criticality_neighbourhood_radius.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.6 (default, Jan  8 2020, 13:42:34) \\n[Clang 4.0.1 (tags/RELEASE_401/final)]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import packages\n",
    "#---------------------------------------\n",
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) \n",
    "\n",
    "#Import your modules\n",
    "#---------------------------------------\n",
    "import admin_functions as adfn\n",
    "import criticality as crfn\n",
    "import IS as isfn\n",
    "\n",
    "# Define paths\n",
    "#----------------------------------------------------------------------\n",
    "Fcode = '/Users/dominicburrows/Dropbox/PhD/Analysis/my_scripts/GitHub/'\n",
    "Fdata = '/Users/dominicburrows/Dropbox/PhD/analysis/Project/'\n",
    "Fdoc = '/Users/dominicburrows/Documents/'\n",
    "F10t = '/Volumes/Dominic 10tb/'\n",
    "\n",
    "F10t2 = '/Volumes/Dominic 10tb2/'\n",
    "Ftm = '/Volumes/Dominic Time Machine/'\n",
    "Ffig = '/Users/dominicburrows/Dropbox/PhD/figures/'\n",
    "\n",
    "%load_ext autoreload\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process data\n",
    "- Calculate critical statistics over a range of neighbourhood radius sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create lists of coord data\n",
    "#---------------------------------------------------------------------------\n",
    "import glob\n",
    "\n",
    "experiment = 'PTZ-WILDTYPE'\n",
    "os.chdir(Fdata + experiment)\n",
    "\n",
    "nnb_l = sorted(glob.glob('*PTZ20*_nnb*'))\n",
    "bind_l = sorted(glob.glob('*PTZ20*0.590*'))\n",
    "len(nnb_l), len(bind_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nnb_list = ['0.5', '1.0', '1.5', '2.0', '2.5', '3.0']\n",
    "nnb_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count=0\n",
    "num_list = 0.5,1.0,1.5,2.0,2.5, 3.0\n",
    "for i in range(len(bind_l)):\n",
    "    for e in range(len(num_list)):\n",
    "        nnb = nnb_l[count]\n",
    "        bind = bind_l[i]\n",
    "        if np.load(nnb).shape[0] != np.load(bind).shape[0]:\n",
    "            print('errror')\n",
    "            break\n",
    "        else:    \n",
    "            av = crfn.avalanche(np.load(nnb), np.load(bind))[0]\n",
    "            name = nnb\n",
    "            np.save(Fdata + experiment +'/' + adfn.save_name(name) + '_nnb-' + str(num_list[e]) + '_av.npy', av)\n",
    "            count+=1\n",
    "    print('Done '+ nnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*dcc*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_nnb-0.5_dcc.npy',\n",
       " 'PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_nnb-1.0_dcc.npy',\n",
       " 'PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_nnb-1.5_dcc.npy',\n",
       " 'PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_nnb-2.0_dcc.npy',\n",
       " 'PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_nnb-2.5_dcc.npy',\n",
       " 'PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_nnb-3.0_dcc.npy']"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*dcc*'))\n",
    "curr_l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualise critical statistics as a function of NNB - coarse scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NNB VS DCC\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "bln_mean, sz_mean = np.zeros(6), np.zeros(6)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*dcc*'))\n",
    "    data = dome(curr_l)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "    \n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_dcc*'))\n",
    "    data = dome(curr_l)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(Ffig + 'dcc_long_k.svg')\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 18.0   p = 0.375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 22.0   p = 0.625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 11.0   p = 0.10546875\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 12.0   p = 0.130859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 22.0   p = 0.625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 20.0   p = 0.4921875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dominicburrows/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1604: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_dcc*'))\n",
    "    bln_data = dome(curr_l)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_dcc*'))\n",
    "    sz_data = dome(curr_l)\n",
    "\n",
    "    alpha = 0.05\n",
    "    \n",
    "    \n",
    "    adfn.stats_2samp(bln_data, sz_data, alpha, 1, 'rel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#NNB VS avsize exponent\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose= 0\n",
    "bln_mean, sz_mean = np.zeros(6), np.zeros(6)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*exp*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "    \n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_exp*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "#ax.set_ylabel(dtype, size = 20)\n",
    "plt.savefig(Ffig + 'exps_long_k.svg')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 20.0   p = 0.4921875\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 20.0   p = 0.4921875\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 12.0   p = 0.130859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 14.0   p = 0.193359375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 19.0   p = 0.431640625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 23.0   p = 0.6953125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dominicburrows/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1604: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_exp*'))\n",
    "    bln_data = dome1(curr_l, choose)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_exp*'))\n",
    "    sz_data = dome1(curr_l, choose)\n",
    "\n",
    "    alpha = 0.05\n",
    "    \n",
    "    adfn.stats_2samp(bln_data, sz_data, alpha, 1, 'rel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nnb vs avdex exponent\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose= 1\n",
    "bln_mean, sz_mean = np.zeros(6), np.zeros(6)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*exp*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "    \n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_exp*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth =10)\n",
    "\n",
    "\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "#ax.set_ylabel(dtype, size = 20)\n",
    "plt.savefig(Ffig + 'expd_long_k.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 9.0   p = 0.064453125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 12.0   p = 0.130859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 13.0   p = 0.16015625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 12.0   p = 0.130859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 14.0   p = 0.193359375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are the same\n",
      "w = 22.0   p = 0.625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dominicburrows/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1604: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_exp*'))\n",
    "    bln_data = dome1(curr_l, choose)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_exp*'))\n",
    "    sz_data = dome1(curr_l, choose)\n",
    "\n",
    "    alpha = 0.05\n",
    "    \n",
    "    adfn.stats_2samp(bln_data, sz_data, alpha, 1, 'rel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nnb vs loglikelihood ratio\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose = 0\n",
    "bln_mean, sz_mean = np.zeros(6), np.zeros(6)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*llr*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_llr*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "\n",
    "\n",
    "\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "#ax.set_ylabel(dtype, size = 20)\n",
    "plt.savefig(Ffig + 'llr_long_k.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZWZmasCAASoqKnIgGQAAAIBgQdErg3r06KHp06crPDzcZzZz5kzde++98ng8DiQDAAAAEAwoemVU7969NXHiRBljfGYTJkzQww8/LGutA8kAAAAAlHUUvTIsISFB//jHP/zOxo0bpyeeeIKyBwAAAMAHRa+MGzRokN5++22/s5EjR+rFF18MbCAAAAAAZR5FLwgMGTJEr776qt/ZSy+9pNdffz3AiQAAAACUZRS9IPH0009r2LBhp5z9/e9/D3AiAAAAAGUVRS+I/O///q8ee+wxv7MhQ4YoLS0twIkAAAAAlEUUvSBijNGbb76pBx54wO980KBBmjZtWoBTAQAAAChrKHpBxhijsWPHauDAgT4za63uvvtuzZs3z4FkAAAAAMoKil4QCgsL0/jx49WnTx+fWVFRkfr27auFCxc6kAwAAABAWUDRC1IRERGaMmWKbrvtNp9ZQUGBevXqpU8++cSBZAAAAACcRtELYpGRkZo1a5ZcLpfP7Pjx47rtttv0+eefO5AMAAAAgJMoekGuYsWKmj9/vjp27OgzO3LkiLp3767Vq1c7kAwAAACAUyh6ISA6Oloffvihrr32Wp/ZoUOH1KVLF61bt86BZAAAAACcQNELEVWqVNHHH3+stm3b+sz27dsnl8ulDRs2OJAMAAAAQKBR9EJIjRo1tHjxYrVu3dpn9vPPPys2NlZbtmxxIBkAAACAQKLohZg6depoyZIlat68uc9sx44dio2N1fbt2x1IBgAAACBQKHoh6KKLLlJWVpYaNWrkM9uyZYtiY2P1888/O5AMAAAAQCBQ9EJUkyZNtHTpUtWvX99ntnHjRrlcLv3yyy8OJAMAAABQ2ih6IaxFixZasmSJateu7TNbt26dunbtqoMHDzqQDAAAAEBpOmPRM8akG2N2G2O+PmntRWPMDmPMGu9Xj5NmzxhjNhtjNhhjup603s4Ys847G22MMd71KGPMTO/6SmNM05L9Fsu3yy67TG63W9WrV/eZrV69Wj169FBubq4DyQAAAACUluJc0ZsgqZuf9bestW29XwskyRhzmaR+ki73nvOOMSbce/w4SfdJaun9+vU9kyTtt9a2kPSWpNfP8XvBKbRt21Yff/yxKleu7DP7/PPPFR8fr2PHjjmQDAAAAEBpOGPRs9Yuk7SvmO8XL2mGtTbPWrtF0mZJ1xpj6kuqaq1dYa21kiZJ6nnSORO9r2dLiv31ah9KTvv27fXRRx+pUqVKPrOlS5fqT3/6k/Lz8x1IBgAAAKCknc9n9B42xqz13tpZw7vWQNKPJx2z3bvWwPv69+u/OcdaWyjpoKRa/v5AY8x9xphsY0z2nj17ziN6+dSxY0fNnz9fFSpU8JktWLBA/fv3V2FhoQPJAAAAAJSkcy164yQ1l9RW0i5JI73r/q7E2dOsn+4c30Vr37PWxlhrY+rUqXN2iSFJ6tKli/75z38qIiLCZzZ37lzdc889KioqciAZAAAAgJJyTkXPWvuztbbIWuuRlCrpWu9ou6STH97WUNJO73pDP+u/OccYEyGpmop/qyjOQVxcnKZMmaKwMN//+adOnaoHHnhAJ+6wBQAAABCMzqnoeT9z96vbJf26I2empH7enTSb6cSmK6ustbskHTbGXOf9/N3dkjJOOmeg9/WfJC21tIxS17dvX6Wnp/udjR8/Xo8++ihlDwAAAAhSvvfv/Y4xZrqkmyTVNsZsl/SCpJuMMW114hbLrZLulyRr7TfGmFmS1ksqlDTYWvvrfYAP6sQOnpUkLfR+SVKapMnGmM06cSWvX0l8YzizgQMH6ujRo3rooYd8ZqNHj1Z0dLRSUlLE3jgAAABAcDHBetUmJibGZmdnOx0jJIwaNUqPP/6439nw4cP13HPPBTgRAAAAgDMxxqy21sb4m53PrpsIEY899pheeuklv7Nhw4Zp1KhRAU4EAAAA4HxQ9CBJeu655/T000/7nT3++OMaN25cgBMBAAAAOFcUPUiSjDFKSUnRkCFD/M4feughTZw40e8MAAAAQNlC0cP/Mcborbfe0r333ut3npiYqFmzZgU4FQAAAICzRdHDb4SFhendd9/VnXfe6TPzeDxKSEhQZmamA8kAAAAAFBdFDz7Cw8M1ceJE9erVy2dWWFioO+64Q4sXL3YgGQAAAIDioOjBr4iICE2fPl09evTwmeXn56tnz55atmyZA8kAAAAAnAlFD6dUoUIFzZ49WzfffLPP7NixY7rlllu0cuVKB5IBAAAAOB2KHk6rUqVKysjI0A033OAzy83NVbdu3bRmzRoHkgEAAAA4FYoezqhy5cpasGCB2rVr5zM7cOCAXC6X1q9f70AyAAAAAP5Q9FAs1apV06JFi9SmTRuf2d69e9W5c2dt3rzZgWQAAAAAfo+ih2KrVauWlixZolatWvnMdu3apdjYWG3bts2BZAAAAABORtHDWalbt66ysrLUrFkzn1lOTo5iY2O1c+dOB5IBAAAA+BVFD2etYcOGysrKUoMGDXxm33//vTp37qw9e/Y4kAwAAACARNHDOWrWrJmysrJ04YUX+sy+/fZbuVwu7d+/34FkAAAAACh6OGeXXHKJlixZopo1a/rMvvrqK3Xr1k2HDh1yIBkAAABQvlH0cF6uuOIKLV68WFWrVvWZrVq1SrfeequOHj3qQDIAAACg/KLo4by1a9dOCxcu1AUXXOAzW758uXr27Knjx487kAwAAAAonyh6KBE33HCDMjMzFRUV5TNzu93q06ePCgoKHEgGAAAAlD8UPZSYm2++WXPnzlVkZKTP7IMPPtBdd92loqIiB5IBAAAA5QtFrxRkpqRoTKdOykxJcTpKwPXo0UMzZsxQeHi4z2zWrFlKSkqSx+NxIBkAAABQflD0SlhmSoryU1PVJCdH+amp5bLs9erVSxMnTpQxxmc2ceJEDR48WNZaB5IBAAAA5QNFr4R9PXWqosJO/FijwsL09dSpDidyRkJCglJTU/3O3n33XQ0dOpSyBwAAAJQSil4JO+7xKM97a2Kex6Pj5fg2xaSkJI0ePdrvbNSoUXrhhRcCnAgAAAAoHyh6JSxmwAB9n5enr48d0/d5eYoZMMDpSI565JFH9Nprr/mdDR8+/JQzAAAAAOcuwukAoSYuOVmSlON2q7HL9X+/L8+eeuopHTlyRMOHD/eZPfPMM4qOjtaQIUMcSAYAAACEJhOsn5OKiYmx2dnZTsdAMVlr9cQTT2jkyJF+56mpqbr33nsDnAoAAAAIXsaY1dbaGH8zbt1EQBhjNGLECD344IN+5/fdd5+mltONawAAAICSRtFDwBhjNGbMGA0cONBnZq3VwIEDNXfuXAeSAQAAAKGFooeACgsLU1pamvr06eMzKyoqUr9+/bRgwQIHkgEAAAChg6KHgAsPD9eUKVMUFxfnMysoKFCvXr20dOlSB5IBAAAAoYGiB0dERkZq5syZ6tKli88sLy9PcXFx+uyzzxxIBgAAAAQ/ih4cU7FiRc2bN08dO3b0mR05ckQ9evQQO6sCAAAAZ4+iB0dFR0frww8/VPv27X1mhw4dUteuXbVu3ToHkgEAAADBi6IHx1WpUkULFy5U27ZtfWb79u1T586dtWHDBgeSAQAAAMGJoocyoUaNGlq8eLFat27tM9u9e7diY2O1ZcsWB5IBAAAAwYeiV8LyDx7UxrFj5SksdDpK0KlTp46ysrLUvHlzn9mOHTt08803a/v27Q4kAwAAAIILRa8EWY9H/338cW0YNUorBgzQ8d27nY4UdOrXr6+srCw1btzYZ7Z161bFxsbq559/diAZAAAAEDwoeiVo87vvavcnn0iS9q1apWVxcfpl1SqHUwWfJk2aKCsrS/Xr1/eZbdy4UZ07d9Yvv/ziQDIAAAAgOFD0Ssiezz7Td2+99Zu1vD17tOKuu/R9aqqstQ4lC04tWrTQkiVLVLt2bZ/Z119/ra5du+rgwYMOJAMAAADKPopeCTmydavfdVtUpPWvvabswYNVcPhwYEMFucsuu0xut1vVq1f3ma1evVo9evRQbm6uA8kAAACAso2iV0KaJiTo+smTFeXnCpQk/bRokZb37KlDPCbgrLRt21Yff/yxKleu7DP7/PPPFRcXp2PHjjmQDAAAACi7KHolqPZ116ljZqZqxsT4nR/ZulXLe/XSj/PmBThZcGvfvr0++ugjVapUyWf2ySefqHfv3srLy3MgGQAAAFA2UfRKWMW6dXX9lCm6OCnJ79xz/LjWDB2qtcOGqYhyUmwdO3ZURkaGKlSo4DNbuHCh+vfvr0IeaQEAAABIouiVirDISF2enKx2Y8Yows8th5K0bdo0fd6vn47u2BHgdMHL5XJp9uzZioiI8JnNmzdPAwcOVFFRkQPJAAAAgLKFoleKLureXR3mzVOVli39zg+sXatlcXHa/emnAU4WvG677TZNnTpVYWG+/+hOmzZNDzzwgDwejwPJAAAAgLKDolfKKl98sf4wd64axMf7nRccOKCVSUnaMHq0LAWlWPr06aP09HS/s/Hjx+vRRx/lcRYAAAAo1yh6ARARHa2rR47UFS+9JBMZ6XuAtdr49ttamZSk/P37Ax8wCA0cOFDjxo3zO/v73/+uZ555JiTLXmZKisZ06qTMlBSnowAAAKAMo+gFiDFGTRMSdOOMGapYv77fY/YsW6ZlcXE6sHZtgNMFpwceeEAjR470O3v99df18ssvBzhR6cpMSVF+aqqa5OQoPzWVsgcAAIBTougFWI22bdUxM1N1OnTwOz+2c6c+69tXW6dNC8krUiXtscce0/Dhw/3Onn/++VMWwWCU43YryvvZxKiwMOW43Q4nAgAAQFlF0XNAVM2aap+WplaPPOJ37snP17phw7Rm6FAV8jDwM3r22Wf19NNP+50NHTpU77zzToATlY7GLpfyvJ/jzPN41NjlcjgRAAAAyiqKnkNMeLguefRRXZuWpsjq1f0es33+fP27Vy/lbtkS4HTBxRijlJQUDRkyxO988ODBmjBhQmBDlYK45GRVGDRI2xo3VoVBgxSXnOx0JAAAAJRRJlhvD4yJibHZ2dlOxygRR7dvV/bgwTr49dd+5xGVK6vtG2+ofteuAU4WXKy1uu+++zR+/HifWVhYmKZNm6a+ffs6kAwAAAAoecaY1dbaGH8zruiVAdENG+rGWbPUpH9/v/PC3FxlP/SQ1r/6qjyFhQFOFzyMMXr33XeVkJDgM/N4PLrrrruUmZnpQDIAAAAgsCh6ZUR4VJSufPlltR0xQmEVK/o95vvx47ViwAAd3707wOmCR3h4uCZMmKBevXr5zAoLC3XHHXdo8eLFDiQDAAAAAoeiV8Y06tVLHebM0QVNmvid71u1Sstuu02/rFoV4GTBIyIiQtOnT1ePHj18Zvn5+erZs6eWLVvmQDIAAAAgMCh6ZVDVSy9Vh4wM1TvFrop5e/dqxV136fvUVB7BcAoVKlTQ7NmzdfPNN/vMjh07pltuuUUrV650INn54YHpAAAAKA6KXhkVWaWKYsaNU+unnpIJD/eZ26IirX/tNWUPHqyCw4cdSFj2VapUSRkZGbrhhht8Zrm5uerWrZv++9//OpDs3PDAdAAAABQXRa8MM8aoxX336brJkxVVu7bfY35atEjL4+N16LvvApwuOFSuXFkLFixQu3btfGYHDhxQly5dtH79egeSnT0emA4AAIDiougFgdrt26vjBx+oZozfnVN1ZNs2Le/dWz/OmxfgZMGhWrVqWrRokdq0aeMz27t3r2JjY7Vp0yYHkp0dHpgOAACA4qLoBYmKF16o66dM0cVJSX7nnuPHtWboUK0dNkxFeXkBTlf21apVS0uWLFGrVq18Zj/99JNiY2O1bds2B5IVHw9MBwAAQHHxwPQgtHPhQn319NMqzM31O692xRWKGTtW0Q0aBDhZ2bd9+3Z17NhRW7Zs8Zk1b95cy5Yt00UXXeRAMgAAAODs8MD0EHNR9+7qMH++qvi5OiVJB9et07K4OO3+9NMAJyv7GjZsqKysLDXwU4K///57de7cWXv27HEgGQAAAFByKHpBqnKzZvrDnDlqEB/vd15w4IBWJiVpw+jRst7PdeGEZs2aaenSpapbt67P7Ntvv5XL5dL+/fsdSHZmPF4BAAAAxTCbDZUAACAASURBVEHRC2IR0dG6euRIXfHSSzKRkb4HWKuNb7+tlYmJytu3L/ABy7BWrVppyZIlqlmzps/sq6++Urdu3XTo0CEHkp0aj1cAAABAcVH0gpwxRk0TEnTjzJmqWL++32P2LF+u5fHxOrB2bYDTlW1t2rTR4sWLVbVqVZ/ZqlWrdOutt+rIkSMOJPOPxysAAACguCh6IaLGVVepY2am6nTo4Hd+bOdOfda3r7ZOm6Zg3YCnNLRr104LFy7UBRdc4DNbvny5br/9dh0/ftyBZL54vAIAAACK64xFzxiTbozZbYz5+qS1msYYtzFmk/fXGifNnjHGbDbGbDDGdD1pvZ0xZp13NtoYY7zrUcaYmd71lcaYpiX7LZYfUTVrqn1amlo98oh04sf7G578fK0bNkz/HTpUhUePOpCwbLrhhhuUmZmpihUr+szcbrf69OmjgoICB5L9Fo9XAAAAQHEV54reBEndfrf2tKQsa21LSVne38sYc5mkfpIu957zjjEm3HvOOEn3SWrp/fr1PZMk7bfWtpD0lqTXz/WbgWTCw3XJo4/q2vHjFVm9ut9jdsyfr3/37q1cP48YKK9uvvlmzZ07V5F+Puv4wQcfKCEhQYWFhQ4kAwAAAM7eGYuetXaZpN/v5BEvaaL39URJPU9an2GtzbPWbpG0WdK1xpj6kqpaa1fYE/cNTvrdOb++12xJsb9e7cO5q3vTTeqYkaFqbdr4nR/euFHLe/bUrkWLApys7OrevbtmzJih8PBwn9k///lPJSUlyePgDqZsxgIAAIDiOtfP6NW11u6SJO+vF3rXG0j68aTjtnvXGnhf/379N+dYawslHZRUy98faoy5zxiTbYzJ5llnZxbdsKFunDVLTfr39zsvzM1V9kMP6ZuUFHnKwK2JZUGvXr00adIk+ftvDZMmTdLgwYMd+4wjm7EAAACguEp6MxZ/V+LsadZPd47vorXvWWtjrLUxderUOceI5Ut4VJSufPlltR0xQmF+PoMmST+kpWnFgAE6vnt3gNOVTXfeeadSU1P9zt59910NHTrUkbLHZiwAAAAornMtej97b8eU99dfG8J2SY1OOq6hpJ3e9YZ+1n9zjjEmQlI1+d4qivPUqFcvdZgzRxc0aeJ3vu8//9Gy227TL6tWBThZ2ZSUlKTRo0f7nY0aNUovvPBCgBOxGQsAAACK71yLXqakgd7XAyVlnLTez7uTZjOd2HRllff2zsPGmOu8n7+7+3fn/Ppef5K01LL/f6moeuml6pCRoXqnuBKUt3evVtx1l75PTeURDJIeeeQRvfbaa35nw4cP16uvvhrgRCfK3sOffELJAwAAwGkV5/EK0yWtkHSJMWa7MSZJ0muSXMaYTZJc3t/LWvuNpFmS1kv6WNJga22R960elDReJzZo+V7SQu96mqRaxpjNkh6TdwdPlI7IKlUUM26cLnv6aRk/m47YoiKtf+01ZT/0kAoOH3YgYdny1FNP6fnnn/c7S05O1ttvvx3gRAAAAMCZmWC9chMTE2Ozs7OdjhHU9q5cqS+HDFHe3r1+5xc0aaKYd95R1UsvDXCyssVaqyeeeEIjR470O3/vvfc0aNCgAKcCAABAeWeMWW2tjfE3K+nNWBBEardvr44ffKCa11zjd35k2zYt791bP86bF+BkZYsxRiNGjNCDDz7od37//fdrypQpAU4FAAAAnBpFr5yreOGFun7yZDW/916/c8/x41ozdKjWDhumory8AKcrO4wxGjNmjO655x6fmbVW99xzj+bMmRP4YAAAAIAfFD0oLDJSlz3zjNqNHauIypX9HrNt2jR91revju7YEeB0ZUdYWJjGjx+vvn37+syKiorUv39/LViwwIFkAAAAwG9R9PB/LurWTR3mz1eVVq38zg+uW6dlcXHa/emnAU5WdoSHh2vy5MmKj4/3mRUUFKhXr17KyspyIBkAAADw/1H08BuVmzXTH+bMUYOePf3OCw4c0MqkJG14+23ZoiK/x4S6yMhIzZw5U126dPGZ5eXlKS4uTp999pkDyQAAAIATKHqlIDMlRWM6dVJmSorTUc5JRHS0rn7zTV3x0ksykZG+B1irjaNHa2VSkvL2lc9n20dFRWnevHnq2LGjz+zo0aPq3r272BUWAAAATqHolbDMlBTlp6aqSU6O8lNTg7bsGWPUNCFBN86cqUoXXeT3mD3Ll2t5fLz2f/VVgNOVDdHR0frwww/Vvn17n9nhw4fVpUsXrV271oFkAAAAKO8oeiUsx+1WVNiJH2tUWJhy3G6HE52fGlddpQ4ZGarToYPf+bGdO/VZ377aOnWqgvWZjOejSpUqWrhwodq2besz279/v1wul7777jsHkgEAAKA8o+iVsMYul/I8HklSnsejxi6Xw4nOX1TNmmqflqZWQ4ZIxvjMbUGB1j3/vP47dKgKjx51IKGzatSoocWLF+uyyy7zme3evVudO3fWDz/84EAyAAAAlFcUvRIWl5ysCoMGaVvjxqowaJDikpOdjlQiTHi4LvnLX9Q+LU2R1av7PWbH/Pn6d+/eyt2yJcDpnFenTh0tWbJELVq08Jnt2LFDsbGx+vHHHx1IBgAAgPLIBOvtdjExMZbNLpxxdMcOZQ8erIPr1vmdR1SurKtef10XdesW4GTOy8nJUYcOHZSTk+Mza9WqlT799FPVq1fvnN8/MyVFOW63GrtcIfMfEQAAAHBujDGrrbUx/mZc0cNZi27QQDfOnKkmd97pd16Ym6vVgwfrm5QUeQoKApzOWY0bN1ZWVpbq16/vM9u4caNcLpf27t17Tu8dKhv9AAAAoPRR9HBOwqOidOXw4Wo7YoTCKlb0e8wPaWlaMWCAju/eHeB0zmrRooWysrJUp04dn9nXX3+trl276sCBA2f9vqG20Q8AAABKD0UP56VRr17qMGeOLmjSxO9833/+o2W33aa9K1cGOJmzWrduLbfbrep+Ps/45ZdfqkePHsrNzT2r9wzFjX4AAABQOih6OG9VL71UHTIyVK9LF7/zvL179cWAAdr83nvl6hEMV111lRYtWqQqVar4zFasWKG4uDgdO3as2O8Xqhv9AAAAoOSxGQtKjLVWP4wfr29HjJAtKvJ7TL0uXdT2jTcU6af8hKrly5era9eufktdt27dNH/+fEVFRTmQDAAAAMGMzVgQEMYYNR80SNdNnqyo2rX9HvPT4sVaHh+vQ+XoIeIdOnRQRkaGKlSo4DP7+OOP1b9/fxUWFjqQDAAAAKGKoocSV7t9e3X84APVvOYav/Mj27Zpee/e+nHevAAnc47L5dLs2bMVERHhM5s3b54GDhyoolNcBQUAAADOFkUPpaLihRfq+smT1fzee/3OPcePa83QoVo7bJiK8vICnM4Zt912m6ZOnaqwMN+/dtOmTdP9998vj3ezFQAAAOB8UPRQasIiI3XZM8+o3dixiqhc2e8x26ZN02d9++ro9u0BTueMPn36KD093e8sLS1Nf/nLX8rVhjUAAAAoHRQ9lLqLunVTh/nzVaVVK7/zg+vWaVl8vHZ/+mmAkzlj4MCBGjdunN/ZmDFj9PTTT1P2AAAAcF4oegiIys2a6Q9z5qhBz55+5wUHDmhlUpI2vP32KXfsDCUPPPCARo0a5Xf2xhtvaPjw4QFOBAAAgFBC0UPARERH6+o339QVL72kMD87UMpabRw9WiuTkpS3b1/gAwbYX//611MWuhdeeEFvvvlmgBMBAAAgVFD0EFDGGDVNSNANM2ao0kUX+T1mz/LlWhYXp/1ffRXgdIH37LPP6plnnvE7e+KJJ/TOO+8EOBEAAABCAUUPjqhx1VXqkJGhOh06+J0f37VLn/Xtq61Tp4b059WMMXrllVc0ZMgQv/PBgwdrwoQJgQ0FAACAoEfRg2OiatZU+7Q0tRoyRDLGZ24LCrTu+ef136FDVXj0qAMJA8MYo7/97W8aNGiQ33lSUpJmzpwZ4FQojzJTUjSmUydlpqQ4HQUAAJwnih4cZcLDdclf/qL2aWmKrF7d7zE75s/Xv3v1Uu6WLQFOFzjGGI0bN04JCQk+M4/Ho7vuuksZGRkOJEN5kZmSovzUVDXJyVF+aiplDwCAIEfRQ5lw4R//qI6Zmap2xRV+54c3bdLynj218+OPA5wscMLDwzVhwgT17t3bZ1ZYWKg+ffpo0aJFDiRDeZDjdisq7MS/EqLCwpTjdjucCAAAnA+KHsqM6AYNdOPMmWpy551+54W5uVo9eLC+SUmRp6AgwOkCIyIiQtOmTVOPHj18Zvn5+erZs6c+LSfPG0RgNXa5lOfxSJLyPB41drkcTgQAAM4HRQ9lSnhUlK4cPlxt33xTYRUr+j3mh7Q0rRgwQMd37w5wusCoUKGC5syZo5tvvtlndvz4cd1666364osvHEiGUBaXnKwKgwZpW+PGqjBokOKSk52OBAAAzoMJ1h0NY2JibHZ2ttMxUIoObdig7Acf1JFt2/zOo2rX1v+MHq3a7dsHOFlg5Obmqlu3bvrss898ZhUqVFDPnj2VmJiozp07Kzw83IGEAAAAcJIxZrW1NsbfjCt6KLOqXnKJOmRkqF6XLn7neXv36osBA7T5vfdC8hEMlStX1kcffaSYGN+/u/n5+Zo1a5a6deumpk2batiwYfrhhx8cSAkAAICyiCt6KPOstfph/Hh9O2KEbFGR32PquVxqO2KEIqtUCXC60vfLL7+oU6dOWrdu3RmPvemmm5SYmKjevXsrOjo6AOkAAADgFK7oIagZY9R80CBdN3myomrX9nvMT263lsfH69B33wU4XemrVauW3G63Lr300jMe+69//Ut333236tevr/vvv18rV64MyaudAAAAOD2KHoJG7fbt1fGDD1Tzmmv8zo9s26blvXvrx7lzA5ys9NWtW1erVq3Sk08+qTp16pzx+EOHDum9997TddddpzZt2mjkyJHaHaKb1wAAAMAXt24i6HgKCvTdm2/q+/HjT3lMk/79dfmwYQqPigpgssDIz8/XRx99pPT0dC1YsEAe75b4ZxIREaFbb71ViYmJ6t69uyIiIko5KQAAAErT6W7dpOghaO1atEhrnnxShbm5fufVrrhCMWPGKLphwwAnC5ydO3dq8uTJSk9P18aNG4t9Xr169XT33XcrMTFRl1xySSkmBAAAQGmh6CFk5W7ZouyHHtLhU5ScyOrVdfXIkap7002BDRZg1lp9/vnnSk9P18yZM3XkyJFin3vjjTcqMTFRd9xxh6qE4GY2AAAAoYqih5BWePSo1g4bph3z5/s/wBi1evhhtXrkEZkgf95cZkqKctxuNXa5TvlA69zcXP3zn/9Uenq6/v3vfxf7vS+44ALdcccdSkxM1B/+8AcZY0oqNgAAAEoBRQ8hz1qrbdOn65vhw+XJz/d7TJ0OHXT1qFGKqlkzwOlKRmZKivJTUxUVFqY8j0cVBg06Zdn71caNG/X+++9r4sSJ2rVrV7H/rJYtW+rPf/6zBg4cqIsuuuh8owMAAKAU8HgFhDxjjJreeadumDFDlU5RTPYsX65lcXHa/9VXAU5XMnLcbkWFnfgrGxUWphy3+4zntGrVSq+++qpycnL04YcfqlevXsXahGXTpk1KTk5Wo0aNdOutt2ru3LnKP0WBBgAAQNlD0UNIqXHVVeqQkaE6HTr4nR/ftUuf9e2rrVOmBN3z5Rq7XMrz7rCZ5/GosctV7HMjIiJ0yy23aM6cOdq5c6dGjRqlyy+//IzneTweffTRR+rdu7caNGigxx57TF9//fU5fw8o2zJTUjSmUydlpqQ4HQUAAJwnbt1ESLJFRdo4dqw2jh4tneKf8Qbx8bry5ZcVER0d4HTnrjif0Ssua62ys7OVnp6uadOm6dChQ8U+95prrlFiYqL69eun6tWrn1cOlA3ncmswAABwFp/RQ7m1+9NP9eVjj6ngwAG/8yotWypm3DhVbtYswMnKlqNHj2revHlKT0/X0qVLi31exYoV1bt3byUmJuqmm25SWBg3CQSrMZ06qUlOzv/9flvjxnr4k08cTAQAAM6Ez+ih3Lrwj39Ux8xMVbviCr/zw5s2aXnPntr58ccBTla2REdHKyEhQVlZWfrhhx/0/PPPq1GjRmc87/jx45o6dapiY2PVvHlzvfTSS9q2bVsAEqOknc+twQAAoOzhih7KhaK8PH3zyivaNnXqKY+5OClJrZ94QmGRkQFMVnYVFRVp6dKlSk9P17x585SXl1es84wx6ty5sxITE9WzZ09VrFixlJOipJTkrcEAAKD0cesm4PXjvHla+9xz8hw/7nde85pr1G70aFW88MIAJyvb9u3bp+nTpys9PV1ffvllsc+rXr26EhISlJiYqKuvvppn8wEAAJQgih5wkkMbNij7oYd0ZOtWv/Oo2rX1P6NHq3b79oENFiTWrFmj999/X1OmTNG+ffuKfd5VV12lxMREJSQkqFatWqWYEOeKK3oAAAQXih7wOwWHD2vNk0/qp8WL/c5NeLguHTpUzQcN4irUKeTl5SkzM1Pp6elatGhRsR9XUaFCBcXHxysxMVEul0vh4eGlnBTFwa6bAAAEH4oe4Ie1Vj+MH69vR4yQLSrye0w9l0ttR4xQZJUqAU4XXH788UdNmjRJ6enp+uGHH4p9XoMGDXTPPffonnvuUYsWLUoxIc6EXTcBAAg+7LoJ+GGMUfNBg3T9lCmKqlPH7zE/ud1aHh+vQ999F+B0waVRo0Z69tlntWnTJv3rX//S3XffrUqVKp3xvB07duiVV15Ry5YtddNNN2nSpEk6cuRIABLj99h1EwCA0MIVPUDS8d27tXrIEO37z3/8zsMqVtSVw4erUa9eAU4WvA4ePKhZs2YpPT1dX3zxRbHPq1Klivr166fExES1b9+eW2cDiM/oAQAQXLh1EygGT0GBvnvzTX0/fvwpj2nSv78uHzZM4VFRAUwW/NavX6/3339fkyZN0u7du4t9XuvWrZWYmKgBAwaobt26pZgQAAAg+FD0gLOwa9EirXnySRXm5vqdV2vTRjFjxyq6YcMAJwt+BQUFWrBggdLT0/XRRx+p6BSfjfy9iIgI3XLLLUpMTFT37t0VybMOAQAAKHrA2crdskXZDz2kwxs3+p1HVqumq0eNUt2bbgpssBDy008/afLkyUpLS9OGDRuKfV7dunV19913689//rNat25digkBAADKNooecA4Kjx7V2mHDtGP+fP8HGKNWDz+sVo88IsMjAs6ZtVZffPGF0tPTNWPGDOWe4kqqP9dff70SExPVp08fVa1atRRTAgAAlD0UPeAcWWu1bfp0fTN8uDz5+X6PqdOhg64eNUpRNWsGOF3oOXLkiGbPnq309HQtW7as2OdFR0frjjvuUGJiojp06MAGLgAAoFyg6AHn6cDatcoePFjHdu70O69Yv75ixoxRjbZtA5wsdG3atEkTJkzQhAkTtPMUP3d/WrRooT//+c8aOHCgGjRoUIoJAQAAnEXRA0pA/v79+vKvf9We5cv9zk1kpNo895yaJCRwRakEFRYWyu12Kz09XRkZGSooKCjWeWFhYeratasSExN12223KYqdUgEAQIih6AElxBYVaePYsdo4erR0ir87DeLjdeXLLysiOjrA6ULf3r17NXXqVKWlpWndunXFPq9WrVq66667lJiYqCuvvLIUEwY3nqMHAEBwoegBJWz3smX68q9/VcGBA37nVVq2VMw776jyxRcHOFn5YK3Vl19+qfT0dE2dOlUHDx4s9rnt2rVTYmKi+vfvrxo1apRiyuCSmZKi/NRURYWFKc/jUYVBgyh7AACUcacremGBDgOEggs7dlTHzExVP8XVocObNmn57bdr58cfBzhZ+WCMUbt27TR27Fjt2rVL06ZNU+fOnYt17urVqzV48GDVr19fd955p5YsWSKPx1PKicu+HLdbUWEn/pUQFRamHLfb4UQAAOB8UPSAcxTdoIFumDFDTRIS/M4Lc3O1evBgfZOSIk8xP1eGs1epUiX1799fbrdbW7Zs0YsvvqgmTZqc8by8vDxNnz5dLpdLF198sV588UVt3bq19AOXUY1dLuV5C2+ex6PGLpfDiQAAwPng1k2gBPw4b57WPvecPMeP+53XjIlRu9GjVbFu3QAnK588Ho8++eQTpaena86cOcrLyyv2ubGxsUpMTNTtt9+uSpUqlWLKsofP6AEAEFz4jB4QAIc2bFD2Qw/pyCmuCkXVrq3/GT1atdu3D2ywcm7//v2aPn263n//fZ3N/2dUq1ZNd955pxITE9WuXTt2UgUAAGUORQ8IkILDh7Xmqaf006JFfucmPFyXPv64mt933zkVB664nJ+1a9fq/fff1+TJk/XLL78U+7wrr7xSiYmJSkhIUO3atUsxIQAAQPFR9IAAstbqh7Q0ffvGG7JFRX6Pqedyqe2IEYqsUqXY78uuiCUnLy9PH374odLS0rRo0aJib8YSGRmp+Ph4JSYmqkuXLgoPDy/lpAAAAKdWartuGmO2GmPWGWPWGGOyvWs1jTFuY8wm7681Tjr+GWPMZmPMBmNM15PW23nfZ7MxZrThHikEMWOMmt97r66fMkVRder4PeYnt1vL4+N16Lvviv2+7IpYcqKiotS7d28tWLBA27Zt0yuvvKLmzZuf8byCggLNnj1bPXr0UJMmTfTss89q8+bNAUgMAABwdkpi181O1tq2JzXJpyVlWWtbSsry/l7GmMsk9ZN0uaRukt4xxvz6n8PHSbpPUkvvV7cSyAU4qta116pjZqZqXnON3/mRbdu0vHdv/ThnTrHej10RS0fDhg2VnJysTZs26dNPP9U999yj6GI87H7Hjh1KSUlRy5Yt9cc//lETJ07UkSNHApAYAADgzErj8QrxkiZ6X0+U1POk9RnW2jxr7RZJmyVda4ypL6mqtXaFPXEf6aSTzgGCWsULL9T1kyer+b33+p17jh/Xmief1FfPPquiM+wMGZecrAqDBmlb48bctlkKjDHq2LGj3n//ff30008aP368brjhhmKdu2zZMt1zzz2qV6+eBg0apBUrVihYb4sHSkNmSorGdOqkzJQUp6MAQLlxXp/RM8ZskbRfkpX0D2vte8aYA9ba6icds99aW8MYM0bSF9baKd71NEkLJW2V9Jq1trN3vYOkp6y1t57uz+Yzegg2uxYt0ponn1Rhbq7febU2bRQzdqyiGzYMcDKcznfffaf09HRNmjRJP//8c7HPu/TSS5WYmKgBAwaoXr16pZgQKNv4fDEAlJ5S+4yepButtf8jqbukwcaYjqfL4WfNnmbd9w2Muc8Yk22Myd6zZ8/ZpwUcVL9rV3WYP19VWrXyOz/49ddaFhenn//1r8AGw2ldeumleuONN/Tjjz8qIyND8fHxxdqE5bvvvtOTTz6phg0bKj4+XhkZGSooKAhAYqBs4fPFAOCM8yp61tqd3l93S5on6VpJP3tvx5T3193ew7dLanTS6Q0l7fSuN/Sz7u/Pe89aG2Otjalzik0ugLKscrNm+sOcOWrY0//dyQUHD2rVvffqu7feOuWOnXBGZGSk4uLiNH/+fO3YsUNvvvmmWrdufcbzioqKlJmZqZ49e6phw4Z64okntH79+gAkBsoGPl8MAM4451s3jTEXSAqz1h72vnZLeklSrKRfrLWvGWOellTTWvukMeZySdN0ogxepBMbtbS01hYZY/4j6RFJKyUtkPR3a+2C0/353LqJYGat1bbp0/XN8OHy5P+/9u49tq3zTBP48x1eRcqSdYtlx6YcxzfJkuxY2rQzCdpxukaTepuixQSdNskm0SCD7owXbZoCu/AGU8wiNXaBYIstPLNBvabjtGmNZNxmnCbuwJ11m50AzVq2E0mW7Fh2JFl2LFl3ShSv59s/RFKkSPEi8vBy9PwAIhR5ePi6OT3R4+/y+hIeU/vww9j74x/DUl2d5+ooXVJKfPjhh3A6nTh58iRcLlfan/385z+Pjo4OfPOb30RFRYWGVRIV3isHDmCupwf25mb84N13C10OEZFuaNJHTwixBQujeABgBPALKeWPhBA1AN4E4AAwBOAJKeVE6DP/BUAHgACA70kpz4RebwfwGoAyLKzb+48yRWEMeqQHU11d6Pybv8H87YSD2LCuX4/2I0dQtWdPniujTM3NzeHUqVNwOp34wx/+kPbnysrK8MQTT6CjowNf+MIXwO4ypDdco0dEpB02TCcqYr7JSVz8/vdx9/33E74vTCY0v/QSGp58kiGgRPT39+O1117DiRMnMDw8nPbn7r//fjz33HN45plnsJGb8pBOHNm3Dw1DQ5GfBx0OHDx3roAVERHph5absRBRlsxVVfjcsWPY8b3vAQmCnPT70f3DH+LSiy8i4HYXoELK1NatW/Hyyy9jYGAAZ86cwRNPPAGTyZTyc9evX8dLL72EhoYGPPbYY3jrrbfgTdF2g6jYcY0eEVFhcESPqIiMvv8+Lr7wAvxTUwnfX7NtG9r/4R9QvmVLniujbI2NjeEXv/gFjh07hq6urrQ/V11djaeeegodHR3YvXu3hhUSaef04cMYOnsWjv37OW2TiCiHOHWTqIS4b93ChYMHMbVMGDCWl2P7d7+Luocewppt2yAUDsyXEiklLl26BKfTiTfeeANTy4T6RPbu3YuOjg58+9vfRlVVlYZVEhERUSlg0CMqMUGvF5d/9CMMvvFG0uNMFRWo2rsX1e3tqG5vx9rWVhgsljxVSdnyeDx4++234XQ68bvf/Q7p3o8tFgu+/vWvo6OjA1/60pegMOwTERGtSgx6RCXq5q9/ja6XXoLq8aR1vGI2o3LXrkjwq9q7l+0ZSsTg4CBOnDiB48ePY2BgIO3PORwOPPvss3j22Wdx3333aVcgERERFR0GPaISNnP1Kjr/+q8xl8Ev/9HKt25FdVvbwqO9HTaHg7t3FjFVVfH73/8eTqcTp06dgifNkA8AjzzyCDo6OvCNb3wDZWVlGlZJ1ieeSAAAHBhJREFURERExYBBj6jE+V0unPrTP4U9B7tuWurqFkb8QuGvoqkJitGYgyop16ampnDy5Ek4nU6cP38+7c9VVlbiW9/6Fjo6OtDe3s5gT0REpFMMekQ6cPrwYfiPHoVZUaBKCWE2Q/j9WZ/XYLOhas+exemee/bAaLfnoGLKpe7ubhw/fhw/+9nPMDY2lvbnmpub0dHRgaeeegp1dXUaVkhERET5xqBHpBOvHDiAuZ4e2Jub8f1/+ie4rl7FRGfnwuPCBXhGRrL+DmEwoGLnzkjwq25rg3XduhxUT7ng8/nwm9/8Bk6nE2fOnIEa6k+Wislkwle/+lV0dHTgy1/+MowJRnG5BT4REVFpYdAj0oHThw/Dd/QoLIoCr6rC/PzzMb+MSykxf+vWYvDr7ITr2rWcfLfN4YhZ51d+//1s61AEbt++jddffx1OpxPXMvh3vX79ejzzzDN47rnnsH37dgCpry8iIiIqPgx6RDpwZN8+NAwNRX4edDhw8Ny5pJ/xTU1h8uJFTFy4gInOTkx1dUH1+bKuxbR2bUzwq2xuZluHApJS4oMPPoDT6cSbb76Jubm5tD/78MMPo6OjAxNOJ7bfvh15PZ3ri4iIiAqLQY9IB3Ix4hL0ejHd3R0JfhMXLsA/PZ11bYrZjLWtrTFtHcyVlVmflzLncrnw1ltvwel04oMPPkj7c1azGX9iNuORNWuwyWhE1Xe+wxE9IiKiIsegR6QTuV5DJVUVs9evx6zzc9+8mYNKgTXbt0dG/Krb21F2773c/THPrl69iuPHj+PEiRO4c+dORp91OBxoaWlBa2tr5J/bt2+HyWTSqFoiIiLKFIMeEaXNMzKyOOLX2Ynpvj4gzQ0/krHW18dM96zYuRPCYMhBxZRKIBDAb3/7WzidTrzzzjsIBAIrOo/ZbEZjY2NcAFy/fj1DPBERUQEw6BHRigVmZzH50UeREb/JS5cQnJ/P+rzG8nJUPfBAJPit3b0bRpstBxVTMqOjo/j5z3+OY8eOobe3NyfnrK6ujgl+ra2t2LVrF+xs00FERKQpBj0iyhnV78dMX1/MqJ83g75uyxFGIyqbmmLaOlhqa3NQMSUipcT58+fhdDrxy1/+EjMzMzk9vxAC999/f9zo35YtW2DgSC4REVFOMOgRkWaklHAPDsZs8DJ7/XpOzm3fvDlmnZ/9vvs4RVADbrcbv/rVr3D69Gl8/PHH6O/vT7s/X6bKysrQ3NwcFwBrGeqJiIgyxqBHRHnlHR+PbevQ0wPp92d9XnN19WLwa2tD5a5dUMzmHFRM0dxuN3p7e9Hd3Y2urq7IP+/evavZd9bX18cEv5aWFjQ2NsJqtWr2nURERKWOQY9IJ3K962a+BD0eTHV1Le7uefEiAi5X1udVLBZU7dkTCX9Ve/fCtGZNDiqmREZGRmKCX3d3Ny5fvgyv16vJ9xkMBuzYsSNu9M/hcHBkt8SU6r2LiKjYMegR6UAu+ugVC6mqcF27thj8OjsxH9Wse8WEQMWOHZERv+r2dpRt2JD9eWlZgUAA/f39MQGwq6sLAwMDmn1nRUUFWlpaYgJgS0sLKtm7sSjp6d5FRFRsGPSIdODIvn1oGBqK/DzocODguXMFrCi33LdvYzI02jfR2YmZK1eAHNyfyjZsiFnnt2bbNrZ1yIOZmRn09PTETf+cnp7W7DsdDkfc9E/2/is8vd+7iIgKiUGPSAdW29+K+12uhXV+4bYOH30ENQdTBI1r1qB6796FqZ5tbajavRsGrgPLCyklhoeH46Z/XrlyZcW9/VIxm81oamqKm/5ZX1/P6Z95struXURE+cSgR6QTq3mdi+rzYbq3d3G654UL8E1MZH1eYTJhbXNzzDo/S01NDiqmdHm9Xly9ejUuAN66dUuz76ypqYkb/WPvP+2s5nsXEZGWGPSISHeklJj79NOYfn5zOVoXZt+yBTVR6/xsDQ0c/SmAiYmJmHV/3d3d6O7uhtvt1uT7wr3/lgZA9v4jIqJixaBHRKuCd2wspp/f9OXLkDmYEmiprY1M9axpb0dFYyMUrvsqCFVV8emnn8aN/l27dg1a/ffMZrNh165daG1tjdn8hb3/iIrDKwcOYK6nB/bmZvzg3XcLXQ5RXjHoEdGqFHC7MfXxx4vr/C5dQmB2NuvzGsrKFto6hNf57dnDtg4FFu79t3T3z7GxMc2+c/369Ql7/1ksFs2+k4hivXLgABp6e2FVFHhUFYNNTQx7tKow6BERAZDBIGauXo1p6+AZGcn+xIqCisZG1ITX+bW1oay+PvvzUlaklBgZGYnb+bO3t1fz3n9LAyB7/xFp4+8aGrDXaIz8fDEQwA8HBwtYEVF+MegRESUgpcT8rVsxG7y4PvkkJ+e2bdqEqrY2VIeme5Zv3QqhKDk5N2UnEAjg2rVrcdM/89H7LzoANjc3s/cfUZY4okerHYMeEVGafFNTmLx0KRL+prq6oPp8WZ/XVFmJ6ra2hfDX3o61LS0wcIpfUQn3/lsaALXs/dfQ0BAJftG9/4xRIxR6wF03SUtco0erGYMeEdEKBb1eTPf0REb8Ji5cgH9qKuvzKmYzKltaFnb3DLV1MK9dm4OKKZeklLh582bc9M8rV64gGAxq8p0WiwWNjY1x0z9Ltfcf++gREWmHQY+IKEekqmL2+vWY4OceGsrJucu3bYu0dKhpb0fZxo0l+Yv9auD1enHlypW40b/bt29r9p21tbVx0z937doFm82m2XfmwpF9+9AQ9f+RQYcDB8+dK2BFRET6waBHRKQhz8hITD+/6b4+QFWzPq913brIOr/q9nZU7NwJRcMpfZxel73x8fG40b+enh5Ne/9t3bo1bvrnli1boBTJmlCO6BERaYdBj4gojwKzs5gMt3Xo7MTkRx8hmINf9A12O6oeeAA1oameVXv2wGi356Bi/jKuJVVVcePGjbgA2N/fr2nvv+bm5rjpnzU1NZp8Xyr8SwQiIm0w6BERFZAaCGCmr29xumdnJ7x372Z9XmEwoKKpCdWhdX7VbW2w1tWt6FycXpd/c3Nz6O3tjQuAWvb+27BhQ9z0z507d7L3HxFRiWLQIyIqIlJKuIeGFts6XLyI2f7+nJzb5nAsBr/2dpRv2ZLWOj+O6BWHcO+/cMP36N5/vhzs/pqI0WjEjh074gLgpk2buEaUiKjIMegRERU578QEJi9eXGzr0NMD6fdnfV5TVdXCGr9wW4fmZihmc8JjOb2uePn9fly7di1u9G9Qw8bQlZWVCXv/VVRUaPadRESUGQY9IqISE/R4MNXVtbjJy4ULCLhcWZ9XsViwdvfuxfDX1gYTf3EvWdPT0+jp6YkLgDMzM5p95+bNm+MC4LZt23TX+4+IqBQw6BERlTipqnBduxazzm/+1q3sTywE1mzfHpnqWdnYCEttLUxr13LaXomSUmJoaCgu/F29elXT3n9NTU1xAXDdunW8joiINMSgR0SkQ/O3b0d6+U10dmLmyhUgR/d0YTTCUlMDS21tzMMcfh71nrmqCqJItvKn5Xm9XvT19cUFwM8++0yz76ytrY0Ev5aWFmzcuBHr1q1DfX09amtrOQpIRJQlBj0iolXA73Jh8tKlxbYOH38M1ePR/HuFwQBzOPglCIcxQbGqCsJg0LwmSt/Y2Bi6u7tjAqCWvf/ChBCora1FfX091q1bF3lE/xx+XldXBwOvGyKiOAx6RESrkOrzYbq3N2a6p29iorBFKQos1dXLjg5a6upiQqGWDeJpeeHef9Ejf93d3Zr2/ksmUShcLiAyFBLRasKgR0REkFJibmBgsa3DhQuY+/TTQpe1PCFgrqpKOkIYCYXV1VBMpkJXrHvh3n9L2z+Mj48XurQIIQTq6upSjhIyFBJRKqWwGzWDHhERJeQdG4us85vq6oJnZATe8XEE5+YKXVrGTOFQWFMDa13d4ojh0lHDmpplW0xQ5qSUuHPnTtzon5a9/3JlaShMNo2UoZBodTl9+DD6jhyBCkAB0HjwYFGGPQY9IiLKSMDthndsLObhGx+PPPfcvQvf2Bi84+MIzM4WutyMmSorF6aJJllTaA6FQoPFUuhyS1K4919XVxdOvfoqbvT2wldejoDFgpGREUxOTha6xIwoioLa2tqUo4QMhUT68LeNjWj1eGBRFHhVFV1WK/5rX1+hy4qTLOhx8QMREcUx2mwwOhywOxwpjw16PHGhcLlHLnoB5oJ/ehr+6WnM9venPNZUUbH86OCSdYUMhYtMJhOamprQ//bb+ObgICxr1sCrqjA//TQeP3QIXq8Xo6OjGBkZiTzu3LmT8HkxhEJVVTE6OorR0VF0d3cnPTY6FKbabKa2tpahkKgIWRUFltCO0hZFgbUEd5dm0CMioqwYrFbYNm6EbePGlMcGvV54x8cXRgNDI4PesbHIz96oUUP/9HQeqk/NPzMD/8wM5m7cSHmssbw8vTWFtbUwlpXlofrCGzp7Fg1RvywNnj0LHDoEi8WCTZs2YdOmTSnPsTQURodAvYbCRAGRoTCxUlhHRaWn+ckn4T16NDKi1/zkk4UuKWMMekRElDcGiwW2DRtg27Ah5bGqz7cQ/O7ejQmAMY/Q6/4i+OUeAAKzswjMzmJuYCDlsQa7fdkdR5eOGhrtdu2L14hj//6YX5Yc+/dnfA6GwgXhUJhOS4rVEgpPHz4M39GjaFAUeI8exWmAYY9y4vFDh3AawGAJ/yUC1+gREVHJU/3+mDDoWzI66B0biwRG3+RkzhrL54uhrCz5WsKocGgsL4cQotAlxyjWEZdkoXBpQCyGUJiJRKFwuYBYyqHwyL59aBgaivw86HDg4LlzBayIKL+4GQsREVGIGgjANzGR1ppC38REyYVCxWqNDYOJNpwJjR4WYygsVtGhMNkoYamGwnRbUhRbKAyP6IVHjM3PP19Uf5lApDVuxkJERBSiGI2w3nMPrPfck/JYGQzCNzkZPzKYYE2hb2ICMhjMw58gOdXjwfzwMOaHh1Meq5jN8SOEoWC4tEWFqaJiVYfClU4fTRYK79y5g6mpqTxUn5yqqpGaUlkaCpNNI81XKLzu9S5uga/5txGVDo7oERER5YBU1fhQmGBNYXjjmWIIhZlQzGaYq6vjRwYTjBia1q5d1aEwE8uFwkQBsRhCYSbyEQo5dZNWO47oERERaUwoykLoqakBduxIeqxUVfimphZ3G020pjDqden35+lPsTzV54Pnzh147txJeawwGmGpqYn0IrTW1cFcUwOjzQaDzQZDWdnC87IyGGw2GMvKIs+j31NMpjz8yQprJSOFqaaOFksozNVI4dKAGB0Kc7HZD5FecUSPiIgAFO+GGaudlBL+6enEawjHxyMtKsI/qz5foUvOGWEyLYTAZUKhMUE4XPr60vfCPysWi65HHdMJheHnxRAKMxEdCuvr6+EbHQXGxnDP1q3Y++ijsNvtKC8vT/qw2WxQSrAvGtFS3IyFiIiSOn34MPqOHFlc53LwIMNeCZJSIuByxYfCcBhcMmqoer2FLrlwFGUh9CULi8u8l87nRBFtWJJKolC4XEAstVCYTDqBcOkj1WdMq2AUmooLgx4RESX1nxoa8G8UJTL96byq4r8PDha6LNKQlBKB2dnku45GBcPg/HyhSy4pisWS9qij0WaDwWqNH7lc5nOK2VywP5fX640JgKslFKbLbDZnHB5ThciysjJdjz5TdrhGj4iIkjIHArBYrQAAi6LArKPpf5SYEAKmNWtgWrMG5ffdl/L4wNzcsiODSx9BtzsPf4Lipnq9UL1e+DVotSCMxmWnpMYFxVCATGuaa+j4ZKHCYrHA4XDA4XCkrDNVKIx+rpdQ6PP5MDExgYmJiZydUwix4vC4XIC02+0wGhkD9I7/homICJV79sDT2wurosCjqqjcs6fQJVGRMdrtMNrtsG/enPLYgNsdCX3/+uqrCJw9C5OiICAlDM3NaNi1C8H5eQTm5xF0uxeeh/4ZdLsjr5daD8N8kYEAAi4XAi5X7k8uRMK1jXHrHhOtnUwQJGvKynBPQwP27NwJo8227JRWj8ezbPN6vYbCdEkp4XK54Mrxv2+r1ZrzAGnR+drXUsOpm0REBAB45cABzPX0wN7cjB+8+26hyyEdWclGP1JKqF5vTABMGAhThMWlgTL8XjHsZLoaKWZz4nWPVmvyqa1RP6tGI6bn5zE5N4cxlwv/5803MdzbC/Pmzbi3rQ2zs7Mxj7m5ubjX3Bx11oTBYMgqPCYKkHa7nRvnJMGpm0RElNL2hx7CkNsNx0MPFboU0pnHDx0CMtzcRwixMO0wNKU411S/P2k4XPpeukEy4HZD9Xg0qVkPVJ8Pqs8Hfw5H5R4JPxkYgBwchMFshmIyQTGbIYzGhee1tVDWr4cIvSeMRkiDAaoQUIVAEEAAQEBK+KWEX1XhCwbhCwbhDQTgCQTg8fsx7/dj3ueD2+eD2+vFnMeDWY8Hs/PzcM3Pw6+qkXMEpIycM9FrpTnUklwwGMT09DSmp6dzel6bzZbzAGlOY61rqe9GzRE9IiLC6cOH4YvqRWV+/vmS/I8aUTGQqrp8WEwnOC4XMEPPoaqF/iNSDshwyEwRNCPhEYuhMRAVGv0JXosJlikC53KvhV8PAroMpSaTKWkwHL92DaZPPkG5osAkBOwPP4yf/OM/phUQ84kjekRElNTQ2bNoCE2NsSgKBs+ezXgEhogWCEWJrGm05PjcUkqoPl92I5BJRjH11Iex2AkpYZASCVctCgEYjQuPIrA0YKYVOpc5JtMQGnk9jdcCGfyZ/H4/JicnMZnuhknvvIOfZPS/WuEVx9VDREQF5di/H96oET3H/v2FLomIEhBCwGCxwGCxwFxVlfPzq4FAxiOQmQRJKk1GIWAskU1WcjHCGR0kg6Gfg0DJ9Ulk0CMiIjx+6BBOAxgs4bUIRJQ9xWiEEmq7kWtSVRH0eCKhL92AmCpgeiYngdDmOqURRUhLJiFg0iCUBqUsuR1FuUaPiIiIiEpS3Priv/xLfOXFFyH9/oWNXwKBhefhh88XeS4zfS3q9ZjXQq/Hveb3Q0adJ+Z8nCJbcrxS4s9v3Ch0GXG4Ro+IiIgKhq07SCtx64v/5V9gfOkloKyswJUlJ6WEDATiwp8aek0uCZs5DaYZhli2IlkgSrDFA4MeERERaeaVAwcQvHQJZgDBS5fwyoEDDHuUM6W6vlgIAWEyQSmBNV9SyuSjleFR00Sjl0lGNNN6LcNgKgOZbMeSGaUEZ0Ey6BEREZFm7ly8iM9ZrbAqCjyqig8vXix0SaQjXF+sPSHEQv/BImsrkIhU1dRTa8OjpimC6Xsvv4wNfj+MioKAlLhTJDugZqJoKhZCPArgfwIwAPjfUsr/VuCSiIiIKEtWRYE1NOUp+jlRrvzx5EkY7t7F7fFxBr1VTigKDBYLYMm+scmm4WH0HTkCFYACoPHgwazPmW9FcbcVQhgA/D2AxwA0AfiWEKKpsFURERFRtpSKCnhDDb69qgqloqLAFZGeHGptxQMuFx602fCAy4VDra2FLol04vFDh3BTSrhVFTelLMm/RCiKoAfgQQD9UsobUkofgJMAvlbgmoiIiChL7U8/jeteL3rm53Hd60X7008XuiTSETE6GjNiLEZHC1wR6cULmzfji1YrHrTb8UWrFS9s3lzokjJWLFM37wVwM+rnYQCfK1AtRERElCPhvwUf4hoq0oAntAlLeDMWD6cGU46Uezyw2u0AFv4SoXxursAVZa5Ygl6i7oNxW9sIIf4KwF8BgMPh0LomIiIiyoHHDx0CGPBIA188eDBmHdUXS3AdFRWnWasVHlWNbCQ1a7UWuqSMFUvQGwawKernjQBuLz1ISvlTAD8FFhqm56c0IiIiIipGHDEmrfx4YAAvbN6M8rk5zFqt+PHAQKFLypiQRdATQghhBPAJgC8BuAXgPIBvSykvL/eZ9vZ22dnZmacKiYiIiIiIiosQ4oKUsj3Re0UxoielDAghDgL4Zyy0V3AmC3lERERERES0vKIIegAgpXwPwHuFroOIiIiIiKjUcWsiIiIiIiIinWHQIyIiIiIi0hkGPSIiIiIiIp1h0CMiIiIiItIZBj0iIiIiIiKdYdAjIiIiIiLSGQY9IiIiIiIinWHQIyIiIiIi0hkGPSIiIiIiIp1h0CMiIiIiItIZBj0iIiIiIiKdYdAjIiIiIiLSGQY9IiIiIiIinWHQIyIiIiIi0hkGPSIiIiIiIp1h0CMiIiIiItIZIaUsdA0rIoS4C2Cw0HUkUQtgrNBFkC7x2iIt8foirfDaIi3x+iKtFPu11SClrEv0RskGvWInhOiUUrYXug7SH15bpCVeX6QVXlukJV5fpJVSvrY4dZOIiIiIiEhnGPSIiIiIiIh0hkFPOz8tdAGkW7y2SEu8vkgrvLZIS7y+SCsle21xjR4REREREZHOcESPiIiIiIhIZxj0siCEeFQIcVUI0S+E+M8J3v8zIcS0EOKj0ONvC1EnlR4hhFMIMSqE6FnmfSGE+Eno2usSQuzNd41UutK4vnjvohURQmwSQpwTQvQJIS4LIb6b4Bjev2hF0ry+eP+ijAkhrEKI/yeE+Dh0bf1dgmNK7t5lLHQBpUoIYQDw9wD2AxgGcF4IcVpK2bvk0P8rpfx3eS+QSt1rAI4AeH2Z9x8DsC30+ByA/xX6J1E6XkPy6wvgvYtWJgDgRSnlRSHEGgAXhBBnl/y3kfcvWql0ri+A9y/KnBfAI1LKWSGECcC/CiHOSCn/GHVMyd27OKK3cg8C6JdS3pBS+gCcBPC1AtdEOiGlfB/ARJJDvgbgdbngjwDWCiHW56c6KnVpXF9EKyKl/ExKeTH03AWgD8C9Sw7j/YtWJM3riyhjofvRbOhHU+ixdCOTkrt3Meit3L0Abkb9PIzEN5s/CQ0DnxFC7MpPabQKpHv9Ea0U712UFSHEZgAPAPhwyVu8f1HWklxfAO9ftAJCCIMQ4iMAowDOSilL/t7FqZsrJxK8tjT5XwTQEBoG/gqAt7Ew3EuUrXSuP6KV4r2LsiKEKAdwCsD3pJQzS99O8BHevyhtKa4v3r9oRaSUQQB7hBBrAfxaCNEspYxey15y9y6O6K3cMIBNUT9vBHA7+gAp5Ux4GFhK+R4AkxCiNn8lko6lvP6IVor3LspGaH3LKQBvSCl/leAQ3r9oxVJdX7x/UbaklFMAfg/g0SVvldy9i0Fv5c4D2CaEuE8IYQbwFwBORx8ghKgXQojQ8wex8L/3eN4rJT06DeDfh3aA+jyAaSnlZ4UuivSB9y5aqdB1cwxAn5TyfyxzGO9ftCLpXF+8f9FKCCHqQiN5EEKUAfi3AK4sOazk7l2curlCUsqAEOIggH8GYADglFJeFkJ8J/T+qwD+HMB/EEIEAMwD+AvJDvWUBiHELwH8GYBaIcQwgB9iYWFw+Np6D8BXAPQDcAN4rjCVUilK4/rivYtW6iEATwPoDq11AYBDABwA71+UtXSuL96/aCXWAzgR2lVfAfCmlPI3S36vL7l7l+C1T0REREREpC+cuklERERERKQzDHpEREREREQ6w6BHRERERESkMwx6REREREREOsOgR0REREREpDMMekRERERERDrDoEdERERERKQzDHpEREREREQ68/8B+Zd9PaQyVxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose = 1\n",
    "bln_mean, sz_mean = np.zeros(6), np.zeros(6)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*llr*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 10, c = 'k')\n",
    "    \n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '_2p*BLN*_nnb-*llr*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 10, c = 'firebrick')\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_llr*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_llr*'))\n",
    "    data = dome1(curr_l, choose)\n",
    "    sz_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 5)\n",
    "plt.plot(nnb_list, sz_mean, c = 'firebrick', linewidth = 5)\n",
    "\n",
    "\n",
    "\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "#ax.set_ylabel(dtype, size = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['02', '04', '05', '06', '07', '08', '09', '10', '11', '12']"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list = ['02', '04', '05', '06', '07', '08', '09', '10', '11', '12']\n",
    "num_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nnb_list = ['0.5', '1.0', '1.5', '2.0', '2.5', '3.0']\n",
    "nnb_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "bln_mean, sz_mean = np.zeros(20), np.zeros(20)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*BLN*_dcc*'))[:20]\n",
    "    data = dome(curr_l)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 10, c = 'k')\n",
    "    \n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*PTZ20*_dcc*'))[:20]\n",
    "    data = dome(curr_l)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 10, c = 'firebrick')\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_dcc*'))[:10]\n",
    "    data = dome(curr_l)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_dcc*'))[:10]\n",
    "    data = dome(curr_l)\n",
    "    sz_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 5)\n",
    "plt.plot(nnb_list, sz_mean, c = 'firebrick', linewidth = 5)\n",
    "\n",
    "\n",
    "\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "#ax.set_ylabel(dtype, size = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualise critical statistics as a function of NNB - granular scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['04',\n",
       " '05',\n",
       " '06',\n",
       " '07',\n",
       " '08',\n",
       " '09',\n",
       " '10',\n",
       " '11',\n",
       " '12',\n",
       " '13',\n",
       " '14',\n",
       " '15',\n",
       " '16',\n",
       " '17',\n",
       " '18',\n",
       " '19',\n",
       " '20',\n",
       " '21',\n",
       " '22',\n",
       " '23']"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nnb_list = adfn.name_zero('front', 4, 23, 1)\n",
    "nnb_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nnb vs DCC\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "bln_mean, sz_mean = np.zeros(20), np.zeros(20)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*BLN*_dcc*'))[:20]\n",
    "    data = dome(curr_l)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*0.' + str(nnb) + '*_dcc*'))[:10]\n",
    "    data = dome(curr_l)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_dcc*'))[:10]\n",
    "    data = dome(curr_l)\n",
    "    sz_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "\n",
    "plt.savefig(Ffig + 'dcc_short_k.svg')\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dominicburrows/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1604: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 6.0   p = 0.02734375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 5.0   p = 0.01953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 4.0   p = 0.013671875\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 4.0   p = 0.013671875\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 4.0   p = 0.013671875\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_dcc*'))[:10]\n",
    "    bln_data = dome(curr_l)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_dcc*'))[:10]\n",
    "    sz_data = dome(curr_l)\n",
    "\n",
    "    alpha = 0.05\n",
    "    \n",
    "    \n",
    "    adfn.stats_2samp(bln_data, sz_data, alpha, 1, 'rel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XsrOzjfybZ3JtAIDjo0cOUUXLb/s1BeGioiI5HA4FAgGCMAAAQJwiyCHqXC4X4aOdCMIAAACQCHKA7RCEAQAAwBw5AAAAALAZghwAAAAA2AxBDgAAGM3n86miooJ9MwHgKK2aI/foo4/qjTfeUEJCgr773e/q+uuvD91WVVWlOXPmhL7+4osv1LNnT/31r39VaWlp2Kp6kyZN0i233BLhbwEAAMSq0tJSFRcXh63WW1BQYHVZAGC5FoOcx+PRBx98oKVLl6qhoUGXXnqpJk6cqCFDhkiSsrKytGTJEknSwYMHdfXVV+uee+6RJK1bt05z5szRt7/97c77DgAAQEzy+XwqLi6W3++X3++XJBUVFSk/P59FnwDEvRaHVrrdbs2bN09JSUny+XxqbGxU9+7dj3vfJ598UmeddZbGjRsnSaqsrFRpaammTp2q4uJiffXVV5GtHgAAxCyv1yuHwxF2zOFwyOv1WlQRAJijVXPkHA6HHnvsMV122WXKy8tTenp6s/vU1dVp8eLFuvnmm0PH+vTpoxtvvFFLly5Vv379dN9990WucgAAENMyMjIUCATCjgUCAWVkZFhUEQCYo9WLncyePVsrVqzQjh07tHjx4ma3L126VBdccEHYUIcnnnhCubm5SkhI0E9/+lMtX748MlUDAICY53K5VFJSIqfTqdTUVDmdzrC59wAQz1qcI7d582bV19crKytL3bp105QpU1RdXd3sfm+99ZZ+9rOfhb6uq6vTyy+/rOuuu06SFAwGlZiYGLnKAQBAzCsoKFB+fr68Xq8yMjIIcQDwby32yG3btk133XWX6uvrVV9fr/LycuXm5obdJxgMav369crJyQkd6969u5555hmtXbtWkrRgwQJdeOGFES4fgGlYJhxApLlcLmVnZxPiAOAoLQa5iRMnatKkSSooKNBVV12lnJwcXXbZZZoxY4YqKyslHdlywOFwKDk5OfT/EhMT9cgjj+iee+7RJZdcovXr1+u2227rvO8EgOVKS0vldrtVWFgot9utsrIyq0sCAACISQnBYDBodRHH2rZtmyZPnqzy8nINHDjQ6nIAtILP55Pb7Q4tES5JTqdTHo+HVnQAAIA2aikTtXqxE/wHQ8eA5lgmHAAAIHoIcm1kh6FjBE1YgWXCAcQr08+7ptcHoH0Icm3g8/lUXFwsv9+vuro6+f1+FRUVGfWH0Q5BE7GJZcIBxCPTz7um1weg/Zgj1wYVFRUqLCxUXV1d6FhqaqoWLVqk7OxsCys7gjlK8cHn8xm9DLfp9QFApJh+3jW9PgAnxxy5CDJ96BhzlCLD5CEodmhZZZlwAPHC9POu6fUB6BiCXBuYPnTM9KBpByYHJTsM7QWAeGL6edf0+gB0DEGujQoKCuTxeLRo0SJ5PB4VFBRYXVKI6UHTdKYHJVpWAcAspp93Ta8PQMckWV2AHblcLmP/CBYUFCg/P585Su3QFJSOnkvQFJRM+DnSsgoA5jH9vGt6fQDajyAXg0wOmiYzPSg1tawWFRXJ4XAoEAjQsgoABjD9vGt6fQDahyAH/JsdghItqwAAAJAIckAYOwQlWlYBALGEbWuA9iHIAccgKAEAEB2lpaUqLi4OGwlj0kJygMlYtRIAAABRZ/pq0YDpCHIA4obJm71L5tcHAJHEtjpAxxDkAMQFkzd7l8yvDwAizfTVogHTEeQAxDzTh++YXh8AdAY2LAc6hsVOAMQ80zd7N70+AOgsdlgtGjAVQQ5AzDN9+I7p9QFAZ2K1aKB9GFoJIOaZPnzH9PoAIF6ZvgiV6fWhc9EjByAumD58x/T6ACDemL7Hnen1ofMlBIPBoNVFHGvbtm2aPHmyysvLNXDgQKvLAQAAQBzx+Xxyu91hc5edTqc8Ho8RDW2m14fIaCkTMbQSAAAAOIrpe9yZXh+igyAHIKIYrw8AsDvTF6EyvT5EB0EOUceFfseY/PNjU2sAQCwwfREq0+tDdDBHDlFlh4m5Pp/P2AUnTP75MV4fABBrTL4mkMyvDx3DHDkYw+fzqbi4WH6/X3V1dfL7/SoqKjKqZ8nkHiXTf36M1wcAxBqXy6Xs7GxjQ5Lp9aFzEeQQNaZf6BOUOobx+gAAANFDkEPUmH6hT1DqGMbrAwAARA8bgrdRbW2t/vnPf+qzzz7T4MGDde6556pfv35Wl2ULTRf6RUVFYXO8TLnQt0tQMvXnJ7GpNQAA+A/m8HUuglwrBYNBPfLII3r88cfDFnNISEjQZZddplmzZmnUqFEWVmgPJl/oE5Qiw+VyGVkXAACIHpMXaIsVrFrZSk8++aTuu+++k97n/PPP180336yzzz5bCQkJUaoMkUbrEXB8vDcAAK3BStaRwaqVEbBnzx498MADLd7vnXfe0VVXXaUrrrhCb731lgzMyGgFVoACmjN5RVcAgFlMX3cgVhDkWuH9999XQ0NDq++/cuVK/ehHP9KUKVO0ZMkSNTY2dmJ1ANC5TF/RFQBgFtPXHYgVBLlWqK2tbdf/27Bhg2688UZNmDBBCxcu1KFDhyJcGQB0PlpWAQBtwUrW0UGQa4VBgwZ16P9v3bpVt99+u8455xw9+eST2r9/f2QKA4AooGUVANBWBQUF8ng8WrRokTweDwuddAKCXCuMHz9ep556aocfZ+fOnbrvvvvkdrv18MMPa+/evRGoDgA6Fy2rAID2YN2BzkWQa4XevXvrzjvvjNjjffnllyopKZHb7dZ9992nnTt3RuyxAaAz0LIKAIBZCHKtdO211+qPf/yjUlNTI/aYBw4c0JNPPqm8vDzdfvvt2rp1a8QeGwAijZZVAADMQZBrg+985zt6//339fOf/1w9e/aM2OPW19dr4cKFys/P10033aQNGzZE7LEB/IfP51NFRYWxqy2aXh8AADAHQa6Nevfurdtuu00ffvih7rrrLqWlpUXssQ8fPqyysjJdeOGF+tGPfqSVK1dG7LGBeGf6Pmim1wcAAMySEDRw1+qWdjE3id/v1+LFi/XHP/5Rn332WcQfPy8vTzfffLMmTpyohISEiD8+EA98Pp/cbrf8fn/omNPplMfjMWKYoOn1AQAQi3w+n7xerzIyMow837aUieiR6yCn06kf/vCHWr58uebOnasRI0ZE9PFXrFihadOm6ZJLLtFf//pXNhcH2sH0fdBMrw8AgFgTCyNhWhXkHn30UV166aW67LLL9NxzzzW7/fHHH9f555+vyy+/XJdffrkWLlwoSdq+fbumTZumiy++WDfccENM75+WlJSkK6+8Un//+9/13HPPKScnJ6KPX1lZqZ/97Gc6//zz9cILL6i+vj6ijw/EMtP3QTO9PgAAYonP51NxcbH8fr/q6urk9/tVVFRkuznqLQY5j8ejDz74QEuXLtXLL7+s+fPna8uWLWH3WbdunR5++GEtWbJES5Ys0bRp0yRJ9957r6699lq9/vrrGjVqlP7whz90zndhkC5dumjKlCl69dVXtXjxYk2YMCGij79582bdeuutOvfcc/Xss8/q4MGDEX18IBaZvg+a6fUhMljMBgDMECsjYVo1Ry4QCMjhcKi2tlbXXnutXnjhBfXt2zd0+3nnnadRo0aptrZWZ511lv7f//t/6tKli84++2x5PB4lJSVpx44d+sEPfqDy8vIWi7LTHLnWWLt2rR5//HG99tprivSURJfLpZ/85Ce67rrrIrqSJhCLTB8Lb3p9pjP551daWqri4mI5HA4FAgGVlJSwFx8AWMQuc9MjMkfO4XDoscce02WXXaa8vDylp6eHbtu/f7+ysrJ02223qbS0VF9//bX+8Ic/aO/everRo4eSkpIkSX369NGuXbsi9G3Zy5gxY/T000/rnXfe0dVXXx36mUSCz+fTQw89JLfbrQceeEB79uyJ2GMDscb0fdBMr89kJs91iJUhPAAQK2JlJEyrFzuZPXu2VqxYoR07dmjx4sWh4ykpKXr66ad1+umnKykpST/+8Y/17rvvKhgMNltlMd5XXRw2bJgeeeQRvffee7r++uvldDoj9tj79u3TE088ofHjx+uOO+6wXdcwALSX6UEpVobwAEAsKSgokMfj0aJFi+TxeGw5SqLFILd582ZVVVVJkrp166YpU6aouro6dPv27dv10ksvhb4OBoNKSkpS7969VVdXF1plcc+ePRHdc83OBg4cqF/96lf68MMPdfPNNys1NTVij+33+/XnP/9Z5557rmbPnh167QAgVpkelFjMBgDMZPeRMC0GuW3btumuu+5SfX296uvrVV5ertzc3NDtTqdTv/3tb+X1ehUMBrVw4UJdeOGFcjgcGjdunJYtWyZJKisri/jCH3Z32mmn6Re/+IU8Ho/mzJkT0V+ixsZGvfzyy7rgggt0xRVX6JVXXgkbBwx0FhZ0QLSZHpRiZQgPAMAsrVrsZO7cuXrttdeUmJioKVOmaNasWZoxY4Zmz56tM888U2+88Ybmzp2rQCCgsWPH6t5771XXrl1VW1urOXPmyOfzqV+/fnr44YdbtSBHrC120loHDx7UokWL9Mc//lG1tbURf/xevXrpmmuu0bRp0zRkyJCIPz7Agg6wSllZmYqKioz+3TN5MRYAgHlaykStCnLRFq9BrkkgEFBpaameeOIJbdq0qVOeIz8/Xz/84Q9DvadAR9llBSi0n+lBxPT6AABoi4isWonocjgc+t73vqd33nlHzzzzjMaMGRPx51i+fLlmzJihs88+W7/97W87pQcQ8cX0eUroGJNXhWxi97kOVmJINADYD0HOYF26dNEll1yiv/3tb/rLX/6ic845J+LPsWvXLj3yyCMaP368rrvuOr399tuhBWqAtjB9nhLaz/RVIdExdgjpAIDmCHI2kJCQoAkTJujFF1/U0qVLNWXKlIg/x+HDh/X3v/9d06dP17nnnqu5c+eyJx3ahAUdYhe9rbGLkA4A9kWQs5nc3Fw999xzKi8v15VXXqnExMSIP4fX69Wvf/1rnXXWWZo5c6bee+89GTiVEgaKhT1Z0By9rbGLkA4A9kWQs6kRI0Zo7ty5Wr58uaZPn67k5OSIP0cgENCrr76q733ve5o4caKeeuop7d27N+LPg9jCPKXYQ29r7CKkA4B9sWpljNi1a5eeeeYZzZs3T/v27eu053E6nZo6daqmT5+usWPHKiEhodOeC4BZWBUyNtlh6wYAiEdsPxBn9u3bp9LSUs2bN08bNmzo1OcaOXKkpk+friuvvFI9evTo1OcCAHQeQjoAmIftB+JMjx49NH36dL355ptaunSprr76ajmdzk55rg0bNugXv/iFxo4dqzlz5mj9+vWd8jwAgM7FkGgAsB+CXIxKSEhQbm6uHnnkEa1atUr33HOPTj/99E55rv3792v+/PmaMmWKpk6dqsWLF+vgwYOd8lwAAAAACHJxoVevXpoxY4beffddLV68WFOnTlVSUlKnPNfq1at1yy23aNy4cbr77ru1adOmTnkeAAAAIJ4R5OJIQkKCzj33XP3v//6vVq5cqTlz5nTaHMQvv/xSzzzzjCZOnKirr75aS5cuVX19fac8FwAAABBvCHJxKi0tTbNmzdL777+vefPm6cILL1SXLp3z6/D+++/rhhtu0FlnnaUHH3yQ/YlgGZ/Pp4qKCjY7BgAAtkeQi3OJiYmaPHmynn/+eX3wwQf6+c9/rvT09E55rs8//1yPP/648vLyNH36dL366qvatm0bm40jKkpLS+V2u1VYWCi3262ysjKrSwLQSjTCAEBzbD+AZgKBgN58803Nnz9fy5cv7/TnO+WUUzRixAhlZWVp5MiRysrK0ogRI5SSktLpz4344PP55Ha75ff7Q8ecTqc8Hg+r9AGGKy0tVXFxMfvcAYg7LWWizlnxArbmcDh02WWX6bLLLtOWLVu0cOFCvfDCC9q7d2+nPN/XX38tj8cjj8cTdnzQoEHKysoK+/jmN7/ZaUNAEbu8Xq8cDkdYkHM4HPJ6vQQ5wGA+n0/FxcXy+/2h929RUZHy8/N57wKIewQ5nNSQIUP0P//zP7rtttv0t7/9TfPnz9fKlSuj8txbt27V1q1b9dprr4WOde/eXZmZmaGeu6aPnj17RqUm2FNGRoYCgUDYsUAgoIyMDIsqQmdgU+vYQyMMAJwYQQ6t4pu6u8AAACAASURBVHQ6ddVVV+mqq65SVVWVFixYoJdeekn79u2Lah0HDhzQmjVrtGbNmrDjAwYMCAt2I0eO1ODBgzttmwXYi8vlUklJiYqKisKGZ3EhGDsYfhebaIQBgBNjjhzabf/+/SorK9O8efO0bt06q8tpJjk5WcOHD28W8Lh4j1+m99iYXp+pmAMZ28rKypo1whDSAcQD5sih06SkpGjatGm69tprtXbtWs2fP19lZWVhF1NWOnTokCorK1VZWRl2PC0tLSzYZWVlaejQoeratatFlSJaXC6XsRf29Ci1H8PvYltBQYHy8/Np5ACAY9Ajh4j66quv9PLLL2vevHmqqamxupxWS0pK0rBhw8J677Kzs9WrVy+rS0McsEuPkqk9hnb5+QEA0Bb0yCGqevbsqR//+Me6/vrr9eGHH2r+/Pn629/+1myOg2kaGhpUVVWlqqqqsONZWVk655xzlJeXp7PPPlu9e/e2qELEMjv0KJncY8gcSABAPKJHDp3u888/1+LFi7VgwQL961//srqcDsnKylJeXp7y8vI0fvx4gh0iwvQeJdPra2JqjyEAAO1Bjxwsd9ppp+nGG2/UzJkz5fF49N5774V6v7Zu3Wp1eW3SVPezzz4r6UiwGz9+fCjYcfGI9jC9R8kOPYaS2XMgAQCINIIcoqZLly4aP368xo8fHzq2b98+bdy4MRSQmj7q6uosrLT1mup97rnnJEkjRowIBbu8vDwuKtFqJi/oYJcl4OmRA46P9wYQmwhysFSPHj00btw4jRs3LnQsGAyqtrZWGzZsCAt3W7Zs0eHDhy2stmUbN27Uxo0b9fzzz0uSMjMzQ711eXl5Ou2006wtEEYztUfJ9B5Dyew5fIh9Jgcl3htA7GKOHGzj4MGDqqmpUVVVVSjkbdiwQXv37rW6tFYbPnx4WLDr06eP1SUBrWbqxapd5vAhNpkclHhvAPbGHLlOYOrFTKzr1q2bRo8erdGjR4eOBYNB7d69OyzcVVVVqaamRg0NDRZWe3yffPKJPvnkE/35z3+WJA0bNiw0DJNgB9OZ2mNolzl8iD0+n0/FxcXy+/2h37+ioiLl5+cb8bvHewOIbQS5NjK55S0eJSQkKD09Xenp6Zo0aVLoeH19vTZt2tRs7t2uXbusK/Y4ampqVFNTo3nz5kmShg4dGhbs0tLSLK4QMJ9d5vAh9pgelHhvALGNINcGpre84T+6du2qkSNHauTIkWHHfT5fs3BXXV2tQ4cOWVRpuE2bNmnTpk2aP3++JOn0009XXl6ezjnnHI0fP17p6ekWVwiYxw5z+BCbTA9KvDeA2EaQawPTW97QMpfLpfPOO0/nnXde6FhDQ4O2bt2qDRs2aMOGDfroo4+0evXqsNfZKps3b9bmzZu1YMECSdKQIUNCwe68885j8RTg30xe9ROxyw5BifcGELtY7KQNmDQcPw4dOqSKigq9//77WrFihT766CMjgt3REhISlJ2drcmTJ2vy5MkaNWqUunTpYnVZAE6A+dWxi9cWQGdoKRMR5NqorKysWcsbc+RiX1OwW7FihVasWKFVq1YZF+zS09P1rW99S5MnT1Z+fr569OhhdUkA/o351QCAtiLIdQJa3nDo0CGtXbs2FOxWrlxpVLBzOBwaP368Jk+erAsuuECDBw+2uiQgbjGaAwDQHmw/0AlMXYIb0ZOcnCy32y23263//u//Vn19vdauXRsairlq1SodPHjQsvoCgYCWL1+u5cuX65577tGQIUNCQzDPPvtsde3a1bLagHjD/GoAQGcgyAER0LVrV5111lk666yzwoLd0T12Vga7LVu2aMuWLXr66afVo0cPTZgwQZMnT9a3vvUttjgAOpnpKxsCAOyJIAd0gqOD3ezZs1VfX6+PP/44FOw8Ho9lwW7fvn1atmyZli1bJkkaM2ZMqLdu9OjRLJgCRJgdVjYErMSUFaB9mCMHWCAQCDQLdgcOHLC6LPXp0ye0YMqECROUmppqdUlAzDD5YtXk2iTz60P7sRAQcGIsdgLYQFOw++CDD0LBbv/+/ZbW5HA45Ha7QwumnH766ZbWA6BzmH4hbXp9aD8WAgJOjiAH2FAgEFBlZWUo2H344YeWB7tBgwaFQt3ZZ5+t5ORkS+sB0HGmX0ibXh86pqKiQoWFhaqrqwsdS01N1aJFi5SdnW1hZYAZWLUSsCGHw6GxY8dq7NixuvHGG9XQ0KDKykq99957evvtt7Vq1So1NjZGtaatW7fqT3/6k/70pz8pJSVF+fn5oQVT+vbtG9VaADsycXig1+vVse25wWDQmBU1WfEztrEQENAxBDnABpKSkpSTk6OcnBzdfPPN2rt3r959912Vl5fr7bff1pdffhnVevbv36/XX39dr7/+uiTpzDPPDC2Ykp2dzYIpwDFMHR6YkpKiQ4cOhR07dOiQUlJSLKooHBf6sY2FgICOadXQykcffVRvvPGGEhIS9N3vflfXX3992O1vvfWW5s6dq2AwqIEDB+rBBx9Uz549VVpaGvaGnDRpkm655ZYWi2JoJdB6jY2NWr16tcrLy1VeXq4NGzZYWo/L5dL555+vCRMmKC8vT/3797e0HsBqJg8PrKio0FVXXdWstpdfftmYoW1lZWXNLvRNCMGIHBN7qwETdHhopcfj0QcffKClS5eqoaFBl156qSZOnKghQ4ZIOrKU+T333KOXX35Z6enpevTRRzV37lzdddddWrdunebMmaNvf/vbkf/OLMQfHJgkMTExtNXBnDlzVFtbq7ffflvl5eVavnx52AVaNPh8Pr300kt66aWXJB2ZW5eXl6dzzjlH48ePJ9gh7pg8PPBEPVsm9XgVFBQoPz+f824Mc7lcvK5AO7QY5Nxut+bNm6ekpCTt2rVLjY2N6t69e+j2QCCgu+++W+np6ZKkzMxMvfrqq5KkyspKbd26VU8++aQyMzP1P//zP+rZs2cnfSvRYerwGKDJgAEDNH36dE2fPl1+v18rVqzQW2+9pfLycnm93qjXs3XrVm3dulV/+ctfJB0Jduecc47y8vKUl5enfv36Rb0mIJpMHh5ol6FtXOgDQHOtXrXyscce07PPPquLL75YDz74oBISEprdx+/369prr9X06dN1xRVX6KabbtKPf/xjjR07Vg8//LC2b9+ukpKSFp/L1KGVJg+PAVoSDAZVU1MTGoLp8XiivmDK8TQFu6YeO4IdYpHpwwMZaQIA5ono9gMHDx7UzJkzdemll+qaa64Ju62urk433XSTBg4cqAceeKDZ//3qq6904YUXyuPxdLhoq9hhLgHQWl999ZX+8Y9/qLy8XO+8846++OILq0uSJA0ePDisx44VMRErCEsAgLbo8By5zZs3q76+XllZWerWrZumTJmi6urqsPvs3r1bP/nJTzR+/Hjdcccdko4Eu5dfflnXXXedpCO9AYmJiRH4lqyTkpLSbL6R3+83ZnUvoC169uypyy+/XJdffrkaGxtVUVGh8vJyvfXWW1q/fr1ldX366af69NNPtXDhQknSkCFDwubYEexgVwwPjF2E9Nhl+mtren3oXC2uEb5t2zbdddddqq+vV319vcrLy5Wbmxu6vbGxUTNnztQll1yiO++8MzTksnv37nrmmWe0du1aSdKCBQt04YUXdtK3ER379+9vtglycnKy5Rs1Ax2VmJio3Nxc3X777XrzzTe1atUqPfTQQ7rooovC5sRaYcuWLVq4cKFuuukm5ebmKj8/X7fffrvKysq0a9cuS2sDgNLSUrndbhUWFsrtdqusrMzqkhAhpr+2pteHzteqoZVz587Va6+9psTERE2ZMkWzZs3SjBkzNHv2bO3cuVOzZs1SZmZm6P6jRo3S/fffr1WrVun++++X3+/XoEGD9NBDDyk1NbXFokwdWskcOcSjQ4cO6YMPPggtmPKvf/3L6pLCnH766WE9dk0LLwGIHab2OnBdELtMf21Nrw+R0eGhlZI0a9YszZo1K+zY008/LenIRsAbN2487v8bN26cSktL21qzseyyuhcQScnJyZo4caImTpyo++67T5s3bw6FOo/Ho4aGBkvr27x5szZv3qwFCxZICg92eXl5SktLs7Q+AB1j8mrRJm8tgY4x/bU1vT5ER6uCHP6joKBAZ5xxhtasWaOcnBwNGzbM6pKAqElISNDQoUM1dOhQzZw5U19//bXeffddlZeX6+2335bP57O6xGbBbujQoaGFUwh2gL34fD4VFxfL7/eHLliLioqUn59vxMWqyVtLoGNMf21Nrw/RQZBrI5NbBpuYOgQFseeUU07R1KlTNXXqVB0+fFjr1q3T+++/rxUrVujDDz9UXV2d1SVq06ZN2rRpk+bPny9JGjZsmM466yxlZmZq+PDhyszMVFpa2nG3VAFgLdN7HRipE7tMf21Nrw/R0abtB6KFOXLtZ4egifjQ0NCg9evXa8WKFXr//ff14Ycfat++fVaXdVw9e/bU8OHDwz4IeID17HDelcxvQDW9PpOZ/rMzvT50TET3kYsWU4NcRUWFCgsLw3oZUlNTtWjRIiP2kbPLCQ/x6ehg995778nj8Rgb7JocL+ANHz5c6enpBDwgSkzfTN10NPAC9hWRxU5whOnjkU0fgoL4lpSUpDFjxmjMmDGaOXOmGhoatG7dulCPnYnB7quvvtLKlSu1cuXKsOMEPCB6CgoKlJ+fT69DO5g+xxBAxxDk2sD08cimB03gaElJScrOzlZ2drZuuOEGNTQ0qLKyUitWrAjNsTN1j0YCHhBdbKbePjTwArGNINdGJrcMmh40gZNJSkpSTk6OcnJydOONN9oq2DUh4AEwCQ28sBpz+DoXc+RiUE1NDdsjIOYEAoFmwe7AgQNWl9UhPXv21LBhw0IraBLwAHsy+WKVOYawCvMzO47FTuIMbxrEi6Zg17TdgcfjsX2wa3LqqacqKysr7CMzM1Pdu3e3ujQAx7DDedfkoInYxAJ8kUGQiyO8aRDPAoGAPv7441CPXSwFO+nIZuyDBg1qFvC+8Y1vqEuXLlaXB8QlzrvA8Zm+0rtdsGplHPF6vSc8zgkFsc7hcCg3N1e5ubm6+eabFQgEtHbt2rBgd/DgQavLbLdgMKhPP/1Un376qZYtWxY63r17d40YMSIs3I0YMUKnnnqqhdUC8YHFRCKDHsPYw/zM6CDIxZCUlJSwk4kk+f1+paSkWFTR8fEHG9HgcDg0btw4jRs3TrNmzVIgEND69etVXV2tTz75JPSxbds2q0vtkAMHDmj16tVavXp12PH+/fs3670bMmSIHA6HRZUCsYeL1Y4zfWgq1yztwwJ80UGQiyH79+9XcnKyDh06FDqWnJxs1Ep/pv/BRuxyOByh7Q6Otm/fPtXU1ISFu1gIeNu3b9f27dtVXl4eOta1a1cNGzasWcDr06cPi6sA7cDFaseYvs8d1ywdY/JK77GCOXLtYGrrjOlj9U2vDzjavn37tGnTppjrwTsel8vVLNwNGzZM3bp1s7o0wBZMvS4wncnzqLhmgQmYIxdhJrfONLUM3nrrrUpMTFRjY6NRLYPM4YOd9OjR47g9ePv371dNTY2qq6tD/9o94Pl8Pv3zn//UP//5z9CxLl26aMiQIWHhbuTIkRowYAC9d8Ax2LC8fUwemsr8R9gBQa4NTB8CIB1ZEKHpIsu0iy27zOEDTiYlJeWkAa+p564p6J2oAcN0hw8f1qZNm7Rp0ya9+uqroeOpqakaNWqUcnJyQj+H/v37G/f3BoD5TB6aanLIBJoQ5NrA9NaZo4NmE5OCph3m8EkMkUH7tDbgNX3YNeDV1dWFVgJtkpaWFvrec3JyNGbMGPXs2dPCKgHYhanzqEwOmUATglwbmN46Y3rQzMjIaNZqn5CQYMzPTzJ76GwTgqa9nCzgNc3BaxqiWV1dbcshmrt379abb76pN998M3RsyJAhoWCXnZ2tM844Q8nJyRZWCcQv088bpg5NNTVkHs301xadiyDXBqa3zpgeNE3/+dlh6CxBs2NMqi0lJUVjxozRmDFjwo5//fXXqq6u1oYNG1RVVaWNGzeqqqpK+/bts6jS9tmyZYu2bNmiV155RdKRRqUzzjgjrOduyJAhbGYOdDLOGx1jasiU7PHaonOxamU7mPwHp6ysrFlQMu1NberPz+TVsyR7rKBl8knF5NqanOi9EQwGtW3bNlVVVYUCXlVVlT799FMdPnzYwoo7JjU1VWPGjAnruevbt6/VZQExg/NG7LLDa4uOY9XKTmBy64wdhgGY+vMzvUfT9KGzJvdomlxbk5NdzDQNQc7IyNCUKVNC/+fgwYOqqakJC3gbNmzQ3r17rfo22qSurq7Zapl9+/YNW0hlzJgxSk1NbdXjmdpIBFiF80bsMv21RXQQ5GKQqUGpiakXWy6XS4WFhXr++edDxwoLC42pkaDZfibXJrX/YqZbt24aPXq0Ro8eHToWDAa1e/fu0LDMpoBXU1PT7PfHRDt37tRrr72m1157TdKREDt06NCwIZlZWVnq2rVr2P+jVR9ojvNG7DL9tbULU69JW4sgh6gy+WLL5/Np0aJFYccWLVqkW2+91Yg3t+lzDE0+qZhcmxTZi5mEhASlp6crPT1dkyZNCh0PBALavHlzaFhm08eOHTsi9W10imAwqJqaGtXU1OjFF1+UdGS125EjRyonJ0c5OTkaNGiQioqKdOjQIVr1gaNw3ohdpr+2TUwOSiZfk7YWc+QQNaaP5zZ9jlwTk/8omjxH0+TarHxv7N27N7SgStPHxo0bdfDgwU593s6WkpKixYsXG/XeBazCeSN2mfzamhyUTL8mbcIcORjD9CEUdmkZNHnorMlzNE2uzcqW1V69eikvL095eXmhY4cPH9a//vWvsHC3cuVKff75551eT6Ts379fM2bMUG5urrKzszV27FideeaZ6tatm9WlAVHHeSN2mframj7/0fRr0tYiyCFqMjIywt4wkuT3+40JSnYZpmA6U08qktm1mXQx06VLFw0ePFiDBw/WpZdeGmq5PPY+vXv3Njrcbd++Xdu3b9err74qSUpMTNSIESNCQzJzcnI0dOhQJSYmRqUek1vOTa4Nsc/kv81oH9ODkl0a71tCkENUHTuS17SRvSZdTCP+mHoxc7wTckpKip5//nmlp6eroqJCFRUVWrNmjdauXav9+/dbWO2JNTY2av369Vq/fr0WLFggSerRo4dGjx6tsWPHduoWCCYPMTK5NgD2ZHpQipXGe+bIIWrsMgcNQLi2zCVobGzU5s2btWbNmlDA27BhgxoaGqJddrv169cvrNdu9OjRSklJaffjmTwXw+TaANibHeY/mj4agTlyMIbprTN2YfofHcSetrRcJiYmavjw4Ro+fLiuueYaSUeGUK9fvz7Ua7dmzRpt3bo1yt9F6+3YsUM7duzQsmXLJB0ZRpqZmRnqscvJyVFmZmarh2SaPMTI5NoAE3DObT9GOXU+glwMMvWPTqx0Y1uJIVCwSkdOyE6nU7m5ucrNzQ0d27t3r9auXRvquVuzZo18Pl9nlN5hhw8fDi368n//93+SpO7du2v06NGhXrvs7Gz1799fCQkJzf6/yY1YJtcGWI1zbseZOmVAio3Xl6GVMcYOv5SmBk3TMQQKsSwYDKq2tjZsSObatWtttQ1Cenp6qMcuJydHY8aMUWpqqiSzhxiZXBtgFc65sc0ury9DK+OI6Uu9NjG5dcZkDIFCLEtISNDAgQM1cOBATZ06VZLU0NCgmpqaUI9dRUWFNm7cqMbGRourPb5du3bpjTfe0BtvvCHpyPc0bNiwUI/dn//8ZzmdTg0ePNio9yzDn4DmOOfGtlh5fQlyMSRWfilxfAyBQrxJSkpSVlaWsrKy9P3vf1+SdPDgQVVWVob13H322WcWV3p8wWBQn3zyiT755BO98MILko78TR4wYIAGDhyojIyM0L9Nn6enp0dtO4Sj0cAGhLPLOdf0UU6m1meX17clBLkYEiu/lFYz9Y8OcwwBqVu3bnK73WH72vl8vrBgt2bNGn355ZcWVnligUBAW7duPeFiL01B7+hwd2zQ69KlS3SLBuKQHc65pk+nMbk+O7y+rcEcuRjDXIeOMfmPThNTgyZgimAwqE8//TRslcz169ervr7e6tI6LF6DHn/3YBVTf/dMn+Nlen1NTH19mzBHLs4w16H9mGMIxIaEhAQNGTJEQ4YM0ZVXXilJOnTokDZs2KCKigqtXr1aa9as0aeffmpxpW0Xjz16dmhgQ+wy9Zxr+nQa0+trYurr21oEuRhk919Kq9jljw6AtktOTg6tJnn99ddLCt8Coenjiy++sLjSjom1oGeXBjYg2kyfTmN6fbGCIAf8G390gPjSq1cvTZo0SZMmTZJ0ZEim1+vVmjVrtHr1alVUVGjdunVhjTt211LQ69atm4YPH67MzEyNGDEi9JGWlnbcPfI6Gw1swPGZPsfL9PpiBXPkgKMwxxDA0QKBgDZu3BgajllRUaGamhqry4q6U089NRTqMjMzlZWVpeHDh6tnz56d+rx2mWcDWMX0OV6m12e6ljIRQQ44Bn90AJzM119/3WxI5p49e6wuyxL9+vVTVlZWWA/e0KFD5XQ6I/YcNLABiFcsdgIAQASdcsopys/PV35+vqQjQzK3b98eGo65Zs0affzxxzp48KDFlXa+HTt2aMeOHXr77bdDx7p06aLBgweHeu4yMzOVmZmpwYMHt2uPPBbxAtBZ7N5436og9+ijj+qNN95QQkKCvvvd74YmijepqqrSnXfeqf3792vcuHG69957lZSUpO3bt+u2226Tz+fT4MGD9bvf/U4pKSmd8o0AkcDqaADaKiEhQQMGDNCAAQM0depUSVJDQ4Oqq6tDwzGbVsk8dOiQxdV2vsOHD2vz5s3avHmzli1bFjrudDo1dOjQsCGaI0aMUL9+/Vqcf2f6Il52vxgE4lEsXPO1OLTS4/Ho97//vebPn6+GhgZdeumleuaZZzRkyJDQfb797W/rV7/6lbKzs3XHHXdo1KhRuvbaa/Wzn/1M3/nOd3TZZZfpiSee0IEDB3Tbbbe1WBRDK2EF5mIA6EyHDx/Wnj175PV6tW3bNnm93rDPa2tr4yLoHatnz56hXrujQ16vXr2sLq1VYuFiEIg3drnm6/DQSrfbrXnz5ikpKUm7du1SY2OjunfvHrq9trZWfr9f2dnZkqQrr7xSjz32mK6++mqtXLlSTzzxROj4D37wg1YFOcQ2U1suWR0NQGfq0qWL0tPTlZ6ernHjxjW7PV6D3ldffSWPxyOPxxN2vG/fvqFw1/Tv8OHD1a1bN4sqbY7tEQB7ipVrvlYNrXQ4HHrsscf07LPP6uKLL1Z6enrott27d6tPnz6hr/v06aNdu3Zp79696tGjh5KSksKOI76Z3HKZkZHRbE7LwYMH2X6gjUwN6oDpCHrhdu7cqZ07d+rdd98NHUtMTNTw4cM1evRojR49WmPGjFFWVlZEF1dpi1i5GATiTaxsOdXqxU5mz56tGTNmaObMmVq8eLGuueYaSUdOLEePbQ8Gg0pISAj9ezQr9qCBOezQcsnvbMeYHNQBuyPoSY2NjaqqqlJVVZVeeOEFSVJSUpIyMzPDwt2IESOUnJzc6fXEysUgEG9iZZ+7FoPc5s2bVV9fr6ysLHXr1k1TpkxRdXV16Pa+ffuGLbv8+eefKy0tTb1791ZdXZ0aGxuVmJioPXv2KC0trXO+iyijx6F9TG+59Hq9cjqdYSdlp9NpTH2ms0NQB2JZR4LeZ599ps8++0yNjY0WVN4xDQ0NWr9+vdavX6+//OUvko6cW0aMGBEW7jIzM9W1a9eIPnesXAwC8SgWVsRtMcht27ZNjz32WOiPY3l5ua666qrQ7QMGDFBycrI++ugj5ebmasmSJZowYYIcDofGjRunZcuWaerUqSorK9OECRM67zuJEnoc2s/0lkvT6zOd6UEdiHctBT2/369Nmzapurpa1dXVqqqqUnV1tWpray2otmMCgYAqKytVWVmphQsXSpK6du2qrKwsnXnmmRozZoxGjx6tzMxMORyODj1XLFwMWo0GcljF9BVxW9KqDcHnzp2r1157TYmJiZoyZYpmzZqlGTNmaPbs2TrzzDO1ceNG3XXXXdq3b5/OOOMMPfjgg+ratatqa2s1Z84c+Xw+9evXTw8//LB69uzZYlGmrlpplxVuTGb6xq6m12cy3h9AbPr6669VXV2tjRs3hv7duHGj9u7da3VpHZacnKyRI0eGhbvhw4eH5vfHCpODEg3kwIm1lIlaFeSizdQgV1FRocLCQtXV1YWOpaamatGiRaFVO9Eyk08okvn1mYwgDMSHYDCoPXv2hELd0UHP7huhO51OjRw5MmxY5tChQ20b7kwOSjQAAifX4e0H8B8MvYsM07uxTa/PZAwxAuJDQkKC0tLSlJaWFjZt4vDhw/rss8/Ceu6qq6u1efNmNTQ0WFhx6/n9fq1evVqrV68OHXM6nRo1alRYuDv99NOVmJhoYaUtM33uMkPygY4hyLUBk5qBlhGEgfjVpUsXDRo0SIMGDdJFF10UOn7o0CFt2bKlWQ+e1+u1sNrW8/v9WrVqlVatWhU61r17d40aNSpsWOY3v/nNiC+o0hGmByUayIGOIci1ET0OsBpDPwHYTXJysrKyspSVlRV2fN++faHFVY4OeZ9//rlFlbbegQMHmm1k3tRT2b9/fw0YMCD0cfTXvXv3jtrWNqYHJRrIgY4hyLUDPQ6wSmlpqYqKipSYmKjGxkY9/PDDxsx1AIC26tGjh3Jzc5Wbmxt2/PPPPw/ruauqqtKGDRuM3wcvGAxq165d2rVrl9asWXPc+zidTvXr1y8s6DWFvabA161bt4jUY4egRAM50H4sdgLYhM/n09ixY8PmmSQlJWn16tWc+FqJ3kzAvgKBgKqrq1VZWam1a9fq448/VlVVlerr660uLeJ69+7drCev6fP+CsG69gAAIABJREFU/fsrLS2tTfPz+NvXfvzsYCUWOwFixLp165otFtDQ0KB169Zp4sSJFlVlHyav3AagZQ6HQ6NGjdKoUaP0/e9/X5JUX1+v6upqffzxx1q7dq0qKytVVVXVbDih3XzxxRf64osvVFlZedzbk5KSQr16R/fkHf2Rmpoauj8jidqH8wZMR5ADEPNMX7kNQPt07dpVZ555ps4880xNmzZN0pGFVaqrq0O9dh9//LE2btxom1UzW6OhoUFer/eki8WkpqY268lLT09X3759lZaWpvT0dPXq1Stq8/XshvMG7IAgB9hE//7923Qc/2H6ym0AIic5OTm0TUATv9+vjRs3hnrt1q5dq+rqajU2NlpYaeeqq6sLLSBzIl27dlV6enoo2PXt2zf0ddPn6enp6tmzZ9wFPs4bsAOCHGAT+/fvV3Jycthk/+TkZO3fv9/CquzB9JXbAHQup9Op7OxsZWdnh44dPHhQVVVVoV67jz/+WJ988klMh7tj1dfXt9izJx051zSFupN9nHLKKTET+DhvwA4IcoBNZGRkNDtBJiQkcFJpBTus3AYgurp166axY8dq7NixoWMHDx7U+vXrwxZUqamp0eHDhy2s1HqHDh3SZ599ps8+++yk93M6nWFDN48dytnUy9ejRw/jAx/nDdgBq1YCNlJWVtbspMLE69Zj9TEAbXXgwAGtX78+bEGVmpoaGXj5ZBvdunVrNpSzf//+Gjx4sAYPHqxvfOMbxmysznkDVmopExHkAJvhpAIA1tq/f7/WrVsXGpJZVVWl2tpaff3111aXFhMSExOVkZERCnZDhgwJfT5w4MA2bb0A2BnbDwAxhmWkAcBaKSkpOvvss3X22WeHHa+rq9P27dtVW1sb9rFjxw7V1tZq+/btMbV6ZmdpbGzU1q1btXXrVr3zzjtht3Xt2lXf+MY3mgW8IUOGqG/fvurSpYtFVQPRR5ADAEPQ2wrYW2pqqjIzM5WZmXnc2xsbG7Vnz55QqDv636bPfT5flKu2l/r6em3atEmbNm1qdpvT6WzWi9f072mnnWb8vDygrQhyACKqpqZGa9asUU5OjoYNG2Z1ObbBxrNA7EtMTFTfvn3Vt29f5ebmHvc+Bw8eDAt5x/bwbd++PWxJfPyH3+9XVVWVqqqqmt3Wo0eP4/biDR48WL169bKg2sgwvQHQ9PrsjiAXg3jTwCp33nmnnn/++dDX1113ne6//37rCjqGqe8NNp4F0KRbt246/fTTdfrppx/39mAwqL179zYbvnl0+Nu1axeLsRxj3759qqysVGVlZbPbTj311GYBr+nrHj16WFBt65jeAGh6fbGAxU5iDG8aWKWmpkaTJk1qdvwf//iHET1zJr83KioqVFhYqLq6utCx1NRULVq0KGzfKwBojfr6eu3cuTOsN2/37t3atWuXdu7cGfr82H3S0FxaWpoGDx6sAQMGqE+fPkpLS9Npp52mPn36hD569+4d9QVYfD6f3G53WO+s0+mUx+MxogHQ9PrsgsVO4git+rDS8uXLT3jc6iBn+nuDjWcBRFLTgiDf+MY3Tnifpp69o4Pdzp07tWvXLu3evTvs83heoGX37t3avXv3Se/TpUsXuVyuZgGvT58+Ou2008LCX6RCn9frlcPhCAtKDodDXq/XiPOa6fXFCoJcDOFNAyv16dOnTcejyfT3BhvPAoi2hIQE9e7dW71799bIkSNPeL/Dhw+HAt+xIe/or3fv3q3GxsYofgfmOHz4sPbs2aM9e/Ycd37e0Y4Ofcfr3Ts6/PXq1euEoc/0BkDT64sVBLkYwpsGVjrnnHOUmJgYdiJPTEzUOeecY2FVR9jhvVFQUKD8/Hwj5/A1MXWOIYDO0xQ8XC6XzjjjjBPe7/Dhw/L5fKGAd6KPPXv2xG3gk9oX+o4OeEcHvp/+9Kd66qmn5HA41NDQYFQDIA2U0cEcuRhTVlbW7E1jyjwgxL6ysjLdeuutSkhIUDAY1MMPP2zM7x/vjY4xeY4hAPtobGwMBb6mnjyv16tPP/009HHgwAGry7Qdh8OhAQMGqH///urfv78GDBgQ9tG/f3+lpKREvS4aADumpUxEkItBvGlgJZN//0yuzWRMWgcQLcFgULt27QqFui1btoQ+37p1qw4dOmR1ibZ16qmnhoW8Yz9PT09XUlJ8DdYz/bqAxU7iUNMQCMAK/P7FHtPnGAKIHQkJCaG99vLy8sJua2xs1I4dO7Rly5awgLdlyxZ5vd64XpSlNb788kt9+eWX2rBhw3FvT0xMVHp6erOevKMDX8+ePWNmY/VYGGlCkAMQF0pLS1VUVBSax2fSsE/T2WGOIYDYl5iYqIEDB2rgwIGaMGFC2G2BQOD/t3fvwVFW9x/HPyEJBBAqhqBWY2JjUIdgEgrRqYWoCCpJSqgVolSM1NE6jHKJpngZ8ceIUosUK9qh1cJ0pMaUGqi0gLfqFLEGMUjjMBpuSgyXEKyEkIRcnt8fzq7ZZEOWW57vs7xfM86wz2bwk2cTzn73nPM9qqys7FDg7dy5U5WVlZyrF4KWlhb/IfQbN24M+jV9+/bttMi74IILdN5556lXr17dnPz4We9mHSoKOQBhr6amRjNmzAj4tHb69Ome+wfbLWxaB2BddHS0/0Dv9hoaGvTll18GFHi+Im/v3r0upPWuuro6ff755/r88887/ZpBgwZ1KPQuvvhiJSUlKT4+3sTyzXBZaeL+nQSA06y8vLzDkpvm5maVl5crMzPTpVTe4oWumgAQTExMjAYPHqzBgwd3eO7IkSNB9+Pt2LFDNTU1LqT1Pt/Ze2VlZR2e69mzpxITE5WUlNThv7PPPrvbMobLShMKOQBASNj/CCDc9OnTR0OGDAl6tMKhQ4e0c+dOVVVV+Y8MqK6u1oEDBwIe02UzdEePHu10Rm/gwIFBC7yLLrrolM/ihctKEwo5dDvrHYIQflJSUvz/UPtER0crJSXFxVQd8bsBAHb0799fqampSk1NPebX1dXVBRR5+/fv9xd7bR/v379f9fX13ZTeew4cOKADBw7oww8/DLgeHR3tn8W75JJL9IMf/MBf5A0YMOCE/3/hsNKEQg7dygsdgngzHX5iY2O1aNEiFRQUqEePHmptbTX3yZsXfjcAAB317dtXffv2VWJiYpdfe6yir/11ir5vNTU1qaKiQhUVFR2ei42N9Rd4SUlJ/iIvISEhpFk8r6804Rw5dBsvnEXFm+nwZrVI98LvBgCge7Ut+o5V/O3du1dHjx51O64p0dHRSkhI6FDkXXLJJSc1i9fdOEcOZljvEBQurWjROaufvFn/3QAAdL9QZ/paW1tVU1Ojr776Sl999ZWqqqo6/Lm6urp7QhvR1NSkbdu2adu2bVq3bl3Ac+ecc05AgZecnKyrr75avXv3dintiaOQQ7ex3iGIN9Nwi/XfDQCAXT169FBcXJzi4uKUlpYW9GsaGhq0d+9ef4HXtsirqqpSZWXlGbOU8+DBgzp48GDAWXn9+vXTbbfdpocfftjE8Qih8k5SeJ71DkG8mYZbfL8bs2bNUkREhBzHMfW7AQDwtpiYGCUmJnY6u+c4jv73v/91KPDaFn779u1Ta2tr9wbvJrW1tVqyZIl27dqlF198UT169HA7Ukgo5NCtLHcIsl5oIrxt3LhRjY2NAY/Zn3l8rO6BBADrIiIiNGDAAA0YMKDTjs5NTU3at29fhwLPV/RVVVXpm2++6ebkp9a6deu0bt063XTTTW5HCQnNToB2eDOI7lZRUaFrrrmmw/V3331XycnJ3R/Ig2hUBADuq62tDSj0KisrtWPHDm3fvl07d+70RFOWUaNG6ZVXXnE7hiSanQDHzWpDDISvsrKyTq9TyHWNRkUAYEO/fv106aWX6tJLL+3wXEtLi3bv3q3t27dr+/bt2rZtm7/I279/vwtpg/NSYxgKOQBwWXp6+nFdRyAaFQGAfZGRkf59eqNHjw547tChQwEF3vbt27Vjxw7t3LkzYNtBd4iOju7W/9/JoJADAJclJycrPz9fy5Yt81/Lz89nNi5ENCoCAG/r37+/0tPTO3yA2dLSosrKyqBF3r59+05LlnHjxp2Wv/d0oJADPIY9fOFp3rx5ys/PV1lZmdLT000WcVZ/9mhUBADhKTIyUgkJCUpISNB1110X8NyhQ4f8SzPbFng7duw44Vm8Cy+8ULfeeuupiN4tKOQAD6GhQ3hLTk42WcBJ3/7sFRQUKDIyUi0tLVq4cKGpn73c3FwNGTKEQhgAzhD9+/dXWlpah7PzWlpa9NVXXwUUeL7/jjWLl5iYqD//+c8aOHDg6Y5+ytC1EvCImpoaZWRkBOwDiomJUWlpKW8KcVrV1NRo2LBham5u9l+LiorSxx9/bOZnz/qHHNbzAcCZoLa2Vjt27PAXeAcOHFCfPn2Unp6urKwsc4eB07USCBM0dIBbysvLA4o4SWpublZ5ebkyMzNdSvUd610rrecDgDNFv379lJqaqtTUVLejnBIhFXKLFy/WmjVrJEmZmZkqLCz0P7d161bNnj3b//jgwYP63ve+p9WrV6ukpCRgn8I111yjmTNnnsr8wBmDhg5AcNY/5LCeDwDgTV0Wchs2bND69etVUlKiiIgI3XXXXXrzzTc1ZswYSdLll1+uVatWSZLq6+t1yy236PHHH5f07ae4s2fPVnZ29un7DoAzBA0d4Jbvf//7x3W9u1n/kMN6PgCAN/Xo6gvi4uI0e/Zs9ezZU9HR0UpKSlJVVVXQr12yZIlGjBih4cOHS5L++9//qqSkRDk5OXrggQf0zTffnNr0wBkmNzdXpaWlKioqUmlpKXts0C3q6urUq1evgGu9evVSXV2dS4kC+T7kiImJUb9+/RQTE2PqQw7r+QAA3tTljFzbzl+7du3SmjVr9Morr3T4utraWhUXF+v111/3X4uLi9PUqVM1bNgwLVy4UHPnztUzzzxziqIDZ6bY2FjeAKJbxcfHKyIiIuBaRESEqRkl610rc3NzNXLkSLpWAgBOmZCbnVRUVOiee+5RYWGhEhMTOzz/97//Xddff33A4PT888/7/3zXXXf5l2MCALzDC8t6vdAV0vKHMByNAADe0+XSSknatGmT8vPzVVBQoAkTJgT9mrfeeivgJPTa2lotW7bM/9hxHEVGRp5cWgDm1dTUaPPmzaqpqXE7Ck4hy8t623aFrK2tVUNDgwoKCvgZDFFJSYkyMjKUl5enjIwMrVy50u1IAIAQdFnI7dmzR9OmTdOCBQuUlZUV9Gscx9Gnn36q9PR0/7U+ffroxRdf1CeffCJJevnll5mRA8IcbwjDW2xsrNLS0szN2Pi6Qrbl6wppicUPOSiCAcC7ulxa+dJLL6mxsVHz58/3X8vLy9M777yj+++/X0OHDtXBgwcVHR0dsBk+MjJSixYt0uOPP66GhgYlJibq6aefPj3fBQDXcVYW3OKFrpBWl35yNAIAeFeE4ziO2yHa6+oUcwD2bN68WXl5eaqtrfVf69evn4qKipSWluZiMpwJVq5c2WEPn4VCSfr2Q46MjIyAYikmJkalpaWuF0uWs7XFHj4AZ6KuaqKQ9sgBQFe8MCuC8GV5D5/lpZ9eOBqBJdsAEFzIXSsB4Fi80NkQ4c1qV8j4+PiAGS9JamhoMPMhh+WjEViyDQCdo5ADcMpYfkMIuKn9LgZruxqsFsHs4QOAzlHIATilrL4hlNhnA3fs3r1bvXv3Dtg/2rt3b4qRELBkGwA6xx45AGcE9tnALRQjJ84Le/gAwC0UcgDCHmdlwU0UIyfHciMbAHATSysBhD322cBt7B89OZaXbAOAWyjkAIQ9lrbBAsvFiPX9o+QDgI5YWgkg7LG0DehcSUmJRowYoVtuuUUjRowwt3/U+v5W6/kAhK8Ix1oPZHV9ijkAnAjrn5pbz2cd9+/41dTUaNiwYWpubvZfi4qK0scff2ziHtbU1CgjIyNgWXRMTIxKS0vJByDsdVUTMSMH4IwRGxurtLQ0k2+w+FT/5HD/Tkx5eXlAESdJzc3NKi8vdylRIN/+1rZ8+1stsJ4PQHijkAMAl9FV8+Rw/8KX9f2t1vP51NTUaPPmzfxOAGGGQg4AXMan+ieH+3fiUlJSgt67lJQUlxIFsr6/1Xo+idlqIJzRtRIAXOaVT/Wt4v6duNjYWC1atEgFBQXq0aOHWltbzRUi1o9usJyv7Wy1bx9fQUGBRo4caSongBNDIQcALvN9ql9QUKDo6Gg1NTWZezMt2W0m4pX7Z5XlQsTH8tENkt18nKEJhDcKOQAwwPqb6ZKSEj3wwAMBhVJubq7bsfxyc3M1ZMgQlZWVKT09XcnJyW5H8hSrhYiP1Q8RfKzm88pstdX7B1jHHjkAMMJqV00vNBMpKSnRjTfeqMcee0w33ngj+4DCiPU9XpbzsYcPCG+cIwcAOKbNmzcrLy9PtbW1/mv9+vVTUVGR0tLSXEz2Lc7yCl/WX1vr+Xysznh55f4BbuEcOQDASbG+PMsrXStpAX/8rL+21vP5WJ3t98L94/cWllHIAQCOyfryLOuFpvTd8rFbbrmF5WPHwfprGx8fHzCbJEkNDQ1m8lln/fVl2Seso5ADAHQpNzdXpaWlKioqUmlpqalGJ9YLzZqaGs2cOVMNDQ06cuSIGhoaNGPGDD7hD4H111aS2u9QMbhjxeyskuXX1wt7gwG6VgIAQmK5s6Hlrp/l5eVBZx3Ky8uVmZnpUirvsPza7t69W7179w7YP9q7d29T7f290HHW4uvrlaMbrO5/RPegkAMAhAXLhSZOjtXX1vrSQK8cCG7x9bX+2kr2i3ScfiytBADgNEpJSVFUVODnplFRUUpJSXEpEU4Vy0sDJW80E5GkiooKFRcXq6Kiwu0oftZfW5Z+QmJGDgCA0yo2NlbPPvusZs2apcjISLW0tGjhwoVm3hDi5FhdGih9O6tUX18fcK2+vt7UrNIjjzyiZcuW+R/n5+dr3rx57gVqIzc3V0OGDFFZWZnS09OVnJzsdiQ/ryz9xOlFIQcAwGlm+c2+xD6bk2VxaaBPRETEMR+7qaKiIqCIk6Rly5YpPz/fRNFkeemiF5Z+SvzbcrqxtBIAgG5g9SwvjkYIX7t371ZMTEzAtZiYGDNLK8vKyo7reneyvnQxNjZWeXl5Adfy8vJM/fvC8Q2nH4UcAABnKI5GCG/WZ23S09OP63p3sr6/sKamRkVFRQHXioqKzPzuWi+EwwWFHAAAZ6hjHY0A77PesCM5OVn5+fkB16wsq7ReBFsvNHfv3h30jEUr+XysnrEYKvbIAQAAhCnr+zPnzZun/Px8cw1FfEVwQUFBwB45K/fPeqHZt29fNTY2BlxrbGxU3759XUrUkeU9kKGikAMA4AzlOxqhubnZf42jEcKP5WYs0rczc1YKuLYsd620XmjW1dUpJiYmoKtmTEyM6urqXEz1Ha+csdgVCjkAAM5QXjkagc53cIP1GRvLs62dzQxamTE81tJPS/exK+yRAwDgDJabm6uNGzfqr3/9qzZu3GjqjapEV024wyvNOqx2w7W+P9MLSz9DwYwcAABnOKtL73xdNdvuBZoxY4bnlj/Bezhw++RZnjG0vvQzVMzIAQAAk+iqCbdYbybiFVZnDK0v/QwVhRwAAADQhvWlgTg54fL6srQSAACYRFdNuMny0kCcvHB4fZmRAwAAJvm6avbq1Ut9+vRRr1699Oyzz5p7w+X1Q4XROatLA3FqeP31ZUYOAACYZf1Tc1+L+h49eqi1tdVci3qEN47mOLMxIwcAAEyz+qm5r6tmQ0ODjhw5ooaGBs2YMYOZOXQL39EceXl5HM1xhqKQAwAAOAF01YRbvHLOHU6vkJZWLl68WGvWrJEkZWZmqrCwsMPzf/vb39S/f39J0sSJEzV58mRVVVXpwQcfVE1NjS6++GItWLDAcwftAQAAAJZwzh2kEGbkNmzYoPXr16ukpEQrV67Up59+qjfffDPga8rLy7Vw4UKtWrVKq1at0uTJkyVJ//d//6fbbrtNa9euVUpKil544YXT810AAAB0M19Xzbboqnn8aBZz/DjnDlIIhVxcXJxmz56tnj17Kjo6WklJSaqqqgr4mvLyci1ZskQ5OTmaO3euGhsb1dTUpI0bN+qGG26QJP30pz/V2rVrT893AQAA0M280lWzoqJCxcXFqqiocDtKB+zzOjHhcg4aTk6XSyuTk5P9f961a5fWrFmjV155xX+trq5Ol19+uR588EElJCRo9uzZeuGFFzR58mSdddZZ/k+q4uLitG/fvtPwLQAAALjDelfNRx55RMuWLfM/zs/P17x589wL1EbbfV6+JYIFBQUaOXKkuftokfWfPcl+V03r+boScrOTiooKTZ06VYWFhUpMTPRf79u3r/74xz8qKSlJUVFRmjp1qt577z05jqOIiIiAv6P9YwAAAK+z2lWzoqIioIiTpGXLlpmZmfPt82rLt88LobH6syfZn221ni8UIRVymzZtUn5+vgoKCjRhwoSA56qqqrRixQr/Y8dxFBUVpXPOOUe1tbVqaWmRJFVXV2vQoEGnMDoAAAA6U1ZWdlzXu5sX9nmxf+/EWO+qaT1fqLos5Pbs2aNp06ZpwYIFysrK6vB8TEyMfvOb32j37t1yHEfLly/XmDFjFB0dreHDh+uf//ynJGnlypUaNWrUqf8OAAAA0EF6evpxXe9u1vd5hcOMjVusz7ZazxeqLvfIvfTSS2psbNT8+fP91/Ly8vTOO+/o/vvv19ChQzV37lzde++9ampq0rBhw3TnnXdKkubMmaPZs2fr97//vc4//3wtXLjw9H0nAAAA8EtOTlZ+fn6HPXJt+x+4LTc3V0OGDFFZWZnS09PNZPPK/j2re7ysz7ZazxeqCMdxHLdDtFdZWanRo0fr7bff1oUXXuh2HAAAAM+qqKgwVyj5lJSU6IEHHlB0dLSampr0zDPPKDc31+1Y2rx5s/Ly8lRbW+u/1q9fPxUVFSktLc3FZN+xeu98Vq5cqYKCAvKdhK5qIgo5AAAAdLuamhplZGQEHGodExOj0tJS12eXLGeT7OfzsTpj6GM9X1c1UchdKwEAAIBTxfI+Jev79yzfu7Ysd9WU7OfrSpd75AAAAIBTzfo+JcvntFm/dz7WZ7y8jhk5AAAAdDvrs16WeeHe0fXz9GOPHAAAAFxjddbGejMRye6988oePuu6qolYWgkAAADXxMbGmntz75XjByzeO+m7PXxtCznfHj6Leb2KpZUAAABAG15pJmKVl/bwbd68WTU1NW5HOSEUcgAAAEAbFCInhz183YNCDgAAAGiDQuTk5ebmqrS0VEVFRSotLTW1v7Dt0tna2lo1NDSooKDAXEHcFfbIAQAAAO1YPn6APXwnJ1z28FHIAQAAAEFQiIQnryyd7QpLKwEAAAAPCZdCxC1eWDobCmbkAAAAAA/xFSIFBQUB59xZK0SsnnMn2V46GyoKOQAAAMBjrBciXjhQ3erS2VCxtBIAAADwoNjYWKWlpZkrRsKlK6R1FHIAAAAAThmvHKhu9Ry+UFHIAQAAADhlvNCMxfo5fKGgkAMAAABwyljvChkuSz9pdgIAAADglLLcjCVczuGjkAMAAABwylntCumFpZ+hYGklAAAAgDOG9aWfoWJGDgAAAMAZxfLSz1BRyAEAAAA441hd+hkqllYCAAAAgMdQyAEAAACAx1DIAQAAAIDHUMgBAAAAgMdQyAEAAACAx1DIAQAAAIDHUMgBAAAAgMdQyAEAAACAx1DIAQAAAIDHUMgBAAAAgMdQyAEAAACAx1DIAQAAAIDHUMgBAAAAgMdQyAEAAACAx0S5HSCYlpYWSdLevXtdTgIAAAAA3c9XC/lqo/ZMFnLV1dWSpMmTJ7ucBAAAAADcU11drYSEhA7XIxzHcVzIc0wNDQ0qLy9XXFycIiMj3Y4DAAAAAN2qpaVF1dXVSklJUUxMTIfnTRZyAAAAAIDO0ewEAAAAADyGQg4AAAAAPIZCDgAAAAA8hkIOAAAAADyGQg4AAAAAPIZCDgAAAAA8hkIOAAAAADyGQg4AAAAAPIZCLkSvv/66xo0bp7Fjx2r58uUBz7388su6/fbbXUr2rWD5HnroIY0dO1bjx4/X+PHj9eabb5rJVlZWpokTJyorK0uzZs3S0aNHXckWLN97773nv2fjx4/XVVddpXvuucdMPklav369fvKTnyg7O1uFhYWm7p8kvfbaaxo3bpxycnL0xBNPqLm52bV8hw8fVnZ2tiorKyVJGzZsUE5OjsaOHavf/va3ruXyaZ9PkpqamnTHHXfoww8/dDHZt9rne/XVV5Wdna2cnBw99NBDrv7sBcv3l7/8RVlZWRo3bpx+/etfy3EcU/l8LIwb7bNZGTM6y2dp3Gifz9q40T6fZGvcCJbP0rixePFiZWVlKSsrS08//bQkO2NHsGySnXEjWD5L40awfNbGjePioEt79+51rr32Wufrr7926urqnJycHKeiosJxHMepqKhwRo4c6fz85z83ly87O9vZt2+fa7k6y7Z161bn6quvdrZu3eo4juPMnDnTWb58uZl8vtfWcRxn//79zujRo52dO3eayjdq1Chn27ZtjuM4zn333ecUFxebybd161Zn5MiR/p+9OXPmOH/6059cybd582YnOzvbGTJkiLN7926nvr7eyczMdL788kunqanJmTp1qvPuu++6ki1YPsdxnO3btzuTJk1yhg4d6vznP/9xLVuwfDt27HDGjBnj1NbWOq2trU5hYaGzdOlSM/m+/PJLZ8yYMU5dXZ3T3NzsTJo0yfn3v/9tJp+PhXEjWDYLY4ZP+3y1tbVmxo1g+dpye9xwnOD5rIwbwfJt377dzLjx/vvvO5MmTXIaGxudo0ePOlOmTHFef/11E2NHsGxvvPGGmXEjWL4lS5aYGTeC5Vu6dKmpceN4MSMXgg0bNuiqq67S2WefrT59+uiGG27Q2rWK8M/CAAAHFUlEQVRrdfToUT322GO6//77TearqqrSww8/rJycHP3ud79Ta2uriWxvvfWW0tLSdNlll0mSHn30UY0ZM6bbs3WWb+3atf7nn376aeXl5SkxMdFUvpaWFh0+fFgtLS1qbGxUr169zOTzvb6DBg2SJF177bV66623XMlXXFysOXPm+LNs2bJFCQkJio+PV1RUlHJycgJeb7fzSdKKFSt01113KTU11bVcPu3z9ezZU3PmzNFZZ52liIgIDR48WFVVVWbyxcfH6x//+If69OmjQ4cO6fDhw+rfv7+ZfJLMjBvts9XX15sYMzrL9/7775sZN4Lla8vtcUMKns/KuBEs32effWZm3IiLi9Ps2bPVs2dPRUdHKykpSbt27TIxdgTLVlVVZWbcCJbv6NGjZsaNYPkiIiJMjRvHi0IuBPv371dcXJz/8aBBg7Rv3z4988wzuvnmmxUfH+9iuuD5Pv74Y1111VV68sknVVxcrI8++kgrVqwwke25555Tnz59NHPmTI0fP17PPfeca780nb22krRr1y6VlpZqypQprmSTOs/3+OOP6/bbb9fIkSP19ddf68YbbzSTr7S0VJ988on27NmjlpYWrV27VgcOHHAl37x58zR8+HD/42O93m5on0+SCgsLdf3117uUKFD7fBdccIGuvvpqSdLBgwe1fPlyjR492q14Qe9fdHS0iouLdf311ysuLs7/xt8NwfJZGTfaZztw4ICJMaOzfF988YWZcSNYPh8L44YUPJ+VcUPqmO+yyy4zM24kJycrLS1N0rev55o1axQREWFi7AiWLTMz08y4ESxfdna2mXGjs/tnadw4XhRyIWhtbVVERIT/seM4+uCDD7Rnzx7dfPPNLib7VrB8F154oZ5//nkNGjRIvXv31u2336733nvPRLaoqCitX79es2bN0muvvab6+nr94Q9/6PZsneXzPX711Vd12223qWfPnq5kkzrPt2DBAq1evVrr169XamqqnnrqKTP5EhMTVVBQoHvvvVeTJ0/WpZdequjoaFfytXes1xuh27dvn+644w7dfPPNuvLKK92O08HEiRP14YcfauDAgVq8eLHbcfzef/99M+NGe/Hx8SbGjM60tLSYGTeOxcK4EUx1dbWZcSOYiy++2Ny4UVFRoalTp6qwsFDx8fGmxo622dyc+e1MsHyWxo1g+ayOG12hkAvBeeedp+rqav/j6upqVVZWqqKiQuPHj9ejjz6q8vJyzZgxw0y+QYMGad26df5rvgLKQrZ7771Xqampio+PV2RkpG666SZt2bKl27N1ls+3tOPtt9/WuHHjXMnl01m+wYMH66KLLlKPHj00ceJElZaWmsp3xRVXaOXKlSoqKtK5557r+uyDz7Feb4Rm+/btysvL04QJEzRt2jS34wTYs2ePNm3aJEmKiopSVlaWPvvsM5dTfWf16tVmxo32PvvsMxNjRmcGDhxoZtw4FgvjRjAfffSRmXEjmMbGRlPjxqZNm5Sfn6+CggJNmDDB1NjRPps1wfJZGjfa57M+bnSFQi4EP/rRj/TBBx/o4MGDqq+v1xtvvKHi4mKtWbNGq1at0hNPPKGUlBQtWrTITL4rr7xSTz75pL755hs1NTXp1VdfdWU/QbBsGRkZ+vTTT7Vnzx5J0r/+9S8NGTKk27N1lm/UqFE6ePCgGhoaXC9AOrt/W7Zs8S87efvttzV06FAz+UaOHKn8/HwdPnxYR48e1csvv2zmjU1qaqp27typL774Qi0tLVq9erVGjRrldizPOHz4sH7xi19o+vTpmjp1qttxOqitrdWDDz6oQ4cOyXEcrVu3Tj/84Q/djuX31FNPmRk32nMcx8SY0Zkf//jHZsaNzlgZN4IZPHiwmXEjmCNHjpgZN/bs2aNp06ZpwYIFysrKkmRn7AiWzZJg+SyNG8HyWR83umLn4zbDzj33XM2cOVNTpkxRU1OTfvazn+mKK65wO5ZfsHwjRozQ3XffrVtvvVXNzc0aO3assrOzTWTLyMjQ3Llz9ctf/lKNjY26/PLL9atf/arbs3WW74orrtCWLVt03nnnuZKpq3wZGRmaPn26pkyZosjISCUkJGju3Llm8qWmpmratGmaNGmSmpub/S2HLejVq5fmz5+v++67T42NjcrMzHR1n4jXrFixQgcOHNDSpUu1dOlSSdJ1112n6dOnu5zsW4MHD9bdd9+tvLw8RUZGavjw4brzzjvdjuUJl112mYkxozPnn3++mXGjM5WVlSbGjWCSkpLMjBvBDBgwwMy48dJLL6mxsVHz58/3X8vLyzMxdnSW7dZbb+32LMEEyzdu3Dgz40Zn98/L40aE43jpsAQAAAAAAEsrAQAAAMBjKOQAAAAAwGMo5AAAAADAYyjkAAAAAMBjKOQAAAAAwGMo5AAAAADAYyjkAAAAAMBj/h+AYfruSqj2jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nnb vs avsize exponent\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose = 0\n",
    "bln_mean, sz_mean = np.zeros(20), np.zeros(20)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*BLN*_exp*'))[:20]\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*0.' + str(nnb) + '*_exp*'))[:10]\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "\n",
    "\n",
    "plt.savefig(Ffig + 'exps_short_k.svg')\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 4.0   p = 0.013671875\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_exp*'))[:10]\n",
    "    bln_data = dome1(curr_l, choose)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_exp*'))[:10]\n",
    "    sz_data = dome1(curr_l, choose)\n",
    "\n",
    "    alpha = 0.05\n",
    "    \n",
    "    \n",
    "    adfn.stats_2samp(bln_data, sz_data, alpha, 1, 'rel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nnb vs avdev exponent\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose = 1\n",
    "bln_mean, sz_mean = np.zeros(20), np.zeros(20)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*BLN*_exp*'))[:20]\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*0.' + str(nnb) + '*_exp*'))[:10]\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "\n",
    "\n",
    "plt.savefig(Ffig + 'expd_short_k.svg')\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dominicburrows/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1604: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 0.0   p = 0.001953125\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 1.0   p = 0.00390625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 2.0   p = 0.005859375\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n",
      "At least one sample is non-Gaussian - performing non-parametric test\n",
      "Samples are significantly different\n",
      "w = 3.0   p = 0.009765625\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_exp*'))[:10]\n",
    "    bln_data = dome1(curr_l, choose)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_exp*'))[:10]\n",
    "    sz_data = dome1(curr_l, choose)\n",
    "\n",
    "    alpha = 0.05\n",
    "    \n",
    "    \n",
    "    adfn.stats_2samp(bln_data, sz_data, alpha, 1, 'rel')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PTZ-WILDTYPE-02_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-04_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-05_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-06_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-07_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-08_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-09_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-10_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-11_2photon_sess-01-6dpf_BLN_run-01_0.23-nnb_llr.npy',\n",
       " 'PTZ-WILDTYPE-12_2photon_sess-01-6dpf_BLN_run-09_0.23-nnb_llr.npy']"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curr_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nnb vs loglikelihood ratio\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose = 0\n",
    "bln_mean, sz_mean = np.zeros(20), np.zeros(20)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*BLN*_llr*'))[:20]\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 20, c = 'k')\n",
    "\n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*0.' + str(nnb) + '*_llr*'))[:10]\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 10)\n",
    "#ax.set_yticks(20)\n",
    "\n",
    "plt.savefig(Ffig + 'LLR_short_k.svg')\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "choose = 1\n",
    "bln_mean, sz_mean = np.zeros(20), np.zeros(20)\n",
    "for num in num_list:\n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*BLN*_llr*'))[:20]\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 10, c = 'k')\n",
    "    \n",
    "    curr_l = sorted(glob.glob('*E-*' + num + '*PTZ20*_llr*'))[:20]\n",
    "    data = dome1(curr_l, choose)\n",
    "    \n",
    "    plt.scatter(nnb_list, data, s = 10, c = 'firebrick')\n",
    "    \n",
    "count=0\n",
    "for nnb in nnb_list:\n",
    "    curr_l = sorted(glob.glob('*BLN*' + nnb + '*_llr*'))[:10]\n",
    "    data = dome1(curr_l, choose)\n",
    "    bln_mean[count] = np.mean(data)\n",
    "        \n",
    "    curr_l = sorted(glob.glob('*PTZ20*' + nnb + '*_llr*'))[:10]\n",
    "    data = dome1(curr_l, choose)\n",
    "    sz_mean[count] = np.mean(data)\n",
    "    count+=1\n",
    "    \n",
    "plt.plot(nnb_list, bln_mean,c = 'k', linewidth = 5)\n",
    "plt.plot(nnb_list, sz_mean, c = 'firebrick', linewidth = 5)\n",
    "\n",
    "\n",
    "\n",
    "#ax.set_xscale('log')\n",
    "#ax.set_xlabel('clusters', size = 20)\n",
    "#ax.set_ylabel(dtype, size = 20)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}