InteractionMatrix_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "224a8ca4-2206-47d3-b8a5-1dd4b5714aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f869335-c4eb-4c3e-bd67-c46e76c45923",
   "metadata": {},
   "source": [
    "### Plot the loss plots (chi2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9737ac9-2af6-4894-b7ae-7ad5980db2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.load(\"mouse586468_sweep0_param25_J_2000iters.npy\")\n",
    "b = np.load(\"mouse586468_sweep1_param25_J_2000iters.npy\")\n",
    "#c = np.load(\"beforesweep2_matrix_2000iters_screen.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2f939d6-fa57-478c-bc66-a3f1badeb3ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6587fa1e-bb47-4c31-a1a7-d69f14d1647a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Chi2s v/s Epochs, Mouse 599975')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atAlms7nSNX4q9rNv377o0KEDAEtrTFBQENq2bXvT6wQs13FydXXFqFGj6rQ/ACq1LlLzxSBEzd6LL74IFxcXPPLII0hNTa00PyEh4ZaHT1c4deoURowYgbCwMKxatarKETEAKo0+U6vViI6OhhDihiNW3N3dMWTIEKxYsQLLli2DWq3GmDFjbJb57bffMHjwYJsQlpubW2nIc4cOHaBQKKyjcuzls88+s9mPhQsXwmg0YtiwYQCA2NhYqNVq/Oc//7FpZVu0aBFycnKsp/S6du2K8PBwfPjhh5XCQG1b5673189doVCgY8eOAGCXz+D222/HP//5T8yfPx/+/v7VLjd8+HDs3bvXJpwXFBTgs88+Q1hYGKKjowH8GQS3bt1qXc5kMlW6+Gdtju24ceOgVCoxZ86cSp+dEOKGIyLT09MrTTty5Ah++eUXDB48GApF9V8nRUVFeO211xAQEIBJkybVuJ3ly5dj3759eOaZZ25pnenp6diwYQPGjh1rHV12vdzc3ErHXAiBN998EwAwZMiQGuuk5oNXlqZmLyIiAt988w0mTJiAqKgomytL79y5E9999x2mTp16y9vJy8vDkCFDkJWVhRdeeKHSNWEiIiLQu3dvAMDgwYPh7++P2267DX5+fjh16hTmz5+PESNGwM3N7YbbmjBhAu6//358/PHHGDJkCNzd3a3zioqKsGnTpkqtPH/88QeefPJJ3HPPPWjdujWMRiP+97//QalUYvz48TfcptFoxFdffVXlvLFjx8LFxcX6urS0FIMGDcK9996L+Ph4fPzxx+jbt6/1f+Y+Pj545ZVXMGfOHAwdOhSjRo2yLte9e3fcf//9ACxBZeHChRg5ciQ6d+6MBx98EAEBATh9+jROnDiBdevW3bDu6z3yyCPIzMzEHXfcgaCgIFy8eBHz5s1D586drf1zAFhbrup6HzmFQoFXX331hsu9/PLL+PbbbzFs2DDMnDkTnp6e+O9//4vExET88MMP1gDQrl079OrVC6+88goyMzPh6emJZcuWVQo9tTm2ERERePPNN/HKK6/gwoULGDNmDNzc3JCYmIiVK1fisccew/PPP19tzRMmTIBOp0OfPn3g6+uLkydP4rPPPoOzszPeffddm2XvvfdeBAYGIjo6Grm5uVi8eDHOnz+P1atX2/x+b926FW+88QYGDx4MLy8v7N69G0uWLMHQoUMrXdurtuussHz5chiNxmpPix08eBCTJk3CpEmT0KpVKxQVFWHlypXYsWMHHnvssSove0DNlGzdtIka2JkzZ8Sjjz4qwsLChFqtFm5ubuK2224T8+bNE8XFxdblUM2VpUNDQ0VcXJz19V9H9FSMDqrucf17P/30U9G/f3/h5eUlNBqNiIiIEC+88ILIycmp1b7k5uYKnU4nAIivvvrKZt6qVauEJEkiNTXVZvr58+fFQw89JCIiIoRWqxWenp7i9ttvFxs2bLjh9moaPn/9Z1DxmWzZskU89thjwsPDQ7i6uorJkyeLa9euVVrv/PnzRdu2bYWTk5Pw8/MT06dPrzRMXgghtm/fLu68807h5uYmXFxcRMeOHW0uZVDdlaVnzZolrv8z9/3334vBgwcLX19foVarRUhIiHj88cdFcnKyzfu8vb1Fr169avW53OiK1lWNGhNCiISEBHH33XcLd3d3odVqRY8ePcSqVasqvT8hIUHExsYKjUYj/Pz8xN///nfrqLTrR43V9tj+8MMPom/fvsLFxUW4uLiItm3bihkzZoj4+Pga9+Ojjz4SPXr0EJ6enkKlUomAgABx//33i7Nnz1Za9v/+7/9E27ZthVarFR4eHmLUqFE2owsrnDt3TgwePFh4e3sLjUYj2rZtK9555x1RUlJy0+us0KtXL+Hr61vtpRLOnz8v7rnnHhEWFia0Wq1wdnYWMTEx4pNPPrG5NAM1f5IQN9G+TESN1hNPPIH9+/dj7969Db7tpUuX4sEHH8S+ffvQrVu3Bt++PZw8eRLt2rXDqlWrbEbdEVHzxFNjRM1M586dMXLkSLnLaLI2bdqE3r17MwQROQi2CBGR3TSHFiEiciwcNUZEREQOiy1CRERE5LDYIkREREQOi0GIiIiIHBZHjdXAbDYjKSkJbm5u1d7zh4iIiBoXIQTy8vIQGBhY4xXKAQahGiUlJVlvwEdERERNy+XLlxEUFFTjMgxCNai4bPvly5eh1+tlroaIiIhqIzc3F8HBwbW6ZRGDUA0qTofp9XoGISIioiamNt1a2FmaiIiIHBaDEBERETksBiEiIiJyWOwjRETkgEwmE8rKyuQug+imOTk5QalU3vJ6GISIiByIEAIpKSnIzs6WuxSiW+bu7g5/f/9butYfg1AVFixYgAULFsBkMsldChGRXVWEIF9fXzg7O/NisdQkCSFQWFiItLQ0AEBAQMBNr4s3Xa1Bbm4uDAYDcnJyOHyeiJo8k8mEM2fOwNfXF15eXnKXQ3TLrl27hrS0NLRu3drmNFldvr/ZWZqIyEFU9AlydnaWuRIi+6j4Xb6V/m4MQkREDoanw6i5sMfvMoMQEREROSwGISIioioMHDgQzzzzjNxlUD1jECIioiZj165dUCqVGDFihNyl2NWFCxcgSRIOHz5c5/dKkoSffvrJ7jU5CgYhGZjNAleyCnElqxBmMwftERHV1qJFi/DUU09h69atSEpKkrscagYYhGRQYjSj7/9tQt//24RiI69VRERUG/n5+Vi+fDmmT5+OESNGYOnSpdZ5q1atgru7u/X6b4cPH4YkSXj55ZetyzzyyCO4//77AViGXU+aNAktWrSAs7MzOnTogG+//bbG7a9evRoGgwFff/01AODy5cu499574e7uDk9PT4wePRoXLlyo9v1ZWVmYPHkyfHx8oNPpEBkZiSVLlgAAwsPDAQBdunSBJEkYOHAgAGDfvn2488474e3tDYPBgAEDBuDgwYPWdYaFhQEAxo4dC0mSrK8B4Oeff0bXrl2h1WrRsmVLzJkzB0ajscZ9dEQMQjLjVZyISE5CCBSWGhv8cTOXsFuxYgXatm2LNm3a4P7778fixYut6+nXrx/y8vJw6NAhAMCWLVvg7e2NzZs3W9+/ZcsWa8AoLi5GTEwMVq9ejePHj+Oxxx7DAw88gL1791a57W+++QaTJk3C119/jcmTJ6OsrAxDhgyBm5sbtm3bhh07dsDV1RVDhw5FaWlplet47bXXcPLkSaxZswanTp3CwoUL4e3tDQDW7W7YsAHJycn48ccfAQB5eXmIi4vD9u3bsXv3bkRGRmL48OHIy8sDYAlKALBkyRIkJydbX2/btg1TpkzB008/jZMnT+LTTz/F0qVL8dZbb9X5c2/ueGVpGVw/2o85iIjkVFRmQvTr6xp8uyffGAJndd2+ghYtWmRt0Rk6dChycnKs4cZgMKBz587YvHkzunXrhs2bN+Nvf/sb5syZg/z8fOTk5ODcuXMYMGAAAKBFixZ4/vnnret+6qmnsG7dOqxYsQI9evSw2e6CBQvwj3/8A7/++qv1/cuXL4fZbMYXX3xhHcK9ZMkSuLu7Y/PmzRg8eHCl+i9duoQuXbqgW7duAGDTeuPj4wMA8PLygr+/v3X6HXfcYbOOzz77DO7u7tiyZQvuuusu6/sqbjVRYc6cOXj55ZcRFxcHAGjZsiX++c9/4sUXX8SsWbNq+5E7BLYIyYwX9iYiurH4+Hjs3bsXkyZNAgCoVCpMmDABixYtsi4zYMAAbN68GUIIbNu2DePGjUNUVBS2b9+OLVu2IDAwEJGRkQAsV9n+5z//iQ4dOsDT0xOurq5Yt24dLl26ZLPd77//Hn/729+wfv16awgCgCNHjuDcuXNwc3ODq6srXF1d4enpieLiYiQkJFS5D9OnT8eyZcvQuXNnvPjii9i5c+cN9zs1NRWPPvooIiMjYTAYoNfrkZ+fX6nOvzpy5AjeeOMNa22urq549NFHkZycjMLCwhtu15GwRUgGbBEiosZC56TEyTeGyLLduli0aBGMRiMCAwOt04QQ0Gg0mD9/PgwGAwYOHIjFixfjyJEjcHJyQtu2bTFw4EBs3rwZWVlZNkHmvffew0cffYQPP/wQHTp0gIuLC5555plKp7W6dOmCgwcPYvHixejWrZu19Sc/Px8xMTHW/kLXq2il+athw4bh4sWL+O2337B+/XoMGjQIM2bMwL///e9q9zsuLg7Xrl3DRx99hNDQUGg0GvTu3bva028V8vPzMWfOHIwbN67SPK1WW+N7HQ2DkAwk/JmE2CBERHKSJKnOp6gamtFoxJdffom5c+dWOuU0ZswYfPvtt5g2bZq1n9AHH3xgDT0DBw7Eu+++i6ysLDz33HPW9+3YsQOjR4+2nmozm804c+YMoqOjbdYfERGBuXPnYuDAgVAqlZg/fz4AoGvXrli+fDl8fX3rdC9KHx8fxMXFIS4uDv369cMLL7yAf//731Cr1QBQ6WbfO3bswMcff4zhw4cDsHTQzsjIsFnGycmp0vu6du2K+Ph4tGrVqta1OSqeGpOBzRXBGYSIiGq0atUqZGVl4eGHH0b79u1tHuPHj7eeHvPw8EDHjh3x9ddfWztF9+/fHwcPHsSZM2dsWoQiIyOxfv167Ny5E6dOncLjjz+O1NTUKrffunVrbNq0CT/88IP1AouTJ0+Gt7c3Ro8ejW3btiExMRGbN2/GzJkzceXKlSrX8/rrr+Pnn3/GuXPncOLECaxatQpRUVEAAF9fX+h0OqxduxapqanIycmx1vm///0Pp06dwp49ezB58mTodDqb9YaFhWHjxo1ISUlBVlaWdVtffvkl5syZgxMnTuDUqVNYtmwZXn311Zs7CM0Yg5AMeJcfIqLaW7RoEWJjY2EwGCrNGz9+PPbv34+jR48CsPQTMplM1iDk6emJ6Oho+Pv7o02bNtb3vfrqq+jatSuGDBmCgQMHwt/fH2PGjKm2hjZt2uCPP/7At99+i+eeew7Ozs7YunUrQkJCrH2RHn74YRQXF1fbQqRWq/HKK6+gY8eO6N+/P5RKJZYtWwbA0ufpP//5Dz799FMEBgZi9OjR1n3PyspC165d8cADD2DmzJnw9fW1We/cuXOxfv16BAcHo0uXLgCAIUOGYNWqVfj999/RvXt39OrVCx988AFCQ0Nr96E7EEmwt261cnNzYTAYkJOTU6emzxsxmsxo9Y81AIDDr98Jd2e13dZNRFSd4uJiJCYmIjw8nP1EqFmo7ne6Lt/fbBGSwfV3y2UMJSIikg+DkAzYRYiIiKhxYBCSgc3weTYJERERyYZBSAY2p8ZkrIOIiMjRMQjJjA1CRERE8mEQkplgmxAREZFsGIRkIvFiQkRERLJjEJKJNQexQYiIiEg2DEIyqegwzRxEREQkHwYhOZQW4pDTwziqeRgoLZC7GiIiugVLly6Fu7t7vax76tSpNd76wx7CwsLw4Ycf1us2GjMGIZnopULopSJeR4iIqA527doFpVKJESNGyLL9+goNFy5cgCRJOHz4sM30jz76CEuXLrX79uxl9uzZ6Ny5c53fV5/hsa4YhORgc4sNBiEiotpatGgRnnrqKWzduhVJSUlyl1PvDAZDowkMzRWDkCwYhIiI6io/Px/Lly/H9OnTMWLEiEotJZs3b4YkSdi4cSO6desGZ2dn9OnTB/Hx8TbL/fzzz+jatSu0Wi1atmyJOXPmwGg0ArD8TZ49ezZCQkKg0WgQGBiImTNnAgAGDhyIixcv4m9/+xskSbK5OC4ArFu3DlFRUXB1dcXQoUORnJxsM/+LL75AVFQUtFot2rZti48//tg6Lzw8HADQpUsXSJKEgQMHArA9NVbRavTXR8WyALB9+3b069cPOp0OwcHBmDlzJgoK/uyCkZaWhpEjR0Kn0yE8PBxff/31DT/3zZs3o0ePHnBxcYG7uztuu+02XLx4EUuXLsWcOXNw5MgRay0Vx+T9999Hhw4d4OLiguDgYDzxxBPIz8+3ru/BBx9ETk6O9X2zZ88GAJSUlOD5559HixYt4OLigp49e2Lz5s03rPGWCKpWTk6OACBycnLsu+KyYiFm6YWYpRdXU1Lsu24iomoUFRWJkydPiqKioj8nms1ClOQ3/MNsrnP9ixYtEt26dRNCCPHrr7+KiIgIYb5uPZs2bRIARM+ePcXmzZvFiRMnRL9+/USfPn2sy2zdulXo9XqxdOlSkZCQIH7//XcRFhYmZs+eLYQQ4rvvvhN6vV789ttv4uLFi2LPnj3is88+E0IIce3aNREUFCTeeOMNkZycLJKTk4UQQixZskQ4OTmJ2NhYsW/fPnHgwAERFRUl7rvvPut2v/rqKxEQECB++OEHcf78efHDDz8IT09PsXTpUiGEEHv37hUAxIYNG0RycrK4du2aEEKIuLg4MXr0aCGEEEaj0brd5ORkcejQIeHl5SVee+01IYQQ586dEy4uLuKDDz4QZ86cETt27BBdunQRU6dOtdYxbNgw0alTJ7Fr1y6xf/9+0adPH6HT6cQHH3xQ5WdeVlYmDAaDeP7558W5c+fEyZMnxdKlS8XFixdFYWGheO6550S7du2sNRUWFgohhPjggw/EH3/8IRITE8XGjRtFmzZtxPTp04UQQpSUlIgPP/xQ6PV66/vy8vKEEEI88sgjok+fPmLr1q3i3Llz4r333hMajUacOXOmyvqq/J0Wdfv+VtVvzKKqXd8iZJaxDiJyeGWFwNuBDb/dvycBapc6vWXRokW4//77AQBDhw5FTk4OtmzZYtMiAgBvvfUWBgwYAAB4+eWXMWLECBQXF0Or1WLOnDl4+eWXERcXBwBo2bIl/vnPf+LFF1/ErFmzcOnSJfj7+yM2NhZOTk4ICQlBjx49AACenp5QKpVwc3ODv7+/zTbLysrwySefICIiAgDw5JNP4o033rDOnzVrFubOnYtx48YBsLQAnTx5Ep9++ini4uLg4+MDAPDy8qq07gpKpdI6r7i4GGPGjEHv3r2trSnvvPMOJk+ejGeeeQYAEBkZif/85z8YMGAAFi5ciEuXLmHNmjXYu3cvunfvbv1Mo6Kiqv3Mc3NzkZOTg7vuusu6b9cv7+rqCpVKVanmihoAS7+qN998E9OmTcPHH38MtVoNg8EASZJs3nfp0iUsWbIEly5dQmCg5Xfy+eefx9q1a7FkyRK8/fbb1dZ5KxiE5HB9HyEzT40REd1IfHw89u7di5UrVwIAVCoVJkyYgEWLFlUKQh07drT+HBAQAMBySigkJARHjhzBjh078NZbb1mXMZlMKC4uRmFhIe655x58+OGHaNmyJYYOHYrhw4dj5MiRUKlq/rp0dna2BoWK7aalpQEACgoKkJCQgIcffhiPPvqodRmj0QiDwXBTn8dDDz2EvLw8rF+/HgqFpZfLkSNHcPToUZvTXUIImM1mJCYm4syZM1CpVIiJibHOb9u2bY19kDw9PTF16lQMGTIEd955J2JjY3HvvfdaP9fqbNiwAe+88w5Onz6N3NxcGI1G62fs7Oxc5XuOHTsGk8mE1q1b20wvKSmBl5fXjT6Sm8YgJAvpup8YhIhIRk7OltYZObZbB4sWLYLRaLS2FACWL3mNRoP58+fbBAonJyfrzxX9eMxmS+t7fn4+5syZY22ZuZ5Wq0VwcDDi4+OxYcMGrF+/Hk888QTee+89bNmyxWa9lXbnL/MkSbL2Aa3oG/P555+jZ8+eNssplcpa7f/13nzzTaxbtw579+6Fm5ubdXp+fj4ef/xxa5+m64WEhODMmTN13hYALFmyBDNnzsTatWuxfPlyvPrqq1i/fj169epV5fIXLlzAXXfdhenTp+Ott96Cp6cntm/fjocffhilpaXVBqH8/HwolUocOHCg0ufi6up6U7XXBoOQHNgiRESNhSTV+RRVQzMajfjyyy8xd+5cDB482GbemDFj8O2332LatGm1WlfXrl0RHx+PVq1aVbuMTqfDyJEjMXLkSMyYMQNt27bFsWPH0LVrV6jVaphMpjrV7+fnh8DAQJw/fx6TJ0+uchm1Wg0AN1z3Dz/8gDfeeANr1qyxaYGq2LeTJ09Wu29t27aF0WjEgQMHrKfG4uPjkZ2dfcN96NKlC7p06YJXXnkFvXv3xjfffINevXpV+XkcOHAAZrMZc+fOtbZWrVixotL+/vV9Xbp0gclkQlpaGvr163fDmuyFQUgW1wUhsI8QEVFNVq1ahaysLDz88MOVTiWNHz8eixYtqnUQev3113HXXXchJCQEd999NxQKBY4cOYLjx4/jzTffxNKlS2EymdCzZ084Ozvjq6++gk6nQ2hoKABLf5etW7di4sSJ0Gg08Pb2rtV258yZg5kzZ8JgMGDo0KEoKSnB/v37kZWVhWeffRa+vr7Q6XRYu3YtgoKCoNVqK+3r8ePHMWXKFLz00kto164dUlJSAFhChaenJ1566SX06tULTz75JB555BG4uLjg5MmTWL9+PebPn482bdpg6NChePzxx7Fw4UKoVCo888wz0Ol01dadmJiIzz77DKNGjUJgYCDi4+Nx9uxZTJkyxfp5JCYm4vDhwwgKCoKbmxtatWqFsrIyzJs3DyNHjsSOHTvwySef2Kw3LCwM+fn52LhxIzp16gRnZ2e0bt0akydPxpQpUzB37lx06dIF6enp2LhxIzp27Fh/1466YXdqBzR//nwRFRUlWrduXT+jxsxm66ixi5cu2nfdRETVqG6ETWN31113ieHDh1c5b8+ePQKAOHLkiHXUWFZWlnX+oUOHBACRmJhonbZ27VrraCm9Xi969OhhHRm2cuVK0bNnT6HX64WLi4vo1auX2LBhg/W9u3btEh07dhQajUZUfIUuWbJEGAwGm7pWrlwp/voV+/XXX4vOnTsLtVotPDw8RP/+/cWPP/5onf/555+L4OBgoVAoxIABA4QQtqPGlixZImC5M5PNo2JZISyjz+68807h6uoqXFxcRMeOHcVbb71lnZ+cnCxGjBghNBqNCAkJEV9++aUIDQ2tdtRYSkqKGDNmjAgICBBqtVqEhoaK119/XZhMJiGEEMXFxWL8+PHC3d1dABBLliwRQgjx/vvvi4CAAKHT6cSQIUPEl19+WenYTJs2TXh5eQkAYtasWUIIIUpLS8Xrr78uwsLChJOTkwgICBBjx44VR48erbI+e4wak4TghWyqk5ubC4PBgJycHOj1evuufLYl6V966ChCQkLtu24ioioUFxcjMTER4eHh0Gq1cpdDdMuq+52uy/c3L6goM8HO0kRERLJhEJKJuaKfkJl9hIiIiOTCICQTYX1mixAREZFcGIRkIspbhNhFi4iISD4MQjKpCELgLTaIqIHxP2DUXNjjd5lBSG78g0REDaTi6seFhYUyV0JkHxW/yzVd9ftGeEFFmYjrLqpIRNQQlEol3N3drffAcnZ2tt6CgqgpEUKgsLAQaWlpcHd3v6lblVRgEJIJ+wgRkRwq7vZdEYaImjJ3d3ebO9jfDAYhmTAIEZEcJElCQEAAfH19UVZWJnc5RDfNycnpllqCKjAIyY2dpYlIBkql0i5fIkRNHTtLy4QtQkRERPJjEJIJgxAREZH8GIRk8ueVpXlqjIiISC4MQjKT2CBEREQkGwYh2fDUGBERkdwYhGTy5y02GISIiIjkwiAkE2sfIQYhIiIi2TAIyeTPW2ywszQREZFcGIRkwz5CREREcmMQkglPjREREcmPQUgm7CxNREQkPwYhmfzZR4hBiIiISC4MQjLjqTEiIiL5MAjJhKfGiIiI5McgJBMOnyciIpIfg5BcpPLh88xBREREsmEQkomo4iciIiJqWAxCMhG8oCIREZHsGIRkw+HzREREcmMQkok1/pjZSYiIiEguDEIysZ4aY4sQERGRbBiEZFNxHSF5qyAiInJkDEIy+bOzNE+NERERyYVBSCZsCCIiIpIfg5BsOHyeiIhIbgxCMhHlV5aW2DZEREQkGwYh2bBFiIiISG4MQjKxxh8GISIiItkwCMmkYtSYmUGIiIhINgxCsikPQmaTzHUQERE5LgYhmZT3lYbRxBYhIiIiuTAIycaShExsESIiIpINg5BcpIpTY2wRIiIikguDkGwsQcho4i02iIiI5MIgJBdrixCDEBERkVwYhGRT0UeIQYiIiEguDEIykcpbhEw8NUZERCQbBiG5VAQhwSBEREQkFwYhuUiWj95k4vB5IiIiuTAIycQsKS3PJqPMlRARETkuBiGZCElleWYQIiIikg2DkEyEojwImctkroSIiMhxOUQQGjt2LDw8PHD33XfLXYqVKD81JthHiIiISDYOEYSefvppfPnll3KXYcPaImRiixAREZFcHCIIDRw4EG5ubnKXYUtR3iLEm64SERHJRvYgtHXrVowcORKBgYGQJAk//fRTpWUWLFiAsLAwaLVa9OzZE3v37m34Qu1MKJwsP7BFiIiISDayB6GCggJ06tQJCxYsqHL+8uXL8eyzz2LWrFk4ePAgOnXqhCFDhiAtLc26TOfOndG+fftKj6SkpIbajbor7yMEtggRERHJRiV3AcOGDcOwYcOqnf/+++/j0UcfxYMPPggA+OSTT7B69WosXrwYL7/8MgDg8OHDdqmlpKQEJSUl1te5ubl2WW9VKvoIgaPGiIiIZCN7i1BNSktLceDAAcTGxlqnKRQKxMbGYteuXXbf3jvvvAODwWB9BAcH230bFSQFryNEREQkt0YdhDIyMmAymeDn52cz3c/PDykpKbVeT2xsLO655x789ttvCAoKqjZEvfLKK8jJybE+Ll++fEv110hZ3iIkGISIiIjkIvupsYawYcOGWi2n0Wig0WjquZpyFafGeB0hIiIi2TTqFiFvb28olUqkpqbaTE9NTYW/v79MVdmHVNEiZGaLEBERkVwadRBSq9WIiYnBxo0brdPMZjM2btyI3r17y1jZrZOUlpYnyVRygyWJiIiovsh+aiw/Px/nzp2zvk5MTMThw4fh6emJkJAQPPvss4iLi0O3bt3Qo0cPfPjhhygoKLCOImuqJLUOAKBgECIiIpKN7EFo//79uP32262vn332WQBAXFwcli5digkTJiA9PR2vv/46UlJS0LlzZ6xdu7ZSB+qmRuFkCUIqc7HMlRARETku2YPQwIEDIYSocZknn3wSTz75ZANV1DAktTMAwMnEIERERCSXRt1HqDlTalwAACrBU2NERERyYRCqwoIFCxAdHY3u3bvX2zaUGsupMTVPjREREcmGQagKM2bMwMmTJ7Fv375624ZKbWkRUrNFiIiISDYMQjJRaS1BSIsSmMw195EiIiKi+sEgJBMnjaWztBZlKDHy6tJERERyYBCSiZPuzxah4jKzzNUQERE5JgYhmSjLh8/rpFIUl7FFiIiISA4MQnIpv6CiFqUoMbJFiIiISA4MQnIpD0I6lLBFiIiISCYMQnJxKu8sLZWhpIx3oCciIpIDg5BcVFrrj6XFhTIWQkRE5LgYhORSfmoMAMqK82UshIiIyHExCFWhIW6xAYUSpXACABhL2CJEREQkBwahKjTELTYAoFTSAACMPDVGREQkCwYhGZUpLEHIVMogREREJAcGIRmVKSwdps0lBTJXQkRE5JgYhGRktLYIFclcCRERkWNiEJKRsbxFSJTx1BgREZEcGIRkZFSWByG2CBEREcmCQUhG5vIgBLYIERERyYJBSEYmleU2GxKDEBERkSwYhGRkdnIBACgYhIiIiGTBICQndUUQ4vB5IiIiOTAIVaFBbrEBQNK4Wp4ZhIiIiGTBIFSFhrrFhrI8CCmNPDVGREQkBwYhGSl1liCkYhAiIiKSBYOQjNQ6PQDAycwgREREJAcGIRk5OVuCkMbECyoSERHJgUFIRpqKICSKYDYLmashIiJyPAxCMtK5WoKQC4pRWGaSuRoiIiLHwyAkI3V5Z2kXqRh5xWUyV0NEROR4GIRkJKktQcgZJcgvNspcDRERkeNhEJJTeRByAVuEiIiI5MAgJKeKW2xIAoUF+TIXQ0RE5HgYhOSkdoEZEgCguCBb3lqIiIgcEIOQnCQJxZIzAKAsP1veWoiIiBzQTQWhhIQEvPrqq5g0aRLS0tIAAGvWrMGJEyfsWpwjKFJa+gmVFmbLWwgREZEDqnMQ2rJlCzp06IA9e/bgxx9/RH6+pW/LkSNHMGvWLLsXKIeGuvs8AJSqLEHIVJhT79siIiIiW3UOQi+//DLefPNNrF+/Hmq12jr9jjvuwO7du+1anFwa6u7zAFDm5AYAEEXZ9b4tIiIislXnIHTs2DGMHTu20nRfX19kZGTYpShHYioPQijJlbcQIiIiB1TnIOTu7o7k5ORK0w8dOoQWLVrYpShHYlZbgpBUkidzJURERI6nzkFo4sSJeOmll5CSkgJJkmA2m7Fjxw48//zzmDJlSn3U2LxpDQAAqZQtQkRERA2tzkHo7bffRtu2bREcHIz8/HxER0ejf//+6NOnD1599dX6qLF5Kw9CqjK2CBERETU0VV3foFar8fnnn+O1117D8ePHkZ+fjy5duiAyMrI+6mv2lM6WO9BrGISIiIgaXJ2DUIWQkBCEhITYsxaH5OTsDgDQmHiLDSIiooZW5yD00EMP1Th/8eLFN12MI1K7uAMAtOYCeQshIiJyQHUOQllZWTavy8rKcPz4cWRnZ+OOO+6wW2GOQuvmCQBwEYUwmsxQKXnXEyIiooZS5yC0cuXKStPMZjOmT5+OiIgIuxTlSHRuHgAAPQqQX2KEu7P6Bu8gIiIie7FL84NCocCzzz6LDz74wB6rcygqnTsAwE0qQl6xUd5iiIiIHIzdzsMkJCTAaOQXeZ2VD5/XowC5RSUyF0NERORY6nxq7Nlnn7V5LYRAcnIyVq9ejbi4OLsV5jCcLX2ElJJAYV42AA9ZyyEiInIkdQ5Chw4dsnmtUCjg4+ODuXPn3nBEGVVBpUERtNChGCW5GQDC5a6IiIjIYdQ5CG3atKk+6nBoBUo36EzFKMtLl7sUIiIih8Kx2lVYsGABoqOj0b179wbZXpHK0k/ImH+tQbZHREREFrVqEerSpQskSarVCg8ePHhLBTUGM2bMwIwZM5CbmwuDwVDv2yt1cgdKgDIGISIiogZVqyA0ZsyYei7DsZm0HkA+YCrIlLsUIiIih1KrIDRr1qz6rsOxOVtGiolCBiEiIqKGxD5CjYDC2QsAoCzOusGSREREZE91HjVmMpnwwQcfYMWKFbh06RJKS0tt5mdmslWjrjR6b8tzWba8hRARETmYOrcIzZkzB++//z4mTJiAnJwcPPvssxg3bhwUCgVmz55dDyU2fzqDj+XZmAMhhMzVEBEROY46B6Gvv/4an3/+OZ577jmoVCpMmjQJX3zxBV5//XXs3r27Pmps9lzdfQEAepGHglKTzNUQERE5jjoHoZSUFHTo0AEA4OrqipycHADAXXfdhdWrV9u3OgehLW8R8pDycS2f9xsjIiJqKHUOQkFBQUhOTgYARERE4PfffwcA7Nu3DxqNxr7VOQoXSx8hb+QwCBERETWgOgehsWPHYuPGjQCAp556Cq+99hoiIyMxZcoU3mvsZrlYTo1ppTLkZHPkGBERUUOp86ixd9991/rzhAkTEBoaip07dyIyMhIjR460a3EOQ+2MIkkHnShCYVYSgAi5KyIiInIIdQ5CxcXF0Gq11te9evVCr1697FqUIypQeUJXdhUl2Slyl0JEROQw6nxqzNfXF3FxcVi/fj3MZnN91OSQijSeAABzXprMlRARETmOOgeh//73vygsLMTo0aPRokULPPPMM9i/f3991OZQyrSWDtPIZxAiIiJqKDfVWfq7775Damoq3n77bZw8eRK9evVC69at8cYbb9RHjQ7B7GIZQq8qypC5EiIiIsdx0/cac3Nzw4MPPojff/8dR48ehYuLC+bMmWPP2hyK0tUycsypmEGIiIioodx0ECouLsaKFSswZswYdO3aFZmZmXjhhRfsWZtD0Xn4W55Lea82IiKihlLnUWPr1q3DN998g59++gkqlQp33303fv/9d/Tv378+6nMYrp6BAAC9ORslRhM0KqXMFRERETV/dQ5CY8eOxV133YUvv/wSw4cPh5OTU33UJasFCxZgwYIFMJka7r5fLl4BACxXl07LLUGwp3ODbZuIiMhRSaKOtzvPy8uDm5tbfdXTqOTm5sJgMCAnJwd6vb5+N3YtAZjXFflCi5NTT6FHuGf9bo+IiKiZqsv3d537CDlKCGpw5aPGXKVipGfyNhtEREQN4aY7S5OdadxQJllOM+ZmXJW5GCIiIsfAINRYSBIKnbwAAIWZvM0GERFRQ2AQakRKtZYgVJrDIERERNQQ6hSEysrKoFKpcPz48fqqx7G5WC6qaMxLlbkQIiIix1CnIOTk5ISQkJAGHVbuSJw8LNcSUhWwRYiIiKgh1PnU2D/+8Q/8/e9/R2Ymr4Bsby7eoQAAT2MGsgtLZa6GiIio+avzBRXnz5+Pc+fOITAwEKGhoXBxcbGZf/DgQbsV52icPIMBAIFSBi5lFsLdWS1zRURERM1bnYPQmDFj6qEMAgDoWwAAAqVrOJVZiI5B7vLWQ0RE1MzVOQjNmjWrPuogADAEAQACpEysu1YgczFERETNX52DUIUDBw7g1KlTAIB27dqhS5cudivKYektnaWdpRKkp6UCiJS3HiIiomauzkEoLS0NEydOxObNm+Hu7g4AyM7Oxu23345ly5bBx8fH3jU6DicditWe0JZmoujaRQB95a6IiIioWavzqLGnnnoKeXl5OHHiBDIzM5GZmYnjx48jNzcXM2fOrI8aHYrJzdIqJLKvyFwJERFR81fnFqG1a9diw4YNiIqKsk6Ljo7GggULMHjwYLsW54iUHqHAteNwLryCUqMZahUv/k1ERFRf6vwtazab4eTkVGm6k5MTzGazXYpyZBrfVgCAEKQiKbtI5mqIiIiatzoHoTvuuANPP/00kpKSrNOuXr2Kv/3tbxg0aJBdi3NEkmc4ACBUSsWlzEKZqyEiImre6hyE5s+fj9zcXISFhSEiIgIREREIDw9Hbm4u5s2bVx81OhbPlgAsQegigxAREVG9qnMfoeDgYBw8eBAbNmzA6dOnAQBRUVGIjY21e3EOqTwIBUtpOJ+aI3MxREREzVudglBZWRl0Oh0OHz6MO++8E3feeWd91eW49IEwS0qoYUJq0iUAHeWuiIiIqNni3ecbG4USRpcAAEBRxgV5ayEiImrmePf5RkjhbrnVhmtRMnKKymSuhoiIqPni3ecbIZVnKHBlN4KldJxPz0eXEA+5SyIiImqWePf5xsjbco+xVoqrOJ9ewCBERERUT+oUhIxGIyRJwkMPPYSgoKD6qol8LFftbi1dwZqMfJmLISIiar7q1EdIpVLhvffeg9ForK96GoUFCxYgOjoa3bt3l6cAX0sQaiVdRWJarjw1EBEROYCburL0li1b6qOWRmPGjBk4efIk9u3bJ08BHmEwKTXQSmUoSj0vTw1EREQOoM59hIYNG4aXX34Zx44dQ0xMTKXO0qNGjbJbcQ5LoYTJMxLK9OPQ5ZyB0WSGSsmbrxIREdlbnYPQE088AQB4//33K82TJInXGLITJ/9oIP04ws2XcTolD+1bGOQuiYiIqNm5qbvPV/dgCLIfqbyfUGvFFRy8lCVzNURERM0Tz7c0Vr5/jhzbf4FBiIiIqD7UOggNHz4cOTl/3gT03XffRXZ2tvX1tWvXEB0dbdfiHJpPWwBASykJRy5myFwMERFR81TrILRu3TqUlJRYX7/99ts2t9kwGo2Ij4+3b3WOzD0UwskZGskIZc4FpOUVy10RERFRs1PrICSEqPE12ZlCAam8VShKuoSDF7PlrYeIiKgZYh+hxiywMwCgg+I8Dl/OlrUUIiKi5qjWQUiSJEiSVGka1aOAzgCADlIiDl9mh2kiIiJ7q/V1hIQQmDp1KjQaDQCguLgY06ZNs15Q8fr+Q2Qn5S1C7RWJOHYlGyazgFLB8ElERGQvtQ5CcXFxNq/vv//+SstMmTLl1iuiP/lEQSjVMJgK4VWajLNpeWjrr5e7KiIiomaj1kFoyZIl9VkHVUWlhuTXDkg6ZDk9dimbQYiIiMiO2Fm6sQvsAoAdpomIiOoDg1BjV95huqPEIERERGRvDEKNXXBPAECM4iwup6ajoMQoc0FERETNB4NQY+fTBnAPgUYqQy/pBI5dzbnxe4iIiKhWGIQaO0kCIgcDAPoqjmNnwjWZCyIiImo+GISagpDeAIAYxRlsOp0mczFERETNB4NQUxDSCwAQLV1EwtVUpObyBqxERET2wCDUFBiCAH0QVJIZnRQJbBUiIiKyEwahpiLEMnqsuxSPjQxCREREdsEg1FSE9QMAjFLuxPaz6SguM8lcEBERUdPHINRUdLgbQu2GVooktDeexK7zHD1GRER0qxiEmgqNG6Q2QwEA/ZRH8ccpnh4jIiK6VQxCTUl4fwBAb8VJ/HE6DUIImQsiIiJq2hiEmpLyINRZSkBWdhbiU/NkLoiIiKhpYxBqSjzCAEMInCQTuivisZGnx4iIiG4Jg1BTU94q1FdxDL+fSJG5GCIioqaNQaipKe8wPUG5GReuXMWFjAJ56yEiImrCGISamjYjAL/20EuFmK76FT8fTpK7IiIioiaLQaipUSiAQa8DAOKU67D5yBmZCyIiImq6GISaosjBMPlEQyeVolPmWpzh6DEiIqKbwiDUFEkSlN0fAgBMVm7EL4euylwQERFR08QgVIUFCxYgOjoa3bt3l7uU6nWcAKPSGZGKqzi7fz2MJrPcFRERETU5DEJVmDFjBk6ePIl9+/bJXUr1tHpIHcYDAIaV/IZN8ekyF0RERNT0MAg1YcoeDwMAhin24tddx2SuhoiIqOlhEGrKArug2LcTNJIRAYnf42p2kdwVERERNSkMQk2ctqelVWiCYhOWbDsvczVERERNC4NQU9d+PIwqF7RUpODs3jXILCiVuyIiIqImg0GoqdO4QtnpXgDAeGzA4u2JMhdERETUdDAINQNS1ykAgDsVB7Bi12nkFpfJXBEREVHTwCDUHAR2gXAPhU4qRUzpAfx3xwW5KyIiImoSGISaA0mCFD0aADBcuQdfbE9kqxAREVEtMAg1F9FjAACxysMoLirAZ1s4goyIiOhGGISaixZdAUMwdCjGAMURfLbtPC5nFspdFRERUaPGINRcSBJQfnrsadeNKDWa8O6a0zIXRURE1LgxCDUnPR8HVFq0Kz2Ke5VbsPpYMvacvyZ3VURERI0Wg1Bz4h4CDHwZAPCm5ktESFcx59eTMJmFzIURERE1TgxCzU2fp4HwAVCbi7FAMx8JyRn4bv9luasiIiJqlBiEmhuFAhj7KeDshba4iOdVK/Dv3+ORU8Th9ERERH/FINQc6QOA0R8DAB5WrUFgwSm88N0RmHmKjIiIyAaDUHPVZijQcQIUEHhbvRgbTibjhe+Psr8QERHRdRiEmrPBbwJaA9pLiXhEtQY/HLyCmcsOodRolrsyIiKiRoFBqDlz9QVi5wAAXlavQCflBaw+mozpXx1AmYlhiIiIiEGouYuZCrQZAYW5DMs8FiJIlYONp9Pw+P8O4NcjScguLJW7QiIiItkwCDV3kgSMng+4h0KXfxlrvT6Al6IAf5xOw1PfHkLs+1ux/WyG3FUSERHJgkHIETh7AlN+Blz94ZpzBpuDP8PIaE+0cNchI78E9y/ag9m/nEBRqUnuSomIiBoUg5Cj8AwHHvgR0OjhlroP83SfYsPf+mFyzxAAwNKdFzBu4U4k5xTJXCgREVHDYRByJH7tgAlfAQon4MRK6H55DG8NDsTSB7vD21WNU8m5GLNgB04k5chdKRERUYNgEHI0LQcAYz4GIAEnfgQWdMfAki1YOb0PWvm6IjW3BPd+sgubTqfJXSkREVG9YxByRB3vBR7ZAPhGA4XXgB8fQfCGafjhkc7oE+GFglITHv7vPvxv90W5KyUiIqpXDEKOKqgb8NgW4PZXAaUaOPULDMvHYum94bg7JghmAbz203G8/vNxpOUWy10tERFRvZCEELznQjVyc3NhMBiQk5MDvV4vdzn15+IuYNkkoCgL8G4N8dA6zN91DXPXn7Eu0sJdh3FdW+DR/i2h1zrJWCwREVHN6vL9zRYhAkJ7Aw9vAPQtgIwzkFZMwVO3t8TSB7sjOkAPSQKuZhdh3h/nMGjuFvx8+CqYn4mIqDlgi1ANHKZFqELqSeCLWKCsAOj/InDHPwAA+SVGbIlPx9z18TifXgAAGNjGB+/f2xmeLmo5KyYiIqqELUJ0c/yigZEfWX7e+h4QvwYA4KpRYUTHAKx5uh+eu7M11CoFNsenY+S87Th2hUPtiYio6WIQIlsd7wG6PQRAAN9OAtb9AzBa7kemUSnx1KBI/DzjNoR6OeNqdhHGf7ITX2w7D5OZDYtERNT0MAhRZUPfBbrcD0AAu+YDS0cAOVets6MC9Pjlyb6IjfJFqdGMN1efwuP/O8BbdBARUZPDIESVqTTA6AXAxG8AjQG4shf4/A4g6bB1EYPOCZ9P6Ya3x3aAWqXAhlOpmPj5bmTkl8hXNxERUR2xs3QNHK6zdFUyE4Fl9wFpJwEnZ2Dc50DUXTaL7L+QiUe+3I/swjIEGrS4OyYIXUI94OOqQStfV2idlDIVT0REjqgu398MQjVgECpXnAN8NxVI+MPyut/zwMCXAeWf1xM6n56PqUv24VJmoc1b3bQqPHhbOKYPiIBOzUBERET1j0HIThiErmMyAmtfBvZ9bnkd2AUY9wXg3cq6SH6JEeuOp2DzmXScTc1DSm4xsgvLAFguyPjc4NYY3bkFlApJjj0gIiIHwSBkJwxCVTj+A7Dqb5ZWIpUOiJ1tGWWmqnw9IbNZYO2JFLy1+hSuZhcBAFr7ueKZ2NYY2s4fCgYiIiKqBwxCdsIgVI2cq8BP04HELZbXHmHAwL8DHe4GFJVPfxWVmrB05wV8vPkc8oqNAIAOLQx4ZVhb9Gnl3YCFExGRI2AQshMGoRqYzcCBxcDm/wMK0izTvNsAt78CRI0GFJUHJOYUlWHRtvNYtD0RBeVD7ftFeuOZ2NaICfVoyOqJiKgZYxCyEwahWigtAPZ8Auz4D1CcbZkW0AkY8BLQemiVLUQZ+SWYt/Esvt5zCcbyCzEyEBERkb0wCNkJg1AdFOcAuz62XICxNN8yzT0EGPwmEDUKkCr3B7p0rRALNp3DDwev2ASih/uGo3+kD/sQERHRTWEQshMGoZtQkAHsnAcc/C9QlGWZ1mY4MPw9wBBU5VuqCkSRvq6I6xOGoe394e2qaajqiYioGWAQshMGoVtQWghsfx/Y/iFgLgPUrpbRZQGdAJ+2gG9UpdNmlzMLsXTnBSzfdxn5JZZO1QoJ6BbmiREdAjC6cyDcnXm3eyIiqhmDkJ0wCNlB2ing12eAy7ttp2sNQHh/oOVAwKsV4OoPaNwAjStyhQ7L913BL0eScOzqn3e31zkpMblnCB7r3xK+em2D7gYRETUdDEJ2wiBkJ2YzcOJHIHErkH7aEo5KcqtfXqMHvCMB9xDkaQOxCd3w8TkvnE619D1SqxQY3SkQ/Vr7oEMLA0I9ndmfiIiIrBiE7IRBqJ6YTUDSISBhE3BpF5BzBchPtXSyNhurfItoEYNjEY9j9qkWOHg5x2aes1qJqAA9Brb2wfCOAYjwcW2IvSAiokaKQchOGIQamBCAsRjIugBknLUEpOTDwMlfAKPlytQioDPOtH0CK3LbY//FLJxOyUOJ0WyzmpY+LmgfaEB0oB4dgwzoGuLBG78SETkQBiE7YRBqJPLTLCPR9n0BlJXf1NW7NdDjMRjbjsKFAiccO38FO04kYvOFYmSYXQD8earMw9kJD/QKxcP9WsKgc6p6G0RE1GwwCNkJg1AjU5BhuU7R3i+A0rxqFytTG5Dq3BqnEI7NeS2wszAIF4Qf3LRqPD4gAlP7hMFFo2rAwomIqCExCNkJg1AjVZwLHFkGHFhi6XiN8l9hldZyaq0KBdDhuDkUO03tsNZpENpFtcMdUb7oF+nDViIiomaGQchOGISagNICwFhiuU6RSg2UFQEZZ4DkI5ZH0mEg9bhNQDILCX+YO+NL02DsREd0DfXC7W18cXtbH7Txc4NUxVWwiYio6WAQshMGoWbCZAQy4oGrB2A+ugKKC9uss86b/fGV6U6sMvVCGjwQaNDi9ra+GNEhAD1bekHJYflERE0Og9B1Ll++jAceeABpaWlQqVR47bXXcM8999TqvQxCzVTGOUvH68NfW69nZIaEwyISe01tcMIcioPmSJS6BWFgax9E+rkiyMMZLdx1CPdxgV7LU2lERI0Zg9B1kpOTkZqais6dOyMlJQUxMTE4c+YMXFxcbvheBqFmriTP0tfo6HLgyr5Ksy+bfbDF3BFrzT2w2xwFI1RQKiTEhHjgjihf3N7GF639XHkqjYiokWEQqkGnTp2watUqBAcH33BZBiEHknMVOLfe2q9IJB+BJEzW2XmSG7ZI3fB9cTdsN3eAEZZRZwEGLdq3MCDCxxWRvq5o5euK1n5u0Kl53SIiIrnU5ftb9jHEW7duxXvvvYcDBw4gOTkZK1euxJgxY2yWWbBgAd577z2kpKSgU6dOmDdvHnr06FHnbR04cAAmk6lWIYgcjKEFEDPV+lIqyQMu7gJOrwJOr4ZbYQbuEptwl3oTip3csUtzGxZlx2BHTmsk5xRjPVKt71UrFejZ0hO3t/HFoChfhHrduPWRiIjkIXuL0Jo1a7Bjxw7ExMRg3LhxlYLQ8uXLMWXKFHzyySfo2bMnPvzwQ3z33XeIj4+Hr68vAKBz584wGivfmuH3339HYGAgACAzMxP9+vXD559/jj59+tSqNrYIEQDLLUEu7QJO/ASc/AkoSLfOKtH54Zzvndiu7oct+cE4k16IjPxSm7e38nVFbJQfYqN80SXEgx2wiYjqWZM9NSZJUqUg1LNnT3Tv3h3z588HAJjNZgQHB+Opp57Cyy+/XKv1lpSU4M4778Sjjz6KBx54oMblSkpKrK9zc3MRHBzMIER/MhmBC9uA498DJ38FSq6775nGABHWB9c8Y7CzJALfJXth58UCmMx//hPzdFFjYBsfxEb5oW+kNzteExHVg2YThEpLS+Hs7Izvv//eJhzFxcUhOzsbP//88w3XKYTAfffdhzZt2mD27Nk1Ljt79mzMmTOn0nQGIaqSsQQ4t9ESis5tAIptbwYLpRpGv05IdG6PDUVtsehqC2QUK6yzJQkI93aBj6sGbloV3LRO8Ddo0bulF3pHeMFJqQAREdVdswlCSUlJaNGiBXbu3InevXtbl3vxxRexZcsW7Nmz54br3L59O/r374+OHTtap/3vf/9Dhw4dKi3LFiG6aWaT5eKNF3cAl/cAl3YDhRk2iwgnZ2T534bdii74Pj0YmzI9IFB12PF2VWNslxa4p1swWvu5NcAOEBE1H02qs3R969u3L8xm840XBKDRaKDRaOq5ImqWFEogKMbyAAAhgMzzllB0cSdwbgOkvGR4Xl6P4ViP4QDM7u7I8uyCNPfOuOLWEZe0bXA6w4hN8enIyC/B59sS8fm2REQF6DGqUyBGdgpAkIezrLtJRNTcNOog5O3tDaVSidTUVJvpqamp8Pf3l6kqolqQJMArwvLofJ8lGKUcBc6sAxK3Alf2Q1GcDa+kTfBK2oQoAFA4AQGdYOrWC8dU7bHkcgBWny3EqeRcnErOxf+tPY0uIe4Y0NoH/SJ90CnIABVPnxER3ZJGfWoMsHSW7tGjB+bNmwfA0lk6JCQETz75ZK07S98sjhqjemMqswSjS3uAy7stz/kpf1lIgtGvA87qOuPn3Fb4MjkIhUJrneuiViImzBM9wy2PDkEGaFS8fhERUZM6NZafn49z585ZXycmJuLw4cPw9PRESEgInn32WcTFxaFbt27o0aMHPvzwQxQUFODBBx+UsWqiW6R0AlrEWB69n7C0GGVftPQturjT0tfo2jmoUo8iCkcRBeAlZzXSDJ2wW+qE1dcCcKzYF1vPGLH1jGU4v0alQJcQd/QM98JtrbzROdgdahVbjIiIaiJ7i9DmzZtx++23V5oeFxeHpUuXAgDmz59vvaBi586d8Z///Ac9e/as99rYIkSyyk22BKLErUDCJiDnUqVFjAot0lT+OFPqjXNGH1wUfrgg/HHaHIICtSd6hHuhbytv3NbKG2383KDgNYyIyAE02VFjjQ2DEDUaFZ2vE/4AErcAaaeBrETAXPlCohUyhB6nzCE4LUJwyhyCFF0E/Ft2Qq/Wgegd4YUgDx3vk0ZEzRKDkJ0wCFGjZioDsi9ZAlFmIpB1wfKccQYiMwGSqDxa0igUuCx8cEH446oiAPkuoRAeLSEFdoRvQAjCvV0QHahnXyMiatIYhG7RggULsGDBAphMJpw5c4ZBiJqe0kIg/RSQegJIPQFz8jGYU45DVZpT7VuOmcOwydwZuxQxcIvoidjoQAyK8oWXKy8pQURNC4OQnbBFiJoVIYC8ZOBaAowZ55CXdAalaWfhlH0e7gXnocCffwqyhCu2mTvgsGgFs297tGnXBb3at0Goj4H9jIio0WMQshMGIXIY+WnAuQ0QZ3+H+exGKEtzq1wsR7gACiWUCgkKSYKkUkOh0kDh6gOVbytIgTFA2G2AbztAwRFrRCQPBiE7YRAih2QyAlf2Aec3o/DyIZiSjsGlOAUK1O4K7QBQpNTjmkcnlPh3hVNYT3hEdIebh289Fk1E9CcGITthECIqZzahLP8ariZdwYWMPCRnFyM5uwhpOXm4lp0HVWEqInAV3RXxiFGcgatUXGkVmXBDsjIIWc6hKHNvBZVfaxiCouEf1hY+BleOYCMiu2EQshMGIaLaKTGacCWrCJeuFeJSeg5Krx6Ga/phBOQdQ2TpSbRAerXvLRNKXIYf0tTByHNriTKvKKiDOsIrtB1a+nrA4OzUgHtCRM0Bg5CdMAgR2UdBXjauXTyJvKunUJoaD2VmAvQFifAruwIdSqp8T6lQ4pwIwkVVOArdW8OpRQf4topBm4hW8OBINiKqAYOQnTAIEdUzsxnFWZeRceEE8q6chDn1NFyyT8O3KAHOorDKt+QIZyQr/JGnawGjPgQanwgYAiPhGRQJ94CWkFQMSUSOjkHIThiEiGQiBJB9CYVXjiAj4TDKko7BJfs0fEqvQFlDp22jUCBRGYbLzu2Q6dkRRX4x0PpGwt9dBz+9Fn56LfRaFfsjETVzDEJ2wiBE1MiUFSE/5RySL5xCTtJZlGUkQp17Ge4lVxEoUqGTSiu9JVu44Jg5HEdFSxw1t8RZZSTMboHwM+jgb7CEoyAPHcK8XBDu7YJAdx2UvFYSUZPGIHSLeGVpoqanpMyI1CsJKDi/B9KV/XDLOAzf/FNwEpXDUb7Q4qLwQ6Lwx4WKh9kPF4U/cpQeCPFyKQ9GzgjzdkG4lwuCPZ3hp9dCreL1kYgaOwYhO2GLEFETZyy13Gok6RCQdAjmKwchpZ+EVMPNaitC0gVhCUYXhB8umv2RKPyRLrnD102LQHcdgjycEebljNDywBTq5QIvFzVPuxE1AgxCdsIgRNQMGUuB7IvAtQQgMwG4dg7IPA9knofIvgwJ1f9JLBAaJIoAnBcBlmezPxLLf86DM1w1KoR6WVqRAvRa6HVO0GtV5c9OcHd2gqeLGp4uaui1TrxdCVE9YRCyEwYhIgdjLAGyL5WHpPPXPRIgsi9BEtV31M4QeiSIQCSaLafakoQX0uGONOGOVOGBfOgA/Bl8lAoJge5ahHu7oqW3C1r6uKCNnxva+ut57SSiW8QgZCcMQkRkZSwFsi5YWpCsj/IWpfyUG769GBpkSB5INXsg2WywBqQ04Y5UWJ7ThDty4YIAgw5t/d3QNkBvefbXo6WPC5yU7J9EVBsMQnbCIEREtVKcW36aLeHPgJSXDOSnAnkpQEnVN7GtclXCCWnCHTlwQQnUKBFOKIETTJIKWo0GLjoNXHVauDnrYHDVQafRQFI6AQoVoHYFXH3LH36AIRhw8wfYb4kcTF2+v1UNVBMRUfOl1QOBXSyPqpQWWlqN8q575KcAeanXBaZkoDgHWqkMIVI6UNVtScrKH7XPVYCTM+DZ0vLwagX4twf8O1peK5R131eiZoZBiIiovqmvCyM1KSsqD0mpQEkeYCwGjCUQZUXIzi9EanYB0nIKkJFTgGu5BcgpLIZSmKCSTHCCCW4ohLeUAx8pG36KHPjjGpRlhUDqccvjek7OgF87wL+D5dm3HeDbFtB51N/nQNQI8dRYDXhqjIgas+IyE86l5eN0Sh5OJ+fidEoe4lPzkJ5nuX+bE4wIktIRKqUgXEpBpHQVUYpLaCtdqvLikwBQ5uIP4RMFJ/9oSB5hgHvInw+NawPuHdHNYx8hO2EQIqKmqLDUiEuZhbh4rRCXy58vZhYiKbsIV7OKUFJWhnApGdHSRUQrLqKNdBmtFVcQJGXUuN4SJwPMOi8oXLzg5OYDhYsXoHMHtAZAY7A8aw2WU4UVP2v0gMaN/ZSoQTEI2QmDEBE1N0IIZBeW4Wp2kSUYlT8nZRcjMzMDztln4Ft8HhFSEoKkDARJ6QiS0uEuFdz8RiWFJRBVBCSNwdKJ2yMU8AgD3Muf9S0AJXts0K1jZ2kiIqqSJEnwcFHDw0WN9i0MVSwxGCVGE5Kzi5GUXYRT2UXYkF2EzGvpKM28jOKcdBgLrkFvzoUH8mCQCqBHAdykQuhRCL1UCLfyZz0KoJZMgDADxdmWR00UKstIN4+wqh86d3t+FEQAGISqdP29xoiIHI1GpUSYtwvCvF2um9ra+pMQAtcKSnE1y9KalJFfgvj8UmTklyAjrwQZ+SVIzy9Bel4xRFkJ9CgoD0aWgGRAAfylawiW0hEipSFEYWl1cjIbgaxEy6MqWndLIDIEAW4Bllalvz7rPHgajuqEp8ZqwFNjREQ3TwiB3GIj0vOKkZpbgrTy56tZRbhwrQAXrhXgalYRzAKQYIY/shAspSFEkWZ5ltIQocpAqJQKgzm7dhtVaqoOSIYgQB9oebgFAip1ve47yYunxoiISHaSJMGgc4JB54RWvm5VLlNiNOFyZhEuZBQgMaMACen5OJ9egE3p+bhWUGq5bhIAZxRbw5G/lIkWymxEaPPRQpUDH2RBb8yApjQbMJVY7iWXfbHm4lx8LaHo+oCkv/7nQEClse8HQo0SW4RqwBYhIiL5ZBeWIiG9AOfT8697zsfFa4Uwmit/dWlQCh8pB9GuBWjnVohIXT6CnXLghywYjGlQFyRDyk2yhKXacPGxDUiGFpYO3foWDEuNHEeN2QmDEBFR41NmMuNyZiHOp//ZgpSQno/zGQXILKj6+kgA4KxWItzLGR09TWjvlo8ITQ5aKLPgbUqHtijFEpJyrwK5SZaLWdaGi8+f4cjND9B5As6elr5K1p89LaPl1C6Ak459mBoAg5CdMAgRETUtWQWlOJ+Rj4S0AiSUP5/PyMelalqRKrhpVAjxckaolzOCPXRo7VZmDUqexjQo85OBnKvlQamOYel6ksJyTzi1qyUYaVyrf60pn6Z2u25e+Wvrz668VUoVGITshEGIiKh5KDOZcSmzEAlplpajhLT88gtNFiA1t+ZTZSqFBD+9Fv4GLfz1WvjptQjQaxCsK0aQMgt+IgPuxjQ4FV0DijKBwkzLc1FW+c9Zdbrxbp2pdH8GKGcvy013XXz+fHbxKb8Jb/kpPqVT/dXSSDAI2QmDEBFR81dUasKVLMsVuC9lFpZflbsAlzILcTmrCKVGc63W4+Wihr9BiwCDDgEGLQLctZZngw4BejX8dSZozMVAST5QWvEosNxXzvrzX+fV8NpsrPvOSgrLqDlDEOAebLlukyHIclkCz5aW183gopYMQnbCIERE5NjMZoHUvGIk5xQjNaf8ObcYKbl//pycU1ynsBTgroW/XodAd0srU6BBB1+9Br5uWvjqNXDTqCDdqB+REICxxBKOSvP+DFQF6ZZHfjpQkAbkp1le5yVbTueZqu9DBcByUUv3UEso8or482bBni0t95trIq1JDEJ2wiBEREQ3UnHbkuScYqTkWm5XkpJTjKScIqSUh6ek7CKU1DIs6ZyU5cFIA1+91vLsZnn2dtPA21UNH1cNPF3UUCkVtS/UbLaEo+zLQE75o+LnrAtAZmLNI+okpSUMXR+OKh4eoY1qBB2DkJ0wCBERkT1UhKWKcJSUU4yUnCLLrUxyipCWV4L03BLkldT+dJckAZ7Oani7auDtZglHlp8tzz51DU1mM5CXBGSetzyuJZT/nGh5NhbVVI3lFJtn+F8CUrhlmtqlhvfaH4OQnTAIERFRQyosNSIttwRpeX9eiTstrxjpuRW3LSlBRn4pMgtKUMMguEpuOTQJAeSllAejvwSkzETL6bmauPpfF5DCr2tF0gKQAL/oOn9WNWEQshMGISIiaoxMZoHMAsv93dLL7+9meZRaX9srNPm6WTp9B7pb+jUFuusQYNBBry3vyyQEUJBhCUVZiX+2KFU8irJq3qCTM/CP5Fv7QP6Ct9ggIiJqxpQKCT5ulpacqICal72Z0HStoBTXCkoRn1r9el3USksoctehhbsWAQZPBBgC0SI4Fv7tLaPldGqlJQhd33pUEZiyLlpGvjlp7fvh1BFbhKpw/d3nz5w5wxYhIiJyCFWFptTcEiTnFCEp29IRPDmnCFmFZbVan0HnhACDtvyyApbRchWXFfDXa6FTKyFJElq46+y6Hzw1Zic8NUZERFRZYakRyTnFls7e2UVIKg9KFSPkknOKUVhqqtW6dE5KnPrnULvWx1NjREREVG+c1SpE+Lgiwse1yvlCCOSVGK2XD0jJKSp/Lr7u2XJJAY1THS4BUA8YhIiIiMiuJEmCXusEvdYJrf3c5C6nRvLGMCIiIiIZMQgRERGRw2IQIiIiIofFIEREREQOi0GIiIiIHBaDEBERETksBiEiIiJyWAxCRERE5LAYhIiIiMhhMQgRERGRw2IQIiIiIofFIEREREQOi0GoCgsWLEB0dDS6d+8udylERERUjyQhhJC7iMYqNzcXBoMBOTk50Ov1cpdDREREtVCX729VA9XUJFVkxNzcXJkrISIiotqq+N6uTVsPg1AN8vLyAADBwcEyV0JERER1lZeXB4PBUOMyPDVWA7PZjKSkJLi5uUGSJLuuOzc3F8HBwbh8+XKzPO3G/WvauH9NX3PfR+5f01bf+yeEQF5eHgIDA6FQ1Nwdmi1CNVAoFAgKCqrXbej1+mb5S16B+9e0cf+avua+j9y/pq0+9+9GLUEVOGqMiIiIHBaDEBERETksBiGZaDQazJo1CxqNRu5S6gX3r2nj/jV9zX0fuX9NW2PaP3aWJiIiIofFFiEiIiJyWAxCRERE5LAYhIiIiMhhMQgRERGRw2IQksmCBQsQFhYGrVaLnj17Yu/evXKXdEPvvPMOunfvDjc3N/j6+mLMmDGIj4+3WWbgwIGQJMnmMW3aNJtlLl26hBEjRsDZ2Rm+vr544YUXYDQaG3JXqjR79uxKtbdt29Y6v7i4GDNmzICXlxdcXV0xfvx4pKam2qyjse4bAISFhVXaP0mSMGPGDABN79ht3boVI0eORGBgICRJwk8//WQzXwiB119/HQEBAdDpdIiNjcXZs2dtlsnMzMTkyZOh1+vh7u6Ohx9+GPn5+TbLHD16FP369YNWq0VwcDD+9a9/1feuWdW0j2VlZXjppZfQoUMHuLi4IDAwEFOmTEFSUpLNOqo67u+++67NMnLt442O4dSpUyvVPnToUJtlGvMxvNH+VfXvUZIkvPfee9ZlGuvxq833gb3+Zm7evBldu3aFRqNBq1atsHTpUvvujKAGt2zZMqFWq8XixYvFiRMnxKOPPirc3d1Famqq3KXVaMiQIWLJkiXi+PHj4vDhw2L48OEiJCRE5OfnW5cZMGCAePTRR0VycrL1kZOTY51vNBpF+/btRWxsrDh06JD47bffhLe3t3jllVfk2CUbs2bNEu3atbOpPT093Tp/2rRpIjg4WGzcuFHs379f9OrVS/Tp08c6vzHvmxBCpKWl2ezb+vXrBQCxadMmIUTTO3a//fab+Mc//iF+/PFHAUCsXLnSZv67774rDAaD+Omnn8SRI0fEqFGjRHh4uCgqKrIuM3ToUNGpUyexe/dusW3bNtGqVSsxadIk6/ycnBzh5+cnJk+eLI4fPy6+/fZbodPpxKeffir7PmZnZ4vY2FixfPlycfr0abFr1y7Ro0cPERMTY7OO0NBQ8cYbb9gc1+v/zcq5jzc6hnFxcWLo0KE2tWdmZtos05iP4Y327/r9Sk5OFosXLxaSJImEhATrMo31+NXm+8AefzPPnz8vnJ2dxbPPPitOnjwp5s2bJ5RKpVi7dq3d9oVBSAY9evQQM2bMsL42mUwiMDBQvPPOOzJWVXdpaWkCgNiyZYt12oABA8TTTz9d7Xt+++03oVAoREpKinXawoULhV6vFyUlJfVZ7g3NmjVLdOrUqcp52dnZwsnJSXz33XfWaadOnRIAxK5du4QQjXvfqvL000+LiIgIYTabhRBN+9j99UvGbDYLf39/8d5771mnZWdnC41GI7799lshhBAnT54UAMS+ffusy6xZs0ZIkiSuXr0qhBDi448/Fh4eHjb799JLL4k2bdrU8x5VVtUX6V/t3btXABAXL160TgsNDRUffPBBte9pLPtYXRAaPXp0te9pSsewNsdv9OjR4o477rCZ1lSO31+/D+z1N/PFF18U7dq1s9nWhAkTxJAhQ+xWO0+NNbDS0lIcOHAAsbGx1mkKhQKxsbHYtWuXjJXVXU5ODgDA09PTZvrXX38Nb29vtG/fHq+88goKCwut83bt2oUOHTrAz8/POm3IkCHIzc3FiRMnGqbwGpw9exaBgYFo2bIlJk+ejEuXLgEADhw4gLKyMpvj1rZtW4SEhFiPW2Pft+uVlpbiq6++wkMPPWRzQ+GmfOyul5iYiJSUFJvjZTAY0LNnT5vj5e7ujm7dulmXiY2NhUKhwJ49e6zL9O/fH2q12rrMkCFDEB8fj6ysrAbam9rLycmBJElwd3e3mf7uu+/Cy8sLXbp0wXvvvWdz6qGx7+PmzZvh6+uLNm3aYPr06bh27Zp1XnM6hqmpqVi9ejUefvjhSvOawvH76/eBvf5m7tq1y2YdFcvY8/uSN11tYBkZGTCZTDYHHgD8/Pxw+vRpmaqqO7PZjGeeeQa33XYb2rdvb51+3333ITQ0FIGBgTh69CheeuklxMfH48cffwQApKSkVLnvFfPk1LNnTyxduhRt2rRBcnIy5syZg379+uH48eNISUmBWq2u9AXj5+dnrbsx79tf/fTTT8jOzsbUqVOt05rysfurinqqqvf64+Xr62szX6VSwdPT02aZ8PDwSuuomOfh4VEv9d+M4uJivPTSS5g0aZLNTSxnzpyJrl27wtPTEzt37sQrr7yC5ORkvP/++wAa9z4OHToU48aNQ3h4OBISEvD3v/8dw4YNw65du6BUKpvVMfzvf/8LNzc3jBs3zmZ6Uzh+VX0f2OtvZnXL5ObmoqioCDqd7pbrZxCimzJjxgwcP34c27dvt5n+2GOPWX/u0KEDAgICMGjQICQkJCAiIqKhy6yTYcOGWX/u2LEjevbsidDQUKxYscIu/9gak0WLFmHYsGEIDAy0TmvKx87RlZWV4d5774UQAgsXLrSZ9+yzz1p/7tixI9RqNR5//HG88847jeL2BjWZOHGi9ecOHTqgY8eOiIiIwObNmzFo0CAZK7O/xYsXY/LkydBqtTbTm8Lxq+77oKngqbEG5u3tDaVSWannfGpqKvz9/WWqqm6efPJJrFq1Cps2bUJQUFCNy/bs2RMAcO7cOQCAv79/lfteMa8xcXd3R+vWrXHu3Dn4+/ujtLQU2dnZNstcf9yayr5dvHgRGzZswCOPPFLjck352FXUU9O/M39/f6SlpdnMNxqNyMzMbFLHtCIEXbx4EevXr7dpDapKz549YTQaceHCBQBNYx8rtGzZEt7e3ja/k83hGG7btg3x8fE3/DcJNL7jV933gb3+Zla3jF6vt9t/UBmEGpharUZMTAw2btxonWY2m7Fx40b07t1bxspuTAiBJ598EitXrsQff/xRqTm2KocPHwYABAQEAAB69+6NY8eO2fzxqvjjHR0dXS9136z8/HwkJCQgICAAMTExcHJysjlu8fHxuHTpkvW4NZV9W7JkCXx9fTFixIgal2vKxy48PBz+/v42xys3Nxd79uyxOV7Z2dk4cOCAdZk//vgDZrPZGgJ79+6NrVu3oqyszLrM+vXr0aZNm0ZxSqUiBJ09exYbNmyAl5fXDd9z+PBhKBQK6ymlxr6P17ty5QquXbtm8zvZ1I8hYGmhjYmJQadOnW64bGM5fjf6PrDX38zevXvbrKNiGbt+X9qt2zXV2rJly4RGoxFLly4VJ0+eFI899phwd3e36TnfGE2fPl0YDAaxefNmm6GchYWFQgghzp07J9544w2xf/9+kZiYKH7++WfRsmVL0b9/f+s6KoZLDh48WBw+fFisXbtW+Pj4NIoh5s8995zYvHmzSExMFDt27BCxsbHC29tbpKWlCSEsQ0FDQkLEH3/8Ifbv3y969+4tevfubX1/Y963CiaTSYSEhIiXXnrJZnpTPHZ5eXni0KFD4tChQwKAeP/998WhQ4esI6beffdd4e7uLn7++Wdx9OhRMXr06CqHz3fp0kXs2bNHbN++XURGRtoMvc7OzhZ+fn7igQceEMePHxfLli0Tzs7ODTZ8vqZ9LC0tFaNGjRJBQUHi8OHDNv8mK0bc7Ny5U3zwwQfi8OHDIiEhQXz11VfCx8dHTJkypVHsY037l5eXJ55//nmxa9cukZiYKDZs2CC6du0qIiMjRXFxsXUdjfkY3uh3VAjL8HdnZ2excOHCSu9vzMfvRt8HQtjnb2bF8PkXXnhBnDp1SixYsIDD55uLefPmiZCQEKFWq0WPHj3E7t275S7phgBU+ViyZIkQQohLly6J/v37C09PT6HRaESrVq3ECy+8YHMtGiGEuHDhghg2bJjQ6XTC29tbPPfcc6KsrEyGPbI1YcIEERAQINRqtWjRooWYMGGCOHfunHV+UVGReOKJJ4SHh4dwdnYWY8eOFcnJyTbraKz7VmHdunUCgIiPj7eZ3hSP3aZNm6r8fYyLixNCWIbQv/baa8LPz09oNBoxaNCgSvt97do1MWnSJOHq6ir0er148MEHRV5ens0yR44cEX379hUajUa0aNFCvPvuuw21izXuY2JiYrX/JiuuDXXgwAHRs2dPYTAYhFarFVFRUeLtt9+2CRJy7mNN+1dYWCgGDx4sfHx8hJOTkwgNDRWPPvpopf8wNuZjeKPfUSGE+PTTT4VOpxPZ2dmV3t+Yj9+Nvg+EsN/fzE2bNonOnTsLtVotWrZsabMNe5DKd4iIiIjI4bCPEBERETksBiEiIiJyWAxCRERE5LAYhIiIiMhhMQgRERGRw2IQIiIiIofFIEREREQOi0GIiOgGJEnCTz/9JHcZRFQPGISIqFGbOnUqJEmq9Bg6dKjcpRFRM6CSuwAiohsZOnQolixZYjNNo9HIVA0RNSdsESKiRk+j0cDf39/mUXFnbUmSsHDhQgwbNgw6nQ4tW7bE999/b/P+Y8eO4Y477oBOp4OXlxcee+wx5Ofn2yyzePFitGvXDhqNBgEBAXjyySdt5mdkZGDs2LFwdnZGZGQkfvnlF+u8rKwsTJ48GT4+PtDpdIiMjKwU3IiocWIQIqIm77XXXsP48eNx5MgRTJ48GRMnTsSpU6cAAAUFBRgyZAg8PDywb98+fPfdd9iwYYNN0Fm4cCFmzJiBxx57DMeOHcMvv/yCVq1a2Wxjzpw5uPfee3H06FEMHz4ckydPRmZmpnX7J0+exJo1a3Dq1CksXLgQ3t7eDfcBENHNs+stXImI7CwuLk4olUrh4uJi83jrrbeEEJa7YE+bNs3mPT179hTTp08XQgjx2WefCQ8PD5Gfn2+dv3r1aqFQKKx3Mg8MDBT/+Mc/qq0BgHj11Vetr/Pz8wUAsWbNGiGEECNHjhQPPvigfXaYiBoU+wgRUaN3++23Y+HChTbTPD09rT/37t3bZl7v3r1x+PBhAMCpU6fQqVMnuLi4WOffdtttMJvNiI+PhyRJSEpKwqBBg2qsoWPHjtafXVxcoNfrkZaWBgCYPn06xo8fj4MHD2Lw4MEYM2YM+vTpc1P7SkQNi0GIiBo9FxeXSqeq7EWn09VqOScnJ5vXkiTBbDYDAIYNG4aLFy/it99+w/r16zFo0CDMmDED//73v+1eLxHZF/sIEVGTt3v37kqvo6KiAABRUVE4cuQICgoKrPN37NgBhUKBNm3awM3NDWFhYdi4ceMt1eDj44O4uDh89dVX+PDDD/HZZ5/d0vqIqGGwRYiIGr2SkhKkpKTYTFOpVNYOyd999x26deuGvn374uuvv8bevXuxaNEiAMDkyZMxa9YsxMXFYfbs2UhPT8dTTz2FBx54AH5+fgCA2bNnY9q0afD19cWwYcOQl5eHHTt24KmnnqpVfa+//jpiYmLQrl07lJSUYNWqVdYgRkSNG4MQETV6a9euRUBAgM20Nm3a4PTp0wAsI7qWLVuGJ554AgEBAfj2228RHR0NAHB2dsa6devw9NNPo3v37nB2dsb48ePx/vvvW9cVFxeH4uJifPDBB3j++efh7e2Nu+++u9b1qdVqvPLKK7hw4QJ0Oh369euHZcuW2WHPiai+SUIIIXcRREQ3S5IkrFy5EmPGjJG7FCJqgthHiIiIiBwWgxARERE5LPYRIqImjWf3iehWsEWIiIiIHBaDEBERETksBiEiIiJyWAxCRERE5LAYhIiIiMhhMQgRERGRw2IQIiIiIofFIEREREQOi0GIiIiIHNb/AyD//65oGcZfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_0 = np.load(\"mouse_599975/mouse599975_sweep0_param50_chi2s_2000iters.npy\")#(\"beforesweep0_pvars_2000iters_screen.npy\")#[:500]\n",
    "loss_1 = np.load(\"mouse_599975/mouse599975_sweep1_param50_chi2s_2000iters.npy\") #(\"beforesweep1_pvars_2000iters_screen.npy\")#[:500]\n",
    "#loss_2 = np.load(\"mouse_599975/mouse599975_sweep0_param70_chi2s_2000iters.npy\")\n",
    "#loss_3 = np.load(\"mouse_599975/mouse599975_sweep0_paramwhite_chi2s_2000iters.npy\")\n",
    "\n",
    "plt.clf()\n",
    "plt.plot(loss_0)\n",
    "plt.plot(loss_1)\n",
    "#plt.plot(loss_2)\n",
    "#plt.plot(loss_3)\n",
    "plt.ylabel(\"Error value\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend([\"Awake state\", \"Anesthetized state\"])\n",
    "#plt.legend([\"Sweep 0\", \"Sweep 1\", \"Sweep 2\", \"Sweep 3\"])\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"Chi2s v/s Epochs, Mouse 599975\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2878213-fe3f-4fcf-95c3-cb514295dc17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa571f4a-29b5-4acc-882e-fbaae0b18a48",
   "metadata": {},
   "source": [
    "### Plot the interaction matrix by clubbing neurons based on peak channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91dd90f8-caa1-4158-a5e3-6786feaed9c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "neurons_order = np.load(\"mouse586468_final_allprobes_peakchannel.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "17387d7c-b4bb-4bda-aa11-4f9a35493a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.load(\"mouse586468_sweep0_paramwhite_J_2000iters.npy\")\n",
    "b = np.load(\"mouse586468_sweep2_param45_J_2000iters.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "651fc2a3-101e-41ea-aac3-70994f486ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_neurons = len(neurons_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "44ec1387-e38e-481b-9090-c409bef06924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2129"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(neurons_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4063b97e-aa2a-496d-b05e-b20fc1795d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_0 = np.zeros((num_neurons, num_neurons))\n",
    "\n",
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        mat_0[i, j] = a[neurons_order[i], neurons_order[j]]\n",
    "        \n",
    "mat_1 = np.zeros((num_neurons, num_neurons))\n",
    "\n",
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        mat_1[i, j] = b[neurons_order[i], neurons_order[j]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0b42c7b1-2c0e-442c-9ff7-3d99a727c96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_neuron = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c65f6dd6-9425-4b21-b1d7-af8fc8d8ce7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_mat_dim = num_neurons//avg_neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8e10e1d2-787f-4e74-9fb1-49191f13b94b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_mat_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dd8c7aeb-e9cc-4060-81d4-6413800b75ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_mat_0 = np.zeros((avg_mat_dim, avg_mat_dim))\n",
    "for i in range(avg_mat_dim):\n",
    "    for j in range(avg_mat_dim):\n",
    "        avg_mat_0[i, j] = np.mean(mat_0[avg_neuron*i:avg_neuron*(i+1), avg_neuron*j:avg_neuron*(j+1)])\n",
    "        \n",
    "avg_mat_1 = np.zeros((avg_mat_dim, avg_mat_dim))\n",
    "for i in range(avg_mat_dim):\n",
    "    for j in range(avg_mat_dim):\n",
    "        avg_mat_1[i, j] = np.mean(mat_1[avg_neuron*i:avg_neuron*(i+1), avg_neuron*j:avg_neuron*(j+1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b704924a-391a-417b-ba13-14916304c30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Interaction Matrices, Parameter=25')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 4000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,10))\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(avg_mat_0, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "plt.clim(-0.12, 0.06)\n",
    "#plt.clim(-0.10, 0.05)\n",
    "#plt.clim(-0.32, 0.27)\n",
    "#plt.clim(-0.4, 0.21)\n",
    "#plt.clim(-0.042, 0.035)\n",
    "#plt.title(\"Awake state\", fontsize=40)\n",
    "plt.title(\"Sweep 0\", fontsize=30)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.xlabel(\"Source Neurons\"), plt.ylabel(\"Target Neurons\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(avg_mat_1, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "plt.clim(-0.12, 0.06)\n",
    "#plt.clim(-0.05, 0.04)\n",
    "#plt.clim(-0.10, 0.05)\n",
    "#plt.clim(-0.32, 0.27)\n",
    "#plt.clim(-0.4, 0.21)\n",
    "#plt.clim(-0.042, 0.035)\n",
    "#plt.title(\"Anesthetized state\", fontsize=40)\n",
    "plt.title(\"Sweep 2\", fontsize=30)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.xlabel(\"Source Neurons\"), plt.ylabel(\"Target Neurons\")\n",
    "\n",
    "plt.suptitle(\"Interaction Matrices, Parameter=25\")\n",
    "#plt.savefig(\"interaction_matrices_mouse599975_paramwhite.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "43b8303b-b87e-48ca-a480-3ffed2df5959",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_01 = avg_mat_0-avg_mat_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c5304b24-6a6f-494d-af5d-e8ffb35f8ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"mouse586468_diffmatrix_param45_peakchannels.npy\", diff_01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "585390a6-549b-4335-86c0-5840ddae5243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Difference in Interaction Matrices')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 9))\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(diff_01, cmap=\"RdBu\")\n",
    "plt.colorbar()\n",
    "#plt.clim(-0.045, 0.02)\n",
    "#plt.clim(-0.06, 0.02)\n",
    "#plt.clim(-0.045, 0.02)\n",
    "#plt.clim(-0.09, 0.105)\n",
    "#plt.clim(-0.04, 0.02)\n",
    "#plt.title(\"Awake state - Anesthetized state\", fontsize=25)\n",
    "plt.title(\"Sweep 0 - Sweep 2\", fontsize=25)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.xlabel(\"Source Neurons\"), plt.ylabel(\"Target Neurons\")\n",
    "\n",
    "\n",
    "plt.suptitle(\"Difference in Interaction Matrices\")\n",
    "#plt.savefig(\"diff_interaction_matrices_mouse599975_paramwhite.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "303f9b9d-a193-4887-984a-b8ec7a9b7f01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7018b0-86f0-4247-baee-5d87c108a55c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "02d776cc-7090-4d20-acda-1280db4efcfc",
   "metadata": {},
   "source": [
    "### Plot the interaction matrix by clubbing neurons based on regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "598687c9-0305-4794-b75b-67b7da16b42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "areas = np.load(\"mouse586468_final_allprobes_area.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dd2c6f7-aa14-4fa5-bd72-0620c0b607b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set(areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccb1d1a9-71a2-4b5b-a0e7-eb8eaa35cca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_areas = {}\n",
    "all_indices = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8d6822e-90d0-4fe9-a373-ebd9e44e472e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for y in sorted(s):\n",
    "    indices = [i for i, x in enumerate(areas) if x == y]\n",
    "    ind_areas[y] = indices\n",
    "    all_indices.extend(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95f37098-004c-46dc-9ea6-bc92ad0d11d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_neurons = len(all_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d90c1032-cc06-490c-9b47-49a8b89634d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.load(\"mouse586468_sweep0_paramwhite_J_2000iters.npy\")\n",
    "b = np.load(\"mouse586468_sweep1_paramwhite_J_2000iters.npy\")\n",
    "c = np.load(\"mouse586468_sweep2_param45_J_2000iters.npy\")\n",
    "d = np.load(\"mouse586468_sweep3_param45_J_2000iters.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "da00b407-1e9e-4b35-901e-5554d66fa252",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_0 = np.zeros((num_neurons, num_neurons))\n",
    "\n",
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        #print(all_indices[i])\n",
    "        mat_0[i, j] = a[all_indices[i], all_indices[j]]\n",
    "        \n",
    "mat_1 = np.zeros((num_neurons, num_neurons))\n",
    "\n",
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        mat_1[i, j] = b[all_indices[i], all_indices[j]]\n",
    "        \n",
    "mat_2 = np.zeros((num_neurons, num_neurons))\n",
    "\n",
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        mat_2[i, j] = c[all_indices[i], all_indices[j]]\n",
    "        \n",
    "mat_3 = np.zeros((num_neurons, num_neurons))\n",
    "\n",
    "for i in range(len(a)):\n",
    "    for j in range(len(b)):\n",
    "        mat_3[i, j] = d[all_indices[i], all_indices[j]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "08e8cf50-7d63-4141-abc5-442216ec18ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_mat_dim = len(sorted(s))\n",
    "\n",
    "start = 0\n",
    "areas_sorted = sorted(s)\n",
    "avg_mat_0 = np.zeros((avg_mat_dim, avg_mat_dim))\n",
    "\n",
    "start_x = 0\n",
    "for i in range(avg_mat_dim):\n",
    "    len_area_x = len(ind_areas[areas_sorted[i]])\n",
    "    start_y = 0\n",
    "    for j in range(avg_mat_dim):\n",
    "        len_area_y = len(ind_areas[areas_sorted[j]])\n",
    "        avg_mat_0[i, j] = np.mean(mat_0[start_x:start_x+len_area_x, start_y:start_y+len_area_y])\n",
    "        start_y += len_area_y\n",
    "    start_x += len_area_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "874d4399-d30a-4177-a6c6-fafbf7a69dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_mat_dim = len(sorted(s))\n",
    "\n",
    "start = 0\n",
    "areas_sorted = sorted(s)\n",
    "avg_mat_1 = np.zeros((avg_mat_dim, avg_mat_dim))\n",
    "\n",
    "start_x = 0\n",
    "for i in range(avg_mat_dim):\n",
    "    len_area_x = len(ind_areas[areas_sorted[i]])\n",
    "    start_y = 0\n",
    "    for j in range(avg_mat_dim):\n",
    "        len_area_y = len(ind_areas[areas_sorted[j]])\n",
    "        avg_mat_1[i, j] = np.mean(mat_1[start_x:start_x+len_area_x, start_y:start_y+len_area_y])\n",
    "        start_y += len_area_y\n",
    "    start_x += len_area_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5c7d5cc5-f4c4-4fcb-94c3-8a6dade8a2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_mat_dim = len(sorted(s))\n",
    "\n",
    "start = 0\n",
    "areas_sorted = sorted(s)\n",
    "avg_mat_2 = np.zeros((avg_mat_dim, avg_mat_dim))\n",
    "\n",
    "start_x = 0\n",
    "for i in range(avg_mat_dim):\n",
    "    len_area_x = len(ind_areas[areas_sorted[i]])\n",
    "    start_y = 0\n",
    "    for j in range(avg_mat_dim):\n",
    "        len_area_y = len(ind_areas[areas_sorted[j]])\n",
    "        avg_mat_2[i, j] = np.mean(mat_2[start_x:start_x+len_area_x, start_y:start_y+len_area_y])\n",
    "        start_y += len_area_y\n",
    "    start_x += len_area_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6df5e791-f3a0-4291-bf67-2e51dd685e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_mat_dim = len(sorted(s))\n",
    "\n",
    "start = 0\n",
    "areas_sorted = sorted(s)\n",
    "avg_mat_3 = np.zeros((avg_mat_dim, avg_mat_dim))\n",
    "\n",
    "start_x = 0\n",
    "for i in range(avg_mat_dim):\n",
    "    len_area_x = len(ind_areas[areas_sorted[i]])\n",
    "    start_y = 0\n",
    "    for j in range(avg_mat_dim):\n",
    "        len_area_y = len(ind_areas[areas_sorted[j]])\n",
    "        avg_mat_3[i, j] = np.mean(mat_3[start_x:start_x+len_area_x, start_y:start_y+len_area_y])\n",
    "        start_y += len_area_y\n",
    "    start_x += len_area_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "73634eef-cf40-4852-b51a-e614d0c39696",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff_01 = avg_mat_0-avg_mat_1\n",
    "diff_23 = avg_mat_2-avg_mat_3\n",
    "diff_02 = avg_mat_0-avg_mat_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c1191935-f3f0-4ae3-93a1-c361ff24dbdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10, 10)\n",
    "im = ax.imshow(np.tanh(diff_01/0.01)[:28, :28], cmap=\"RdYlBu\")\n",
    "cbar = ax.figure.colorbar(im, ax=ax, shrink=0.8)\n",
    "#cbar.ax.set_ylim(0, 0.1)\n",
    "#ax.colorbar()\n",
    "#ax.figure.colorbar.ColorbarBase.set_clim(0, 0,10)\n",
    "\n",
    "# Show all ticks and label them with the respective list entries\n",
    "ax.set_xticks(np.arange(28))\n",
    "ax.set_yticks(np.arange(28))\n",
    "ax.set_xticklabels(areas_sorted[:28], fontdict={'fontsize':10})\n",
    "ax.set_yticklabels(areas_sorted[:28], fontdict={'fontsize':10})\n",
    "\n",
    "#Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=60, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "#for i in range(len(areas_sorted)):\n",
    "#    for j in range(len(areas_sorted)):\n",
    "#        text = ax.text(j, i, avg_mat_0[i, j],\n",
    "#                       ha=\"center\", va=\"center\", color=\"w\")\n",
    "\n",
    "ax.set_title(\"Sweep 0 - Sweep 1, Parameter=white\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e029ebba-dddf-468a-a854-ab0b44080877",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "btp_lakshika",
   "language": "python",
   "name": "btp_lakshika"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}