Michaelis-Menten_Basic_Tau.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sys.path.append(\"/home/edixon1/GillesPy2/gillespy2\")\n",
    "sys.path.append(\"/Users/ghilman/Documents/Coding/repo/GillesPy2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/Library/Frameworks/Python.framework/Versions/3.6/lib/python36.zip',\n",
       " '/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6',\n",
       " '/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload',\n",
       " '/Users/ghilman/Library/Python/3.6/lib/python/site-packages',\n",
       " '/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages',\n",
       " '/Users/ghilman/Library/Python/3.6/lib/python/site-packages/IPython/extensions',\n",
       " '/Users/ghilman/.ipython',\n",
       " '/Users/ghilman/Documents/Coding/repo/GillesPy2']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    }
   ],
   "source": [
    "%matplotlib\n",
    "%matplotlib inline\n",
    "import os\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gillespy2\n",
    "class MichaelisMenten(gillespy2.Model):\n",
    "     def __init__(self, parameter_values=None):\n",
    "            #initialize Model\n",
    "            gillespy2.Model.__init__(self, name=\"Michaelis_Menten\")\n",
    "            \n",
    "            #parameters\n",
    "            rate1 = gillespy2.Parameter(name='rate1', expression= 0.0017)\n",
    "            rate2 = gillespy2.Parameter(name='rate2', expression= 0.0001)\n",
    "            rate3 = gillespy2.Parameter(name='rate3', expression = 0.1)\n",
    "            self.add_parameter([rate1,rate2,rate3])\n",
    "            \n",
    "            #Species\n",
    "            A = gillespy2.Species(name='A', initial_value=301)\n",
    "            B = gillespy2.Species(name='B', initial_value=120)\n",
    "            C = gillespy2.Species(name='C', initial_value=10)\n",
    "            D = gillespy2.Species(name='D', initial_value=0)\n",
    "            self.add_species([A, B, C, D])\n",
    "            \n",
    "            #reactions\n",
    "            r1 = gillespy2.Reaction(name=\"r1\",reactants={A:1,B:1}, products={C:1},\n",
    "                   rate=rate1)\n",
    "            \n",
    "            r2 = gillespy2.Reaction(name=\"r2\",reactants={C:1}, products={A:1,B:1},\n",
    "                    rate=rate2)\n",
    "            \n",
    "            r3 = gillespy2.Reaction(name=\"r3\",reactants={C:1}, products={B:1,D:1},\n",
    "                    rate=rate3)\n",
    "            self.add_reaction([r1,r2,r3])\n",
    "            self.timespan(numpy.linspace(0,100,101))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gillespy2.basic_ssa_solver import BasicSSASolver\n",
    "from gillespy2.basic_tau_leaping_solver import BasicTauSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MichaelisMenten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'r1': 0, 'r2': 0, 'r3': 0}\n",
      "{'A': [301, 258, 232, 208, 192, 181, 172, 167, 152, 147, 135, 127, 121, 117, 112, 103, 97, 91, 84, 80, 68, 57, 52, 49, 44, 39, 33, 31, 27, 25, 20, 20, 17, 15, 14, 12, 9, 8, 5, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'B': [120, 78, 62, 42, 30, 31, 34, 35, 28, 31, 31, 30, 33, 37, 41, 40, 47, 52, 51, 52, 52, 48, 54, 58, 59, 63, 60, 67, 68, 70, 72, 79, 80, 80, 86, 90, 90, 97, 97, 98, 103, 107, 110, 111, 114, 117, 118, 118, 118, 118, 118, 120, 121, 122, 122, 123, 125, 125, 125, 125, 126, 126, 126, 126, 126, 126, 126, 126, 128, 128, 128, 128, 128, 128, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130], 'C': [10, 52, 68, 88, 100, 99, 96, 95, 102, 99, 99, 100, 97, 93, 89, 90, 83, 78, 79, 78, 78, 82, 76, 72, 71, 67, 70, 63, 62, 60, 58, 51, 50, 50, 44, 40, 40, 33, 33, 32, 27, 23, 20, 19, 16, 13, 12, 12, 12, 12, 12, 10, 9, 8, 8, 7, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'D': [0, 1, 11, 15, 19, 31, 43, 49, 57, 65, 77, 84, 93, 101, 110, 118, 131, 142, 148, 153, 165, 172, 183, 190, 196, 205, 208, 217, 222, 226, 233, 240, 244, 246, 253, 259, 262, 270, 273, 276, 281, 285, 288, 289, 292, 295, 296, 297, 298, 298, 299, 301, 302, 303, 303, 304, 306, 306, 306, 306, 307, 307, 307, 307, 307, 307, 307, 307, 309, 309, 309, 309, 309, 309, 310, 310, 310, 310, 310, 310, 310, 310, 310, 310, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311, 311], 'time': [0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0]}\n"
     ]
    }
   ],
   "source": [
    "ssaResults = model.run(solver=BasicSSASolver)\n",
    "tauResults = model.run(solver=BasicTauSolver)\n",
    "\n",
    "print(tauResults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import time\n",
    "#%time time.sleep(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x108947eb8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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uueceYmJisFgsZGVlMXfuXI4dO1apsE9amjLm9euXnGBNSYFTp1SWjZ4i0Whq\nFnXLo0coCySlelmrJwRyozLFH3/8MWazmZEjR/LBBx84PPGiEr1bt25l1KhRgFJebNiwIceOHWPz\n5s2O9qioKMLCwso936ZNm3jySaXXbzKZ8Pb2rtR4QYVr4uNVclPDhsW3Xb4MJ0+qB6q6uihKo6nN\n1BqP/loeuLt7ke3JmYW1Y1u1AnwICLgxD74oVSFTXFYpQY8biHeUd/6rZXMry9WyBnbsK189PVX2\njTbyGk3No+79W9pDKD4+KvevirlZMsW9e/fm448/BpSYVWJiIi1btuSOO+7gk08+AZTM7oEDB8o9\nf9++fR0Tr1arlfT09HIlgotis6kJVrusgZ30dJVH7+amjLzOl9doaiZ1z9C7uICzs3Iti+b9VRGD\nBg0iKyuLdu3aMXPmzFJlinv06FGs1ur18Mwzz5CdnU1ERAQjR45k+fLlODs7ExMTQ0pKCm3btuWv\nf/2rQzb4WudfuHAh//vf/4iIiKBTp04cOnSI4OBgOnbsSERERLmTsQYDtG6tDL2dK1cK5Q1at9b5\n8hpNTaZSMsXVRZXIFBfl5Ek1c9i8OVxHPFqjkFJlqQYEqHsnKO8+KUmlUTo7Q5s2eiHyzULLFGuu\npqIyxXXPowcVvrFY1NLN/PxbPZpaidWqPPZz51QcHpRoWVycSrH09laevDbyGk3Np24+cBf14rOy\nVLxeU2Fyc+HoUfWzaVOVF5+TA4cOqRBNixaFujYajabmUzcNvbOzCh7n5GhDX0mys+HwYRW2adVK\n3TPtImUGA7Rrp714jaa2UTcNPSjjnpOj4g2aCmMyqVWtjRsXzmWnpqoMm8aNtZHXaGojdTNGD4Ve\nvL2OrOaa5OWpiVYnJ+XJ24281QpnzqgUyrpW61WjuV2ou4be01PFGup4ScGqwGJR4ZpTp0puO39e\n3QS0SJlGU3upu4bebuRvQNxszpw5hIWFERkZidlsZvv27QCsW7eOqKgo2rdvT7t27fj3v/9drJ/Z\nbGb48OFlHvfw4cP06dPHIVU8YcKEcsfi6elZ4XFv2rSJbdu2VXj/8+fVxGtiYizr1xfWk8nJUdv8\n/fX9UqOpzdTdGD2o8E1amlI1q6Q4+i+//MK6devYs2cPLi4uJCcnk5eXR35+PhMmTGDHjh00atSI\n3Nxc4u2SC6hcZ6vVypYtW8jMzCxV1mDy5MlMmzaNIUOGALBv374busyr2bRpE56envTo0aPENovF\ngqnI6qa8PJUX7+cHmzfHsmvXLgYOHFhsAvYG135pNJpbTN316KEwzfLChUp3TUxMJCAgAJcCGYWA\ngAAaNmzIlStXsFgs+Pv7A+AfvgWWAAAgAElEQVTi4kKbNm0c/VauXMljjz1GdHQ0a9euLfPYRVeu\nRkREALBs2TJiYmIc7ffeey+bioj0TJs2jbCwMO6++24uXrwIwLvvvku7du2IjIxk+PDhxMfH8957\n7zF//nzMZjNbtmxhzJgxTJw4ka5duzJ9+nR27NhB9+7diYqKonv3Hpw8eZjAwDxmzZrF6tWrMZvN\nLF26mgsXMnn99XH07NmFqKioMq9Ho9HUbGqHRz91qiomcj3YJ2OvDn2YzVCKsJid6OhoZs+eTevW\nrenXrx+PPPIId955J35+ftx33300bdqUu+++m3vvvZcRI0ZgKFDzWr16NRs2bODQoUMsWLCARx99\ntMSxp02bxl133UWPHj2Ijo5m7NixxbTkSyMzM5NOnToxf/58Zs+ezauvvsrChQuZO3cuJ0+exMXF\nhdTUVHx9fZk4cSKenp4899xzACxZsoSEhAS2bduG0WgkPT2dLVu2YDCYWLJkIx988AIPPvg5s2fP\nZteuXbzzzkIOHIAFC15g4MC7eOyxpaSmptKlSxf69et3Q+JrGo3m5lO3PXpQsQe7bHEl8PT0ZPfu\n3SxevJjAwEAeeeQRli1bBqhygD/88ANdunThzTffZNy4cQDs2rWLgIAAmjRpwt13383evXu5dOlS\niWOPHTuWuLg4hg0bxqZNm+jWrRu5ubnlXIaBRx55BIBRo0axdetWACIjIxk5ciQrVqwoFpK5mmHD\nhmEsUB1LS0tj2LBhREaG8+670zh58kCxfe0TsLt3f8+8eXMxm8306dOHnJwcTp8+XbEvUKPR1Bhq\nh0d/Dc+7XBIT4exZVfeuqCpXBTAajfTp04c+ffoQERHBRx99xJgxYwAVbomIiOCxxx6jWbNmLFu2\njJUrV3Lo0CFCQ0MBSE9P5/PPP+dPf/pTiWM3bNiQcePGMW7cOMLDw9m/fz8mkwmbzebY51pSx3bZ\n4W+++YbNmzfz9ddfM2fOnDLj/UW98JkzX6JDh758+ukXnD0bT58iVV3y8pSh9/MDISSff/55sdCU\nRqOpfdR9j94+CZuWVqluhw8f5ujRo47PsbGxNG3alIyMjGJxc3u7zWbj008/Zd++fcTHxxMfH8/a\ntWtZuXJliWN/99135Bdo8Jw/f56UlBRCQkIIDQ0lNjYWm83GmTNn2LFjh6OPzWbjs88+A+CTTz6h\nV69ejv369u3LvHnzSEtLIyMj45oSxOnpkJCQhotLCKmpOJ5SAJycvDh37gouLmpxVP/+/VmwYIFD\n537v3r2V+g41Gk3NoFxDL4RoLIT4SQhxUAhxQAgxpaDdTwixQQhxtOBnvYJ2IYR4VwhxTAjxuxCi\nQ3VfxDVxclJLPYt4yhUhIyOD0aNHOyY6Dx48yCuvvIKUktdff502bdpgNpt5+eWXWbZsGVu2bCEk\nJISGRcov3XHHHRw8eJDEqwqVf//994SHh9O+fXv69+/PG2+8Qf369enZsyfNmjWjXbt2TJ48mQ4d\nCr86Dw8PduzYQXh4OD/++COzZs3CarUyatQoIiIiiIqKYvLkyfj6+jJ48GC++OILx2SsnfR0pfM2\ndux0Fi9+nujoKEch8qwsCAnpS3z8QUaONPPf/67mpZdeIj8/n8jISMLCwnjppZeu5zeg0WhuMeXK\nFAshGgANpJR7hBBewG5gKDAGuCSlnCuEmAHUk1L+RQgxEHgGGAh0Bd6RUnYt4/BANcgUX825c+rV\nvv1tu4ZfysKi3mFhxStB2fVtDAYlO1wN9Vo0VYCWKdZcTZXJFEspE6WUewreXwHigBBgCPBRwW4f\noYw/Be3LpeJXwLfgZnHrsK/2OXv2lg7jVmK1Kq23hg2LG/mcHOXlC6Fkh7WR12jqHpWajBVChAJR\nwHYgWEppj0mcB4IL3ocAZ4p0SyhoKx6/uJnYUysrGaevS5hMqtxfUXJzlZGXUnny1VCQS6PR1AAq\nPBkrhPAEPgemSinTi26TKv5TqfxFIcQEIcQuIcQu++KfakMIZcXy82/LQiRpacpzL4q9LovVqkTM\n3Nxuzdg0Gk31UyFDL4RwQhn5j6WU/y1ovmAPyRT8TCpoPws0LtK9UUFbMaSUi6WUnaSUnQIDA693\n/BXHrmaZklL956pBWK0QH19SsOzsWeXRt2ql5qo1Gk3dpSJZNwJYAsRJKd8usukrYHTB+9HA2iLt\njxdk33QD0oqEeG4d9jTLUhYw1VXsejX5+WoZgZ3MTLh4UckOV0IrTaPR1FIqEqPvCTwG7BNC2HUI\nXgDmAp8KIcYDp4CHC7atR2XcHAOygLFVOuLrxdUVjMZKr5CtzVy8qB5gGjYsNOh2428yqXaNRlP3\nqUjWzVYppZBSRkopzQWv9VLKFCnl3VLKVlLKflLKSwX7SynlJCllCyllhJRyV3nnuCkIAb6+yr2t\noLGvLpniV155hZCQEMxms+OVaq/AXUVkZqqCIT4+xRcEp6SobY0aKWOv0WjqPrfXv7q3t7J0ycmq\n4vU1qE6ZYlDCZnbRserAzU0Z+KCgwoIhFgskJCjvvkB8U6PR3AbUfQmEothli0+dUjOR16A6ZYrL\nYtmyZTzwwAMMGDCAVq1aMX36dAC++uorh+ffpk0bmjVrxo8//sjQoUMdfTds2MD999+PlEqQ7S9/\n+TP33BPGgAH92LFjB3369KFZs+b8+ONXNGkCNpuVP//5z3Tu3JnIyMgSTyUajabuUCs8+qNHp5KR\ncZ0yxVeTmwVWK54HzbSKWlLmbtUpUwwwf/58VqxYAUC9evX46aefAKWds3fvXscN5JlnnuG+++7j\nvvvuA+Dhhx/mzjvvpG/fvjz99NNcvHiRwMBAPvzwQ8aNG8fFi0rSuHfvu3jjjTe4//77mTlzJh9+\nuIFffjnI3/42mmefvY/Fi5fg4+PDzp07yc3NpWfPnkRHR9OsWbOq+Z41Gk2N4fby6KFQAiE7W+Ue\nlkF1yhSDCt3ExsYSGxvrMPIAd999Nz4+Pri6utKuXTtOFcmLfP3113Fzc2PSpEkIIXjsscdYsWIF\nqamp/PLLL/Tt+0cSEsDJyZl77x0AKJXNzp3vJCXFie7dI0hIiAeU3s7y5csxm8107dqVlJSUYiJu\nGo2m7lArPPpWrW5Apvhq8vPht9/U+5QUFcQug+qUKS4LlyIaBEaj0SE6tnHjRtasWcPmzZsd28eO\nHcvgwYNxdXXloYeGceaMCSHA2dkJg0EUXK6B7GwXPD2haVOD43hSShYsWED//v0rPDaNRlM7uT09\nem9vNUN5DUmE6pQpriynTp1i0qRJrFmzBrciS1gbNmxIw4YNee211xg6dCyZmdCkSWG/5GR1LzOZ\noHnz4ho3/fv351//+pdDLvnIkSNkZmbe8Fg1Gk3No1Z49FWOn5/S7L1GIZKMjAyeeeYZUlNTMZlM\ntGzZksWLFztkip988knc3Nzw8PCokExxg6vOVTRGD/Dll1+WOZZly5aRkpLimHxt2LAh69evB2Dk\nyJFcvHiRBg3akpOjLg1Uds3580qkLChICZoV5YknniA+Pp4OHTogpSQwMPCaY9BoNLWXcmWKbwbV\nLlN8NRaLCt8EBSljbzQW5iDWMmJiYoiKimLcuPFYrcp7P39eGfrAQFVAxHD7PbfVSbRMseZqKipT\nfHt69CaTWkl06ZKKbzRpUisTyzt27IiHhwevv/4WQqjLystT0vs+PtC06a0eoUajqQncnoYeoF49\nSE1VsY1z59TnWub67t69m5wcOHhQxeB9fZUnL2XxWL1Go7m9qV2WrSrx9VUTs1KqxVPJybd6RNdF\nUpK6BA8PuHJFPaTUr68LiGg0mkJuX0NvNEJoqIp1ODtDYuI18+prIhaLuj/5+anLOX1aXco15pg1\nGs1tyO1r6EEFsgMDlbHPz1cucS3iwgVV8zwwEE6eVGvAmjSpdREojUZTzdy+MXo7jRqpVEubrbC2\nbC0gP19l19Srp1QqMzOV5ryv760emUajqWlo388ewsnPV+GbjAxVfknKGi1T7OSkJmAzMpQn36JF\n9YRsDh8+TJ8+fTCbzbRt25YJEyaU28ezEtVMNm3axLZt2yo9rtjYWMdagspw5MgRBg4cSKtWrejQ\noQMPP/wwFy5cqPRxKsqyZcuIiYmptuNrNBVBe/SgPHk/PzWzabXCxYv88vvvNVamODtb1VG5cEFN\nxP7hD+Dufl2HKpfJkyczbdo0hgwZAsC+ffuq9PibNm3C09OTHj16lNhmsVgwlSGaHxsby65duxg4\ncGCFz5WTk8OgQYN4++23GTx4sOP8Fy9eJDg4uJzeGk3tRXv0dkJCCguSuLqSeOxYjZQpbtWqDS1b\nNmPVqh+ZOHEoISHKyNtlikF51H/+858JCwujX79CmeLmzZvz1VdfAWC1VkymODExkUaNGjk+R0RE\nOMZa1FO99957i0lDTJs2jbCwMO6++27sxd/fffdd2rVrR2RkJMOHDyc+Pp733nuP+fPnYzab2bJl\nC2PGjGHixIl07dqV6dOns2PHDrp3705UVBQ9evTg8OHD5OXlMWvWLFavXo3ZbGb16tVkZmYybtw4\nunTpQlRUVKnf/SeffEL37t0dRh6gT58+hIeHk5OTw9ixY4mIiCAqKsohNLds2TKGDh3KPffcQ2ho\nKAsXLuTtt98mKiqKbt26OUTr+vTpw5QpUzCbzYSHh7Njx44S57948SIPPvggnTt3pnPnzvz8888A\nTJkyhdmzZwPwv//9jzvuuAObzVbm34lGU1lqhUc/9bupxJ6vIpniAsz1zfxjQBGxNBcXNauZlASB\ngUS3b8/sDz6ocTLFAwc+TPv2d9K2bV9On34aKS8ChTLFoGSK77qruEzxhg0bOHjwIKNHj+a+++5j\nyZKKyRRPmzaNu+66ix49ehAdHc3YsWPxLWciIDMzk06dOjF//nxmz57Nq6++ysKFC5k7dy4nT57E\nxcWF1NRUfH19mThxIp6eno6nmyVLlpCQkMC2bdswGo2kp6ezZcsWTCYTGzdu5IUXXuDzzz9n9uzZ\n7Nq1i4ULFwLwwgsvcNddd7F06VJSU1Pp0qUL/fr1K/ZEtX//fjp27FjqmP/5z38ihGDfvn0cOnSI\n6Ohojhw54ui3d+9ecnJyaNmyJfPmzWPv3r1MmzaN5cuXM3XqVACysrKIjY1l8+bNjBs3jv379xc7\nx5QpU5g2bRq9evXi9OnT9O/fn7i4OP7+97/TuXNnevfuzeTJk1m/fr3j70mjqQr0X1NRGjRQKSu5\nuXi6u7P7++9rlEzx3/72OgaDG48/PgmLRckUf/xxoUzxH//4RwCcnZ0ZMKBQpvjOO+/EycmJiIgI\nR5ipojLFY8eOJS4ujmHDhrFp0ya6detGbjlFWwwGA4888ggAo0aNYuvWrQBERkYycuRIVqxYUWZI\nBmDYsGEYjUYA0tLSGDZsGOHh4UybNo0DBw6U2uf7779n7ty5mM1m+vTpQ05ODqdPn77mOIuydetW\nRo0aBcAf/vAHmjZt6jD0ffv2xcvLi8DAQHx8fBxPBEW/T4ARI0YASuMoPT29xLzLxo0biYmJwWw2\nc99995Genk5GRgbu7u68//773HPPPcTExNCiRYsKj1ujqQi1wqMv5nlXJ05OEBysJmW9vTG6uNQo\nmeJPP13DwoWbycxUDx9PPlkoUzxs2DCH8XRyckIUaPcYDAbHMQ2G8mWKX3zxRb755htAPV2AElEb\nN24c48aNIzw8nP3792MymYqFF3Jycsq8JvtYvvnmGzZv3szXX3/NnDlzyoz3F/XCX3rpJfr27csX\nX3xBfHw8ffr0KbWPlJLPP/+8WBjtasLCwvh//+//lbm9LIr+Tsr6PqHwOsv6bLPZ+PXXX3F1dS1x\njn379uHv78+5c+cqPT6Npjy0R381wcFgNHI4Pp6jBbFlqBkyxfPmrcHLyw2jUU0pFJUpHjt2bKWO\nWZZM8Zw5cxxPGgDfffedY5/z58+TkpJCSEgIoaGhxMbGYrPZOHPmTLGYtM1m47PPPgNUXLxXr16O\n/fr27cu8efNIS0sjIyMDLy8vrlxj/UJaWhohISEAjicqoES//v37s2DBAuwifXv37i1xrEcffZRt\n27Y5bmQAmzdvZv/+/fTu3ZuPP/7Y8V2cPn36mjeN0li9ejWgng58fHzw8fEptj06OpoFCxY4Ptu/\n41OnTvHWW2+xd+9evv32W0d2l0ZTVdQKj/6mYjJBcDAZ+/fzzEsvkZqejsnJqUbIFE+fPpTsbGjU\nqCE//lhcpriyqoYVlSn+/vvvmTJlisMLfeONN6hfvz7BwcE0a9aMdu3a0bZtWzp06ODo4+HhwY4d\nO3jttdcICgpi9erVWK1WRo0aRVpaGlJKJk+ejK+vL4MHD+ahhx5i7dq1xYygnenTpzN69Ghee+01\nBg0a5Gjv27evI1Tz/PPP89JLLzF16lQiIyOx2Ww0a9aMdevWFTuWm5sb69atY+rUqUydOhUnJyci\nIyN55513ePrpp3nqqaeIiIjAZDKxbNmyYp58RXB1dSUqKor8/HyWLl1aYvu7777LpEmTiIyMxGKx\ncMcdd/Cvf/2L8ePH8+abb9KwYUOWLFnCmDFj2LlzZ6mev0ZzPdyeMsXlYbHA77+r9z4+Kkm9BmDX\nmI+IKNSyscsUjx8//tYO7janT58+vPnmm3TqVK5i7HWjZYo1V1NRmeJyQzdCiKVCiCQhxP4iba8I\nIc4KIWILXgOLbHteCHFMCHFYCFE769SZTEqr3mZTVahucapbXh4cOAAXL6qVr3Yj37FjR37//XfH\nJKJGo9GURkVCN8uAhcDyq9rnSynfLNoghGgHDAfCgIbARiFEayll7VILA2Xo7WIy6em3TFtASuXJ\nZ2erz0XX9ezevfuWjElTkqLzNhpNTaNcj15KuRkoPUewJEOAVVLKXCnlSeAY0OUGxnfrcHaGgAD1\n/tQpJWV8k5FSKVJeuqQeMtzdoRLqAhqNRgPcWNZNjBDi94LQTr2CthDgTJF9Egraaif166ufFotS\nDbuJ2D35ixeVcJnFoh4yamnFQ41Gcwu5XkP/L6AFYAYSgbcqewAhxAQhxC4hxK6LRdIYaxQuLkrd\n0l6cBAplEm4CeXnKuOfmqhR/e+FvjUajqQzXZeillBeklFYppQ14n8LwzFmgcZFdGxW0lXaMxVLK\nTlLKToGBgdczjJtDcLByqc+eVSUHK7Ha8nqRUnnuzZsr8bKsLHW/0aviNRrN9XBdpkMIUTQp/H7A\nnpHzFTBcCOEihGgGtAJKqjvVEubMmUNYeDiR99+PedQotm/YAMnJrFu7tspkisPDwx1CYwA5OXD4\nsPLirVZ1b7GLa5ZFaGgoyddRCjE+Pp5PPvmk0v00Gk3totysGyHESqAPECCESABeBvoIIcyABOKB\nJwGklAeEEJ8CBwELMKlWZtwAv/zyS3GZ4rNnydu/X8kUP/kkO3btqhKZ4ri4OHr37k1SUhJSGjh2\nTMXjLRYL58+bsFhU1ajqiM3bDX1ZwmsajaZuUK6hl1KOKKV5yTX2nwPMuZFB1QQSExOLyxSHhIAQ\nXIqLw5KXh3+Bi12WTHFcXBxr164t14i2bdsWk8lEcnIykyZNx2Zz5dSpvXTv3pOhQ2fy+uvjSEw8\ngbu7O4sXLyYyMpKUlBRGjBjB2bNn6d69u2PZf3x8PPfee69DNfHNN98kIyODV155hWPHjjFx4kQu\nXryI0WhkzZo1zJgxg7i4OMxmM6NHj2batGnV8VVqNJpbTK2QQJg6FWKrVqUYsxn+cQ2ttOjoaGbP\nnl1cprhnT/wSE7nvjjuUTHG/ftctU2xn+/btGAwG6tULJCcHsrIS+PXXbRw7ZuTVV5+hW7coXn31\nS3788Ucef/xxYmNjefXVV+nVqxezZs3im2++YcmSMu+7DkaOHMmMGTO4//77ycnJwWazMXfuXN58\n880SUgEajaZuoaf3ysDT05Pdu3cXlylesQKCgvhg5kx++OKLG5IpthfbeO6551i9ejWXLgmkhEce\nGUZmppGMDNi/fyujRz8GwF133UVKSgrp6els3rzZsRp20KBB1KtXr9Rz2Lly5Qpnz551FCZxdXXF\nvbpKUmk0mhpHrfDor+V5VydGo7GkTPHIkZCURESjRkT06nXdMsVXlxK02cDbG+rV8yAhQWV2XkOy\nvVQqIx2s0WhuH7RHXwaHDx8uVojDIVOcm8umY8cgJQVSU6tMpthgUItx7cW+GzakmHTupk2bCAgI\nwNvbmzvuuMORLfPtt99y+fJlAIKDg0lKSiIlJYXc3FxHSMbLy4tGjRo51Clzc3PJysoqVyJYo9HU\nDWqFR38ryMjI4JlnniE1NRWTyVRcpvjDD3kyLg43V1c8/P2vS6a4KCdOKG9eSiV34O6u0ilfeeUV\nxo0bR2RkJO7u7nz00UcAvPzyy4wYMYKwsDB69OhBkyZNAFVwZNasWXTp0oWQkBD+8Ic/OM7xn//8\nhyeffJJZs2bh5OTEmjVriIyMxGg00r59e8aMGaMnYzWaOoqWKb5eTp9W9WXd3KBdu+vOf8zMhLg4\naNxYHeL0aWjVSqkjazRFqfH/E5qbTkVlirVHf700aaLiLFlZcPIkNGt2XcY+KUmFbXx94eBBtTjK\n27saxqvR3CIslitcvPg5+flJVXI8KSVSWjAYnLBas7lypWRFLje3Vri4hGC1XuHKlZIqr+7ubXF2\nDsZiSSUjo2RKn4dHOE5OAeTnp5CZWbLkpadne0ymeuTlJZGVdbCU7R0wmbzJzT1HdvaRYttMJl88\nPc2Oz97eXfH1vbNC1369aEN/I7RqpQqUXLqkZk9DKqffZrXC5csqTJOYqCZkq2txlEZTFtnZx7FY\n0ou1GQwuuLm1ICvrKNnZx7DZsottNxq9cHEJQUrJkSNPYLGkFdvu5dWVRo0mceHCSs6dew+42esm\nv73m1kuXbu32pKTCubvGjf9yext6KWWJAss1CicnZZnPn1eW2t1d6eJUkNRUZdw9PJQScnCwigRp\nNFdTkRCrzZZLRsbvXLmyg+zs46XuY7VmkZm5n9zccwQEDEEIwcWLX5CbG19sP4PBFSltSJl3XePN\nyTnBxYsrEcIZV9emuLg0wWQqjEf6+NxBSMhEAOLiHkdKS7H+fn7R1K8/Bpstn0OHRhfb5uTkT2Dg\nA/j69kVKGzZbyewyIZwwGJyusd0Zg8GElFZstpIS5OVtNxhcEMKIzWYp9Tu69nYDRmNhmUghqt8M\n19gY/cmTJ/Hy8sLf379mG3tQ1vrwYZUu07Ztha31lSsqeScrC/LzITwcjMZqHqvmlmC1ZiFlfrE2\ng8EVg8EFKa1YrRmAMtZWaxY2WyYuLo0xmbzJz0/l/PmjJCXtJjv7eZSWIDRtOgsnJ3+SklaRmvpD\nCWNpMHgghAEp84sYK/v/uxGj0Q0QKJWSQjsghAlX1+b4+fXH09NMTs5Jx/gK93HGyyuqnKsWeHt3\nx9m5BosW1nJqfYy+UaNGJCQkUGMljK8mJ0eJx1+6BA0aVFhq8soV1SUgAI4cKX9/Te3DZssjLy+x\nRLvJ5IfJ5FXKdgMGgzNGYy5gw2bLwmI5RGbmLGy2VMdeJ06odRgGgwdOTkE4OQXh7ByMk1MQRqMn\nTZvOwNW1CampW0hKWlVwTh+Cg0fh4dGuOi9ZU8OosYbeycmJZs2a3ephVJzNm2HQIOXdDx4MX355\nzWD7zz+rSM8f/6g8+U2bdGy+NpOS8g15eUlIaSUrK44rV3bi5taSNm2WkJNzhoSEDzEaPRFCIKUk\nNzeBnJyTpKX9jsWSes1jC+GCp6eZBg2G4+XVBTe3lo6nXKPRB3f31ghRtmPh69sbX9/eVXq9mtpF\njQ3dVAS7J+Ts3BCDwakaRlZJFiyAyZPV+yVLoEAa4WqkVBmZqanqISA2Vhl7Tc0mLy+J9PRfuXJl\nJ+npO5AyD7P5JwD27OlJevo2QBlmD492SGkjL+88+fkXShxLCBMeHhF4eXXGxSUEKHmXd3IKxNu7\nMx4eERgMztV6bZraSUVDN7Xa0CckLOLYsUmYzT/j69ujGkZWSaSEp5+G995T+gX790MRZUs7O3dC\nly7Kg586Fd5++xaMVVMpjh+fwZkzr6Ni2QInp2BcXZsWTGgaMJn88fBoh7NzfZKTvyI+fhZSWggM\nfAgvry54e3fG2bm+43gqvKJn3jU3Rq2P0VcE+2x2evq2mmHohYBFi1Tmzbx5MHEi/PBDiXj9Bx+o\nXYOC4JVXbs1QbzcyM+OIj3+FrKyDFC2R0LLlu/j59SM1dQtHjjxZol+bNkuwWjO5cGE5hROWkvz8\n8+Tnny+Rw20wuGOzZVGvXn9at16Em1vzarwqjaZi1GpD7+6uVglmZx8tZ8+biBDwt7+ptMunnlKW\n/MknHTn2+/fD++8r5//NN/XiqOokI+N3bLY8vL07IYSR9PTteHq2x2BwcexjMqlfgNHohYdH8fiZ\nxZLGqVOvcenSetzcWtG+/Ua8vXuWOI+UFkdcPiPjN+rVu4vAwIdrfraY5rahVoducnPP8csvIdSr\nN4D27a+9QOGmIyUMGwaff67SLTdsgJ49ee89mDQJOnSAHTv0BGx1YLVmER8/m4SEt/D27kFU1P+r\ncF+bLZ/k5C84e/afpKVtRggXmjSZTpMmLxTLfdZoagK3RejGHvPMyzt3i0dSCkLAp5/CjBnwxhvQ\nuzfMnUtQy+nYbPD889rIVwcpKd9x9OhT5OTEU7/+OFq0eL1C/XJzz3Lu3PskJi4mLy8RV9dmNG/+\nOg0ajMPJyb+aR63RVC+12tALYUAIJ/LzK18Y+6ZgMMDrr0PnzlgeGYnhL8/zTsdJhIZ6MGTIrR5c\n3cG+gjo5eR379w/Gza0N7dv/RL16fcrta7VmcerUa5w58wZSWvHzG0BIyPv4+Q1ACL16TVM3qNWG\nHsDNrSUuLo1u9TCuzbBh/HWBP323vMrm3R689abEaNTu/I0gpZVz597j3Ll/U6/e3bRsOR8/vwG0\navVPGjQYXywOX3p/yagRic8AACAASURBVKVL/+Po0Unk5JwgOHg0oaEv4ebW4iZdgUZz86j1ht7F\npQkWS+nl+moKp07B3O19+dHFC4/cDMb5/g948FYPq9aSkfEbhw9P4MqVHXh7d3dMohoMJkJCni6z\nX35+CgkJ7zpy4S2Wy7i5ta6w96/R1FZqvaE3GFzIyjqCxXIFk8nrVg+nVFQKpWCH7MSEwNX4zvo/\neDhaaRJrKkVi4lIOH56Ak5Mfbdt+TFDQiAplt2RmHmTfvsHk5MTj4RFBYOCDeHt3Jzh4ZLnev0ZT\n26kTpQSt1jSys0/c6mGUyoEDsHw5dOwIeXmCZ172h3PndAL9deLrexcNG06gS5dDBAc/WiEjn5Ly\nHXv2dMdqzSQq6mc6d46lTZv3adBgnDbymtuCcg29EGKpECJJCLG/SJufEGKDEOJowc96Be1CCPGu\nEOKYEOJ3IUSH6hw8gKtrKABZWXHVfarr4vhxVf81Nhbuvx9aNy9QGPzHP1RpKU255Oae5+TJl5HS\nhptbKK1bL8LJye+afaSUpKdvJy7uMfbtG4SrazM6dtyBj0+3mzRqjabmUJHQzTJgIbC8SNsM4Acp\n5VwhxIyCz38B/gi0Knh1Bf5V8LPacHNrBUBW1qEqP7ZN2nh106uEBYUxrN0wLDYL+5L2sfPsTnYn\n7iY1p1CMqm9oXyZ0nIDRUDxTY+BAaNRIFaNasABoOAC6d4dffoHx45W6mc6zLJO8vAvExvYhN/cM\nQUEP4+ERds39rdZskpJWcfbsP8nI2I3R6EVISAzNms3BZPK8SaPWaGoW5Rp6KeVmIUToVc1DgD4F\n7z8CNqEM/RBguVSrsH4VQvgKIRpIKUtqtFYR7u7K0GdnHyt3XyklXx3+iiCPINoHt2fjyY34ufnR\noUEH3J3cS+y/cMdCZm+eDcBffP/C5ezLpOWqSjp+bn4EewSTb8snPTedNQfX8GHshywevBhzfTNS\nKgHLuDj49Vf45BP74lgBH3+sVMx++QX+8x94/PEq+z7qEvn5l/jtt3vIzT1NZOT/yjTyFssVMjL2\nkJz8NefPL8ViuYy7eztatVpEcPCoGjt3o9HcLK53Mja4iPE+DwQXvA8BzhTZL6GgrYShF0JMACYA\nNGnS5DqHAc7ODQHIzT1d7r4bTmxg6OqhABiFEWuB5olRGAkPCmdy18mMNY9FCEF6bjov/vgiQR5B\nJGUmEZ8aTyu/Vrx373t4OHkwd+tcjl0+RlKmqoNpEAaOXz5Op8WdGB81ntCkZ3jhiXCMRrVAdvjw\nIgNp1kytmP3jH9Uy2QceAE/tbRbFYknj998HkJV1hIiIdSVkdqW0cvbsIs6d+3dBzU6JECYCAu4n\nJGQSPj53aAkCjaaAG866kVJKIUSldRSklIuBxaAkEK73/M7O6h4TGPhQufsu3r2YAPcA5vSdw5T/\nTaFnSE+e6/4c72x/h6TMJMZ/NZ6PfvuIBX9cwNeHvybPqkTTXr7zZe5tfS+t/Vvj7eJN7PlYXEwu\n/LHlH+kS0oX2we3ZcnoLEzpMYOZPM1m6dym5lsUYguLwtjVj0SKXktGZAQNgzhx48UX461+VCNpt\nyJUre0hJ+cbx2curA/7+g5DSSkZGLGFh/8XPr99VffZy5MgErlzZhbd3T0JDX8bLqzPe3l31KlaN\nphSu19BfsIdkhBANAHt597NA4yL7NSpoqzbUP7aB/PxrV6K6kHGBtYfXMqXLFFYfXI3JYGLZkGU4\nG/9/e+cdH1WxNuBndje9k5CQhE4ChN4/kSZNmoJ6UVCxIAoq2JALXCvXXrChoqhYUARFUcoFlaYg\nCASkhRZIoSQhhfS6bb4/JgnEBAikwTLP77dmz8ycc2Z28D1z3nmLM9sTtmO2mekQ1IHtCdvp+HFH\nAEa3Gc17Q98jxCukzLU6NejE+nvWlynr1VgFu3qy55N88VYLSAzDntKajJvu5avo9kz1n1p+hfnU\nU2q39q23oEMHuPPOqv0YVxiFhSfZvXsANtuZxNL169+Kv/8InJzq0bXrDjw9O5TWWa25xMfP4uTJ\nd3Fy8iciYhGBgWP0yl2juQCXal65HCjJ2HsPsOys8ruLrW+uAbJqUj8PIIQRk8mH1NQfS3NpVkR9\nj/r8747/4eXixfq49bx9/ds082tGqHcoBycf5N5O92KxWSi0qkTCD3d7mCW3Likn5C9EalwDCn9/\nHOL74dc4gVvG5DNtzTQmLJ9Q+oZQhrfeAhcXpaffv/+i7nWlk56+Cimt9OhxmL59LUREfIuTUwDR\n0ZOJjp5MSsr3pKb+TFFRImlpK4mMbMvJk28RHHxfsXnlWC3kNZpKcMEVvRBiEWrjNUAIcRJ4HngN\n+F4IMQE4BtxW3HwVMBw4CuQD42ugz+UwGNzJzz+I2ZyCi0uDitsIA2H1wrhp8U0MDx/O/V3uL60L\n9Q7l4xs+BiC7KJt9yfvoEnxplqHS7EGzppK4OF8yrr2bbHMB06+dzhtb3mB/6n4+vfFTOgSdWaXi\n66ti2N97L/Trp9xoPTwu6d5XGiEhE/H3H4nNlsu+fcPIyFiL0ehTmi3Mas0sk/Da3b0NnTptwte3\nd111WaO5IqmM1c3t56gaWEFbCUyuaqcuFmfn+pjNCRQVHa9Q0G88tpEVh1ewL2UfRoORT2745Jwr\nQW8X71I1zKXQqRMUFQl69oT7/30zD/5vEp0adGLJrUt4+H8P02VeF6b2nMrMXjNxdVJhb13uGofx\nhx9g5UqVb3bdOoczuZTSjt2u3pYKC49htWbi5dWVU6fmEx//IgaDC+HhcwkJmVgaTMxmKyA3dzc5\nOZEI4VQcw0an1NNoLpYrPgQCgLNzKLCboqITQI9y9e9vf59fj/5KjjmH2YNnE+odWu19sNvVwjwj\nQzm+LloEfbuMp11QW9oHtsfNyY2diTtZsHcBb255kze3vFl6rpvJjc4jO9LD6MqIfRsYNGcOPPZY\ntfexrigoiCEq6iby8kp97jAavXFxCSE//xD1648mLOw9XFzKqsmMRjd8fHri49Oztrus0TgUDiHo\nS7xjCwrKm1im5KXw08Gf8HT2pE39Njz6f4/WSB++/x4eeURpXYYOhb59VXmP0DMPni0ntxDkEURE\nQATr4tbRv2l/hoYNJSknicjESOZ1lbzbGZ7d8Byzbh2NIaT6H0h1QV7efszmUzRt+iIGgzPp6WvI\nzFyLzeZH+/Yr8fcfUddd1GgcGgcR9E0AsFpTytWti12HTdrIKsrip2E/4WR0qvb7WyzwzDNQvz6k\npsKrr1bcbsM9GzAItf9937L7+OvkXzz6f4/ialIqnCJrEZO/vJUXWcGB5yL46r978AhtVu39rS0K\nCmJwc2tBQMBIfH1jMZm8yM6OJDZ2JoGBt9Oq1acYjVfHfoRGU5c4RFCzkkxTDRrcV67uf0eUjfZt\nbW6jf7P+NXL/zz5TVpLZ2XDHHUpPXxElQh7gvaHvseOBHaVCHsDF5MKnE5bxdpNJLG2YQ/u3w3ng\n5/v4ZOcnHDl9GeXFrQTp6WvYvr0t2dkqebbJ5IWUdo4cmYyzcxAtW36shbxGU0s4iKBXTlNmc3K5\nut2ndmMURmZfP7tG7p2SAs8/D8HBYLPBCy9U7jwvFy88nD3It+TzXdR3peVCCJ645yNW/9Wclik2\nlu78hkkrJxHxYQQf7/i4RsZQ3RQUxHHgwFjc3Vvi7n4mbEFS0nxyciJp0WJ2aVJujUZT8ziUoI+L\ne7pMeVRKFAdSDzC151Qa+TSq6NQqU1QELVsqlc3EidDiIhMUzdk2h7E/juXljS+fKRSCIYt38Mv6\nENJesnAkfiRDWlzPQ/97iEdXP4rVbkVKycsbX6bd3HbEZlw+IZpttjyiom4C7LRr91NpIDGL5TSx\nsTPx8elLYOAdddtJjeYqwyF09E5OStBnZW3CbreU2mE/te4pvF28mdl7Zo3du2FDCAwEZ2d49tmL\nP396r+nsT93PMxuewcPZg8eveVxV+Pkh9+5FNGtG2JfLWZ45iukPPsHbW98hKiWKJj5N+HLPlxiF\nkZu/u5kt923B3cm91hyIbLbCYtPH7RQWxhMW9jYAhw5NIC9vH+3br8LFpQk5ObvIyYkkJWUxVmsW\n4eEfaCcnjaaWcRBBHwAIQGI2J+Lq2oQtJ7awInoFAe4B+Lr6Vvs9T5yA6dNVkqifflIbsA0q9tU6\nLwZh4ItRX1BgKeCJX58gNiOWOcPmADB63UQCJobw0duHOZAXT1i965nSfQrf7PuGDfEbCPYMpl+T\nfizev5jA2YG4GF344bYfGNBsQOn18y35rDi8gkHNB+HvXj1xYMzmFHbu7FZszgoeHh1L65yd69Oy\n5Uf4+PRk584u5OXtA8BkqkeLFq/j6dm+Wvqg0Wgqj0MIeoPBhMnki9WaQW7uPlxcGjNz7UycDE60\nrNeyzCZodTFpEqxdqyxuHn0UZsy49GuZDCa+/de3jPlhDF/s/oJ3hryD0WCkR0gPfuuQydxrY5my\nZg9PBk3GOqg/CVMTWLRvER9Gfsji/Yup51aPRt6NMNvMjFw0kt/u+o1rG11LQnYCoxaPYmfSTlyM\nLoxtN5aHuj1E99Dupb9JviWfRfsW8fPhn+ndqDcTukwgwD3gnH2VUnL48ETM5hQiIr7F17cvLi5n\nzEDDw99HSjtRUTeTl3eAli3n4ec3CFfXZnolr9HUEUI5s9Yt3bp1kzt27KjSNSIj25OXd5CQkAdI\ndB5D/6/6YxAGZvaaycsDX77wBS6CqChoX7wwvf9++OST6nNkNdvMOBvLen/a8/Owt2+HSE4he8dm\n/Forsx4pJQXWgtJY+qdyT9Fzfk/SC9J5d8i7PL3+aXLMObwz5B12Je1iwd4F5Jpz8XbxpmtwV5r4\nNmHZoWVkFGYQ7BlMUm5S6QPhzcFvUt+jfrn+2Wz5HDgwBl/f62jU6MkKxxAf/1/i42cRFjaHhg0f\nqZ4fRqPRlEMIsVNK2e1C7RxiRQ/KxFJZ3Rj5cveXuDu5k2/Jp0+TPhc892J54w31d+RI+Pjj6o1W\n8E8hD2Bw98Dwwkswbhx+d0+E7dsBZaFzdsKUfEs+KXkpGISB+5bfRxOfJmy5bwvtg9RT6dVBr/Lz\noZ/ZdnIb2xO3s/TgUoaGDeXhbg/Tt0lfDqQeYG7kXD7b9Rl5ljyW3LqkXF+MRnfatVsOnFkg5Ocf\nxWw+Vfz9APHxswgKupvQ0CnV98NoNJpLxmFW9AcOjCM7ewvtu+wjaHYQLf1bsid5DxkzMvB2qT5T\nvoIC8PEBKSEhQW3E1hohIZCUpJKW3HJLuWq7tPPM+md49c9XaejdkCeueYKBzQbSIajDRalNXtr4\nEs9ueJY1d61hUHMVCz4v7xAJCR/QqNFU3NyaY7dbSEv7mYSED8nK+qPM+Z6eXejc+U+MRreqjVej\n0ZyXq3BFH4TZnMzSg0vJs+RxV4e7uNl8c7UKeVBpAS0WGD++loU8KLOehx9WuWa7d4dGZU1GDcLA\nKwNfISIggid+fYInf3sSHxcf0mekIygr6G12G0KICvcvpl07jS93f8mUVVNYN/oFUk99QmbmOsBE\nUVECUhaSnb0dqzUdV9emNG/+Op6eKtqnEAJv72u0kNdoLiMcZkV//PgbxMbO4D8HgziRm8nxafk1\nsgl7/fXw998QGwvete3zk5OjTHssFuV+u3EjuLpW2FRKydH0oxzLOsag5oOw2CwsilrEXR3uYtep\nXUxaOYlJXSeVCdd8NisPL+fGxaOY2AzuDW+En98QTp2aDwg8PNri5dWdgICb8fcfVhptUqPR1C5X\n5Yo+tQi2pSTzr1DYk7CRTqH9qtXS4+uvYc0amD27DoQ8KFvOhx+GI0dg2TL1ff78CjcJhBCE+4cT\n7q+Spy+OWsw9P9/Dh5EfsiNxB/Xd6+PvpswtCyz5ZJ1eQkbGBiyWZDp0WM0NrUYyuHFbvjkRw4zh\nq0g5eiOurs3p2nUHTk7Vb66q0WhqDofwjAVwdW3G2mS1RWi2Q/fPB5JZmFlt109Ph6lT1ffbbjt/\n2xrlzTfh55+VGueLL9RucCUY12Ecrw18jb+T/mZil4kcmnKImyNuZvaW2XSa24Ld++8lPX01Qjhh\nt1sAmHfTCiSC+5YOp6DgGBERC7SQ12iuQBxG0Ht59eS3FBOd/euxP8eJzv5++Ln5Vcu1Dx9WKvG0\nNOjdu5xqvPaREgYMgBEjlBH/ypUXPEUIwYzeM8h7Ko+Pbvio1Imsta8/0Zmn+DqpKddee4r27ZeX\nehY382vGsz3v4o+kE/xVNAQfn2trdFgajaZmcBhBvzclivg8K/3rZZJWZKGHTzY2W2GVr7t2LVxz\njUomYjRWegFds6xdC/37q1eLTp1g9GhVdgFOn/6FvbuuJTHxs9L8uq2d93FLQxe+jYlnxLcjmPX7\nLCw2taK3WDLp47KMrv4evLrrT+Iy4mp0WBqNpmZwGEF/MO0gAB187AyoD3d0e5Gzbb0vhagouOEG\n8PeHwkJ4/HFo2/bC59U4AwZA8+bKU+vXX1VUtVGj4M8/z3lKaupP7Ns3goKCaKKjH2D37n7k5R2g\nRYvZzBsdyeTukzmedZz5u+ZjMqitm993343VksKCm7/DIAyMXzYe+3kSsGs0mssThxH0ybkqRLFV\neDEy1IVerWdUycTPbIa771abri+/DF27qnDElwVGo0pntXmzMv9Zs0bpk4YNg3nzVF5DVJ7WoqJE\nAHx9ryM0dAo9eybQqtV88vL2k5z8LUIYCPBpzwfDPyDq4ShiHo1BCEFm1g6MuStYliiZuXEe93e+\nnz+O/UHbuW0Zv2w8H0V+xMnsk3X5K2g0mkriMOaVM9fO5J2t7xB15ywSjj1F+/arkNJMQMCoS7re\nc8/Biy/C0qVw881KLX5ZhWrJzlahM0eNUuZACQnqybR+PbYeHTg63YOUwAMYjW707JlYzvrIbE7B\naPSq8GEopWT37r7k5B1gXdE9fLxrISl5KdR3r09YvTCOpB8hLT8NozAyqvUoHu72MF1DugIgEPi4\n+tTKT6DRXO1U1rzScVb0eckEegTSJHQCYCQ+/nn27x+D3V500deKjIRXXoExYyA+Xq3uLyshD+pV\nY/x42LVLreBDQ2HtWrLfn4Ll2F585/5Fo7QBNGv2MlBe3eLsHHjON57k5G/IyvqT8BZv8N+Bb3P8\n8eMsvGUhzf2a82L/F0mZlsLhKYd5sueT/BH/B4O+HoTf6374ve6H7+u+3PXTXVwOCwiNRqOo0ope\nCBEP5AA2wCql7CaEqAd8BzQF4oHbpJQZ57tOdazohy8czt7kvfyn93/o6/YLWVlbsFrT6dz5T3x8\nelX6OlYrdOigfJMGDoRvvoG9e6FNmyp1r2ZITVVCPkjF48/PP8r27a0wSXesIhffo960vSsGJxcV\njVJKSX7+QbKzt5KdvZ2Cgmjq1RtKcPAEnJz8sdkKSElZRGzsTFxdm9OlyxbEBZzOCq2FLDu0jKTc\nJAAOph7kk78/4dWBr9ZoHgCNRlO7DlP9pZRpZx3PBNZJKV8TQswsPq5CEN/KcSr3FKn5qfx54k9u\n7XM3p08rk8PMzE0XJehXr4aDB1V8+aeeUhuwl6WQB5WNHEpjJbtOug9Pz07k5u4iJGcASY3X8/fG\ntrS55ldyc/8mIeFDcnP/BsBo9MHVtTGxsTOIi3uOevWGkJW1Cas1A3f3trRuPb9CIW+xWXh508t0\nbtCZUa1H4WpyZUy7MaX1UkqyirJ4at1TdGrQiaFhQ2vlp9BoNOemOlb03c4W9EKIw8B1UsokIUQw\n8LuUstX5rlMdK/rANwNJzU9l4S0LGdv2NiIj21JYeAxf3/507Li60te54QYV4iA0FE6eVDb0teEF\nK6Xk+PFXSEtbRpcufyGEkbS0FWRlbSI7ezuFhTEAeHtfS9u235GTs5vExLlkZf2Jye6Jx+r9mL0s\nnO5moVWr+QQH3UvWnR2JGrsfi4+aY3f3toSGPoSf32Dc3MIQwkBubhSJiXNJS/sJH58+hIZOxsen\n7zk9is02M9d8dg0ns0+y+b7NhNULK9c2z5xHz/k9OZF9grV3raVLcBcdi16jqQEqu6KvqqCPAzJQ\ndozzpJSfCCEypZS+xfUCyCg5PhdVFfR2acf5RWds0kb0lGjC/cNJSVnCgQO3YTC406dPzgVVEADH\nj0OzZjB0KKxapdQ2d955yd26KOLjXyA+/nl8ffvTqdN6AHbvHkBW1mY8PTvh7h6BEEYMBndyc3eR\nnb0ZIVzw8upOdvZmSkxJw9+DUPtIeP99SE+nYHgXEmZ1JOC2d88rwC+GqJQoun3SjSJbEUEeQVzf\n4noW3LwAUDlwOzXoREPvhnT7pBsZhRn4ufrRLaQbj1/zOMPDh1f5/hqNRlFbqpveUsoEIUQgsEYI\ncejsSimlFEJU+CQRQkwEJgI0bty4Sp3IKMjAJm0ANPRuCED9+v/Cw6MDVmsGUloQwuWC1/n8c2Vd\nc//90Lgx3FFLOaxPnHiL+PjnCQq6h9atPy8tj4j4BienAAwGFaM+OflbDh26D2fn+vj6DiQzcz2F\nhUdxcqpPhw6/YsINt14rlB3ooEEQFYXbvyYT9tBc8EqE26tnVd0usB17H9rLmpg1bE/cTkJ2Qmnd\niugVPL3+abbdv409D+7h15hf2Z6wnd9ifmPsD2M5POUwwV7B1dIPjUZTSaSU1fIBZgHTgMNAcHFZ\nMHD4Qud27dpVVoX9Kfsls5AN32pYpvz06d/khg3IEyfeveA1LBYpGzaUcujQKnXloklM/Fxu2ICM\nirpN2myWCtvY7TYZG/uM3LAB+fff/WRRUaq0WHLkvn03yd9/d5YZGX+UPSEuTsojR9T3rCwp+/SR\nEqR87jkpbbYaHc+JrBMy8M1AGT4nXGYWZJaWR6dFS+cXneW4peNq9P4azdUEsENWQj5fsnmlEMJD\nCOFV8h24HogClgP3FDe7B1h2qfeoLCXOUiXqgxL8/Abh5taa2NinsVqzz3uNVauUTl5JxBrrajl8\nffvSqNE0IiK+wWA484JVUBBDbOzT7NlzPZs3B3Ds2EsEB99Px46/4ewcgMnkSdu2S+nVKwVf375l\nL9q0KYSFqYHMng1vvaVMMV94AcaOhaKLNzmtLA29G/LDrT8QlxnHuJ/GlXrShvuHM63nNL7Z+w2b\njm2qsftrNJryVMWOPgj4UwixB9gO/E9K+QvwGjBYCHEEGFR8XKMk5ylBH+QZVKZcCIGbW1Ps9jyO\nHXvpvNd4+mn1t1u32rGZl9KOlBI3txa0aPFmaSAxgIyMdezc2Y3jx1/HYkmlfv3RtGnzPS1bflKq\nxgE1PpPpPM5JaWnw5ZcwfDhMm6YiXy5ZcmawNUSfJn14d8i7rIxeycZjG0vLn+rzFI28GzF51WSs\ndmuN9kGj0ZxFZZb9Nf2pqurm3b/elcxCvr3l7XJ1CQnz5IYNyN9/N8nc3IMVnr92rVrHh4XVuGaj\nlPj4l+XevSOl1VpQpvzkyY/khg1GuW1bW5mfH1v1Gx0+LGVQkJTBwVIePSrlpElSCiHlH39c+Nwq\nYLfb5U8HfypzLKWUPx74UTIL+eIfL5aWaTSaS4OaVt1cThzLOgZAka28SsLPbyAAQpg4evSRch6b\nJZuvAIsXg6EGf5Ho6Ic5fPgB4uJmERf3HFZrFtnZW0hOXsiRI4+xc+f/ceTIQ9SrN5QuXbbg5tas\n6jdt2VJFtjSblQfYY48p06J771VeYTWEEIKbWt8EwJ5Te+jzRR9+OfoLPi4+9GrUi2c3PMuwhcOI\nzYitsT5oNBqFQwj6uEwVPreJT5NydW5uLfDx6YfB4EFGxlpSU38sU794sQpzEBGhApdVN2ZzaunD\nxWxOIzl5IceO/RewkZX1B3v2DOTgwXEkJX2GweBC8+av0b79MkymajTeb9cOfvsN8vNVELSvvlKD\nnjat+u5xHtLy09iXso9hC4cx6OtBbD6xmQC3ADaf2Ey7ue14/c/XS0MjazSa6schUgmWmPc19qnY\nTDMkZBKJifOwWNKIiZmKv/8wjEYPcnKUrOvYUQn86iY7eztRUaMIDX2MgIAbMZtPYLcX4O19LaGh\nj+LioswMTSY/3N0jymzGVjtduigh7+mpjkt09jfdpKJe1iADmw/k8JTDRJ+OBuBY5jHyLHmMCB/B\nY788xsx1M1m4byHzbphHz0Y9y5ybb8lnV9IunIxO9AjtUaP91GgcFYcQ9CWbsY18Kk79FBg4lqCg\n28nK2syuXb2JjX2K8PD3eOEFlVDkxx+hdesz7W22QgwGlyo5FyUnL+bw4fE4OwdjNLqzc2cPjEZ3\nWrf+mqCgO+vGU7REyH/9tYqR07YtTJigAu/Xq1ejt27g2YAGng3UwVkvXktuXcLK6JVMWT2FXp/3\n4rqm1+FiUj4PSTlJRKVElfpIjG03lneGvHPmOhqNplI4hOom15xLgFsAIV4hFdaXCFVX1xYEB08i\nIWEOmzZt4913VbgYg8FKYuJnHDx4F1u3tmLTJjeOHXvxkvoipZ24uOc5ePB2PD27ERh4B0ePPoaH\nR3u6ddtLgwbj6j4cwN9/K5PLMWNUYLQpU+qkG8sOLaPDxx3o26QvBx4+wJM9nyTPkkd6QTrpBek0\n8GzAf3r/h2VjlzGr3yyWHlxKxIcRfLLzE50ARaO5CK74Fb2UklxzLk9c80RpZqSKKCpKYuvWxjRp\n8jxGY1vGj/fG1dVOaqqBxMQbyc//BZPJHymV2d/x468ihDONGj1ZxvTxQuTl7ePYsZfx8xuCEILj\nx18mMPB2WrWaX6VEKNXKG2/A7t0qFnO/frBokVLh1HLW84beDYk+HU2neZ2o56beKAY3H8wbg98A\nILsoG28XtVcxstVIxrYby4P/e5BJKyexYM8C5t0wj7aBl0PKL43m8uaKX9FnFmZitpkJ8gg6bzsX\nl2C8va/l1Kkv+eGHpcTERDBq1HvcfHMCjRvn4Oc3BKs1A6PRnVatPqdevWHExf2HHTs6kZa2Eput\nAHux7XdBQQwxZ/kIIwAAExZJREFUMdM5efIDsrO3YbPlk56+Bint5OTsxNW1CRkZv5KdvZ3mzd8g\nImLh5SPkAZyc4LvvlJBft05lrHroIYiOrtVudA3pylc3fUWnBp1o7NOYxj6NaerbFFC6+VYftOJA\n6oHS9q0CWrH+7vV8MeoLDqYdpNO8Tjz+y+OsjF5Z6jSn0WjKc8VnmDqUdoiIDyO4seWNLL99+Xnb\nJicvZMmSD3nssU306bOUWbPOrGCFMBEcPJHmzV/BZPLBas1m69YWSFmEzZaDEE74+V1PWNi7FBUl\nsHfvEKQsMecUgMTDoyN5eXvw9OxCaOgjBAaOubwEfEUcOgS//AL//S8UFCg1ziuvgLPzhc+tQZJz\nk+k8rzMSycZ7NxLuH16mPjUvlX+v+Tdf7/36jPdtvXAmdJ7AhC4TCHAPqItuazS1Sq1Er6wuqiLo\nf4/7nf4L+jO6zWiW3LrkvG3z8sy0bZuDzeZEu3YFtGjxAdOmBeDn1wNPz07lhPKRI4+SkPA+AEK4\nlAp2k8kPL6/uGI1eWCynsVhOkZ9/BJPJlxYtZtOgwT11r4e/WJKSYORI2LEDQkJUUDRQbsItW0L3\n7spt2M+v1rp0IPUA/b7sh5vJjU3jN9HEt7z5bJ45j7+T/iYyMZIV0Sv4Pf53XIwujGk3hsndJ2tL\nHY1Dc9UI+k///pSJKyYy49oZvDb4/NEWPvhA5dT+7Tc4cUL5ED344Lnb22z57N9/Kx4ebWna9HmK\nik6SmbmRnJxIcnIisVozS9v6+g6gefPXcXa+gleSaWlw3XWwfz+4ualA/CaTykdbwoABMHmyeiiY\nan6LZ1fSLgYsGICvqy/r7l5Hc7/m522/P2U/cyPnsmDvAnLNuXQL6cajPR5lXIfLYBNco6lmKivo\n6zz8gaxiCIR///ZvySzkl7u+PG+7oiIpGzWSslcvKbXn/XkoKpJy1iwVNgGkvOMOKdPTpVyzRkW/\nbNRIlTdsKOXHH9dKzIjIhEg5fOFwmVuUW+lzsgqz5AfbPpARH0RIZiHH/jBW5pvza7CXGk3tQyVD\nINS5kJdVFPSjvx8tmYXcdnLbedvNn69Gu2KFlJ9+KmVGxiXf8uqgqEjKRYukXL++bLnFIuXPP0vZ\nu7f6Qa+9Vsp9+2qtW9mF2fKznZ9Jm71yDxi73S5f2/SaFLOE7PFpD5mYnVjDPdRoao/KCvor3uqm\nwFKAq8n1vK/0Nhu89hp07qxi2TzwAKxZU4udvBJxdlYhjfv3V85V06cr+3uTCUaNgo0bVWTMw4fV\nD/vBB7XSrY93fMz9K+6n9+e92Ze874LthRDM6D2DpWOWsj9lP53ndeb+5fczb8e8Sp2v0TgCV7yO\n/sZFN3Iy+yS7Ju06Z5vvvlMya8kSWLgQNm9Wsefr2LDkyiE1VW3G5ubCPfeoDVohVIz7wEC47z5Y\nsQLmzYOJE2u0K1JKvt77NVN/nUpWURYPdn2Q0W1G069pvwueu+fUHp5a/xRbT24lvSAdgH9F/Is5\nw+ac09lOo7mcuWp09N0/6S6HfD3knPV2u5QdOkjZurWU69ZJaTRKOX36Jd/u6uXoUSnbtJHSw0N9\nAgKkTE5WdUVFUg4frsIfL1hQK91JzUuV438eL43/NcoJyyaUll9IhSelUufEpMfIF/94Ubq+5Cq9\nXvGS7297XxZZi2qyyxpNtcPVoqN3edFFNnq70TnrFy1So3zySSlNJikjIqRMSrrk22lKKNnRttmk\nvPlmKd9+W8q+faU0GKR87bVa2wTJM+fJ5Fz1wFkVvUoyCzn6+9EyITuhUucfOX1EDl4wWDILGfRm\nkHx2/bPyRNaJmuyyRlNtVFbQX9E6eiklRbYivFy8KqzPyYEnn1SBG595Rjl//vUXNNAxsapOiani\n6dOQnAxTp8LOndCwIcycCaGhSo1z+HCNdsPdyZ1Aj0BARcl8qf9LrDi8gogPI5gbOfeCMXHC6oXx\n67hfWX3narqFdOOljS/R9N2m/Ov7f7E+br1aDWk0VzqVeRrU9OdSV/RpeWmSWcgBXw6osP7RR9Vq\nfsOGS7q85mLYtk3Ku++W0sVF/eiDB0vp6iqlk5OUU6ZIWVBw4WtUE9Fp0XLgVwMls5AjF428qHNj\n02Pl9N+mS//X/SWzkBEfRMhlh5bVUE81mqpBJVf0V3RQs/0p+4GK49D/8gvMmaMWnhad06Lm6dFD\nfd59F7Ztg549obBQOVh98AF8/rnyVhs/HsLDazSVV7h/OGvuWsPCfQtxMylv58ScRB5Z/Qgdgzri\nanItbXtT65to6d+S2IxYfjjwAwD+7v48cc0T7E3ey65Tuxi1eBS3RNzCnKFzCPUOrfCehdZC9pza\nw97kvfRu3JuI+hE1Nj6N5mK5sgV9qhL0YfXCypTHxsKNNyoh/+OPMHhwXfTuKsXPD4YOVd99fJQd\n64wZKkLm66+rj5eXsuLp3l09HJo2PaMKCg1VljxVRAjBuA7jSo8TshPYc2oPSw8uLdMuvF44Lf1b\nEn06mhlrZ5S7zuo7V7Pn1B6e//15Vh1ZRXO/5hhE2YeU1W4lJj0Gi12tKEwGE9Ovnc4zfZ/Bzeky\nj3WkuSq4os0r39v6Ho//+jir7ljFsPAzWZJ69YItW+C551SsLs1lQG4uPPGEWtmHhSlhv2cPWK1l\n25lMcMstKsxCnz5nHgDVRKG1sIze3tnojMlgwma3VZhz2MXogtFg5PYfbmfxfpWGzNvFGz9XP+q5\n1aOZbzOEEITVC6NHaA9a+rfkzS1vsmDPAlr4teCRHo/QI7QHnRp00kJfU+1cFbFuEnMS2XpyK0Na\nDMHD2QOADRuUtqBxY4iLq9lk35pLICoKfH3Vpu3q1TB8OLRvrzZuGzaEP/+E+fMhM1Plup08GcaN\nO5Mdq46w2q2si13HlhNbiEyMZHvCdnxdfTn66FEARnw7gu0J2wFo7tecYM9gdibu5GTOSUCt8n1d\nfUuvF+oVSveQ7vQI7UFD74YVxuEJcA+gQ1AHnI3a4UNTMVeFoP8nCQkqwbeXF6xdC03KBzvUXE7E\nxKi0hnPmqBX/zJnw1FPKE3fRIpg7V3njennB9ddX7OHm4aEmvUcP9cBwqnySmKogpeR0wenScMhv\nbXmL2IxYbNLGobRD7EzaSb8m/Zh3wzwiEyNZfWQ1BoMBgUBKSUxGDDsSd5BRmHHe+zgbnekY1JEe\noT1KHwxh9cJK1UcGYdDB2q5i6lzQCyGGAu8BRuAzKeU5Q0tWh6AvLIS+feHgQbUX2KZNlS6nqU1S\nUpQd7DffqJAL69ercinVZH74ofpbEadPQ7rycsXFRYVjKNH9d+9e4xu/58Jmt5FZmIm/uz8x6TGE\nvx+OpOz/ax8O+5Drw65ny4kt3PPzPYBSC7Wp34aIgAi6h3QnLjOOTcc3sTtpN4W2wnL38XDyoE39\nNvi7+9PMtxnN/Zrj7+aPEIIbW96Iv7s/h9MO89fJv0qvPyxsmFYjOQh1KuiFEEYgGhgMnAQigdul\nlAcqal9VQZ+crDLhbd2qclwfO1bnb/qaS2H9esjOVpOZn6/+/lOHP3Uq3HCDss9/6CH1MCgsVE4T\n2dlKLRQTo84HpfP38Dij62/eXG0SW63q4+pKOYxGlTi9e/czr4j/RAjlkFGJh0ieOY/v93/PyeyT\nZcqHhQ+jW0g3TuWe4tOdnyKRHM86zvaE7exP3c/yscsZ0XIEyw8vZ9TiUeWue1+n+3B3cmf10dXE\nZMSUq+8W0g0vZy8SchKIPn0me5jJYCLEM4Smvk0xGoxY7JYy+xZOBiciAiLo36w/nRt0psBSgNlu\nLvsTCWOp/8K5EEIQ7Bms3zhqkLoW9D2BWVLKIcXH/wGQUr5aUfuqCPo9e2DECBVK3WBQMuD77y+1\n55rLhu3bYdq08uXTpqlY+IcOVRxX57nnVEz9pUuVl1xOjsqcVfLvvFkzFWc/JUU9LJyclCD39FT/\ngIKD1QMgKkqddz68vM6ojUreIho1qpYN5DxzHkII3J3cybfkk5KXUq5NA88GuJpcyTXnkpiTyKG0\nQ+xN3sve5L0cyzqGk8EJgzBgs9tKLYIKrYUk5SaRU5RD95DuCCGISokqp0ISiHJvIJdCS/+WtKnf\nhg6BHcq8RbQPbM+IliMAeGPzG+Uc27oGd2Vwi8FY7VZmb5ldpi7APYDrml5XztruaqSuBf1oYKiU\n8v7i47uA/5NSTqmo/aUK+t9+U0K+ZNHn6akClnXocMld11wtxMXBqlUQGakeKocOqYfBsWNqJ//p\np1VKxXr1lEqohBIzrp9/Vm7WBQXqjaKEwEAICFCvmbm5Ze9pNCpTUlAZvUreOkpwclL3BrXhVPgP\nVY2Li9qwBhWVr+gfVkKurso8FeD48fIOJO7u6kEG2OPjMdhsAOSY7FiFBFcX9RAErGmpHPcT7Ksv\nyXGyYwNwdip9AzJm5eJqVw+DU6425D+eba42gas0sLiNna2hEjcLhOSc9VNIMNnVSUUGqbJxco56\nY3kZZbKDUQokYDEU14uS/0hMdjBIgRQSS8m1L6HeLiTWCuqd7CDK1Zd8OX+9AYHJeGYvaULgEKbO\nXFZujJWhsoK+zuzohRATgYkAjRuXd3iqDM2aqf23ESNU5rsuXSp+y9ZoytGsmbLoKcFiUfGsS4T6\nmDHKJ2D37rIC9YEH1F+7XQlOUOdlZam3h3bt1F8nJ/WaeTbOzmc2jwwGyPjHRqyb25l6KZUq6mw8\nPc/U22zlHyTe3mfqzebybyR+ftC6tbp9QYFqA5T+LxMQoPY0ALZvp77VRteks84PCoL6xeHAo/4q\nLbYDUkilymrSBGw2xI6dGCQ8Fg+RAUV80SqfFD8XcHcDu4TMDM6IxWLc3cHNFWx2yMosX+/hAS4u\n2KxmRE4ORimwCUmmofiBZjKB0QB2iUeRxFUasApJVkm9kxMYBNglnkUSF2nAIiTZ/6y32fEyg7M0\nYBaSnArqvc0CJykoEnZyDcUrTWcnJcxtdnzMApMUFAo7eaX1ziDACSPebmcssIJ8K3bCq06ueNWN\nRqPRXK1UdkVfU+YIkUC4EKKZEMIZGAssr6F7aTQajeY81IjqRkppFUJMAX5FmVd+LqXcXxP30mg0\nGs35qTEdvZRyFbCqpq6v0Wg0msqhAwRoNBqNg6MFvUaj0Tg4WtBrNBqNg6MFvUaj0Tg4WtBrNBqN\ng3NZhCkWQqQCxy7x9AAg7YKtHAs95qsDPearg6qMuYmUsv6FGl0Wgr4qCCF2VMYzzJHQY7460GO+\nOqiNMWvVjUaj0Tg4WtBrNBqNg+MIgv6Tuu5AHaDHfHWgx3x1UONjvuJ19BqNRqM5P46wotdoNBrN\nebiiBb0QYqgQ4rAQ4qgQYmZd96cmEEI0EkJsEEIcEELsF0I8VlxeTwixRghxpPivX133tToRQhiF\nELuEECuLj5sJIbYVz/V3xeGvHQYhhK8Q4gchxCEhxEEhRM+rYI6fKP43HSWEWCSEcHW0eRZCfC6E\nSBFCRJ1VVuG8CsWc4rHvFUJ0qa5+XLGCvjgB+YfAMKANcLsQok3d9qpGsAJPSinbANcAk4vHORNY\nJ6UMB9YVHzsSjwEHzzp+HXhHShkGZAAT6qRXNcd7wC9SytZAR9TYHXaOhRChwKNANyllO1Q487E4\n3jx/CQz9R9m55nUYEF78mQh8VF2duGIFPdADOCqljJVSmoHFwKg67lO1I6VMklL+Xfw9ByUAQlFj\n/aq42VfATXXTw+pHCNEQGAF8VnwsgAHAD8VNHG28PkBfYD6AlNIspczEgee4GBPgJoQwAe5AEg42\nz1LKjUD6P4rPNa+jgAVSsRXwFUIEV0c/rmRBHwqcOOv4ZHGZwyKEaAp0BrYBQVLKkoyep4CgOupW\nTfAuMB2VjhTAH8iUUhYn33S4uW4GpAJfFKurPhNCeODAcyylTABmA8dRAj4L2Iljz3MJ55rXGpNp\nV7Kgv6oQQngCPwKPSynLZI2WynTKIcynhBA3AClSyp113ZdaxAR0AT6SUnYG8viHmsaR5higWC89\nCvWQCwE8KK/icHhqa16vZEGfADQ667hhcZnDIYRwQgn5hVLKpcXFySWvdcV/U+qqf9VML2CkECIe\npY4bgNJf+xa/4oPjzfVJ4KSUclvx8Q8owe+ocwwwCIiTUqZKKS3AUtTcO/I8l3Cuea0xmXYlC/qr\nIgF5sX56PnBQSvn2WVXLgXuKv98DLKvtvtUEUsr/SCkbSimbouZ0vZTyTmADMLq4mcOMF0BKeQo4\nIYRoVVw0EDiAg85xMceBa4QQ7sX/xkvG7LDzfBbnmtflwN3F1jfXAFlnqXiqhpTyiv0Aw4FoIAZ4\nuq77U0Nj7I16tdsL7C7+DEfprdcBR4C1QL267msNjP06YGXx9+bAduAosARwqev+VfNYOwE7iuf5\nZ8DP0ecY+C9wCIgCvgZcHG2egUWoPQgL6s1twrnmFRAoS8IYYB/KIqla+qE9YzUajcbBuZJVNxqN\nRqOpBFrQazQajYOjBb1Go9E4OFrQazQajYOjBb1Go9E4OFrQazQajYOjBb1Go9E4OFrQazQajYPz\n/2T72oVv8iMsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10868c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tauResults['time'],tauResults['A'],'--r', label='Tau Substrate')\n",
    "plt.plot(tauResults['time'],tauResults['B'],'--y', label='Tau Enzyme')\n",
    "plt.plot(tauResults['time'],tauResults['C'],'--g', label='Tau Enzyme-Substrate Complex')\n",
    "plt.plot(tauResults['time'],tauResults['D'],'--b', label='Tau Product')\n",
    "plt.plot(ssaResults['time'],ssaResults['A'],'-r', label='SSA Substrate')\n",
    "plt.plot(ssaResults['time'],ssaResults['B'],'-y', label='SSA Enzyme')\n",
    "plt.plot(ssaResults['time'],ssaResults['C'],'-g', label='SSA Enzyme-Substrate Complex')\n",
    "plt.plot(ssaResults['time'],ssaResults['D'],'-b', label='SSA Product')\n",
    "plt.plot([0],[11])\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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