{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating a model with basico\n",
    "First some jupyter magic for plotting and convenience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline\n",
    "import sys\n",
    "if not '../..' in sys.path:\n",
    "    sys.path.append('../..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now import basico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from basico import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we are ready to load a model, just adjust the file_name variable, to match yours. \n",
    "The file can be a COPASI or SBML file. For this example, we use the brusselator model, that is distributed with the package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = get_examples('brusselator')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we are ready to simulate. Calling `run_time_course` will run the simulation as specified in the COPASI file and return a pandas dataframe for it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.345934</td>\n",
       "      <td>2.368188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>0.182762</td>\n",
       "      <td>2.658596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.147433</td>\n",
       "      <td>2.863681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>0.141129</td>\n",
       "      <td>3.048319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.140734</td>\n",
       "      <td>3.228345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             X         Y\n",
       "Time                    \n",
       "2.0   0.345934  2.368188\n",
       "2.5   0.182762  2.658596\n",
       "3.0   0.147433  2.863681\n",
       "3.5   0.141129  3.048319\n",
       "4.0   0.140734  3.228345"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time_course().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for plotting you would then just plot that as one does"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = run_time_course()\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The run_time_course command\n",
    "you can change different options for the time course by adding named parameters into the `run_time_course_command`. Supported are: \n",
    "\n",
    "* `model`: incase you want to use another model than the one last loaded\n",
    "* `scheduled`: to mark the model as scheduled\n",
    "* `update_model`: to update the initial state after the simulation is run\n",
    "* `duration`: to specify how long the simulation is run\n",
    "* `automatic`: in case you would like automatic step size being used\n",
    "* `output_event`: in case you would like to have the event values before and after the event hit listed\n",
    "* `start_time`: to change the start time\n",
    "* `step_number` or `intervals`: to overwrite the number of steps being used\n",
    "* `method`: a method name to use for the simulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so lets run two simulations that will be different slightly, as we will use the `update_model` flag:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = run_time_course(update_model=True)\n",
    "df2 = run_time_course(update_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='Time'>, <AxesSubplot:xlabel='Time'>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(), df2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now you could plot the difference between them too: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SkJMccVgOVgzmHccRqV1ulo6FlGOzTkKBl6XjJ4YJ0VaI9485qSl8aZm2/1YiqFpFhsVJqeeIeGIjjAE/iLaLNKrX1AjDidroezrgl0ImLTOAZjOjCGm7G69U5wydrZNO+m/pADTMgsRAHaRlBhC0Yq3Q1Mkspx8zrIu2QXn4OZbOiLZ0QoiTVjn4fuSRj8cwMaStFH5EDezQbYBJx/1ftAXl449T+NU86KZFUD40gGEhpO2mZVrhExpOKhhLZ4yHXxt9z7eAL4QwhRDPCCHCe/x7UD4qgGEicLAMQbNbxCp3mhgKgkjLBOXjx/tJpJXCNw1R1YNuWgQa8E0EdngrtQY1bqNuwHc9fE+o1YuH/zGg+CN2apGAVZbhWjptjeoeA/FUMPeqFLbPG688MpaOClSzm6NV7aNOC+kEY+kACHd2mbF00uFKGLBTwWfpJGzmtam+Xc1iw5eAL4RYBFwOfN+P61UtQS2cQSbgR0yDWY1q+tk/ErKAH8SiLbiWTjbgdzZHq1plTQvPTgwCw8JwlKXTHLNwZMgqZgYl1AxT9WfXw+9siWGI6s4Q80vh3wD8I1AwrUQIca0QYp0QYl1PT49Pt51hglo4AxAGhrQxDZEpUxs6hZ8OIC0TJlg6c1piY/KiQ0HQs0tsTCNHbIyGqO8F5eGDsnVcS6c5ZtESs8Kt8IUQVwAHpJSFjyECpJQ3SinXSinXdnV1lXvbyiCD9PDVoLNMEc5BB+6ibXCWTiKZRgjoaI5WtcqaFjO0aBvKvmcH2HaR5syibUvMpLUhEu6AD5wDvF4IsR24BbhQCPEzH65bfQQ86EypFm29uuQDo9XbcaaF38XTPBrawEljJ0eIWQatDRZDYVvwdnwunpaDNAxMVC2dUAb8IMdtpFFZOok0TVFX4Vex2Cg74EspPy2lXCSlXAZcDfxRSvnOsp+sGnHsAH1UL1PCwDINWmJWuAadlO6ibUBZOgCJfhoipjutDlHbgRu0gsmilsLECvPsciYsnaRNc9SkpSHklk5dEaSHb1iY2EQMleM/qzESLg/fSatMk0AWbWcBIOIDNFgq4MdTDmk7RDuVA1SpUlgYbkqwF/AHQhXwA7RiI03IVNbDb45ZDNZLwJdSPiSlvMLPa1YVAefhm9LBdFVca0PIFH46rl6D8vABIzlIQ8QI5z6GQAO+gYU6AKUtjAo/qLRMUAE/OUzakTTHLFpjFkNVLNS0wi+FQLN0TAwcImZW4Ydq0KWT6jXAgG+lBohZJq1uwB8Mk60TYMKAIyxMoRZtW2MWQoRN4Qdr6cjkCABNUTP8WTp1RdCLtqi0TIC2xki4Bl2QCt/18M3kUIgVfnB7QKQwM4u2hpsWHCqxEeiibTbgN0ct5eGHedG2rghwt6M0TEx30RZcDz9Mg85OqNdAiqepgB9NDRCLqIUzIFwLtwFuvJLCUH3PCOnsMtC0zCZIuQHfzcMfTto4TnXuVNYBvxQCVAqOq7LGLtpWr1IombQb8M0giqcpSydiD7lZOiowhio1M+C+pzz8kAb8gBW+SI0C0BQzMynV1brxTwf8UghUZalBZ7qDrs3dwBGaTJOMpROAwo80gTCJpYdosAxaYsqvreapdcnMgNjInV2GK+AH6+Eb6RFAKksn5s0uq7Pv6YBfCgEOOhvTTcv0Bp278BiWoBXkoq0Q0NBGozNELGLS7Cr8UBVQC3DjlZPpeyFU+I7jWrEBKnwkMVI0x3LsxCodtzrgl0KQux0xsISDK/DDlx4X5KItQMMsGuxhGiyD1jDWIgpw49V4hd/WGKE/LLu8gzqW1MOtmNlIguaolUkYqNZcfB3wSyHAtEzbtYqiplrsyWyACUvQsj2FH4ClAxBro8lRHn5rzMI0BIdHksHcqxIE7uGPXbQdGE2Fo0Sy446fIHfaAk0klIcfq+6ZuQ74pTADAT8i1CALrcIPYtEWoGEWTXKEhohKLexoitI7HJK2g2ADPiamGLtom7SdcJypPFMKXyiF396kxm1flYoNHfBLIchBJ9V/hWWoQRa6mibpANMyAdnQRoscpsE9dWh2c4Te4UQg96oIAfrQNtniaRCyvmd7AT8ghe8G/CaRoDFiMrtZWZa9wzrg1z4B7nbMWDpibMAPTcXMTMAPxsN3om20ClUtE6CjKcrhsCl8EZyHb2GP2eUNIQn4GYUf0A5519LpsFIYbi0iIeCwDvghwLGDG3Tuf0XEDfjeISihGHQQ+KJtOtJKKyMZhd/ZEqW3SqfV0yLgDDEDJ2eXt7pPKPpe0B6+q/DbI2rPh+naiYd0wA8BAQ86yHr4DRGDqGmEb9E2IA8/HWmlTYwSs1TQUh5+dQ66aRFo3zNchR9GS8f9GwK2dNqtbF+b3Vy9fU8H/FIIeNABWK7CF0IdZh6KQQfB7rQFUkJdt8nIHmTeN5LErtIt7iUhZaAevuN5+KG0dNzd1kEegALMsrJtNbtZK/xwEGB55IzCzzkWOFQF1DylFZSl47Zfo5vWOrs5iiPDFrSC8aHTmHkXbUPR9zKWTkABP9oMQJuZXWvr1Ao/JARYsTCj8I2cgB+mqoV2AhCBvWGm3CynBlO13+xmpfirdeCVRMALj0rhZxdtW8O0fjRDaZmtZjYjrEMH/JAQYB5+Oo/CD1UBNe88WyECuXxSTlT4QDg2XwUctGzcOk7uoq3pnqscioA/Qx5+s5Ftq87mKIer1E7UAb8UgkzLHOfhg1vTJAwBC9SibRDHG7p4Ab/BnSF1NKmAf2goBO0ng/Wh0xgYQhLJiQahKc/tvVkGZemYFkksWkRW4c9ujiKr1E7UAb8UAly0TecJ+HNaYhwMQ8ACV+EHtMsWSDn5LZ1wKPxgA35GbIybXfZVYcAqmaAtHWCUGE1ibJYOUJUb/3TALwXHDqw8si0nDrqu1hhDiTQjVVpbuySCVviuJRZqDz+gPSCenZgrNjpbYhwaqr6AVTIBWzpSSkZkjEaybdXp7ratxtll2T1ICLFYCPGgEGKTEOIFIcTH/HiwqiRID196gy57aEdXq+o4PYMhGHjpRHCbX4CEq/BjbtBqiJg0Rc1wBfygZpcZsZHte3NbYxwIQ78LuO0SaWdCwO9oVv28GvueH5IhDfyDlPIY4Ezg74QQx/pw3eojwLTMfJbO3DAFfDsZXGlkIOGoBcdYTpZTaDZfBR3wPYVvZBcZ57bG6BlMVO1RfUUTsIc/nEgTJ0pDPoVfhX2v7IAvpdwrpXza/XwQ2AQsLPe6VUmgHr6bITHO0gHCobTsZHCVMoGEM7YWEbjlFapw0JVMwB5+Wqq+l5shNrc1RtqRtb8GkrF0gmm7kaTNCDGiTjzztbAr/AxCiGXAKcCTeb53rRBinRBiXU9Pj5+3nRmkdLN0ArZ0xk2rISQK30vLDAjP0oka2fbraIrWfsCCwDdeeYu2hszpe22qqmnNiw0n2GqZw8k0ozJGTGYDfsxSdfFDHfCFEC3A7cDHpZQD478vpbxRSrlWSrm2q6vLr9vOHAGrLG/jkCnGWhKmITgwGC/0a7VDwIu2w+64HqPwm6NVuXBWMgFvvEq5YgM5UWyEJuAHtH40nEgzSgzLGTtGZ1fp7NKXgC+EiKCC/c1Syjv8uGbVIQPe3u5Oq02ZDViGIZjTEg2Rwg/O0hlIqvYTTjajqaM5LAo/6EVbMfY+wNxWV+EP1LjYCPjNcjihLB3LHh3z9WpdP/IjS0cAPwA2SSm/Wv4jVSmZ1LiZy9IBNfBCEfDtRKAKfyDpLi7mBK3ZzVFGkjbxlF3gt2qEoDdeeQo/N+C3hUThB5yWOZJMk5QRTGdscO+s0gJqfij8c4B3ARcKIZ51P17nw3Wri4BVlmfpGIw9Vq4rLOlxdipQhd8Xzx/woTqzJUoiYLGRzCj8bN9riJi0Nli1LzacYBdthxM2KUwMZ+wmNVUiufraruxWkFI+CgRTIKWaCDxTYuLGK1Be6obd/YHcc0ZJJwLN0unzxpadHXieD71/IM7C9sbA7h04Qe+0dfte7psleLn4tW7puG0XlIefTGNjjbESIevhSykRAdWPmg56p22xBJwpkVH4cqz90NUa4+BQoioLMZVEwIu2fXH3jTJn4C3sUEF+T99ovl+pHQL2oZMFA34DBwaqT6WWRMBpmUrhTwz481obSNmy6maXOuAXywwNOjOPpePI6szpLYmAF20PZyydrMJf4Kr63YdrPeDPTIYYecRGzduJAVuxI8m0OsvCHjs+F3VUZ9/TAb9YZihTQsjxi7be4lmNT60DVPjxlM2QJ7DsrNJqa4jQGrNCpPAD6nvO5JaOlDU8uwz4TNuhRBphRhBOSu3VcfFml7urrO/pgF8sAWdKpAoMutDU0wlQ4Q+MpjLlAca338KORnb31fibZdB2orcE54wTG20x4imHwUQNF++zA1b4CTvbr3P63qJ2VSdfK/xaJeBpddZHnZiWCTUe8KUMNC3z8EgqJ7VwbLbEgvbGqlNZJRO0nehM3vdq2sd30qiT1gLKw0+mMbzZQ07CQFujRUvMovvwSCD3nS464BdLwCVqU87EzS+gauJDjedDe39TQAq/byRJ2ks4s8cG/IXtjdrSmYJknjx8CImd6KQCrdI6krQxMgo/2/eEECysQrGhA36xBD7o8ls6jVGVD72/lnc8pt03q4DSMvtGU5lqo+NV6oL2RvpHUwzVsi0RuJ3ofjI+4LeFwE4MsOAhKA8/E/DHiY1FHY10a0unRgk6U6LAtBpg+Zxmth0cDuS+M4KXwRCQpaMUfn5LJxSpmYFvvMrf9+a5BdSqTaWWhJ0O7jxbVJaOGckf8NX6UXW1nQ74xRKwj5rwFvjlxIC/Yk4zW3tqOOB7Cj8wSydFqqClE4KgNVNiY1zfa22IMLc1xisHarjvOanAxiyoRVszo/DHpmYubG9kMJ5mIF49R0XqgF8sM5UL7Uy0HlZ2tbC7b7R2jzq0PUsnIIU/msIwPYU/0dKB6suWKInA94DkXz8COHJuCy/3DAVy3xnBSQfq4Q8n01iRiVk6kJOaWUV9Twf8Ygl60Nn5F84AVnS1ANSurZN2lU9A9fD7RpI0N7ilE8ZZOnNbG7AMUeOWTrBiI2EXFhtHzm3hlQNDtZuLbwfr4Q8nbKyI268nePgqNbOafHwd8Isl4IWzZGbhzJnwvZVzmwF4pVZtnYyHH5yl09EcVf834wadaQiOmNVQ4wE/6Hr4+fPwQQX8oUS6drPEAly0lVIynEwTiRa2dAB2V1Fqpg74xRLwwlmhtEyAZZ3NCAFba3Vq7Vk6gSn8FO1NETWw87RfNabHlUTQlVqdSQK+O7t8+UCN9r0A0zLjKQcpIRKNZe+Vw5yWKDHLqKq+pwN+sQSelinUUXN5AlZDxGRRR2PtLtx6lk5AA+/wSJJZjVGVjZGn/ZZ2NvFKz3Dt2hIBWzrxScTGkXNrPeAHp/C9VN9oNL+l4+Xia0unFvGslqBK1DoODkbeLB2AFXNaeKXWFX5Ai7b9o67CN/Mr/FOXdtA7nGRrra6BBHzaWrJAlg6o0h6tDVbtBvwA0zK9JIpCAR/gmPltPLerr2rEhg74xRK0j2pLHMy8AQtgRZdKzXRqsUxy4Iu2KTo8SyfPoFu7bDYA67b3BnL/wAlwduk4ksyxv3n6nhBCZerUasAPMC1zOKHeIKOx/JYOwBkrZrOnP86u3upQ+TrgF0uAAT+esnnlwBCOYeb1UUGlZo6mbPbV4o5bO7idtvGUzWjKpr3Js3QmDroVc5qZ3RzlL9sP+37/GSHA9aOHt/TkPUsgl5VdNZyaGWBapqfwYzG11yOf2DhjeScAT2w7FMgzlIoO+MUSoMr673W7ODScJGJZBQO+56XW5OlX6eAWbftH1SCb1egt2k5sPyEEpy7tqGGFH5yH/9PHdzCrxUtpnZghBqrv9Qwm6B+png1ERWOnArN0PA8/Fits6aya28Ls5ihPbq2OvqcDfrHIYDz8tO3wX3/ayilL2jGt/IuOAGuWdNDZHOVXz+z29f4zgjcQAlD4z+3qA6CjKao8/DyDDuC0ZR1sPzRSm4XAAiqPvKt3hAc3H+BNa5a498nf905friyx36/f6+v9ZwTHDszSefwVpdpbmlS+/fi0TADDEJy+bDZP1rPCr8lCVgFUyxyMp/i3P2yi+/AoHzpvJaJAWiFA1DJ485qF3LdxPweHaiwnOoC0TCkl923cz0d+8Qyr5rZw7qo5BS0dyPr4T9WireOklZ3j49mou3pH+Myv1mMIwVtOW5q9Tx5OWdzOMfPb+Mnj26tm8bFoAkjLTNkO33rwZf7rT1u56tRFLJ7T5t4rf/udsWI23YdHq6JUsi9yVQhxGfB1wAS+L6X898l+flfvCIeGEnS2BHfGqe+UaekMxFN0946yu2+UXb0jPL3zMI9sOUj/aIq3rFnExcfMg7vNglk6AG87bTHfe2Qbv3p6N+9/9YppPUdFSJe38cp2JD2DCXb3jbKzd5gNuwd48MUDbD04zDHz2/jZ+06f1NIBOH7BLBojJr9ct4vXHHcEhlE9B0tPiZMuS6UOxlPsODTCzt4Rth0c5omth3hyay+mIfiny4/hiPaW7H3yIITgXWcu5TO/Ws9TOw5n3jxrAjtV1qw8kbbZ1x9nd98o2w4O8/yufu7ftJ9Dw0muOHE+X3zLiYj+Hdl75eHMFcrH//3ze/nAeSun/Sx+UHbAF0KYwLeAS4Bu4C9CiN9IKTcW+h3bkfzv25/ne9esraoT3SelhIAvpeQP6/fxwKb9vHRgkJ2HRhiIjzvkuC3GhUfP5T3nLOPERe3ZaxcIWABHzm3l1KUd/PSJHbz2hCMyW7ernhIXbZ/b1cc9L+zjqR2H2d03yr7+OOmc7KSYZbBmSQfXvnoFV560gOaY+38yiaUTtQw++ZrV/MvvNvLdP73Ch88/sqw/aUYpIZc8nrL5xZ938vTOPnb2jrCrd2TCechHzm3hnWcu5f2vXs78WY3ZNptEbLzxlAX8vz9s4psPvsx33nEqjdHgCpL5imMX7eE7juSPLx7goZcOsH73AHv6RieUhp7VGOHslZ28de0izj9qrhIOZv6dth5HH9HK+au7+PI9mzlh0SzOXjmnrD+pHPxQ+KcDL0sptwIIIW4B3gAUDPhHzGrg/k0HuPKbj3L+UXNpb4rQHLNoippETANDCCxDYBb6ENnPLUNgeK9CYJljv+99RE0DyyzDjily4Wxff5yP3vIMf97WS1drjKOPaGXNkg4WdTSyqKOJhe2NLOxopLM5OvHNziiclunx8YtX8YGfPsWlX/sTb16zkGWdzbTELExDEDEN91VgGgamgXrN0x657VWwvYXANAUNlknEFNN/cy5h0fa/1+3iU7c9j2kITlw0i9OWzWZBewML2htZ0N7IovZGls9pzv9/OYmlA/Dec5bxzM7DfOWezTy5tZfTlnXQErNoilo0RE0iU/S1gm1oGBgGY15NITKfRy31/zJtHLuogP+X7b18/JZn2d03yuLZjSyd3cxlxx/B4o4mlnU2saSziSWzm2htGBcARf7Cc7k0RS0+dMFKvnT3Zi7+6sNccdJ8FrY30mCZmfE3fjyObatsfxwzVnPaOPfflmG4fa/ccZtSQmAKpJR87s4N3PzkTpqjJictbufC1XPdftfAwvZGFs9uYlFHY55x67bnJDOkb7z9FN7y7f/h/T9ex8XHzuO4BW00Ri0aLIOGiIllqPFlCFUOxBCqHQ2B+tz9Xrn4EfAXArty/t0NnDH+h4QQ1wLXAixZsoTrrzyW3zy3h28++LIPj1AcUdOgIWLQGDXVII+YNEVNZjVGmNMSpbMlxrzWGMu7WjhuQVvmtCmg6IWzr9y7mee7+/i/bzqBt522uLSBPklapserVnVx7ydezRd+u5FfP7NnxtZDDAGNEZOGzIdBU9SivSlCR1OUjqYIc9saWDy7iWPnt7J8Tkv2by+yls4jW3r49B3redWqOXzzr9com6akhyy8BgJq4H3xLSeyoL2R+zfu5+GXekq7fhnELLffRUwaoqrfNUUsOpojdLbEmNMSY/6sBla6fS8zawGlvKfod44j+eyv1gPw8789g7OPLEFFGgYgphQbHz7/SE5d0sG/3/0iP3x0O0k7f1aP30RMkel3je5HU8ykvdHte81R5s9qYMnsJo5fOIv5sxqyQbnI2dGX7tnMzU/u5APnreAfLllN1CrhTSbPEYfjaWuI8MP3nMZX7tnMoy8f5M5n9xR/fR/xI+Dni2gTVnaklDcCNwKsXbtWvuec5bznnOUk0w6jSZuRVJqRpE3alqQdB8dBvUpJ2pbYUmI7eT7cr0/2M2lHkrIdRpI28ZTNSDLNaErddzSVZv9AnI17Bjg0nCBlZx/96CNaee85y7nq1EUYReTh9wwm+M2ze7j69MX89RlLSm/JKQKWx6KOJr53zVqklPSNpBhN2dju36hepWq3gu3lYDtkXse0szO2HdO2JJG2iaccRlN2Ju89nrIZTtj0jabY2TvC4eHkGNuqoynCG05eyHvPWc4SO6mC/SQzBCkl192+nhVdzXz7HWsmqtBiMCPZQ6sL0Byz+MzrjuEzrzvG/f93+0PSHttehfrbZN9z29xxxr4m0uo+o0mbkWT28+FEmu0HR1i3/TC9I0m89dCIqdJIP3bRUZy1sjO7aDsJD24+wEv7h/ja204qLdh7TGEnepyxopNfffgcHEdyaDiZ6XOq7ZxMG+Ybj7ltktv3Jn5PZvpzIp3td+pDjdvhZJqeoQQv7R/i8EiSkWT22ZfMbuJtpy3mnWcuZVYRaZk9gwm+89ArXHXqIq677OjSZ7KZgJ/f0vFY1NHEDVefgpSSwUSaeMom4Y4rR0ocB/Xqtpsj1bjwPnek5NwvlvZo4/Ej4HcDi3P+vQgo+u0raqkp7yyCq1ldLFJKDg4leaVniKd3HubuDfv4x9uf55a/7OSXJyTVE06iFm5+cgdJ2+Fvzl42vQcoctB5CCHoaI7SMb27+c5wIs3O3hE27hngwc0H+Pmfd/K75/fyx+OHaZuirMK2g8Ps7hvlX994/PSCPaj2SxefdtkYNWmMmsxuDqaKZynYjmRP3ygv9wzx5NZefvvcHt7+vSd491lL+YKYWqV+56FXWNjeyBUnLpjeAxRhJ475cUPQ1VodSRdSSvpHU2w9OMz67n7u2rCXL9+zmfs37ecOO6Wy3ybhsZcPAvDus5ZNz7bMWDrF7VMQQtDWEKFtuv28DPwI+H8BVgkhlgO7gauBv/bhujOOEKoTd7XGOHNFJx86byU/eXwHn//NC2zp6udYKDjwEmmbnz2xgwuPnpupX1/6AxSupVMLNMcsjpnfxjHz23jLqYt4+cAgV333cR7YsIs3RKKT5gD/j5vTfM501KlHkTOkasQ0BItnN7F4dhMXrJ7Lxy5axf+5cwM/fnwHnzw5TuskQev57j7W7TjM9VceS2S6fneJYqOaEELQ3hRlzZIoa5Z08O6zl/GH9Xv58M1PM9is2m6yMP6nLT10NEU4bkHb9B6gCEunWig7qVxKmQb+F3APsAm4VUr5QrnXrQa8dLQFsxrYtNvdKVdgar1xzwAHh5Jcdeqi6d+whgNWPo6c28q337GGRHyUuDO5JfH4K4eYP6uBZZ1lZB4VYenUCo1Rk49etAqAvYeHJ7USn3U3n732hPnTv6ExeUpwrfG6E+bz0YtWkU4nOTRa+O+SUvLIloOcu6pr+qm6QhSs41Rt+LKLSEr5BynlUVLKlVLKf/PjmtWCYQjetGYhuw4Oul/Ir7S2uMWljpk/TZXgXTtEAR/g2PltREUaexIf1XEkj289xFkrO8tL0w1Z+y2e3cTxC9vY1zc0acDfvG+QWY3q/NlpE7K2AzjvqDlYOJOKjc37B+kZTPDqVWWmSprRoi2dSqJLKxTBm05ZhMBBItyMhom8fGCIqGWwZHYZCrWILJ1aI2aZREmRFoUD/ub9g/QOJ8vPTzasmhh0pfCaY49gYCROmsJB66X9g6ye11rem6UozcOvBSzDwMLGnmTB+5GXlH//qlVd5d3MiNSPwg87R85tYUFrZNJBt2X/ICu7WsrLt65hH7UQUcsgRpq0KLww6vn3Z6/sLO9mZm0MulK47PgjsHAYTOYvaSClZPO+QY46YprrRh4h7HuWKbBIY08ybp/YeoiVXc0cMauhvJtNsumvmtABv0jaG4Q6oKQAL+0fYtXccgdd+FSWaQiiIk1qEoW/cc8A89piLHDPAJ02IQxaR85toTkCQwViyf6BBAPxNKvntZZ3oxC2XcQ0sHCwJ8lN2X5omFVzy2w7UJbOFGmZ1YAO+EWiOk7+5hpOpNndN8pR88oM+CJcC2ceDSJNepK02+7DIyz2o0xECC0dIQQNpiQt8/e9zfvV2tJRZQf8/Mdr1jIWDoaQpAuMWykl3YfVruSyKXC8ZrWhA36RmMIpaOl4Rw8eWa5SCOHCGUBMpEmJwiqr+/Aoizp8GHQhtHRgcrHx0j6/An74+l5EqJ3A6QIKv2coQSLtsLicdTePGul7OuAXiYVT0NJ5ab8K+KvKVfghnFaDCvhJmX/QpW2HfQNxfwrBhbT9LGEXFBub9w8ytzVGR7mbxwwrdLPLiFB/T6G2844d9GV2aUa0pRMmLAoPui0HBomaBkvLVQqGEcqAFSNV0MPfNxDHdqQ/Cj+Elg6AicQuYOm8tH+Q1Uf44EGL8GWIWXgBP3/befXptaWjmYAlCk+rX94/xIquAhUcSyGE02qAqEiTKKDwuw8rleWLwq+RaXWpmAXEhpSSLfuH/Fl0DGHCwNQKXwX8he1a4WvGYeKQlvk7ztaDw6ycbjmFXEIa8COkScr8Cj8b8P1S+OFrPwsbO09xAK9wnj8KNXx2WEbhFxi3u3pHmdMS86e2f42IDR3wi8TEyTvopJTs64+Xn8cLoc3SiZIiUWDhrPvwCELA/HYf2s+IADJ8gUvkFxv73fN557X50XbhU/gmatE2VUDhd/eN+PNmCe5O2+pvPx3wi8TCzjvohhJpRlM289p8qBwYQpUFEJGpSS2dea0NxCw/VJZ7jxpQWqVgYpPKM1T3D6iDZfzre9UfsEohUoTC92XBFtxaOtrSCQ3KRzWwnbE7Hg+4R6DNbdUqqxAR0sQLWjoj/tg5kK1zFLI2bLKHGJUTg/r+AaXw/el7FsiZOdBkpjCl6gepPAveXjlq/xS+tnRCRaM9yCgxUuNO+ckOOj9UVvgyJQAsmSxYwMq3HHwouS55TTDUw+z0fjaxfMK3Drh9z5e69GEUG+7fk8yj8Pf2j5J2pH8K34zqgB8a7BTzBjfwrLNyzGHaQOaQ47m++Kjhm1bj2Jg4xPMMurTtsLc/zkK/Ar5XlzxMb5rdfwbgWVZP+Nb+gQTtTREaIj7YYSEsnua98edT+JkcfD82XUHNpATrgF8Me54lYo/yhHMs6UIKX3v4+XEPMB91Jnr42Rx8vwadG/hqQGkVTfdfsDF5wVk64Vv7B+LM88POgXD2PfcNLN+irZeD79vsUiv8ELH9EQD+7Bw95sxbgAMDCRojJq2xwqUDiqbGT7zKi7uQlS/g73ZTMheWWzTNI4yWzq6/sK/pKIadiTtp9w8m/BEaEM6A7x6Gk3Qmhjlv7c2XDCfQHn6o2PEY/S0rOcQs0s5YhX/AHXRl1SL3CKOlk1H4E1VWz5DPgy5si7Z2GvY8zZ7WE0g5ExdUDwzEfWy78BVPIzEAkLesR+9wkpaY5Y8dBtrSCQ12CnY+QU/naQCkxyl8/6fVIRt0m38PwEv2xOP3eoeV+vftEPHM2aIhacMDL0BqhH1tx0/od44j6RlM+JOSCeHse0/9iAFa2B47asK3Dg0l/D28XpdHDgl7n4PkEL1dpwNMyNLpGUzQ5dugM1VqnMx/2EXNkU7An/6DPa0n8KfU0chxf9fBITVAOpoKl04uiYzCr36lVRQ7nwTgQNtJpB05pv16R5KkHenv7ChMduKBF+HF3/GryOsYZqJleGg4SWeLnwG/Ns5T1gF/MuwU3H89WA30zTsDYEKWju8KH8LjpT7zMxjoZt2yDwBiwvpH73CCjqZI+TWIPMJk6QzsgYe/CPOOZ7RpAcCY9vM1Bx/Cl6Xz2A1gNXJn7PUTbFhQYqOz2SehBirg14DQKGukCSG+LIR4UQjxvBDiV0KIdp+eq/JICfd+Ti3YXvl1nEZ13mquwh9OpBlO2j4unLl+YhgG3o7H1Zvl4jM40HU2AIn02Dey3uGkz9PqkFg6qTj893sgNQpX3YTl7kLODVwH/NxlC+FatH3yRnjuF7D2vYxYsyYIDVBio9PPvmfUR/G0+4DjpZQnAi8Bny7/kaqA+ADc8X548jtwxgfhpKuJmGpRNnenbXaXrU+DzjtsuZan1lLC+tvgp2+Clnlw1U3E3IWxRHqs0jrkt8oKg6XTuxVuuhR2PQGv/wZ0rcZyz0nOp/D9s3RCsOkvnYAH/hnu+hSsvhwu/jwR05iQSi2lVH3Pb0tHOlXfhmXlEkop78355xPAVeU9ToWxU/DszfDQF2FoH1z4T3Du3wNkDicPdtDVuCWx+2n447/CKw/AwlPh7b+Eli6i1k4AkuMD/nCy/HOAc6nl9hvtg//5BjzxHbUA+PZbYPVrAXUQPDAmcHl1dHzZZQu1vdPWcVRywAP/DAdfgpPfCVfeAGYEyxQTbNiB0TRpR9LZ4rOlAyqGGD5l/gSAD8njGd4L/LLQN4UQ1wLXAixZssTH2/rAoVfg+Vvh6R/D4F5YdBr81Y9h8emZH4mYEwed7wq/Fj380cOw8Teq/XY8CrFZ8NovwWl/m+n4XmG08Qo/OEunRhS+lND9FyUynr8VUiNw/Fvg4uuhPTtGLMPtezmBa/9gnDkt0Uy/LJtazNIZ3A/rb4Wnf6ICfcdyeMftsOrizI9EDGNCosWhYTVufbd0wJ1d+iQAA2DKgC+EuB84Is+3PiulvNP9mc8CaeDmQteRUt4I3Aiwdu3ayqahpJNqy/rLD8DL98O+59XXj7wYrvwGrLoExuXVe9Pq3EF3ILPL1sdpNVR3wJdS2Q5bH4It96n2c1IweyVc8s9w6nugoW3Mr3gKNVfh247k8EgyoEFXxYErMQjb/qTabct90L8LrAY44SplHx5xwoRfsUxvdjnWw+/ya8EWaqN4muPA/g3w0j3w0l2w+yn19YVr4S0/gGPfmK2Y6mKZIu/MEvDZ0nGvVeViY8qAL6W8eLLvCyHeDVwBXCTH591VC8MHld3Q/RcV6LvXQXJIdfJFp8Ml/wLHvQnaFxe8hJdJkjvoegYTxCyDtgafJkrVuGibGIJ962HPM+pjx//AQLf63qzFcMYHVLCaf/KEN0mPmBvwcxdt+0aSSOljDj5UX/s5DvTvVIFpl9v39j6nni/aAstfDRd8Bo6+HBpmFbxMxJxoJ/aNJP1LZ4XqtHRG+9RY7f4z7Pqzakd3MxULT4UL/gmOuRLmHl3wEpZpMJwcK6AODXkK309LpzZKc5cVqYQQlwH/GzhPSjnizyOVwUgvHN6mcnAPbFRqYP9GGD6gvi9MmHccnHQ1rLhADbhxarQQ3qDL3QBzeCRJR1PUn122UFkPOp1Uyv3gZjU97nlJzXx6NgPu39y6ABathVd9ApafD50rCwb5XPJZOr0ZlRWQjzqTSAmD+6D3FTiwCfa/oD4ObILkoPqZSBMsWANnfxSOvEgJDau4N7uMpZMjNvpHU6ya5+P6RyXTMpPDylbtccftgU3qtW+n+2wGzD1OCYvFZ6ix2zqvqEtHTTFh0TYQhV8jZT3KlabfBGLAfW7Qe0JK+cGyn6oQqTgM7lE5yod3qODeuxV63dd4X/ZnrQaYewysuhTmHQtHnAgL10C0eVq3zvqo2c7TN5Ki3U+VJQJUqPF+6N8NA7uhv9t93a3Uen+3as/c7KC2RXDE8WrmM/9kWHAytOZz9qYmn6XjbbqqGUsnPqD63UB3Tt/L6X/p0ezPNrTDvOPh5LfD3GNhwSlKaJjT6yt5Ff5oilmNfrZdgGmZdkq9IQ7sybbb4e3Zzz1B5j3HnKPUOtqadyuBsfBUiE3v3F7LMCbsUj6U2fAXhKXjc2qmlErIDuxW7Vcm5WbpHFn2E3gkhtSCqfeHZV5zPh85NPZ3hKlsmI7larFr9gqYvRzmrFavPq6W5xt0/aMp2hr9nFa7/x2leqnJ4ZzgnS+o78kqTQ9hQOt8aFsI80+C494MXathziroXAUx/9RjPksnU1bBV5U1jRmSlGrheWA3DOzN3+/ytZ/VAB3LVJ9beWH287nHqHb1a9bHRLEhpaR/JMUsv/uetFV7lPLsyWGlxAf2uON3ryvKcl6He8jMEgEQ0LZAjdujLs1pu2PVWlCRM59isEwxoQ7RoaEEbQ1WRoj4wnT2gDg2DB3IithC/c9O+PaYfmbplMbWh2HjnbDzCRWo4v0Tf6apU3WMtoXqHd/7vG2B8o/bl0xbNZWK5+HnKvz+0ZR/9bSheA863q82l2x7WE19x78RgsqBb1sIXUepgDRrodt2C9XnLUdMWOAKilhkosLvdTMl/M3SKcJHdWy1T2DLPWpNYmAPpONjf0YYqn3aFqg3wZUXun3P/WhfqoK6MTMb1SOWt36kguZoyiZpO/7OLnMTBibrF73b4Mnvwp5nlUIf2j/xZxpnq3Zqna/EROsCaJuvXjuWqXEbmZlMFpWHP66kx3CSOX5aiZBzFsMkfS8+oDKKtj6kxu3gvol7boxINs4tPFWtUXgxr20hfGFtWY9ZmYA/dAB+8nqINMPSs2HZOWODeet89RrxqWyuD+Tb/DIwmqLdV5VVRMB/+X647X3Kvlp4Khx9hRpEsxZlg3nrAl9VUrlETU/hZwO+56PO9nNaPZWlk06qDXUbf63eEJecpRZMWxfk9L/5M/pmWAwRL0PM9aL7R1VQCaTvSZuCYeGxb6jd04alBNiqS5Qyb1+aHbet82csmBeDZUz08Hv93nQF2b5XyNLp3w0//yu1rjjnKFh+ntvn5uf0vwXQNCdQIVGZXj2wG457F7zxu1XVOSYjm4c/3kcNwNIp5KUmR+A3H1PB6po7la9eA2R22qZyAv5QknY/6+jA1Dttb38vbPqtyso6+yO+2i5Bks0QU32vb0T9fcH0vTRqWW4cBzbBA19Qa2JXfE0FqhrAMg1S4zZeHRpOsHzO9NbyCjKZpZMYgpteo7KO3nmHWrSvEJUpntbYAW/+fs0Ee8jmQnuWTsp2GEnaAQ66PDz2dWV/XXlDzQR7yFH49tgsHV/tHJh80O1br4L9+Z+Bcz5aM8EecvLw3b6XCfgzlTAgJfz+H9TC6Ru+VTPBHtTa24QsnaGkv9lhkNP38ij8Dbep/RZX31zRYA+VUvgdS6tqylwMEWOsyvKm1cEMujwKf2CvqgB43JuVDVZDeB5+IpX9uw75XbwKJrfEnrlZZVKc/n5/7zkDRIyxs8uspRPEgneevvfi72HHY2pTYnOnf/ecAcZn6Xgb/ub43vcm8fCf+rFakF7+an/vOQ0qVB65dtSVR0bh2+NUVmA+6jhe/J1aXDz/Ov/uN0Nks3TGKnxfN75A4UGXTsLzv4TVr4Om2f7ecwYY3/f6R5WK9FVsTLbLe+OdKoHilHf6d78ZIjIuS6dvJInj94Y/yEnLHCc29j4Pe55WKaZVMKvU9fCLJGvpjFP4M2XpbLlXLZB1rfbvfjOEZ+nkZukcGkr6m5IJhS2dl+6C0V445V3+3m+G8NaPPC/aExv+LtoW6Ht2WvW9Va+p6qJghbBMMWHdDaDdz2QByMkQG2fpPP1jMGNw4l/5e79pogN+kWQtHRW0BgIJ+AUsieSIqr+y6lL/7jWDCCGIWkZG4Usp6RtN+ZuhA4WD1vr/VtkjKy/w934zRGSCwk8RMQVNUR8DcKG+t+sJlRG2+jL/7jWDWIYx5rSwobj6+1r9KofikW926TiqsODR1TOz1AG/SAxDYIisj9rnTqt9VQqFfNTtjyg7p0YDPkDMNDIKfyRpYzvS/0EnhFsiIGfQ2Sm152PVpTWpUCFnD0im76nsMN9KekDOpr9xfW/zXcquWHmhf/eaQcZvmBzMBHyf9+/kK562f4PaRVxF41YH/BJQKV6uygo0NW7coNtyr6rFsvQc/+41w8QiRman7VBCDboWvwM+uGeL5gy67nWq4FaFsyPKwcvDT9rZvudrv4PCfW/zXbDsVdMubVBpxm+YHIyrvtES87nv5dv098oD6rWK3ix1wC+BiCFyMiVU0PKtUiaoHZ4wdlotJbx0L6w4v6bSWMcTs8yMpeOpLN8HHUysCfPKA0r1Lz/P/3vNENa4sxj6RpP+e9D5+t7BLaognHsQSy0yfsPkYCIgS8dT+Lmzy5cfUDWVplmDKgh0wC8BK+e4tL7RJC0xK5iNQ7nT6n3rVYndo2rTQ/WIWllLZyioQQduwM8ddPerAlyN7f7fa4bIlzAwIwp/813qtYb73viDizyx0ea3pTN+p21iSJWNqbJ1Ix3wS8AyRCZTIthBl6OyXvy9Ul+rX+fvvWaYmJVj6QTlo4KydLz2Gz6kar6srF07BybuAekb8bmkB+Tve5vvgnknTHpORLUz/s3S63vNMZ/Xc8ZniG1/VAmPKut7OuCXgJWza28gkICfJxf6xd/B4jOhpcvfe80wuQo/MB8VVODyfNQt9wKypv17yJOlM5LyNwcfJmbpjPSqDJ0azc7xGJ9dNxhP0RQ1/Z2Zw8TiaZt+qw65WXKWv/cpEx3wSyB3196MTKt7t6mV/qMv9/c+FSCWk5bp+ajBBPwchf/MT1UJ3gVr/L/PDGJ6PrQjSdsOg4m0v7tsYWJp7i33qs9r2L+H3E1rrsJPpIPrd6AsncQgvPArdZZEla276YBfAmrXXs60OmiV9eLv1esxV/h7nwqQu2gbWC40qGwJJ60WHHc8Bqe+e8bKGAeFECJTE2bAbbtZjX7nkY/ve79TRfrmn+LvfWaYiVk66YD6XY6l88KvITVclRv9anskzDCWaWA72c0vviv83AJWqTg8+V9KnXYs8/c+FSDfom1zkJbOUz9Sn5/8Dv/vUQEswyBlO/SNBLD/A8b2vf0bldg48a9q/s0ykidLpyWItSPDVGttdlLNLOccBYtP9/8+ZVLb/5szjGWIMcXTArN0pA1PfEtl51x8vb/3qBC5i7aD8RSNETOTQeErRkSdsPTcL9RCd8tc/+9RASxT9b1AivbB2EXb+69Xeffn/r2/96gA4zetDcZT/qZS52JEYOfjsOtJpe6roHbOeHTAL4GIm5YZT9kk0k5wg+7gFnjkq7D6clhRu/njueR6+EOJdDCbrkAprR2PKR/13I8Hc48KEDEN0o6TqQUTmNjY+Bt1Gti5f1815QDKYXxp6cF4QB4+qFz8HY+pdaM11wRzjzLRAb8ELFOQdmQwhdMg66M+8W1AwKX/4u/1K8jYLJ00rYENOvf/5I3fUSeChQTL3fTXH0ThNMgG/HU/gLnHwRkf8Pf6FWJ8aemhoDx8UH0v2gpvv6Vq93348pcLIT4JfBnoklIe9OOa1UjE9VEDC/jRFug6Wh2Efem/qeMKQ8KYRdsgFf7p1ypb4oSrgrl+hYiYBilbMuCmtPq+h6F9ifo47s2qBHcVHS9aDuNLSw/GU8Hs/wC46HMwZzXMPTqY6/tA2aNOCLEYuATYWf7jVDeWKUimnWBq4YM6h/bvnvT3mlXCmEXbIKfVJ10dzHUrTMQUpB11yhoEkNLa0gUfX+/vNauATPE0R2I7kuGkHVzfW/veYK7rI35YOl8D/hGQU/1greOdjzkcZB55SBm7aBvgtDqkWKaaXY4k0ggBDRHtxhaDZWRLKwRa0qNGKKvXCCFeD+yWUj5XxM9eK4RYJ4RY19PTU85tK4YqnuYwnAwwrTCkxCyTlC1xHOlufgloWh1SvAyx4aRNU8T0tzRyiLFyyiPrgF+EpSOEuB/IV+7ts8BngKKKPUspbwRuBFi7dm1Nzga803O8aXVjpDbrq1eCqHvMYdJ2XB+1fgfddPAyxEaSNo1R3XbFEs3ZeDUY1PpHDTFlz5FSXpzv60KIE4DlwHOu2lgEPC2EOF1Kuc/Xp6wSvHr4o27A9/XEoZDjnWsbT9nBbW8PMV6G2Egy7X/hrxCTm4c/FGRZ7hph2n+5lHI9kNnVIoTYDqwNd5bOWIWvLZ3iibme86FhdYh0YFk6ISXiefhJW88sSyBbD9/JOe2qfvueXvkpAa8e/mhSLZx5qlUzNQtmqTS/zfsGgfpWWdMhkrET01polECmHr4TYEprDeFbz5FSLvPrWtWKVzxNL5yVzvI5zQA8390P1LfKmg6WYTDk2NhBphWGkNw8/KGEWjqs575Xv3/5NFDlkfXC2XRY1NGIZQjW7+4D6nvQTYeIKUilHWxHMrc1VunHqRlyD49JulZsPfe9+v3Lp4GXpTOaTOsF2xKxTIMlnU0Zha/TMkvDMlQtndGUTZMWG0WTTct0GIqnMQ1R12sg2oQuAXXEoVL4OuCXzoo5LcEeYB5iMinBCd33SiH3iMPBeIqWmFXXVqwO+CWgFm2lDvjTZEVXc+bzep5WT4eImxI8krT1om0J5B5xOKjTgbWlUwoRI5sLrafVpeMt3II/AT+VStHd3U08Hi/7WkHS0NDAokWLiESmb2NF3DpOoymdllkKhiEwhMrD1yU9dMAvCW8Tx2A8TWeLXjgrldyA74dK7e7uprW1lWXLllXtNF1KyaFDh+ju7mb58uXTvo5lGhk7TG+8Kg1vw6Te4a0tnZLw/MCBeIpmbemUzAo34DdEDF9Ou4rH43R2dlZtsAd1Hm1nZ2fZs5CIIbIlPfTssiS8DZNDiXRd5+CDDvgl4fmB/aMpPeimQVdrjJaY5WuGTjUHew8/ntHKeYPUYqM0vA2TfSMBHEtaY+ioVQKewo+nHL1oOw2EECyf05ypWqgpHq/vga7hVCrehsne4SSdzT4f/l5jaIVfArkqSw+66XH5ifO58OhwHCy+a9culi9fTm9vLwCHDx9m+fLl7Nixw/d7ebNLQCcMlIhlqPWPkaTN7BYd8DVFEjGyKqtRB/xp8cHzVvK5K46t9GP4wuLFi/nQhz7EddddB8B1113Htddey9KlS32/l1b408cyBfsH1BrKnOb6TrbQUqEExvqouumqiS/89gU27hnw9ZrHLmjj81ceN+nPfOITn+DUU0/lhhtu4NFHH+U///M/fX0Gj4ipFf50iZgGB9yAP7vOLR3dc0ogYmqFrxlLJBLhy1/+Mpdddhn33nsv0WgwASWiFf60sQzBrsOjAHTWuaWjA34JWIb28KuVqZR4kNx1113Mnz+fDRs2cMkllwRyjzF9T+fhl4RlGsRTDgCddW7paA+/BLSPqhnPs88+y3333ccTTzzB1772Nfbu3RvIfcYqfK3TSiG37epd4euAXwJ60GlykVLyoQ99iBtuuIElS5bwqU99ik9+8pOB3Ct3/UiXVigN79SrhohR90JNB/wS0JaOJpfvfe97LFmyJGPjfPjDH+bFF1/k4Ycf9v1euUHLNKp/s1k14b1ZdjbHamKjXpBomVoC2tLR5HLttddy7bXXZv5tmiZPPfVUIPfysnR0dljpeDPzerdzQCv8kshNjdOlFTQzidf3dHZY6Xgz83pPyQQd8EvCyplKN2kfVTODeLNLrfBLJ6Pw6zxDB3wI+EKIjwghNgshXhBCfMmPh6pWdGqcplJ4QUsr/NLxxq22dMr08IUQFwBvAE6UUiaEEOEoklIAT2WZhiDqQ3lfjaZYvKCla+GXjpVR+Drglxu1PgT8u5QyASClPFD+I1Uvnspqiph1v9qvmVm8oNUY0ZZOqXjrH9rDLz/gHwW8SgjxpBDiYSHEaYV+UAhxrRBinRBiXU9PT5m3rQyeytLTas1Mk8nS0Qq/ZDyhNkefUjd1wBdC3C+E2JDn4w0oS6gDOBP4FHCrKCB9pZQ3SinXSinXdnV1+fpHzBSZhbM6PwhZo5BScu6553LXXXdlvnbrrbdy2WWX+X4vL+DrdODSsbTCzzBl5JJSXlzoe0KIDwF3SCkl8GchhAPMAWpTwk9BJjVOZ+hoUAe6fPe73+Wtb30rF1xwAbZt89nPfpa7777b93t5GWJ6h3fpeGXN9aJt+Ruvfg1cCDwkhDgKiAIHy32oaiU76HTArzruug72rff3mkecAK/990l/5Pjjj+fKK6/ki1/8IsPDw1xzzTWsXLnS3+dAK/xyyN1pW++UG/BvAm4SQmwAksC7XbUfSiy9+UWTh89//vOsWbOGaDTKunXrArmHZydqhV86izsaWdbZpMctZQZ8KWUSeKdPz1L1ZLJ0dMepPqZQ4kHS3NzM2972NlpaWojFglGR3hGHuu+VzrvPXsY1Zy2r9GNUBVoulEAmF1qrLM04DMPAMILbmzGnNcqKOc0cu6AtsHuEFSEEOotaoSNXCejdjppK0RS1+OMnz6/0Y2hqHL1dtASEEFiG0NNqjUZTk2iFXyKfft0xnLWis9KPoakyrr/++ko/gkYzJTrgl8j7zl1e6UfQaDSaaaEtHY1Go6kTdMDX1DS1sO2jFp5RUx/ogK+pWRoaGjh06FBVB1QpJYcOHaKhoaHSj6LRaA9fU7ssWrSI7u5uqr36akNDA4sWLar0Y2g0OuBrapdIJMLy5XoRXaMpFm3paDQaTZ2gA75Go9HUCTrgazQaTZ0gKpHhIIToAXbM+I0ryxxCfFbANNFtMhbdHmPR7TGR1VLK1un+ckUWbaWUtXnGYRkIIdZJKddW+jmqCd0mY9HtMRbdHhMRQpR14IK2dDQajaZO0AFfo9Fo6gQd8GeOGyv9AFWIbpOx6PYYi26PiZTVJhVZtNVoNBrNzKMVvkaj0dQJOuBrNBpNnaADfgAIIRYLIR4UQmwSQrwghPiY+/XZQoj7hBBb3NeOSj/rTCKEMIUQzwghfuf+u27bQwjRLoS4TQjxottPzqrn9gAQQnzCHS8bhBC/EEI01FObCCFuEkIcEEJsyPlawb9fCPFpIcTLQojNQojXFHMPHfCDIQ38g5TyGOBM4O+EEMcC1wEPSClXAQ+4/64nPgZsyvl3PbfH14G7pZRHAyeh2qVu20MIsRD4KLBWSnk8YAJXU19t8iPgsnFfy/v3u/HkauA493e+LYSY+rBtKaX+CPgDuBO4BNgMzHe/Nh/YXOlnm8E2WOR22AuB37lfq8v2ANqAbbhJEzlfr8v2cP/ehcAuYDZqQ+jvgEvrrU2AZcCGqfoE8Gng0zk/dw9w1lTX1wo/YIQQy4BTgCeBeVLKvQDu69wKPtpMcwPwj4CT87V6bY8VQA/wQ9fi+r4Qopn6bQ+klLuBrwA7gb1Av5TyXuq4TVwK/f3eG6RHt/u1SdEBP0CEEC3A7cDHpZQDlX6eSiGEuAI4IKV8qtLPUiVYwBrgO1LKU4Bhwm1VTInrTb8BWA4sAJqFEO+s7FNVNSLP16bMsdcBPyCEEBFUsL9ZSnmH++X9Qoj57vfnAwcq9XwzzDnA64UQ24FbgAuFED+jftujG+iWUj7p/vs21BtAvbYHwMXANillj5QyBdwBnE19twkU/vu7gcU5P7cI2DPVxXTADwAhhAB+AGySUn4151u/Ad7tfv5ulLcfeqSUn5ZSLpJSLkMtNP1RSvlO6rc99gG7hBCr3S9dBGykTtvDZSdwphCiyR0/F6EWsuu5TaDw3/8b4GohREwIsRxYBfx5qovpnbYBIIQ4F3gEWE/Ws/4Myse/FViC6uBvlVL2VuQhK4QQ4nzgk1LKK4QQndRpewghTga+D0SBrcB7UAKsLtsDQAjxBeBtqCy3Z4C/BVqokzYRQvwCOB9VFno/8Hng1xT4+4UQnwXei2qvj0sp75ryHjrgazQaTX2gLR2NRqOpE3TA12g0mjpBB3yNRqOpE3TA12g0mjpBB3yNRqOpE3TA14QaIUSnEOJZ92OfEGK3+/mQEOLblX4+jWYm0WmZmrpBCHE9MCSl/Eqln0WjqQRa4WvqEiHE+Tl1+a8XQvxYCHGvEGK7EOLNQogvCSHWCyHudstkIIQ4VQjxsBDiKSHEPd6Wd42mVtABX6NRrAQuRxXw+hnwoJTyBGAUuNwN+v8JXCWlPBW4Cfi3Sj2sRjMdrEo/gEZTJdwlpUwJIdajDt+42/36elSN8tXA8cB9qtQLJqqMr0ZTM+iAr9EoEgBSSkcIkZLZxS0HNU4E8IKU8qxKPaBGUy7a0tFoimMz0CWEOAtU+WshxHEVfiaNpiR0wNdoikBKmQSuAr4ohHgOeBZVr12jqRl0WqZGo9HUCVrhazQaTZ2gA75Go9HUCTrgazQaTZ2gA75Go9HUCTrgazQaTZ2gA75Go9HUCTrgazQaTZ3w/wGWVTVYAuRkpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df1-df2).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.014562</td>\n",
       "      <td>-0.123916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.592729</td>\n",
       "      <td>3.615933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.630084</td>\n",
       "      <td>-3.816620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.056936</td>\n",
       "      <td>-2.428037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.024780</td>\n",
       "      <td>-2.245888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.077394</td>\n",
       "      <td>4.837513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.051794</td>\n",
       "      <td>6.844179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X           Y\n",
       "count  197.000000  197.000000\n",
       "mean    -0.014562   -0.123916\n",
       "std      1.592729    3.615933\n",
       "min     -6.630084   -3.816620\n",
       "25%     -0.056936   -2.428037\n",
       "50%     -0.024780   -2.245888\n",
       "75%      0.077394    4.837513\n",
       "max      7.051794    6.844179"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df1-df2).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}