4vho4OOU3mgT9XTgbjM7t/Ii5WdmrwWqeo5SFIjjLxL08A0P1ZsC/MbMnlqzQu67bq53AVKgGtfXAJhZM/AL4AIKN9pEnQ7clv3OVDM1vr7+PMFNgxEfd/dtZeRTU6nucWNmL2e0JXKkkcYIGmM6CYZO5eXuGTP7JaMtaC8FflqVwtbPAHBPlfJeTjD/QdTjZeRVaRkrHsuYbXn+EUGX2WrbQfB3Wq7HkipIJczseQRf2oS944N/Ru5n5w+A97p7f2i/IwguFkcq5JMI7nh8j+JjoYNgXogR3cCqcfZv5HgdJBi3XI7VZR6Xz2SCu9TfryCPt1ClO2gVGiKY+2Ilwd/SBmA3wfs/jWBowInAcwjmmQlbClxnZivKubjJfoH6I0G38KgBgt4RNwKbCOr3qcA84BjgqeTGQi18mtxrjNXAC9z9UTNbRDBPhhHMFTQrtN8AwbCuUhzK+HcXIeVxbGZfIPiMi3LgLoIh7uuALQQN9bMJusMfT3DxnfSNxIkcxxC8rw8QDG14jGBoUh9BzJwEvDD7PGwycLGZnePuf0y6QNmeLd9IOt8K5Y3jkQQzayeom9+YTWoF/o/ghnEpMRSOYwceIfidxGmU+nolwbQUlZhFMGVFWDdBo0I5dH1dnK8TDAWK6iS4jn2QYOjUUuB5jB2JsBi4ysxO9ITmfxpPAtfX55dwruOB94aSbgQuLK/kNVbvsVrjjDvbj2AM73Do8U1gWfb1pQTj8TLA8Dj5nBrab0u9f65GeQBPI36OkYuLOHZt9Lh6/zzZcn0oUq5bYn6+a8rI9/SYfC6q98+bwPs1naByHPmZBoE7Yn7W08vIe0VMPiOPx4FLCS5Io6+dV+f35PWR8twINOXZ93hyx+d2AlOLPE9T5O9zEDhmnP0bKl4ZO8fN2hr+DuPitTOyfXMF+TcR3CGKjseOnnNpDX7W5xF8af737M89ucjjZgJfIn58+c/LLMslMXn1Zcs2vYjjlwOfAh6twfs2ldy5MAaBo/Ps2wzcFvm5XlvCud4TOTbuPU9lHI/zM4w8fk32mq3A8XOAN2c/855W5Dn3iTiO+TlvBd4FzClwXAfBHBBx9cK2QseXWdbLIueJu8a6oIZ/l/WM4y+Os29D1dcJ/B6+G/OzFjWXSJ44v6jeP1MC70nVrq+z+b8kJi8n6C0aex2Q/bvcFHPML2v0nqTy+jptj7oXYJw39juMNrgMEpksiaClrZiGmxaCbpGZ7C+54EXEvv4gaGXdGBO89+QL+MjxqatYCO6E9ITKtB14SszPeE0ZeU/UiuXCyM/0eeInlT29jLxXZI9dT3Cx92/AC4C5oX0uiDnXeXV+T6IXpmcV2P/SyP4vK/I874oc96Vx9m24eI35O1pbw99hXLx+m7ET5B5RZv4viOSzg+AuUvScS+v5t1zkz/KamHKXXHaCecWieWwBji2jTFaDn/ucSFnH/ULF2Ivk3xZ5noUEd7NHjoubpDm1cZw974kE11ge+TleX2Z+Rf1+95U4zv6cGeA3wIoyjn9OzO/HgW8kXM7oJM7XETTGRc97QS3+LrNlqlccr80XszRgfV3h72AywWqF0Z+3kgbai+r9cyXwvlTz+rqVoFdRNK+vFHHsMuIbqM+owXuSuuvrND5SOcdNdlzePzH6pv6nu19aTl7uPgQ8FEo6vPISTlzZ7uy/IaiIwjqBs70BJ3fODpG6iNyu6B8g+OIgMczshQQXXSNWElQsSXkE2M/dF7v7y9z98+5+had/fOnTQs/7gasL7P/7yPbTC53AzOYT9EIY8QTw2Tz7Trh4rZMNwFWRtHInJY0e93Pyd5dPNXe/GPhlzEuvKDYPM9ufsfOK7Sa4ELy3jDJ5qceU4WmR7UITlf6Z3HH3BeM866vAjNB2c+T1VMdxtm79PmPnKHuzu/+snDwr/P1OxDheC5zg7ue6+92lHuzuVxP0VIt6bXZp7Iplhw7+TyipF3grwfV7PdUrjt8XF7P7aH39SoLeJWEPuvuN9ShMGtTg+voNBKNSwh4g6IE3Lnd/DHh/zEv/VnmxCkrV9XVapbLhBjiFINCNoLfNlyrMb13o+QEV5jXRfYegJ0rYMPDqbEA3oo8QDJkb8cdyLyr3BWY2k9x5ATIEq1EMxB9ROnfvcfdNSeVXC2Y2maBL/4hHsw3D43kgsh0d5x3nKwRDVUa839278+w7EeO1XqLjm99Q6hcbM5tDMJfaePk2mrjPyiNLOP5zBBN+hn3K3aOxkSbROB13zhp3HwTCq/XMNbNx56wxs2cT3KDKpxHi+PWMXannUnev51yCEyqO3X1tOQ02EV8nuIseNpvc66JKfJ/cOuszXpvVqwqpRxz/zt0vz7P7vlhfvzUm7Yc1L0VK1OL6mqBHSdRnij2Hu/8fcH8k+QwzO6zikuWR0uvrVEprw83ILNYO3ObuuyrML3x8RasMTGRm9j5yW4FHfCJ716bhZCexCrem7iJYyUjy+zq5M63/t7v/o16FSZGZke2dRRzTFdmeFbfTCDM7g+DL0IjL3f2yPPtOuHits98TzP0wYgHBBJ+leD25vQ/udfc7Ki1YncVN/rxfMQdm78S/PpL8AMGQljSbGdlONNazd94LvQeNEMfRpVOHgA/WoRxhiuOIbINE3N9SpStVYWZvIff9vZXcJXbraWZku9px3E18b4V9sr42s0MJlpcOG2TiLRJTiqpeX5vZcoLJesM2MrZ3SiHfi0kb70ZDpWZGtut6fZ1maW24Cc+E/2QC+WVCz1O9kla9mNmzCLp7Rv3C3f+r1uVJQnbI3UVAeyj5I+6+Lv4IMbOXEHSzHLGKYFUGCbpuhhWz/GB0ZZ68Xe2zyxmGLwJ7COYGidt3wsVrvWXvRv1fJLnUYRbR/Ru9tw3kfn6OKPbu4BsZuwzp9909E7dzilQ11gmWdx5vlazUx7GZnQIcFUn+Q73rV8VxXnGr8ESH7JQkOwwyXA8NEPQeqHgV0ITUOo4vcPcx31n24fo6rrfN7919n5ymoEbX12fFpP2uiN4rUb+OSXtBGeUpVmqur9MurQ034XGx0THf5Zgdet6VQH4TipkdQLDiR7RR627gbTUvUHI+Rm631L+5eyVLg05oZjYb+N9QkgNvc/feOhUpbXaQu2zmonw7jrPPeHP4fIzcObg+5+5jlgadwPGaBtEu3C/KjokuyMxOAo4NJQ0QP8yo0Zwck1bsEuyvjGwP0Rh3W6NxWmqsD5PnWiN7R3S8uQbupjHiOPq7BfhRzUsRT3E81uSYtErr9h+QO7fLF1I2BLKWcXwf8N8x++2T9XV2eOIbY176Qa3LkgY1vL6O9nACuKHUTLJTGayKJJ+YHdJUDam4vm4EaW242Rp6Xswvr5CjQ8+j43z3aWbWDvyW3F5OELxP5zTqZGlmdhTBqkQjupnAlWRCvkHuHbjvuvu19SpM2mR7CYTH/S42s8UFDntqZPvuuJ3M7CByJ5B8gJg7dBM1XtPC3e8jWJJzRAu5d8jGE71L/3t3b+j6JnuX6gMxL0UngI07dg5j5z+51907kyhbld0T2Y7OS5Ej2/MgfK1y7zi9ir5FfC8maKw4fm5kOwOkor5QHMc6OCZtY7mZmdk/A88PJd0DfLHc/KqkVnHswLuivRr28fr6RYzt0fUkRdQdE1Strq9PjEm7ucy8bopsNwPHlZnXuNJwfd0o0tpwM9Kl04DjzSza1bpo2TGW4V9+yatYTHDfZ2ygj0yWtrb2xalctqX/x+R2tfvUBJ78rWJmdjbw2lDSEwTdgCXX3yLbb8q3Y3aoXvTLwt/z7P5NRlc9G7kIHIzZb8LFa9ZkM/ukmf3RzFab2S4z6zezjWb2gJn91sz+xcyqctEQER0WUXCYRfYCPTr+u6GHV5jZFIIVpaITET8AXFlEFqcS1OFhOReCZvZ0M/uGmd1pZl1mNmhmW83swezv/D1mtrTcn6EC0Th/Qzae84nOXREb52b2KnK/7IY1TByb2TRyb4gBrHT3naF9lpvZBWZ2nZltyf5uu8xslZn9zcw+bWZxvbmSojjOyt7tPz3mpdvLzO9Agkk+RwwRDJGKq7PqqVZxfGGeVZIman1djLgbpT9qgGGyiavV9XX2u3J03qpBoNyeJXE9a6s2QTH1v75uDEmvL57Eg2DcWjfBB9ww8MaYfZYQ3OHJAMPj5PXN0H4b6v2zpekBfIjctexHHh+qMN+10Txr/HP9W+T8NwJNMfstjfnZrynjfKfH5HNRvX+/JZR/DrApUv6z8ux7UczPenqVynVBzLnOq/N7dWj2s2SkPDuBw/Pse36k7Nfn2e8Vkf0uzLPfhInXPH9HxT5uBF5awbnj4vXToddnEgwhCL/+1AJ5vjay/zqgOfT6D2LOubSef8t5fo4mgguzjwHrY8rcB5xcZF6fjDn+LdnXlgB/LfL3PZT9e6np+5X9OwuX41N59juKYHnzkf2GgUNi9pue5z1tuDgmaJSLlv8n2demEPRGGC7y93st8PQyyqA4Lv69iqs77ikzLwP+EsnrP/Lse17MeS+o8c9e7TjeCswp8j1vqDivoIz7EXxuh8uZKSdW8sT5RfX+GUsof82ur4HlMcevqaDscfH7uSq+V3W7vm6kRyp73Lh7P8FFnWUf/y+7hFpJzOxpwDsY/YX9JsFiNjQzO5P4Zdb/z90btguZmR1L0HAzoo/gy8I+18pfgm8TrLwx4iJ3/3O9CpNm7v4IuUs5TgeuM7N/NrP5ZjbJzI4xs++TO1RviJg7LGY2ldxx8dsZu1LLhI3XMp0GXGZmvzKzxFcJdPcugu7tYYXu1kcnYfyxp2eCzhxmNtPM7o487jWzNcAe4CHgPxk7THk3cLa731bkqeJWrFlvZi8jmA/izCLzaSa483a3mcVNvFgtHyd3zP3/M7PvmNlR2ThfaGbvJGh4mBra73sevxTy58k/9LvR4jjf7/YQguEp76b4Ht3PJPgM/VTBPUsw0eO4WGY2l9xhAiPKne/vncBzQtsPkVvXpU214/hjHhlKp/qa8xg7P+nVPvF7GcWp5fV13GTjlSzwE3fsgpi0RNTr+rrRpHmFpf8HvJigwWUxcJWZvdiLnI3cgmW/fk1w8WAEv9iJPHt70bJdz3/F2A/Wu4B/rtI57y7z0O+6+3eLPEcrwRCp8NC6z7r7w2WeuxIvreBnfqG7b0iyMPmY2SuAV4WSNhHcKZL8PgisYHS8/DyCSef+N8/+EKxmFh0vDMFFYHgo58fdPWeCtYkar3l0EkwG2UewtONcxq5KNOKVBENpn+HBRHpJupDcIROvMbMPesxEgtnfzxmR5B8lXJ4ktVDaOHUn+AL8ES9tuGnckuHLCJYKjs7x0k/w2dNHcGE4M+bYGcAfzOy17v6rEspRFne/wcw+Tu51wzuzj3xuIubz08yOB96T55hGjOO43+0g8GfGNupkCH63XQR3n+czdgjdyA26/dw9yZU+JnIcF+v7BO972GrKaLgxs2XkNkhkgLdmb7amUpXj+HqCXhLhfZay79TXcWUx4htIk5yUWNfX8WbHpO2pIL+4Y6OfJUn7IDW8vm5EqW24cfd/mNnFwGsILhxPAh4ys68RfCiOWY40O+btdIIPx1cyenHgwP/so629OSyYEfx3jA2+bQSTpVVrBaFSviiElbJc5acJAn7EHcCXyzxvpWZlH+UoZhm8ipnZPHKXx4Ng7OeOWpy/Ubl7b/aO2vcZOx9CVCfwfnePLk9Ldr6W8JeUG4nMpzDB4xWCnhd/IOh6f2/MnctJBKsanUvwuT41cvzBBF/mn+Xu3eUVOdZfCcaFL8luTwdeTvzqMm8m94vode4eXY2hUV0HvNeDyV5LFff5F220uZngJs3V4S9/ZnYM8HaCL1fh65Rm4EIzu9/dV5ZRppK4+1fMrDNb7hnj7UqwWtY73T1nSVIzayK46IybW6NR4zjud/tRcn+3TwKfA34bjmsLJpx8DfCvMfm818xud/cfl17kWPt0HJvZh4CzI8kOvKPUxpbsF/ILyf0M/kaeL0ypkmAcf5fROB4kuF7y0D4Tvb4uxrMYOxH2doL3JSm6vo43JSatkr+5uGOrtaoUUNvr60aVyqFSIW8laKU2gg/UmQTdo1ZmH3uZ2YME8+JcxWijzcgH6k3AJ2pR4AbwQ8Z+yA8Br/IGXRoN9t4JCXcHHiQYItXQ3Zyr7DvkrnbwS3f/XZ3K0lDcvdvdX0swq/23CD6PdhI0KG8kmGTtI8DyPJWKkXsROETkIjArLl4h+Oz+qZl93ILVexrNHwnmSjnW3T/l7n+PNtpAMGzW3W9w9w8RzEl1eUxeJ5LwaibZ38NFkeQxdxGzF/NviiRPiIuDrGcCt5rZDyxYmaEUk2LSwl/s/8PdT3P3P0a/RLr7fdmeF6czdjneKdRwSVl3/xFBL5JPEDRkbSaoX3YQNDx+HTjR3d+U54vZOwluPEXl1Ltm1mJmb7RgUubHzaw3O5nvA2b2TTOLrqBRT4V+t1cDR7r7D6Jx7e7r3f0rBJNexzUIfjM7mW7F9uU4NrMXET9c56vu/tcysnwvuRMcP0bQ+NYQEorj8KpUX/WxS5/nq687gD+lMI6rITrcEOCn7j7mZvsEV4/r67jeyX0xacWKi4OqN3zV8PoaM9vfzM43s1vMbLONLopxXWqvr4udDKdeD4Ku8leTnYQ4+8iEtjMxj/B+fwam1/vnSMODYGxf3GRpH0z4PGvznKecxwVFnK+NYLWw8HGfLeK4pTHnu6aMn/f0BH9epwaTHRK0ZIfPuRWYV8RxF8WU9/QqlfGCmHOdV+u4qdLP9o7Iz/WlmH3yxWv00Qm8voKy1DReE3jvfhhz3gHgoCKPj4vXT8fst4TcifIywLLIPs+L5LMLmBKTV6onNSW44JtP0LvpvQQ3O6Ll3UN2cuEi87xjnL+ToicIJJhPIxOTxxn1ft+KKPsCoCfPe/DB0H4nAg8WEV8/ATrynKtmcUywolC+Y1cC7SW8P1tj8iim/lYc539vnpKN12hZrwFayshvOcGN0fB7eGYRx51X6t9WGh/Zv9MdoZ9hLTA5sk+x9fW4cVxEWWoW52WUbSbxn3dHVZBnXJxX8lhag7+XulxfE6yyFD3+pxX8HAfH5PfXar9/Cbz/Ba+vs/t9NM/fa/hR0fV1NR5p73GDB+PRnksw8dA2coc/jfwffpDdZyfB3YAXufuumhU4pczsecC/x7z0U3f/72qf392tzMcFRWR/PnBMaPt+gu739fTjCn7mtdUsmJktAL4RSX6/u2+t5nklkO1C+x+hpCeAz0b2eR7F9yKZBfzEzPLNoVGyKsdrpd5B0CgQ1gq8P8mTeNATIrw8pRF8EQmL3r3/pSc7ZKsm3H3Q3be4+23u/k13Pw14NsGXhBFTgB+aWbHvc77lNncSjGMvtmxXA2PuqhEMpUq7nzO6DGnY3nrXgkUU/g4cXkR+bwCutGDZ6oKqGMfjLaX6Lo8MMxmnfJuJ7w2d2O92X4pjADM7GvgTY4dN3EswXGeoxPyaCOb6CQ+R+L67R5funci+Qu68W+9z956RjXGur/MpKY4LSVF9/VrGft79w8f2TKqUrq/jxX0ux9U/xYo7NtU9p4q5vs7u91WCHomF3p/Er68rlfqGGwAPfJngzslbgYsZXY7PQo8ugi747ye4o/IfrqEymNlygvcsOsb+Thrj4jcvMzuJ3FnChwnuCqf6w6XOvkfuGOzL3f0X9SrMPugr5I7Pfn/4S0I2Xi9l7ASem4AjCIYkHEHu+GkD/jsbDxNa9otH3Je9F1ThdNHhEudlv8hgZrMYO39EQw+vCMt+MTsViM7z8VUzO7WILPJ98f1JGTdTvhmTdnqJedSUmb2O+JWz9ta7FqyWeTEwLfT63wmGqE0h6Gr/FoKbViOeQcJDA8uQ73d7n7tfW2Je/0fQmyFsoZkV05BVrH0ijs3sUIL5wqLd+x8Cnuvlza/xAYK/uRHrCO5U7xOy8228LpT0O3e/PPT6cuCXjL2+3k3QWzDNcZy0uGFSNRvWmhL1vL7uiUlLuuEm7Q3a415fw95Jo/8llDREcLN/OQ1wfd0QDTcj3L3P3X/k7q919wMJ7rLOJViab5K7z3H3l2TvGO6sb2nTIbsc2u8YO5HXVoK7L5WMf6yr7MSlF5E7eeVXvfjlavc5ZvZ64GWhpC7GX11BEmRmzyK42zbicne/LPT6SLxGJ+HdBTzV3R/yYN6Xh9z9PeTO69QC/E91Sp46fyWYoyDsUDPbP+Hz/IbcOVYOJOiJAsHFfHiuj4fc/eaEz19XHqzW9XpGe7NC8AWlmEnfx8xZlHVVGUW5lbFz3Sw0s+gkmKmQbQz4YcxL0Xr3E0D4b/bPBF+wr3f3Hnff5sHcHM8g+AwY8f7sl/R6Sex3m30vro956eml5jWOCR/H2Tmo/sbYCWdXA8/2IldkjeR5CGN7L7+jjIbXhmRmbeR+gesmaMgaeX2kvp4ZOXQAONbd/5ryOE6Mma0ATogkdxM0TO8TUnB9Hfe5HL2WLEXcsfk+++uu0PV1dp8WgknKw97k7p929zWNcH3dUA03UdmeOJ3uvsndx+u6uy+7CDg6kjYyKeITtS9Ooj4AHBXafhT4TJ3KknrZhq6vR5I/7DVaGnFfZ8Fy9d8JJfWQO+s9xMerAy/LE6//Se5E7adlJ+qe0NzdCSaYjDow4fP0AdG7ZW/O/h8dXtGQd+kLcfd/ENzFD3uamR0Vt3/Ixjzpd5VRBgfuiXlpUal51cgNjJ3AN6fezX4evy30+gDw9rhewu7+ELndv438y4vXQmK/23GOS+x3O9Hj2MyWEDTaLI68tJZgLppy6/j/Ifeu+0/d/U9l5tWIPg4cFtq+IFIPX8TY+hqCOTHWRhNTGMdJeltM2i/dvZLlqBtGSq6vozezIPfGQKkOKPIcdVfk9TUEDWvh9+QKd/95nmxTeX2d2uXApXJm9q8ES19Gfdjdr6lxcaohemE3DbglmFC8KHGzo59kZndHE919RUklS6cOxva8en8Jc1ZA/BfjH5hZtHL+rrt/t6TSTXwfJeiCOeJzHlrJbZx4vTBfvLp7xswuBP4rlHwW5X+BaiRxXx7nxaRV6kLgXaHtc8zsDCBcgQ8RLCM7UV1BMIFr2OnAeHMXrMmTXu4du7jjUrfig5n9F8GKSVHRevckcst/pbs/OU7WFwJfYHRIxlmVlLNCjfi7nZBxbGYHEAyvWxJ56UmCRptKbtBFr7FOjbs+Gkfc6mDvNLOzI2m3u3vcF/+6yfZgCt9xvw/479Dr+err+939knGyTlMcJyI7V89rY17al4ZJpeH6+gmCicPDnTIOMLMmd8+UUI7xyvNYGfnUwrjX1yHReMv7N5rW62s13ExQZvZC4HMxL/3Y3aOtwhPFQsZ2Ey7VFOKXc5yokvhZl8ekVfp7mFDMbCnw6VDSA8BXQ6/ni1eIn88lLDrMYKIvNzoibqx1JeO5Y7n77WZ2H6MToLcDP4vsdkV2WNFEtTYmrdDy4A/mSS93/rG4Yb2JTO6ZlGwcfzjmpbh69ymR7bgeZHu5+xYze4TRi9NDzWxWmfOWVOpRgvnkovN69MfsW4yq/24nYhyb2WKCRptlkZfWE6y6lvSXrCSGJi7IPsK6Esg3ad9i9G/QCSbdHoKC9fWPx8s0ZXGclHMZ22ixshGHHCasptfX7j5gZo9FjmklaIBZm9C5Hyojn6oqdH0dUVK9Swqvrxt2qJSZLTOzp5rZC8zsrOzzaOW1T8qOmf05Y3+/t6P5TETq4ZuMNiqMXAQOwrjxCrDTg5X1xhOdPHa/SgraQOJ61xR6r8r1o8h29E50ww2vKFHcF+tpMWlht+dJn15mGWbGpKVmvH02juPutOerd6N/Q9E4jhPdpy4N5B6sqLMy5qUZZWY5MyatGr/bCRPHZraIoNEm+uVqA0Gjzeral2piMLNXkntn/kJ3vzH72nj1NTRQHCcorrdU3BxfUn3RFTchWGSgHKdFtoeJH7Jcb3mvr2OEP/Mb8vq6YRpuzKzJzM42s9+Z2VaCN/Mm4A8EK0ndBKwys63Zfc4eWTVgX2Jm0wgmS4teQG0Bzm3kyYhFGpGZnQu8KJR0kbtfn30tX7yO2F3EKaITRUbvfE1UR8SkVWvJzZ+Sv6fIFoJ6aCKbH5M27gWPu3cSDC+IKvcGy9KYtFossVpQNo5/T+6SyRCUL1+9OzOy3Wixfk1MWtp/txMijs1sIcGcNodEXtpEMDzq0dqXamLIxnJ48tJtBHPdFFNfQ+PFcUWyQ8pOjyQPAD+pfWmEhCZ6zy5rHv18uSPbaJ8a411f5zEz9LwhY7UhGjbM7NkEXXMvBV5CMPbZ8jzmZPe5FHgke+w+wYLJXX7K2C80Q8ArC4yfbzju/kF3t3IfxF9kXptn34bn7l2VvF/Z9yGuG/AZMfteUOMfL5Wyq06EZ6LfTnb5+nHiNay1iNNE52qa8I2zZjYbOCWS3Ac8Uo3zZe/K5PtS99ORLvQTWPS9huBLYiF/jEmL3sUrKLtKUzRO+sg/HKtmQnF8WOSlYeAV49S70WFFjRbrifxuxzku8XkEJkIcm9l8gkab6N/bZoJGm4eTOpe7r6jweuHNMdl+Nmbf05MqcwI+T+4kzx9z9+3j1NfRuUMaLY4r9RaC715hlxXRk2FCSdH19Z9j0l5mZtFhrYXEzd8Ul3fdjHd9PY5wvduQsZr6hhsz+wLBEpPLGP1wcHKXJyUm3QjG4F9lZp+vdjlT4nxyl6Ib8S/uXmgcn4gk77PkzmD/8dAFTb54DY91n51dvnA80SFDjTxWvlgfYez8Gte4e28Vz5lvGEXDDK8oh5l1AGfHvFTM/AXROUQgd7nOYr2esV8ObkpJD9J8cfzBAvVuZ2Q7rldTVJpi/a+Mbbx7UbZRtWhmdgywIpLcS3F/X+Vo2Dg2s3kEjTbRxoOtBEt+170hs5Fll7R+byjpeoKVoyB/nF8V2W60OC5btjHgvJiXNEyqTtx9FWOHSy0m6NBQinfEpEVX5qu38a6v8wnXuw15fZ3qhhsz+xzBrO7GaKPMyMXbGoLWv19mH39mdKWD6P6fMrPP1q7ktWdmLyV+KeyL3P2btS6PyL7OzI4FwisK3Ej2y8F48QrcFtpuJX6FmrDoBHhxc09MGGZ2EvDBmJd+XeVT/4lgfHP4scDdJ/T7DfwbY8d1ryP/HDZ7ufsDwLWR5KdmJ/csSnZ4wkdiXqr277ugceL4d0XUu9Ev2SsKnKuJ0Yl1AfYQrCJSF9neKd+LJHdQeDL1qLhrs8vdvdyJjgtpyDjONoj9BTgq8tI2gp42463wJgVke9R8l9EbAoMEc2V4gfo6OiRoRYHzpCqOK3QWY5egf4Lg71Tq5zsxaZ/LLpldkJm9Bjg2knyNB8vZp8J419cFhOvdhry+Tm3DjZmdCvwruQ0wGwh+UfPd/RB3f6G7/1P28UJ3P4SgtfsDBLPqW+jYfzWzus8GXQ1mdjhBF87oHcnb0GTEIjWXvQj8DqMr9w0xehFYKF5vjKSfXeB050S2o8enRnZS+XeY2aQyjz+ZYKhDdPWoRyiwmkelPLAp8thSzXNWwsyeb2b/XOzFWp48Pgh8Mualb7h7XK/XOOfHpP0wOzdCofM3EyzXGV2WdB117iExThxvBF5TRBbROH1p9nMjn2eQu0T2P9x9uIjzVNP/MLbn0IfN7MXFHJxdKjf6+eUEw1WqotHiGMDMZhJ8GY5+idhO0NPm/poXauJ5B7krxnzV3R8oo75uxDgu11tj0i708paeluT8FIguhX0M8O+FDsyu0BR30+ELlRcrGeNdXxdxeMNfX6e24Qb4ErkflJcAh7v7N90972oD7r7d3b9B0JX0EkYbb5qAL1exvHVhZjOAyxi7UsdmgkkRq3XXSkTyexu58zb8t7vfV2S8/h+54+bfl2/4gZkdArw2lLSLYPLEtJpBcFdzjZl93syKWi7TzOZlh7zeyNilZDMEw1JSPz9Fje0H/C/BXG8XmNl4cynlMLNTzOxKcifpHPFAnvRY7n4tY+9KLwRuMLOzYg4ZKcMBBBP+virm5U/Us24bJ44zwDOLKZu7P0HuUqTLyTOMLHuhGm0Aq2pDZTE8WMI42huqCfiNmX0wXzd0M5tsZl8kd36CET9QQ8SobI+zPwMnRF7qBJ7j7vfWvlQTS3beoPCX2scJeigUrK8nQhyXI/ueRRtoM4xduU1qzN0HiO+V/BEz+1Z2+PMYZnYawRDVOZGXfu3uf022lBWJvb4u8tiGv74uNLarLsxsf4Jfykjr2dXAa0q4w4e7d2e7e80CnpNNPs3M9nf3dYkWuL6+Dhwak94E/GH8hv/yufuKqmQs+wwzeyfj9wiLWyLzc9leAPl8192/W1HBKmRmc4EvhpKeBC7IPi82XncxOvv9XOAJM3uMYMLTEa0E83iFJ0/rJfhCnJN5CuN1EfBp4NNmtg64E7iXoLfCToIJ5GYRdMM+jeAuZXuevD7g7ldUvcSNaynBl4XzzewJgklf7yFYRaeL4L2eRnCxdizB+x1dYnjEeuAcz7/UZj7vBU4md26O/YArzOx2goluHyOY+G8h8EzgBYztWQXwdXf/vxLPn7R8cbwb+HUJ9W50SfUfmdlnCIZPhC0m+BwYsYZgiHjdufuPzOxMgnmIRrQSNO590Mx+B9xPENezgRMJ5gqJmwvkNuB9VS1w43k/uT1BRgwBF1V4jfd7d48bArSv+Qq5q8W8z917zOzHFFdflxvHAwRfpj8c3imF9XWcNzF2cte/ZBuypM7c/Xdm9j3GzlXzbuDVZvYzgmFDuwl6tD6fsauDQdCIGTffTV0UuL4uyN1Xm9mlwCuzSXOB35nZueH5ccxsCUEjTfj6+hvuHo3pmktlww3BhWN4IuL3ltJoMyI7LOG9wEOMDpk6DfhVUgVNgSV50ucxdlIlkTRZyNiu34UckH2Ml2e9/RfBF5QR73f37uzzcuN1CnB0EedewNgeKWm3f/bx0hKP6wM+4u7fSr5IE9aB2UfcJJuFrARe6u6rSz3Q3Xeb2XMJJlaNfhE6KfsoxveBDxfcq/ryxfEMSv9MC2sif6PZiCHgDdm7qmnxVoLPqGi38iUEQ9eLcQtwtnoJj5FvqON8ipsIdzx3V3h8wzOz08ltdLzM3S/PPi+3vi4mjiH4UhidS6RRvCUm7Qc1L4WM570EDRPRFaLmUNzn8kbgee4eHQ5bT+NdXxfrPQQ3h0aulZ8BPGpmvye4ObWM4Hp0cuiYe4DPlVXihKV1qNSi7P8O3Ovuj5abkbs/QvCGjzQELRpndxGRspnZMwnuRI243N1/V6fiTGQ3ACeq0WZcPcSvvliqboLeUceX02gzwt3XA0+hvJ4iXQRj2N+ekiFx1enKWlg/wRLjN9Xp/LGyjUivIJjIutSGlyHg28Dp7r456bKJ5GNmbeRO5NpN7oSnEsPMngYcHkneRjC0VVIiW1e+iqDBodRestcCJ2e/Q6dCUtfX7r4VOJOgkWbETOCNBHP6vYaxjTbPT8vNkrQ23ITLtSqB/MJ5pPVnFpEGlp0ENnwR2IO6/UfdT3Cn4zMEy6huLeHYxwnu6J3o7s9I+yow9ebuvyLomv8WgmW5S7kBsptgiPJbgIXu/v+SuGhx953u/hqCO1y/pfCX/EcIVh1aVu8hkBHj9fqrphPc/bI6nXtc7p5x9y8QDIf7FmMnLY7aTBDPR7j7e9TTRurgo+Q2QHxWQ32KEjcp8U/S8sVWRmU/l88n6Nl1EWOH7+XsTjCP4Kvc/fTszZZUSPr6Onv9uCKbZ1+e3XYRNHqdlqabClbGCKSqM7NXE6wX78Bv3P2VBQ4plN+vCO4GOfA6d7+48lKKiIzKrj4RXk3mrqS+ZJnZZOBZBF04ZxJ8KXoUuL7RL5bMbD+CuXoOIOh+PplgeMBuYAdB486daao4G1V2dZqDCf6O5hMMb2kjuJjbRfB3tRJ4tJzhyWWUZzLB3DeHEXR/NoKVcrYCt7v7k9UuQ6myE4x+lNE7cgPAl5LqCWRmxwPHE8TCAMFdwWvSvvJRVHbZ4xUEy1cvJPg76yK4M/8gcF8t/sZE4iiOZV+U7WU2Mt/cXIIpU3YRzJt2a1r/PnV9PSqtDTcrCCarHBkqdXyF+d1JcAHhwEnuflelZRQRERERERERqbZUNtwAmNmDBHfhHDjM3csaMpVdzuvhbD6r3P2w5EopIiIiIiIiIlI9aZ7vJTzp5DfLySDbFfIboaRvV1QiEREREREREZEaSnOPmybgSuDZBL1lfgm8zd17ijx+MvBD4NXZpL8Dz3X3TBWKKyIiIiIiIiKSuNT2uMk2sJwN/IFgwsJXAw+a2bvMbHa+48xstpm9m2Dyu1cRNPr8EXipGm1EREREREREpJGkucfNhaHNlwGzss89+1hNsFToruz2DOBQYDlBQ49l9+8ELs/uUwx397il7kREREREREREairNDTcZ4htbLPQ8+vp4rxV1WoKGm+YyjhURERERERERSVRLvQtQhvEaZNLZCiUiIiIiIiIiUoa0N9xY4V1ERERERERERCamNDfcLKt3AURERERERERE6im1c9yIiIiIiIiIiOzrUrscuIiIiIiIiIjIvk4NNyIiIiIiIiIiKaWGGxERERERERGRlEptw42Z/dLMnlXvcoiIiIiIiIiI1EtqJyc2swzgwCPAd4CfuHtXXQslIiIiIiIiIlJDjdBwY9n/+4CLge+5+631LJuIiIiIiIiISC00QsMNBI03hLbvJuiF83N376lx0UREREREREREaiLNDTcvBt4JnEUwF09cI85u4KcEvXDur3khE2Jmy4AVwCJgKrAReBy4yd0H61g0ESmBYllkYlAsizQ+xbGITCSpbbgZYWYHAO8A3gzsl00eGUI18hzgJuDbwK8b5cPYzF4BfAg4Nc8uncAvgc+4+7aaFUxESqJYFpkYFMsijU9xLCITUeobbkaYWTNwDkEjzpmMzn1D5Pl24ELgf919Ta3LWQwzmwp8H3hNkYdsBt7k7ldWr1QiUirFssjEoFgWaXyKYxGZyBqm4SbMzJYD7wLeBMzJJkd74ThwNcFcOJe7e6bW5YyTbYD6PfDCyEtbgbuAncBy4HhGfx6AfuA57n5DLcopIuNTLItMDIplkcanOBaRia4hG25GmFkb8CqCXjhPyybH9cLZQNAC/wN331DTQkaY2ZeBj4SSBgm6c/6vuw+E9jsS+AG53Ty3A8e4+8ZalDWOmc0AnhVKehIYyLO7yET2YYIhnCOGgP8ELsk+bwMOyD7eCxwe2lexLJIe48VyE0EMk/3/jcBTQvsqlkXSQXWyyMQ3EscjrnX3nfUqTK01dMNNWLah493A64AZMbs4MAxcDnzH3a+uYfEAMLODgIeA1lDy2e5+WZ79O4C/ktt48z13f2f1Sjk+M3spEFteERnXQ+ReKCqWRRrPKxk7d4ZiWaTxqE4WaXwvc/ff17sQtdJU7wIkxd1Xuvt7CWaOfztwx8hLjA6jagHOBq40s0fM7P1mNq2GxTyf3Eabi/I12gC4ey9wHrkt7m/NNgCJSGP5HxTLIo1uANXLIhOB6mQRaSgTpuFmRLax48fA1wiW/IvOe0M27eDsPmvN7ONm1hrNK0nZ3jOviCT/Z6Hj3P0R4HehpBbgtcmVTERqZAOKZZGGp3pZZEJQnSwiDWXCDJWCvZMWv53gbtjc6MuR7ehkxvcTdLdaW6WynQ38NpR0s7ufVuSxZwFXhJLucvcTEixe0czseODOke3f/GghBy+rvM3rXa98esV5RA1vTXaFx+Z50T+pyiRdvmpo6uhINL9Mb2+i+VVD5ilHjvv6jh2refTRP+zdnjp1P4488lVj9uvp2c799/8snHQCsICUxvKcd7yR1vmV/40v/1nyQ40zU9oTzzNJfv8jiebXPDNutG9lBg9ZlGh+Tbc9mGh+1dA8d864r2/vX8dDu0fnI53WModjZz43Z5+eoZ3c1RUOWU5w97vSXC/P/cDraV0w/s9ejIMuTP7zenhKW7IZ3vNwsvklLOk6FMAWzks0v+HHnkg0v2rY8eoVeV/bve5RNtz4u73b7XMWseQ5rxuzX//Obaz984/CSamukw89/xzaF82qON9pnxuqOI8xBgYTzS7x6/XZsxPNb7izM9H8oArX1/0JT4eUGU42P6Dr5SsqzqN/1zYe+0tuHLv7XRVn3CBa6l2ASmVnkX8Z8E5GlwkPN8iMPL8G+BbBrPJvzx7TGtrnGODvZnasu++uQlHPimxfU8Kx1xNMrDby+zrezBa4++YkClainE+Gg5e1ctRhkyrOdFpr5ReZUUOWbMXSknAZky5fNTQ1TU40v4wlfNFeBcNTF4z7+rp1N+dsz55zKFMKHJM1QIpjuXX+XNoWLaw406mTkv8dZyYn/+UnSW7J/vqaW5K96AQYnFLU32jRmqyu8/wXpdBn9hM99+Vsz5u0pJi6aCRu0hvLC+bQtn/lv++p7d0V5xE1PLny64UctinZ/BKWdB0KYJMSbrix9M/r2Ts7f9207b7cxaCmH3gE7ePsH5LqOrl90SwmL638Zsq0tio03HiyjQRJXw8nXYcOW/KNGMlfX/cnmh9V+Jn7Z1V+jRljn5rAu2GHSpnZAWb2eeAJghnjn83ozzPSjWgP8G3gKHc/090vdfer3f1VwIHAl4G+0P4HAh+oUpGPjmzfHLtXDHfvBu6LJB9VcYlEpGTd3bnXczNmHFj0sYplkfTYPZh7F3VmW/EXlYplkXTo7cxdCGrywiVFH6s4FpFG0lANNxZ4sZldDqwBPgXsR+4wKANWAu8BFrv7e919TJ9ud9/s7h8n6Cq5mdGeN2dXqfhHRLZXlXj86sj2+OM5RKQquru35Gx3dJTcE0uxLJIC3UO5DTeTm0seoqZYFqmz/h25dfKkGSX3UlEci0hDqNtQKTML36Ze5+6ZcfZdCLwt+xhZu93IXTFqmGAOmW+7+7XFlsPdHzazLwDfzCYdWvQPUSQzmw1E++2VOqg4uv8h5ZdIRMoxONjD0FDuvA/t7TNLzUaxLFJnA5k+Bj23a3l7c8mLTCqWRepoqK+H4f6enLTWqTNLzUZxLCINoZ5z3KxltOHlIGIaMszsuQRz17yYoKzhuWvIbm8E/hf4X3ffGM2jSOFhS1PKzGM8MyPbPdnumaXYEtlOfvZKERnX0FBfznZTUyvNzSXP6aJYFqmzoUxuo02ztdDSVPJE+4plkToaHsi9kdLU0kZza8nzKCmORaQh1Hty4uhKT6MvmD1K0KAzsl+4d40B1xLMX/Nbd6905q2R/tLVWmJramS7nKUaoseUfGswyszmA6XOcre80vOKNKrh4ciXveayVlRTLIvU2bDnTobZVN7lkGJZpI4yg7l1srUojkVk4qp3w814DSXLQ6+PNNjsAX4KfMvdV1ahPCMNREmLNtz0xe41vmjFEs2zHO8Gzk8gH5F9wvBw7uT1TU2JXCQqlkVqbCjacGPN5WSjWBapo2jDTVMyN1MUxyKSSvVuuCmmoWRksuHvAD929z1VKMcm4Iwq5JtPOY1D1eoNJCJly9tpcDyKZZGUMcWyyL5KcSwiDaHeDTfjGQIuI+hdc001T+Tu/QRDr6ol2tjUUUYe0WOq0YAlIuOIzmeTyQzm2XNcimWROmux3Dvzw+WNuFYsi9RRU2Q+m8yw6mQRmbjS3HCz1N031LsQCUlrw823gUtKPGY5QYOayD6nuTn3InE4PReJimWREjRHGm4ypKbhRrEsUqRow40PKY5FZOJKbcPNBGq0AdgZ2Z5sZlNKXFlqfmS7q7IigbtvYexs+uMyK6s7uciE0NLSnrOdyQwyPDxQ6spSimWROmtpijTC+hBDmcFSV5ZSLIvUUXNbpE4eGmB4sL/UlaUUxyLSEJrqXYB9gbtvB3ZEkg8sMZslke1Hyy+RiJSjtXUyLS25N+f6+rpKzUaxLFJnbU3ttFrul7u+4d2lZqNYFqmjlvYpNE/KrZMH93SVmo3iWEQaghpuaufByPbBJR5/UGQ7mp+I1MDkKbkrfPb2bi81C8WySApMaZmVs90zHO0cW5BiWaTOJs1akLPdv3NbqVkojkWkIaR2qNQEdD9wWmj7BWY2GTgJOBk4AZgWev1xd18KYGZTgGNj8hORGps6ZQG7dj6xd3v7tocZHh5k9+717N61jt27NzA83B97rGJZJD2mts6ma3DT3u11PSvpHNjAzsEt7BrcyrDnn8NKsSySDu2zF9Kzae3e7d2PP0RmaIDeLevo2fokvVvXjVk2fITiWEQaSb0bbkaW4DvFzJbW5ITu19XiPDH+DLw9tP2u7KMYzyD3d3WXu29OqmAiUrzZcw5lw4bb9m5v2HArGzbcWuzhimWRlGhvmpqzvaV/LcR/v4ujWBZJgekHHE7nylv2bm9feRPbV95U7OGKYxFpGPVuuAEw4Bc1OpdTv5/5SmAQKGnmw6zzItu/rbg0IlKW2bMPwawJ90w5h58X2VYsi9RJkzVXcvh5kW3FskgdTDvgMGhqgozqZBGZ2NLQcANB482E5u49ZnYPwdCoqH5gHcFSgDnM7FDgnFDSEPDzqhSyDO943rFMbZpZcT6/WPWbygsT8fqTzym8UwmGNm4qvFMpnnJMsvndel+y+QGZnp7E80y7ll3j33JvAaZ1LGBXz8YxrzVZM5PaptPbH52LHAgmQExtLB90SQ9TOypfBfWAn65PoDS5Hj+trGXX87K2klYBK5zf0Ycnmp+vWptofgBN19+beJ5p59OmjPu6DXVAzHzETdbMpJZp9A52xR+X8np5yadWMtUqj8M/bbi78sJEPH/RisTzTNLA8+Mu0crXduXtieYHwMOrEs2uee6cRPMb3lbyvG8F7ffn8f+eH2+Zx66BsR1lmqyZ9uap9AzFzl+V6jp58jueZKrtqjifP+6DcWxTJyeb4dZks4N98/p69srKf+Y9Pb0JlKRxpWVyYq/BIw3+HJP2LwRz27wt+oKZtQM/AsLfMn7o7qurUzwRKcacGWPnFj9k/+dxxvGf5MglL8132BdQLIukisVcBh278MUcveCsfIe0oXpZJFXmdiwdk3b4rGfxnCXv5ai5z8t3mOpkEWkoaehx48AGgpbuie6rwBSCxpoRXyL42R+K7NsK/JXcCY23A5+tZgFFpLAlC05leHiAJ7b8Y2/aqvVX09TUzJRJee9eHh96rlgWqbPF04/mgBkrWLX9etbuGJ236p6Nl7P/jBX5DvshimWRVFk240SGfIAndt21N+3hHddjTU1MaZmd7zDFsYg0lHo33BhBw83T3P2JQjs3OnffYWYfBQ4HXpBNbgW+AUTHVizKPkYMAOe4+9jxGSJSU60tHRx6wPPo7tvO9l1Bt3X3DA8/cQUtTe2FDlcsi6RAa3MQq4fOfRZ7+rezrWcNAE6GJ3feme+w8Jc9xbJICrQ2t3PE7GfRM7iDbb1rgSCOH9z+d1psUqHDFcci0hDSMlRqn+Huw8CrgF9GXpo1zmFbgJe5+/VVK5iIlMSsiWOXv5IFs47KSR/K9I13mGJZJGXMmjhu0UtZOK2kuYoUyyIpYtbEivkvZuGUw3LSh3zceesUxyLSMNRwUwfuvsfdXwO8ErhlnF07ge8AR7t73Pw4IlJHLc1tHLv8FRx70CuZMWX/QrtfjGJZJJVamto4br+Xctx+L2NG+6Lxdt2J6mWRVGppamPF/BexYv6LmTFpv0K7q04WkYZS76FS+zR3/zXwazNbBryB3PG1m4ED3X2gLoUTkaItmH0kC2YfSW//DjZsv4c1G66N2+0L7l6FtQlEJCkLpx3GwmmHsWHXSu7b9Ie4XZ7l7vfUulwiUryFUw5l4ZRD6Rncyfo9D7C6K/YeqepkEWko6nGTAu7+GHBdJLlPjTYijaVj0ixmTV1a72KISIXaW6bme2lfWEhBZEKY3DqD2e0H1LsYIiKJUMONiIiIiIiIiEhKaajUPszM5gPzSjxseTXKIiLlUyyLTAyKZZHGpzgWkWpQw82+7d3A+fUuhIhUTLEsMjEolkUan+JYRBJX76FSXufzi4iIiIiIiIikVr0bbqzO5xcRERERERERSa16DpX6cej5nrqVYt/2beCSEo9ZDlxWhbKISPkUyyITg2JZpPEpjkUkcXVruHH3N9fr3BJw9y3AllKOMVMnKZG0USyLTAyKZZHGpzgWkWqo91ApERERERERERHJQ6tK1YmZNQMHA0cCi4AV0V1qXSYRKY17hp7+Trp7t9I/uJuh4X76+nfVu1giUiL3DD2DO9jTv53+4T3s6ttc7yKJSIncM3QPdtE9uJ2+4W6GMv30De2ud7FERBKhhpsaMrMDgXOB5wDPAKaPs/uBZnYF8E13/2MtyicihfX272RL14N07lpD154nGBrur3eRRKQMvYO72LznETp71rKjdx1DmYFiDnsm8ECViyYiReod2sXm7lVs732cHX3rGfKi4lhEpOGo4aZGzOznwD+VeNhZwFlm9gfgbe6uW4AidXTfmkvZ1Hl/uYd/08xeozgWqb97Nl7Opt0PlnPot83shahOFqm7e7b8kY3dD5d7uOpkEWkomuOmdg7Nk74euAb4+zjHvhi4zswWJl0oESled9/22PRJrdOYNW0pC2YdxcypS/IdfjqKY5FU6BnojE2f1DKVWR0HMLvjwPEOV50skgLdg12x6ZOapzK7fX8WTjmMWZMW5zv8dBTHItJA1OOmPu4CLgSucPfVAGZ2OnBGaJ89wNTQ9qHAJWb2THf3GpVTRPKYNnkhi+Yez9zpBzO5ffbe9M5da7njkR/nO0xxLJIy0ybNZ/H0Y5k3ZRmT22YB0NnzBJ3rnhjvMMWySIpMb5vP4mlHMa9jGZNbZ+5N3977JLdtyrsyt+JYRBqGGm5qx4E/Ahe4++1F7L8d+DjwrVDa04FXAxcnX7zy+MAAbpXP8fHaw5+bQGlyfeCeaxLN72sHH5Fofs2PJ9w7d8H8ZPMDhjeXtJplQc1HHZZofsMPlN1FOq/mHd15X7PhDPMmH8TBs05jRnv2Jl0v0Dt6TFNvb6FTpC6OATIPrSZj2yrOZ+1TEihMxJUb7k40vxc97WWJ5jd0/0OJ5tdchVi27fE9TMrlmUSzo6m9PdkMgeFH1+R/0QeYy0IO4kimD8yGbcC2HQyzIzjWi/rsS2Us29GHYpMr/xs687yTEihNrt7XJnvZOfOy+xLNr33jnkTza9ov+c4cQxs3JZ5nkqoSy3Omxb+wuZm5HYdw0KLTmTFl0ej+oV0yuybD+G9ZKuO4ee4cWlrnVJzP8xetqLwwEY/94rhE81v2T/ckmp/v6Uk0v5al4/bALMvQ408mml/mmSsSza9tVRVGED6xteIsWgZ3JFCQxqWGm9p5pbuvLeUAd/+2mZ0JvDyU/AZSVrmI7CtWLHgpk1tnJJGV4likjo7hFDpsSjmH/gUI32lQLIvUybHLX0nHpJlJZKU4FpHUU8NNjYw02pjZ/sS/79HbMi1mthT4NbkNN2ea2Vx3r/zWuIiUJNxo0ze0m0xMt4OBoaLu9JyZje89imWR2htptOnzHpyxIyQGyNuT9CpyG25UJ4vUSbjRpm9gFx5bJxfVi0p1soiknhpuau8GIO/spSGLgcdi0tuBrwOvTbJQIlKaW9b/gr6hXeUe3k4Q3z8GzkuqTCJSmtu5hj5K6lb/5ci26mSRFLjtoQvpG9hZ7uGqk0Uk9bSqVGPS701ERCQdVCeLiIhIVeliozH11bsAIiIiAqhOFhERkSrTUKkac/elpexvZl8A/jWUdIu7n5dkmUSkdKcvefu4r+8e2MaNT16U7+Vb3P3UpMskIqV5ur1w3Nf3+E5u4S/hpO8B7whtq04WSYFnHPvBcV/f07uFmx/4Tr6XVSeLSOqpx02KmdlU4K2R5N/WoywikijFsUhjenlkW7Es0vgUxyKSemq4Sbf/IHe1qS7gB/UpiogkpAvFsUijmht63oViWaTRdaE4FpEGUJehUmb2xnqcF8Ddf1Kvc5fCzM4B3htJ/ld370zwHPOBeSUetjyp84vsoxKNY1Asi9SJYlmk8SmORaQh1GuOm4sAr9O5U99wY2bHMbacVwF5B+eW6d3A+QnnKSJA90DsdWA14hgUyyK1plgWaSDdfdvikhXHItIw6j05sdXoPJ49V70ai4pmZgcCfwSmhpIfB17v7qkvv4hA7+AuVm67Opq8AcWxSEPppzcuWXWySAPp7d/JQ4//KZqsOllEGko957ipVaNNrc9VtmzXyr8Ai0PJm4DnuvvW+pRKRErRP9TNbRsvYWC4J/rS2xTHIo1jwPt4kDujydtQnSzSMAYGu7nzkZ8yMNQdfUl1sog0lHr1uHlznc6bWmY2G7gaODSUvA14jrs/WqXTfhu4pMRjlgOXVaEsIg1vYLiX2zZeQs/gjriXn6jiqRXLIgka9AHu5Hr6GNMA+9Yq1smgWBZJzOBQL3c88hN6+rfHvaw6WUQaSl0abtz9x/U4b1qZ2QyCcbbHhJJ3ENzVe6Ba53X3LcCWUo4xa4jOSyI1Nzjcz+0bf82egdhx9FWlWBZJzpAPcifXs4edcS+vrua5FcsiyRgc6uOOR37Knt6SwikRimMRqYZ6z3GzzzKzDuBw4DDg34FloZd7gfcB99ehaCJSpOHMIN2DnewZ6OSRzuvpG9q19zWjGWe4jqUTkWIN+zDd7KKb3azm/rieNiLSAIYzg+zq3sDKtZfn9LQxa8ZddbKINC413NSQmb0ZOBN4KkGXyHxzDHUAPwO+a2a/Ar7h7nfXpJAiMq51u+6js/dJuvo30jPYRb45z2MabQ4DqtaDTkRKs8HX0skWdtFJD3uKPexzZvZ51cki6bF+213s2LWWnd3r6OnfQVy9HNNoo1gWkYZSz8mJ90WfB14PHEJx7/1U4C3A7Wb2NTNTQ5tInT3aeSMb9qzMzmNT0mIUv1Ici6THah5gE0+U0mgDcC6qk0VSZfX6v7Ox8156+jspoV5WLItIQ1HDTXoMA9cQrCq1KfJaM/BB4BIza65tsUSkeMbs9gOY07GE1qaO6IuKY5EGMou5zGB23EuKZZGGYMyatpQFs45ixpT943ZQLItIw1DDTW11A5czdrhEP/Aidz/D3Z/n7vsBpwJ/jex3NvChqpdSRPJqbmpl3uSDmNI6Jye9yZo5cb9zecriV3PyolfylMWvzpfF2SiORequmWbmsh9TmJ6T3kQTx/N0TrTTOYITx8vibBTLInXX3NRKa8uUnLQma+b4Q17LSYe9iWOXv4Ijl75kvCzORrEsIiln7iV19ZcKmFk7cDHwslDyIHCuu/8hZv8m4McEw6tG7AQWuHt/Ncuaj5kdRWjS5GNe+BEmz1hYcb7TLr6l4jyq7eS7k53U7rYVyd7caZoypfBOpeY5N/Zuc9ky2zqTza+7O9H8AFr2Xzzu60OZQe7t/DNbetfsTTOaOH7ui5jfcdDetN2D27lx08/yZVPXOIaxsfy0JW9h6qS5lWfctbvyPCJscnui+S38Zexy7WVbd0pJQ23qYu3nT000v6X/dnOi+dXDkA/xALeylQ170wzjWE5lni0CYI/v5Bb+Ej7sciD8DTB1sXzqUe9iasf8ivP1ux6qOI8oO+7wRPN7/CUzEs3vwM/dlGh+1WAnHpVofk27+xLNb2Bxsr8TgOa/35n3tYxnuJeb2cbGvWnROIb0x3I0jo9+yUfomFn59fWMnyV/fd08a1ai+T32gSMSzW/pl+5OND8fHEo0P4C+5x2XaH4df70v0fxoqkLfjkym4iz2ZLq4qT/nK/PR1VyBOW3qMqbTzNYU3qsq3N2X1+PE2S6YPya30WYIeHVcow2Au2fM7D3AOcDIt/IZwBnAn6tYXBGJ4Z7h/s6/jGm0WTHnBTmNNnn0Ekw8Dopjkbpydx7k9jGNNsdwSs6XvRhfIFhkQHWySJ25Ow9w65hGmyLiGBTLItJg6jUZ11KC2cOsxuetZ/eiC4FXRdI+BdxlZksLHHsH8MzQ9uGoYhGpufs6r2ZT76M5aYfOOI1pbfPpCS0FDtA3NKYXxgPASaFtxbFInazkdjazLidtOUczjZn0+mhPvj56o4fOQHWySCoUG8dQdCwfXIViiogkot6zqNeyIaXWjURRb4xJ+1L2UaojKyyLiJRhQ8+DY9Ie3nkDD++8oZjDT4psK45F6mQjj49JW8V9rKJgd/OrYtIUyyJ1UEEcQ3wsT6u0TCIi1VKvhpsnqG/vl0a3vd4FEJGKKY5FJgbFssjEsKHwLiIi9VGXhht3X1qP804g99S7ACJSMcWxyMSgWBZpfA5cW+9CiIjkU++hUvsMdy95qJaZ7Ucwbv7YUPIl7n5xYgUTkaKddcAHit63wKpSimOROnqOvaKo/WJWonkYOCy0rVgWqZNi4xiKiuVfu/vaZEomIpI8NdykiJm1ALOAI4AXA+8Apod2WQO8tw5FE5EiZTzDUKaPnQOb8+2iOBZpABnPMMhANDn8RU+xLNIAFMsiMhGo4aaOzOy/gWJv4f8deIO7b6leiUSkVA/uuJbH99xd7O6KY5GUetjv5klWFbu7YlkkpRTLIjIRqeEm/X4PfMvd42a/r4iZzQfmlXjY8qTLIbKPeLu7f78aGSuWRWrm78AXq1Eng2JZpIaqFsuKYxGpBjXcpN8LgGYz63P36xLO+93A+QnnKSLxXmdmD1chjkGxLFIrzwB6qlQng2JZpFaqGcuKYxFJnBpu6utzwH+HtjuAOcAK4BzgTKAVeBHwIjP7FvABdx+ubTFFJJ/l05/KkmnH793O+BCDmV629j7Omt23hXd9FnCt4lgknQ7iSA7kkL3bGYbZSScruT28Wwuqk0VSTbEsIhNRQzXcmFkzQaPGEQST+M4AmkrJw90/l3zJyuPunUBnzEs3AN80s6cDPwOWZNPfQ9C489balFBECmlrbqeN9jHpLU3t0YabEYpjkRRqtTZaactJc/fobhuARdnnimWRFFIsi8hE1BANN2Z2FPBh4FUEH6yVSE3DTSHufoOZnQHcRtATB+AtZvZ7d78sgVN8G7ikxGOWA0mcW2Rf0wXMzD5PMo5BsSxSK28Gfk516mRQLIvUSjVjWXEsIolLfcONmX0I+A+Cslqe3cLN6HH7eDZ9THN72rn7Y2b2OeB/QskfI4EP9+wM+iXNom+W71cgIgV8B/hkaDuROAbFskgNrSe4AZR4nQyKZZEaqlosK45FpBpKGmZUa2b2EeC/COZ5ifLQw0KP6GuQv8GnUVwc2T7FzGbWoyAiUrYrItuKY5HGpDpZZGJQLItIw0htw42ZHUPQ0ybcOPMHgq6Nrye3oeYM4GXAu4CfAntCr23J7n8GwWS/DSfbcr8jlNQELKtTcUSkPJ0ojkUanupkkYlBsSwijSTNQ6U+DjRnn2eAt7j7jwHMbEl4R3e/NrT5PTObDnwWeD8wD/gS8Bx3f6jqpU6AmV0MvDqSnIlsT6pRcUSkDA93XR9Nuh/FsUijuz87pEGxLNLYFMsi0lBS2ePGzFqBcxntbfO9kUabYrj7Lnf/F+DtBD1vFgF/MLOp1ShvkszspYxttIGxv6vNNSiOiJRhS+8atvU9HveS4likgXTmn6ZCsSzSQBTLItLoUtlwA5wItDM6ofCXy8nE3X8I/DC7uYxg0rHUyo6r/U4Ru/YQTKomIikzmOnngR1/K2ZXxbFIig36AI9RVEddxbJIiimWRWQiSOtQqcOy/zuw2t3XjrezmTW5e7Sr44gLgLdkn78R+EwSBaySrxD0DgLYDUzLs9+f3X2gNkUa35Q/3cNUW1txPs1HHFJ5YSKsfzDR/G5bsTbR/B753smJ5nfEf3clmh/A0IOPJprfExeclmh+B15wU6L5Afiu3RUd/1DP9fQPdxeza2riGIBdu6Elbh740ux5+vIECpNr8h/vTjS/dack+7Y/8t2nJJrfoRf1JZofwNJ/uznR/FqWHJBofkOPP5lofgBDZ55Y0fEPr7yUwY39xeyaqlhu2tJFc2tz4R0L2PiOZP+uARZenOyI9QM/90Ci+TUfnPD0Jjv3JJsf4I88kWh+j33omETzO/CzydfLlf5eHtz8ZwZ3NVYsT708metrO+noygsTMXz7/Ynml/S13Nrzk73WXPrluxPND2DSH29LNL+Hv/XURPM75D3/SDQ/SOZv0Xu3BBMP7KPS2uNmVuh5XC0fbaRpz5eRu68H7iHovXOAmVV2JVcmM3ufme03zuvPYbSBaYjxG5i+lWTZRKQ4j/evpD/Tk/f17UPrWT8YNHZZ4cXsFMcidbLuyZvo79+V9/XOzlVs2nh7sdkplkXq5PGuO+kfyt9Atr1nLet33VdsdoplEUmttDbcTA493xnzevQTemaB/NaEnh9cToES8FZgtZn9zMxeYmZ7e9OY2RTg+6F9vw7MzpPPT929qHEYIpKs9QOPcN3uS7i351q2DD7BkI/26hryQR7ouXHv9sLWcXueKI5F6mjjhtv5x03/xcoHfsm2rQ8yNDR6N354eIBHHvxNsVkplkXqaP3Oe7lu7fe5d9Mf2LJnFUOZ0Q4zQ5kBHth8VbFZKZZFJNXSOlQq3DAT15smOpZhMbBhnPzC/c4XlluoBHQAr8s+3MxWAWsJ5t9Zmt1nAHgfEDdm4Q/AP1e9lCKSV4ZhNg6uZuPgagAmN02no2kqPZnd9Hrw0WU0sXFwVb4sFMciKZDJDLJl091s2XQ3YHR0zKa9YxZ9vTvo6wtWCDZrIv9IbMWySBpkfIiNux9k4+4HAZjcOouO1un0DO6kdyi4/2s04WM67O+lWBaR1Etrw0146vcZ0RfdfcjMNgIjQ49WAOMNFgwPxs87rKrGDDgk+whry7N/F3Cuuyc7cYuIVKQns4ueTO6Qi3EuDrtQHIukkNPbu53e3u25qfkbbbpQLIukUs/gDnoGd+SkqV4WkUaX1qFSD4eeH5Znn/DURM/Pl5GZzQaeQjDRMcD2fPtW2T8DXwBuBoqaIS1ipyoVkfo6quNpHDTpOGY2z6eJsib/VByLpMBhR5zDkqVnMH3GgVhTWfewFMsiKXDUgudz0OxTmdm+iCZTvSwiE1dae9ysJBgy1EYwofBMd++K7PM34LkEPVdeYmbHufs9MXl9EZiUfe7A3VUpcQHufhtBr6B/M7NW4AiCCYhfnt2lG/h3YBPB8KnJwOW1L6mI5DOjZR4zWuYBkPEM3ZkuVvXdxZahxwFopoVlk45lUtNkcHig74Z6FldE8pg+/QCmTz+AZUAmM0xP9xbWPvZXtm0NVilqamrlwKWng8Pax/5S17KKSH4z2vdjRvt+MOfpZHyY7oHtrNp+E1u6g4UCmq2VZbOeCjirOm8cPzMRkRRLZY8bd+8HwuuQPS9mt18SrC7lBPPBXGVmbzSzOWbWYmZHm9nPCCYFHultswG4s4pFL0q2Zb8FeFko+e3u/u/ufmF2crTk15AUkcQ0WTBefuvQ6NKsR3Y8jeXtK9i/7VBmZht4RCTdmpqacc+wfduDe9MOO+Jcli47k3nzj6pjyUSkFE3WjLuztXv13rQj5z+P5XNOZcHUQ+tYMhGRyqWy4SbrT6Hn50RfdPe1wI8Jetw4MA/4EcH8OP0ES4D/U/b1kX2+5OMMWK8VM2sBLmS0x9Of3f3ndSySiJQo4xnu77kBz7YLz21ZzKK2cVeSEpEUymSGeejBS/fOZzN7zqEsWLiivoUSkZJlPMP9m6/YO5/N3MnLWDT9yDqXSkQkGWkdKgXwK+A/CBpdzjGzhe6+KbLPR4FTgcMJGmYs8rqH0v/o7t+obpGL9gnguOzzbuBd9SiEmc0naPAqhb6ZigCP9d/L7kwnEAyROrLjtLqVRbEsUr4nHr+W7j0bAWhqbuOQw86uW1kUyyLle2zHP9g9sBUIhkgdOf+5dSmH4lhEqiG1DTfu/piZzWG0V9CumH06zexM4IfAC2KyMWAY+F/gg1UqaknM7Ejg06Gkf8v2HqqHdwPn1+ncIg1rz/AO1vSPTql1cPsJdDRNq2OJFMsi5ejes5nH1/597/ayg55LR8esOpZIsSxSjj3921jTefPe7YPnPJ2O1jEL09aK4lhEEpfahhsAd99RxD6bgBeZ2ckEc8YcAswEdhAMl/q1uz9azXIWy8yaCBqZRiZLvgP4ev1KJCKlcnfu772BDMMATG+ew5I2dcUWaTTuGR5+8FI8MwTA1GmL2f+A+vWcE5HyuDv3b/kzGc/Wy5MWsGTmCXUulYhIslLdcFOK0KpNafYB4JTs8yHgbe7ZWkZEGsLjAw+wczjoim0YR3U8naBNVkQaybonb2LXricBMGvisCPOVSyLNKDHu+5gZ18w3NFo4qgFz1csi8iEM2EabtLOzA4CvhBK+qq7312n4oz4NnBJiccsBy6rQllEUq8ns4tVfaML0y1pO5rpzXPqWKK9FMsiJejt7eSx1Vft3d7/wKczbdqiOpZoL8WySAl6BrtYtf2GvdtLZp3E9EkL6lgiQHEsIlWghpsaMDMDvg9MziatAS6oW4Gy3H0LwSpcRQt+FJF9j7vzQM+NDBMMq+homsbB7cfXuVQBxbJI8dydhx/8DZnMIADtHbNZuuw5dS5VQLEsUjx354HNVzLsQSx3tM7g4Nn1H+6oOBaRalA/wtr4Z+DM0PY73L23XoURkdKtG3yYzuGNe7eP6jiNZlPbt0ij2bjhNrp2rN67fejh59Dc3FrHEolIOdbtupfO3if2bh81/3k0NymWRWRiquu3jmxPlI8C7aHkn1S6ypKZLQPeEErqcff/qiTPCn029PxPwCozW1rgmIWR7ZaYYza4+0CFZRORIqzqu2vv87kt+zO5aTq9md159+/LdMclK45F6mztmqv3Pp895zA6OubQ2xu/FkJ//8582SiWReps1fYb9z6fO/kgJrfOoncwPmb7hvLW14plEWkI9b5d/Frgi4Bnt3+TxNLY2aXEjwHOHUkzszXu/ptK8y5TR+j5C4HHyshjccxxxwN3l1kmESlBJjSP+LahdVy3u9Th64DiWKTuhrNDpAA6tz/MP276UjnZKJZF6izjQ3ufb+tZw3Vr/7ecbBTLItIQ6jZUKrs09udGNoFHgTcleIrzgNXZvA34fwnmLSIiIiIiIiJSdfWc4+Y5wDKC3jYOfNLde5LK3N27gU+Ekg41szPz7S8iIiIiIiIikjb1HCr1+tDzO9z9t0mfwN1/Y2Z3ACdmk14H/C3p8xRRjpkAZnYBcH7opX8Cbslz2CnAL0Lb64EV7r6tCkWsu+EHH008z+ZDDko8zyQd8T95504oy6P/NiXR/AAOem2y+U3e6IV3qjNfGr8k8Jl8bO/zVZuuZc3m6/duH3PgOcycsjhn/+7+Tu5c8/NoNuuBpwN70hbLmd17yFhzxflM+8cThXcqkXe0F96pFFM6Cu9TgiM+mezn17ofRac4q9x+ZyebX/+yeYnm17ZzV6L5AbSs2Rqb/twD3g3AoztuYnXXaBV83LwXMmPSfjn7dg/u4I7NsaOsU1snD2/dxpANFt6xgP3+kPwlYqY35esy7I6dm6xsG195SKL5Acz/9k2J5tee8F9w82EHJ5shkJk6KTb9jBOC+7Or11/Dmo3X7k0/5qCXM2PK/jn7dvdt465H/y8um9TGchL89vsTz7Nl/8WFdyrB0Lr1ieZ34GeTjZEtlx+aaH4Ac1/ySKL5Tdpa+fVbWFW+Qz1Z0kJrsZoGOxMoSOOqZ8PNWaHnP6zieX5I0HBjBPPLpMmmfHP6xEyUNjRRKxWRRjepdQodbTNz0sLzaIQMJTGPl4gkr615CpNbZ+SkjSwzHEN1skhKtbVMpWPSzJy04UzeuYYVyyLSEOoyVMrMlgBzQ0l/qOLpwnnPN7MDqnguEREREREREZHE1GuOm+Oy/zuw2t2T7SMX4u7rgFWhpBXVOpeIiIiIiIiISJLq1XATHhy/oQbnC59jfg3OJyIiIiIiIiJSsXo13MwKPd9Ug/OFzzGzBucTEREREREREalYvRpu2mpchvA54qemFxERERERERFJmXqtKtUTep7smqLxwufoybtX7b3DzD4NHAHMAQaB7cDjwA3AM939+nGOF5EUWLf9TtZsvoHuvu0MDvdg1kxLU1t0t6Pd/YF6lE9ECnty972s6foHewa3M5jpw2hSHIs0oHXb7uCxjdfR3beNwaFsndw85r6tYllEGkq9Gm7CQ5eW1OB84XNsrsH5ivWayPYkYCpBeZ8JfMrMbgc+6e5X17pwIlKcTV0rcxN8OG7p0YvN7F8UyyLptKn74UjKMMPDY5YDVxyLpNzmzvtzE1Qni8gEUK+Gm9XZ/w1YYmYHu/uq8Q4ol5ktB5bGnLtRnARcZWb/AXza3T2pjM1sPqX3eFqe1PlF9jFHo1gWaXRVi2NQLIvUkOpkEWko9Wq4uQcYAFqz2y8Fvlqlc50dej4I3F2l85RiPfAn4FbgQaATyBAMlzoBeDHw/ND+BnyKYK6eTyZYjncD5yeYn8g+ZVLrNOZOO5gZkxcxpX0urc0dGMbgcA9bd63isS03Rg9RLIukzKTmqcybvIwZkxYytXU2rU3tgDGY6WNrzxrW7Lw1eki14hgUyyJlm9Q6jbkzDmH6lMVMbZ9LS0sHZsbgUC9bux5h7aYbooeoThaRhlGXhht3HzCza4HnZpM+Zmbfc/fuJM9jZlOAjwIjrejXufuYvpI1dCtBg8xfxmnZvwn4ppmdBPwcOCT02ifM7BZ3v6zK5RSRccyYvJgTDvon5kw9CDOL2WMOLc3tcQ03IxTLInU2c9JCTlpwLnM6luSJY2hpaotruBmhOBZJgelTFnPCIa9n9vR8dTK0NE+Ka7gZoVgWkdSr16pSAL/I/u8E3Qm/VIVzfBGYT9CiDkFDSN24+5/c/apiumO6++3AKcAjkZe+aGbNVSmgiBRl3vSDmTtted4LxDzWRrYVyyJ1NG/yQcydvFRxLNLg5s08hDkzVCeLyMRWr6FSEDSi/D9gIUHDyjvNbL27/3sSmZvZJ4D3EDQMGcGEyHVtuCmVu3ea2T8BtzPa+HQ4cAaQxGRq3wYuKfGY5YDuSIiU7qPAr1AsizSyasYxKJZFakV1sog0lLo13GSHS30SuIjRxpXPm9mxwLvcfUc5+ZrZTIIPzFeH8nXgU3UeJlUWd7/TzK4id86bs0igYnH3LcCWUo4p8W6GiIx6EFAsizS2qsUxKJZFakh1sog0lHr2uMHdf2Jm5wAvY7SR5ZXAc8zsQuD77v5oMXmZ2cHA24E3A7MZbbBx4HJ3/3EVfoTEmdnhwHHA/kAH0Adsi+x2bK3LJSLF6+7bxrZdYxbKeynwELkXiYplkRTrGdwZTToP1ckiDaW7dxvbdqpOFpHGVteGm6w3AH8jWPZ6pPFmNvBh4MNmtpHR1Ze6sg+AGcBM4AjgKcCibPpIk/VIXncAr6/uj1CZbC+hDwBvAQ4s4pCjqlogESnZ4HAfT2y9lfWdd9M3uCtul7hhoKUuFyoiVTY43Mfju+5i3e776RveHX35IzGHqE4WSZnBoT6e2HILG7bdTd/AmAZYUJ0sIg2m7g037r7HzJ5NMGTqHEZXgBppgFlE0CPnZeNkE+5fGD7+MuBN7r4nsQInzMxeCXyHYCnwYi2oUnFEpAybulby4LorGBzuLfXQjmqUR0TKs6n7ER7YdjWDmb5SDlOdLJIimzsf4MEn/sjgkOpkEZk46rmq1F7uvtvdX07Qy6aX3GFO4YaYuAeRfY1geNHH3P0cd4+99Z0GZnY+wcRo0UabJwjG2P4C+C0Q7d+ZqX7pRKQYqzddx72P/2ZMo01by9TorvcA/ZG06JALEamTVTtu5u4tfyjUaHM1qpNFUmv1hmu4d82vxzTatLWqThaRxpaKhpsR7v41YAnBalNd5DbQeJ5HeJ+u7LFL3P2/alv60pjZh4ELIsm/AI519yXu/lx3f627nwtcEdmv5FsIIpK8tVtuYfXm63LSFs48ilMPfTsnLX9ddPfXAf8bSdtQxeKJSJEe23k7q7puzknbb8phrJj/kuiuH0R1skgqrd10E2s2XJuTtnD20Zxy5Ds58dA3RHdXnSwiDaXuQ6Wi3H078G9m9jngqcCzgKcBiwnmvhnpndIJbCf4kL0RuBb4RyOsHGVmxwFfDCUNAq9191/H7NsOnBtJ/tcqFk9EirC7dzOPbvzb3m2zJo458BwWzjwCgD19W6OHtDE2lq+pYhFFpAi7+rfySOcNe7eNJo6b/0IWTjmU3QNjbsDHxbHqZJE6292ziVXr/7p326yJY5a9nAWzjwRgT++YRZ5UJ4tIQ0ldw80Idx8Ebsg+JgwzawEuJPe9f0dco03WxwkarUYMA5dXqXgla168Hy1tcyvOZ2jtEwmUJtfwqscSzzNJwysfSTS/g16baHYAXLnh7kTze/6iwvvUmz+4puA+Gc9w/9CVeGiExBFNJ7Ng4yR8Y3C8Z7qih72VsbH8xwqLm5imqVNoapleeUbuhfcpUe+phyaa36Rr7ks0P++P9ravzP5vS74z7FfW3pRofh9cmmh2DCebHQDWXbgjTMYz3BeN5eaTmdc1neGuTWSKi+PU1MkAu85eweCshRXnM+f2HQmUJqIrdpLY8vWVNBdRQcObS1rBuaAZa5ckmh/Ao998aqL5HfLeZD8bqhHLzYcdPO7rGc+wcu3vcB+N46MWnMV+bcthT/D53NQ/5r5uquvk5jlzaG6dXXE+Sf9NA/j0KYnnmWYLPpL8aNg/pfz6uhpxnIRhT7gOaTCpbbiZaMzsDcBVwJnACaGX/uruP8pzzD8D50eSL3L3x6tTShEpZMPwYwwzyG4f/VIz2xawuHl5oUPfHdlWLIvUyYbhx5jTtJDOzOZSY1lxLJIiG3bez1BmkF39m/emzZ68hMUzC67srVgWkYaihpvaeSvwPSA6WfKY5QjN7CTgUwSrbIWtBz5dldKJSFE2ZFazw3PvYC1rLmo14PDqd4plkTrakFnNg8O30kJrTnoRsaw4FkmRdV33sKP3yZy0g+acWsyhimURaShquKmtDnKXGhwEPmZm7yDolTYHOI74pUU7gbPcfVPVSykieQ370Ji0lUO3Mdmm0mKtGMag97PLO/NloVgWSYEMwwyEOoQbxmNDK1nXtEpxLNIghjODY9JWbrqSjraZtDZNAmticLiXXX15Q1WxLCINQQ039dUKPL+I/f4KnOfu66pcHhEpYIixF4m97KbXdwfr3I1PsSySUo7TyaZiFvdWHIukxFBm7BxjPYM76Bksao4mxbKINIxULQc+wf0PsLrEY3qBO4GrUSObSCq0W9mT8l0KvE0XiCL1d2DzYXQwtZxDH0R1skhqtLeWPam+6mQRaShquKkRd/8tsDuSfD7wOfI36HQQTGT8H8AjZvYtM5tcvVKKSCGD5N7dO7z5ZA5rOoHJTCt06MtRHIukwvymA2ix3PltDmo6moOajirUoHMEqpNFUmNwOHf1uCMWPJ/D5z+Hya2zCh2qOllEGooabmprv8j2TQSz2hdcjoZgWNW7gRvMLJqPiNRIv+deJE62aazJPEDPmHbZWIpjkZSIxvLMpvk8mVlFL3uKOVyxLJIC/UPdOdtT2maxevuNxQ6VUhyLSMNQV9/amhnZ/hEwN/u8G/gucAWwDphCMFHxW4Cnh445HrjUzJ7l7mMn2yiBmc0H5pV4WDGNTCIT1hADOdsPDN2ytxdOMy3s33Qwc5oWkfEh7h6+Ll82icUxKJZFylFMLE9hOiszt4Z3u5OgJ+wIxbJIHQ1l+nK279/4x729cJqtlQNmHc/cKQcxnBnkrvWX5stGcSwiqaeGmxoxs0nApEjy/tn/VxLMaP9k5PU7gR+Z2YeB/wqlnwp8HPhChcV6N8FwLREpQsaHyURmLu2nB4ApNoMTWk7fOwfOnkxX9PAvAx8NbScVx6BYFilJsbG8J9MVnaz4jcBZVKdOBsWySNEymSEyPpyT1jcU9H6d0jaXEw94FR3ZOXD29G+NHq46WUQaioZK1U5znvSdxDfa7OXuXwG+Fkn+FzMra2ZFESmP51k2qoXWnEabPH6M4lgkFSqJZdXJIumQN46bJuU02uShOllEGooabmrE3XuIX2T0q+M12oT8G0Ejz4jZwAuSKJuIFKfZWgAbk35g8+HFrjalOBZJAcWySONrbmolLo6XzD65UKPNCMWxiDQMDZWqrW4Ys/TMT4o50N27zew3wJtDyacDl1RQnm+Xcfxy4LIKzinS0JppZpihnLRFTcuKOrZKcQyKZZGSKZZFGl9zUyvDmdz5qhZPP7qoYxXHItJI1HBTW13kNtxsdve1JRx/C7mVyxGVFMbdtwBbSjnGbOydDZF9SSttOV/22mino7Se1YnGMSiWRcqhWBZpfK1N7TkNN23NU+hom1lKFopjEWkIGipVW49EtjeWePyGyPacCsoiImWYbLmd5iZZR6lZKI5FUkCxLNL4JrfNytme1FLUUMcwxbGINAQ13NTWA5Ht/hKPj+7fXkFZRKQMU2xGznZT6R+jimORFFAsizS+qZPm5mw3WcmDCRTHItIQNFSqRszsIuBNkeSnmln8lPjF2V7BsSJShl3embO907fzl4GfF3Po/Xm6QiuORWrs/qGb2Zh5LCetyFjOF8egWBapuV29m3K2d/Zt4MqHvljMoaqTRaShqMdNY1tX7wKI7GsmUfJwikIUxyITg2JZpMYmtUbX/KiY4lhEUkkNN43t+noXQGRf01x6N+xCFMciE4NiWaTGVCeLyL6iLkOlzOwz9TgvgLt/rk6n/ghwAfBqINyH8ybgdeMcdxLxSwpmgD8lVTgRKc6hzcczmemsztyzN20Gczmm9bSc/bozu7hr+Jpw0r8AX4tkpzgWqYNDm49nefMxbBx+fNxYLjKOQbEsUheHzT+TKW1zeHTbtXvTZrQv5rhFL8nZr3ugkzvW/SqcpDpZRBpKvea4uQCoZG6XStSl4cbdtwHbzOy/COa6GVlu8DTgXHf/atxxZvaJPFn+yt1XJ1/S0gyv38iQ9dS7GPFOPjrZ/G69L9Hsmo86LNH8evdPvLswz1+UbH7vX/VQovl964SnJJofwPCuXeO+3koTS/1gNrGGbnYDsJNtbB5cyxI7dO9+Q94aPfT0mOxSEccAw107GU7paqRtmzYnml/TwcsSzS8zbXKi+T3xvBmFdyrRB5cmO23D2l8em2h+B39y/Lgrx9CatXlfa6WJVtoKxnJMHJ+aJ8vUxPL0393NVKv8b8gOXZ5AaXINd6f0emHEU45JNLv2Lb2J5gdwyHvvTzS/w24f8zdekYdPGkw0PwCGh/O+1GaTWDbzJDbsup/ugeBzbmffejbveoils04ezaJpzHogp8dkl5o4Ht6+nWEbqjgfa0n+q97wyugiuenSPG9eovn1L06+Tn7+ohWJ5nflhrsTze9FJ78w0fwAfKjyv+fmwZZ9ehaqfWmoVCq+krj7MPABglb9EV8xs/8xs5w1Dc2sA3h9TDY7gE9Vr5QiMh4z41BW5KQ9yr087Hcz6AP5DntpZLsbxbFIXZURyy+JSVOdLFJHZk0cPvdMwpf6D2+7hge3/pXB4b58h6lOFpGGUs9VpVLRkFIP7v4XM/sA8I1Q8vuBd5nZLcB6oAN4GjAlcvgA8E/u/hgiUjdzbAGH+Qoe5u69aU+yinWsZobPoZnm6CHRz7zXKI5F6m+8WJ7KmDut0dnJVSeLpMDcKUs5fN6ZPLT1r3vTnui6kye77mZm+yKam8b0LFKdLCINpV4NN2fU6byp4e7fNLNh4L+AkX72rcAzxjmsB3iuu99U7fKJSGEH2MGYG49wLxmCrtyO08W2Qode6u5/qHoBRaQo+WJ5N13jHaY6WSRFlsw8AcN4ZNu1DHswZMvJsKOv4EJRqpNFJPXq0nDj7tcW3mvic/fvmNlVBHP+vAwoNEnJ83WBKJIu+9tyZvsC1rCSrWxgmKLG8H6h2uUSkdKUEcuqk0VS5sCZxzN38lJWdd7Elu5VDGfyDl8OU50sIqlXz6FSAmQnQXtDdj6bpwH7AwuBZwPPCe16p7vfUIciikgBk20qR/MUhn2YLrbRTy+76eJJVsXtfqe7313jIopIEaKxvJNO1vBA3K6qk0VSanLbLI5d+CKGM4N09a2nb2gPu/u28PjOO+J2V50sIg1BDTcp4e69wNUAZtYMvDeyyw9rXigRKUmzNTOHBQDs8Z35Gm4UyyIpNxLLk7w9X8ON4lgk5ZqbWpkzeSkAeyZty9dwo1gWkYawL60q1UjOAhaHtnuBn9epLCKSHMWySONTHItMDIplEWkYarhJp7dEti919656FEREEqVYFml8imORiUGxLCINQ0OlUsbM5gEviSRXpRunmc0H5pV42PJqlEVkohkkdkJExbJIA6llHINiWaRaBof74pJVJ4tIw2jYhhszmwbMoMReQ+7+RHVKlJg3EiwLPmI1UK1VuN4NnF+lvEX2aVvZEE16EsWySEOpcRyDYlmkKrZ0j5lzTnWyiDSUhmm4MbNnAq8DTgMOp7xhXk76f+Y3R7YvdHevS0lEpGwxX/h+o1gWaSyKY5GJYUv3o9EkxbKINJS0N2JgZsuA/wOeOpJUx+JUlZmdAhwVShoGLqpPaUSkXDt9O710R5N/V4eiiEiZFMciE0NX7wZ6Bruiyb+rfUlERMqX6oYbMzse+CvBkCgj6DEzIvzc8qRHX0u7t0a2r3D3Mbf7EvRt4JISj1kOXFaFsohMGOtZG5e8tYqnVCyLJKwOcQyKZZHErdt1X1yy6mQRaSipbbgxs+nApcBMRhtjhoCbgB3A2dk0B34MTAcWAccDbaFjtgBX1KLMlTCzKcCrI8lVmwARwN23ELw/RTNrpHYwkdob9iE282RNz6lYFklWPeIYFMsiSRvKDLBpz0M1PafiWESqIbUNNwQTey1ltAHmSuDN7r7JzJYw2nCDu++dF8bMJhHMhfPp7PHzgObsscO1KHipzKwD+AQwLZS8G+gws0OAVRqHK5Juwz7MbrpYz2MMM1Tv4ohImYZ9mMd4KF8cH2hmK1Uni6TbcGaQ3f1bWLfrXoYzsavDiYg0lLQ33IxcGN0FvNTdBwsd5O79wIVm9mvgl8DzCRpyHHhTlcpaFjM7FfggQSNUW+TlacDPs8/Xm9kPgf9x986aFVBECury7TzJo2xhA05mvF3/ambfQ3EskkpFxvKfUJ0sklpdvet5vOsONnevosD9WtXJItJQylmZqerM7CBgf0bnp/lEMY02Ye6+CzgXuDebz+vN7JxEC1omM2sxs28CNwKvYmyjTdRi4DPASjM7q9rlE5HCMp7hIb+L2/k7m1lXqNEGYAGKY5HUKSOWVSeLpEzGM6zccjX/WPdzNu15uFCjDahOFpEGk8qGG+DE0PNOd7+6nEzcvRf4SCjpg5UUKgkWDGL9BfAe8k+cvBX4C8FcPmELgMtUwYjUl7tzP/9gHavLOVxxLJISimWRxufu3Lvpcp7ceVc5hyuORaQhpLXhZm72fwfuiXk9Z2x5dl6bWNlGn40EjSRPM7NFSRWyTG8DXhFJiw6+/Zi7Pw+YD7wF2Bl6rQ34sZnNqF4RRWQ8G3iMLazPSZvJXFrG7zy3O/RccSySAnGxbOMvRvlpVCeLpMq6Xfeyec8jOWmz2ventaljvMNUJ4tIQ0nrHDczQ8/jluvri2xPBvrHye9uYD+CxpuTgN9XULZKfSqyfT9wdGh7N9klBN19CPiRmd0H3ACMNFDNB94J/Gd1i9rYmveM9ydRuqRnts60Jxt+bTvTP/nexy58S6L59Xy5pBGURTniU4XvvK/tvJLwaIpZLfuxpP1o7t7zl71pzbREJzd9G/ATFMcl2/Le0xLNb+ENXYnm53c9kGh+C2eekGh+1TDzD1MSze/Js6cmmh/Afl9dW3Cfx8hdbWYK0+lm197tZpoZzv30/x3BYgkTvk62nuilVuWaZyX8vXgw2Yngh5qTvZ+57jnJxgnA/rcnm99ff3Nyovntz02J5gdA165xX35s+80527Na92NJ6xHc3ac62YeSXyzBTzsu0fzsprh79OUb3prsSu/rnn1wovkBLLsu2e8AB/3mHYnmN+Ps5kTzA5j/rco/G4Z9Z+GdJrC09rgJfwON+768O7JdqBdNeNKxhWWVKAFmdgzBSldh0U+Xi929O5zg7rcD34/s95JkSycixdg91ElvJvcj6PCpp7FhIPdu39y2A6KHPoDiWCQ19vhO+ujJSWsjtwPvnJhLBtXJIumxe2h7TJ38NDb0P5qTpjpZRBpdWhtuukLPx9yeyc5dE77aKtQUGs5jdvnFqthBke31wDMiaT/Ic+xlke1DEimRiJSkdzj3zl970xQmNXWwdeCJnPQFbcviDlcci6REDzn3SJhEO11sy0mbz+J8hyuWRVKgdzi30aa9aWp8nTwpegkOKI5FpIGkteEmPFZh/zz7rAw9jzZ+7JWdDPikUNKeCspVqWif2SFyh6vd7+635jn2ycj2rMRKJSJFG/LcbsftTVPZ0PcoHpp6a2rzLKa1xLYRK45FUiKTO2wCoyknjqcwnalj7x2NUCyLpMBQZNHZ9qYpbOh7JGd1uKnNs1Uni0jDS2vDzUijjAGHm1lcOW8L7fM6M8s3A9k/kTs8ak0yRSzLpsj2gsj2D8c5tj2y3Rm7l4hU1aTIZIfDPsT6/odz0ha3H57vcMWxSEq0RcKxPzJ93mJie82NUCyLpMCkpsk528MMs74vWicflu9wxbGINIxUNty4+xZgZHBqG3BKzG6/HtmdYEKxn5tZzuyGZvYc4NuMrkI1SDChYL3cRu4kyuEKYwD42TjHnhjZvi12LxGpqhkt82hidNK2PcM76B7u2rttNLFoUt7e1opjkZSYziyaQpdB4Tv0RhMLOXC8wxXLIikwpk4e6hxbJ7erThaRxpfWVaUArmZ0rOmLYMw09dcA9zG6ItNLgfVmdh3BUp2HA8fD3nU9HfiFu48/NX0VuftuM/sJ8M+h5Pe5+zfHO87MmoH3RpJ/nHT5RKSwlqY2Fk06hHX9wWo0TobDp5zGko6jc/YbyPRED21CcSySGi3Wyn6+hPU8tjftMFZwgI1OmzfgY1dWUp0skh4tTW0saj+EdX2hOnnq02Lq5N7ooaqTRaShpLLHTdZIjxoDzsteKO3l7k7wgRsepD4NeCHB8KgTsseO9LbZAnyimgUu0ieAtaHtL2V7BsUys1bgfwkaoUb8Dbi0KqUTkYIOmfIUOpqm7d1+pPsfbB9YV+iwC1Aci6TKwRxDO6NDLR7lPrb75vEOaUF1skiqHDLlqbl18h7VySIy8aS5x821wIcZbVyaR2SOGHe/3sxeB1wETIbQrIKjzw3YALzYffyrsVpw904zOwP4DUGF0QFcaWa/JmisegjoBeYCpwLvAMKDc28FXpFtuBKROmhraufkGS/mrl1XsXt4OxmGuX3XFSxsW8aCSQcxpXkmfcNj5kE/N/RccSySAq3Wxon+LO7lZnbTRYZh7uJ6Fvj+zGf/vV12Q34LOZPfKJZF6qytqZ2TZ74kqJOHtpFhiNt3/omFk8J1cnf0MNXJItJQUttw4+4Z4GtF7PdrM7uVoCfLS4FFoZcfBX4JfMXdd1aloGVw97Vm9lTgPODtBGNsX5V95LMd+CrwZffIFPplMrP5BA1ipViexLlFGl1H8zROmXkO6/sfZl3fQ+wa2sqmgTVsGhh3/vPE4xgUyyKV6LApnOxnspG1rOcxdrGDzaxjM7F37EcabRTLIikS1Mlns77vEdb1PRjUyf1r2NSvOllEJobUNtyUwt2fAN4NvDu7utRMYId7zOD09GjOPvoJegfF3Njb60ngM8DFSVYsBO/Z+QnmJ7JPcTLgnp3gNDwyM9Ym4JMkH8egWBapiGf/WXEjyKtVJ4NiWaRsjgOenaxYdbKITCxpnuOmLO7e6+4b09xoY2ZPAx4EvgM8jcK/hwOAHwFPmNnbqlw8ESnCjsFN3LjjElZ230DX0GYKXCACLERxLJI6Xb6Nm7mSh7iLnWwv5hDVySIps2NwEzd2/oqVe66na2gTqpNFZKKZED1uGomZPRv4A7lLga8HvgFcCTwG9ACzgRUEEy2/juB3NQ/4vpk9BXiHxuKK1Mf2gfXcuevPZBjemzapaQoHth/F3Lb96WiaTu/wLm7e+ZvwYUMojkVSpdM3czc3kgktBT6JDg5gOXNYyDBD3M414UMuI1jpUrEskhLbB9Zx586YOrnj6GydPI3e4d3c3JUz97DqZBFpKGq4qSEzmwf8gtxGm8uB18csU76ZoCHnSjP7LkFjz5zsa/8MrAb+s8IifRu4pMRjlhNcuIrskwYyvdy7+685F4jz2g7k2Kln0tLUtjetPzOmI90bCRpok45jUCyLlGzA+7mPW3MabeayH0fzFFqsFYA9Y6fH+1fgi1SnTgbFskhJBjK93LsrWicv4dhp0Tp5zOTEqpNFpKGo4aa2PkTuZGUPAa8qNKzL3W8xs1cDV4eSzzezH7n7lnILkz22pOPNxpuKR2TiW9t7HwOhkJ3SPJPjpj2HZiv4cXovkHgcg2JZpBxP8AiD9O/dnsw0juEUmq153OOqVSdn81Ysi5Rgbc+9Y+vk6aqTRWTiUcNNbb0ysv2fQGt2efD9CZYA302wfPn97v7IyI7u/lczux54RjapA3gN8PWql1pE9oquULGs4ziarYWhzAA7hjbRl+lmMNPHcMxch4pjkfSIrhq1lMNotmaGfJAuttFPL3uIdoYNKJZF0mFMnTx5xWidPJitk72P4YzqZBFpbKltuDGzC6uQrbv7W6uQb0FmNoWxS/2dB3yfPL8HM7sH+C7wveyY26sZrVwAnpp8SUUknyEfpDeT+0Wu2dq4feef6Bxcn13RIq9XmdkFKI5F6m7Yh+gld+hECy3c5dfTyZbxYvlSM/tv4HsolkXqKrZOppXbu/5I5+CGYOXH/FQni0hDSW3DDUGjRpIThI2sC1iXhhuCJcqjnlXgmOMIVp76JzN7PcHShWFzEyiXiBRpKDMwJu2e3X8p9vDPAKcDf4qkK45FamyQsXff7+WWYg49jGy9jGJZpK5UJ4vIviTNDTeViA4UTcMM8V3jvDYI/ANYB0whaLA5MPT6M4G/AD+LHLcnwfKJSAHhiQ7jGE3MaJlPe9MU+jM97BjaGN3lmcDhkTTFsUiNtdI67uuGMYPZNNPCdjbH7aJYFqmzouvk5mydPKg6WUQaV9obbiqZqWukscYqzCcR7t5tZgNAtJb5OnCBu+8IJ5rZ8wju6h2UTToMeF/k2GgPHBGpohZrpcVaGYqZv+bA9qM5ePKJtDZNAmDPUCc3dv06Lpv5kW3FsUiNNVsLzd7CMENjXjuAgzmII2m1Nvb4TraTcwf/SeCA7HPFskgdBXVyG0M+tufNgR1Hc/Dkk3Lr5B2xCz0pjkWkIaS54WZZifs3A7OAo4AXAedm0zqBtwF3JVq6EpnZEYxttLnL3T8Qt7+7X2VmTwNuZLTxJlq5XJ9sKUWkkGkt89gxuCEn7bApp7C049hCh4a/8IUpjkXqYBoz6WJbTtohHMsSO3S8w14P/IjRejlMsSxSY9Na5sbUyaeydLLqZBGZWFLbcOPuj5d56B3AT8zsKOASgi6QFwEvcPebEypeOT4ek7bCzF7v7tEhUAC4+yYzewtwTczLe4ArEyxfWZoX70dLW+XDgTObKlp5MZbt6i68Ux01Pfpkovn5Qfsnmh8w/rR+ZVhwa3/hnUrQ+u93JJofwOpfjn+x13t+KzyYm9b15sWsfmbu+9//ZBt8OCfps8APItmlIo4BmhcupKVtTsX5DD25rvBOJdrvl48mmt/w1q2J5td8xCGJ5jcwafzlqMvR3JRsnnNuTfY9HH54VaL5ATzyvZPHfb33y7fDo7kNN51vOYL+U07Yuz2wYRNckNPjZjvwDiA6kUZqYjkpq951YOGdSnTQ55O9h5bp6yu8UwlaOpO9bjjgyrE9uiqV9Nj/eXeP7UFaCZs0KdH8AHqeGtdOGhi+qS24RRt2yIH07J97TO/uyXBtTlK66+TZs2lumV1xPoOHJ39t2HR9Xe+FF5Zwfbf0suRHz/lQsp8Ni65JNDum/PqmZDMEdv7p4Irz6H18K7wzgcI0qKZ6F6Ba3P0B4AzgCWA68Fszi/ZYqQkzM4JeQGNeImhk+h8z2y/P4U1AT0z6f0aHV4lIdbk7A+vGfmHd/K3fsPVHf2Jox+7xDv9ITNolimOR2nN3BjeOjeXtP/oVnRf/nuGu+GXAgacAX45JVyyL1Ji707NnbBw/fPevWH3/7xnoyxvHoDpZRBpManvcJMHdN5vZR4FfAvOA84H31KEoR5J/lnoD3g+818zuBdYAvcBs4HhgYcwxTwD/WYVyisg4BtZtJbM7ph3VnZ1X3MLOP/+DtiULaJ0/O+5uSnQCRCBmaRsRqbrBjZvJ7InpXeHOnr/dyJ6/30Tr/vvRNHVydI8L82WZcBFFpICePZsZGojrJeVsWHsjG9bexJTp+9E+eTaZjOpkEWlsE7rhJutSYAfB/DevNbMPuXuy4zUKi/ZTfBTYCZwUSmsCVmQfhTS5x8yOKiJVNbR9Z862tbbgg6GLQXcG1m5iYG3Rcxu+ILHCiUjRhncUjuXBJzdQAsWySI0N9ObGcVNTS6SBxunetYHuXUXHsuJYRFJrwg6VGuHuGeC27OZ0gqX/ai06SHULcCrwJuBmCg9ZjjY07W9mMxIqm4gUKbOnN2d70kH7Mf8959J+6AFgBRev6wP+Gkk7QLEsUnuZ7txYbj1wMbPf/CraDjqwmFjuRbEsUneDg7lxPHXGYg5d8SqmzTqQIhaUVZ0sIg1lX+hxA+QsG5H8bHuFRdcpnOTuQ8BPCOa4mUHQ+2YZMBOYBOwm6Cl0P8Gs95dF8jiSoNFHRGrEWnInvPOhYaY/awXTn7WC4Z4++levZ3BLF5nuXoa272LnFbeEd38qwefPsyPZKpZFasyaI5NXDg0z9dQTmXrqiWR6ehl4fD1D2zoZ3LSF3X/JWWTmFQT18VkolkXqqikyCW0mM8yC/U9kwf4nMjTYy+6d6+nv6WRosJe+3i42rs2ZcFV1sog0lH2l4WZK6Pm8Opx/e2Q7ZyJid9/J2Fb/vczspJjkw1DFIlJTTdNy57sY2jG60kDz5HYmH7N873b/k1uiDTfDRGI/S7EsUmPRuWuGd45OYto0uYP2I4LVLwY2bIo23Dzk7kN5FhRQLIvUUEtrbhwP9O8KvdbBrLmjq9h0794UbbhRnSwiDWXCD5XKCjd8jDvFfJU8FNlebGalrM93akyaunKK1Fjb4tw5xoc7d42Z96YAxbJICrQuzF1kcrhrF0M7ukrJQrEsUmeTp+bG8UDfLvp7u0rJQnEsIg1jwve4MbPXAItDSWtrXQZ332RmDxO04o94A/AfhY41synAuTEvTau0XNnl0UvtgbS88C4iE1PLzGm0LprL4IbR0Ze7rruH2ecUNXVWB4plkVRonjGNlgXzGNo8upRw9y13MeMFZxQ8VvWySDq0tU+jY8o8ertH43jL+rs44ODCcYzqZBFpMBO64cbMXgp8n2DyXwOGgOvqVJyfAZ8PbX/MzH7i7usLHPd54lv/K65YgHcTLJEuIkWa9oxj6fzl3/Zud/3+BqY/awUts6cXOvR9KJZFUmPKKcez87Kr9m7vuvJappxyAi2zCt5wV70skhLz9z+exx8ejeN1q69l/uITmNRRMI5VJ4tIQ0ntUCkze2YZjzPN7Fwz+7SZ/QP4LcH8NkbQePN/7r5n3BNXzzcJlgAfMRO4wswWx+8OZvYh4IN5Xs4kVjIRKdqMs55K0+T2vduZ7j42/PtPGeosOArzDXnSFcsidTDtjNOwjtFY9p5etn79QoZ2jDv88Y2oXhZJjUVLT6O5ZTSOhwZ7uf/WC+nvLTiMWXWyiDSUNPe4uYbCy2SPZ2QdwJHeNpuAf62wTGVz9y4zewtwaSj5GOBBM/sucAWwgaDr5grgrcDTQ/uuA8Lz4nRVs7wiEq95Sgfz33U2m75y8d60gSc28/i/fIMZzz2JySsOoWX2dAbWb40eOvKZpFgWSYGmyR3MedMr2fbdn+5NG1y/iY3nf4WpzzyFjqMPIzPQHz3sY6HnimWROmtp7eDQ417Jg3eMxnHP7k3cce1X2O/AU5g1/zDa2qfTs2dL9FDVySLSUNLccDPCCu8yhjPa6GPAY8DZ7r4xsVKVwd1/Y2YfAL7GaG+nacBHs498vk7QnfNNobSuBIr0beCSEo9ZztilyUX2KVOfeiRzz3sh2358BXjwUeO9/XT9/ka6fn/jeIcqlkVSZPIJRzPr1S9lx68uH43lvn52X3Utu6+6drxDFcsiKTF3v6M56KiXsuaByxm5/B8e6mfdmmtZt0ZxLCITQ9obbspptAkf9yTwA+Cr7t6dTJEq4+5fN7OHCCqLwwrsvgf4OPAd4KrIa5sSKMsWYMwtiPGYlfsrEZlYZr7wFNoWz2Xrj/6UM1nxOL4AfAbFskiqTHv202hZOI8dF/8+Z7LiPHoIbrSoXhZJkcXLnsbkqfNYff/vcyYrHofqZBFpKGluuPlsGccMESz3vQW4090fTbZIyXD3q8zsKOBlwAuB04AFBL1vthD0EPodwZw8mwDM7PBINrfXrMAiEmvycQdz4FffS/dtD9Fz1yP0Pvwkwzv3kOkdoHlqO8M7c9qLL3Z3VyyLpE/HUYfS/tkP0Xv3Snrvf4j+1Y+T2bWHTG8fZHKmvHiBu18HqpdF0mbWvEM58fQPsX3TSjq3PMSuHY8z2L+H4aF+Wlo7GBzImeZSdbKINJTUNty4ezkNNw3D3YeB32Qf4zKzA8gdf7u+iNWoRKQGrKmJqU89kqlPPTInvf/JLTz54W/m7qtYFkkta2pi8glHM/mEo/emDWzYxKYLvhbebTsolkXSyqyJufsdzdz9js5J7969iTuv/VpkX8WxiDSO1K4qJTmeHdm+ph6FEJGKKZZFJgbFskjjUxyLSMNQw01jeGtk+wd1KYWIVEqxLDIxKJZFGp/iWEQaRmqHSknAzJ5O7rLgD7v7NXUqDkBbeKNnsCuRTD2TTD5hNphsfsO+M9H8moeHE83Pe9sK71SiTMI/89CezYnm15Jw+SAY4pSEwU3bo0lPYR+I5aTjBKB5qDnR/BKP5b6iJsIsWt/uvkTzAxhI+mfuT/ZnrsbfzcCGiucYZXDLmInH21JfL7NndF3NCvRvrvz9i9qTcF2f8THLtVck6b/rTHPy9bInHCsDu5P9PQ9V4XquP4Ey9naPieV018lDyfyeB7tbE8knrKkKn9eJ8mSvGehJ5rowR8LvYe/OZOM46c8ZgN7HK/987duwI5qU/Idsipl7ArV7FZjZyLdaBw5y9yfKzGcJsGYkL3dvmMYqM5sM3AocFUp+hbtfWqciYWYvRcsVipTjCeDA0LZiWaTxvBK4ANXLIo1OdbJI43uZu/++3oWolTQPlbLQI015lV8Is6IbjcxsKvBHci8OL61npSIiFQlfICqWRRrTZ1C9LDIRqE4WkYaS5oYbSKSzb6q8w8z+Zmbnmdm8uB3MbKqZvQl4CDg99NJa4N3VL6KIVNlaFMsijeqY0PO1KJZFGt1aFMci0gDSPmzImFiNNwackX24mT0GPAzsACYDC4ETGDte7zHgee5ehUGWJbsWeFlo+0lgIGa/5eR2+XwZsLrCcyed576WXzXy3Nfzg+Azah3Bxd8uoB2YCxwJRAeXK5bTn1818tzX8qtGnvWK5XkEsRy9XlIs73v5VSNP5Zf870R1cmkUJ+nLrxp5TpT82oADQtvXVnDOhpP2hpskhBtB4j4A68WAg7KP8fweeJu7JztjXpk8mK2q4FhCszGj0la7+wOVnDvpPPe1/KqRp/ILkgkqkQPiXgxRLDdAftXIc1/Lrxp5KpbzUyzXJr9q5Kn8qvI7URyXQHGSvvyqkecEy++ucs/T6NI+VCoJ+4ee765bKQI3AJcQ9LAZzxBwBfBcd39ZWioVERljV4HXFcsijUGxLDLxKY5FpGHtCz1uzsn+7wTdJuvG3e8GXmVBs+KhBN029wemE5SvC3gE+Ie717uRSUQKO43gQlCxLNLYFMsiE8OLCXrbK45FZEKpa8ONmT2zyF1PMbOlJWQ9CdgPeB7wmlD6HSXkUTUerMH+cPYhIg3M3RXLIhOAYllkQlhb6TAVEZE0qnePm2soPPmwAb+o4BzhQXO/qiAfEREREREREZGaqnfDzYjYWQJLeD0fzz4M+L27/73MfEREREREREREai4NkxOX2yhTbN7dwFeAV1fxPCIiIiIiIiIiiat3j5vPjvPa+dn/Hfg6wcRixXCgP7v/g8Dt7t5TZvlEREREREREROqmrg037p634cbMzmd0/puvufsTtSmViIiIiIiIiEg6pGGo1HiqOYxKRERERERERCTV6j1UajxnhJ5vqlspRERERERERETqJLUNN+5+bb3LICIiIiIiIiJST6ltuJGGt5Xcyae3pjDPfS2/auSp/Ca+tL+HipP05VeNPNOeXyNI+3uY9vyqkafyq9y+Fsv74u9kX8uvGnnua/lNSObuhfcSEREREREREZGaS+3kxGZ2lpkNZx/dZja/jDwWmFlvNo8hMzuj8FEiIiIiIiIiIumQ2oYb4M2Mrir1C3ffUmoG7r4Z+EU2nybgLckVT0RERERERESkulLZcGNmTcDzQkm/qCC7/8v+78BZFeQjIiIiIiIiIlJTqZzjxsyOA+7KbvYC09w9U2ZezcBuoJ2g8eZYd38gkYKKiIiIiIiIiFRRKnvcAEdk/3fgvnIbbQDcfRi4NyZvEREREREREZFUS2vDzcLQ8/UJ5BfOY1EC+YmIiIiIiIiIVF1aG24mh573JJBfOI8pCeQnIiIiIiIiIlJ1aW242RV6PjuB/MJ59CaQn4iIiIiIiIhI1aW14WZb6PnhCeQXzmNb3r1ERERERERERFIkrQ03D2f/N2CpmR1WbkZmdiiwLJS0upKCiYiIiIiIiIjUSlobbu4GdhCsKgXwyQryCh+7B7i1grxERERERERERGomlQ037u7AHwh63BjwBjN7Tan5mNmrgTcSNAA58Mfs8uAiIiIiIiIiIqlnQRtJ+mSHRz3AaOPNAPBpd/+vIo//EPDvQGv2+AxwnLs/UJ0SS5iZLQNWECy/PhXYCDwO3OTug3UqUzNwMHBktlwzgH6C3l2rgdvdvbuE/DoI5k9aks1vGsHf2y5gO3A/8IC7DyX4Y6SOmR0OHAfsD3QAfcAWYBVwTynvacLl6iD4GzwCmAW0E/xutgB3Aqu8hA9AM5sGPJ3g55wL7AY2APe7+yOJFj5F0hbLScdxNk/FMumMZcVxMtIWx9kyqU6uEsWyYrmG5VGdXCVpjONsuRTL9eLuqX0A3yBocBkO/f8o8DHgKcCU0L5TgJOBjwKPRI4ZBr5V759nX3gArwBuYrSXU/SxHfg2MLeIvA4CXg18GbiG4EMhnNfaIvI4EPggQQ+uneOUy4Eh4ArgRePk92bgp6G/sfHyc4IPnx8CKyp8Xy+Oybvgz1/F3/NM4Pz/3959x09S1Pkff302wC5xCUsOGwiCkpGMLkpSEFQy50kQAZHzAAkGfoJ64oHHHaiIoLhLOBAOEE5FEDhQkkhaUYLEL7IkSbskN/L5/VE9bE9Nz0xP7pnv+/l49GO3e6qqa6b70/2dmqpqwh8L9T7Te4GvJPmm5fjM8i6Z7x/YGric8IdDrfwzgG8Cy9Z5r9sC1wPzapQ1HTiSpDF8EJYixXK74zgpU7HsxY1lxfHgxXFShu7JnTvWDcdyp+M4qZdiuT3HtzCx3O44TspULHtzcZzkUywPg6XnFahzQEcBt1HeeJNujFmQnDxzom3vRnluAUb2+v0M8kJo9b+sgYvCi8AuGeVMAW4g3ICaurCkyrq0hYvWL4EVM8qc0WR584H/AkY18dnu0eCF9dQW3rcD0+rUZx/C09kaKfPFJO+0FuuWXh6J6jUK+CEh5huqG7BrxvscDfy4wbJ+B6ze63gcpFimA3HcD7FMh+O4qLGM4ngg47hTsUzB47josYzuyYVfKFgso3ty4eI4yTutxfoplvtgGUWBuft8M/sYoQV2T8KBgzD0qWR0VtZUuiuBQ1xz23RM0k3ycuDj0UsvAw8QWuMnA5uw8NitCFxrZju6++2pPBsDO7epautU2f4coefWS4SL0SRCV8T0nE+7A783sw+7+4s19vEOoSvo3wi/WIwAlgU2AFZKpRtJ+HVigpntnfd8NLNxwLl50naDmZ1CuHnF/kb4leRlQpfJlQmfweIdrM5VqXoZ4Q+bvTPSPQo8AvwDGA9sTujaWVI6F/d09+uT8kYR/sDYJSprHnA34Q+MxQnnzRqp1z8E3Ghm27r7q82/td4oaCx3I45BsVzSs1hWHLdHQeMYdE/uiALFsu7JbVbQWNY9uQMKFMegWC6uXrcc5V2Aw4GnWNibJu55E/e2eRw4uNf1Hg4LoatlulV0LnA0sEiUbn0qu3m+AqycSnMM2S2tswljOmu2iEf7uzeV9v6kTpOrpF0VOC9jv7eR6p5HeFT9tYRuexsCI2rsfyvgpowyT2jgs70glS9XV1Y69KsA8OWMtJcCG1RJP4LQDfI/CWORIYxdndDEsne033eBSal9fT6jbr8DPpBRr1GELrkzo/QvAUsnac7MKO9sYJmM8nYm/HER77vvunVSwFimA3HcD7FMB3/do6CxjOJ4YOO4U7FMweO46LGM7smFXihgLKN7cuHiONmmWB4GS88r0FBlQ6vqJ4CzCI/1fpbQIvs2oUXy7uQk/jg1Al5LW4/JJMKNJB1Ue9ZIP5bKm8uPU68fk5T3APATQoPdpoSeVVOifEN16nYPYfzt5g28n6MyLij7p14f3eDnM4LQYyxd3kxg0Rx5d0zlmUflTTfz/VN5c9mfxi7iFeOjCS3f6TGoc4G9G/gcGu7CGuWPu1TeHL3+dMaFveaxIvwyMDvKdxJhsrV4fPVxdcpaicqby/6tvOduLxQ0lulAHCdpCh3LdCCOk3ILG8sojgc2jpOydE8ekFhG9+RhG8vonjwwcZzkVyz30dLzCmjp7wW4MAqmqTnyrEP5xFbzWPiL6zLAmCr5ct9YkvQTmnxPV0b7+XWLn9FSwFtRmRVjPqM8i0cXy9Pzvv+Mm8uUFus/CrgvKvOQLp5jY6lsvT8w9foG0WtOeIJcnrJ/EOW7ncpxwjfmLOvDUb6n6KO5tYoay0WJ46TMrsVyu+M4KbOwsaw4bttnXMg4TtJPaPI96Z5cWWbPYrlWHCevK5bb8zkXMpaLEsdJmbont7Z/xXKfLelxhwPPzBbpdR0GSfI4uHjM4+n18nl4tNs1qU2jgAOT115399ntqJ+7DzWZ9ZxofYcW6/EG4YKVtladbN8ltNBDuECd2kodWrQP4VeZkpvdfWoX97834fGSJTOBq1Prk6L0z7r7n3KWfW20vjawW7TtP/IU5O6/I/wSVTKR8MdA4RU5losSx6BYboNasaw4blGR4zgpa6jJrLonV+plLOue3GFFjuWixDEMRCwX+Z4MiuXCGRYNN2a2oZmdTZg4S9pnF2Cx1Ppd7v5ozrzxhenT7alSWzwQrY9NJjFrxWvR+pLVEprZNsAXU5uOcPd/tLj/VhwRrZ/W5f1/Llr/7+iPj3iCthkNlP1stL4sYZxwyVzCozLzuj5az5rMrYgGMZY7EcegWG5FrVhWHLduEOMYdE/O0stY1j258wYxlnVPrlTkezIolgtnYBtuzGwpMzvSzO4hXCyOJpw00j67Ruu3NpD3NsIj/Eo2MbMVW65Re8zP2NZqb601o/XnsxKZ2aLAz1gYmxe6+00t7rtpZrYWoYtiyRBwSxf3P5kwk3zaT6P1+MkEYxrYRZz2zWj9cXef00B5f47W418XimoQY7kTcQyK5Wb3Xy+WFcetG8Q4Bt2Ty/QylnVP7ppBjGXdk8vrU/R7MiiWC2fgGm7MbIqZXQy8QOiWtxnhEXlWM6M04wPR+l15M7r721QG4ftbrlF7xN0s5xNm52+Kma0DbJna5ITJvbKcCqyb/P9lwkzzvRR3Zb3ZkwGnXXIo5bF7v7tPj9LcQxjTXbJe0s04j82i9aej9Zk5y6mWfnUzWzorYcEMYiy3NY5BsdyierGsOG7dIMYx6J4c62Us657cHYMYy7onlyv6PRkUy4UzEA03ZraKmX3NzJ4AbiaM5xxLOCFLExlJ+60XrT/RYP4no/X1W6hLO8Xd7+5193ebKcjMVgb+h/BEtJIrs8YIm9mmwPGpTce4+6vN7LeNtojW7wKwYEczm2pmD5vZLDN728yeMbObzOwrZjahlR2b2UjgoGjzBXE6d38TuCi1aQyV3T+rlX90tPn/ovVF69e0bvqinNe1DGIsty2OQbHcyo7zxLLiuC0GMY5B9+RYT2JZ9+SuGsRY1j25XKHvyaBYLqRez47c7EKYcOvTwK8Js6YvIDx7/t3k/+n1NwgnXs2ZxrU09PkvS+VM44s3WMaZUf6z66SfEqUf6sD7WoLQWyu9nxMbyD8KGE/ofngGMCsq60lghSr5pqfS/abZ90/lzPeXATcR5niaTeiuOET4VeI7wPY13s8DUVnbECZ1uznj+MfLXEKvt8WaPBa7ReW9A4yrcT4+HaXdsUbZowk3qXT5N2d8xjMarPPnMz6Hg7sVl01+zgMXy63GcVJGT2O5nXGclFf4WFYcK44z9qF7cmV5PYnlvHGcOh8Vy82d8wMXy63GcVKG7sldvienzkfFckGWnlegiZNtfcIs1C9R2UCT/v9swszq+1Ll8XdaWjoOk6LgebuJMk6KyphWJ33dC2sb3lf8+LrXgWVrpD8rxwW2tPwfsGqVck5OpXuLjMct5n3/VN5c8iz3ZF2ICeNb0+l2JHQxbaTs+4GVmzgWV0XlXFwn/YRkX6X0C4DLCbP2b0DoprsVcCzwaFT23YRHZa6UUf/VGqjzzzLy/2uv47VOnQculmkwjpM8ZzVwTnc8lmljHCfl9UUsozhWHJfvQ/fkyvJ6EsvonqxYbv496Z5cWV5f3JOT9BNQLBdi6XkFch6sJYDDCN3IshprFqT+f2uSdlyv6z3IC7BhFDyvNFHGl6IyrqqTvu6FtcX39KmMi8JRdfKclePCei2wc40y1ic0NJbSH9vK+6e5m4snMfQdwFJlzY7SPJv6/1uERtSPEsYNbwocQpgYLy77TmB0A8diPOEXhXQZU3LkG01olb8neT/13vMrwNfSdaPypvPVnHVenDAGN97Hyb2O1zr1HqhYpok4TvKdleN86Vos08Y4Tsrrm1hGcTzs4zgpX/fkgsQyuicrlpt/P7onFySOk/0qlvt4GUWBmdl2hMmT9mHhY/HS89ZYar3ks+7+t27Wc5haIlqfnZmqtvgRfHGZXWNmG1E+jhPgt8C5bSj+Y8BIM5vt7r+P9juC0KWwNHbzPuD7bdgnhO6b1wF/BB4hPDLxXWA5wk1gd8IjJ9+rDuEiOwL4ajIDfzymdLXk34cJQw/jx/3dD0w1sy8TbjolWxN+Afq3nHX/LOEmUfIk1SecSxuZLHNYeI2o5lngG8DP3X1eavslwLdT6yea2UXu/lydfX8bWDpje9VHUxbEwMRyh+MYehPLLcVxUrd+i2XFceMGJo5B9+RSdShOLOue3D0DE8u6J4eqUJw4BsVyf+t1y1FGq9qKwImEVrlavWvmE57pvl/0+hq9fg/DYSGMxSxrKW6ijMOiMm6ok35KlH6oTe9lDWBGXDYwPkfeZQldCEvLesB2hEm5ssap/hAYmcp/bOq1ecDGrb5/4OPAzkSt+1XSbg48llHPPQmNpVmt6DOB1XOU/Z9RvleBJXIek79Eeb+WI8+2lI/Dzbv8HTgsVc44Klv2H6RKN9wkz3FU/wXiu72K05yf9UDEMi3EcZK/ULFMm+I4eb1vYhnF8bCO46Rc3ZMLFsvonqxYbrwOuicXLI6TvIrlPl56XoHkoIwA9iDMSTOX2hMNPwJ8BVgllV8NN90/Zu3oyvkvURld78oJrAD8NSr3BWDtNn1O2xFuVOnyL0hemwS8ndp+erfff1LushmfwSOElvUFGRfKb+QsN6tr4z458m0V5ZmfjvcqeT5K+IUpnW8G4VeIjQkt9aMJDcO7ANMIN/J0+vNJbsiEic/j9/0GYUK8HQhdVzcGDqay6+qz0fpJ3YrLJo9/38dyp+M42UehY7lWHCevFz6WFcfDO46TMnVPLlgsNxrHSR7FcvPHv+9judNxnOyj0LFctDhO8imW+3zp7c5hHeDfgeepPdHw68B5wFZVylHDTfePXTsmTzsxKuPCOunbemFNLqoPRmW+DLy/zZ/VRMJYz/R+9qT8F4MngbHdfP9R2ZtS2aK9Y3IxjS+wExooN55M7JwceX4S5fllnfTjCa366Tz/CyxVJ99WGcflpNTrXyL7xlprOZtw00pvO6Kd51O7l36P5W7FcbKvQsdytThOXit0LCuOWz72fR3HSXm6Jy8suzCx3EgcJ+kVy60d+76O5W7FcbKvQsdykeI4yaNY7vNlBD1iZr8ntDyeQJhpujRWzlP/3gj8E2HG7CPc/Q9dr6hUMytaX8zMFm+wjBWi9ZnNV6cxZrY0YZztBqnNrwM7uftD7dyXuz8NfCvafCbwkdT6Ee4ej0nuGne/n/B5pO1K5TF5yd2HGig6jtn1aiVOzqH9os0X1NnHcYSbS8mjwL7u/katTMn1JN7XKWa2QvL69wnjp/9aZ/8QJpL7InAMsGr02os58vdS38ZyN+MYih/LNeIYih/LiuPW9G0cg+7JsaLEsu7JPdG3sax7crmixDEolgdFzxpuCF3c4smFDXgC+Dqwprvv4u6XuXszE3NJB7n7q4SLcdoaDRazZrT+ePM1ys/MliTMj7RZavMbhMnApndotz+P1ien/n8d8ISZTai1EBo400ZlpFukhTpeH61vSBijm/ZCg2U+H60vVyf9PpRPNvYS8KscedJOz3vNcPebCV0xS8YC+6de/y3wfmAvwg2uNBHdPMIEdbcDxxO6//7Iw08J74t2c2+euvRKv8Zyj+IYih/LWXEMxY9lxXEL+jWOQffkGooQy7ond1m/xrLuyVUVIY5BsTwQev1UKWdhT5vrgO+4+109rI805hHCJGolayXb8pqUUV5HJS3O1xG68ZW8BXzM3f/Yqf26+9/N7HVgmYyXP06Y9KtRq2bk2wSY3kRZEMYKp40Hfk8Y31oyp8Ey4/Rj6qT/XLR+kbvPr5Y4OZ6To80356xbyU3A9qn1LdMvuvsC4OpkqcnMVmfhkwEAnvP6s+UXQV/Fcq/iGPoiloei9dKvZQ9R0FhWHLdNX8Ux6J5cx1C03otY1j25N/oqlnVPrmkoWi/8PRkUy0XVyx43JaXGm10I3aj2Sx6TJsX3l2h967wZkwvChtHmuLy2MrOxhNbl7VKb3wF2c/c7O7nvxLz6SXoq7ko6ljBOOW1cg2XG6V+tltDM1qH82ED9bpxZ9Wm062ScfvkG86d9NFq/tYWyuqlvYrkAcQzFjuWsOIZix3JWXRTHjeubOE722etYLnIcQ49jWffknuqbWC5AHEOxY7kf78nV6qNY7rFeN9yk57UZCewEXAq8aGbnmtlWVXNKEcTd/6Y0kHd7ynt8PeDuL7VcoyrMbAxhQq0pqc2zgT3c/fed2m+0/1YuWN0Q1+8V4DcsHMoIMCl5L3l9IFqfUSPtodH67e5eb/zrzIxtjY4FXyJaf6vB/GnxLxo/baGsbuqLWO51HKfqUORYzopjKHYsz8zYpjhuXF/EMfQ+lvsgjqH3sax7cu/0RSz3Oo5TdShyLPc6jkGxPDB62XCzC3AF4fHf8Tw3SwOHA3eY2aNm9hUziyckkt67gfKW5K3NLB5/WM3B0fov2lKjDMm41KsJT0kqmQN8MhmD2Q0fpTze3gEWdXfLuxAek5f2TEa66S3Uccto/Xl3fx5ID18cTWWrdy27Ruu3ZSUys5HAZ6PN9X4NwN3fJoyhTtskd+2CzaL1piY7M7PtKP9F46/ufmszZfVA4WO5IHEMxY/lijgGKHIsK47bpvBxDIWJ5aLHMfQwlnVP7rnCx3JB4hiKH8t9d09O6qdYLiLv8WOtCGMSv0QYN5h+rHf8aPB5hAvZ/oSATJehx4H37vhdRPmj2abmyLMO4eJeyjMPmJwj35RoX0M58owCronyzQV27+JnNIIw+3u6Dlc1UU7D77+BsscQWuvT5R+VvHZYtP3mnGVuH+VbUO04A5+I0r4BLJ5zP9dGeS9r4H2vBLwZ5d+/ic9vMUJX5HQ5e3XrHGvTOVDYWC5CHCf1KHQs14rj5PXCxrLiuG3naGHjOMnT81guehwnZfc0lpuN4ySvYrk950BhY7kIcZzUo9Cx3Os4TtIqlgdo6XkFooOzGXAuYTb1dGNM3KDzOnAesFWSTw03vTtmk5KLdTqg9qiRfgxwR5T+xzn31eiNZSRweZRnHvCpJt/rvxAeTd9IntHA1KgODnykif135MaSlH1KVPZ8wpPdSp/jw9Hrx9UpbwXCE+JyXfCp/APg/AbqfmCU913gMznyLQrcGOV9k9CYPKqB/S8B3BKVc2W7jk23lqLGcrvjOClzIGO5VhynPstCxrLiuD1LUeM4df7pnpyv7J7GcrNxnORVLLfnHChkLLc7jpMyBzKWex3HSXrF8gAtPa9AlQM1Bvjn5GCVGm6yeuEsIDwDXg03vT1e34uCai5wNLBIlG69jJvKK/HFmjBr+ISMZf8o74wq6SYQxpRemHFBP6FGnlrLGEKvsHeASwgt2EvW+EzGAgdQ2UrshJncm/mcp0TlDGWk+WdgxQbL/XwSP+myfxql2SmJr3Sas4FlMsrbkfDoyXTa14CJVfa/IuGmn06/RQP1H5Ecm/jmcnZ8bqXy7AA8kHFsTk5e/yLwf4Qux+OrlLEEcBCVv6Y8DazQ67gcoFj+ecZxajqOk3pNp8CxTIfiuMixjOJ40ONY9+Q+iWV0Ty7MQjFjWffkPohjxfJgLj2vQI6TZjJwWnLwag2lKv27PzCi1/UeTguhxfi6jCB9iTD51hXAvRkXsDnA9hnlDWWU1egyrQ1lpJcpVS5ejwG/Td7jJYRuhdOp/JWktPySaKhfA5/zlKisoYw0txJufhcCu1GjOySwOWF8clzHGcBKGemPzkg7l/DI8MsIrfpZx24OsEuNepwQpf9zE5/NWsn5Fu97AeEGclVyfK4DXqhybH4NjM54r+8CTyZ5/5swXvwuyrsjl5angLV6HZMDFsttjeOkXtOj7YWKZToYx0WOZRTHgxzH09pQRlksU/A47tdYRvfkwiwUM5bbGsdJvaZH2wsVy/RhHCuWB3PpeQUaOHFGJMHyi+SAphtw0g05C5IT7AfANr2u93BZCK2jWa3w1ZaXgF2rlJV1cWp0mdaGMspuLlTeWBpZ3gG+VrpwNfkZT4nKHMpIc2uUZgHwKOEJBZcTntp2A2GCsKx6vgp8oEYdvgC83cD7frFeHAKPRHmOafLzeR9wTxPH5l3C0MuxqbKybqL1lmup8utBPy0UL5bbGsdJvaa3UEbHY5kOx3GRYxnF8aDG8bQ2lFEWyxQ8jvs1ltE9uVALxYvltsZxUq/pLZShe7JiedgsPa9AkyfQeOB44CFq98JZQOhW9Z16AaOlbcdmb0JrabXgexX4Ua0ApLh/JH4Q+DZwJ+Fxh3nyPQKcDKzWhs92SlT2UEaaW1t4jzflqSehF9zFhAnOqpX1AmFs79J1yto2yjcHWL6Fz2gUYfb8O6n8BSpeSt1yt8ooZ2PCrzyv1SljHuGXgh17HXsDHMttjeOkXoWOZboQx0WOZRTHgxjH09pQRlksU/A47sdYRvfkwi4UJ5bbGsdJvQody/RZHCuWB3ex5EPsW2a2NWFW7n1Y+Lz40puyaP0hd9+wi9UbtsxsIrApsAqwOKFl+BngDnef28u6tYOZjSaMKZ4ErEo490YDbxEuuEPAA+7+epfr9SnCzX1bYM0cWd4mdEU9xxt8fKOZjU32sxph9vi5wMvAn9z9wUbK6gQzW5rQZXUiMI4wWdqbhMnN/0LoMjq/ThlGeErD+oT3uRThejKT0I33bnd/szPvoBgUy92P5W7GcbK/wsay4rg9FMeDf09O9qdYViz3tSLGsuK4nGK5d/q+4abEzBYnzG9zKLB1sjn95gxwdx/Z7bqJ9IKZjQPeD6xOmKBsMcKQw5mEi+sjwIPuvqBHVRSROhTHIoNBsSzS/xTH0ksD03CTZmbvI/TC+QzhsWklargRERERERERkb4xkA03JWY2CtiD0AtnF8LTptRwIyIiIiIiIiJ9YaAbbtLMbFXgIHc/rdd1ERERERERERHJY9g03IiIiIiIiIiI9JsRva6AiIiIiIiIiIhkU8ONiIiIiIiIiEhBqeFGRERERERERKSg1HAjIiIiIiIiIlJQargRERERERERESkoNdyIiIiIiIiIiBSUGm5ERERERERERApKDTciIiIiIiIiIgWlhhsRERERERERkYJSw42IiIiIiIiISEGp4UZEREREREREpKDUcCMiIiIiIiIiUlBquBERERERERERKSg13IiIiIiIiIiIFJQabkRERERERERECkoNNyIiIiIiIiIiBaWGGxERERERERGRglLDjYiIiIiIiIhIQanhRkRERERERESkoNRwIyIiIiIiIiJSUGq4kfeY2ZCZebIM1Ul7aiqtm9mUrlRyGDKzCdFnPa1H9VjUzLYwswPM7Fgz+7qZnWBmh5nZnmY2oRf1qqfB8/rg6LM+uDu1FBERERERyTaq1xVol+QL2Zp1kr0LzAJmAo8A9wBXufufO1o5kT5lZosCBwAHAh8GFqmT/hXgeuAS4CZ3X9DxShZc0tB2UBd2NdHdh7qwHxERERER6aLh1uNmBLAMMBH4OHAK8KCZ/c7M1utpzaQpRemNMojM7LPA08BUYCfqNNoklgc+Q2i8edzMPmNmw+06IyIiIiIi0jYD0+OmRR8C7jezA9z9ml5XRqSXzGxJ4CLgk1WSPJMsLxN6sa0ErAJMjtJNBC4GVgP+vRN1FRERERERGXSD3HBzAPCHaNtIYDlgU8LQha1Sr40BLjOz7d393u5UUaRYzGwp4Cbgg9FLLwPfA65198eq5F0d2B34HLBZ6qUxHahqPzkeODVn2tuBVVPrWdexamY0UCcREREREekTg9xw82KV+R6eBP4I/NjMjga+D1jy2hjgv4Dtu1LDPubup5L/y6j0j6lUNtqcB5zg7m/WyujuzwLnAuea2V7Ad4G1O1LLPuLurwCv5ElrZvOjTdWuYyIiIiIiMkwM67kn3P2HwH9Gm7czs016UR+RXjKzY4FPR5u/4e5H1mu0ibn7VYSebVe3q34iIiIiIiLD0bBuuEmcBsyLtu3ci4qI9IqZLQ/8W7T5Snf/drNluvtb7r4XYb4cERERERERacIgD5XKxd1fM7N7ga1Tm9dvtBwzWwHYEliZ8GSdt4Drq80Hkso3Jtn3GsB4wrCtl4EngD+4ezx0omFmtiGwUVK3fwDPAQ+4+9Otlt0qM1sF2ILw3pcjTHY7E3gMmO7uM3tYt2WBbQiT7y4PzCYcm+nu/lAbyh8JbEcYTjQeeJVwbO7owfv+V2Cx1PrrwJHtKNjdn8qTLomF9YF1gRWAJYB3krr8FbjP3ee2o07SPkkPxXUJc/O8CzwP3JkMnWtH+R2/RoqIiIiIFNmwb7hJPEt5w83ycQIzGwLWTFafcfcJyfbtgW8AOxAmP047ltAAUcHMtgS+DuwIjK1SrzfM7DLgW+7+fJ43Eu3jAMI8NOtkvOxmdhvwXXe/vomyTyU8Tr1kB3e/NWfexYGjCJPYrlsj6btJo9qlwDR3n5UqY4iFxyPtIDM7qEaZh7j7tBp1M2A/4BjCXC+ZvdLM7DngB8D33f0fNfaXlXdR4CvA0WSca8AcM7sGOMXd/9pI2c0ws0WAL0abp7r7q13Y9yRgX+DjhIbPWo8cn21mVwOnu/uDna5bUZnZqsAQC6/fTwFrubs3WM7uwC9Tm650932iNBMIj4QvudDdD04aHb9AOIezYtjN7E7gRHe/s5F6pfbd8WukiIiIiEg/0FCpwOonychk9i3gVsIXi7jRplqexczsUsKTYj5B9S8kAEsBRwCPm9neDdRrkeQL7qVkN9pAeM8fAn5jZl17VLOZ7Ub40nkGtRttIJyfWwBnAZ/qaMV4rxHhPuAyQiNCrfhYlfCI64fN7P0N7GN1YDqhQS2r0QZgUULj0QNm1vH3TXivy0Tbzu/0TpMeTU8SJjHentqNNhAmDz8QuN/MTuhw9QrL3Z8Drk1tmkRzwzvjHlXn5slkZksA1xMaLqvFsAHbArebWUPD7bpxjRQRERER6SfqcROsFq3XfQKMmR0D/L/UpmeAvwBvACsCFRMcm9l4wheeTaOX/gE8QBhisABYndDbY3Ty+mLAFWZ2mLv/rE69RhAmhN0temkecDdhKM4SwIbJfgBOMrNcT71phZkdR3ikdNwg8hZwP/AS4IQhUx8gfI5dYWZbAL+msjHlVcKxeYXQoLJ2UreSCcAdZjbF3afX2cfKhIa+SdFLMwlPOns12f8WwNKEL6w/Bw5p8O006sPR+gvd6OlDdsPYs4RhUbMIQ9OWBt4HTGZhA+tI4Awzc3f/jy7Us4jOAfZKrR8J3JA3s5mtAXwstekx4Jac2S8jNFaXPEgYtgSwFuHa8t6ugJPNbKS7fy1HvTp+jRQRERER6TfDvuHGzJYBNos2P1In2wqEBgiAu4Bj3f3uqNxFCQ0QpfURhC886S8kzwMnA5e6+5wo/zjgROAkwhdcA84xs/vc/U816vZlyhttHDibMJTg9VT5RviV/keEhoTvUDlJc9skPUf+g/LeTQ8T3v+v3L1i32a2DrA34Rf12HaE83c14LbU9quA42tUpaKBysxWAq6hvNHmbkLD3E3xEBQzW4tw/D+ZbFqa8KVxszpPX/oJ5Y02bxCO8dT03C3JuXMYoUfPEsAPa5TZDttG63/s8P7S5hE++6uB37r7a1mJkt5QJxDOhdI59F0zu8Hd/9yNihaJu99iZg+zcD6u3c1slQaGCx1OecPZj3MOtdqDhb2zbgKOjhv5zGxdwjmbbtz5qpnd4u43Viu4i9dIEREREZG+oqFSYa6ReIhG1S8XibGERoNfAVPiRhsAd58TfYn6MvDR1Pr9wIbuPjX+QpLkn5n8Qr0fofEFwlCR/6pWqWTui29Fm49y92PTjTZJ+e7uNxDm9nmM8BksXq3sViRPLJpGeaPN1cDm7v6LrEabpI6PuftphN4Wv4pem+HuQ8CMKNtb7j5UY3krY1cXECZuTq9v6+43Zn2Zdfcn3P1TwPdTm9cmzGmUycw+TXmD2lvAju5+XjzhbnLunAPsSpicNx7G1G6rRuv1Gi7b5Q1ggrvv6+4/r9ZoA2GCY3f/AnBoavMoQlwNV+mhTaMIjX11mdkoyj/H2cCFOfdZOhevAHbJ6pmVbNsV+J/opfOTfVfT8WukiIiIiEg/GtYNN2Z2JOFX/LQ73f2+HNlfBg7K85QbMxsb7WcWsHueyV/d/UrKv6DtYGbxMIKSIwhfXEqucvcf1yn/78BnCE+D6ZRjCXNRlPwJODDvpL7uPt/dOzKUy8w2J0yMW3IXcLi7L8iR/TjCMJGSo5PeMlmOida/6u731Crc3e8gTHzdactG6zO7sE/cfW6jE8omE0unG/H2NbPRVZIPugsJDYAlhyWTBtfzScobKi+v1WiWYQbwOXeves1I4udQwtDMkgmEHjsVuniNFBERERHpO4PccLOSmU2IlslmtrmZHW5mtxP+2E/3AplD+DKex/kNfNk5gPAY25Kz3P2FnHkBzozWM7/8AJ+N1k/JTBVJGhD+t4H65JYMf4iHOh2T9Qt6j3wpWv9arS+kacmX07NTm8ZT/nQy4L0n82yf2vQiOSeCJfTqeTln2mYtF63P7PD+WnVV6v9jgY17VI+eSoblXZzatDqVc1tliSclrtm4m+H0Kj3XyiRpTo82H1wlebeukSIiIiIifWeQG24uIzzGNr08AdwDnEflvB5zgH/KGvZUxTUN1GWnaP3yBvLi7k8Bf0tt2j5OY2arUf547Afd/aEGdvPfjdSpARtR3jDwV8/52PAuSc/D8SLwuwbzxxO6Vhwbwnw8aVfk7NFDMozsigbr1PfMbLSZLWtma8QNsFTOxbReD6pYFOdE61nzQb0nmZ/pI6lN0939Dw3szwkTZud1GQuHMQFsm8yvFev4NVJEREREpF8N+8mJE3cCR7j7X3KmX0D5EJl60l/c5wJzki+gjXgNWCP5/+SM1zeP1vM2QDWbPq9tovVbO7SfhpnZZMqHjDwBrJn9vbKqeH6kTh2bLzaYpxGvAauk1pfu4L4ymdlEwlwlHyE8tWvl2jnKdHoOoMJy94fM7HcsfDLYrma2prs/UyXLkZT3Mmy0t82TjQxbdPdXzOwpFsbFsoQJup+MknbjGikiIiIi0peGW8PNu8CbhKEgjxKennN1vcc4Z5iVZ24beG+oUPpL8SJUfmlpVDwnCVQ+OvvxRgp092fNbDblc+S0Q/wFvJFeQJ22erS+HaFnVivafmwIk0d30quUn6PjOry/95jZcsAZhCE0zfYA7HpDU8H8iIUNNyMIT4z6epwomX/p4NSmN2m8p10z5+LjlDekrEDqGtjFa6SIiIiISF8a5IabHTo4JKfWI59jy9D+IWlLVtlP2htNlDuL9jfcxPOnvJ6ZqjfiurVDJ47NrCbrktfzwAap9a4MPTKzlYGb27C/QR7ymccvgBdY2Eh6qJmdmvG0tn0oP+cvyTNXTaTZ60pa3KjSrWukiIiIiEhfGu5feLohHkrTLRWPsS6IItWrE8cmzzirIn0GAHdE61t0ab8/pbzRZj6hEeJIYCtCj6glgdHubqUF2KFL9esLSQPN+alNKwF7ZiRtdVJiaM+5G5fRq2ukiIiIiEhfGOQeN0URP872MXdftwP7iXuyNDN8pBNDTuL5MIo0hCGu2/nuXnNy1ya1emw6PRQonpB5FTNbx907NkTLzLag/DHsrwC7uPv9ObIvVT/JsHM+YXhU6Zp+JHBl6UUzez/lE7Lf6e6NzNNV0o7rShwP3bpGioiIiIj0JfW46bBkLpz0F5WJZja6A7t6KVpfu5HMZrY67R8mBWEIR9r6HdhHs+LPbJ0u7aehY0Pn6lVyN5Vfpg/v8D7jHiEn5my0gfL5UARw9+cJvZVKPmJm6fPsC1GWvI+jjzVzLsbn+9/TK128RoqIiIiI9CU13HTHnan/jwamdGAf90brWzWYf8t2VSQSD8P5cGaq5rUydOMhyuff2NrMOjE3RlGPDQDuPocwwW3aIWbWyd5R8Zf5XzWQN35SmQTpY2gkjW9mthjwmdRrrwL/0+Q+JpvZ8nkTJ2knpTa9BjyVkbQb10gRERERkb6khpvuuCFa/3y7d+DuM4D0I4A3SIZH5PVPba5SyYPAy6n19czsQ20sf060vmjejO6+gDA5bjrvP7ejUpHbo/V9zGxknoxJz4N921+lCmcD/0itL0tlY05TzGxSxuZx0XquCZjNbGngky1WaSAlk7Gnn9p2cPIkqQMpH640NWmsa4YB+zeQ/gDK5326w92zGls7fo0UEREREelXarjpjosJjyAv2cfMPtqB/VwUrX8zTyYz+yCwR/urA8mXtLgB4Cwza9eEpDOj9fjx4/X8IFo/JXnaUdu4+xBwW2rTSlQOXanmS8D4dtYni7u/DJwSbd7PzL7abJlmtoSZXQl8NuPleGjW+3IWeyp6YlAt6VhbHtib8kmJHTivxX2cZGZL1EuUpDkx2nxhleTdukaKiIiIiPQdNdx0gbvPBL4Xbb7SzLZrpBwzG2lmn64xhOU8YHZqfS8zqznZrpmtAFxCZ8+F71P+pWwT4JKkN0BdZjaq2vAMd58NDKU2fdDMxuWtWNJL4cbUphWA68xstbxlJHVc0swOrJHk7Gj9NDPbvE6Z2wDfaqQerXD37wHXRptPM7Mf5vminmZmnwbuB/aqkuRP0fpXcpT5OeBfG6nHMHQx8GZq/TvAZqn1m9z9iRb3sRpwgZlVvWYkr12QpC0ZovL8Arp6jRQRERER6TtquOmeM4DfptbHAbea2TlmVvUJKmY22sy2MbPTgSeBq6jyVB13fw74RrT5R2Z2ppktk1H2zoS5JdYB5gJvN/B+cnP31wi9LtJDJPYB7jGzPcws8+lmZrZ20uPjCWD3Gru4JfX/xYDrzWw/M/uAmU00swmpJasB4iBgRmp9Y+BBMzux1nweSWPNHmZ2AfAccFq1tO5+FXBdatOSwE1mdnjc+8jMFjGzo4Drk/cT907ppIMIDS5pXwSeNLPjzWytahnNbHUzO9LM7iWcp7UmYb4SWJBaP8DMfmJmy2WUu5qZ/YTw+HCjfOidpLj7m4TGm5I1oyTNPAI8rXQu7gv8xswqJitOtl1P5RC/I9x9fo2yO36NFBERERHpR5Y93UD/MbMhyr+k7JD0puhE+c+4+4Qmylia8OU9a3LV54C/ECbvHEH44rEaYQhJ/ISVicnwm6x9jCT8qr1b9NI84A/JfhYHNgLWSL1+EnAUOd+jmZ1K+dCaup+3mR0DnEllg+GbwH2Ep804sBywAbBiKs0h7j6tSrkbA/eQ7/H2meWY2UaEYxM/sciBRwgTqs4izIMzDpgMTKB8/o56n9nKhMmaJ0YvzSQ82ek1wnvfkoVzkswDDqX8y/iF7n5wtf20ysyWIvTC+kSVJEOE+ZReBt4lDP1aBajWqHOSu5+RsZ8fUTlkbA7wR+BZwlPOJhHO1dLn/CJwAuWfxzfd/dQa72eI/Of1wcDU1Kaq510ntOM6ZmbrUz7XTcnzwJp1Gk/isiYAT6c2XUQYgpV+lPt0QuOqEc6BjTKK+q67fy3H/jp+jRQRERER6Td5vuhKm7j7LDObAvw7YchHeoLaVZOlnneonJA3vY8FZrYX8HPKJ3EdDWxfJduZ7n5G0sujY9z9LDN7ivDFOD2UYUlaeIqMu083s8MJ83s09Uhzd/+TmW1CaBDYOfWSER5hnucx5jV7xrj7C8nx/y2Q7kEwDtglI8scwtOA4qdSdZS7vwHsYWaHEHoRrRQlmZAs9TwGnOzu1Z5gdAyh8TDdyLgo1c/TIULPq47P+dPP3P1hM7uVypj6aSONNtWKJ0xOfC2wQ7Jt42Splv40dz85V+FduEaKiIiIiPQbDZXqMnef5+5fJgxPOhd4KUe2V4GrgYOBFd39hTr7mOPunyI8KeqxGknvBD7h7sfnqXs7uPv/Er70/z/Kf8nPMp/QQ+ULhOEPtcqdSvjl/RvATYShT2/TwOPC3f3v7r4L8CHgGvINHXuaMITnY0DNOWuSffyN0CPhm8ArVZLNJbzfzdz9yhx16IjkM50IfI7w9K15ObL9HZgG7ASsV6PRBnefS5gU+xhCb5BqniU0IG3k7lk9SaTSOdH6AuAn7Sg4GY61M3AcoadNZjLC9WW7vI02qfI7fo0UEREREeknAzNUqp+Z2XrAhoRhMuMIDRZvEL6wPgo8VeURunnL34jwi/hKhEc+Pw/c7+5PtVTxNjCzyYTJU8cT3vtcwlCIx4HpSe+PXtVtFKExZi3CsVmS8Gv+LMLQqUfc/cUWy9+OMBfM8oQeO88Bt7t7N+e1ycXMxhDOo8mEYWyLEXo2zCQ02Ex392eq5a9T9ihg06T85QiTbL9IaBi4t5Xzfzgys50ony/mWnf/ZBPlTKC8gbVimJ6ZbUboQbYKYejc88CdSSNlW3T6GikiIiIiUmRquBERGTBmdgVhAvCSXd39hibKmUCdhhsREREREeksDZUSERkgZrYS5fNbPUl57xsREREREekjargRERksx1L+lKVzNIxIRERERKR/qeFGRGRAmNm6wL+kNs0Eftab2oiIiIiISDvoceAiIn0omSy69Kj2ZYGtCE9rG5tK9j13n9XtuomIiIiISPuo4UZEpD9tBdxS4/WHgDO7VBcREREREekQDZUSERk8zwJ7uvucXldERERERERaox43IiKD4W3gMeBa4CwNkRIRERERGQymh42IiIiIiIiIiBSThkqJiIiIiIiIiBSUGm5ERERERERERApKDTciIiIiIiIiIgWlhhsRERERERERkYJSw42IiIiIiIiISEGp4UZEREREREREpKDUcCMiIiIiIiIiUlBquBERERERERERKSg13IiIiIiIiIiIFJQabkRERERERERECkoNNyIiIiIiIiIiBaWGGxERERERERGRglLDjYiIiIiIiIhIQanhRkRERERERESkoNRwIyIiIiIiIiJSUGq4EREREREREREpKDXciIiIiIiIiIgUlBpuREREREREREQKSg03IiIiIiIiIiIFpYYbEREREREREZGCUsONiIiIiIiIiEhBqeFGRERERERERKSg1HAjIiIiIiIiIlJQargRERERERERESkoNdyIiIiIiIiIiBSUGm5ERERERERERApKDTciIiIiIiIiIgWlhhsRERERERERkYJSw42IiIiIiIiISEH9f1zlxIDsVYn6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x3600 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the overall confusion matrix\n",
    "\n",
    "cmap='viridis'\n",
    "fig = plt.figure(figsize=(4,12), dpi=300)\n",
    "axBirds = fig.subplots(1, nBirds)\n",
    "\n",
    "for ibird, bird in enumerate(birdUnique):\n",
    "    ax = axBirds[ibird]\n",
    "    im_ = ax.imshow(confMatTotBirds[ibird]*100.0, interpolation='nearest', cmap=cmap, extent = (-0.5, ncalls-0.5, ncalls-0.5, -0.5), vmin=5, vmax = 50)\n",
    "\n",
    "    # if ibird == (nBirds-1) :\n",
    "    #    fig.colorbar(im_, ax=ax)\n",
    "    \n",
    "    if ibird == 0 :\n",
    "        ax.set(xticks=np.arange(ncalls),\n",
    "               yticks=np.arange(ncalls),\n",
    "               ylim = (ncalls-0.5, -0.5),\n",
    "               # xticklabels=callNames,\n",
    "               # yticklabels=callNames,\n",
    "               ylabel=\"Actual Call Type\",\n",
    "               xlabel=\"Predicted Call Type\",\n",
    "               title='%s %.0f %%' % (bird, np.mean(pccBirds[ibird])))\n",
    "    else:\n",
    "        ax.set(xticks=np.arange(ncalls),\n",
    "               yticks=np.arange(ncalls),\n",
    "               ylim = (ncalls-0.5, -0.5),\n",
    "               # xticklabels=callNames,\n",
    "               # yticklabels=callNames,\n",
    "               # ylabel=\"Actual Call Type\",\n",
    "               # xlabel=\"Predicted Call Type\",\n",
    "               title='%s %.0f %%' % (bird, np.mean(pccBirds[ibird])))\n",
    "        \n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/confMat%dUnitGoodPCPerBird.eps' % numUnits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}