{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7bde442d6490>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "from src.dataset import PandemicDataset\n",
    "from src.problem import SIRProblem\n",
    "from src.dinn import DINN\n",
    "from src.plotter import Plotter\n",
    "\n",
    "torch.manual_seed(10004645917449800112)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "different_datasets = ['SIR_RKI_1'] #, 'SIR_RKI_3', 'SIR_RKI_5', 'SIR_RKI_10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch  0\n",
      "physics loss:\t\t0.0002626387710236112\n",
      "observation loss:\t4.641225446805014\n",
      "loss:\t\t\t4.641488085576038\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.00032559855026192963\n",
      "beta:\t\t\t0.09014467149972916\n",
      "#################################\n",
      "\n",
      "Epoch  1000\n",
      "physics loss:\t\t8.516573932758325e-05\n",
      "observation loss:\t0.5881097407181334\n",
      "loss:\t\t\t0.588194906457461\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.007406014949083328\n",
      "beta:\t\t\t0.08217237889766693\n",
      "#################################\n",
      "\n",
      "Epoch  2000\n",
      "physics loss:\t\t3.744264051511477e-05\n",
      "observation loss:\t0.3629036673508007\n",
      "loss:\t\t\t0.36294110999131585\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.01244969293475151\n",
      "beta:\t\t\t0.07527147978544235\n",
      "#################################\n",
      "\n",
      "Epoch  3000\n",
      "physics loss:\t\t2.2488903684638793e-05\n",
      "observation loss:\t0.3351137848635904\n",
      "loss:\t\t\t0.335136273767275\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.01644785702228546\n",
      "beta:\t\t\t0.06965437531471252\n",
      "#################################\n",
      "\n",
      "Epoch  4000\n",
      "physics loss:\t\t1.1671624451999599e-05\n",
      "observation loss:\t0.3014856688548675\n",
      "loss:\t\t\t0.3014973404793195\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.020783189684152603\n",
      "beta:\t\t\t0.06397118419408798\n",
      "#################################\n",
      "\n",
      "Epoch  5000\n",
      "physics loss:\t\t5.748865396680308e-06\n",
      "observation loss:\t0.26069648743834306\n",
      "loss:\t\t\t0.26070223630373973\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.025329839438199997\n",
      "beta:\t\t\t0.05841492861509323\n",
      "#################################\n",
      "\n",
      "Epoch  6000\n",
      "physics loss:\t\t2.7409750088373968e-06\n",
      "observation loss:\t0.2047972958936241\n",
      "loss:\t\t\t0.20480003686863293\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.02986917272210121\n",
      "beta:\t\t\t0.05310284346342087\n",
      "#################################\n",
      "\n",
      "Epoch  7000\n",
      "physics loss:\t\t2.3953860096516563e-06\n",
      "observation loss:\t0.14625131427317423\n",
      "loss:\t\t\t0.14625370965918388\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03393588587641716\n",
      "beta:\t\t\t0.04829966649413109\n",
      "#################################\n",
      "\n",
      "Epoch  8000\n",
      "physics loss:\t\t3.6347580235888025e-06\n",
      "observation loss:\t0.10231428342772422\n",
      "loss:\t\t\t0.10231791818574781\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03620445355772972\n",
      "beta:\t\t\t0.04531411454081535\n",
      "#################################\n",
      "\n",
      "Epoch  9000\n",
      "physics loss:\t\t5.869482792007702e-06\n",
      "observation loss:\t0.08100686539324195\n",
      "loss:\t\t\t0.08101273487603396\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03618756681680679\n",
      "beta:\t\t\t0.04449731111526489\n",
      "#################################\n",
      "\n",
      "Epoch  10000\n",
      "physics loss:\t\t7.3072389994298555e-06\n",
      "observation loss:\t0.0717683924078327\n",
      "loss:\t\t\t0.07177569964683213\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.034104421734809875\n",
      "beta:\t\t\t0.04563898965716362\n",
      "#################################\n",
      "\n",
      "Epoch  11000\n",
      "physics loss:\t\t8.816711632506803e-06\n",
      "observation loss:\t0.06663665580154264\n",
      "loss:\t\t\t0.06664547251317515\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03337372466921806\n",
      "beta:\t\t\t0.04507675766944885\n",
      "#################################\n",
      "\n",
      "Epoch  12000\n",
      "physics loss:\t\t9.776682738700162e-06\n",
      "observation loss:\t0.058904072275154123\n",
      "loss:\t\t\t0.058913848957892825\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03299642726778984\n",
      "beta:\t\t\t0.04365518316626549\n",
      "#################################\n",
      "\n",
      "Epoch  13000\n",
      "physics loss:\t\t9.743923045297006e-06\n",
      "observation loss:\t0.050619574126966896\n",
      "loss:\t\t\t0.050629318050012195\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03283131495118141\n",
      "beta:\t\t\t0.04255611076951027\n",
      "#################################\n",
      "\n",
      "Epoch  14000\n",
      "physics loss:\t\t8.866789682776572e-06\n",
      "observation loss:\t0.04295989462607499\n",
      "loss:\t\t\t0.04296876141575776\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03343890607357025\n",
      "beta:\t\t\t0.042593058198690414\n",
      "#################################\n",
      "\n",
      "Epoch  15000\n",
      "physics loss:\t\t9.599858339788549e-06\n",
      "observation loss:\t0.036144848335881025\n",
      "loss:\t\t\t0.03615444819422081\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03558468073606491\n",
      "beta:\t\t\t0.04485592618584633\n",
      "#################################\n",
      "\n",
      "Epoch  16000\n",
      "physics loss:\t\t8.196450957937106e-06\n",
      "observation loss:\t0.031455641777083064\n",
      "loss:\t\t\t0.031463838228041004\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.03958951309323311\n",
      "beta:\t\t\t0.04959242045879364\n",
      "#################################\n",
      "\n",
      "Epoch  17000\n",
      "physics loss:\t\t7.093790666750134e-06\n",
      "observation loss:\t0.027600079371000646\n",
      "loss:\t\t\t0.027607173161667397\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.044546812772750854\n",
      "beta:\t\t\t0.055899348109960556\n",
      "#################################\n",
      "\n",
      "Epoch  18000\n",
      "physics loss:\t\t5.521170898216214e-06\n",
      "observation loss:\t0.023374954476309617\n",
      "loss:\t\t\t0.023380475647207834\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.049693092703819275\n",
      "beta:\t\t\t0.06302155554294586\n",
      "#################################\n",
      "\n",
      "Epoch  19000\n",
      "physics loss:\t\t4.2573548526017875e-06\n",
      "observation loss:\t0.018871720035331722\n",
      "loss:\t\t\t0.018875977390184324\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.05453198403120041\n",
      "beta:\t\t\t0.06991928815841675\n",
      "#################################\n",
      "\n",
      "Epoch  20000\n",
      "physics loss:\t\t3.5550732592339104e-06\n",
      "observation loss:\t0.015757650556672068\n",
      "loss:\t\t\t0.015761205629931302\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.05863002687692642\n",
      "beta:\t\t\t0.07569078356027603\n",
      "#################################\n",
      "\n",
      "Epoch  21000\n",
      "physics loss:\t\t2.582923094956737e-06\n",
      "observation loss:\t0.013518483451621346\n",
      "loss:\t\t\t0.013521066374716303\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.061724212020635605\n",
      "beta:\t\t\t0.08021965622901917\n",
      "#################################\n",
      "\n",
      "Epoch  22000\n",
      "physics loss:\t\t2.272518001647618e-06\n",
      "observation loss:\t0.011754612082508633\n",
      "loss:\t\t\t0.01175688460051028\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.0639999508857727\n",
      "beta:\t\t\t0.08349073678255081\n",
      "#################################\n",
      "\n",
      "Epoch  23000\n",
      "physics loss:\t\t2.1015029007195647e-06\n",
      "observation loss:\t0.010462922018919789\n",
      "loss:\t\t\t0.010465023521820509\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.0653863400220871\n",
      "beta:\t\t\t0.08549153059720993\n",
      "#################################\n",
      "\n",
      "Epoch  24000\n",
      "physics loss:\t\t3.267445245303824e-06\n",
      "observation loss:\t0.0092998586697264\n",
      "loss:\t\t\t0.009303126114971704\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.06673039495944977\n",
      "beta:\t\t\t0.08717170357704163\n",
      "#################################\n",
      "\n",
      "Epoch  25000\n",
      "physics loss:\t\t3.466631547577182e-06\n",
      "observation loss:\t0.00846185119076541\n",
      "loss:\t\t\t0.008465317822312987\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.06874528527259827\n",
      "beta:\t\t\t0.08975958079099655\n",
      "#################################\n",
      "\n",
      "Epoch  26000\n",
      "physics loss:\t\t3.884010924908015e-06\n",
      "observation loss:\t0.007413181213996863\n",
      "loss:\t\t\t0.007417065224921771\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07079681009054184\n",
      "beta:\t\t\t0.09220335632562637\n",
      "#################################\n",
      "\n",
      "Epoch  27000\n",
      "physics loss:\t\t4.201580756886954e-06\n",
      "observation loss:\t0.006786243733933726\n",
      "loss:\t\t\t0.006790445314690613\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07230375707149506\n",
      "beta:\t\t\t0.09404034912586212\n",
      "#################################\n",
      "\n",
      "Epoch  28000\n",
      "physics loss:\t\t4.5657114996443715e-06\n",
      "observation loss:\t0.006389622595630189\n",
      "loss:\t\t\t0.006394188307129833\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07400451600551605\n",
      "beta:\t\t\t0.0960889607667923\n",
      "#################################\n",
      "\n",
      "Epoch  29000\n",
      "physics loss:\t\t4.10737694862486e-06\n",
      "observation loss:\t0.005944900041995046\n",
      "loss:\t\t\t0.005949007418943671\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07587942481040955\n",
      "beta:\t\t\t0.09825523942708969\n",
      "#################################\n",
      "\n",
      "Epoch  30000\n",
      "physics loss:\t\t5.545502084035884e-06\n",
      "observation loss:\t0.005610746370077902\n",
      "loss:\t\t\t0.005616291872161938\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07720818370580673\n",
      "beta:\t\t\t0.09985308349132538\n",
      "#################################\n",
      "\n",
      "Epoch  31000\n",
      "physics loss:\t\t4.982880808871798e-06\n",
      "observation loss:\t0.0053296108082894565\n",
      "loss:\t\t\t0.005334593689098328\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07848984003067017\n",
      "beta:\t\t\t0.10143924504518509\n",
      "#################################\n",
      "\n",
      "Epoch  32000\n",
      "physics loss:\t\t5.317613643121745e-06\n",
      "observation loss:\t0.005059270993277027\n",
      "loss:\t\t\t0.005064588606920149\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.07957851141691208\n",
      "beta:\t\t\t0.10281424969434738\n",
      "#################################\n",
      "\n",
      "Epoch  33000\n",
      "physics loss:\t\t6.747756384934829e-06\n",
      "observation loss:\t0.004846530744607481\n",
      "loss:\t\t\t0.004853278500992416\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08056166023015976\n",
      "beta:\t\t\t0.10408066213130951\n",
      "#################################\n",
      "\n",
      "Epoch  34000\n",
      "physics loss:\t\t7.102196313222368e-06\n",
      "observation loss:\t0.0046397047178300494\n",
      "loss:\t\t\t0.004646806914143272\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08179917931556702\n",
      "beta:\t\t\t0.10560827702283859\n",
      "#################################\n",
      "\n",
      "Epoch  35000\n",
      "physics loss:\t\t6.219588891756858e-06\n",
      "observation loss:\t0.004481988879490172\n",
      "loss:\t\t\t0.004488208468381928\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.0830327495932579\n",
      "beta:\t\t\t0.10714659094810486\n",
      "#################################\n",
      "\n",
      "Epoch  36000\n",
      "physics loss:\t\t6.693666229607881e-06\n",
      "observation loss:\t0.004339014303826681\n",
      "loss:\t\t\t0.0043457079700562895\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08411721885204315\n",
      "beta:\t\t\t0.10852637141942978\n",
      "#################################\n",
      "\n",
      "Epoch  37000\n",
      "physics loss:\t\t6.913034125352385e-06\n",
      "observation loss:\t0.004205424861208154\n",
      "loss:\t\t\t0.004212337895333507\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.0851905569434166\n",
      "beta:\t\t\t0.10992693156003952\n",
      "#################################\n",
      "\n",
      "Epoch  38000\n",
      "physics loss:\t\t7.054415547354161e-06\n",
      "observation loss:\t0.0041203523748157885\n",
      "loss:\t\t\t0.004127406790363142\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08627694845199585\n",
      "beta:\t\t\t0.11129483580589294\n",
      "#################################\n",
      "\n",
      "Epoch  39000\n",
      "physics loss:\t\t7.30465189206374e-06\n",
      "observation loss:\t0.003978380468400584\n",
      "loss:\t\t\t0.0039856851202926475\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08708962798118591\n",
      "beta:\t\t\t0.11231888830661774\n",
      "#################################\n",
      "\n",
      "Epoch  40000\n",
      "physics loss:\t\t7.583628165367575e-06\n",
      "observation loss:\t0.0039177623598079855\n",
      "loss:\t\t\t0.003925345987973353\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.087773896753788\n",
      "beta:\t\t\t0.11319407820701599\n",
      "#################################\n",
      "\n",
      "Epoch  41000\n",
      "physics loss:\t\t7.880912303033405e-06\n",
      "observation loss:\t0.0038008713931158185\n",
      "loss:\t\t\t0.003808752305418852\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08840202540159225\n",
      "beta:\t\t\t0.11399751156568527\n",
      "#################################\n",
      "\n",
      "Epoch  42000\n",
      "physics loss:\t\t8.092172458157662e-06\n",
      "observation loss:\t0.003718016397164523\n",
      "loss:\t\t\t0.0037261085696226803\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08893223851919174\n",
      "beta:\t\t\t0.11469309031963348\n",
      "#################################\n",
      "\n",
      "Epoch  43000\n",
      "physics loss:\t\t8.44329570216976e-06\n",
      "observation loss:\t0.0036484628034419795\n",
      "loss:\t\t\t0.003656906099144149\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08936411887407303\n",
      "beta:\t\t\t0.11531399190425873\n",
      "#################################\n",
      "\n",
      "Epoch  44000\n",
      "physics loss:\t\t8.67140366401083e-06\n",
      "observation loss:\t0.003682291650295039\n",
      "loss:\t\t\t0.0036909630539590497\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.08979722857475281\n",
      "beta:\t\t\t0.11586518585681915\n",
      "#################################\n",
      "\n",
      "Epoch  45000\n",
      "physics loss:\t\t9.048615164922593e-06\n",
      "observation loss:\t0.003513215857099602\n",
      "loss:\t\t\t0.0035222644722645246\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.09013684839010239\n",
      "beta:\t\t\t0.11631626635789871\n",
      "#################################\n",
      "\n",
      "Epoch  46000\n",
      "physics loss:\t\t9.262601101768972e-06\n",
      "observation loss:\t0.00347386359011784\n",
      "loss:\t\t\t0.0034831261912196087\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.09043703228235245\n",
      "beta:\t\t\t0.11674117296934128\n",
      "#################################\n",
      "\n",
      "Epoch  47000\n",
      "physics loss:\t\t9.416169464308715e-06\n",
      "observation loss:\t0.003412295039728308\n",
      "loss:\t\t\t0.003421711209192617\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.09071660786867142\n",
      "beta:\t\t\t0.11713622510433197\n",
      "#################################\n",
      "\n",
      "Epoch  48000\n",
      "physics loss:\t\t9.739690366261128e-06\n",
      "observation loss:\t0.00338372787888427\n",
      "loss:\t\t\t0.003393467569250531\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.09093859791755676\n",
      "beta:\t\t\t0.117407888174057\n",
      "#################################\n",
      "\n",
      "Epoch  49000\n",
      "physics loss:\t\t9.882686908766499e-06\n",
      "observation loss:\t0.003363522780896052\n",
      "loss:\t\t\t0.0033734054678048186\n",
      "---------------------------------\n",
      "alpha:\t\t\t0.09113705903291702\n",
      "beta:\t\t\t0.11772657185792923\n",
      "#################################\n",
      "11740129004170718876\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yBgciIkLOse57GQBgapKIX1+t78cjIn/F4EBENMC9/Iml2/tLTE0SsfQKHUIM7GWgTn0SHGpqapCdnY2CggKEhIRg7ty5WLBggcvlRpuamrB7927s2rULGo0GTzzxBADAYrHgwIED2LVrF3744Qe89NJLMJlMfXG4REQD1r1r212WCwKweJYWV4zXcKloUvB6cGhvb8fq1asRHx+PlStXor6+HuvWrYPFYsGtt96qqr98+XIAQExMDM6cOeMo/+tf/4oDBw5g7NixaGxsRGtrK4MDEZGXyLKMt3d1v5jTypv0SIoW+/GIKFB4PThs374dNpsNy5Ytg1bbuXutVousrCzMmzcPoaGhivrPPfccIiMjsXPnTmzcuNFRfs8990Cn06G1tRUPPfSQtw+TiGhA+/h7O77Md31zqjV3G2DUs5eBXPN6nMzNzcWMGTMcoQEAJk2aBKPRiH379qnqR0dHK+qeFR4ezh4GIqI+8GWeDR/sdd3bsPaXDA10fl4NDpIkobS0FKmpqcoXEUWkpqaiuLjYmy9HREQe2vGDDW+5GKIYGSXgf35pgMj5DHQBXh2qMJvNsFqtCA8PV20LDw9HfX29N18OAFBUVOT1fZaVlXl9nwMZ29P72KbeNVDac3fZYGwvjlCV/2RsPdJHtsKbv+0GSpv2l75qz9GjR3v8HK/2OHR0dK5p7mqIwWQyoa2tzZsvR0REbpBl4MuicJeh4bLkRqSPPM+KT0ROvNrjYDAYAHT2PDgzm83Q672/eEhP0pI/7HsgYnt6H9vUu4KxPe2SjNe/sWLPCfU6DddM0eCW6cMgCDF99vrB2Ka+5A/t6dXgYDKZoNPp0NTUpNrW2NiIqKgob74cERGdh8Um4+9brTjkIjTcmqHFvMlco4E859WhClEUkZSUhMLCQkW5JEkoLCxEUlKSN1+OiIi6Ye6QkbXFogoNogD8fI4O10zRMjRQj3j9cszp06dj9+7dsNvPXR+cl5cHs9mM9PR0b78cERE5aTDL+O+PLDh2WlaU6zTAA1frMGss72xJPef14JCZmQlRFLFmzRqUlJRg//79WLt2LebPn4+IiAjk5eVh+fLlqKio8PZLExENeOVnJCx/swOVdcrQYNIDv71OjymJDA3UO15fOdJoNGLFihXIzs7GqlWrYDAYkJmZiYULFwIA2tra0NDQAIvFonheVFQUYmLUE3RMJhOGDBnCxaCIiC7gm0Ibsr9Rr9EQbgIevlaP+CFcQpp6T5BlWb5wtYHl7NoQ/jB7NRiwPb2PbepdwdCeB8rsWPOZ+rbYw8IEPDJfh+iw/g0NwdCm/sSf2pO31SYiCnAvbrag8KT6yomowcCKG/UIG8RJkOQ9DA5ERAFKkmX8ycUkyLP+cJOBoYG8jsGBiCgASbKM7G9s3YYG3uGS+gqDAxFRgLHZZaz7yoo9xerhiaGhAp6/TQ9RZGigvsHgQEQUQNqtMl77wor8CnVoGBEp4JlFBh8cFQ0kDA5ERAHC3CHjwdc7XG67a7YWV6TxlE59j58yIqIA0NAq44X/qEODQQv8+modJozkwk7UPxgciIj8XE2ThJc+tuJMi7LcpAceulaPUTFc2In6D4MDEZEfq2mS8KePLKhrVZaHDgJ+O5+rQVL/Y3AgIvJTVY0SXtxsQX2retsjXEKafITBgYjID3UXGuKiBDwyX48IEy+3JN9gcCAi8jMn6yU89a4Fzks7JQwR8Nvr9BhsZGgg32FwICLyI5V1Ep5+z0VoGCrgt/MZGsj3GByIiPxEXYuMlz9Rh4ak6M7hiRADQwP5HoMDEZEfaLN0hgbVnIbIzp4GE0MD+QlOySUi8jFJkvGPbVacrFffsGr5AoYG8i/scSAi8iFZlvF/X1mRV66890RshIDHbuCcBvI/7HEgIvKhj7+3Y3eRMjSEGIAHr9ExNJBfYnAgIvKR0hoJH+2zKcr0GuCBq/WIDuPpmfwThyqIiHygqlHCqk0WVfndc3QYG8vQQP6Ln04ion7W3CZj5QZ1aJgYL2L6KN7lkvwbexyIiPpRSbWE17aqQ8OISAHL5ul8cEREnmFwICLqB5Is4/NDdmz6zga7pN7+8yt00IqcDEn+j8GBiKiP1bXI+L/tVvxwykViAHB/pg7JwzhyTIGBwYGIqI9Ikoxvf7Bj424bzOrRCYwZLuD+ubzTJQUWBgcioj7Q0i7j4ewOl9sEAPOmaHDTNC2HJyjgMDgQEXnZiVoJz7zvoosBQNRg4Bdz9Bg3gkMTFJgYHIiIvGjvcTv+vtXqclvGaBGLZ+l47wkKaAwORERecqDMjv/50nVoePhaHSbGc40GCnwMDkREXvB9aWdPg/OllgYd8PQtXEKaggeDAxFRL+0vseMfW62wq++KjRcXGzBIz6EJCh4MDkREvfBVgQ1v7rCpymeP02DJ5VqIAkMDBRcGByKiHtryvQ3vf6cODZelanDXZQwNFJwYHIiIeuDTg65DwxXjNVg8m6GBgheDAxGRB2RZxju5Nnxx2K7adtUEDX52KUMDBTcGByIiN0myjHdybNiapw4NQ0MF/GwmQwMFPwYHIiI3mC0yHvyX6yWk507U4LaZWggMDTQAMDgQEV3A8WoJz3/gegnpsbECfsrQQAMIgwMR0XkUnrTjxc3nVoMUBECWAY0A3HOlDhmjuRokDSwMDkRE3Th6SsKrnyqXkJZ/XOTpgXk6TE5gaKCBh2ugEhG5cOy0hFc+scCivuISD17D0EADF3sciIicnG6Q8N8fup7T8KfFBkSGcD4DDVxeCQ41NTXIzs5GQUEBQkJCMHfuXCxYsMDlZKGmpibs3r0bu3btgkajwRNPPKHYvm3bNmzevBmNjY0YM2YMlixZgri4OG8cJhHRBVU1SnjiHXVouChRxK9+ooNWZGigga3XwaG9vR2rV69GfHw8Vq5cifr6eqxbtw4WiwW33nqrqv7y5csBADExMThz5oxi27Zt2/DWW2/hnnvuwciRI7F161Y899xzeP755xEREdHbQyUiOq+6FhlZW9S3xZ4YL+L+uQwNRIAXgsP27dths9mwbNkyaLWdu9NqtcjKysK8efMQGhqqqP/cc88hMjISO3fuxMaNGx3lVqsV7733Hu68807MmjULAPCLX/wCZWVl2LJlC+64447eHioRUbfMFhH/2mJBbbP6Fpe/ytRBp2FoIAK8MDkyNzcXM2bMcIQGAJg0aRKMRiP27dunqh8dHa2oe9aRI0fQ0tKCjIwMRfmsWbOQm5vb28MkIupWu03AhoNDcbpBHRpeutMAI2+LTeTQq+AgSRJKS0uRmpqq3KkoIjU1FcXFxW7vq6SkBImJiTCZTIryCRMmoLa2Fo2Njb05VCIilzpsMt47OBRVzXpFuVHXGRrCTQwNRF31aqjCbDbDarUiPDxctS08PBz19fVu76uhocHlfsLCws67vaioyIMjdk9ZWZnX9zmQsT29j23qHXYJ2Hh4CCoaBynKw4w23Dm1BjUn7ajx0bEFOn5Gvauv2nP06NEeP6dXPQ4dHZ3rtjv3Epwta2tr82hf3e0HgEf7IiK6EEkGPiqIwvEzytAQorfjZxfVIsyovpEVEfWyx8FgMADo7HlwZjabodfrVeXn25er4Yiz++5uXz1JS+7qy30PRGxP72Ob9owky1i7zYrCaklRbjIAv1swCCOjknxzYEGIn1Hv8of27FWPg8lkgk6nQ1NTk2pbY2MjoqKi3N5XRESEy/2cLfNkX0RE3ZEkGW98a8Pe48rQoNNIeOgaPUZGcUFdovPp1TdEFEUkJSWhsLBQUS5JEgoLC5GUlOT2vpKTk1FaWqoaksjPz0dkZCTXcSCiXpMkGWu/tOLbQuUwhEaQccukMxgVw9BAdCG9/pZMnz4du3fvht1+7ouYl5cHs9mM9PR0t/eTmpqKkJAQ7NmzR1Gek5OD6dOn9/YwiWiAs9ll3Pe/HaqeBkEAbphwBklRHT46MqLA0uvgkJmZCVEUsWbNGpSUlGD//v1Yu3Yt5s+fj4iICOTl5WH58uWoqKg47370ej0WLVqEN954A7t27cKJEyewbt06nDx5EjfccENvD5OIBjBJkvHaF+oVIQUBWHq5FuOGtfvgqIgCU69XjjQajVixYgWys7OxatUqGAwGZGZmYuHChQA6r4ZoaGiAxaJc+z0qKgoxMTGKsszMTMiyjHfffRf19fVISUnBE088gcjIyN4eJhENYBv32HDwhLKnQasBHrpGh/FxGvTBVd1EQUuQZVm9VNoAd3ZtCH+YvRoM2J7exzZ13+4iO/7nS3VvwyPzdZgwsvPW2GxP72Obepc/tSdnAhFR0CqtkfDPr9Wh4aZLtI7QQESe8cpttYmI/E2bRcY/tlphc1rHacnlWlyeylMfUU+xx4GIgtL6XTbUON3pcvY4DUMDUS8xOBBR0DlQasfOo8quhlExAu6czdBA1FsMDkQUVFraZWR/q5zXYNAB916pg1bDO10S9RaDAxEFlbd2WtHkdE+82y/VIjqMpzsib+A3iYiCxt7jduwpVq7XMDlBxKyxvIKCyFsYHIgoKDS1yXhzh3KIwmQAllymgyBwiILIWxgciCjgybKMN761osVp5eg7LtUhIoShgcibGByIKODlFkn4vlQ5RHFxkoiM0TzFEXkbv1VEFNBO1kt4w+kqisFG4C4OURD1CQYHIgpYFlvn6pAWm7L8ztk6hA1iaCDqCwwORBSwNuTYUFmvXh3ykhReRUHUVxgciCgg7T1ux9dHlKtDxkUJuGMWV4ck6ksMDkQUcGqaJLz+jXJeg14L3J+pg17LIQqivsTgQEQBxSbJ+J8vrWizKMsXz9JiRCRPaUR9jd8yIgoo/9lrw/Fq5byGjNEiLuXqkET9gsGBiAJGfoUdnxxQzmsYFibgrtm89JKovzA4EFFAaDTL+L/tynkNGhG4L1MHo56hgai/MDgQkd+TZBnrvlLf9fKW6VokRfM0RtSf+I0jIr/3+SE78iuUS0pPihfxk0mc10DU3xgciMivHa+WsGmPcmnIcBNwzxzOayDyBQYHIvJbZouMtdussHe5iEIA8MurdAjlktJEPsHgQER+SZZlZH9jRW2z8tLL6y7WIHUEhyiIfIXBgYj80rc/2LH3uHJew+gYAQvSuaQ0kS8xOBCR3zlZL2H9TuW8BpMB+GWmHhqRQxREvsTgQER+xWqX8Y9tVliU6zzh7st1GDKYoYHI1xgciMiv/GevDZV1ynkNV6ZpMDWZ8xqI/AGDAxH5jR9OSvjsoLKrYWSUgEUzOK+ByF8wOBCRX2i3yPjn11Z07WvQisAvruStson8CYMDEfmF9Tk21aWXC6drET+Epykif8JvJBH53LeFNuz4QTlEMTZW4JLSRH6IA4dE5BMdVhmnG2XkHLVja54yNBh0wM+v0EHkktJEfofBgYj6XWmNhFWbLN1uv+NSLaLD2CFK5I/4zSSifiXJnZMgu3PtRRpcOpZDFET+ij0ORNSv9h6XVOs0AIAgADdP0+Lai3haIvJn/IYSUb+x2jrvduksYYiAxbN1GBXDTlAif8fgQET95lOnxZ0A4Jbp7GUgCiSM90TUL1o7ZHx+SHnjqvghAuZN4XwGokDC4EBE/WLrYRvanEYp5k7U8JJLogDD4EBEfc7cIavWapiWImLWOA5REAWaHn9ra2pqkJ2djYKCAoSEhGDu3LlYsGABhG5+PVgsFmzYsAE5OTmw2+1IT0/H4sWLERISoqi3fft2fPLJJ6iqqkJkZCTmzJmDBQsWQKNhdyZRoNqaZ0dbl2UbBADXT2VoIApEPfrmtre3Y/Xq1YiPj8fKlStRX1+PdevWwWKx4NZbb3X5nLVr16K8vBwPPPAAdDod3n77bWRlZWHlypWOsPH+++9jy5YtuP322zFmzBiUl5fj3//+N06cOIEHH3yw5++SiHzGbJGx9bBybkN6ioi4KHZ4EgWiHgWH7du3w2azYdmyZdBqO3eh1WqRlZWFefPmITQ0VFG/tLQUOTk5ePHFFxEbGwsAeOSRR/Dwww/jwIEDuPjii9HU1IRNmzbh4YcfRnp6OgAgISEBI0aMwMqVK3HttddizJgxvXmvROQD2/LsMDstEsneBqLA1aPIn5ubixkzZjhCAwBMmjQJRqMR+/btc1k/JSXFERoAICwsDJMnT0Zubi4A4OjRo9Dr9Zg6dariucnJyRg5ciSOHj3ak0P1yIEyO97JteLdg0Pw913DsS3PduEnEVG3KuskbPneqbchWcRI9jYQBSyPv72SJKG0tBSpqanKHYkiUlNTUVxcrHpOSUkJxo8frypPS0tz1JckCRqNxuUcCa1WqwgpfWXfcTs+P2RH8ZlBaGjX4mS9enU7InKPLMtYn2OD1WnpBvY2EAU2j7/BZrMZVqsV4eHhqm3h4eGor69XlTc2NnZbv6GhAQCQmJiIlpYWHDt2TDEkUVNTg/LyckycONHl8RQVFXn6Frqlt4cCOHecJadaUVRU67X9D1RlZWW+PoSgEwhtevyMAUcqoxVlU+Na0FHfgCL1acKnAqE9Aw3b1Lv6qj1Hjx7t8XM87nHo6OgAAJhMJtU2k8mEtrY2l8/prn57ezsAICYmBrNnz8aaNWtw9OhRSJKEEydOICsrCzNmzEBcXJynh+qxqBDlReZnzLo+f02iYCTJwPYi5Y+FUIMNV45u8M0BEZHXeNzjYDAYAHT2PDgzm83Q6/Uun+Oqfmtrq6L+L3/5S/z73//Gc889B5vNBlmWYTKZ8Nhjj3V7PD1JS90JGSph0+Fzs7haLRrExo9CiIEL1HiDN/+tqJO/tunOH2yoaVXObbh1hhHjx/nn8Z7lr+0ZyNim3uUP7elxj4PJZIJOp0NTU5NqW2NjI6KiolTl4eHhLus3NTUhMjLS8Vin0+Huu+/G//7v/+KFF15AWFgYbrvtNpfDHH0hOkyAxikjnG7gPAciT1hsMj7YqwwNcVECZo7hWixEwcDj4CCKIpKSklBYWKgolyQJhYWFSEpKUj0nOTkZR44cUZUXFBQgOTlZVa7T6bBt2zYMGTIEmZmZnh5ij2lFAdHhyuRwqkHqt9cnCgbbC+yob1WWLcrQQhTZc0cUDHp0TdT06dOxe/du2O3npkvn5eXBbDY71mDoKiMjA8XFxaiqqnKUNTc349ChQ8jIyFDVP3LkCL788kv84he/gCj272VbsRHKkxt7HIjc126V8ckBZW/D+DgRE0by8kuiYNGjb3NmZiZEUcSaNWtQUlKC/fv3Y+3atZg/fz4iIiKQl5eH5cuXo6KiAkBnj8PMmTORlZWF/Px8HDt2DFlZWUhISFAFjY6ODqxduxZXXXUVUlJSev8OPeQcHE7xkkwit23Ls6OlXVm2cJq226XoiSjw9OiCaqPRiBUrViA7OxurVq2CwWBAZmYmFi5cCABoa2tDQ0MDLJZzEw3vu+8+rF+/HmvWrIHFYsHUqVOxdOlSVY/C2XUdbrvttp6+p16JjRABnOtJqWRwIHJLu1V92+zJCSJShrG3gSiYCLIs8y9jFydqJTzzvnJ93FfvNsCk5y+mnjq71oY/zAYOFv7Ypp8fsuGdXGVwePJmPRKH+n9w8Mf2DHRsU+/yp/b0/290P4uNFCAKyixVcYbZiuh8bHYZXxxW9zYEQmggIs/wW+1EpxEwxKRcCKriDK+sIDqfHT+or6SYfxGXliYKRgwOLgwbrAwO5XXscSDqjrlDvW7DmOECRg/n6YUoGPGb7cKwUKfgwB4Hom5t/t6mupLihnT2NhAFKwYHF5x7HE7WyZAk9joQOSs/I2FbnvL2lxcniRgfx1UiiYIVg4MLzsHBYgeqmhgciLpqaZexdpsV9i4dchoRWDSDvQ1EwYzfcBdC9BJC9Ha0Ws79aio/IyM2wnfHROQv2i0ylv2rw+W2qydrMCyMv0eIghm/4d1w7nU4Uct5DkSyLOOVTy0utyUMFTi3gWgAYHDoRkyo8uR4opZDFUQff2/HsdPq70JkCHB/pg4659vLElHQ4c+Dbgx3urKitFaCLMtcc58GpOZ2GU9u6ECLixGKmHABj16nR9RgfjeIBgIGh27EOvU4mDuA2mYZ0WE8OdLAYrPLeP4Di8vQMCVBxAPzdBAZqIkGDAaHboQZ7RhshOL69NIaGdFhvjsmIl/4cJ8NNS6uKlqUocW8KTyFEA00nOPQDUEAkqKVzVNawwmSNLBU1En49KBdVX7vlTqGBqIBisHhPJKGKrtfyzhBkgYQSZbxxrdWOK999tA1OswYwwWeiAYqBofzcO5xKKuRIPEu5DRAfHHYjuIq5ef9+qkaTEpgaCAayBgczsM5OLRZgepGBgcKfsVVEt7fo7xx1bAwgXe8JCIGh/OJCBEQblKWcbiCgll9q4y9x+145VOLYilpAFg8Wwu9lldPEA10/PlwAUlDRRw8ce4MWlItIWM0u2op+GzIseKLw+qJkABwzRQNJozk556I2ONwQap5Dlx6moJQu0XuNjRMGCnipmn8jUFEnRgcLiAxWtk1e6KWt9im4PNlvuvQkDpCxK+v1kErcoiCiDrxZ8QFJA5VZqsOG3C6UcaISJ5IKTg0tcnYcsCmKv/JJA0WTuO8BiJSYnC4gHCTgMgQoL71XFlpjYwRkb47JiJven+PDe3KW7PgwWt0mMzLLonIBQ5VuIHzHChYvfqpBTt+UA5TzBwjMjQQUbcYHNzgPFzBpacpGFQ3STh0QvlZ1muBhdN0PjoiIgoEDA5uSHKaIFleK8POCZIU4LZ8r54QeccsLW+PTUTnxeDgBuceB4sdON3A4ECBq/yMhJ0/qIPD7HGc9kRE58fg4IbQQQKiBivLOFxBgUqWZbyTa4Nz9H3xToNPjoeIAguDg5uSVPMc2ONAgemTA3YcqVQG3xvTtYgwcYiCiC6MwcFNvLKCgsGhE3a8/51yzYbIEODqKbyKgojcw+DgJucVJMvPyLBxgiQFmFc/tarKbp6mg4GLPBGRmxgc3OQ8QdJqB07VMzhQ4OhuXk7GGJ4GiMh9PGO4abBRwNBQ5a8yTpCkQLJ5v3pZ6XmTNRAF9jYQkfsYHDyQONQ5OLDHgQJDZZ2EA2XKoCsKwMLpvPySiDzD4OABTpCkQPWJ002sTAbg1aUG3vWSiDzG4OAB5x6HijoZNjt7Hci/nWmWsadYGXIzJ2hg1DM0EJHnGBw8kOjU42CzAyc5QZL83Jf5NnS9AEivBTIncoiCiHqGwcEDIQYB0c4TJDlcQX6s3Srjm0Ll0tKzx2kw2MjeBiLqGQYHDzmv51DGCZLkx3KO2tFmOfdYAJA5kYs9EVHPMTh4yHk9B06QJH8lyTK25il7GyYniIgJ59eeiHqOZxAPOd9iu+IMJ0iSfyqokFDVqPxszp3E3gYi6h0GBw859zjYJKCSEyTJD23PV/Y2xEUKSB3BrzwR9Q7PIh4yGQQMC+MKkuTf6lpkHCpXfi6vmqCBwFUiiaiXenxNVk1NDbKzs1FQUICQkBDMnTsXCxYs6PbEZLFYsGHDBuTk5MButyM9PR2LFy9GSEiIol5JSQn+/e9/o6ioCCaTCZdddhkWLVoErdZ/Lh9LihZQ3XSul6GsRgbG+/CAiJzsPGqH3KUjzKgDMsZwmIKIeq9HPQ7t7e1YvXo1NBoNVq5ciaVLl+Kzzz7Dxo0bu33O2rVrkZeXhwceeACPPvooTp06haysLMhdzm719fV47rnnEBMTg6eeego///nPsWPHDqxfv74nh9lnnIcreEkm+RNJlrGjULlSZMZoDYw69jYQUe/16Gf89u3bYbPZsGzZMkdPgFarRVZWFubNm4fQ0FBF/dLSUuTk5ODFF19EbGwsAOCRRx7Bww8/jAMHDuDiiy8GAOzZswchISG49957IQgCkpOT0d7ejuzsbNx55529eZ9e5XxJZmWdDKtdhk7DEzP53pFKCWdalGWXpbK3gYi8o0c9Drm5uZgxY4Zi+GDSpEkwGo3Yt2+fy/opKSmO0AAAYWFhmDx5MnJzcx1lkiRBp9MphjtEUfS7cdkEpx4Hu9QZHoj8wbdOCz7FDxFUy6UTEfWUx8FBkiSUlpYiNTVVuSNRRGpqKoqLi1XPKSkpwfjx6kkAaWlpivqXXHIJamtr8dFHH8FisaCyshLvv/8+Zs2a5elh9imTXkBMuPNCUByuIN9rbpfxfanys3jZOE6KJCLv8Xiowmw2w2q1Ijw8XLUtPDwc9fX1qvLGxsZu6zc0NDgeR0dH44YbbsDbb7+N9evXQ5ZlDB48GDfffHO3x1NUVOTpW7igsrKyC9YZYoxCVaPJ8fhQcQPi9A1eP5Zg4E57kme6a9MP8qJgl859LjWijGGaMhQVsUfsfPgZ9T62qXf1VXuOHj3a4+d43OPQ0dEBADCZTKptJpMJbW1tLp/TXf329nbH44MHD2LTpk248cYb8eyzz+LRRx9FZGQkXnrpJdjtdtXzfWl4qEXx+HSz3kdHQtRJloHCauX3bOzQNhh1DA1E5D0e9zgYDAYAnT0PzsxmM/R69R9Qg8Hgsn5ra6ui/ptvvonrr78et912m6NszJgx+O1vf4vdu3fj0ksvVe2jJ2nJXefbt90k4cuic+GhtlWPxKRR0GnZJdydvvy3Gqi6tmnhSTsAq2L7FZPDMTolqp+PKnDxM+p9bFPv8of29LjHwWQyQafToampSbWtsbERUVHqk1R4eLjL+k1NTYiMjAQAtLS0oLKyEjNnzlTUCQsLw4QJE3Ds2DFPD7VPJQwV0DUi2GWgghMkyYdyj6nn2UxN4hpvRORdHp9VRFFEUlISCgsLFeWSJKGwsBBJSUmq5yQnJ+PIkSOq8oKCAiQnJwOAY/KWJKlPfjabDaLoXyfAQS4mSHI9B/IVq03GvhLlcN7CaVqIInvAiMi7evTXePr06di9e7di3kFeXh7MZjPS09NV9TMyMlBcXIyqqipHWXNzMw4dOoSMjAwAQEhICOLj47Fr1y7Fc+vr61FQUIAJEyb05FD7FG+xTf7i+zJJdfvsmVwpkoj6QI+CQ2ZmJkRRxJo1a1BSUoL9+/dj7dq1mD9/PiIiIpCXl4fly5ejoqICQGePw8yZM5GVlYX8/HwcO3YMWVlZSEhIUASNO++8E59++ineffddnDhxAgcOHMDq1asxbtw4xyJR/oS32CZ/kXNU2dswboSIqMHsbSAi7+vRypFGoxErVqxAdnY2Vq1aBYPBgMzMTCxcuBAA0NbWhoaGBlgs534C3XfffVi/fj3WrFkDi8WCqVOnYunSpYohiEmTJuGxxx7Du+++iy1btsBkMmH27Nm45ZZb/PI69KRoZXA4WSfDYpOh5wRJ6kfN7TLyK5Sh9dKx7G0gor7R4ztHRUdH49FHH3W5bdq0aZg2bZqiTK/XY8mSJViyZMl59zthwgS/HJZw5ewEybMDFGcnSKYMY3Cg/rPvuB1Sl1EyvYaTIomo7/Ds0gtGnYDhEVxBknxrT7FymGJyogijnuGViPoGg0MvOd8DoJQTJKkf1bfKOHZK+ZmbPorDFETUdxgcesl5nkMpexyoH+07bkfX2DBIB0yK59eaiPoOzzC9lDzMaYJkvYzWDvY6UP9wXrvhoiSRq5cSUZ9icOilhKECdF16hmUARafZ60B9r9Uioui0MqReksJhCiLqWwwOvaTTCEh2uoriGIMD9YOiWqNimMKgA9Li+JUmor7Fs4wXjBmubEYGB+oPR2sGKR5PiucwBRH1PQYHL3AODqU1nQtBEfWVDpuA0jqjouziJA5TEFHfY3DwglExIroubGmXeFkm9a3jZ4ywy+c+dBoRmJTArzMR9T2eabxgkF5AfJSyi/joKQ5XUN9xHqZIHSHCxEWfiKgfMDh4yWjOc6B+YrPLKD6jHKbgEtNE1F94tvGSsbFOweGUBKudwxXkfcdOS7DYlZ+3KZzfQET9hMHBS1JHiOjaUWyxA8VV7HUg7ztcrvxcJUULiDBxmIKI+geDg5cMNgpIcLpvxZFKBgfyvkMnlJ8rLjFNRP2JZxwvGu+0+A6DA3lbTZOE0w3KIbBJCRymIKL+w+DgRc7BoaRGhtnCeQ7kPQUVyjAaauwcqiAi6i8MDl40ZrgIbdf7VsjADyfZ60DeU+DUi5U2UoQoMDgQUf9hcPAivVbA6BgOV1DfkCQZR5yCKO9NQUT9jWcdL3M+kTv/QiTqqbJaGeYOZdn4OM5vIKL+xeDgZc7zHE43yKhr4TwH6j3n3qshJiuiBnOYgoj6F4ODlyUOFWAyKMuOVNp9czAUVJx7rxKjOrqpSUTUdxgcvEwUBYwfweEK8q4Om4wip2XMkyLbfXQ0RDSQMTj0AVfzHCSZwxXUc0WnJdi65AZBkJEQyR4HIup/DA59IG2kslmb24CKMwwO1HPO6zeMCLXAqOVnioj6H4NDH4gOExEdqpy0xuEK6g3nyzA5v4GIfIXBoY849zo4/2IkcpfZIqPcqccqkcMUROQjDA59xDk4HDstwWpj1zJ5rui0hK5TZLQiEBfG4EBEvsHg0EfGjxDRdSVgq70zPBB56ugp5ecmeZigWNqciKg/MTj0EZNBQHI05zlQ7zkHzrGx/NoSke/wDNSHVJdlcp4DeajDJqO0WjnExeBARL7EM1AfShup7E8+cUZGUxvnOZD7jldJsHf5yIgCMGoYv7ZE5Ds8A/WhlGECDFplGe+WSZ5wnt+QMFSAUc/7UxCR7zA49CGtRsA45+WnOVxBHjh2WtlDNY7DFETkYzwL9TH18tN2yFx+mtxgs8sorlIGzTEMDkTkYzwL9THn9RzqW4HTjQwOdGElNTKsXW6sKgAYM5xfWSLyLZ6F+lhshIDIEGUZhyvIHQUVytuxx0UJCDFwfgMR+RaDQx8TBAHj45RXVzA4kDvynT4nzr1XRES+wDNRP5jgdMIvPCXBJnG4grrX2iGjpEb5GXH+HBER+QLPRP1gvNMEyQ4rcLyKwYG6d6RSeX8KvQYYy/kNROQHeCbqB2GDBMQPcV5+2t5NbSIgr9xpmekRInRazm8gIt9jcOgnXH6a3CXLMvKdJkZymIKI/IX2wlVcq6mpQXZ2NgoKChASEoK5c+diwYIFEATXv4osFgs2bNiAnJwc2O12pKenY/HixQgJ6bzk4NixY3jmmWdgt7v+JT537lzcc889PT1cn0sbKeKzQ+feW0mNDHOHDBNnyZOTUw0y6luVZQwOROQvehQc2tvbsXr1asTHx2PlypWor6/HunXrYLFYcOutt7p8ztq1a1FeXo4HHngAOp0Ob7/9NrKysrBy5UoIgoAxY8bgz3/+syo41NTU4IUXXsC4ceN6cqh+Y8xwEVoNYPvx7ckyUHhSwtRk3h+ZlJyvpogK6bysl4jIH/QoOGzfvh02mw3Lli2DVtu5C61Wi6ysLMybNw+hoaGK+qWlpcjJycGLL76I2NhYAMAjjzyChx9+GAcOHMDFF18MAIiOjla91ieffILo6GjMmDGjJ4fqN/RaAWOGi4p7VRRUMjiQmvoyTE23PXlERP2tR/2fubm5mDFjhiM0AMCkSZNgNBqxb98+l/VTUlIcoQEAwsLCMHnyZOTm5nb7Ok1NTfj6668xf/58aDSB/wfWeZ6D8x8IIqtdVt3YisMURORPPD4jSZKE0tJSpKamKnckikhNTUVxcbHqOSUlJRg/fryqPC0tzWX9sz7//HMYDAZceeWVnh6mX3L+A1DTJKOmieGBzimukmCxnXssQH05LxGRL3k8VGE2m2G1WhEeHq7aFh4ejvr6elV5Y2Njt/UbGhpcvk5HRwc+//xzXHPNNdDr9d0eT1FRkfsH76aysjKv7xPonNdg0sXCbD3Xe/L191W4KK71PM8KfH3VnsFoZ3EYgDDH45hQC05XVKjqsU29i+3pfWxT7+qr9hw9erTHz/H4p0xHRwcAwGQyqbaZTCa0tbW5fE539dvb212+zldffQWr1Yqrr77a00P0W4IAJEZ2KMpK6gw+OhryRyV1RsXj5CjX3w8iIl/xuMfBYOj8Q2c2m1XbzGazy94Bg8Hgsn5ra6vL+pIkYcuWLbjqqqswePDg8x5PT9KSu/pi3xk2G45Un+uLLm8yISUlEqIY/JPf+vLfKhi0tMuo+lIZLGdNGoLRI4Z1+xy2qXexPb2Pbepd/tCeHvc4mEwm6HQ6NDU1qbY1NjYiKipKVR4eHu6yflNTEyIjI1Xlubm5qKurw/z58z09PL+XNlI5ydPcAZTVcvlp+nGZ6S6P9VpgVAznNxCRf/H4rCSKIpKSklBYWKgolyQJhYWFSEpKUj0nOTkZR44cUZUXFBQgOTlZVb5582bMmjULQ4YM8fTw/F7UYAHDI5yXn+YESVJ/DsbGitBpgr8niogCS49+zkyfPh27d+9WLNaUl5cHs9mM9PR0Vf2MjAwUFxejqqrKUdbc3IxDhw4hIyNDUffw4cMoKyvD9ddf35NDCwjqyzJ53woC9hQrPwfOnxMiIn/QozNTZmYmRFHEmjVrUFJSgv3792Pt2rWYP38+IiIikJeXh+XLl6Pix9ngycnJmDlzJrKyspCfn49jx44hKysLCQkJqqCxefNmTJ06FSNHjuz9u/NTaU6XZRZXyeiwcrhiIDtZL6HDqiwbG8vgQET+p0crRxqNRqxYsQLZ2dlYtWoVDAYDMjMzsXDhQgBAW1sbGhoaYLFYHM+57777sH79eqxZswYWiwVTp07F0qVLIYrnTo6SJOHkyZN46KGHevm2/FtqrAhRAKQfs4JdAo6dljAxPvAXuaKe2XpY3evkfEdVIiJ/0OObXEVHR+PRRx91uW3atGmYNm2aokyv12PJkiVYsmRJt/sURRF/+ctfenpIAcOoF5AyTEBR1blehvwKBoeB7JtCdXDQDIArbYgo8LAv1EecVwM8wgmSA5YkqYepEoYyNBCRf2Jw8BHnyzIr6mQ0mjnPYSCqrFf/u985W+eDIyEiujAGBx9JHibA6PS3gb0OA1PRaeW/+2ADkDKMX00i8k88O/mIVhSQOoKXZVLnja26mhjPryUR+S+eoXzI+bLMggoJsszhioHmeLXy35yrRRKRP+MZyoecb7Pd2OZ6vJuCV3O7jOomBgciChw8Q/nQsDABQ0OVs+fzKzjPYSA57jRModcCcVG8ooKI/BeDgw8JgqBaVriAwWFAKalR/nsnRQtcv4GI/BqDg485D1ccPSXBYuNwxUBRWqP8t06O5leSiPwbz1I+lhonQujyA9Nq71x+moKfLMsoqXbuceBXkoj8G89SPhZiEJAc7XSbbQ5XDAi1zTJaO5RlSdEcpiAi/8bg4Aechys4QXJgKHEaphhshGqyLBGRv2Fw8AOulp9u4PLTQa9UNTFShCAwOBCRf2Nw8APJwwQMcl5+mr0OQc95foPzkBURkT9icPADWlFAahyXnx5IJEnGiVqnKyp4fwoiCgA8U/kJ1fLTlRIkLj8dtE41yOiwKct4RQURBQKeqfyE8wTJpjagso7BIVg5D1NEDQbCBnGogoj8H4ODnxgWJiKay08PGM5XVHDhJyIKFDxb+RHn4QoGh+Dl6ooKIqJAwLOVH3Eerjh2WkIHl58OOla7jAqnYSgu/EREgYLBwY+kjhDR9f5GNjtw7BR7HYJNZZ0Mu9M/ayJ7HIgoQPBs5UdMBgHJwzjPIdg5D1PEhAsw6dnjQESBgcHBz0xwWkWS960IPs53xOQwBREFEgYHP+M8z6GyXkZDK+c5BJOyWmUYTBzKryERBQ6esfxMUrSAQXplWX4lex2ChdUm46RqYiS/hkQUOHjG8jMaUcD4EU6rSHL56aBRXifD3iU3CAAShnKogogCB4ODH3Ieriio4PLTwcJ5YmRspACjjsGBiAIHg4Mfcl4IqrkdqDjD4BAMnCdGJrK3gYgCDIODH4oOEzEsjJdlBiPVxEjObyCiAMOzlp9yHq5gcAh8HTYZJ+udJkbyigoiCjA8a/kp5+GKotMSOqwcrghk5bUyuk5VEQQgnkMVRBRgGBz8lGr5aQk4yuWnA1qp0zDFiEgBBi2DAxEFFgYHPzVIL2BUDOc5BJOyGi78RESBj2cuP5bmvPw0F4IKaGW1XGqaiAIfg4Mfc54gebJeRl0L5zkEonarjFPOEyN5RQURBSCeufxY0lABJqflpwsquYpkIDpRK6NrbNAIwMgo9jgQUeBhcPBjoihgfJx6FUkKPM4rRo6IEqDnxEgiCkAMDn6Oy08HB94Rk4iCBc9efs55PYeWjs5ubwosZTWcGElEwYHBwc8NDRURE678I8PhisDSZpFxupETI4koOPDsFQC4/HRgcx6m0IhAHCdGElGAYnAIAKrlp6sktHP56YDhPEwRFyVAp2FwIKLApO3pE2tqapCdnY2CggKEhIRg7ty5WLBgAQTB9QnRYrFgw4YNyMnJgd1uR3p6OhYvXoyQkJBuX2PLli04ePAgHn/88Z4eZlBIjRWhEQD7j39/7D8uPz05QXP+J5JfcL6igje2IqJA1qMzWHt7O1avXg2NRoOVK1di6dKl+Oyzz7Bx48Zun7N27Vrk5eXhgQcewKOPPopTp04hKysLcjdXCNTU1ODdd9/FRRdd1JNDDCpGvYBRw7n8dKAq5YqRRBREehQctm/fDpvNhmXLliElJQXp6em47777sHnzZjQ3N6vql5aWIicnBw8//DAmTJiAsWPH4pFHHsHx48dx4MABl6/x+uuvIy4uDvPmzevJIQadtDhl7wKDQ2BobpNR08SJkUQUPHp0BsvNzcWMGTOg1Z4b6Zg0aRKMRiP27dvnsn5KSgpiY2MdZWFhYZg8eTJyc3NV9ffu3YsDBw7g3nvvhSjyJAuoJ0iebpBxhstP+73j1cqAp9dyYiQRBTaP/ypLkoTS0lKkpqYqdySKSE1NRXFxseo5JSUlGD9+vKo8LS1NVb+jowPZ2dm49tprkZSU5OnhBa3EoQJCDMqyggouP+3vnINDUrQAjcjgQESBy+PJkWazGVarFeHh4apt4eHhqK+vV5U3NjZ2W7+hoUFR9v7776O2thbbtm3Dzp07MXHiRNx2220YMmSIy+MpKiry9C1cUFlZmdf36Q3x4VEorDY5Hu8ubEKsts6HR+Qef23P/pBXOhSA0fE4SteIoqKmXu93ILdpX2B7eh/b1Lv6qj1Hjx7t8XM87nHo6OgAAJhMJtU2k8mEtrY2l8/prn57e7vjcUVFBbZs2YIrrrgCjz/+OH7zm9+gtbUVTz75pMtAMtAkR7UrHpfWGSBxtMJvSTJwqkl5l7IR4RYfHQ0RkXd43ONgMHT2l5vNZtU2s9kMvV6vKjcYDC7rt7a2Kuq///77SE9Px/333+8oGzduHJ588kl8+OGHWLp0qWofPUlL7urLffdE5HAZnxR2OB632zTQhacgeVhgzAPxt/bsaxV1Eix2ZVC4dEocIkzeG6oYaG3a19ie3sc29S5/aE+P/+KYTCbodDo0Nam7WxsbGxEVFaUqDw8Pd1m/qakJkZGRjseHDx/GrFmzlAcoirj00ktRWFjo6aEGnSGDBQyP4GWZgeJ4lfLfZshgeDU0EBH5gsfBQRRFJCUlqf6QS5KEwsJClxMak5OTceTIEVV5QUEBkpOTHY/tdrvLBaR4ZcU5qrtlVnKCpL86Xq0cRxoVw88xEQW+Hp3Jpk+fjt27d8NuP/dHKy8vD2azGenp6ar6GRkZKC4uRlVVlaOsubkZhw4dQkZGhqNszJgxyMnJUTxXkiTk5ORg7NixPTnUoOMcHIpPy2i3cKKDP3K+oiIlQIaUiIjOp0dnsszMTIiiiDVr1qCkpAT79+/H2rVrMX/+fERERCAvLw/Lly9HRUUFgM4eh5kzZyIrKwv5+fk4duwYsrKykJCQoAgat912G/bu3Yt169bh+PHjOHr0KF555RVUV1fjxhtv9M47DnBjY0Vouvyr2WWg8BSHK/yNuUPGyXploEthjwMRBYEe3avCaDRixYoVyM7OxqpVq2AwGJCZmYmFCxcCANra2tDQ0ACL5dzEsPvuuw/r16/HmjVrYLFYMHXqVCxdulQxDJGSkoKnnnoKGzZswHPPPQdBEDBx4kQ888wzLudODERGnYDRMSJ+6BIWCiokXJTI+1b4kxKn+1NoRSB+COc3EFHg6/FNrqKjo/Hoo4+63DZt2jRMmzZNUabX67FkyRIsWbLkvPtNSUkZ8De1upAJI5XBgRMk/c/xKmVvQ8JQ3hGTiIID+04DkPNttqsaZdQ2Mzz4k2Kn+Q2cGElEwYJnswCUMFTAYNXy0wwO/kKWZU6MJKKgxbNZABIFQdXrwOEK/1HVKMPcoSzjxEgiChY8mwUo5+BwpFKCnetP+4Vip/Ubwk1AVIiPDoaIyMsYHAJUWpzyKgqzBSitYXDwB84rRqYME10ubEZEFIgYHAJU1GABIyKVf4w4z8E/OM9v4MRIIgomPKMFsLQ453kOXH7a19qtMirqnBZ+4sRIIgoiPKMFMOflp49XyzBz+WmfKq2RIHf5JxAFIDGawxREFDwYHALY2FgR2i7/gpIM/HCSwxW+5Hxjq5FDBBi0DA5EFDwYHAKYQSdg9HBelulPnCdGjuIwBREFGZ7VApzzcAWDg++4XPiJEyOJKMjwrBbgnINDTZOM6iaGB1+obZbR1KYsSxnGYQoiCi4MDgFu5BABoUZlGS/L9A3n+Q2DDcCwMAYHIgouDA4BjstP+w9XwxRc+ImIgg2DQxBIG6lcRbKgUoLVzssy+5urFSOJiIINz2xBYKJTj0OHFSg6zV6H/mSxyThxRhnWRsWwt4GIgg+DQxAINwlIclpk6NAJBof+dLxagr1LkwsCkBTNrxcRBR+e2YLEpHjlP+VhBod+deyUsrchYYiAQXr2OBBR8GFwCBKTE5TzHE438rLM/nTUaWhobCy/WkQUnHh2CxKJ0QJCBynL2OvQP2ySjGKniZFjhvOrRUTBiWe3ICEKgnq4opzBoT+cqJVhsSnLGByIKFjx7BZEJsUrhysKT0rosPKyzL527JQyoMVGCAgdxPkNRBScGByCyISRIsQuf69s9s7wQH3r6CnObyCigYNnuCBiMggYPZyXZfYnSZZxrIrBgYgGDp7hgozzcMXhcjtkmcMVfeVkvQxzh7KM8xuIKJjxDBdkJico/0nrWjr/uFHfcB6mGBoqIGow5zcQUfBicAgyIyIFRA1WlnG4ou84T4wcM5yhgYiCG4NDkBEEweVwBXmfLMuqhZ/GcH4DEQU5nuWCkPNwRdFpGeYODld4W02zjEazsmws5zcQUZDjWS4IpY4Qoe3S6SDJnbfaJu9ynt8QOgiICedQBREFNwaHIGTQCUgdofynPXSCwxXe5nxjq7HDRQgCgwMRBTcGhyDlavlpiZdlehXnNxDRQMQzXZBynufQ3AaU1TA4eEtDq4yaJnWPAxFRsOOZLkhFh4kY7jTezpteeY9zb8MgHTAyisMURBT8GByC2KQEznPoK87rN4weLkIUGRyIKPgxOAQx5+GK0hoZjWYOV3gD5zcQ0UDFs10QGzNchEGnLMur4HBFb7W0y6is4/wGIhqYeLYLYlqNgLQ4p6srOFzRa0VOd8PUaYCkaA5TENHAwOAQ5JyHK/IrJNgkDlf0hvP8hpRhIrQaBgciGhgYHIKc830r2ixA0WkOV/TGMaf2GxvL0EBEAweDQ5CLCBGQOFT5h+1AKYNDT7VbZdV6GGM4v4GIBhCe8QaAi5KUvQ4HyiTIXEWyR45XSbB3aTqNAIyK4deIiAYObU+eVFNTg+zsbBQUFCAkJARz587FggULul2n32KxYMOGDcjJyYHdbkd6ejoWL16MkJAQR538/HysXr0adrt68t7cuXNxzz339ORQCcBFiSL+s/fc49pmGSfrZcRxwSKPOQ9TJAwVYNCxHYlo4PA4OLS3t2P16tWIj4/HypUrUV9fj3Xr1sFiseDWW291+Zy1a9eivLwcDzzwAHQ6Hd5++21kZWVh5cqVjrBRW1uL8PBwPPHEE6rnR0VFeXqY1MXIKAFRg4G6lnNlB8okxEXxl7Knjjrf2IrrNxDRAONxcNi+fTtsNhuWLVsGrbbz6VqtFllZWZg3bx5CQ0MV9UtLS5GTk4MXX3wRsbGxAIBHHnkEDz/8MA4cOICLL77YUVcURQwfPrw374dcEAQBFyVq8GX+ud6cg2V2XHdxjzqcBiybXcbxai78REQDm8dnvdzcXMyYMcMRGgBg0qRJMBqN2Ldvn8v6KSkpjtAAAGFhYZg8eTJyc3N7eNjkqSmJyn/q49VcRdJTJTUyrE4jaaM5v4GIBhiPfnJKkoTS0lLcdNNNinJRFJGamori4mLMmTNHsa2kpATjx49X7SstLQ2ff/65xwfsrKioqNf7cFZWVub1ffqaVgL0mhGw2M/9ofv8u9O4OK61z187WNpzV0kogHDH4+gQC05XVPjkWIKlTf0F29P72Kbe1VftOXr0aI+f41FwMJvNsFqtCA8PV20LDw9HfX29qryxsbHb+g0NDY7HJpMJtbW1WLp0KQDAYDAgMTER119/PaZMmeLJYZILGhEYNaQdR6pNjrIjVYP6JTgEi9J6o+JxYlSHj46EiMh3PAoOHR2dJ0qTyaTaZjKZcPLkSZfP6a5+e3u74/G0adOQlZUFm80GoPNKjEOHDuHll1/G7bffjquvvtrlMfUkLbmrL/ftC1dq7DjyhdXxuLzBiKEjRiHC1D9XBQRye3bYZJz6ShkUZqZFYXRitI+OqFMgt6k/Ynt6H9vUu/yhPT0KDgaDAUBnz4Mzs9kMvV7v8jmu6re2tqrqx8TEKB4nJycjOjoaa9euxcyZM1UTL8kzk+JFGHVA+4/ZQQaw97gdcydykuSFFJ2WYOsyL1IUeEUFEQ1MHp35TCYTdDodmpqaVNsaGxtdXjYZHh7usn5TUxMiIyMdjzdt2oSWlhZVvZkzZ0Kj0eDo0aOeHCq5oNMKuMhpkuR3xbzplTsKTyqvpkiKFjBIz/UbiGjg8Sg4iKKIpKQkFBYWKsolSUJhYSGSkpJUz0lOTsaRI0dU5QUFBUhOTnY8/vjjj13WO4srHXrHtFHKVSSLq2TUtbBtL+RIpTI4jI9jbwMRDUwen/2mT5+O3bt3K1Z4zMvLg9lsRnp6uqp+RkYGiouLUVVV5Shrbm7GoUOHkJGR4SibMGECvv76a9Xzd+/eDavVijFjxnh6qOTChJEiTE4jSnvY63Be5g4ZZbXKcMXgQEQDlcdnv8zMTIiiiDVr1qCkpAT79+/H2rVrMX/+fERERCAvLw/Lly9HxY+XqSUnJ2PmzJnIyspCfn4+jh07hqysLCQkJCiCxs9+9jMUFhbi5ZdfxpEjR3DixAl89NFH+Mc//oFFixa5vDKDPKfVCLjY6d4Vu47a2aNzHgWVEro2j1YDjBrG4EBEA5PHs+KMRiNWrFiB7OxsrFq1CgaDAZmZmVi4cCEAoK2tDQ0NDbBYLI7n3HfffVi/fj3WrFkDi8WCqVOnYunSpRDFcyff2NhYPPfcc9iwYQOysrJgtVoRHx+P+++/HzNmzPDCW6WzZo4VsfPouV6Gk/UySmpkpAzjmL0reeXKYYpxsSJ0WrYVEQ1MPZpOHx0djUcffdTltmnTpmHatGmKMr1ejyVLlmDJkiXn3W9MTAwefPDBnhwSeWBsrIjoUAE1zed+Ru/8wY4U/opWkWUZh08oh3ImJbCdiGjg4hlwABIFAbPGKYcr9hTZ0WHjcIWz8jMyGtuUZZPi+bUhooGLZ8AB6tKxGnTtbG+zAvuPS93WH6gOnVC2ybAwATHh/NoQ0cDFM+AAFTVYQNpI5T//tnwbJ0k6ySvnMAURUVc8Cw5gl49XDleU1sg4Xs3gcFZLu4xip/bgMAURDXQ8Cw5gFyWKGDJYWfbFYZtvDsYPFVQoL8PUa7jMNBERz4IDmEYUcJXTfSr2l0g4w5UkAQAHypTDFKlxIvS8DJOIBjgGhwHusnEaGLpkB0kGPjvIXgerTcZBp4mRUzi/gYiIwWGgMxnUl2Z+c8SO+taB3etQUCmh49wdyCEAuMhpxU0iooGIwYFwzRQtNF0+CTYJ+OTAwO512F+i7G0YPVxAuInDFEREDA6EqMECLktV9zrUNg/MdR1skqya3zA1mb0NREQAgwP9aP5F6l6Hd3MHZq/DsVMSWjuUZQwORESdGBwIQGevw5w05R/HfSUSCk8OvFtuf1es7GlJihYwZDCHKYiIAAYH6uKGdC0GG5Rlb++0wS4NnImSVpuM744rw1I6exuIiBx6dHdMCk4hBgE3TtPi3zvODVFU1sv4qsCOzIk9+6hYbDJyy0JRWD0Ilj3tiAoRMCFeg0vHaDAk1P9+xR86IaHNoizLGM3gQER0FoMDKVyRqsE3R+woP3Oul+GDvTZMG6VB2CDP/tC3W2X8+WMLjleHO8rqWmQUVdnw0T4bLkkRceMlWr+6aVTOMWVvw7hYEVEcpiAicvCfMzb5BVEUcMcsnaKszQK8t9vziZLv5Ni6vfeFJAN7iiX8f+9asCHHCrPF98MhzW0yDpcr5zfMHMOvCBFRVzwrksqY4SIyRis/GruO2nGk0v2JkqcbJOz44cL17RLwxWE7nnq3A/kVvp2IueMHO+xdcoNOA0xN4TAFEVFXDA7k0qIMHYzKjgf882srGs0X7hmoaZLw2lYrus6p1IoS7pqtxZw0DfQuBsjqW4GsLVa8ucMKq73/ex8kWcbXR5TB5ZIUESY9hymIiLpicCCXIkIE3DJd+Re+rgV49VMLmtpc/2Fv7ZDxwV4rnnzXgso6ZZ1LRrbgijQt7pytwx/vMGDeZI1i3Yizviqw408fWdDQz0te55dLqG1WvuacNE4BIiJyxuBA3boiTYOxscpf3GW1Mv7rvQ58mW/D6YbOO2nmldvx5g4rfv9WBzbvt8PmNOIQZrBhRlKz4/Fgo4BFM3R49qd6pMWpP4LHq2U8u6kDJ2r7b+XKrwqUBx0/REDKMPY2EBE5408q6pYoCLg/U4/nP+jAmZZz5Y1twFs73ZssGR0qYOGEWhi16h6EYWEiHpmvw9dH7Hgn1wZLl102moEXN1vw0LV6jIrp23xb1SjhkNOkyDnjNRAEBgciImfscaDzCjcJeGS+HkMGe/Y8QQAyJ2rw5C16RJm6DxmCIGBOmhZ/uEmPoU7rOpgtwJ8/tuDoqb7tefj0oB1yl1xj1AEZYzgpkojIFQYHuqDhESL+cJMBk+Iv/HERBGD6KBFP3aLH7Zfq3J5cODJKxBML9aqhkQ4b8MonFhw73Tfhoa5Fxq6jymGKOWkaGHXsbSAicoVDFeSWcJOAB6/RoaBSwreFdhSelNDS3rlNrwFGDhEwJVGD6aNERIf1LI8ONgp4+Fo9/vaFFXldhg46bMDLWyx4eL4eY4Z7N+t+csCmugTzJ5P4tSAi6g7PkOQ2QRAwYaQGE0ZqIMsy2qwA5M6ufVH0zi90vVbAsqt1+PtWKw6U9W14ON0gqS7BnD1Og3ATexuIiLrDoQrqEUEQYNILMBkEr4WGs7QaAb+aq8NFicqP59nw4K1hi/d22xRrTeg0wLUXMUsTEZ0PgwP5pb4OD/tL7IoeDQD4ySQN70tBRHQBDA7kty4UHvLKe7ZEdVObjDe+tSrKQgext4GIyB0MDuTXzhsePrFiQ45nS1Rb7TJe+8KC5nZl+aIMHQZxeWkiogticCC/1114ADpvkPXMRvd6H9qtMv76uRXHTiuDxkWJIu+CSUTkJp4tKSCcDQ/pyeqP7KkGGS9/YkXWFgsKKuyQZHUPRGWdhD9+aFFc5gkAkSHAXZfpuEokEZGbOKhLAeNsePgy3453d9tU98TIr5CQXyEhbFDnrcGHhAqQJKD8jIyjpyQ4xwmDFvjNPD0vvyQi8gCDAwUUQRCQOVGLcSNErPvKihO16t6FpjZgX8n5r7oIMXSGhoSh7HQjIvIEz5oUkEZGiXjiJj3uvkKL8EGePXd4uIDHb9RjtJdXoSQiGgjY40ABSxQFzB6nxbQUDXYdtWN7gR0n67u/wsKkB66aoMH8i7XQazk8QUTUEwwOFPAMOgFXTtDiyglaVDVK+OGkhOomGfWtMjQiEDZIwKgYEWkjRRgYGIiIeoXBgYJKTLiImHAOQRAR9RWeYYmIiMhtDA5ERETkNgYHIiIichuDAxEREbmtR5Mja2pqkJ2djYKCAoSEhGDu3LlYsGBBt8v2WiwWbNiwATk5ObDb7UhPT8fixYsREhJy3tfZt28f3nzzTWRlZfXkMImIiMjLPO5xaG9vx+rVq6HRaLBy5UosXboUn332GTZu3Njtc9auXYu8vDw88MADePTRR3Hq1ClkZWVBdnFPgbNqa2vx97//HVVVVZ4eIhEREfURj4PD9u3bYbPZsGzZMqSkpCA9PR333XcfNm/ejObmZlX90tJS5OTk4OGHH8aECRMwduxYPPLIIzh+/DgOHDjg8jXsdjteffVVGAwGj98QERER9R2Pg0Nubi5mzJgBrfbcKMekSZNgNBqxb98+l/VTUlIQGxvrKAsLC8PkyZORm5vr8jXWr1+Puro63HXXXZ4eHhEREfUhj4KDJEkoLS1FamqqcieiiNTUVBQXF6ueU1JSgvHjx6vK09LSXNb//vvv8emnn+KBBx5AaGioJ4dHREREfcyjyZFmsxlWqxXh4eGqbeHh4aivr1eVNzY2dlu/oaFBUXbmzBm89tprWLhwIcaPH4+CgoILHlNRUZH7b8BNZWVlXt/nQMb29D62qXexPb2PbepdfdWeo0eP9vg5HvU4dHR0AABMJpNqm8lkQltbm8vndFe/vb3d8dhut+Mvf/kLEhMTcdNNN3lyWERERNRPPOpxODtZ0Ww2q7aZzWbo9XqXz3FVv7W1VVH/nXfewenTp/HCCy9AFN3PMz1JS/6w74GI7el9bFPvYnt6H9vUu/yhPT3qcTCZTNDpdGhqalJta2xsRFRUlKo8PDzcZf2mpiZERkYCAA4cOICPP/4Yv/71rx1lRERE5H886nEQRRFJSUkoLCzERRdd5CiXJAmFhYVYtGiR6jnJycku5yoUFBQgOTkZAPDBBx9AkiS89NJLijqSJAEAli5divj4eKxatcqTwyUiIiIv83jlyOnTp2Pr1q1YtGgRNBoNACAvLw9msxnp6emq+hkZGfjoo49QVVWFmJgYAEBzczMOHTqE//f//h8A4He/+51qoiQAFBcX4+9//zuef/55XmFBRETkBzxexyEzMxOiKGLNmjUoKSnB/v37sXbtWsyfPx8RERHIy8vD8uXLUVFRAaCzx2HmzJnIyspCfn4+jh07hqysLCQkJDiCRkhICOLi4lT/DR06FAAQFxeHsLAwL75tIiIi6gmPexyMRiNWrFiB7OxsrFq1CgaDAZmZmVi4cCEAoK2tDQ0NDbBYLI7n3HfffVi/fj3WrFkDi8WCqVOnYunSpRecBBkaGoro6GhPD5GIiIj6iCCf74YRA9TZtSH8YfZqMGB7eh/b1LvYnt7HNvUuf2pPBgciIiJym8dzHIiIiGjgYnAgIiIitzE4EBERkdsYHIiIiMhtHl+OGexqamqQnZ2NgoIChISEYO7cuViwYAEEQfD1ofWrkydPYteuXcjJycHMmTNx6623AgAsFgs2bNiAnJwc2O12pKenY/HixQgJCVE8/8SJE3jjjTdQXFyMiIgI3HDDDZgzZ47qdbZt24bNmzejsbERY8aMwZIlSxAXF6eo09zcjDfffBPff/89tFotZs2ahZ/+9KfQ6XR99v69qbKyEm+//TaOHDkCjUaDqVOn4s4778TgwYMddY4cOYK33noLFRUViImJwaJFi1QLqkmShA8++ADbt29HW1sbJkyYgCVLlmDIkCGKeu5+hvfs2YONGzeiuroa8fHxuPPOOzF27Ni+awgvKi0txYYNG/DDDz9AFEVMmjQJixcvdqz9ArBNe+r48eN46aWX8Kc//Ulxg0K2p3tOnjyJxx9/HFarVbVtypQpeOyxxwD4pq3cPX9fCK+q6KK9vR0rV65EfHw8brjhBtTX12PdunW48sorHX84B4KysjI8/vjjjpU+x40bh1/96lcAgDVr1qC8vBxLliyBTqfD22+/DY1Gg5UrVzo+yHV1dfjDH/6A6dOnIzMzE+Xl5fjnP/+JJUuW4IorrnC8zrZt2/DWW2/hnnvuwciRI7F161bs27cPzz//PCIiIgB0frmeffZZCIKAn/3sZ7BYLHj99dcxatQoxzH5s+rqavzhD3/A1KlTcc0116CtrQ3vvvsuOjo68Oyzz0Kr1aKsrAxPP/00rr/+ekybNs1xgv7d736HiRMnOva1fv167NixAz//+c8RGRmJDz74AKdOnXKspwK4/xk+ePAg/vznP2Px4sVITU3Fnj17sGXLFjzzzDMYOXJkv7eTJ06dOoWVK1di1qxZuOKKKyBJEjZt2oTTp0/jj3/8I3Q6Hdu0h8xmM/7whz+guroar7zyimMdHban+woKCrBq1Sr8+c9/Vm0LCwtzhDFftJU752+3yOSwZcsW+cEHH5StVquj7MCBA/LSpUvlpqYmHx5Z/5IkST516pQsy7L82muvya+99posy7JcUlIi33HHHfLJkycddRsbG+Wf//zn8v79+x1lr7/+uvz0008r9vnFF1/Iv/71rx1ta7FY5F/96lfyl19+qaj35JNPyv/+978dj/fs2SPfc889cnNzs6OsoqJCvuOOO+Ty8nIvveO+849//EN+9tlnFWWtra3y/fffL3/77beyLMvyiy++KP/1r39V1HnrrbfklStXOh43NDTIS5YskQ8fPuwos1gs8m9+8xv5888/d5S5+xlesWKFvGHDBsVrvvLKK/LLL7/ci3fbP9566y35qaeeUpS1t7fLS5YscXwO2aY9k5WVJT/wwAPy7bffLldXVzvK2Z7uy8/Pl2+//fbz1vFFW7l7/nYH5zh0kZubixkzZkCrPTeCM2nSJBiNRuzbt8+HR9a/BEHA8OHDVeW5ublISUlBbGysoywsLAyTJ09Gbm6uo2z37t249NJLFc+dOXMmmpqa8MMPPwDo7PZsaWlBRkaGot6sWbNU+5oyZYqiWz8uLg5JSUmKev6quLhY9R5NJhPGjh2L4uJidHR04MCBA6r2mj17No4fP47q6moAwN69exESEoIJEyY46uh0OmRkZCAnJ8dR5s5n+NSpUygrK8OsWbNUr7lv3z6XXaz+ZOzYsbjhhhsUZRqNBlqtFlqtlm3aQ5999hkOHTqE++67T1HO9vQ+X7SVu+dvdzA4/EiSJJSWliI1NVVRLooiUlNTUVxc7KMj8x8lJSUYP368qjwtLc3RPvX19aivr1fVCwkJQWJioqNeSUkJEhMTFWOoADBhwgTU1taisbHR7df0Z7fccgumT5+uKq+rq3MMU9jtdtV7jI+PR2hoqKK9xo0bp+pOTEtLQ0lJCWRZdvszXFJSgrCwMNVckrS0NNhsNpSVlfX6ffel9PR0TJ061fG4paUFf/vb35CSkoKJEyeyTXugpKTEMWzY9Q8LALZnH/BFW3nzXMrJkT8ym82wWq0IDw9XbQsPD0d9fb0Pjsq/NDY2dts+Z+9uevYPfnf1zm5vaGhwWefszczObu+uXnh4OPLy8nr8XvqLq9Dw/fffo6SkBHfffTfq6+thNBodY5pdOber86Sps3U6OjrQ1tYGSZLc+gx316Znj8PVnWr91TvvvIMPPvgAiYmJeOKJJyAIAhobG9mmHjCbzXj11Vcxa9YszJ49GzU1NYrtbE/PnP0xtHTpUgCdPQlxcXG4+uqrHb0Cvmgrd87f7mJw+FFHRwcAqH4Bny07efJkfx+S3+no6Oi2fdrb2wHA8X9X9QYNGoS2trYL7guAot6gQYPO+5qB5PTp03jttdcwd+5cjB49Gjt27HDZDoC6XbtrU+Bce519nqt9nf0Md9emzq8ZCG644QZMmjQJmzdvxlNPPYUnn3yy288WwDZ15X//93+h1Wodf+icsT09k5SUhFdeecVxo0ebzYajR4/i9ddfR1lZGe644w6ftJU75293MTj86GyaNpvNqm1msxl6vb6/D8nvGAwGl+3T2trqaB+j0Qigs81CQ0MV9cxms2OugsFgcPQ+ONcB4NifwWBQfIlcvWagaGlpwZ/+9CckJibirrvuAtB9mwLqdu3uswl0ttfZbs8LfYa7a1PneoHAaDRi/PjxGDduHJ5//nm8/fbbSE9PZ5u6aevWrdi/fz+effZZlz0KAD+jPeF8V+fExEQkJibiv/7rv3D55Zf7pK3cOX+7i3McfmQymaDT6dDU1KTa1tjYiKioKB8clX8JDw932T5NTU2IjIx01Dlb5qre2XaMiIjotg4ART1XAaPrawYCm82GV155BYIg4OGHH3ZMdgoPD0d7e7ujx6sr53btrr30ej0GDx7s9me4uzY9exz+3K5WqxV/+9vfYLPZFOWiKGLu3Lk4cOAA29RNZWVleOONN7B06VLEx8d3W4/t6ZnPPvtMNdwDdE7qHTlyJPLy8nzSVu6cv93F4PAjURSRlJSEwsJCRbkkSSgsLERSUpJvDsyPJCcn48iRI6rygoICJCcnAwAiIyMRERGhasfW1laUlZU52jE5ORmlpaWqpJyfn+/Yx9l6zvs6+5qB9G+ybt06lJeX43e/+51isZXExESIoqh6j+Xl5Whubna069l2kJ2WXSkoKEBiYiIEQXD7M5ycnIympibV8FtBQQG0Wu15/4j4mtVqxY4dO1BeXq7aJooirFYr29RNH330EaxWK/71r39h6dKljv8effRRAMCjjz6KBx54AAkJCWxPD3z11Vf47rvvut0uy7JP2sqd87e7GBy6mD59Onbv3g273e4oy8vLg9lsVq2QNhBlZGSguLgYVVVVjrLm5mYcOnRIcclhRkYGdu3apXhubm4uQkJCHLN6U1NTERISgj179ijq5eTkKCYUTp8+HQcPHkRra6uj7OTJkygtLcWMGTO8+v76ykcffYSdO3fit7/9rWNRrbMMBgMuuugixSVYALBz504kJCQ4Zrinp6ejpaVF8cW32WzYs2ePou3d+QzHxsYiISFB9W+0a9cuTJkyxTHc5I9MJhMSExPx7bffqrbt3bsXY8eOZZu66d5778Wf/vQnPP/884r/fv/73wMAfv/732PVqlUwGo1sTw9MmDABO3bsUPWKFRUVoaKiAmlpaT5pK3fP3+5gcOgiMzMToihizZo1KCkpwf79+7F27VrMnz/f8Qt4IEtOTsbMmTORlZWF/Px8HDt2DFlZWUhISFAEqwULFqCyshL/+te/cOLECezatQtvv/02Fi1a5FgmWq/XY9GiRXjjjTewa9cunDhxAuvWrcPJkycV1+hfcskliI+PR1ZWFo4dO4b8/Hy88sormDlzZkD0OHz33XdYv349Fi5ciLCwMJw+fdrxX0tLCwBg0aJF2L17NzZt2oQTJ07giy++wCeffILbb7/dsZ+IiAjMnz8fr732muOqjDVr1kAURVx11VWOeu5+hm+//XZs3rwZX3zxBU6cOIH3338f3333HRYtWtRvbdNTS5YswbZt25CdnY3jx4/j6NGj+Oc//4nvvvsOP/vZzwCwTd1hNBoRFxen+u9suI2JiXF0YbM93XfDDTegtbUVq1evxuHDh1FeXo5t27bhj3/8IzIzM5GYmOiTtnL3/O0OLjntpOu64AaDAZmZmVi4cCFEcWBmrPfeew8AFPeqWL9+PXJycmCxWDB16lQsXbpUsUAT0HmviuzsbBQXFyMsLAzXXXcdrr76atX+t27dio8//hj19fVISUnB3XffjYSEBEWdrveqAIBLL70Ud9xxh99PkAKAl19+WdWrctbo0aPxzDPPADh3H4Dy8nJER0fjlltuUfWodF3bvqWlBWlpabj77rtVE7Hc/Qzv2bMH7733HqqqqhAXF4fFixcrFqTxZ0VFRXjvvfdw7NgxAJ09WLfddpvis8M27ZmWlhb8/ve/xx//+EeX91Nhe15YY2Mj3nnnHXz//fdobW1FbGws5s6di8zMTMekR1+0lbvn7wthcCAiIiK3Dcyf0URERNQjDA5ERETkNgYHIiIichuDAxEREbmNwYGIiIjcxuBAREREbmNwICIiIrcxOBAREZHbGByIiIjIbQwORORXtm/fjtdee83Xh0FE3WBwICK/cubMGdTW1vr6MIioGwwORERE5DYGByIiInKb1tcHQET9o6mpCe+99x7279+PlpYWxMbG4rrrrsPs2bNVdSsrK7Fp0ybk5+ejvb0dw4cPx1VXXYW5c+c6bgt8VktLC95//33s3bsXjY2NiIiIwCWXXIKFCxeqbtdrtVrxn//8Bzt37kRdXR0iIiIwc+ZM3HTTTTAajYq6Bw8exDvvvIOKigoMGjQIkydPxk9/+lMMHTpUUS8/Px8bN25EWVkZgM7bld9yyy0YO3asN5qNiJzwttpEA0BLSwtWrlyJyMhI3HjjjYiMjMSxY8fw7rvv4vrrr8cNN9zgqHv06FG88MILuOSSS3DVVVchJCQExcXFeO+99zBmzBg89NBDjvDQ2NiIJ598EhEREViwYAFiY2NRVVWFzZs3o76+Hs888wzCwsIAADabDc8//zxaW1txyy23YMSIEaiqqsLGjRuh1+vx9NNPAwDee+89bN68GVFRUbj11lsRHx+Puro6fPzxxzhx4gReeOEFREZGAgCqq6vxu9/9Dtdddx0yMjJgNpuxY8cO7NixA88//zzi4uL6t6GJBgKZiILev/71L/mxxx6TLRaLorygoEC+66675IaGBlmWZdlut8sPPfSQ/Oabb6r2UVtbK997773yV1995Sj7y1/+Ij/11FOy1WpV1LXb7fLTTz8t/+1vf3OUffrpp/Kvf/1ruaWlRVG3o6NDzs3NdTx+99135bvvvluuq6tT7fMPf/iD/PrrrzvK9uzZI//iF79QHWt5ebnqvRKRd3COA9EAsG/fPvzkJz+BTqdTlI8fPx7Dhw/HwYMHAXT2Npw5cwY33XSTah9DhgzBnDlzsHPnTgCA3W7Hnj17cOONN0KrVY56iqKIG264Abm5uZAkCQCwZ88eXHbZZQgJCVHU1ev1yMjIUJQlJiY6ehW67nPatGkoLCx0lI0bNw56vR5/+ctfkJeXB7PZDAAYOXKk6r0SkXdwjgPRANDQ0IB//etfyM7OVm2z2Wxobm4GANTW1iIsLEz1x/2smJgYfP/99wCA5uZm2Gw2xMTEuKw7bNgwWK1WNDc3Izw8HA0NDYiOjnbreDUajcvyyMhItLa2Oh6HhYXhueeew8cff4w33ngDlZWVSE5OxsKFCzF16lS3XouIPMPgQDQARERE4Nprr8XkyZNV2wRBwLBhwwB09iI0NjaiqanJMTehq+rqakdPQGhoKDQaDaqqqjBixAiXdbVarWOCZGRkJGpqanr9XmSnaVmRkZG48847AQCtra346quv8PLLL+Opp57CqFGjev16RKTEoQqiAWD69OnYu3cvhg8fjri4OMV/I0aMUAw1SJKE//znP6p91NXV4auvvsKMGTMAdPYKTJs2DR9++CHsdruiriRJ+OijjzB9+nRH78HMmTPxzTffoKmpyWvvq6amBi0tLY7HISEhuO666zBixAjFkAYReQ97HIgGgJtvvhlPPfUUnn32WcyfPx/Dhw9HY2Mjvv32W2g0Gtx///2OuoMHD8b+/fvR2NiIOXPmICwsDMePH8fGjRsxatQoXHnllY66d911l2O/CxYsQExMDKqrq/Hxxx+jtrYWDz30kKPulVdeib179+Lpp5/GLbfcgoSEBDQ1NWH79u0wm834/e9/7/H7ev/993H48GEsXLgQSUlJ0Gg02Lt3L06dOoWJEyf2rtGIyCVejkk0QLS1teGDDz7Anj17cObMGYSFhWHKlCm46aabHHMPvv76a2zcuBGrVq3Cu+++i3379qG1tRXDhg3D5Zdfjvnz56vmHzQ3N2PTpk3Yu3cvGhoaEBERgWnTprlcx8Fut2PLli345ptvUF1dDaPRiEmTJuGnP/2pY7hk+/btOHLkCH7961+r3sPBgwfx4Ycf4sknnwTQOT/js88+w86dO3Hy5EmIoohRo0bh5ptvxvjx4/uiGYkGPAYHInI4GxxeffVVXx8KEfkpznEgIiIitzE4EBERkdsYHIjIISoqqtt1GYiIAM5xICIiIg+wx4GIiIjcxuBAREREbmNwICIiIrcxOBAREZHbGByIiIjIbQwORERE5DYGByIiInIbgwMRERG57f8HJvQBEa7hPtEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dataset_name in different_datasets:\n",
    "    covid_data = np.genfromtxt(f'./datasets/{dataset_name}.csv', delimiter=',')\n",
    "    plotter = Plotter()\n",
    "    dataset = PandemicDataset(dataset_name, ['S', 'I', 'R'], 83100000, *covid_data)\n",
    "    problem = SIRProblem(dataset)\n",
    "    dinn = DINN(3, dataset, ['alpha', 'beta'], problem, plotter)\n",
    "    dinn.train(50000, 1e-6, create_animation=True)\n",
    "    print(torch.seed())\n",
    "    dinn.plot_training_graphs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PINN",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}